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Genomic and functional characteristics of DNA copy number variants associated with developmental abnormalities Bagheri, Hani 2017

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GENOMIC AND FUNCTIONAL CHARACTERISTICS OF DNA COPY NUMBER VARIANTS ASSOCIATED WITH DEVELOPMENTAL ABNORMALITIES by  Hani Bagheri  M.Sc., University of Oxford, 2012  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Pathology and Laboratory Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  September 2017  © Hani Bagheri, 2017 ii  Abstract  Small gains and losses of chromosomal DNA, called copy number variants (CNVs), are the cause of many human developmental abnormalities detected before or after birth. Clinically-significant CNVs are found in 2-6% of developmentally arrested embryos and fetuses (termed miscarriage) and in ~15% of children with postnatal developmental abnormalities, typically including abnormal brain function and leading to neuro-developmental delay (NDD).   The overall goal of my PhD project was to characterize CNVs found in both miscarriages and in children with NDD in order to identify candidate genes that cause these two aspects of abnormal development. I used a multi-faceted approach consisting of bioinformatics, human cell-line analysis and transgenic animal model investigations. I characterized CNVs reported in miscarriages from literature as well as from our laboratory by using bioinformatics approaches to determine the CNVs size, gene content, gene density and function, known gene knockout murine phenotype, and biological pathway enrichment for all miscarriage CNV genes. My analysis identified several genes from miscarriage CNVs with important functions during prenatal development and pregnancy (e.g. CDKN1C and TIMP2) and enrichment of genes from miscarriage CNVs in biological pathways and processes relevant to embryo/fetal development and feto-maternal interaction (e.g. immune response).  For discovery of candidate genes responsible for childhood NDD, I characterized CNVs mapping to a chromosome region, 2p15p16.1, which are known to be associated with multiple postnatal developmental abnormalities and NDD (termed 2p15p16.1 microdeletion syndrome). I performed detailed phenotype and CNV analysis of 33 patients with 2p15p16.1 microdeletions and identified 3 candidate genes (XPO1, REL, and BCL11A) for the developmental problems. By iii  studying their expression in patient cell-lines as well as phenotypic consequences of the loss or gain of their expression in zebrafish, I confirmed their role in developmental abnormalities associated with this syndrome. I have also explored the role of non-coding sequences from this CNV in regulation of one of the candidate genes, BCL11A.  The results of my study provide a blueprint for identification of genes with a role in abnormal development by characterizing CNVs. Understanding the cause of the developmental abnormalities opens paths for exploring possibilities for their improved diagnosis, prevention, and potential cure.  iv  Lay summary  During early human development, errors can occur in our genetic material (DNA) leading to loss or gain of some of its segments. These small losses and gains of DNA are called copy number variants (CNVs) and can delete or duplicate multiple genes. In the past decade, scientists have detected unique CNVs in families who lose their babies before birth (miscarriage) or give birth to children with developmental abnormalities. In this thesis, I characterized CNVs in miscarriages and children with developmental abnormalities using bioinformatics, patient cell-lines, and animal models in order to identify genes responsible for the two developmental disorders. As a result, I determined genes and biological processes affected by CNVs in miscarriages and using one CNV causing childhood developmental delay as proof-of-principle I identified 3 candidate genes for children’s developmental problems. This work provides new information and a blueprint for future genetic studies of developmental disorders. v  Preface Recruitment of human subjects described in this thesis was performed with the approval of the University of British Columbia Clinical Research Ethics Committee (C01-0509), unless taken from previously published data by other groups. Participants were enrolled in the study after providing informed consent and were clinically assessed by their respective physicians. Written consent was obtained for publication of the patient’s images shown in chapter 3. Zebrafish (Danio rerio) wild-type AB embryos were raised and maintained with the approval of the Institutional Animal Care and Use Committee (IACUC) of the University of British Columbia and in accordance with the Canadian Council of Animal Care.   Chapter 2 describes copy number variants (CNVs) in miscarriages identified in families from our lab and three other previously published studies. The list of chromosome microarray detected CNVs from our lab and previously published by my supervisor was initially generated by Dr. Ying Qiao, the research associate in my supervisors’ lab. I have expanded this table with CNVs detected in 3 other publications. Bioinformatics analysis including CNV genomic characteristics, gene content, gene function, pathway enrichment and gene mouse knockout phenotypes was performed by me. I also received help and advice from Mr. Eloi Mercier a former bioinformatician at BC Genome Science Centre for the statistical analysis carried out in this chapter. This work was critically reviewed by my supervisor Dr. Evica Rajcan-Separovic, and Dr. Mary Stephenson, obstetrician and department head (Department of obstetrics and gynecology, University of Illinois at Chicago) and published in Molecular Human Reproduction in 2015. I am the first-author of this paper (Bagheri et al. , 2015).     Chapter 3 describes the results of multiple studies carried out by myself and other collaborators. The details of the phenotypes and 2p15p16.1 CNVs in 23 published subjects was vi  collaboratively put together by myself and colleagues Dr. Ying Qiao and Dr. Chansonette Badduke. The phenotypic details of 8 new subjects with 2p15p16.1 CNVs recruited from our lab were initially extracted from patient charts by Dr. Ying Qiao and was double-checked and summarized by myself and Dr. Chansonette Badduke. The 2p15p16.1 deletions in 8 new subjects were presented using UCSC custom-track tool by myself and Dr. Chansonette Badduke. Chromosome microarray analysis for our subjects was carried out by Signature Genomics and Royal Columbian Hospital Cytogenetics Lab. Confirmation and extension of breakpoints for smaller CNVs was carried out by quantitative multiplex PCR of short fluorescent fragments (QMPSF) by Mrs. Sally Martell in our lab with advice from myself and Dr. Chansonette Badduke on where to position primers. Protein expression analysis of candidate genes in patient lymphoblast cell-lines was collaboratively carried out by myself (BCL11A western-blotting as well as statistical and quantitative analysis and presentation of data for all genes) and colleagues, Dr. Sally Martell (BCL11A and XPO1 western-blotting) and , Dr. Chansonette Badduke (XPO1 western-blotting), Dr. Jiadi Wen (REL western-blotting), and Dr. Mark O’Driscoll (USP34 western-blotting). Protein expression analysis of all the candidate genes in the brain tissues was performed by myself using the human protein atlas database accessed online as described in chapter 3. Immunohistochemistry staining and image analysis of two candidate genes, XPO1 and USP34, described in this chapter, was carried out by the histochemistry lab at Department of Pathology and Laboratory Medicine, UBC and with the help of neuropathologist, Dr. Chris Dunham. Two cellular assays performed to identify the nuclear protein export and DNA-repair/stability function of two genes, XPO1 and USP34, respectively, described in the chapter was performed by a colleague, Dr. Mark O’Driscoll, in the University of Sussex, United Kingdom.  vii  Zebrafish knockdown/overexpression modeling and in vivo phenotypic analysis as well as gene knockdown/overexpression confirmation by RNA/protein extraction, RT-PCR, gel electrophoresis, and western-blotting was carried out by myself. Gene morpholino oligomers were designed by GeneTools, LLC under supervision of Dr. Cheryl Gregory Evans. The bacterial culture and propagation of E.coli transformed with human gene cDNA plasmid clones and the downstream purification and linearization of DNA, as well as in vitro messenger RNA synthesis was performed by myself. Help and supervision was received from Mrs.Xianghong Shan (technician) for microinjection of zebrafish embryos. The sequence of cDNA gene-insert for each clone was confirmed by the sequencing core facility at BC Children’s Hospital Research Institute. All data presented in this chapter were carefully collated and written by myself and published in the Journal of Clinical Investigation Insight in 2016 (Bagheri et al. , 2016). For this paper, I share the first-authorship with the previous PhD student in our lab and colleague, Dr. Chansonette Badduke.     Chapter 4 describes a bioinformatics analysis of regulatory/enhancer elements involved in two CNVs from the 2p15p16.1 chromosomal region. All data presented in this chapter was generated by myself and with the use of currently available online bioinformatics web-tools described in this chapter. The data in this chapter is not yet published. viii  Table of Contents  Abstract .......................................................................................................................................... ii Lay summary ................................................................................................................................ iv Preface .............................................................................................................................................v Table of Contents ....................................................................................................................... viii List of Tables .............................................................................................................................. xiv List of Figures ............................................................................................................................. xvi List of Abbreviations ............................................................................................................... xviii Acknowledgements ................................................................................................................... xxii Dedication ................................................................................................................................. xxiii Chapter 1: INTRODUCTION ......................................................................................................1 1.1 Overview ......................................................................................................................... 1 1.2 Copy Number Variants (CNVs)...................................................................................... 3 1.2.1 Mechanisms of CNV generation ................................................................................. 4 1.3 CNV categories ............................................................................................................... 5 1.3.1 Benign CNVs .............................................................................................................. 5 1.3.2 Pathogenic CNVs ........................................................................................................ 6 1.3.3 CNVs of unknown clinical significance ..................................................................... 7 1.4 Biological consequences of CNVs .................................................................................. 7 1.5 Approaches to study CNVs ............................................................................................. 9 1.5.1 In silico analysis of CNVs .......................................................................................... 9 1.5.2 In vitro analysis of CNVs ........................................................................................... 9 ix  1.5.3 In vivo analysis of CNVs .......................................................................................... 10 1.6 Research goal, objectives, and overall significance ...................................................... 13 Chapter 2: CHARACTERIZING CNVs IN PRENATAL DEVELOPMENTAL ARREST (MISCARRIAGE)........................................................................................................................14 2.1 Background ................................................................................................................... 14 2.2 Chapter goals ................................................................................................................ 16 2.3 Materials and methods .................................................................................................. 17 2.3.1 Miscarriage cohorts ................................................................................................... 17 2.3.2 Analysis of CNVs reported in 4 studies of miscarriages .......................................... 18 2.3.2.1 CNV gene content analysis including gene characteristics .............................. 18 2.3.2.2 Functional gene enrichment analysis ................................................................ 19 2.3.3 Statistical analysis ..................................................................................................... 19 2.4 Results ........................................................................................................................... 20 2.4.1 Miscarriage CNV characteristics .............................................................................. 20 2.4.2 Rare versus common CNV’s features ....................................................................... 21 2.4.3 Candidate genes for miscarriages ............................................................................. 29 2.5 Discussion ..................................................................................................................... 30 2.6 Conclusion .................................................................................................................... 34 Chapter 3: CHARACTERIZING CNVs IN POSTNATAL DEVELOPMENTAL DELAY: 2p15p16.1 MICRODELETION SYNDROME .........................................................................36 3.1 Background ................................................................................................................... 36 3.1.1 Review of published cases with 2p15p16.1 deletion and their phenotypic features 38 3.1.2 Genomic findings for all published cases ................................................................. 39 x  3.2 Chapter goals ................................................................................................................ 42 3.3 Materials and methods .................................................................................................. 43 3.3.1 Subjects ..................................................................................................................... 43 3.3.2 Chromosome microarray analysis and refinement of CNV breakpoints .................. 44 3.3.3 Candidate gene selection........................................................................................... 45 3.3.4 Functional analysis for 4 candidate genes in patient cells ........................................ 46 3.3.5 Indirect immunofluorescence .................................................................................... 47 3.3.6 Immunohistochemical analysis ................................................................................. 48 3.3.7 Bioinformatics analysis of pathway enrichment for 2p15p16.1 genes ..................... 49 3.3.8 Design of Morpholinos for knockdown of zebrafish gene orthologues ................... 49 3.3.9 In vivo zebrafish knockdown analysis ...................................................................... 50 3.3.10 Gene-knockdown confirmation analysis............................................................... 52 3.3.11 RNA synthesis for rescue of gene knockdown phenotype and overexpression of genes in zebrafish .................................................................................................................. 53 3.3.12 Rescue and gene overexpression experiments in zebrafish .................................. 54 3.3.13 Statistics ................................................................................................................ 55 3.4 Results ........................................................................................................................... 56 3.4.1 Clinical findings (summary of new and published 2p15p16.1 deletion cases)......... 56 3.4.2 Genomic findings (summary of new and published 2p15p16.1 deletion cases) ....... 59 3.4.3 Candidate gene selection for functional analysis ...................................................... 63 3.4.4 Expression of candidate genes in the human brain ................................................... 67 3.4.5 Expression of the candidate genes in patient lymphoblast cell lines ........................ 70 3.4.6 Functional studies of 2p15p16.1 candidate genes in human cells ............................ 72 xi  3.4.7 Pathway enrichment of 2p15p16.1 deleted genes ..................................................... 76 3.4.8 Knockdown effect on zebrafish phenotype ............................................................... 77 3.4.9 Rescue and overexpression of gene knockdown phenotype in zebrafish ................. 87 3.4.10 Testing the mirror phenotype of 2p15p16.1 duplications by overexpression of the genes……. ............................................................................................................................ 93 3.5 Discussion ..................................................................................................................... 96 3.5.1 Candidate genes for 2p15p16.1 microdeletion syndrome ......................................... 97 3.5.1.1 Other genes in the region ................................................................................ 101 3.5.2 2p15p16 duplications and mirror phenotype .......................................................... 102 3.5.3 Unexplained cases and the role of regulatory elements where none of the critical genes are deleted ................................................................................................................. 103 3.6 Conclusion .................................................................................................................. 105 Chapter 4: CHARACTERIZING TWO 2p15p16.1 MICRODELETIONS AND THEIR NON-CODING REGULATORY ELEMENT CONTENT ...................................................106 4.1 Overview ..................................................................................................................... 106 4.1.1 Enhancers ................................................................................................................ 107 4.1.2 Locating enhancers ................................................................................................. 108 4.1.2.1 VISTA enhancer browser ............................................................................... 109 4.1.2.2 Encyclopedia of DNA elements (ENCODE) project ...................................... 110 4.1.3 Linking enhancers to their target genes .................................................................. 111 4.1.4 Enhancers and disease............................................................................................. 112 4.2 Chapter goals .............................................................................................................. 114 4.3 Materials and methods ................................................................................................ 114 xii  4.3.1.1 Enhancer content in 2p16.1 deletions ............................................................. 114 4.3.1.2 Conservation of 2p16.1 deleted region and its enhancers in zebrafish ........... 115 4.3.1.3 Long-range interactions of BCL11A promoter with deleted 2p16.1 region enhancers......................................................................................................................... 116 4.4 Results ......................................................................................................................... 117 4.4.1 Summary of genomic and clinical findings in our 2 cases without the candidate genes… ............................................................................................................................... 117 4.4.2 Enhancer content in the 2p16.1 deleted region ....................................................... 120 4.4.3 Conservation of deleted 2p16.1 region and enhancers in zebrafish ........................ 124 4.4.4 Long-range interactions between the BCL11A promoter and the 2p16.1 deleted region.. ................................................................................................................................ 125 4.5 Discussion ................................................................................................................... 129 4.6 Conclusion .................................................................................................................. 134 Chapter 5: DISCUSSION..........................................................................................................136 5.1 Overview ..................................................................................................................... 136 5.2 Review and significance ............................................................................................. 137 5.3 Strengths and limitations............................................................................................. 139 5.3.1 Strengths ................................................................................................................. 139 5.3.2 Limitations .............................................................................................................. 140 5.4 Future studies .............................................................................................................. 141 5.5 Conclusion .................................................................................................................. 144 References ...................................................................................................................................146 Appendices ..................................................................................................................................173 xiii  Appendix A Supplementary Tables for Chapter 2 .................................................................. 173 Appendix B Supplementary Tables and Figure for Chapter 3 ................................................ 211 xiv  List of Tables Table 2.1 Comparison of the CNV characteristics in 101 miscarriages from 4 studies. .............. 21 Table 2.2 Gene ontology (GO) terms of rare and common miscarriage CNV genes using DAVID classification. ................................................................................................................................ 22 Table 2.3 Enriched functional pathways for genes from rare/common miscarriage CNVs using the DAVID .................................................................................................................................... 23 Table 2.4 Genes found in rare and common miscarriage CNVs associated with embryonic lethality.. ....................................................................................................................................... 26 Table 2.5 Genes found in rare and common miscarriage CNVs associated with infertility and other reproductive disorders.. ....................................................................................................... 27 Table 2.6 Placental-specific genes found in rare and common miscarriage CNVs. ..................... 28 Table 3.1 A summary of clinical characteristics presented in >50% of all 33 subjects with 2p15p16.1 microdeletion syndrome. ............................................................................................. 57 Table 3.2 Protein homology for candidate genes from 2p15p16.1 in human versus zebrafish. ... 78 Table 3.3 Splice-blocking morpholino knockdown consequence on mRNA size........................ 86 Table 4.1 Positive VISTA enhancer elements in the 2p16.1 region. .......................................... 122 Table A.1 Rare miscarriage CNVs identified in four high-resolution CMA studies (hg19 breakpoints)……………………………………………………………………………………..173 Table A.2 Common miscarriage CNVs identified in four high-resolution CMA studies (hg19 breakpoints)……………………………………………………………………………………..178 Table A.3 Common miscarriage CNV regions (CNVRs) identified in four high-resolution CMA studies…………………………………………………………………………………………..206 Table B.1 Genomic details of 2p15p16.1 microdeletions in 33 cases………………………….211 xv  Table B.2 Sequence of morpholinos used to knockdown the genes of interest in zebrafish…...213 Table B.3 Sequence of PCR primers used to confirm the knockdown of the genes of interest in zebrafish…………………………………………………………………………………….......214 Table B.4 Detailed phenotypes of all 33 cases with 2p15p16.1 microdeletion syndrome……..215 Table B.5 Gene contents of all 33 cases with 2p15p16.1 microdeletion syndrome……………218 Table B.6 A summary of known functions and mutations of genes deleted in >50% of cases and their animal knockout model reports…………………………………………………………...220 Table B.7 Enriched pathways for the 16 most commonly deleted genes in the 2p15p16.1 microdeletion region…………………………………………………………………………....226 Table B.8 QMPSF ratios for primers a-e for two small deletions.………………….…….........227   xvi  List of Figures Figure 2.1 Analysis of the function of CNV genes. ...................................................................... 25 Figure 3.1 Genomic overlap of all published 2p15p16.1 microdeletions. .................................... 40 Figure 3.2 Physical features of the 2p15p16.1 microdeletion carriers. ........................................ 58 Figure 3.3 Summary of CNVs involving the 2p15p16.1 region, including our 8 new cases. ...... 62 Figure 3.4 Protein expression analysis of XPO1, USP34, REL, and BCL11A in human brain…68 Figure 3.5 Protein expression analysis of XPO1, REL, BCL11A, and USP34 in patients’ cells..71  Figure 3.6 XPO1 dysfunction in patients’ cells as determined by abnormal distribution of rpS5........................................................................................................................................................ 73 Figure 3.7 Wnt signaling pathway and DNA double-strand break repair were comparable in USP34 haploinsufficient cells and controls. ................................................................................. 75 Figure 3.8 2p15p16.1 gene synteny in human versus zebrafish. .................................................. 78 Figure 3.9 Knockdown of xpo1a, rel, bcl11aa, and bcl11ab genes in zebrafish causes an abnormal phenotype with head morphology and size defects. ..................................................... 80 Figure 3.10 Measurement of head, otic vesicle, and body size in affected zebrafish embryos.....82 Figure 3.11 No head and body abnormalities were detected for xpo1b, usp34, vrk2, and fancl morphants………………………………………………………………………………………...83 Figure 3.12 Brain structural anomalies in affected zebrafish embryos. ....................................... 85 Figure 3.13 Confirmation of gene knockdown in zebrafish embryos by RT-PCR and western-blotting.. ........................................................................................................................................ 87 Figure 3.14 Human gene RNA dose response and protein production in zebrafish.. ................... 89 Figure 3.15 Rescue of xpo1a, rel, and bcl11aa morphant zebrafish embryos. ............................. 91 Figure 3.16 Overexpression of XPO1, REL, and BCL11A human genes in zebrafish.. ............... 95 xvii  Figure 4.1 Schematic diagram depicting chromatin looping and promoter-enhancer interaction...................................................................................................................................................... 108 Figure 4.2 Deletions in both patients compared to CNVs found in healthy individuals in the same region. ......................................................................................................................................... 118 Figure 4.3 Transcriptome profile for FLJ30838 long non-coding RNA gene involved in 2p16.1 deletions. ..................................................................................................................................... 119 Figure 4.4 Enhancer elements in the deleted non-coding region of 2p16.1.. ............................. 121 Figure 4.5 Distribution of enhancers among human chromosomes. .......................................... 123 Figure 4.6 Interspecies comparison of gene and enhancer synteny and sequence conservation. 124 Figure 4.7 Long-range interactions between BCL11A promoter and other genomic regions in erythroblasts.. .............................................................................................................................. 127 Figure 4.8 Characteristics of the two additional smaller deletions in our case 2.. ...................... 129 Figure B.1 Morpholino titration analysis for 2p15p16.1 zebrafish orthologous genes………...230   xviii  List of Abbreviations ASD: Autism Spectrum Disorder BLAST: Basic Local Alignment Search Tool  BMI: Body Mass Index CMA: Chromosome Microarray Analysis  CNVs: Copy Number Variants CNVRs: Copy Number Variation Regions  DAVID: Database for Annotation, Visualization and Integrated Discovery DD: Developmental Delay  DECIPHER: DatabasE of Chromosomal Imbalance and Phenotype in Humans using Ensembl Resources DGV: Database of Genomic Variants  dpf: days post-fertilization DSB: Double-Strand Break DSB-R: Double-Strand Break Repair ECARUCA: European Cytogeneticists Association Register of Unbalanced Chromosome Aberrations ECL: Enhanced Chemiluminescence ENCODE: Encyclopedia of DNA Elements EST: Expressed Sequence Tag  fHB: Fetal Hemoglobin FoSTeS: Fork Stalling and Template Switching  GO: Gene Ontology  xix  GWAS: Genome-Wide Association Studies HGMD: Human Gene Mutation Database HI: Haploinsufficient hpf: hours post-fertilization HPA: Human Protein Atlas HS: Haplosufficient ID: Intellectual Disability IHC: Immunohistochemistry IQ: Intelligence Quotient IQR: Inter-Quartile Range IP: Immunoprecipitation  IR: Ionizing Radiation KEGG: Kyoto Encyclopedia of Genes and Genomes  LB: Luria Broth LCL: Lymphoblast Cell-Lines LCR: Low Copy Repeat LncRNA: Long non-coding RNA MCR: Minimal Critical Region MGI: Mouse Genome Informatics  MHB: Midbrain-Hindbrain boundary MM: Mismatch morpholino MO: Morpholino MS: Multiple Sclerosis xx  NAHR: Non-Allelic Homologous Recombination NDD: Neuro-Developmental Delay/Disorder NHEJ: Non-Homologous End Joining OFC: Occipitofrontal Circumference  OV: Otic Vesicle Pc-Hi-C: Promoter Capture Hi-C  PCR: Polymerase Chain Reaction PTU: N-PhenylThioUrea QMPSF: Quantitative Multiple Polymerase chain reaction of Short Fluorescent fragments RIPA: RadioImmunoPrecipitation Assay RPL: Recurrent Pregnancy Loss  rpS5: ribosomal protein S5 RT-PCR: Reverse Transcription Polymerase Chain Reaction SB-MO: Splice-Blocking Morpholino SD: Standard Deviation SEM: Standard Error of Mean SNP: Single Nucleotide Polymorphism TB-MO: Translation-Blocking Morpholino TF: Transcription Factor TiGER: Tissue-specific Gene Expression and Regulation  TiSGeD: Tissue-Specific Genes Database  UCSC: University of California, Santa Cruz UV: Ultraviolet xxi  WCE: Whole Cell Extracts WebGestalt: Web-based Gene set analysis toolkit  WT: Wild Type  xxii  Acknowledgements I am grateful for the incredible amount of support I received from my research project supervisors, Dr. Evica Rajcan-Separovic and Dr. Cheryl Gregory-Evans. The self-less dedication of their time and laboratory resources were the major reason for the successful completion of this thesis project. They and their lab members provided an environment in which I could freely share my ideas and thoughts and test my hypotheses. I will take away great memories and lessons from my time working with them and my colleagues in the lab whose generous offer of support always extended beyond my PhD project to non-work related issues.  I would also like to thank the faculty and staff from the Department of Pathology and Laboratory Medicine at UBC, who were always accessible and ready to help with the progress of my PhD project. I would like to thank my committee members, Dr. Catherine Pallen, Dr. Suzanne Lewis, and Dr. Wan Lam, for their continuous support throughout my PhD and presence at annual meetings to ensure the project was on track and provided valuable feedback.   I was very lucky to have Dr. Catherine Pallen, as my PhD committee chair who immensely encouraged my PhD project’s progress by composing detailed committee reports after each annual meeting and providing critical advice during the annual meetings.  I am appreciative of the financial support received from the BC Children’s Hospital Research Institute for 2 final years of my PhD project. I am also thankful for the annual financial contributions and travel awards from UBC’s Faculty of Medicine, UBC’s Graduate School, and BC Children’s Hospital Research Institute, which significantly helped me to be able to present my data at national and international scientific meetings.   Last but not least, I cannot thank my family enough for their continuous support and encouragement throughout my career which allowed me to pursue this PhD project.   xxiii  Dedication I dedicate this thesis to patient families. Without their contribution none of the data used in this thesis would have been generated. 1  Chapter 1: INTRODUCTION 1.1 Overview Early human development is a complex process which depends on genetic and environmental factors. Sometimes, these factors could go wrong leading to arrested development before birth termed miscarriage or birth of babies with postnatal developmental delay (DD) which is the condition of a child being less developed mentally and/or physically than its normal age. Children affected with DD often manifest poor skills in cognition, thinking and learning (referred to as intellectual disability (ID)), socio-behavior (e.g. autism spectrum disorder (ASD)), speech and language, fine and gross motor, daily living activities (e.g. feeding difficulties) as well as multiple physical anomalies such as microcephaly and craniofacial defects. DD, therefore, most commonly affects nervous-system and is often in the literature and throughout this thesis referred to as neuro-developmental delay/disorder (NDD).  In some cases the cause of developmental arrests and delays are linked to the large chromosome abnormalities e.g. aneuploidies where there is an abnormal number of chromosomes (higher or lower than 46) in patient cells. Typical examples include loss of a copy (monosomy) of chromosome X in miscarriage or extra copy (trisomy) of chromosome 21 and Down syndrome. But in karyotypically-normal (euploid) cases of abnormal development, where large chromosome abnormalities are not found, much smaller gains or losses of chromosomal DNA segments known as copy number variants (CNVs) can be detected by a recently developed chromosomal microarray analysis (CMA). These CNVs can be due to a new genetic error(s) (de novo CNVs) during the embryonic development or be passed from the parents to the baby (parental CNVs). De novo CNVs are more likely to be disease-causing (pathogenic) because they are not present in healthy parents 2  and only found in the abnormally developed baby. Also, it is more likely for the CNV to be pathogenic or causative if it is uniquely found in the patient (i.e. a ‘rare’ CNV) rather than commonly found in the healthy population (i.e. a ‘common’ CNV). Available CMA studies have estimated the cause for 2-6% of developmental arrests/miscarriages and 15% of children with NDD to be due to pathogenic CNVs. Although CNVs are much smaller than large chromosome abnormalities, they typically contain multiple genes and it is important to try to pinpoint the exact candidate gene(s) that cause the observed abnormalities in patients when its dose is reduced or increased. The overall aim of my PhD project was to characterize CNVs found in both miscarriages and children with NDD in order to identify genes responsible for human developmental abnormalities. I used a number of approaches including bioinformatics analysis of the CNVs and genes, as well as in vitro and in vivo investigations of the functional effect of the gene copy number change in patient cell lines and zebrafish embryos, respectively.  The zebrafish approach to pinpoint disease-causative genes (or ‘driver’ genes) from the CNVs was first described by Golzio and co-workers (2012) by knocking down or overexpressing each of the genes from a specific CNV from 16p11.2 region in order to identify the one(s) causing physical developmental abnormalities (e.g. microcephaly) similar to those seen in the patients. Several later CNV studies also used zebrafish-modeling and successfully identified culprit genes responsible for the phenotypic abnormalities (mainly microcephaly) noted in patients with previously uncharacterized CNVs in 8q24.3 (Dauber et al. , 2013), and 17p13.1 (Carvalho et al. , 2014) genomic regions suggesting that this approach is an effective way to identify ‘driver’ genes out of multiple genes involved in CNVs.  3  The recent popularity of zebrafish in CNV studies is mainly due to its rapid reproduction and development, transparent embryogenesis, and simple gene expression manipulation in comparison to rodent models which has made it a valuable tool for developmental studies. Therefore, zebrafish was also considered a great model for my PhD project to analyze the in vivo effect of knockdown or overexpression of genes that are involved in previously uncharacterized prenatal or postnatal developmental abnormality-associated CNVs. My main expectation was that similar to the above-described studies, I will be able to characterize CNVs and their integral genes and pinpoint novel developmental genes that are responsible for developmental abnormalities before or after birth.  1.2 Copy Number Variants (CNVs)  CNVs are gains or losses of small segments of DNA (>1Kb) which can cover multiple, one, or no genes (Zhang et al. , 2009a). The advent of CMA has resulted in identification of a large number of CNVs associated with human diseases (Vissers et al. , 2010). CMA allows for detection of CNVs by comparing the patient’s fluorescently labelled DNA with that of one, or many, healthy controls using oligonucleotide and/or single nucleotide-tagged DNA probes sampled throughout the genome and arrayed on a glass slide (Pollack et al. , 1999). Reading the array using special scanners and software then allows for identifying specific probes and genomic regions that have a lower ratio for patient DNA fluorescence (i.e. losses in patients) or higher ratio for patient DNA fluorescence (i.e. gains in patients) in comparison to that of controls, while the majority of regions are often balanced in fluorescence (i.e. they do not demonstrate CNVs in patients) (Pollack et al., 1999).  These submicroscopic CNVs are typically missed by traditional lower-resolution cytogenetic techniques such as karyotyping which are limited to detect large segmental (>10Mb 4  in size) or whole-chromosome gains/losses (Liu et al. , 2015, Reddy et al. , 2012, Wapner et al. , 2012). CMA’s higher-resolution and ability to detect CNVs in cases with normal karyotype findings has resulted in a much higher diagnostic yield (15-20%) over karyotyping (3%) (Miller et al. , 2010). CMA is, therefore, nowadays considered a first-tier clinical diagnostic test for unexplained postnatal DD, ID, ASD and multiple developmental anomalies at birth (Chong et al. , 2014, Miller et al., 2010). More recently, CMA became a standard test for unborn fetuses with ultrasound abnormalities and resulted in increased diagnostic yield for pathogenic CNVs of several percent over karyotyping (Leung et al. , 2011, Liu et al. , 2011a, Wapner et al., 2012). The application of CMA to determine the cause of early embryonic or fetal death is increasing but is still not a standard of care mainly due to the limited number of studies to determine the role of CNVs in miscarriage (Karim et al. , 2017, Maisenbacher et al. , 2017, Reddy et al., 2012, Sahlin et al. , 2014, Shah et al. , 2017).  1.2.1 Mechanisms of CNV generation Regions of the genome containing low copy repeat (LCR) sequences are ‘hotspots’ for CNV generation and recurrence (Hastings et al. , 2009). LCRs that are highly (>95%) identical to each other throughout genome can misalign during meiosis between the homologues and result in non-allelic homologous recombination (NAHR) which results in loss of a DNA segment (copy) on one allele and gain of it on the other allele (Hastings et al., 2009, Lindsay et al. , 2006). This erroneous recombination event typically gives rise to recurrent CNVs with similar breakpoints due to their positioning between LCRs in the region (Gu et al. , 2008). Some of the ‘hotspots’ for recurrent CNVs which are prone to NAHR errors are at 1q21.1, 16p11.2, 15q13, 15q24, 17q12, and 17q21.31 (Sanders et al. , 2011, Watson et al. , 2014). So far, more than 20 recurrent pathogenic microdeletion/microduplications have been identified (Watson et al., 2014). 5  In addition, CNVs can be generated in the absence of homologous sequences and are caused by different types of errors during DNA replication and repair mechanisms including non-homologous end joining (NHEJ) of chromosomal breaks and DNA fork stalling and template switching (FoSTeS) (Hastings et al., 2009). These CNVs, unlike NAHR-generated CNVs, typically do not share similar breakpoints and can occur randomly throughout the genome, or recur in a segment of the genome but result in CNVs of different sizes and breakpoints (Gu et al., 2008). An example of such CNVs are recurrent deletions of variable sizes in the 2p15p16.1 chromosomal region, which are the topic of my chapter 3, and described there in more detail.    1.3 CNV categories  CNVs are typically classified as benign, pathogenic, and variants of unknown clinical significance (Kearney et al. , 2011). 1.3.1 Benign CNVs CNVs are commonly found in the healthy population and account for around 13% of the human genome (Stankiewicz and Lupski, 2010). These CNVs are, therefore, considered benign to the overall development and the copy number change of their genes do not cause any apparent adverse phenotypes (Zarrei et al. , 2015). To assist in distinguishing benign CNVs from pathogenic CNVs, the database of genomic variants (DGV; http://projects.tcag.ca/variation/) was established in Toronto in 2004 and made publicly accessible (Iafrate et al. , 2004). It comprehensively and continuously catalogues all known benign CNVs that have been found in the general healthy populations world-wide (Iafrate et al., 2004). Therefore, clinicians and scientists continuously check the CNVs found in the patients against those catalogued on DGV or other similar databases (Church et al. , 2010, de Leeuw et al. , 2012) to identify if the CNVs 6  are ‘rare’ in the general population or ‘common’. As a result, common CNVs are largely considered benign due to their recurrence in healthy controls (Zarrei et al., 2015). 1.3.2 Pathogenic CNVs Generally, rare CNVs not found in DGV or found to occur at a significantly lower frequency in the healthy population than in patient cohorts could be considered as pathogenic, especially, if they are deletions, large in size (>1 Mb), de novo in nature (i.e. occur in the affected subject as a new event and not inherited from either parents), harbor a large number of protein-coding genes (Vissers et al., 2010, Vissers and Stankiewicz, 2012, Zarrei et al., 2015)  and contain genes with roles in early development (Huang et al. , 2010). Although, features described above increase the likelihood for a CNV to be pathogenic, CNVs that are duplications (Brunetti-Pierri et al. , 2008, Dathe et al. , 2009, Migliavacca et al. , 2015), smaller in size (<100Kb) (Hollenbeck et al. , 2017, Krumm et al. , 2013, Moreira et al. , 2016), inherited (Brunetti-Pierri et al., 2008, Hehir-Kwa et al. , 2010, Mefford et al. , 2009), and are coding gene-empty (i.e. map to non-coding regions) (Dathe et al., 2009, Zhang and Lupski, 2015) have also been reported  as causative of abnormal phenotypes suggesting that there is no single criterion that applies to all pathogenic CNVs. To assist in identifying pathogenic CNVs and their phenotypic consequence, several international databases such as DECIPHER (DatabasE of Chromosomal Imbalance and Phenotype in Humans using Ensembl Resources; https://decipher.sanger.ac.uk/) (Bragin et al. , 2014, Swaminathan et al. , 2012) and ECARUCA (European Cytogeneticists Association Register of Unbalanced Chromosome Aberrations; http://umcecaruca01.extern.umcn.nl:8080/ecaruca/ecaruca.jsp) (Feenstra et al. , 2006) have been developed which catalogue CNVs in patients with NDD.  7  Furthermore, recently provided information on regulatory elements in CNVs (e.g. gene enhancers) can also be considered in determining pathogenicity of CNVs which can be obtained from databases such as ENCODE (Encyclopedia of DNA Elements; https://www.encodeproject.org/) (Consortium, 2012) and VISTA (https://enhancer.lbl.gov/) (Visel et al. , 2007) (to be discussed in more detail in chapter 4). 1.3.3 CNVs of unknown clinical significance  Although databases explained above are helpful in general categorization of some benign and pathogenic CNVs, the interpretation of certain CNVs remain inconclusive and these CNVs are termed as CNVs of unknown clinical significance. Examples of such CNVs include rare CNVs that are found in affected subjects but inherited from physically healthy parents (Fogel, 2011) or CNVs that are gene-empty, lack known disease genes or contain genes with no clear association to the phenotype (e.g. genes of unknown function) (Fogel, 2011, Westerfield et al. , 2014).  1.4 Biological consequences of CNVs  Various mechanisms have been proposed through which CNVs can cause disease or lead to increased disease susceptibility (Zhang et al., 2009a). Examples of ‘local’ CNV effects include  alteration of copy number and expression level (dosage) of their integral genes (Stankiewicz et al. , 2009, Vissers et al. , 2004), disruption of the coding sequence of a gene (Rujescu et al. , 2009), indirect dysregulation of gene function by rearrangement and copy number alteration of important gene regulatory elements (e.g. tissue-specific enhancers) (Beysen et al. , 2005, Velagaleti et al. , 2005). CNVs can also have a ‘global’ pathogenic effect by altering genome-wide expression (Luo et al. , 2012, Schlattl et al. , 2011). Several studies have reported that CNVs can alter the 8  levels and timing of expression of genes involved not only within (Chaignat et al. , 2011) (Henrichsen et al. , 2009) (Luo et al., 2012), but also flanking (Henrichsen et al., 2009, Merla et al. , 2006, Reymond et al. , 2007, Stranger et al. , 2007), and distant from their boundaries (Ricard et al. , 2010). For instance, whole-genome expression analyses of brain and lymphoblast samples from carriers of NDD-associated CNVs e.g. 16p11.2 and 3q27 detected global expression changes involving hundreds of genes within CNVs and beyond their locus (Luo et al., 2012, Mehta et al. , 2014, Migliavacca et al., 2015).  The mechanism by which CNVs affect the expression of genes outside of their boundaries remains uncertain, however, more recent studies have shown that the impact of CNVs on the chromatin’s architecture and the wiring of interactions between genes and their cis and trans regulatory elements can explain some of the spatiotemporal gene misregulations outside of CNVs (Franke et al. , 2016, Lupianez et al. , 2015, Lupianez et al. , 2016, Zepeda-Mendoza et al. , 2015). Finally, gene copy number changes due to CNVs can affect biological pathways that they participate in. For instance, recent studies have shown enrichment of pathways relevant for nervous-system function (e.g. axon growth/pathfinding, and glutathione metabolism and oxidative stress) for genes from the pool of CNVs detected in subjects with disorders of neuro-development (e.g. autism) (Sbacchi et al. , 2010) and neuropsychiatry (e.g. schizophrenia/bipolar) (Mehta et al., 2014), respectively. The identification of a shared biological pathway enriched for CNV genes and relevant to the observed phenotype in similarly-affected patients can, therefore, hint to the key biological processes affected by CNVs which can explain the etiology of disorder and shed light on how future potential therapeutic strategies can be devised (Poptsova et al. , 2013). 9  1.5 Approaches to study CNVs In general, CNVs and their genes are studied by in silico analysis of their effect using bioinformatics databases and web-tools as well as empirically by in vitro and in vivo analysis using cell-lines and animal models, respectively. 1.5.1 In silico analysis of CNVs Bioinformatics approaches use statistical methods and software tools to characterize CNVs and their genes. For example, determining whether a yet uncharacterized gene is sensitive to loss of one copy (haploinsufficient or dosage-sensitive) can be predicted (Huang et al., 2010, Rice and McLysaght, 2017) by assessing the gene’s characteristics (e.g. expression pattern, sequence conservation, interaction network) in relation to known haploinsufficient (HI) and haplosufficient (HS) genes. A “haploinsuffciency likelihood score” is assigned to the gene based on gene’s alikeness to known HI genes (Huang et al., 2010) and the closer the gene’s characteristics are to known HI genes the higher the likelihood that the gene is HI and prone to dosage-sensitivity if involved in CNVs (Huang et al., 2010).  In addition, many softwares and web-tools such as database for annotation, visualization, and integrated discovery (DAVID; https://david.ncifcrf.gov/) (Dennis et al. , 2003) and web-based gene set analysis toolkit (WebGestalt; http://www.webgestalt.org) (Wang et al. , 2013) have been developed to facilitate assessment of global impact of CNV genes, by determining whether they participate in the same biological processes more than a random set of genes (i.e. pathway enrichment) as described above.  1.5.2 In vitro analysis of CNVs Patient cell lines can be used to study the expression and function of genes from CNVs. An example of this approach is a study of gene expression from a deletion and duplication of 10  16p11.2 region, both of which are associated with NDD and variable morphological anomalies (Migliavacca et al., 2015). For the majority of genes (20/34) involved in the 16p11.2 a bi-directional alteration of their transcript abundance was noted to be in keeping with their deletion or duplication in comparison to controls (Kusenda et al. , 2015). Interestingly, the bi-directional expression change of several genes from this CNV region has also been correlated with mirror phenotypes in the CNV carriers such that their duplication and increased expression causes microcephaly while their deletion and decreased expression causes macrocephaly (Kusenda et al., 2015, Migliavacca et al., 2015).  1.5.3 In vivo analysis of CNVs Animal models, serve as a powerful tool to decipher the role and in vivo consequence of copy (dosage) changes of CNV genes (Kohler et al. , 2014). Mouse is the most popular mammalian model used to study the function of genes and identify novel causal developmental genes involved in CNVs or other genetic variants e.g. single nucleotide mutations (Perlman, 2016). Several studies have used mouse models to study the effect of dosage-change of genes involved within CNVs (Girirajan et al. , 2008, Kiehl et al. , 2009, Meechan et al. , 2009). For instance, dosage reduction of the orthologous copies of genes involved in 22q11.2 deletion in mouse showed similar neuropathological (e.g. cortical development) abnormalities seen in human post-mortem brain tissue samples of 22q11.2 microdeletion carriers (Drew et al. , 2011, Kiehl et al., 2009, Meechan et al., 2009). Additionally, overexpression of the orthologue for RAI1 involved in 17p11.2 duplications in mouse, recapitulated human features including growth retardation, increased hyperactivity, and psychomotor developmental abnormalities (Girirajan et al., 2008).  11  Large-scale gene knockout studies in mouse have significantly improved our understanding of the function of previously uncharacterized genes (Dickinson et al. , 2016). A recent example is the analysis of 1,751 unique gene knockouts in mice which revealed 410 lethal genes with characteristic cardiovascular anomalies, craniofacial defects, limb and other abnormalities which can be compared with human genotype-phenotype studies to gain insight about the gene function and identify lethal gene disruptions (Dickinson et al., 2016). Moreover, the international mouse knockout databases such as mouse genome informatics (MGI) database (http://www.informatics.jax.org/) (Blake et al. , 2003, Bult et al. , 2010) which catalogues known published knockout phenotypes are very useful resources to gain insight about gene’s function and identify causative CNV genes in relation to the human phenotypes.   Nonetheless, mouse modeling suffers from high-cost, long generation time for testable transgenics (a few months or even years), and lower statistical power due to limited number of transgenics that can be produced (Lieschke and Currie, 2007). Moreover, the polygenic nature of CNVs and involvement of more than 10 genes in most pathogenic CNVs hinders quick identification of causative genes, by mouse studies, especially if their role in the phenotype is unknown. Recently, animal models with shorter generation times and cheaper maintenance such as zebrafish have been proven to be powerful tools to determine causative genes from CNVs (Davis et al. , 2014). Zebrafish’s fast embryonic development (a 3 days post-fertilization embryo is equivalent to a toddler in humans), large generation of hundreds of embryos at weekly intervals, transparent embryonic development with clear visibility of internal organs under the microscope, ex vivo development of the embryos allowing for early developmental analysis, cheaper and easier maintenance than mice, have made it a valuable animal model for understanding the mechanism of congenital developmental abnormalities (Davis et al., 2014). 12  The zebrafish genome is highly similar to human genome with around 70% of all human genes and 84% of disease-associated genes having an orthologous copy in zebrafish which is higher percentage identity than other popular animal models such as drosophila (60%) and Caenorhabditis elegans (43%) (Davis et al., 2014, Howe et al. , 2013). Moreover, previous studies of the syntenic relationship of the coding-gene regions of the zebrafish and human genome have revealed extensive contiguous blocks of synteny between the genomes in both species with 80% of genes analyzed belonging to conserved synteny groups (Barbazuk et al. , 2000, Howe et al., 2013). The simplicity of zebrafish development in comparison to humans and other mammalian models allows for an accelerated generation of transgenics and phenotypic assessment, hence, a more expeditious identification of culprit CNV genes responsible for physical phenotypic features seen in the patients e.g. increased/reduced head size, or body growth retardation (Davis et al., 2014). For example, three recent studies used knockdown and overexpression of zebrafish orthologues for human genes in CNVs detected in subjects with a variety of congenital abnormalities including reduced or increased head size (micro and macrocephaly), and reported the “phenotype driver” or dosage sensitive genes based on recapitulation of the human phenotype in zebrafish embryos (Carvalho et al., 2014, Dauber et al., 2013, Golzio et al. , 2012). Gene orthologue knockdown and the phenotypic assessment of the zebrafish embryos revealed 4 candidate genes (ASGR1, ACADBL, DVL2, and GABARAP) out of 9 commonly deleted genes for 17p13.1 microdeletion syndrome (Carvalho et al., 2014); 2 genes (SCRIB and PUF60) out of 3 commonly deleted genes for 8q24.3 microdeletion syndrome (Dauber et al., 2013); and 1 gene (KCTD13) out of 29 deleted/duplicated genes for 16p11.2 CNVs (Golzio et al., 2012), as “driver” genes for the phenotypic features, specifically head size anomalies, seen in the patients 13  carrying CNVs in these regions. For 16p11.2 CNVs, in particular, which are recurrent CNVs causing mirror phenotypes of head size in deletion/duplication carriers, the authors of the study reported that bi-directional dosage change of the “driver” gene, KCTD13, by gene-orthologue suppression and human gene RNA-induced overexpression in zebrafish embryos recapitulates the human head-size mirror phenotypes as was shown by significant enlargement or reduction of the head size, respectively, in the affected embryos (Golzio et al., 2012). 1.6 Research goal, objectives, and overall significance Overall goal: The overall goal of my PhD project was to use the above in silico, in vitro and in vivo approaches to characterize CNVs identified in subjects with two human developmental disorders in order to identify culprit genes. Specifically, my objectives were to: 1) Identify candidate genes integral to CNVs responsible for prenatal and postnatal developmental disorders.  2) Confirm their role in development using a zebrafish model. As an example of a prenatal developmental disorder I used miscarriage and as an example of a postnatal developmental disorder I used a microdeletion syndrome described in my supervisor’s laboratory. The overview of these disorders is provided in Chapters 2 and 3. Overall significance: this body of work will improve our understanding of the impact of CNVs in early human development and pinpoints novel genes associated with disease.  14  Chapter 2: CHARACTERIZING CNVs IN PRENATAL DEVELOPMENTAL ARREST (MISCARRIAGE) 2.1 Background It is estimated that only around a third (30%) of human conceptions end up in a live birth while the majority of conceptions are either lost early prior to clinical detection of pregnancy (60%) or end in miscarriage (10%) after clinical-recognition of pregnancy (Larsen et al. , 2013, Macklon et al. , 2002). The majority of miscarriages are sporadic; however up to 5% of couples trying to have children experience recurrent pregnancy loss (RPL) (Sierra and Stephenson, 2006).  Large chromosomal abnormalities are the major etiological factor for early (<20 weeks of gestation) miscarriages and account for ~60% of miscarriages (Tur-Torres et al. , 2017). They are predominantly large chromosome gains and losses such as monosomy X and trisomy 18 (Rai and Regan, 2006, Tur-Torres et al., 2017, Warburton and Fraser, 1964, Yamada et al. , 2005). Other exogenous causal factors for sporadic miscarriages include heavy smoking, drug/alcohol abuse, diabetes, obesity, and anatomic uterine abnormalities of mothers (Ford and Schust, 2009, Oliver and Overton, 2014, Practice Committee of the American Society for Reproductive Medicine, 2012). However, there are still 30-40% of miscarriages with a normal karyotype (euploid) and no other known causes that are left unexplained (Larsen et al., 2013, Macklon et al., 2002, Rull et al. , 2012, Zhou et al. , 2016). The role of CNVs in miscarriage is not clearly understood due to the limited number of studies (Wang et al. , 2017b). To date, there have been only 20 published studies, which used CMA to detect CNVs in a total of ∼4000 early miscarriages (<20 weeks of gestation) world-15  wide (Ballif et al. , 2006, Benkhalifa et al. , 2005, Bug et al. , 2014, Gao et al. , 2012, Lathi et al. , 2012, Levy et al. , 2014, Menten et al. , 2009, Rajcan-Separovic et al. , 2010a, Rajcan-Separovic et al. , 2010b, Robberecht et al. , 2012, Robberecht et al. , 2009, Rosenfeld et al. , 2015, Schaeffer et al. , 2004, Shen et al. , 2016, Shimokawa et al. , 2006, Viaggi et al. , 2013, Wang et al., 2017b, Warren et al. , 2009, Zhang et al. , 2009b, Zhou et al., 2016).  Early low-resolution CMA studies reported CNVs in 1–13% of early miscarriages (Menten et al., 2009, Robberecht et al., 2009, Schaeffer et al., 2004, Shimokawa et al., 2006) and in 6% of miscarriages that failed to grow in culture (Benkhalifa et al., 2005, Zhang et al., 2009b). However, these studies did not determine the origin of the reported CNVs by parental analysis (i.e. whether the CNVs are de novo or inherited) and did not identify the precise size/genomic breakpoints of the CNVs, nor their gene content making it difficult to analyze the clinical significance of their reported CNVs.  More recent high-resolution CMA studies provided more details on miscarriage CNVs, which is particularly important for chromosomally-normal/euploid miscarriages, in search for their cause (Bug et al., 2014, Levy et al., 2014, Rajcan-Separovic et al., 2010a, Rajcan-Separovic et al., 2010b, Robberecht et al., 2012, Viaggi et al., 2013, Wang et al., 2017b, Zhou et al., 2016). So far, clinically-significant CNVs uniquely found in miscarriages (i.e. ‘rare’ CNVs), that are de novo in nature, with large sizes (>5Mb) and high gene content (>10 genes) were found in ~2-6% of euploid miscarriages (Levy et al., 2014, Rosenfeld et al., 2015, Shen et al., 2016, Wang et al., 2017b, Zhou et al., 2016). A large percentage of rare CNVs found in miscarriages are classified as variants of unknown clinical significance noted in ∼1–40% of sporadic and recurrent euploid miscarriages (Bug et al., 2014, Levy et al., 2014, Rajcan-16  Separovic et al., 2010a, Rajcan-Separovic et al., 2010b, Robberecht et al., 2012, Rosenfeld et al., 2015, Viaggi et al., 2013, Wang et al., 2017b, Zhou et al., 2016).  The overall significance of detected CNVs and their genes to miscarriage and how they lead to prenatal embryonic lethality is, therefore, largely unexplored. Knowing the CNV size, their gene content and function, association with established developmental phenotypes, presence in parents or databases of controls (DGV) is essential for their interpretation (Kearney et al., 2011) and is necessary to improve our understanding of their role in early embryonic development and pregnancy. 2.2 Chapter goals My goal, therefore, was to better characterize miscarriage CNVs and identify candidate genes and biological processes for miscarriage. I have specifically aimed to: 1) Determine the genomic characteristics of rare and common CNVs identified in 101 euploid miscarriages reported in four high-resolution array studies that documented both common miscarriage CNVs and rare miscarriage CNVs. This included comparing their size, gene number and gene density.  2) Determine the function of genes integral to CNVs using reported murine model knockout phenotypes and placental tissue-specific expression analysis and identify those of relevance for miscarriage.    3) Perform a bioinformatics-assisted enrichment analysis of CNV genes in biological pathways and processes.   This type of analysis has the potential to identify candidate CNVs and their integral genes that may be responsible for embryonic/fetal lethality and early pregnancy loss by disrupting 17  developmental genes, placental genes, imprinted genes (with uniparental preferential expression pattern), and/or genes with important functions in parental reproductive processes.  2.3 Materials and methods 2.3.1 Miscarriage cohorts  Data from four high-resolution CMA studies of miscarriages (Rajcan-Separovic et al., 2010a, Rajcan-Separovic et al., 2010b, Robberecht et al., 2012, Viaggi et al., 2013) were used. Details of the miscarriage cohorts in the 4 studies are outlined below:  (i) Rajcan-Separovic et al. (2010a) — Twenty-six euploid miscarriages from twenty-three couples with RPL were studied. Couples were recruited based on the following criteria: (1) a history of idiopathic RPL, based on a negative evaluation, as previously described (Stephenson, 1996) and (2) at least one miscarriage with a normal karyotype (46, XY or 46, XX, confirmed with microsatellite analysis). The obstetric history and details on miscarriages can be found in Rajcan-Separovic et al. (2010a).  (ii) Rajcan-Separovic et al. (2010b) — Seventeen euploid embryonic miscarriages, defined as a crown-rump length between 4 and 30 mm without cardiac activity on transvaginal ultrasonography, were included in the study. All of these miscarriages had abnormal morphology, based on embryoscopy findings. The obstetric history and details on miscarriages can be found in Rajcan-Separovic et al. (2010b).  (iii) Robberecht et al. (2012) — Eleven euploid miscarriages were analysed using high resolution array. When parents were available, the origin of both rare and common miscarriage CNVs were followed up by quantitative PCR (qPCR) in parents.  18  (iv) Viaggi et al. (2013) — Forty first-trimester miscarriages with a normal karyotype were investigated by array-CGH. No sample from the parents could be retrieved. 2.3.2 Analysis of CNVs reported in 4 studies of miscarriages The four high-resolution CMA studies of miscarriages (Rajcan-Separovic et al., 2010a, Rajcan-Separovic et al., 2010b, Robberecht et al., 2012, Viaggi et al., 2013) were used because they provided breakpoints for the CNVs, classified the CNVs as rare or common based on the DGV (http://dgv.tcag.ca), and provided origin for some of the CNVs. Only CNVs >~1 Kb were included. Two studies were previous publications in my supervisor’s lab (Rajcan-Separovic et al., 2010a, Rajcan-Separovic et al., 2010b) from which I used published rare CNVs and unpublished data on common CNVs in miscarriages. Miscarriage CNVs overlapping with CNVs from at least two control cohorts reported on DGV were considered common; the remaining CNVs were considered rare. All of the included CNVs were reviewed and re-classified, if required, using the latest version of DGV at the time of analysis (~2013) to determine their current gene content and presence in healthy controls catalogued in DGV. Details of these rare and common CNVs are summarized in Table A.1 (Appendix A) and Table A.2 (Appendix A), respectively. Overlapping common CNVs were merged into CNV regions (CNVRs). CNVRs are summarized in Table A.3 (Appendix A). 2.3.2.1 CNV gene content analysis including gene characteristics  Gene names and chromosomal coordinates for CNVs were mined from the human reference sequence (human genome 19 assembly, hg19, University of California, Santa Cruz (UCSC) database). Gene content of a CNV was defined based on genes located within CNVs.  19  Criteria for assessing the genes from CNVs as possible candidates for miscarriage included: (i) the expression pattern of genes using databases TiGER (Tissue-specific Gene Expression and Regulation; http://bioinfo. wilmer.jhu.edu/tiger/) (Liu et al. , 2008) and TiSGeD (Tissue-Specific Genes Database; http://bioinf.xmu.edu.cn:8080/databases/TiSGeD/search. php) (Xiao et al. , 2010); and (ii) the consequences of gene knockout in mice, when available (e.g. embryonic lethality and/or pregnancy-related abnormalities) using MGI (http://www. informatics.jax.org). 2.3.2.2 Functional gene enrichment analysis  Functional enrichment and pathway analysis for genes from common and rare CNV groups was performed using DAVID (http://david.abcc.ncifcrf.gov/) (Dennis et al., 2003). Gene-enrichment for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms analysis (biological process, cellular component and molecular function) was carried out for rare and common CNV groups separately using the ‘Functional annotation’ tool from the DAVID (Dennis et al., 2003). An adjusted P-value of <0.05 was considered statistically significant for pathway analysis and GO terms. Data were reported as significantly enriched pathways and GO terms. 2.3.3 Statistical analysis  All statistical analyses were performed in R 2.12.0 for Windows (The R project for statistical computing: http://cran.r-project.org/bin/windows/ base/old/2.12.0/). CNV size was described with medians and interquartile ranges (IQR), and CNV gene density with a mean. CNV size and gene density comparison was performed using Wilcoxon rank-sum test. 20  2.4 Results 2.4.1 Miscarriage CNV characteristics  A total of 101 euploid miscarriages were included in my analysis collected from four high-resolution array studies of miscarriages which recorded common and rare CNVs (Rajcan-Separovic et al., 2010a, Rajcan-Separovic et al., 2010b, Robberecht et al., 2012, Viaggi et al., 2013). This included 50 miscarriages from Rajcan-Separovic et al. (2010a and 2010b), 11 from Robberecht et al. (2012), and 40 from Viaggi et al. (2013). Details of all the miscarriage rare and common CNVs/CNVRs including their cytobands, sizes, and gene content are presented in Tables A.1-A.3 (Appendix A).  Out of 44 CNVs initially reported as rare, 25 remained rare after re-assessment using DGV, the remainder were common. Out of the 25 rare: 14 were parental, 1 de novo, and 10 with unknown origin (Table A.1; Appendix A). The de novo CNV was a 1.5Kb gain disrupting G Protein-Coupled Receptor 180 (GPR180) gene, known to be associated with response to vascular injury, but with no evidence of a role in pregnancy and reproduction (Rajcan-Separovic et al., 2010b). Three CNVs initially reported as de novo and rare were either excluded (a 100 base-pair in size CNV, detectable by custom array only and found to disrupt the WD repeat domain 37 gene (WDR37), not known to be associated with miscarriage (Rajcan-Separovic et al., 2010b)) or were reclassified as common (two CNVs at chr11:50414030–51372036 and chr11:2904010–2906824, hg19, covering no genes and cyclin-dependent kinase inhibitor 1C gene (CDKN1C), respectively (reported in Robberecht et al. (2012)).  In total, 371 individual common CNVs in miscarriages were noted in the four studies (Table A.2; Appendix A). CNV from 15q11.2 (mapping to 20– 22 Mb) was the most common and was present in 31/50 (62%) and 17/40 (42%) of all miscarriages studied by Rajcan-21  Separovic and co-workers (2010a and 2010b) and Viaggi and co-workers (2013), respectively. The common CNVs were merged into 118 non-redundant common CNV regions (CNVRs) (Table A.3; Appendix A). The paternal/maternal origin of these common miscarriage CNVs could not be conclusively determined, such as when the common CNV was present in both partners.  2.4.2 Rare versus common CNV’s features I compared the 25 rare and 371 common miscarriage CNVs in regard to size and gene content. In total, 94 and 353 genes were found in these CNVs, respectively (Table 2.1). Overall, the rare and common CNVs did not differ significantly in their size (median of 0.16 versus 0.14 Mb, respectively) but the mean gene density for rare CNVs was significantly higher than common CNVs (56 versus 24 Genes/Mb; P = 0.03) respectively (Table 2.1). Table 2.1 Comparison of the CNV characteristics in 101 miscarriages from 4 studies.   Rare  Common  Number of  CNVs/CNVRs 25/25 371/118 Median CNV size (Mb) 0.16 (IQR 0.08 - 0.51) 0.14 (IQR 0.08-0.55) Number of genes 94 353 Gene density (No. of genes/Mb) 56* 24 GO term enrichment NS Immune-response and reproductive-processes* Pathway enrichment# NS Immune-response  Genes with phenotypes in MGI§ 40/94 (43%) 66/353 (19%) Genes with embryonic lethality phenotype§ 12/40 (30%) 14/66 (21%) Genes with reproduction/pregnancy-related abnormalities § 9/40 (23%) 14/66 (21%) Genes expressed in placenta§§ 3/94 (3%) 3/353 (1%)  NS, Not significant; IQR, interquartile range. *p<0.05.  # KEGG pathway-analysis using DAVID web-tool (http://david.abcc.ncifcrf.gov/).  § MGI web-tool (http://www.informatics.jax.org) was used to identify the number of genes with mouse-knock out phenotypes. §§ TiGER (http://bioinfo.wilmer.jhu.edu/tiger/) and TiSGeD (http://bioinf.xmu.edu.cn:8080/databases/TiSGeD/search.php) web-tools were used to identify the number of genes which have an exclusive or high expression in placenta.   22  No specific pathway or GO term enrichment was noted for the 94 genes from rare CNVs, but 353 genes from common CNVs showed significant enrichment (P<0.05) in genes from pregnancy/reproduction-related biological processes (or GO terms) (Table 2.1 and Table 2.2) and immune-response related pathways (Table 2.1 and Table 2.3).  Table 2.2 Gene ontology (GO) terms of rare and common miscarriage CNV genes using DAVID classification. Rare        No significant GO term was identified for rare CNV genes         Common          Genes (n) % P-value  Biological process    Multi-organism process 22 7.4 0.007 Reproductive process 22 7.4 0.019 Reproduction 22 7.4 0.014 Defence response 19 6.4 0.017 Defence response to bacterium 8 2.7 0.036 Antigen processing and presentation 7 2.4 0.041     Cellular component    MHC protein  7 2.4 0.006     Molecular functions    No significant GO term - - - An adjusted p-value of <0.050 was considered statistically significant      The significantly enriched biological processes for genes from common CNVs included multi-organism processes, reproductive processes, reproduction, defence response, and antigen processing and presentation (Table 2.2). Moreover, major histocompatibility complex (MHC) which is a group of cell surface proteins essential for the immune system was an enriched cellular component GO term for common CNV genes (Table 2.2).  The enriched immune-response related pathways included graft-vs-host disease, allograft rejection, and antigen processing and presentation (Table 2.3).  23  Table 2.3 Enriched functional pathways for genes from rare/common miscarriage CNVs using the DAVID CNV Significantly enriched pathways (p < 0.05) CNV genes involved in pathways CNV characteristics (type, origin, and frequency) Gene function Mouse model (Reference) Rare No significant pathway enrichment _ _ _ _ Common -Graft-vs-host disease  -Antigen processing and presentation -Allograft rejection  -Viral myocarditis -Asthma -Type I diabetes mellitus  -Autoimmune thyroid disease  -Systemic lupus erythematosus KLRC1, KLRC2 12p13.2 (loss) –  unknown origin in miscarriage Receptors for recognition of MHC class I HLA-E molecules by Natural Killer (NK) cells and several cytotoxic T-cells. _ HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-DQA1 6p21.32 (loss/gain) * – present in 18/101 (18%) miscarriages  (unknown origin in miscarriage) Key immune response regulators due to their facilitating role in antigen processing and presentation. HLA-DQA1: Abnormal T-cell morphology and subtype cell number (Kontgen et al. , 1993) RNASE3 14q11.2 (loss) – paternal, transmitted to miscarriage Involved in immune response by its antibacterial, cytotoxic, and low ribonuclease activity. Allograft-rejection is among the super-pathways related to this gene _   CAV1 7q31.2 (gain) – paternal transmitted to miscarriage Facilitates cell cycle progression and stimulates the activation and proliferation of T-cells Increased susceptibility to bacterial infection induced mortality (Medina et al. , 2006), reduced fertility (Le Saux et al. , 2008), and vasculature abnormalities (Schubert et al. , 2002) H2BFM, H2BFWT Xq22.2 (gain) – unknown origin, present in 4/101 (4%) miscarriages Members of the H2B histone. H2BFWT is specifically expressed in sperm nuclei and its 5' UTR polymorphism has been linked to male infertility. _ *Variably deleted or duplicated: In some cases only two HLA subtypes were affected while in others all were affected.     Genes from miscarriage CNVs involved in these pathways included killer-cell lectin like receptor C1 and C2 genes (KLRC1 and KLRC2), human leukocyte antigen (HLA) class II histocompatibility antigen DR alpha, DRB1 beta, DRB5 beta, and DQA1 chain genes (HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-DQA1), ribonuclease A family member 3 gene (RNASE3), 24  Caveolin 1 gene (CAV1), H2B histone family member M and W, testis-specific genes (H2BFM and H2BFWT). These genes are known to have essential immune-response and/or reproduction-related functions which are detailed in Table 2.3 together with their CNV characteristics and known mouse model phenotypes. Although no enrichment for biological pathways/processes was noted for rare CNV genes, they had two times more genes with mammalian phenotypes in MGI which were reported for 43% (40/94) of rare and 19% (66/353) of common CNV genes (Table 2.1, Figure 2.1). Rare CNVs also had slightly more genes with previous reported murine embryonic lethality than common CNVs (30% for rare versus 21% for common) but a comparable percentage of reproduction/pregnancy-associated abnormalities (23% for rare versus 21% for common) in mouse knockout models (Table 2.1, Figure 2.1, Table 2.4, and Table 2.5). Similarly, the number of genes with placental-specific or placental expression was not considerably different in rare versus common miscarriage CNVs, 3% versus 1%, respectively (Table 2.1, Figure 2.1, and Table 2.6). 25   Figure 2.1 Analysis of the function of CNV genes.  Rare and common miscarriage CNV genes associated with embryonic lethality, pregnancy-associated abnormalities, and/or placental-specific expression were identified. This was determined by assessing the 40/94 rare miscarriage CNV genes and 66/353 common miscarriage CNV genes that had reported murine phenotypes in mouse knockout studies, catalogued on MGI, as well as assessing all 94 rare and 353 common genes against human placental-specific genes listed on TiGER, and TiSGeD. For details of the CNVs in which these genes are involved refer to Tables 2.4-2.6. The number and percentage of genes (highlighted) associated with each category is indicated inside and outside of the corresponding colour-coded circles, respectively. 26  Table 2.4 Genes found in rare and common miscarriage CNVs associated with embryonic lethality. This was determined by reviewing mouse knockout studies catalogued in Mouse Genome Informatics (MGI) database.  Rare (Frequency: 12/40 (30%)) of genes with reported mouse knockout phenotype)  Gene CNV (type) – Origin Mouse knockout phenotype Reference NCAPH2 22q13.33 (Gain) - Unknown Embryonic lethality MGI ID: J:175295 RALA 7p14.1 (Gain)  - Maternal Complete embryonic lethality (Peschard et al. , 2012) CPT1B 22q13.33 (Gain) - Unknown Complete embryonic lethality  (Ji et al. , 2008) SCO2 22q13.33 (Gain) - Unknown Complete embryonic lethality between implantation & placentation (Yang et al. , 2010) RAD51B 14q24.1 (Loss) - Unknown  Complete embryonic lethality between implantation & somite formation (Shu et al. , 1999) NSDHL  Xq28 (Gain) - Paternal Complete embryonic lethality during organogenesis due to placental defects (X-linked male lethality) (Caldas et al. , 2005, Liu et al. , 1999) RILP 17p13.3 (Loss) - Maternal Complete embryonic lethality between somite formation & embryo turning (Tao et al. , 2009) MAPK11 22q13.33 (Gain) - Unknown Complete lethality throughout fetal growth & development (del Barco Barrantes et al. , 2011) PLXNB2 22q13.33 (Gain) - Unknown Complete prenatal lethality (Deng et al. , 2007) SIX3 2p21 (Gain) - Unknown Complete perinatal lethality  (Lagutin et al. , 2003) OFD1 Xp22.2 (Gain) - Maternal Partial embryonic lethality during organogenesis (X-linked male lethality) (Ferrante et al. , 2006) REL 2p16.1 (Loss) - Unknown Complete embryonic lethality during organogenesis (Grossmann et al. , 1999)     Common (Frequency: 14/66 (21%) of genes with reported mouse knockout phenotype)    Gene CNV (type) – Origin  Mouse knockout phenotype Reference NSRP1 17q11.2 (Loss) - Unknown Complete embryonic lethality (Kim et al. , 2011) PCYT2 17q25.3 (Gain) - Unknown Complete embryonic lethality  (Fullerton et al. , 2007) RBBP8 18q11.2 (Loss) - Unknown Complete embryonic lethality before implantation (Chen et al. , 2005a) SLC2A3 12p13.31 (Gain) - Unknown Complete embryonic lethality between implantation & placentation (Stuart et al. , 2011) PCSK6 15q26.3 (Gain) - Paternal Complete embryonic lethality between implantation & placentation (Beck et al. , 2002) CYFIP1 15q11.2 (Gain) - Unknown Complete embryonic lethality between somite formation and embryo turning MGI ID: J:175295 MLLT4 6q27 (Gain) - 1 Maternal, 2 Unknown Complete embryonic lethality during organogenesis (Ikeda et al. , 1999, Zhadanov et al. , 1999) PTPRJ 11p11.2 (Gain) - Unknown Complete embryonic lethality during organogenesis (Takahashi et al. , 2003) MAP2K1 15q22.31 (Loss) - 2 Unknown Complete embryonic lethality during organogenesis (Catalanotti et al. , 2009) UTY Yq11.21 (Loss) - Unknown Complete embryonic lethality during organogenesis (Shpargel et al. , 2012) SOX11 2p25.2 (Gain) - Paternal Complete embryonic lethality during organogenesis (Bhattaram et al. , 2010) MAP2K4 17p12 (Loss) - Unknown Complete embryonic lethality during organogenesis (Nishina et al. , 1999) CDKN1C 11p15.4 (Gain) - de novo Complete perinatal lethality (Zhang et al. , 1999) MAFG 17q25.3 (Gain) - Unknown Partial prenatal lethality (Shavit et al. , 1998)  27  Table 2.5 Genes found in rare and common miscarriage CNVs associated with infertility and other reproductive disorders. This was determined by reviewing the mouse knockout studies catalogued in Mouse Genome Informatics (MGI).  Rare (Frequency: 9/40 (23%)) of genes with reported mouse knockout phenotype) Gene CNV (type) – Origin  Mouse knockout phenotype  Reference MAPK8IP2 22q13.33 (Gain) - Unknown  Male infertility (Kennedy et al. , 2007) ACR  22q13.33 (Gain) - Unknown Male infertility (Nayernia et al. , 2005) SBF1 22q13.33 (Gain) - Unknown Male infertility (Firestein et al. , 2002) SYCE3 22q13.33 (Gain) - Unknown Male/Female infertility (Schramm et al. , 2011) LIPC  15q22.1 (Loss) - Paternal Abnormal reproductive system physiology  (Wade et al. , 2002) NSDHL  Xq28 (Gain) - Paternal Placental defects (Caldas et al., 2005) MAPK11 22q13.33 (Gain) - Unknown  Immune-response abnormalities: Decreased T-cell proliferation, abnormal T-helper 1/2/17 cell differentiation, decreased interferon gamma secretion, increased interleukin-10 secretion, decreased tumour necrosis factor secretion Direct Data Submission on MGI MAPK12 22q13.33 (Gain) - Unknown  Decreased angiogenesis (Pogozelski et al. , 2009) REL 2p16.1 (Loss) - Unknown Abnormal humoral immune response (Carrasco et al. , 1998, Ouaaz et al. , 2002)     Common (Frequency: 14/66 (21%)) of genes with reported mouse knockout phenotype)   Gene CNV (type) – Origin  Mouse knockout phenotype  Reference PTPRJ 11p11.2 (Gain) - Unknown Infertility (Zhu et al. , 2008) TRIP13 5p15.33 (Loss) - Unknown Infertility and abnormal reproductive system physiology and gametogenesis in males and females (Li and Schimenti, 2007, Roig et al. , 2010) FOXO3B 17p11.2 (Gain) - Paternal Female infertility (Castrillon et al. , 2003, Miyamoto et al. , 2007) DAZ2 Yq11.223 (Gain) - Unknown Male/female infertility (Ruggiu et al. , 1997) CATSPER2 15q15.3 (Loss) - Paternal Male infertility and impaired fertilisation (Quill et al. , 2003) CAV1 7q31.2 (Gain) - Paternal Reduced fertility, abnormal uterus physiology, and immune-response abnormalities (Carrasco et al., 1998, Le Saux et al., 2008, Li et al. , 2011) PPYR1 10q11.22 (Gain/Loss) - Unknown Abnormal fertility/fecundity (Sainsbury et al. , 2002) MAP2K1 15q22.31 (Loss) - Unknown Abnormal placenta morphology/cell migration/angiogenesis (Bissonauth et al. , 2006, Giroux et al. , 1999) SLC2A3 12p13.31 (Gain) - Unknown Abnormal placental transport and early pregnancy loss (Ganguly et al. , 2007) CDKN1C 11p15.4 (Gain) - de novo Abnormal placenta/uterus/imprinting (Mancini-Dinardo et al. , 2006, Takahashi et al. , 2000a, Takahashi et al. , 2000b)  DOCK8 9p24.3 (Gain) - Unknown Abnormal humoral immune response (Randall et al. , 2009) LYN 8q12.1 (Gain) - Unknown Abnormal humoral immune response (Hasegawa et al. , 2001) MSR1 8p22 (Gain) - Unknown Abnormal macrophage morphology, increased IgM/interleukin secretion (Chen et al. , 2005b, Suzuki et al. , 1997, Zhu et al. , 2001) HLA-DQA1 6p21.32 (Gain/Loss)-Unknown Abnormal MHC II cell surface expression on macrophages (Clausen et al. , 1999) 28  Table 2.6 Placental-specific genes found in rare and common miscarriage CNVs. This was determined by checking CNV genes against placental-specific genes reported on TiGER and TiSGeD databases.  Rare (Frequency: 3/94 (~3%) of all miscarriage genes); Common (Frequency: 3/353 (~1%) of all miscarriage genes) Genes TiGER TiSGeD CNV (type) Type (origin) Frequency in our miscarriage dataset STS + + Xp22.31 (Gain) Rare (Maternal)  1/101 (1%) Miscarriages EGFL6 + + Xp22.2 (Gain) Rare (Maternal)  1/101 (1%) Miscarriages TIMP2 (Maternally imprinted) - + 17q25.3 (Gain) Rare (Maternal) 1/101 (1%) Miscarriages PSG gene cluster + + 19q13.2-13.31 (Loss) Common (Unknown) 2/101 (2%) Miscarriages NOTUM + - 17q25.3 (Gain) Common (Unknown) 1/101 (1%) Miscarriages + 1 unrelated father CDKN1C (Maternally imprinted) - + 11p15.4 (Gain) Common (de novo) 1/101 (1%) Miscarriages   29  2.4.3 Candidate genes for miscarriages The list of genes from miscarriage CNVs causing embryonic lethality or reproductive problems in knockout mice is shown above in Figure 2.1, Table 2.4, and Table 2.5. Genes with highest or exclusive expression in placenta are also listed in Figure 2.1 and Table 2.6.  The vast majority of CNVs containing mouse-lethal genes’ table (Table 2.4) are of unknown origin (~70%) and almost all of the remaining (~30%) are parental. Also, a large fraction of them are gains (~70%) rather than losses (~30%) which altogether hampers the clear-cut correlation of CNVs and their integral lethal genes in mouse models with miscarriage in humans. There were only 3 genes from rare heterozygous deletions, which is the closest comparison to homozygous mutations typical for knockout mouse models. This included RAD51 paralog B gene (RAD51B), Rab interacting lysosomal protein gene (RILP), and REL proto-oncogene, NF- κB subunit gene (REL) (Table 2.4). Furthermore, although, these 3 genes are interesting candidate lethal genes with important development-related functions e.g. DNA repair and protection against aneuploidy (RAD51B) (Rodrigue et al. , 2013), endocytic trafficking and cell-survival (RILP) (Lin et al. , 2014), and apoptotic pathway and cell-survival (REL) (Barkett and Gilmore, 1999), one was maternal (RILP) while the origin of the other two (RAD51B and REL) remain uncertain if they are de novo or inherited, limiting thus their categorical association with miscarriage. There was only one de novo CNV (gain) partially including and disrupting CDKN1C gene sequence which when knocked-out in mice is embryonic lethal (Table 2.4). This gene is highly expressed in placenta and could possibly be related to miscarriage, however, deletions within this gene are also commonly found in DGV controls. CDKN1C is maternally imprinted, 30  and the lack of information on the reproductive history of DGV controls that have this CNV and whether they were males or females hampers the interpretation of its relevance in miscarriage.  Similar to the CNV genes that are lethal in the mouse, the majority of genes in rare and common CNVs associated with reproductive problems when knocked out in mice were of unknown origin (~74%) or gains (~70%) in miscarriages (Table 2.5). There were two paternal losses of genes lipase C gene (LIPC) and cation channels sperm-associated protein 2 gene (CATSPER2) (Table 2.5). LIPC is reported to cause abnormal reproductive system physiology (Wade et al., 2002) in mouse knockouts and is involved in cholesterol and fat processing and breakdown (Wade et al., 2002) (Table 2.5). CATSPER2 is associated with male infertility, and impaired spermatozoa activity and fertilization in knockout mice (Quill et al., 2003) (Table 2.5). The deletion of these genes in the fathers could possibly contribute to miscarriage by affecting the paternal reproductive physiology. In addition to CDKN1C described above, several other genes had strong or exclusive expression in the placenta, namely EGF-like–domain, multiple 6 gene (EGFL6), steroid sulfatase (Microsomal), isozyme S gene (STS), tissue inhibitor of metalloproteinase 2 gene (TIMP2) from rare CNVs and notum pectinacetylesterase homolog (drosophila) gene (NOTUM), and the pregnancy-specific glycoprotein (PSG) gene cluster, from common CNVs (Table 2.6). Copy number changes and dysfunction of these genes due to CNVs could contribute to inadequate placenta function/development and miscarriage. 2.5 Discussion  This study represents a unique analysis of rare and common CNVs and their integral genes detected in 101 euploid miscarriages from 4 studies. Their characterization included analysis of overall genomic features of CNVs and their integral gene function and was performed 31  to facilitate identification of candidate genes for miscarriage. Collectively, the rare and common CNVs were small (median 0.14 –0.16 Mb) in these four studies. Although the number of available rare CNVs was small in this analysis, I noted a significantly (P < 0.03) higher mean gene density and two times more rare CNV genes with an abnormal phenotype in mouse knockout models (43% versus 19%) compared with common CNV genes, which could suggest the importance of rare CNVs and their genes in pregnancy failure. It was intriguing to find that 19% (66/353) of genes from common CNVs are also embryonic lethal in mouse knockout models. A recent large-scale mouse knockout study, however, suggested that not all lethal genes in mouse cause lethality in humans (Dickinson et al., 2016). For instance, human orthologue of several mouse-lethal genes e.g. COQ6, DEPDC5, YARS and KDM5C were associated to milder clinical manifestation mainly NDD and ID in humans (Dickinson et al., 2016). This was suspected to be due to the difference in the underlying biological processes in both species (Dickinson et al., 2016). Moreover, the embryonic lethal genes found in MGI described in this chapter are normally complete homozygous knockouts which do not represent the heterozygous nature of CNVs, and in addition the majority of both rare and common miscarriage CNVs that contain lethal genes are duplications (~70%) and/or are of unknown origin (~70%) (i.e. could be either de novo or parental). Therefore my interpretation of human miscarriage genes that are embryonic-lethal in mice was hampered in most cases due to lack of de novo losses containing lethal genes in mice. The pathogenic role of deletions of genes in miscarriages which are shown to be lethal in mice is easier to envision, because even if the deletions are parental in origin, there is a possibility that undetected secondary variants (e.g. sequence mutations) are present on the other allele in the miscarriage. There were 3 genes (RAD51B, RILP, and REL) from rare CNVs included in losses of maternal (RILP) or unknown 32  origin (RAD51B, REL) which could be candidates for such “two-hit” events. Sequencing data have revealed several embryonic/fetal lethal genes in humans with biallelic mutations, e.g. DYNC2H1 (Qiao et al. , 2016), IFT122 (Tsurusaki et al. , 2014), THSD1 (Shamseldin et al. , 2015), CHRNA1 (Shamseldin et al. , 2013), KIF14 (Filges et al. , 2014), GLE1 (Ellard et al. , 2015), and RYR1 (Ellard et al., 2015). Therefore, euploid miscarriages can be further analyzed by combined CMA and sequencing technologies in future to identify smaller variations e.g. single-nucleotide mutations in genes with CNVs or genome-wide to identify lethal genes with mutations affecting both alleles.  My study also identified several parental CNVs harboring imprinted genes i.e. those that are expressed preferentially from one parent-of-origin and from the parental chromosome that is affected by the CNV. For example TIMP2, is maternally inherited in a family with multiple miscarriages. It is known to be expressed from the maternal allele in placenta, and is strongly expressed in placenta and maternal reproductive organs (e.g. endometrium) raising possibility that the miscarriages occurred because of abnormal expression of the gene due to the CNV in either the placenta or maternal tissues or both. In addition, some parental CNVs contained genes that when knocked out in mice cause abnormal reproductive system physiology, abnormal male fertility and impaired fertilization (e.g. paternal deletions of LIPC and CATSPER2). Therefore CNVs predisposing parents to miscarriage should be considered as well. My analysis of CNVs from euploid miscarriages from four studies did not identify any single miscarriage CNV that was clearly pathogenic, which is similar to the study reported by Bug and co-workers (2014) but in contrast to recent studies reporting that ~2-6% of euploid miscarriages have a CNV that could be clinically significant (Levy et al., 2014, Rosenfeld et al., 2015, Shen et al., 2016, Wang et al., 2017b, Zhou et al., 2016). Overall, the frequency of 33  clinically significant miscarriage CNVs in euploid miscarriages appears to be smaller than in cohorts of postnatal cases with NDD, where ~15% of subjects have a clinically relevant CNV (Vulto-van Silfhout et al. , 2013). Similarly, de novo rare miscarriage CNVs appear to be infrequent in euploid miscarriages as the vast majority of rare CNVs with a known origin were parental (14/15 in 4 studies we re-analyzed and 10/12 CNVs in the recent studies e.g. Levy et al. (2014)).  Although no clear single pathogenic CNV was identified, my study pointed to the role of common CNVs in miscarriage based on significant enrichment of genes from common CNVs in immunological pathways, such as graft-vs.-host disease, allograft rejection and antigen processing and presentation, and biological processes related to immune response and reproduction. Interestingly, several previous studies have also reported immune-response as the top affected pathway in females that experience RPL by performing expression analysis of the endometrium or enrichment for CNV genes from couples with RPL in biological pathways (Kosova et al. , 2015, Krieg et al. , 2012, Nagirnaja et al. , 2014). Moreover, genes dysregulated in endometrium of women with RPL were shown to be significantly enriched in immune response and signaling pathways (Kosova et al., 2015) suggesting that disturbed immune-related pathways are commonly associated with miscarriage. The contribution of CNVs to miscarriages seems, therefore, to be complex, with genes from common, rare and parental CNVs potentially playing a role. As a result, future array studies of additional miscarriages and couples should enlist all CNVs with their characteristics (gene content, size and origin). The interpretation of CNVs detected in parents could be facilitated with a database of CNVs in controls with a known reproductive history, as very little is known about the DGV controls other than they are healthy. One recent example of such effort was provided in 34  a study which recorded CNVs in 411 Japanese women presumed fertile based on one or more live-born children (Migita et al. , 2014).   Finally, the in silico analysis of the functional impact of the CNV should be complemented by in vitro analysis of the function of genes integral to CNVs (e.g. RNA and protein expression) in miscarriage tissues as recently reported by another study from our lab (Wen et al. , 2015) but also in reproductive tissues of carrier parents.      2.6 Conclusion My study showed that both rare and common miscarriage CNVs could have a role in miscarriage. Rare CNVs significantly have higher gene density and contain more genes with abnormal phenotypes in mouse knockout models when compared with common miscarriage CNVs, despite having a comparable size. But, common miscarriage CNVs were found to be significantly enriched in genes involved in pathways and biological processes relevant to pregnancy. No CNVs of clearly pathogenic role were identified. Future studies of euploid miscarriages and couples should record a complete CNV burden (rare and common CNVs) and characteristics (size, gene content, origin) for a more comprehensive assessment of their role in miscarriage. Moreover, CNVs carried by parents are typically considered benign but could impair normal pregnancy development if they contain genes which are relevant for parental reproduction, are imprinted in pregnancy tissues, show variable expression or carry mutations on the second allele in miscarriage. I was not able to shortlist any of the reported miscarriage CNV genes as a reliable candidate to test in zebrafish due to uncertain or parental inheritance, their role in reproduction-specific biological processes and placental expression, as well as immune-response related functions suggesting a role specific to feto-maternal interaction at the placenta level. Therefore, 35  zebrafish as a non-placental model was not a suitable animal to further test the function of any of the identified genes in vivo. Nevertheless, I published the findings of this study which represents a rare example of detailed analysis of miscarriage CNVs to hopefully help guide future more large-scale studies which are highly-warranted to gain insight about the role of CNVs in prenatal developmental arrests.   36  Chapter 3: CHARACTERIZING CNVs IN POSTNATAL DEVELOPMENTAL DELAY: 2p15p16.1 MICRODELETION SYNDROME 3.1 Background Postnatal developmental delay (DD) is the condition of a child being less developed mentally and/or physically than its normal age. It frequently affects nervous-system (neuro-developmental delay; NDD) which can be accompanied with other physical abnormalities of development such as microcephaly, unique craniofacial/body defects, and growth retardation as well as socio-behavioral abnormalities including autism (Ben-Shachar et al. , 2009, Erdogan et al. , 2008, Mefford et al. , 2007). When NDD is severe, it is termed intellectual disability (ID) which is seen in 1-3% of world’s population and is characterized by low intelligence quotient (IQ) score of less than 70 (Hamdan et al. , 2014, Maulik et al. , 2011, Ropers, 2010).  ID can be caused by environmental factors such as complications during pregnancy and birth which can vary from maternal alcohol abuse, intrauterine infections (e.g. rubella), maternal/fetal malnutrition, and/or fetal brain damage due to perinatal hypoxic conditions (Vissers et al. , 2016), however, genetic factors play an important part in ID etiology (Vissers et al., 2016). They range from large chromosome abnormalities such as trisomy 21 in Down’s syndrome to mutations in single genes e.g. PHF8 (Loenarz et al. , 2010) and KDM5C (Brookes et al. , 2015). So far, more than 700 genes have been identified that are associated with X-linked, autosomal-recessive, and autosomal-dominant forms of ID and its associated NDD traits (Vissers et al., 2016). CNVs are considered a strong genetic etiological cause for ID/NDD with ~15% of cases having an established CNV diagnosis (Cooper et al. , 2011, Vissers and Stankiewicz, 2012). 37  Some of the great examples of such CNVs include chromosomal deletions at 15q11.1q13.1, 17p11.2, 7q11.23, 22q11.2 and duplications at Xq28 associated with Prader-Willi and Angelman syndrome (Butler et al. , 1986), Smith-Magenis syndrome (Chen et al. , 1997), Williams-Beuren syndrome (Perez Jurado et al. , 1996), DiGeorge syndrome (Edelmann et al. , 1999), and MECP2 duplication syndrome (Van Esch, 1993), respectively. Each of these syndromes manifest shared recognizable constellation of clinical features including ID and a number of recurrent physical anomalies. In this chapter, I will describe the characterization of one CNV mapping to 2p15p16.1 chromosome region which is associated with a distinct ID/NDD syndrome in order to identify candidate genes. The 2p15p16.1 microdeletion syndrome was first identified and described in our lab (Rajcan-Separovic et al., 2007). I studied this CNV, in particular, because its causative genes were not previously found and characterized by other in vivo modeling studies. The 2p15p16.1 microdeletion syndrome is a rare genomic disorder caused by a de novo deletion of a submicroscopic segment in the short arm of human chromosome 2 (Online Mendelian Inheritance in Man [OMIM] 612513). This syndrome was first described in 2 phenotypically similar children, with idiopathic ID (IQ<70), autism, speech and language delay, microcephaly, structural brain abnormalities, optic nerve hypoplasia, neuromuscular deficiencies, distinctive pattern of craniofacial features (including telecanthus, broad and high nasal root, ptosis, downslanting short palpebral fissures, long and smooth philtrum, large ears), camptodactyly, feeding problems, and renal defects (Rajcan-Separovic et al., 2007). The deletions in the first two subjects were overlapping in the 2p15p16.1 region and had the same gene content (25 genes: 18 coding and 7 non-coding) but had different sizes (5.7 and 4.5 Mb), as detected by chromosomal microarrays (Rajcan-Separovic et al., 2007).  38  3.1.1 Review of published cases with 2p15p16.1 deletion and their phenotypic features    Since the initial report of the two cases, there have been 23 other subjects reported in the literature (Balci et al. , 2015, Basak et al. , 2015, Chabchoub et al. , 2008, de Leeuw et al. , 2008, Fannemel et al. , 2014, Felix et al. , 2010, Florisson et al. , 2013, Hancarova et al. , 2013, Hucthagowder et al. , 2012, Jorgez et al. , 2014, Liang et al. , 2009, Ottolini et al. , 2015, Peter et al. , 2014, Piccione et al. , 2012, Prontera et al. , 2011, Ronzoni et al. , 2015, Shimojima et al. , 2015) and several cases on the DECIPHER (Firth et al. , 2009) with deletions in the same region, who demonstrate common phenotypic abnormalities. Moderate to severe ID, DD, and delayed language skills were reported for all cases that assessed/reported these features (as reviewed in (Bagheri et al., 2016)). The most frequently observed physical feature in the reported patients is head size abnormality which accounts for 20/25 (80%) subjects, who predominantly presented with congenital microcephaly (occipitofrontal circumference [OFC] of <3rd percentile) (16/20 subjects) (de Leeuw et al., 2008, Felix et al., 2010, Florisson et al., 2013, Hancarova et al., 2013, Hucthagowder et al., 2012, Jorgez et al., 2014, Liang et al., 2009, Ottolini et al., 2015, Piccione et al., 2012, Rajcan-Separovic et al., 2007, Shimojima et al., 2015) or smaller head size significantly lower than the normal average head size (OFC of <5th-10th percentile) (4/20 subjects) (Basak et al., 2015, Fannemel et al., 2014, Piccione et al., 2012, Prontera et al., 2011). There has also been 5/25 (20%) cases that did not report microcephaly, but 3 of these reported cases had other head shape abnormalities (Balci et al., 2015, Chabchoub et al., 2008, Jorgez et al., 2014) while the remaining two either reported brain structural abnormalities (Ronzoni et al., 2015) or did not comment on head size/shape (Peter et al., 2014).  The majority of cases had at least one or a combination of distinctive facial features reported in the first 2 cases, which included high/broad nasal root (68%), telecanthus (68%), 39  epicanthal folds (64%), smooth and long philtrum (60%), ptosis (56%), downslanting palpebral fissures (~50%), and a high, narrow palate (~50%) (See review (Bagheri et al., 2016)). Other frequently reported features included digital anomalies (72%), feeding problems (52%) and hypotonia (~50%) (Bagheri et al., 2016).   3.1.2 Genomic findings for all published cases The deletions reported in the 2p15p16.1 region span ~11Mb (chr2: 55,616,146-66,376,496, hg19) and are highly variable in size, ranging from 0.103 Mb (Ronzoni et al., 2015) to 7.89 Mb (Rajcan-Separovic et al., 2007), with average and median deletion sizes of 2.86 and 2.50Mb, respectively (see review (Bagheri et al., 2016)). All 25 reported 2p15p16.1 deletions are de novo in origin and are not detected in healthy individuals reported on the DGV. For the 5/25 cases where inheritance was assessed, deletion occurred on the paternal chromosome (Felix et al., 2010, Hancarova et al., 2013, Liang et al., 2009, Piccione et al., 2012, Rajcan-Separovic et al., 2007).  In order to find a common deleted region for all cases, several early studies compared the smallest region of overlap (Chabchoub et al., 2008, de Leeuw et al., 2008, Liang et al., 2009, Rajcan-Separovic et al., 2007) and initially proposed a ~2.5Mb segment (chr2:59,241,620-61,786,583 Mb; hg19) to be the minimal critical region (MCR) for 2p15p16.1 microdeletion syndrome (Liang et al., 2009). This region was later refined by Hucthagowder and co-workers (2012) to ~1.1 Mb (chr2: 60,525,759- 61,600,000 Mb; hg19) as more deletions were reported (Felix et al., 2010, Hucthagowder et al., 2012) (Figure 3.1, Upper panel). The proposed MCR contains 10 protein coding genes (BCL11A, PAPOLG, REL, PUS10, PEX13, KIAA1841, C2orf74, AHSA2, USP34, XPO1) and 3 non-coding RNA genes (LINC01185, LOC339803, SNORA70B) (Hucthagowder et al., 2012) (Figure 3.1, Upper panel). 40   Figure 3.1 Genomic overlap of all published 2p15p16.1 microdeletions. A summary of deletion breakpoints identified in 25 subjects and published in a total of 18 studies are shown in black bars using UCSC genome browser’s custom track tool. Earlier studies are shown in the upper panel and the two proposed minimal critical regions (MCRs) (Hucthagowder et al., 2012, Liang et al., 2009) are highlighted in turquoise. The gene content of deletions and the MCRs are shown in the middle panel. More recent studies which in some cases only partially overlapped the MCRs, or not overlapped the refined MCR in 1 case (Prontera et al., 2011), are shown in the lower panel. The structural variants identified in healthy controls catalogued on DGV are also shown in the lower panel indicating the paucity of deletions (red) and duplications (blue) in this region.   41  However, cases reported after Hucthagowder et al., (2012) demonstrated variability in breakpoints and overlapped only partially the proposed MCR (Fannemel et al., 2014, Hancarova et al., 2013, Peter et al., 2014, Piccione et al., 2012) while 1 case did not overlap the MCR at all (Prontera et al., 2011) (Figure 3.1, lower panel). This complicated efforts to delineate a common critical region and pinpoint a single or small number of candidate genes for the syndrome. Non-overlapping deletions also indicated that deletion of different genes in this region could lead to similar phenotypic features in the individuals (Figure 3.1, lower panel). For instance, subject 1 reported in Piccione et al., (2012) carries a deletion covering BCL11A, PAPOLG, REL, PUS10, and PEX13 coding genes (Figure 3.1, lower panel) while the subject reported by Fannemel and co-workers (2014) had a non-overlapping deletion containing different genes, XPO1 and USP34 (Figure 3.1, lower panel). Yet, both share similar phenotypic features including ID, DD, delayed language skills, microcephaly, hypertelorism and certain unique craniofacial defects e.g. smooth, long philtrum and down-slanting palpebral fissure.  The observed genomic instability of the 2p15p16.1 region in constitutional cases was also supported by finding of acquired deletions and duplications in cancer (lymphoma) (Dai et al. , 2015, Kwiecinska et al. , 2014), and a suggestion was made that it overlaps with a fragile site (Luo et al. , 2004). Furthermore, germ-line duplications in this region were also reported in two studies (Loviglio et al. , 2016, Mimouni-Bloch et al. , 2015). Mimouni-Bloch and co-workers (2015) reported an isolated duplication case with ID, mild dysmorphic features (including puffy eyelids, broad philtrum, and right ear lobe sinus) and normal head size suggesting a milder clinical phenotype of duplications in this region than deletions. Loviglio and co-workers (2016) recently grouped 9 ID subjects with duplications in the region and found that their head size is significantly bigger than in the deletion cases but comparable to controls. They proposed the 42  2p15p16.1 region CNVs and the phenotype of carriers is yet another example of mirror phenotype (Loviglio et al., 2016). Mirror phenotypes of head size and body mass index (BMI) have previously been reported extensively for 16p11.2 reciprocal CNVs (Golzio et al., 2012, Jacquemont et al. , 2011, Migliavacca et al., 2015, Shinawi et al. , 2010) and therefore identifying whether the same is true for 2p15p16.1 CNVs is highly warranted. Identifying this is significant especially as Loviglio and co-workers (2016) claimed that there is a chromosomal contact between the 16p11.2 and 2p15p16.1 loci suggesting a common pathomechanism that leads to similar phenotypes such as head size alteration in patients with DD. 3.2 Chapter goals My overall goal was to characterize the 2p15p16.1 CNV in order to find candidate genes for ID and physical abnormalities associated with it. I used in silico, in vitro, and in vivo experimental analysis and specifically, I aimed to: 1) Describe 8 new subjects with deletions in the 2p15p16.1 chromosome region and together with previously published cases (N=25), provide a collective in-depth review of CNVs and associated phenotypes for all available cases (N=33).  2) Identify the critical genes for further studies based on several criteria (e.g. how frequently they are deleted and their involvement in the smallest deleted regions).   3) Study the functional consequences of deletion in patient cell lines by assessing specific gene expression in patient cells and affected biological pathways using bioinformatics.   4) Study the phenotypic consequences of knockdown and overexpression of candidate genes in zebrafish  43  3.3 Materials and methods 3.3.1 Subjects Clinical findings for the 25 published cases, including our own first 2 patients reported in 2007, were collected from publications (Balci et al., 2015, Basak et al., 2015, Chabchoub et al., 2008, de Leeuw et al., 2008, Fannemel et al., 2014, Felix et al., 2010, Florisson et al., 2013, Hancarova et al., 2013, Hucthagowder et al., 2012, Jorgez et al., 2014, Liang et al., 2009, Ottolini et al., 2015, Peter et al., 2014, Piccione et al., 2012, Prontera et al., 2011, Rajcan-Separovic et al., 2007, Ronzoni et al., 2015, Shimojima et al., 2015). Added to this list of published cases were our additional 8 newly recruited patients with 2p15p16.1 microdeletions (identified herein as cases 1–8), giving a total of 33 cases so far. The clinical findings of these new cases were obtained from the patient charts and descriptions provided to us by the clinicians who assessed the patients’ phenotype.  Microdeletion breakpoints for all cases described here were from the human genome 19 assembly (hg19, UCSC database) or were converted to hg19 assembly from the initially reported hg18 breakpoints using the UCSC liftover tool (http://genome.ucsc.edu/cgi-bin/hgLiftOver?hgsid=579225243_wVQfxB93EgIHmkr1adOoNI0R3ewX). UCSC genome browser customization tool was used to illustrate the CNV characteristics (including breakpoints, size, and gene content) in the 33 patients. High-resolution arrays were used to obtain breakpoints of our 8 new cases. Breakpoints, array platforms used, and other genomic information for all 33 2p15p16.1 microdeletion cases are summarized in Table B.1 (Appendix B).  DECIPHER catalogue of CNVs in patients and DGV catalogue of CNVs in healthy individuals were used to assess additional CNVs in the 2p15p16.1 region and to confirm the paucity of pathogenic 2p15p16.1 CNVs in healthy individuals, respectively. 44  3.3.2 Chromosome microarray analysis and refinement of CNV breakpoints DNA was extracted from peripheral blood using the ArchivePure DNA blood kit (5 PRIME, #2300740). CMA for new cases was performed by either Affymetrix or Signature Genomics using commercial arrays (e.g. Signature Genomics, SignatureChipOSTM). Whenever possible, high-resolution Affymetrix Cytoscan SNP Array (hg19) or Affymetrix Cytogenetics Whole-Genome 2.7M Array analysis was repeated according to Affymetrix protocols and as previously described (Qiao et al. , 2013). DNA from the case reported previously by Chabchoub et al. (2008) was obtained courtesy of Thomy de Ravel (University of Leuven, Leuven, Belgium) for higher-resolution array analysis. Because our new case 2 had two additional de novo smaller (<30Kb) deletions in the 2p region which were involved in the non-coding regions (within the second intron of BCL11A and its ~50Kb upstream region) they were confirmed and breakpoints refined by quantitative multiplex PCR of short fluorescent fragments (QMPSF), using previously described protocols (Charbonnier et al. , 2000). Briefly, fluorescent dye-tagged primers were designed to amplify short PCR fragments (100-200bp) in the regions several base-pairs or kilo-bases downstream of the deletions to confirm the extension of the CNVs using the patient’s genomic DNA and DNA of healthy parents as controls. PCR fragments obtained were then analyzed by capillary gel electrophoresis, quantitated, and their deletion was confirmed by reduced fluorescent signal ratio in the patient versus controls. Primers designed for the first deletion (in the BCL11A intron) and regions within and surrounding the second deletion (in the upstream non-coding region of BCL11A) were as follows: a) within intron [Forward, 5′-TTCTAGTGCTTTGGGCGAGT-3′; Reverse, 5′-GGAATGCTGCAGTTGTCAGA-3′ (chr2:60710726 – 60710922, hg19)]; b) extension of upstream breakpoint of the intron [Forward, 5′- ACTACGTGGTTGTGCAACTCT-3′; Reverse, 5′- ATAGCTGAAGGGGGCCAAAA-3′ 45  (chr2: 60,718,097-60,718,597, hg19)];  c) within the intergenic enhancer [Forward, 5′-ACATGGCCAGACCTGAAAAC-3′; Reverse, 5′-CAGAAAGGCTGAACCCTGAG-3′ (chr2:60855485 – 60855688, hg19)]; d) extension of upstream breakpoint of the intergenic deletion [Forward, 5′-TGCATGAGGACATTGGTGAT-3′; Reverse, 5′-CTCAAGGGAAGGAGCTGTTG-3′ (chr2:60850362 – 60850585, hg19)]; e) extension of downstream breakpoint of the intergenic deletion [Forward, 5′-GAAGGTTCCCACGTTTTGAA-3′; Reverse, 5′-TTATTTGCCCCCAGTGAGAG-3′ (chr2:60835872 – 60836064, hg19)]. These smaller deletions are presented and discussed in more detail in the next chapter which focuses on the role of regulatory, non-coding sequences in the 2p15p16.1 CNV. 3.3.3 Candidate gene selection  The following criteria were used for candidate gene selection from 2p15p16.1 region for further in vitro and in vivo analysis: haploinsufficiency index/prediction scores, frequency of involvement in patient’s deletions, presence in the smallest deletions, and known information about function in brain. Haploinsufficiency index scores represent calculated probabilities for each gene to be sensitive to reduction of copy number (haploinsufficient) based the alikeness of their features (including sequence conservation and expression pattern during early development) to known haploinsufficient (HI) genes as described by Huang et al., (2010) and in DECIPHER (https://decipher.sanger.ac.uk/). According to both metric versions, a score of ~10% or less is considered as a significant likelihood for a gene to be HI. This metric system was obtained by contrasting the genomic, evolutionary, functional, and network features of 301 known HI genes from literature against 1,079 known haplosufficient (HS) genes (Huang et al., 2010) whereby deletion of one copy were considered benign in CNV studies. As a result of this contrast, a 46  statistical predictive model and metric system was built according to the probability for each gene in the genome to be HI based on having features uniquely found in known HI genes versus HS genes. This included longer and more conserved coding sequences and promoters, higher expression tissue-specificity and levels during early development, interaction with more partners and a greater network proximity to other known HI genes (Huang et al., 2010). The results of this analysis identified 12,433 genes with high predicted probability of haploinsufficiency and their prediction indices were confirmed for some by their enrichment in dominant diseases and phenotypic abnormalities in heterozygous knockout mice (Huang et al., 2010). A comprehensive PubMed literature review and genecards analysis (http://www.genecards.org/) (Stelzer et al. , 2016) was carried out to identify the known biological and neuronal roles of the involved 2p15p16.1 microdeletion genes. The available animal knockdown or knockout phenotypes were obtained from literature or the online MGI database (http://www.informatics.jax.org/)(Eppig et al. , 2017) for each gene. The available mutation phenotype data for each gene was obtained from Human Gene Mutation Database (HGMD; http://www.hgmd.cf.ac.uk)(Stenson et al. , 2003).  3.3.4 Functional analysis for 4 candidate genes in patient cells  Using the above criteria, 4 genes (XPO1, USP34, REL, and BCL11A) were selected for in vitro functional analysis and dosage-sensitivity assessment in patients’ lymphoblast cell lines (LCLs) established in our lab as previously described (Harvard et al. , 2011). Protein expression was tested using western blotting. Protein was extracted from LCLs using RIPA (radioimmunoprecipitation assay) lysis buffer (ThermoScientific, Cat #89900) mixed with 10µl/ml Halt Protease Inhibitor Cocktail (ThermoScientific, Cat #87785) according to manufacturer’s instructions. Protein concentration was then determined using the Bio-Rad DC 47  protein Assay (Biorad, Cat #500-0116) and the EnSpine 2300 Multilabel Reader (Perkin Elmer, Enspire Magager software v1.00 Rev2) according to manufacturer’s instructions. Proteins were then stored in -80⁰C prior to western blotting. Western blotting was performed according to standard protocols (Harvard et al., 2011), using 30µg of protein per lane per sample, and the following primary anti-bodies: antiCRM1/XPO1 (1:1000 dilution; Novus Biologicals, NB100-56493); anti-Ctip1/BCL11A (1:1000 dilution; Abcam, ab19487); anti–c-Rel (1:3000 dilution; Cell Signaling Technology, no. 4727); and anti–β-actin (1:3000 dilution; Sigma-Aldrich, A2066). Depending on the primary antibody host species, secondary goat polyclonal antibodies against rabbit immunoglobulin G (IgG) (Novus Biologicals, cat #NB730-H) or mouse IgG (Novus Biologicals, cat #NB720-H) were used at 1:2000 dilutions. An enhanced chemiluminescence (ECL) kit (Amersham Pharmacia Biotech Inc.) was used to develop the membranes, and the resultant films were analyzed using an ultraviolet (UV) densitometry machine (GeneSnap and Gene Tools software). The absorbance values for the tested proteins were normalized to the corresponding β-actin absorbance values, and the average normalized values for the proteins from 3 independent biological replicates for subjects and controls were used to generate graphs with protein expression comparison between cases and controls. For USP34 expression analysis, urea-based whole cell extracts (WCEs) were prepared: 9 M urea, 50 mM Tris-HCL, pH 7.5, and 10 mM 2-mercaptoethanol with sonication (15 seconds at 30% amplitude using a micro-tip; Sigma-Aldrich). WCEs were immunoblotted using anti-USP34 antibody (1:1000; Bethyl, A300-824). 3.3.5 Indirect immunofluorescence Immunofluorescence staining of XPO1 and USP34 targets or function-related markers, ribosomal protein S5 (rpS5) and p53 binding protein 1 (53BP1), respectively, were carried out to 48  indirectly assess the proper function of XPO1 and USP34 in LCLs using anti-rpS5 (1:1000; Santa Cruz Biotechnology Inc., C-20 sc-169174) and anti-53BP1 (1:1000; Bethyl, A300-272A). rpS5 is one of the XPO1 cargos (Thomas and Kutay, 2003), and 53BP1 is a marker of DNA double-strand repair efficiency, a process proposed to be regulated by USP34 (Sy et al. , 2013). Images were captured using SimplePCI software on the Zeiss Axioplan platform. For rpS5 nuclear export analysis, LCLs were also treated with leptomycin B, an XPO1 inhibitor (Sigma-Aldrich, 87081-35-4) at 20 ng/ml for 3 hours at room temperature. For ionizing radiation (IR)-induced 53BP1 foci formation, LCLs were irradiated with 3 Gy using ultraviolet C irradiation machine, as explained previously (Kobayashi et al. , 2004). 3.3.6 Immunohistochemical analysis Protein expression for the 4 candidate genes (XPO1, USP34, BCL11A, and REL) was determined using the human protein atlas (HPA) database (http://proteinatlas.org) (Uhlen et al. , 2015, Uhlen et al. , 2010). This database gathers the expression level of all human gene protein products in different body tissues (including brain) by using immunohistochemistry staining followed by scoring the expression level as high, medium, low, or not detectable.   In addition, detailed immunohistochemical analysis was performed on formalin-fixed wax sections of brain from a fetus (intrauterine death, with abnormal kidney and gonads) and from a 4-year-old girl who died from lymphocytic thyroiditis, with no evidence of brain involvement in either of the 2 cases. Anti-XPO1 and anti-USP34 antibodies were used according to standard protocols. Images were captured and analyzed for staining patterns by a certified neuropathologist (CD), Dr. Chris Dunham at the Department of Pathology and Laboratory Medicine, UBC. 49  3.3.7 Bioinformatics analysis of pathway enrichment for 2p15p16.1 genes The WEB-based GEne SeT AnaLysis Toolkit (WebGestalt, http://bioinfo.vanderbilt.edu/webgestalt/) (Wang et al., 2013, Zhang et al. , 2005) was used for functional enrichment analysis of genes from the 2p15p16.1 region. The 13 protein-coding and 3 non-coding genes deleted in 50% of the patients with a 2p15p16.1 microdeletion were uploaded on WebGestalt. This included the following genes: BCL11A, PAPOLG, LINC01185, REL, PUS10, PEX13, KIAA1841, LOC339803, C2orf74, AHSA2, USP34, SNORA70B, XPO1, FAM161A, CCT4, and COMMD1. Subsequent enrichment analysis was based on the 16 unique Entrez Gene IDs, 15 of which were recognized by WebGestalt. Parameters used for all enrichment analysis modules included the commonly used reference gene sets (all genes in a genome) and the multiple test adjustment default method proposed by Benjamini and Hochberg (Benjamini and Hochberg, 1995). ). The pathway analysis performed by WebGestalt is based on comparing the observed and expected number of genes in the pathway. WebGestalt uses publicly available pathway data (>1400 pathways) from multiple web-sources including Pathway Commons (http://www.pathwaycommons.org) as well as Wikipathways (http://www.wikipathways.org) (Cerami et al. , 2011) to determine if a gene-set is enriched in a particular pathway(s). In this chapter, for the 2p15p16.1 region genes, the top-10 pathways with a minimum of 2 genes were used. 3.3.8 Design of Morpholinos for knockdown of zebrafish gene orthologues  Zebrafish orthologs for human 2p15p16.1 candidate genes, XPO1, USP34, REL, and BCL11A and their genomic arrangements (synteny) were identified using Ensembl, The Zebrafish Model Organism Database (https://zfin.org/) and the UCSC genome browser, with sequence alignment and comparisons. I also included in the analysis two coding genes (VRK2 50  and FANCL) because they were the only genes in two CNVs detected in our case 2 and patient described by Prontera and co-workers (2011). The zebrafish protein sequences were extracted from Ensembl and aligned against the sequence of human counterparts using the Clustal Omega Multiple Sequence Alignment tool (EMBL-EBI; http://www.ebi.ac.uk/Tools/msa/clustalo/) (Sievers et al. , 2011) to obtain percentage protein homology for each gene. Gene-specific 25-base morpholino oligomers (MO) were designed by Gene Tools, LLC (http://www.gene-tools.com/), to block the splice donor or acceptor site (i.e. splice-blocking MO; SB-MO), closest to the 5′ end of the expected primary RNA. In cases in which a SB-MO failed to knockdown the gene, another MO was designed to target the genes mRNA translation initiation site (i.e. translation-blocking MO; TB-MO). The designed MO sequences are listed in Table B.2 (Appendix B). As an experimental control for each gene, a gene-mismatch control MO (MM), representing a scrambled gene sequence, was also designed and injected into age- and clutch-matched zebrafish embryos as the gene MO.  In Figures, MM controls are referred to as ‘control/ctrl’. All MOs were modified at the 3′ end with carboxyfluorescein to visualize the cellular uptake, location and equal distribution of the MO solution in the post-injection embryos. 3.3.9 In vivo zebrafish knockdown analysis Zebrafish (Danio rerio) wild-type (WT) AB embryos (Johnson et al. , 1994) were raised at 28.5 ⁰C on a 14 hour light/10 hour dark cycle in 100mm2 petri dishes containing E3 zebrafish medium supplemented with 0.003% methylene blue (Sigma-Aldrich, Cat# 66720) as described by Cold Spring Harbor Protocols. For injections, 1- to 2-cell-stage embryos were obtained from natural spawning of WT zebrafish. Approximately, 5 nanoliters of increasing amounts (5ng, 7.5ng, and 10ng) of gene MO were injected into the yolk sac of the embryos with 0.1% phenol red using a variable INJECT+MATIC microinjector. The morphant embryos were grown at 51  28.5°C and observed for morphological changes under a stereoscopic microscope and fixed in 4% paraformaldehyde at specific developmental time points. A p53 MO was used to suppress off-target MO-induced apoptosis, as described previously (Robu et al. , 2007). Typically, 4 ng MO was co-injected with 1.5-fold p53 MO (i.e. 6 ng) to assess whether the phenotype was genuine.  Morphant embryos were assessed visually for phenotypic defects by live imaging at 1 and 3 days post-fertilization (dpf). Ventral and lateral images of 3-dpf fish were taken from 50 embryos per injection at the same magnification (×115 and ×50 for head/brain and whole-body images, respectively) using a Leica MZ16F stereomicroscope. Using ImageJ software (National Institutes of Health; NIH), the distance between the eyes, the size of the otic vesicles, and the trunk length of 5 somites were measured as surrogate assays for microcephaly assessment, ear size, and body growth in human subjects, respectively; as these features are recurrent in patients with 2p15p16.1 microdeletion. These measurements in 3dpf MO-injected embryos were normalized against that of 3dpf MM controls and the normalized measurements were plotted and presented as the mean ± standard deviation (SD). The phenotype assessment was performed in at least 3 independent injection experiments. The embryos were treated with 200 μM N-phenylthiourea (PTU; Sigma-Aldrich, catalog #P7629) 24 hours post fertilization (hpf) to inhibit melanogenesis for better visibility and assessment of the developing internal organs (e.g. brain), as described previously (Karlsson et al. , 2001). Brain structures were evaluated 1 dpf by analyzing the forebrain, midbrain, and hindbrain morphology compared with control brains. Brains that deviated from a normal appearance were recorded and their percentage presented for MO-injected fish compared with controls. 52  3.3.10 Gene-knockdown confirmation analysis Each MO knockdown was confirmed at the RNA or protein level, depending on the nature of the MO used, by reverse transcription polymerase chain reaction (RT-PCR; for SB-MO) or western blotting (for TB-MO). The detailed explanation of the RT-PCR assay used to confirm SB-MO knockdown was previously described (Morcos, 2007). I followed the same approach which evaluates the success of knockdown by assessing the SB-MO-mediated blockage of splice junction and subsequent intron retention or exon deletion (depending whether the SB-MO binds to the first exon-intron boundary or middle exon-intron boundaries, respectively). To do that, total RNA was extracted from thirty 1- to 3-dpf gene MO or MM control–injected embryos. Forward and reverse primers spanning the SB-MO–binding site, listed on Table B.3 (Appendix B), were then designed and used to amplify the region of the SB-MO–mediated gene splice blockage by PCR using Taq polymerase (Invitrogen, cat #18038042). The amplified PCR products were then separated on a 3% weight/volume agarose gel, and the presence of a truncated band (exon deletion) and/or a diminished normal-sized band (large intron retention) in the sample PCR product compared with control indicated successful splice blockage and gene knockdown. For each gene, PCR product for β-actin house-keeping gene was also generated for morphants and controls and run on the same gel to confirm equal sample loading. For 1 gene, rel, the knockdown was not achieved with 2 independently designed SB-MOs (as was confirmed with RT-PCR and gel electrophoresis); therefore, we used a TB-MO, which resulted in successful knockdown as confirmed by extraction of total protein from thirty 3-dpf rel-MO–injected and control embryos (using RIPA lysis and extraction buffer from Thermo Scientific, cat #89900) and Western blotting using rabbit polyclonal anti-Rel (AnaSpec, cat #55483) and infrared fluorescent dye (IRDye 680 LT) tagged goat anti-rabbit secondary antibody 53  (LI-COR Biosciences, cat #926-68021). The membrane was scanned and the protein band visualized with a LI-COR Odyssey infrared imager. 3.3.11 RNA synthesis for rescue of gene knockdown phenotype and overexpression of genes in zebrafish  Full-length human gene sequence-verified cDNA clones were obtained as transformed E.coli bacterial glyercol stocks from Dharmacon. Clone ID’s for 3 candidate genes used in this chapter are as follows: BCL11A (clone ID: 5087967), XPO1 (clone ID: 5267242), and REL (clone ID: 40125742). For each gene, the cDNA plasmid-transformed bacteria were cultured onto 1.5% Luria Broth (LB)-agar plates and a single colony was selected for further propagation and extraction of purified plasmid DNA using QuickLyse® miniprep kit (Qiagen, cat #27405). The presence of correct gene cDNA insert in each plasmid was then confirmed by Sanger sequencing of the 5’ and 3’ ends of the cDNA insert using the plasmid’s 5’ M13 forward and 3’ M13 reverse primers surrounding the gene cDNA. After linearization of plasmid DNA using appropriate restriction enzymes, KpnI-HF (New England Biolabs, cat #R3142S) for XPO1, SnaBI (New England Biolabs, cat# R0130S) for REL, and BglII (New England Biolabs, cat# R0144S) for BCL11A, the cDNA for each gene was used as a template to synthesize RNA using AmbionTM mMESSAGE mMACHINE T7 (cat #AM1344), T3 (cat #AM1348), and SP6 (cat #AM1340) transcription kits for XPO1, REL, and BCL11A, respectively. The integrity and concentration of the synthesized RNA was then determined using a Bioanalyzer (Agilent 2100 System). MO oligo target sequences were checked so they did not overlap with regions in the synthesized human gene RNA sequence to avoid cross binding and blockage of RNA transcription.   54  3.3.12 Rescue and gene overexpression experiments in zebrafish Increasing amounts (40-160pg) of RNA made for each human gene was injected into 1-cell stage embryos to determine the optimum amount at which a genuine phenotype is achieved with minimal embryonic toxicity. Age- and clutch-matched uninjected wild-type embryos were also used as controls. Embryos were then scored as dead, affected, or normal 1-2 dpf and the percentage of embryos in each category for each sample were plotted and compared against each other. Once the optimum RNA amount was obtained, it was used for overexpression and rescue injections. For overexpression experiments, the phenotype of two groups was compared against each other: RNA alone versus age- and clutch-matched wild-type controls. For rescue injections, the phenotype of three groups was compared against each other: MO alone, MO+RNA, and age- and clutch-matched wild-type controls. Dorsal images of head structure were captured for injected and the control groups at 2dpf and distance between the eyes were measured for the embryos in each sub-group (N=50) and normalized against the average of the measurements in the controls and plotted to see if there is any significant difference. Each gene injection was repeated at least once. Data were presented as mean ± standard error of mean (SEM).  To confirm the production of protein in RNA injected/overexpressed zebrafish embryos versus controls, protein was extracted from RNA injected and control 2dpf zebrafish embryos (N=30). Western-blotting was then carried out using human gene antibodies for the three candidate genes, XPO1, REL, and BCL11A (details found in section 3.3.3) to determine presence or absence of human proteins in zebrafish injected embryos. Immunoprecipitation was carried out for REL and BCL11A for better band visibility by isolation and purification of the gene-specific protein from the total protein lysates using human REL and BCL11A antibodies and 55  magnetic beads provided by SureBeadsTM Protein G Magnetic Beads kit (Bio-rad; cat #161-4021).  3.3.13 Statistics All statistical analysis in this chapter was performed using GraphPad Prism software. All pairwise comparisons were performed using the 2-tailed t-test unless otherwise stated. For human protein measurement analyses, differences were considered highly significant if the P value was less than 0.01, while not significant if the P value was greater than 0.05. For zebrafish measurements, differences were considered highly significant if the P value was less than 0.0001, significant if the P value was less than 0.001, and not significant if the P value was greater than 0.01. 56  3.4 Results 3.4.1 Clinical findings (summary of new and published 2p15p16.1 deletion cases) Summary of clinical findings of 2p15p16.1 microdeletion syndrome for all 33 cases (25 previously reported and our 8 new cases) are presented in Table 3.1. Detailed clinical features of each subject are listed in Table B.4 (Appendix B). Common features reported for ≥50% of total 33 cases include DD and ID, language delay and feeding problems, hypotonia, microcephaly (OCF <3rd percentile) or small head (OCF 5–10th percentile), craniofacial features (abnormal nasal root, epicanthal folds, telecanthus, ptosis, downslanting palpebral fissure, smooth and long philtrum, high, narrow palate), and distal limb anomalies (predominantly camptodactyly or metatarsus adductus) (Table 3.1). Structural brain abnormalities were present in 30% of cases (Table B.4; Appendix B). Images of some of our new cases are shown in Figure 3.2.  Only two cases, case 4 and 8, in our cohort did not have microcephaly or smaller head size but one was presented with other head shape abnormalities (case 8) while the other had no reported head structural defects but had cognitive deficit (case 4). This was consistent with the findings in the 25 published cases which reported microcephaly and smaller head size in 80% of the cases while other skull anomalies including craniosynthosis, bitemporal narrowing, other head shape abnormalities, or structural brain defects in the remaining non-microcephalic cases. Only one published case (Peter et al., 2014) has not reported any head or brain structural anomalies and only presence of cognitive deficit in their reported case. This case also did not report any facial dysmorphic features which is unusual as at least one craniofacial abnormality of eyes, nose, ears, or mouth was observed in other previously published data and in our 8 new cases. Nonetheless, it should be taken into consideration that the focus of this paper was language assessment of the case, and possibly no detailed phenotyping was performed. 57  Table 3.1 A summary of clinical characteristics presented in >50% of all 33 subjects with 2p15p16.1 microdeletion syndrome.   Our 8 new cases are highlighted in gray. Detailed phenotypic characteristics of all the subjects are compiled in Table B.4 (Appendix B).   +, present;  –, absent; blank, not assessed or not reported; DD, developmental delay; ID, Intellectual disability; M, Male; F, Female; OFC, occipital frontal circumference; –°, indicates the  patient without microcephaly but having other head shape abnormalities; –#, indicates the patient without microcephaly but having abnormal brain.  Florisson et al., 2013 ( 1)Rajcan-Separovic et al., 2007 ( 2)Case 1Prontera et al., 2011Rajcan-Separovic et al., 2007 (1) Case 2 de Leeuw et al., 2008Florisson et al., 2013 ( 2)Case 3Felix et al., 2010Liang et al., 2009Balci et al., 2015Basak et al., 2015 (2)Jorgez et al., 2014 (4)Ottolini et al., 2015Piccione et al., 2012 (2)Piccione et al., 2012 (1)Case 4Hucthagowder et al., 2012Peter et al., 2014Hancarova et al., 2013Jorgez et al., 2014 (5)Case 5Jorgez et al., 2014 (3)Chabchoub et al., 2008Case 6Shimojima et al., 2015 (1)Fannemel et al., 2014Jorgez et al., 2014 (6)Case 7Shimojima et al., 2015 (2)Ronzoni et al., 2015Case 8Age at last reported examination (years.months)4 6 4 9 8 1.832 13 3 4 4.6 3 6 1.911 0.7 4 1.1 2 11 14 16 16 9 16 5.11 3 21 14 12 18 14 12Sex M M M F F M M F M F F F M F M F F F M F M M M M M M M M M F M MDeletion size (Mb)6.74617.89189.57443.52786.11222.01263.45126.67775.36243.34973.14410.87581.02956.31001.68182.50500.64300.97102.47240.20300.43804.60004.59272.39000.58330.35950.23780.23272.75002.66723.52400.10290.7947DD  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  + 33 (100%)ID  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  + 28 (84%)OFC centile (Microcephaly)<3rd (‡ ); 5th-10th (+) ‡ ‡ ‡  + ‡ ‡ ‡ ‡ ‡ ‡ ‡ –°  + ‡ ‡ ‡  +  – ‡ ‡ ‡ ‡ ‡ –° ‡ ‡  + –° ‡ ‡ –# –° 26 (79%)Delayed language skills  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  + 25 (76%)Digital anomalies  +  +  +  +  +  –  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  + 25 (76%)Abnormal nasal root (Broad/high or other)  +  +  +  +  +  +  +  +  +  +  +  +  +  –  –  +  +  +  +  +  +  +  +  + 22 (67%)Epicanthal folds  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  –  –  –  +  +  +  + 20 (61%)Telecanthus  +  +  +  +  +  +  +  +  +  +  +  +  –  +  +  +  +  +  –  +  –  +  + 20 (61%)Feeding problems  –  +  +  +  +  +  +  –  +  +  –  +  +  +  +  +  +  +  +  +  +  –  +  + 20 (61%)Ptosis  +  +  +  +  +  +  +  +  +  +  +  +  –  –  –  +  +  +  +  –  +  +  –  – 18 (54%)Other head shape abnormality  +  +  +  +  +  +  +  +  +  +  +  –  –  +  +  +  –  +  +  +  + 18 (54%)Hypotonia  +  +  +  +  +  +  +  +  +  +  +  +  –  +  +  +  +  +  + 18 (54%)Smooth, long philtrum  +  +  +  +  +  +  +  –  +  +  +  +  +  +  +  +  –  +  +  +  – 18 (54%)High, narrow palate (or palate abnormality)  –  +  +  +  +  +  +  –  –  –  –  +  –  +  +  +  +  +  +  –  +  +  +  + 17 (51%)Downslanting palpebral fissure  –  +  +  –  +  +  +  –  +  –  –  +  +  –  +  –  +  +  +  +  +  +  –  +  + 17 (51%)                                                                                                                                                                                                                                                                 Total no (%)58   Figure 3.2 Physical features of the 2p15p16.1 microdeletion carriers. Images of the patients in cases 2, 3, 4, 7, and 8 illustrating the craniofacial and digital abnormalities seen in 2p15p16.1 microdeletion syndrome. All patients had microcephaly and/or head shape abnormalities (except case 4), and at least 1 unique facial feature including hypertelorism, epicanthal folds, telecanthus, short/downslanting palpebral fissure, ptosis, and abnormal nasal root. Digital anomalies were also a common feature in these patients. Lower panel in the figure shows abnormal fingers in case 8.  Taken together, ID, head and/or brain structural anomalies, facial dysmorphism and digital anomalies are considered as prominent physical features observed in 2p15p16.1 microdeletion cases (Table 3.1). Body growth retardation was also common and reported in 60% of all cases (20/33) which included intrauterine growth retardation (36%), postnatal growth retardation (36%), low height (<3rd or 3-10th percentile) (36%), or low weight (<3rd or 3-10th percentile) (42%) (Table B.4; Appendix B). Other morphological abnormalities of the thorax or body organs such as cardiac and renal defects as well as urogenital abnormalities were reported less prevalently (≤30%) (Table B.4; Appendix B). 59  It is important to note that symptoms of immunodeficiency was also reported prevalently in more than 50% of all cases (17/33 cases) which included immunoglobulin (gamma type) deficiency and compromised antibody response, recurrent infections of ear and respiratory system as well as abnormal levels of B- and T-cells in some cases who underwent hematological assessments (de Leeuw et al., 2008, Hancarova et al., 2013, Hucthagowder et al., 2012, Jorgez et al., 2014, Ottolini et al., 2015, Piccione et al., 2012, Rajcan-Separovic et al., 2007) and our cases 1, 4, 5, 6, 7, and 8 (summarized in Table B.4; Appendix B). 3.4.2 Genomic findings (summary of new and published 2p15p16.1 deletion cases) The earlier studies used lower-resolution arrays (1Mb resolution bacterial artificial chromosome or BAC arrays) and for a more accurate evaluation of their breakpoints and gene content, my lab, previous to the start of my project, reassessed the breakpoints with higher-resolution arrays for available cases. This included the first two cases reported in 2007 (Rajcan-Separovic et al., 2007) and the case reported by Chabchoub et al. (2008) and their sizes were corrected from 4.50, 5.70, and 0.570 to 6.11, 7.89, and 0.583 Mbs, respectively. In addition, our 8 new cases were also assessed by high-resolution microarrays. CNV breakpoints, array platforms used for the CNV detection and additional reported genomic findings for all cases are shown in Table B.1 (Appendix B).  In addition to the 2p15p16.1 deletions, secondary genomic findings of note were noted. This included one published case reporting mosaic deletion of the 2p15p16.1 region (20/30 cells – 67% mosaicism) (de Leeuw et al., 2008) and 8 published subjects (24% of total cases) with additional balanced chromosomal rearrangements or CNVs of unknown significance (Fannemel et al., 2014, Liang et al., 2009, Peter et al., 2014, Piccione et al., 2012, Prontera et al., 2011, Rajcan-Separovic et al., 2007) (Table B.1; Appendix B). Nevertheless, the majority of these 60  secondary genomic variants were considered benign as they were either inherited (Fannemel et al., 2014, Piccione et al., 2012, Rajcan-Separovic et al., 2007), balanced rearrangements (Prontera et al., 2011), or a known benign fragile site at 12q13.2 (Fannemel et al., 2014). The significance of a secondary de novo CNV found in a published case (29Kb Xq28 deletion (Piccione et al., 2012)) or CNVs of unidentified origin reported in another published study (2q13 and 6p25.3 deletions of 343Kb and 80Kb in size, respectively (Peter et al., 2014)) were considered unknown, since their pathogenicity could not be ruled out and their involvement in the patients’ overall phenotype remained uncertain. In our cohort of 8 subjects, only 1 case (case 2) had secondary de novo CNVs of unknown significance; 2 small (<25Kb) deletions in the same region (2p16.1) and 1 large (6.5Mb) deletion on a different chromosome in the 12p11.21-q11 region. The 2 small deletions in the 2p15p16.1 region covered non-coding regions as one was intronic (17Kb deletion in the second intron of BCL11A) while the other intergenic (22.5Kb deletion ~50Kb upstream of BCL11A). The larger 12p11.21-q11 deletion was also largely covering a non-coding region and the centromere with only a 1.5 Mb coding region containing 11 genes (4 of which were OMIM genes: DNM1L, FGD4, PKP2, and YARS2). The contribution of the genes in this region, specifically the 4 OMIM genes, to the overall phenotype of the patient was unknown but deemed less likely based on the absence of phenotypes associated with the OMIM genes in our case 2. These phenotypes are namely lethal encephalopathy, due to abnormalities of mitochondrial and peroxisomal fission (DNM1L), Charcot-Marie-Tooth disease type 4H (FGD4), arrhythmogenic right ventricular cardiomyopathy (PKP2), myopathy, lactic acidosis, and sideroblastic anemia (YARS2) which were all absent in case 2. Moreover, none of the genes in the region had a strong haploinsuffciency prediction score (as reported by DECIPHER), except DNM1L. However, 61  report of a subject with inherited deletion of this gene from a healthy parent on DECIPHER (patient ID 253252), diminished the importance of the pathogenicity of this gene in case 2. The de novo deletions in published cases (Felix et al., 2010, Hancarova et al., 2013, Liang et al., 2009, Piccione et al., 2012, Rajcan-Separovic et al., 2007) and our own cases occurred on maternal or paternal chromosomes based on parent-of-origin microsatellite analysis eliminating the possibility of an imprinting effect (Table B.1; Appendix B). The 2p15p16.1 microdeletions found in all the 33 cases put together ranged from 0.10 to 9.57 Mb in size and had variable breakpoints (Figure 3.3). The size range is similar when only our cohort of 8 subjects are taken into consideration (0.360-9.57 Mb) as the largest deletion (9.57 Mb) in this region is from our case 1 which expands to p14 region (Figure 3.3). Several other deletions also expanded to p14 region of chromosome 2 (Figure 3.3). The indicated region between dashed lines at 2p15p16.1 in Figure 3.3 determines the region where most of the reported deletions and our 8 cases’ deletions are concentrated within and/or fully or partially overlapping. This region was also proposed as the MCR in the earlier studies (Hucthagowder et al., 2012, Liang et al., 2009).  The gene content of the 2p15p16.1 deletions is shown in Table B.5 (Appendix B). Calculating the gene deletion recurrence and frequency for all 33 cases revealed 16 frequently deleted genes (deleted in >50% of cases) which included 13 protein-coding genes, BCL11A, PAPOLG, REL, PUS10, PEX13, KIAA1841, C2orf74, AHSA2, USP34, XPO1, FAM161A, CCT4, COMMD1, and 3 non-coding RNA genes, LINC01185, LOC339803, and SNORA70B. These genes are consistent with the genes involved in the MCR. The most frequently deleted or disrupted coding genes were XPO1 and USP34 (>70% of cases) followed by BCL11A, REL, PUS10, and PEX13 (~60% of cases) (Table B.5; Appendix B). 62   Figure 3.3 Summary of CNVs involving the 2p15p16.1 region, including our 8 new cases. The breakpoints of the microdeletions are arranged on the basis of their start site. The black bars are published 2p15p16.1 microdeletion cases, and the red bars represent our 8 additional cases. The extended/refined breakpoints after re-evaluation by some published cases are shown on both ends of black bars. The reference citations for published cases (black bars) are indicated in the left-hand column with the subject number indicated in the parentheses. The region between dashed lines is enriched in the smallest deletions reported so far which was also proposed as the minimal critical region (MCR) in earlier studies (Hucthagowder et al., 2012, Liang et al., 2009).     63  Intriguingly, several of these frequently deleted genes were recurrently involved in smallest CNVs with the lowest number of deleted genes (1–4) which were detected in 10 cases (Figure 3.3 and Table B.5; Appendix B). While four of these cases had deletions of only XPO1 and USP34 ((Fannemel et al., 2014, Ronzoni et al., 2015, Shimojima et al., 2015) and our case 6)), BCL11A was the only coding gene deleted alone in 2 cases (Balci et al., 2015, Peter et al., 2014). BCL11A was also found to be deleted along with PAPOLG in 1 patient (Basak et al., 2015) and with PAPOLG and REL in the subject described by Hancarova et al. (2013). The remaining two cases had larger CNVs (~2–3 Mb) that included only FANCL and VRK2 ((Prontera et al., 2011), and our case 2)). Intriguingly, these CNVs mapped outside of the region of highest CNV incidence (region enclosed between dashed lines shown in Figure 3.3).  The 2p15p16.1 microdeletion affects one allele and I have, therefore, assessed the impact of the loss of one copy of all 13 coding genes (deleted in >50% of cases) using haploinsufficiency index (HI) scores which was first provided by Huang et al., (2010), and later by DECIPHER. Checking these scores against 2p15p16.1 deletion genes, indicated that only 5 genes, BCL11A, REL, USP34, XPO1, and CCT4, from our 13 most frequently deleted 2p15p16.1 protein-coding genes had a strong haploinsufficiency likelihood as indicated by a score of ~10% or less (Table B.5; Appendix B).  3.4.3 Candidate gene selection for functional analysis Given the variable breakpoints and the fact that several cases with deletions did not overlap each other (e.g. 3 cases reported by Hacarova et al., (2013), Peter et al., (2014), and Piccione et al., (2012) versus 2 cases reported by Chabchoub et al., (2008), and Fannemel et al., (2014)), identifying “a smallest region of overlap” was challenging. Instead, I considered that genes were most likely involved in the phenotype if: (a) they were frequently deleted (~60% of 64  cases); (b) had a strong likelihood of being HI indicating dosage sensitivity (i.e. a score of ~10% or less by both databases ((Huang et al., 2010) and DECIPHER); (c) occurred alone or in combination in the 10 smallest deletions with the lowest number of genes; and (d) function relevant to brain. Six genes were deleted in ~60% or more of cases and they were XPO1, USP34, BCL11A, REL, PEX13, and PUS10 (Table B.5; Appendix B). Of these, 4 genes, XPO1, USP34, REL, and BCL11A were also predicted to be strong HI candidate genes with a score of ~10% or less by the two bioinformatics databases (Table B.5; Appendix B). Finally, these 4 genes were also recurrently involved in the 10 smallest deletions mentioned above making them strong candidates for further functional analysis (Table B.5; Appendix B). These findings reinforced the strong likelihood of the 4 candidate genes to have a role in the syndrome. Detailed information on the function and characteristics of these genes and their human mutation and animal model findings are provided in Table B.6 (Appendix B). Briefly, XPO1 (named as exportin 1; also known as chromosomal maintenance 1 gene, CRM1) encodes a nuclear export receptor that exports approximately 200 different cargo molecules (e.g. proteins, ribosomal RNAs, small nuclear RNAs, micro-RNAs, and specific mRNAs) from the nucleus to the cytoplasm. This includes molecules required for correct neuronal positioning during development (Honda and Nakajima, 2006) or synaptogenesis (Simon-Areces et al. , 2013). Knockdown of this gene in drosophila has previously been reported to promote neurodegenerative diseases due to nuclear accumulation of proteins and aberrant nucleocytoplasmic shuttling (Chan et al. , 2011). No mouse or other in vivo models are reported for this gene and no mutations for this gene have been reported on HGMD (Table B.6, Appendix 65  B). Therefore, the role of this gene to the physical features seen in the 2p15p16.1 microdeletion patients (e.g. microcephaly) is unknown.  REL (named as REL proto-oncogene, NF- κB subunit; also known as c-Rel) encodes a transcription factor which is a key component of the NF-κB pathway with a role in synaptic plasticity, neurogenesis, and differentiation (Salles et al. , 2014, Sarnico et al. , 2009a). It is also known for its neuroprotective and anti-apoptotic role in NF-κB pathway (Lanzillotta et al. , 2015, Sarnico et al. , 2009b). Phenotypes associated for mouse knockout models for this gene (as listed on MGI) are increased neuronal apoptosis, abnormal skeleton morphology, and various deficiencies of immune-system which are relevant to 2p15p16.1 microdeletion syndrome phenotypic features as well as embryonic lethality. No mutations are reported for this gene on HGMD (Table B.6, Appendix B). USP34 (named as ubiquitin specific peptidase 34) encodes a deubiquitinase enzyme which is also reported to be associated with the NF-κB pathway (Poalas et al. , 2013) in addition to having a role in the Wnt signaling pathway (Lui et al. , 2011) as well as genomic stability (Sy et al., 2013). Early lethality in drosophila after knockdown (Tsou et al. , 2012) and after knockout in mice (Brommage et al. , 2014) has been reported for this gene. However, the only mutation reported for this gene on HGMD is associated with congenital heart disease making its role in the 2p15p16.1 microdeletion syndrome unknown (Table B.6, Appendix B).   Finally, BCL11A (named as B-cell CLL/lymphoma 11A), encodes a zinc finger transcription factor that represses transcription, is a member of the Brahma-related gene 1 (BRG1) associated factors (BAF) complex involved in the modification of chromatin structure and also has a role in regulating genomic stability (Huang et al. , 2015), polarity, and migration of cortical upper layer neurons (Wiegreffe et al. , 2015), axonal branching and dendrite 66  outgrowth (Kuo et al. , 2009), as well as expression of fetal hemoglobin (Basak et al., 2015). De novo nonsense and/or missense mutations in BCL11A gene have been associated with autism and DD (Coe et al. , 2014, De Rubeis et al. , 2014, Dias et al., 2016, Iossifov et al. , 2012) as well as increased fetal hemoglobin levels (Bauer et al. , 2013) as reported on HGMD or in literature (Table B.6, Appendix B). Moreover, the phenotypes associated with mouse knockouts for this gene on MGI are abnormal neuronal/axon morphology and differentiation, abnormal hematopoiesis, various immune-system deficiencies, and complete neonatal lethality (Table B.6, Appendix B). Therefore, identifying the developmental role of these 4 candidate genes in ID, DD and 2p15p16.1 phenotypic features was of interest in this project. Information on the other 9 coding and frequently deleted genes (>50% of cases; PAPOLG, PUS10, PEX13, KIAA1841, C2orf74, AHSA2, FAM161A, CCT4, and COMMD1) such as known biological function, neuronal role, previous reports of single nucleotide mutations and animal knockdown/knockout phenotypes is provided also in Table B.6 (Appendix B). These 9 genes were considered less likely candidates based on the extensive evidence of their involvement in autosomal recessive disorders and/or lack of reported phenotypes in the heterozygous deletion/mutation state as well as irrelevant phenotypes in human or in vivo models. Some examples include, PEX13 associated with autosomal recessive peroxisome biogenesis disorder also known as Zellweger syndrome (Al-Dirbashi et al. , 2009, Ebberink et al. , 2011, Krause et al. , 2006, Krause et al. , 2013, Shimozawa et al. , 1999, Suzuki et al. , 2001, Xiong et al. , 2015), FAM161A associated with and autosomal recessive retinitis pigmentosa and retinal dystrophy (Bandah-Rozenfeld et al. , 2010, Carmichael et al. , 2013, Langmann et al. , 2010, Maranhao et al. , 2015, O'Sullivan et al. , 2012, Wang et al. , 2014), FANCL associated 67  with autosomal recessive Fanconi anemia (Ali et al. , 2009, Ameziane et al. , 2012, Chandrasekharappa et al. , 2013, Grunert et al. , 2014, Meetei et al. , 2003, Vetro et al. , 2015), COMMD1 with normal heterozygous deletion phenotype (Levy et al. , 2011), and CCT4 associated with recessive hereditary sensory neuropathy in rats (Lee et al. , 2003, Sergeeva et al. , 2014). Two of the unselected frequently deleted genes, AHSA2 and C2orf74, were eliminated from further in vivo functional investigation in zebrafish because their role in the syndrome was deemed less likely as knockout of one gene, AHSA2, in mouse was associated with no major phenotypic abnormalities other than decreased total retina thickness (as catalogued on MGI), while the other gene, C2orf74, encodes an uncharacterized protein. They had no orthologous copies in zebrafish. Following sections describe results of analysis of the role of the 4 candidate genes, XPO1, USP34, REL, and BCL11A, in the disease using combinations of protein expression and functional analysis in patients’ LCLs, immunohistochemistry (IHC) staining in human brain, and in vivo zebrafish knockdown modeling. 3.4.4 Expression of candidate genes in the human brain  The protein expression of the 4 candidate genes were obtained from the Human Protein Atlas (HPA) database (http://www.proteinatlas.org/). Data showed detectable expression in brain and additional tissues for all 4 genes. XPO1 was most ubiquitously expressed at high or medium levels (62 of 80 tissues), followed by REL (43 of 78 tissues), USP34 (32 of 80 tissues), and BCL11A (16 of 80 tissues). The expression level of these genes in different brain regions was presented as high, medium, low, or not detectable based on IHC anti-body staining signal intensity obtained from HPA (Figure 3.4, Part A). 68   Figure 3.4 Protein expression analysis of XPO1, USP34, REL, and BCL11A in human brain. (A) Bar graph illustrating expression levels of all 4 genes in 4 different brain regions. Data were obtained from The Human Protein Atlas (HPA) database (http://proteinatlas.org); N, not detected; L, low; M, medium; H, high. (B) Human fetal brain immunohistochemical staining against XPO1 and USP34 performed in the Department of Pathology. For XPO1, mild positivity was seen in immature ependyma or neuroepithelium (black arrows); in the cerebral cortex, positivity was seen in Cajal-Retzius cells (black arrowheads); in immature ependymal cells undergoing mitosis (overlying the germinal matrix), positivity was stronger and associated with the mitotic spindle (red arrowheads). For USP34, strong positive staining was visible in the Purkinje cell layer of fetal brain cerebellar cortex (red arrows), while moderate positivity was seen throughout gray matter in the striatum, tegmentum of the pons, and hippocampus (black arrows); USP34 was diffusely expressed in neurons and could be seen in both the nucleus (N) and cytoplasm (black arrows) for large neurons in the tegmentum of the pons. No staining was visible in white matter or germinal layers.  69  The expression level in the cerebral cortex for all proteins were either high (BCL11A, REL) or medium (XPO1, USP34), while in the cerebellum was high for BCL11A and REL, medium for XPO1, and low for USP34 (Figure 3.4, Part A). Other brain regions, hippocampus and lateral ventricle, also had medium to high expression for most proteins (Figure 3.4, Part A). Overall, all 4 proteins seemed to have a substantial expression levels in different brain regions.   The in situ expression levels of the 2 most frequently deleted genes, XPO1 and USP34, in control fetal and child human brains were further examined using IHC. For XPO1, IHC demonstrated mild positivity in human fetal brain (e.g., immature ependyma, Purkinje cells, inferior olive, substantia nigra, and Cajal-Retzius cells in the cortex; Figure 3.4, Part B). Positivity was more intense along the spindle of some mitotically active immature ependymal cells (a pseudostratified epithelium) overlying the germinal matrix (Figure 3.4, Part B), which constitute a dense stem cell population that sequentially gives rise to neuronal and glial precursors that eventually migrate out into the cerebrum. In the child’s brain, mild positivity was seen only in Purkinje cells and inferior olive cells (data not shown). The staining was predominantly nuclear.  For USP34, strong positivity was seen in the Purkinje cell layer of the fetal brain cerebellar cortex, while a moderate positivity was seen in gray matter, striatum, tegmentum of the pons (both cytoplasmic and nuclear), hippocampus, caudate, putamen, and thalamus and no staining in white matter or germinal matrix (Figure 3.4, Part B). In the child’s brain, positivity for USP34 was more widespread and visible in both the white and gray matter, with the Purkinje layer (Bergmann glia or radial astrocytes) in the cerebellar cortex still having a strong positivity (data not shown). 70  3.4.5 Expression of the candidate genes in patient lymphoblast cell lines In order to assess the dosage-sensitivity of these genes and their potential haploinsufficiency in patient cells, their protein expression level in patients’ LCLs versus healthy controls was assessed. Data revealed reduced XPO1, REL, BCL11A, and USP34 protein expression segregated with the deletion (Figure 3.5). All 4 genes had an average of 40-50% drop in expression level in patients with gene deletions while the expression levels in cases with 2p15p16.1 deletions not involving any of these genes were comparable to healthy controls.  In detail, XPO1 had a significantly reduced expression (p<0.001) in our new cases 3, 6, 8, and our previously published case (subject 1 in (Rajcan-Separovic et al., 2007)) versus that of 5 healthy controls and our cases 2 and 4 which did not have this gene involved in their deletions (Figure 3.5, Part A). REL had a significant reduction (p<0.01) of ~40% in expression for cases with deletion of this gene, case 3, 4, and published subject 1 (Rajcan-Separovic et al., 2007) versus 4 healthy controls and patients without deletion of this gene, cases 2, 6, and 8 (Figure 3.5, Part B). The expression level of BCL11A was also assessed in a case with deletion of this gene, case 3, and showed >50% drop in expression level versus that of 3 healthy controls and 3 cases without deletion of this gene, cases 2, 6, and 8 (Figure 3.5, Part C). The expression level of USP34 was also assessed by our collaborator, Dr. Mark O’Driscoll, in 2 cases with deletion of this gene, cases 3 and 4, which showed ~50% reduction (p<0.001) in protein expression level compared to that of 2 healthy controls and a case with no deletion of this gene, case 8 (Figure 3.5, Part D).  71   Figure 3.5 Protein expression analysis of XPO1, REL, BCL11A, and USP34 in patients’ cells. Expression of (A) XPO1, (B) REL, and (C) BCL11A was analyzed in the LCLs from patients (with and without gene deletions) and controls by western blotting, and the subsequent quantitative measurement of protein density was done using GeneSnap image acquisition software (Syngene). Each data point represents the average of the ratios of normalized protein density compared with the internal loading control from independent biological replicates (N=3 per individual). Expression of (D) USP34 due to the large protein size of ~500kDa was assessed by titrating increasing amounts of whole-cell extracts (WCE) (50mg, 100mg, 200mg) from control and patient LCLs. Antibodies against a protein control of similarly large size (~300kDa), ATR, confirmed equal loading. Case 3 and 4 with USP34 deletion exhibited approximately 50% reduction in USP34 expression. Quantification was performed using Image J. ***P < 0.001; **P < 0.01; and ns = P > 0.05, by 2-tailed Student’s t test. §, Subject 1 in (Rajcan-Separovic et al. , 2007); Ctrl, control; c, case. 72  3.4.6 Functional studies of 2p15p16.1 candidate genes in human cells  The function of two genes with the highest frequency of deletion, XPO1 and USP34, was assessed in patient cells with deletion of these genes versus controls. A previously reported assay that assesses the nuclear export mechanism of XPO1 (Thomas and Kutay, 2003) which is established in our collaborator’s lab, Dr. Mark O’Driscoll, prompted us to examine the function of XPO1 in patient LCLs by assessing the nucleocytoplasmic distribution of one of XPO1’s cargo proteins, 40S ribosomal protein S5 (rpS5) (Thomas and Kutay, 2003) (Figure 3.6, Part A). Distribution of rpS5 was determined in cases with and without XPO1 deletion using indirect immunofluorescence in untreated LCLs and following treatment with leptomycin B, an XPO1 inhibitor that blocks active nuclear export of XPO1 cargo molecules (Thomas and Kutay, 2003) (Figure 3.6, Part B). Nuclear accumulation of rpS5 and failure of its nuclear export was evident in untreated LCLs from subjects with the XPO1 deletion (cases 3 and 8), compared with LCLs from a control and the subject in case 4 with intact XPO1 (Figure 3.6, Part B). In fact, accumulation of rpS5 in untreated case 3 and in LCLs from case 8 was comparable to that observed in LCLs from the control and case 4 (with intact XPO1) following treatment with leptomycin B (Figure 3.6, Part B). Importantly, the level of rpS5 was unaffected in whole-cell extracts (WCEs) from XPO1-deleted and -non-deleted cases and controls, in the presence or absence of leptomycin B, confirming that only the cellular localization of this protein is affected in the absence of XPO1 (Figure 3.6, Part C). 73   Figure 3.6 XPO1 dysfunction in patients’ cells as determined by abnormal distribution of rpS5. (A) Normal physiological XPO1-mediated export of 40S rpS5 and approximately 200 different proteins, mRNAs, and micro-RNAs from the nucleus to the cytoplasm. Suppression of XPO1 leads to blocked XPO1-mediated cargo and retention of proteins, mRNAs, and microRNAs in the nucleus. (B) Immunofluorescence images of patients’ LCLs showing that deletion of XPO1 was associated with impaired nuclear export of a known cargo protein, rpS5. LCLs were either untreated (Unt) or treated with leptomycin B (LeptB), which inhibits XPO1-mediated nuclear export of cargo. Untreated control LCLs (C) showed a preponderance of cytoplasmic rpS5 staining, consistent with its role at the ribosome. Upon leptomycin B treatment, increased immunofluorescence indicated nuclear accumulation of rpS5 was evident, consistent with XPO1 inhibition. A similar response was evident in LCLs from the patient in case 4, who possessed 2 copies (+/+) of XPO1. In stark contrast, LCLs from individuals with a deletion of XPO1 (+/–) (cases 3 and 8) each exhibited significant nuclear accumulation of rpS5 when untreated, and this distribution was unaffected by treatment with leptomycin B. (C) Western blotting of WCEs from LCLs untreated (–) or treated (+) with leptomycin B showed equal rpS5 expression. Ctrl, control.  74  Examining the function of USP34 with two different experiments, however, did not suggest its dysfunction in patient LCLs which was carried out in our collaborator’s lab, Dr. Mark O’Driscoll. USP34 has been implicated in Wnt signaling pathway at the level of AXIN1 by opposing its β-catenin–dependent ubiquitination. Reduced USP34 expression following small-interfering RNA (siRNA) knockdown has been shown to lead to enhanced degradation of AXIN1 (Lui et al., 2011). While patients’ LCLs deleted for USP34 showed reduced USP34 expression (Figure 3.5, Part D), AXIN1 levels were unaffected (Figure 3.7, Part A). Recently, USP34 has also been implicated in promoting DNA double-strand break repair (DSB-R) (Sy et al., 2013). The efficiency and kinetics of DSB-R can be assessed by monitoring the formation and dissipation of ionizing radiation–induced (IR-induced) p53-binding protein 1 (53BP1) foci. However, the kinetics of DBS-R as measured by IR-induced 53BP1 foci were grossly identical in cases with USP34 deletion compared with those without the deletion and compared with controls (Figure 3.7, Part B). 75   Figure 3.7 Wnt signaling pathway and DNA double-strand break repair were comparable in USP34 haploinsufficient cells and controls. A) Increasing amounts of WCE prepared from LCLs with differing USP34 copy number were probed with AXIN1. MCM2 antibody confirmed equal loading. B) LCLs were either untreated (Unt) or irradiated with 3Gy ionizing radiation (IR) and p53-binding protein 1 (53BP1) foci formation monitored at different times post-irradiation. 53BP1 foci are used as a marker of double-strand breaks (DSBs) and can be used to assess double-strand break repair (DSB-R) capacity. IR-induced 53BP1 foci form and dissipate over time post-irradiation in LCLs with normal USP34 copy number (Ctrl and case 8) similar to those with USP34 deletion (case 3 and 4). Images were acquired using the Zeiss Axioplan platform and SimplePCI software. +/+, presence of both copies; +/-, Deletion of one copy (heterozygous); Ctrl, Control.  The function of the other two genes, BCL11A and REL, was difficult to assess in our EB virus (EBV) transformed patient lymphoblast cell lines (LCLs) since the BCL11A dysfunction is typically measured in terms of fetal hemoglobin expression in patient’s erythroblasts or fetal hemoglobin levels (fHb), and REL is a key role-player of NF-κB signaling pathway which was considered potentially impacted due to EB virus (EBV) transformation of patient LCLs (Ersing 76  et al. , 2013, Gewurz et al. , 2011, Montes-Moreno et al. , 2012). Nevertheless, the BCL11A dysfunction was demonstrated in one of our cases with a large deletion involving BCL11A (case 1) and in a case described by Piccione and co-workers (2012) as they both had elevated levels of blood fetal hemoglobin (fHb). BCL11A is a known master suppressor of fHb level (Sankaran et al. , 2008) and the elevated levels of blood fHb have been previously associated with its deletion (Basak et al., 2015, Funnell et al. , 2015). Unfortunately, we were not able to obtain repeated blood samples for fetal hemoglobin expression analysis for all of our cases. 3.4.7 Pathway enrichment of 2p15p16.1 deleted genes  The bioinformatics-assisted pathway enrichment analysis of genes deleted in >50% of cases (13 coding and 3 non-coding) which included our 4 candidate genes, using WebGestalt functional enrichment analysis webtool, revealed NF-κB pathway as the top enriched pathway followed by other immune-related pathways including Toll-like receptors (TLR), CD40, IL2/12/23 signaling pathways (Table B.7; Appendix B). NF-κB pathway is interesting since three of our four candidate genes XPO1, REL, and USP34 have been previously associated with this pathway in literature (Garcia-Santisteban et al. , 2012, Muller et al. , 2009, Poalas et al., 2013) while BCL11A and REL are also associated with B-cell and T-cell development and regulation (Visekruna et al. , 2012, Yu et al. , 2012). Our EBV transformed patient cell lines, however, were suboptimal to test NF-κB pathway since a large number of studies have reported aberrant activation of this pathway by EBV cell infection (Ersing et al. , 2013, Gewurz et al. , 2011, Montes-Moreno et al. , 2012). As described above (in section 3.4.1), more than 50% of all cases (17/33 cases) who mostly had deletions of all 4 candidate genes (or at least 1), reported symptoms of immunodeficiency (summarized in Table B.4; Appendix B). This could point to the NF-κB pathway aberration in the deletion carriers as a key immune-system related pathway. 77  Intriguingly, in one case (11 year old girl) carrying a deletion covering 9 genes in the region (including the 4 candidate genes), immunoglobulin replacement therapy successfully treated the case which suggests the impaired pathway is immune-related and symptoms may be ameliorated by therapy (Ottolini et al., 2015). It is also consistent with abnormal features reported in mouse knockout models for BCL11A and REL including abnormal immune-system morphology and irregular levels of B-cells and T-cells (as catalogued on MGI and summarized in Table B.6; Appendix B). 3.4.8 Knockdown effect on zebrafish phenotype To determine the effect of deletions of the 4 human candidate genes (XPO1, USP34, REL, and BCL11A), I suppressed expression of their orthologs in zebrafish embryos by knockdown with gene-specific morpholino oligomers (MO). I also knocked down 2 additional genes (FANCL and VRK2) because they were the only coding genes deleted in two cases (our case 2 and a previously published case (Prontera et al., 2011).   Reciprocal Basic Local Alignment Search Tool (BLAST) analysis of the zebrafish genome showed that the 6 human candidate genes had 8 orthologous genes in zebrafish. USP34, REL, FANCL, and VRK2 have 1 copy in zebrafish, while XPO1 and BCL11A each have 2 copies (xpo1a, xpo1b, bcl11aa, and bcl11ab). xpo1a and bcl11aa were considered more orthologous to the human copies, as they were similarly syntenic to usp34 on chromosome 1 and rel on chromosome 13, respectively (Figure 3.8).  78   Figure 3.8 2p15p16.1 gene synteny in human versus zebrafish. Location of human 2p15p16.1 genes in relation to each other and their orthologous copies in zebrafish which are dispersed among 4 different chromosomes (chr1, 6, 13, and 17). Gene locations were extracted from the UCSC genome browser (https://genome.ucsc.edu/) and Ensembl database (http://ensemble.org).  Protein homology of each zebrafish orthologue was also assessed and found to be highly conserved for XPO1 (~90%), USP34 (~80%), and BCL11A (~80%), while less conserved for REL (~50%) (Table 3.2).   Table 3.2 Protein homology for candidate genes from 2p15p16.1 in human versus zebrafish. Human gene Zebrafish orthologue %Protein homology XPO1 xpo1a 88  xpo1b 90 USP34 usp34 81 REL rel 51 BCL11A bcl11aa 78  bcl11ab 64 VRK2 vrk2 59 FANCL fancl 58   79  With injection of increasing amounts of MO (5ng, 7.5ng, and 10ng) for each gene, I determined that an amount of approximately 4-5ng caused minimal embryonic toxicity (i.e. lowest lethality) yet yielded more than 60% affected embryos (Figure B.1; Appendix B). In addition to the knockdown of each gene individually, I carried out a combined knockdown of each gene and p53 gene, as previously described to minimize MO-induced apoptosis due to MO off-target effects and give a more genuine phenotype (Robu et al., 2007). We found that knockdown of 4 of the 8 genes, xpo1a, rel, bcl11aa, and bcl11ab, consistently resulted in visible phenotypic abnormalities of the head and body in more than 70% of injected embryos at approximately 5ng MO (or lower), with and without knockdown of p53 (Figure 3.9). No visible phenotypic abnormalities were observed for the other 4 genes, usp34, fancl, vrk2, and xpo1b, at a high MO amount of 10ng at 1 and 3 days post fertilization (dpf) (Figure 3.9 and Figure B.1; Appendix B).   80   Figure 3.9 Knockdown of xpo1a, rel, bcl11aa, and bcl11ab genes in zebrafish causes an abnormal phenotype with head morphology and size defects. (A) Microscopic images of 3-dpf zebrafish embryos injected with gene-specific MO with or without p53 MO. (B) The percentage of normal, affected, and dead fish was scored for all gene MO injections (+p53 MO and –p53 MO) at 3 dpf and plotted. dpf, days post fertilization; MO, morpholino(s).  In order to assess the head size difference in gene knockdown embryos and controls, dorsal microscopic images were captured from the head structure of the gene-MO and MM-control injected embryos (Figure 3.10, Part A; only showing the gene+p53 knockdowns as was similar to those captured for gene-p53). Qualitative assessment of images from the head structure suggested a reduced head size in embryos with the knockdown of xpo1a, bcl11aa, bcl11ab and rel compared to controls (Figure 3.10, Part A). To quantitatively assess the head size reduction in 81  embryos with gene knockdown versus controls, distance between the eyes of gene-MO injected embryos (N=50) and controls (N=50) was measured (as a surrogate assay for microcephaly assessment in zebrafish, as previously described (Golzio et al., 2012)) from 3 independent injections, for both gene+p53 and gene-p53 knockdowns, and the average distance for each gene knockdown was calculated and normalized against that of controls and plotted (Figure 3.10). Significant reduction in distance between the eyes (i.e. reduced head size or microcephaly) (P < 0.0001, N=50 per injection) was consistently noted for 3 genes, xpo1a, bcl11aa, and rel, when suppressed alone or in combination with p53 (Figure 3.10, Part B). As a secondary parameter of the gene morphants head abnormalities, I measured the size of their otic vesicles (developing inner ear) and both the microscopic images (Figure 3.10, Part A) and size measurements (Figure 3.10, Part B) suggested a significant otic vesicle reduction (P < 0.0001, N=50) in rel, bcl11aa, and bcl11ab morphants compared to that of controls; whereas the otic vesicle size in xpo1a morphants was comparable (P = 0.1619, N=50) to that of controls (Figure 3.10). Knockdown of either xpo1a or rel also indicated a dysmorphic, curved body in zebrafish (Figure 3.10, Part A), while bcl11aa and bcl11ab morphants did not have the severely affected body shape (Figure 3.10, Part A). However, measuring the length of 5 tail somites in bcl11aa morphants suggested a significant reduction (P < 0.0001, N=50) in size compared with that observed in controls (Figure 3.10, Part B). Eyes and fins were also noted to be abnormal and visibly much smaller for almost all injected fish compared with control eyes and fins (Figure 3.10, Part A).  82   Figure 3.10 Measurement of head, otic vesicle, and body size in affected zebrafish embryos. (A) Microscopic images and (B) size measurements of the head (distance between the eyes), otic vesicle, and body trunk (distance between 5 tail somites) of 3-dpf embryos injected with the 4 causative gene MOs: xpo1a, rel, bcl11aa, and bcl11ab. Microcephaly was observed for 3 gene morphants compared with those of controls (xpo1a, rel, and bcl11aa). Otic vesicle size was severely reduced for rel, moderately reduced for bcl11aa and bcl11ab, and comparable to that of controls for xpo1a knockdowns. The body trunk was fully dysmorphic and curled for xpo1a and rel morphants, while measurement for bcl11aa and bcl11ab morphant embryos was possible. Smaller eyes and fins for all genes were noted. (B) Measurements for both –p53 and +p53 MO injections were normalized to the sizes obtained in controls, and the ratios were plotted. Data represent the mean ± SD of 3 independent injections. N=50 embryos per gene per injection. ***P < 0.0001 and ns, not significant (P > 0.01), by 2-tailed student’s t test for significance of differences in measured sizes between each gene morphant and controls. MO, morpholino(s).  83  Head and body size microscopic assessment and eye-distance measurements for the other non-causative genes, usp34, fancl, vrk2, and xpo1b, were comparable to those of controls (Figure 3.11).    Figure 3.11 No head and body abnormalities were detected for xpo1b, usp34, vrk2, and fancl morphants. A) Microscopic images and B) size measurements of the head (distance between the eyes), otic vesicle, and the body trunk (distance between 5 tail somites) of the 3 dpf embryos that were injected with xpo1b, usp34, vrk2, and fancl MOs. The size measurements were carried out with ImageJ software, normalized to the sizes obtained in controls and the ratios were plotted. No significant difference in the head, otic vesicle, and body size was noted in morphants versus controls. Data represented as Mean ± S.D. from 3 independent injections. N=50 embryos per gene per injection. Student’s t-test was used to determine the significance of the differences for measured sizes between each gene morphant and controls. ns, not significant (p>0.01). 84  In addition to head and body structure anomalies (assessed at 3dpf), analysis of the brain structure of embryos at 1dpf, suggested structural brain abnormalities for morphants targeting xpo1a, rel, bcl11aa, and bcl11ab (Figure 3.12, Part A). Although the forebrain and midbrain of the 1-dpf xpo1a and rel morphants seemed comparable to those of the controls, the hindbrain ventricle for both genes was abnormally developed and not properly expanded in approximately 50% to 70% of knockdowns compared with controls, as the ventricle walls were not stretched enough to allow brain cavities to form (Figure 3.12). This feature has previously been reported for suppression of other genes that have a role in brain and neural development (Gutzman and Sive, 2010), supporting the role of xpo1a and rel in neurodevelopment. The most dramatic brain abnormality was noted in bcl11aa and bcl11ab morphants, in which the brain lacked differentiation of structures or expansion and resembled an underdeveloped neural tube (Figure 3.12, Part A). Shrinkage of whole-brain volume was detected for these 2 genes in almost all of the injected fish embryos (>90%) (Figure 3.12, Part B). The overall development of the fish was therefore significantly impaired for xpo1a, rel, bcl11aa, and bcl11ab, but the abnormalities also demonstrated gene specificity. Features such as heart edema as well as swim bladder anomalies were considered as nonspecific effects.  85   Figure 3.12 Brain structural anomalies in affected zebrafish embryos. (A) Representative depiction of the forebrain (F), midbrain (M), hindbrain (H), midbrain-hindbrain boundary (MHB), and otic vesicle (OV) structures in 1dpf embryos. (B) Bright-field and green-fluorescence microscopic images of the brain structure of 1dpf embryos injected with the 4 causative genes, xpo1a, rel, bcl11aa, and bcl11ab (all +p53 MO). Moderate-to-severe abnormal brain cavity formation was noted in all injected fish. xpo1a knockdown caused a mild narrowing of the hindbrain (indicated with black arrowheads), rel suppression led to hypoplastic hindbrain cavity development, which was manifested with pinched-off hindbrain (indicated with red arrowheads), while knockdown of both bcl11aa and bcl11ab led to shrunken brain volume with poor development of all compartments. (C) The percentage of observed brain phenotypic abnormalities for each gene was scored. N=50 embryos per gene. MO, morpholino(s).  To confirm the successful knockdown of genes for all SB-MO injected morphants, the structure of mRNA transcript was assessed by total RNA extraction from the 1- to 3-dpf morphant embryos and controls followed by RT-PCR, PCR amplification of the MO target site (the PCR primer sequences used for amplification of the region spanning MO target site are 86  detailed in Table B.3; Appendix B), and gel electrophoresis. Depending on the SB-MO exon-intron boundary target site, the resultant mRNA transcript structure will either be truncated as a result of exon deletion by splice-blockage of middle exon-intron splice-sites or abnormally enlarged as a result of intron inclusion by splice-blockage of first exon-intron boundary; both leading to a successful gene knockdown (Morcos, 2007). Therefore, depending on the SB-MO target site for each gene, a truncated or larger transcript was expected to produce as a result of knockdown (Table 3.3).   Table 3.3 Splice-blocking morpholino knockdown consequence on mRNA size. Zebrafish gene SB-MO target site (exon-intron boundary)  SB-MO knockdown consequence Size of truncated transcript in morphants vs normal product in controls Size of larger transcript in morphants vs normal product in controls§ xpo1a i8-e9  Exon 9 (129bp) deletion  291bp vs. 420bp - xpo1b e1-i1 Intron 1 (1999bp) inclusion - 2125bp vs. 464bp rel* e4-i4 and  e1-i1 (both failed) - - - usp34 e2-i2 Exon 2 (119bp) deletion 370bp vs. 489bp - bcl11aa e1-i1 Intron 1 (3250bp) inclusion - 3613bp vs. 363bp bcl11ab e1-i1 Intron 1 (7226bp) inclusion - 7579 bp vs. 353bp fancl e3-i3 Exon 3 (61bp) deletion 236bp vs. 297bp - vrk2 e4-i4 Exon 4 (88bp) deletion 212bp vs. 300bp - i, intron; e, exon; SB-MO, splice-blocking morpholino  § Presence of large products (>1000 bp) were difficult to detect due to their large size, therefore absence/reduced amount of normal-sized amplicon compared to controls was considered a successful knockdown. *rel knockdown was not successful with two independently designed SB-MOs, therefore TB-MO was used and knockdown was confirmed with western-blotting.  Successful knockdown and change in transcript size in morphants versus controls was confirmed for all genes (except rel) by assessing the size of gene mRNA produced as a result of knockdown compared to that of MM controls. This was done by visualization of full or significant absence of normal transcript and/or the presence of truncated abnormally spliced transcripts for gene-morphants versus controls as was visible after gel electrophoresis of PCR products for each gene and controls (Figure 3.13; Part A). For rel only, the knockdown was confirmed by TB-MO injection and subsequent western-blotting of the protein extracted from the 87  morphant embryos (Figure 3.13; Part B), as RT-PCR confirmed that 2 independently designed SB-MOs for this gene failed to target and block the expression. The western-blot for embryos knocked-down for rel with TB-MO showed suppression of protein production compared to two independent controls.  Figure 3.13 Confirmation of gene knockdown in zebrafish embryos by RT-PCR and western-blotting. A) the agarose gel electrophoresis images of PCR amplicons of RT-PCR products from total RNA extracted from the morphant fish embryos. SB-MOs designed for each gene had specific exon-intron boundary target sites (as indicated in Table 3.3). Primers surrounding the target site were designed to amplify the region of splice-blockage (primer sequences are provided in Table B.3; Appendix B). All SB-MO injections (except for rel) successfully knocked-down the gene-of-interest, as was shown by change in the sequence, hence the size of the transcript, and/or intensity of the PCR product in comparison to controls. Equal loading was controlled with bactin2. B) For rel, the gene knockdown was confirmed by analysing the protein expression through western-blotting in TB-MO injected embryos. C, control; TB-MO, translation-blokcing morpholino; L, protein ladder marker.  Therefore, the zebrafish developmental defects produced as a result of the knockdown of the orthologous copies of human XPO1, REL, and BCL11A support the role of these genes in the 2p15p16.1 microdeletion syndrome phenotype and identify them as 3 key dosage-sensitive genes “driving” the abnormal development. 3.4.9 Rescue and overexpression of gene knockdown phenotype in zebrafish  In order to validate the phenotypic abnormalities observed by knockdown of XPO1, REL, and BCL11A orthologues in zebrafish, I decided to rescue the phenotype by the overexpression of the genes in knockdown morphants. To do this, RNA was made from full-length human gene cDNA templates for each gene and injected alone into 1-cell stage zebrafish embryos to obtain 88  the optimal amount at which the embryonic toxicity is minimal. For XPO1 and REL, RNA amount of 80pg resulted in 20-30% lethality while majority of surviving fish embryos were normal and comparable to uninjected controls and no major affected phenotype was observed with RNA alone (Figure 3.14, Part A), therefore 80pg was considered an optimal amount for further injections. For BCL11A, however, this amount was highly lethal causing almost 80% lethality and I noted that reducing the amount to as low as 40pg still caused a large number of affected fish (~50%) but reducing the percentage of lethality to 20%. This made the rescue of the phenotype for this gene difficult since injection of RNA alone caused an abnormal phenotype in fish. This amount (40pg) was considered as the amount at which minimal lethality is observed for this gene for further injections. The functionality of RNA in vivo was also confirmed for all 3 genes by extraction of total protein from 2dpf RNA-injected fish and western-blotting to see if human protein can be detected. Human protein production was detected for all genes in injected zebrafish protein lysates versus uninjected controls (Figure 3.14; Part B). 89   Figure 3.14 Human gene RNA dose response and protein production in zebrafish. A) Increasing amounts of RNA (40-160pg) was injected in 1-cell stage zebrafish embryos and lethality/toxicity was quantified by scoring the number/percentage of 1-2 dpf embryos with dead, affected, and normal phenotype compared against uninjected controls. This was done to identify an optimal RNA amount for rescue injections. High amounts of RNA (>80pg) caused high percentage (~60%) of toxicity and embryonic lethality for all genes, therefore, <80pg of RNA was considered the minimum amount at which a genuine phenotype can be achieved. For BCL11A, even low amount of 40pg, although reduced the percentage lethality to reasonable levels (~20%), caused a consistent affected phenotype in 50% of injected fish. This suggested that RNA alone for BCL11A also causes a phenotype which has to be taken into consideration for rescue injections. B) The functionality of synthesized RNAs was assessed by detection of human gene protein in RNA-injected zebrafish embryos as was confirmed by extraction of total protein from RNA-injected embryos and subsequent western-blotting. For each gene western-blot, protein extracted from human LCLs was used as a positive control and protein extracted from uninjected zebrafish embryos as a negative control. Human gene antibodies were used to detect the human proteins. For REL and BCL11A immunoprecipitation (IP) was used to allow protein detection.     In order to perform the rescue injections, the appropriate amount of RNA for each gene (40pg for BCL11A and 80pg for XPO1 and REL) was injected together with 4ng of gene MOs into 1 cell-stage zebrafish embryos (i.e. MO+RNA). Moreover, a group of clutch-matched 1-cell stage embryos were injected with gene MO alone and a group of clutch- and age-matched 90  embryos were not injected as controls. The phenotype in the MO+RNA group were milder compared to MO alone injected group for xpo1a and rel (but not bcl11a), although not fully rescued. Representative images of the embryos injected for each subgroup and the uninjected controls are shown in Figure 3.15 (Part A). Scoring the percentage of embryos that were normal, affected, or dead between 1-2dpf per group revealed a noticeable improvement in the survival of embryos in the rescue (MO+RNA) group versus MO alone group; as the percentage of survived embryos was significantly higher for both xpo1a (by 19%; p=0.0009) and rel (by 14%; p=0.0034) in the rescue group versus MO alone group (but not for bcl11aa) (Figure 3.15, Part B). The failed bcl11aa rescue was thought to be due to the fact that injection of RNA alone for this gene caused detrimental effects in zebrafish embryos making the window of RNA/MO amount balance very narrow and difficult to achieve.  Taken together, both the overall body structure phenotype and the lethality of fish embryos was rescued significantly in the MO+RNA group compared to the knockdown (MO alone) group for xpo1a and rel.  91   Figure 3.15 Rescue of xpo1a, rel, and bcl11aa morphant zebrafish embryos. A) 1-2 cell stage clutch-matched zebrafish embryos were injected with gene MO alone and together with the RNA of human gene counterparts (MO+RNA) to rescue the phenotype. The assessment of overall developmental phenotype of MO injected embryos (morphants) against those injected with MO+human gene RNA (rescues) and wild-type controls at 2 days post-fertilisation suggested improvement in the overall body structure for xpo1a and rel morphants but not bcl11aa. For bcl11aa, both RNA and MO individually caused an affected phenotype; therefore, I believe a narrow window of amount balance may be needed for a rescue effect. B) Embryos were scored as normal, affected or dead between 1-2 days post-fertilisation. Significant improvement in the survival and reduced lethality of embryos was noted for the rescue group (MO+gene RNA) in comparison to MO alone injected embryos for xpo1a and rel but not bcl11aa; C) Using microscopy, dorsal images of the 2dpf embryos per group (N=50) was captured to measure the distance between the eyes as a surrogate assay for micro/macrocephaly in zebrafish. Data suggested noticeable improvement of head structure and size for xpo1a and rel only, as shown by yellow dashed lines between eyes and the shape and enlargement of eyes. D) The average of distance between the eyes for MO alone and MO+RNA injections (N=50 per group) were normalized against that of clutch-matched wild-type controls (N=50) and plotted. The normalized distance for xpo1a and rel MO+RNA group were comparable to controls while the normalized distance reduction was significantly lower for MO alone group compared to uninjected controls. Chi-squared test was performed for comparing the percentage of embryos survived in MO versus MO+RNA groups while student’s t-test was performed for pairwise comparison of normalized eye distance measurements. ***P < 0.0001; **P<0.001; *p<0.01; 92  and ns, not significant (P > 0.01). Data are represented as mean ± the standard error of mean (SEM) for eye distance measurements.   In order to see if the microcephalic head phenotype was rescued in the MO+RNA group versus MO group, dorsal images were taken from the 2dpf fish embryos (Figure 3.15, Part C). Microscopic images suggested that although the head structure/size was not fully rescued in comparison to wild-type controls, it was noticeably improved in the MO+RNA group versus MO group for xpo1a and rel (but not for bcl11aa) as was seen by the shape and overall enlargement of the eyes and their distance to each other (Figure 3.15, Part C). To quantitatively assess the head size, the distance between the eyes of the 2dpf embryos were measured for the MO+RNA group, MO-alone injected group, and uninjected controls (N=50 per group). The average distance between the eyes for each group was then calculated and normalized for each injected group (MO+RNA and MO alone) against that of wild-type controls and plotted (Figure 3.15, Part D).  Normalized distance for morphants (MO alone) suggested a significant reduction compared to uninjected controls for both xpo1a (0.94 vs. 1.0 normalized distance, respectively; p=0.01) and rel (0.89 vs. 1.0 normalized distance, respectively; p<0.0001) (Figure 3.15, Part D). In contrast, the normalized distance for rescued (MO+RNA) embryos for xpo1a and rel, was comparable to controls (0.96 vs. 1.0 normalized distance (p=0.233) and 0.95 vs 1.0 normalized distance (p=0.0.85) despite their moderate rescue (Figure 3.15, Part D). For bcl11aa there was no noticeable and quantifiable head size rescue effect as expected due to the failed rescue. Overall, data suggested that head size was enhanced/moderately rescued in the MO+RNA group versus MO alone injected embryos for xpo1a and rel.  93  3.4.10 Testing the mirror phenotype of 2p15p16.1 duplications by overexpression of the genes Recently, a suggestion was made by two studies (Loviglio et al. , 2016, Mimouni-Bloch et al. , 2015) that 2p15p16.1 duplications have a mirror effect in relation to head size, i.e. the head was bigger in cases with microduplication than microdeletion but comparable to normal. This prompted me to analyze additional cases with duplications in the 2p15p16.1 region and their phenotypes. Eighteen unique (unpublished) 2p15p16.1 duplication cases have been catalogued on DECIPHER, ranging 0.117-4.66 Mb in size. Of these 18 cases, only 6 duplications involved one or a combination of the 3 causative genes described above (XPO1, REL, and BCL11A), out of which 5 cases were provided with detailed phenotypes but did not present mirror phenotypes, specifically macrocephaly, except in 1 case. Also, the remaining twelve 2p15p16.1 duplication cases that did not involve the candidate genes did not report macrocephaly. To identify if overexpression of the 3 above-described causative genes, XPO1, REL, and BCL11A, leads to mirror phenotypes, specifically macrocephaly in individuals, I used human gene RNAs used above for rescue experiments for the 3 genes, for gene overexpression analysis in zebrafish. The optimal RNA amount that was obtained from the rescue work for each gene (i.e. 80pg for XPO1 and REL and 40pg for BCL11A) was injected in 1-cell stage embryos and a group of uninjected clutch- and age-matched wild-type embryos served as controls.  Injections revealed no major body or head structure anomalies for XPO1 and REL, but as expected from the rescue work, for BCL11A it caused a dysmorphic body for at least 50% of survived embryos (Figure 3.14, Part A; Figure 3.16, Part A). Interestingly, the abnormality of the phenotype was limited to the body structure and not the head (Figure 3.16, Part A). To carefully check the head 94  structure and size, and to test if overexpression of any of the 3 genes causes larger head size (macrocephaly), microscopic dorsal images were taken from 2dpf RNA-injected embryos for all 3 genes and their age- and clutch-matched wild-type uninjected controls. Images suggested no noticeable difference in the head structure or size of the RNA-injected embryos for all 3 genes versus controls, as was qualitatively/visually assessed by the distance between the eyes and the shape of eyes as well as overall head compartments (Figure 3.16, Part B). To quantitatively assess if there is any difference in the head size, the distance between the eyes was measured for 2dpf gene-RNA injected zebrafish embryos (N=50) for all 3 genes and normalized against that of uninjected controls (N=50). Data showed no significant (P>0.01) and/or noticeable difference in the normalized distance (i.e. head size) between XPO1, REL, and BCL11A gene overexpression versus controls minimizing the possibility that candidate genes’ overexpression causes mirror phenotype (macrocephaly) in cases with duplication of 2p15p16.1 region (Figure 3.16, Part C). 95   Figure 3.16 Overexpression of XPO1, REL, and BCL11A human genes in zebrafish. A) 1-2 cell stage zebrafish embryos were injected with increasing amounts (40-160pg) of the human XPO1, REL, and BCL11A RNAs to overexpress these genes in fish embryos. The assessment of overall developmental phenotype of RNA injected embryos against wild-type embryos at 2 dpf suggested no visible abnormalities for XPO1 and REL but shrinkage of body trunk and aberrant dorsalization for BCL11A RNA injected fish. This affected phenotype was consistent and not due to RNA toxicity as it was seen in very low RNA amounts of 40pg in at least 50% of injected embryos (see  Figure 3.14, Part A). B) Using microscopy, dorsal images of the 2dpf embryos per group (N=50) was captured to identify if there is any head structure and/or size abnormality compared to clutch-matched wild-type uninjected controls. Images suggested no noticeable difference in head structure or size as was shown by comparable distance between the eyes (indicated by yellow dashed lines). C) To quantitatively assess the head size in RNA injected embryos versus controls for all 3 genes, the distance between the eyes were measured as a surrogate assay for micro/macrocephaly in zebrafish, using the microscopic dorsal images (N=50) and ImageJ software. The average distance was then calculated for each gene RNA injected embryos and normalized against that of their clutch-matched wild-type controls. Plotted data suggested normalized distance for all 3 gene RNA-injections was comparable to controls. ns, not significant (P > 0.01).   96  3.5 Discussion In this chapter, I described a detailed review of CNVs and genes as well as phenotypes for all published 2p15p16.1 microdeletion cases as well as our 8 new patients with 2p15p16.1 microdeletions ranging from 0.36 to 9.57 Mb in size and with overall clinical features in keeping with the syndrome.  The identification of critical CNV genes for the phenotype is typically achieved by finding a smallest (minimal) region of overlap or MCR and analysis of the function of genes that map to this region. For example, several genotype-phenotype correlation studies of randomly-sized CNVs involved in the same chromosomal region (similar to 2p15p16.1 deletions) in cases with ID, have narrowed the number of genes in the region to 1-4 causative genes by identifying the smallest region of overlap among CNVs that can explain the phenotype. For example, deletion of HDAC4 was associated with brachydactyly in cases with 2q37 deletions (Williams et al. , 2010), SCRIB and PUF60 with microcephaly in cases with 8q24.3 deletions (Dauber et al., 2013), and ASGR1, ACADVL, DVL2, and GABARAP with microcephaly in cases with 17p13.1 deletions (Carvalho et al., 2014). Consistent phenotypes observed in loss-of-function mutations of these genes and their haploinsufficient animal models further confirmed their genotype-phenotype correlation (Carvalho et al., 2014, Dauber et al., 2013, Williams et al., 2010).  In the case of 2p15p16.1 deletions defining a smallest region of overlap was more challenging due to the variable size of CNVs and several cases of non-overlapping CNVs causing the same phenotype. Nevertheless, the frequency of involvement of 4 genes in all CNVs and their isolated deletions in several cases with characteristic syndromic features helped pinpoint XPO1, REL, USP34 and BCL11A as the best candidates. This was further supported by the observation that non-overlapping CNVs containing any of the above genes could cause 97  similar features. For example, our case 6 (with XPO1/ USP34 deletion) and the case reported by Hancarova et al., (2013) (with BCL11A, REL, and PAPOLG deletions), both had prenatal and postnatal growth delays, microcephaly, hypotonia, spasticity of legs, downslanting palpebral fissures, dysplastic ears, abnormal hematologic findings, and digital anomalies. This suggests that several critical developmental genes in the 2p15p16.1 deletion region could contribute to the phenotype and deletion of one or combination of them leads to the similar distinctive phenotype. Therefore, in this section, I will discuss my findings related to the function of these 4 genes (XPO1, REL, USP34 and BCL11A) and their role in the phenotype.   3.5.1 Candidate genes for 2p15p16.1 microdeletion syndrome XPO1 appears to be the strongest candidate for the syndrome. The elevated nuclear accumulation of rpS5 in LCLs from patients with XPO1 haploinsufficiency supports the previous observation that the biogenesis and subsequent nuclear export of ribosomal constituents is dependent on XPO1 (Thomas and Kutay, 2003) and suggests that its inhibition could lead to repression of ribosomal biogenesis, downregulation of translation, and apoptosis, as previously shown with leptomycin B–induced inhibition of XPO1 (Tabe et al. , 2015). The aberrant nuclear transport due to XPO1 deletion could affect other molecules known to be its cargo and relevant to the phenotype. This includes molecules relevant for synapse formation (e.g., neurogenin 3) (Simon-Areces et al., 2013), neuronal positioning during brain development (Honda and Nakajima, 2006), and stabilization of centrosomes and mitotic spindles of the dividing cells (Forbes et al. , 2015a). In keeping with this is XPO1’s widespread expression in both fetal and child brains. In humans, XPO1 single nucleotide polymorphisms (SNPs) were linked to autism (Liu et al. , 2011b), mutations with genomic instability in cancer (Puente et al. , 2011), and overexpression in multiple sclerosis (MS) (Haines et al. , 2015). To date, patients with isolated 98  deletion of XPO1 were not reported, although 4 cases had a deletion containing only XPO1 and USP34 ((Fannemel et al., 2014, Ronzoni et al., 2015, Shimojima et al., 2015) and our new case 6)), with all patients having microcephaly and/or structural brain abnormalities. Knockdown of xpo1a in zebrafish resulted in abnormal development including small head and brain abnormalities, further implicating this gene in the NDD phenotype and supporting the finding that the vast majority of patients with XPO1 haploinsufficiency show microcephaly. Only 4 of the 25 subjects with XPO1 deletion (our case 8 and the 3 patients previously described (Chabchoub et al., 2008, Jorgez et al., 2014, Ronzoni et al., 2015) had a normal head size, although they had other cranial abnormalities including a high forehead (Chabchoub et al., 2008) or structural brain abnormalities (Ronzoni et al., 2015).  The role of the second most commonly deleted gene in the syndrome, USP34, in the 33 subjects remains uncertain, as no cellular phenotype for pathways previously associated with USP34 dysfunction (Wnt-signaling pathway and genomic instability) was identified in cells with the deletion, despite reduced expression of USP34 in LCLs from patients with USP34 haploinsufficiency. Similarly, there was no phenotypic effect in zebrafish with usp34 knockdown. This is also consistent with the report of a USP34 de novo mutation case on HGMD that has been associated with congenital heart disease but no other physical features (Zaidi et al. , 2013). A small deletion of maternal origin containing USP34 only in a patient presented on DECIPHER (ID no. 314299) reinforces our findings that this gene may not be responsible for the prominent phenotypic features seen in the syndrome. However, the role of this gene cannot be fully excluded and as the function of this gene becomes better known, its precise role in the syndrome may be elucidated.  99  BCL11A is an intriguing candidate for phenotypic abnormalities on the basis of reports of patients with both larger and smaller deletions of BCL11A alone (Balci et al., 2015, Peter et al., 2014). I showed that knockdown of both BCL11A orthologs in zebrafish resulted in microcephaly and significant structural brain abnormalities, supporting the role of BCL11A in the 2p15p16.1 microdeletion phenotype. A recent study also reported that de novo heterozygous missense, nonsense, and frameshift mutations in this gene disrupts the localization and transcriptional activity of BCL11A and cause ID, DD, language delay, microcephaly, unique craniofacial defects (e.g. abnormal external ears and downslanting palpebral fissures), autism and elevated levels of blood fHb. This was supported by defective cellular function and phenotypic abnormalities in the haploinsufficient Bcl11a mouse (e.g. smaller heads and abnormal cognition and socio-behavioral interactions) (Dias et al. , 2016).  BCL11A inhibits fetal hemoglobin expression (Basak et al., 2015, Bauer and Orkin, 2015), which was increased in patients with BCL11A deletion including our case 1. It is of interest to note that even without an obvious deletion of BCL11A, fetal hemoglobin expression was increased in a patient described by Prontera and co-workers (2011) with isolated FANCL and VRK2 deletions (Funnell et al., 2015, Prontera et al., 2011), possibly suggesting an effect of the distant deletion of regulatory elements on the function of BCL11A.  FANCL and VRK2 appear less likely to have a significant role in the phenotype, given the normal development observed following knockdown in zebrafish and given that 1 of the 5 DECIPHER patients with deletions in the VRK2/FANCL gene region (ID no. 274140) inherited the VRK2/FANCL deletion from a normal parent. The possible role of non-coding elements in the case reported by Prontera and co-workers (2011) and our case 2, which contained only FANCL and VRK2 in their deletions, is explored in more detail in the next chapter. 100  Finally, we implicate REL in the 2p15p16.1 microdeletion phenotype, given the reduced protein expression detected in LCLs from patients with this deletion and given the structural brain abnormalities, abnormal growth, and dysmorphism observed following knockdown in zebrafish. This is in keeping with the previously noted neuroprotective and antiapoptotic role of REL in the NF-κB pathway and its role in hippocampal long-term plasticity and memory formation (Lanzillotta et al., 2015, Salles et al., 2014, Sarnico et al., 2009b).  It is also interesting to note the functional relationship between a number of genes from the 2p15p16.1 region suggesting that the disruption of a biological process(es) or their impaired interaction could contribute to developmental anomalies. For example, we noted conserved co-localization of pairs of orthologues of human XPO1/USP34 and BCL11A/REL in zebrafish. Furthermore, XPO1 exports protein products of 3 NF-κB pathway related genes from 2p15p16.1 region, REL (Watters et al. , 2017), COMMD1 (Muller et al., 2009) and USP34 (Garcia-Santisteban et al., 2012). Furthermore, USP34 also interacts with REL (c-Rel) (Engel et al. , 2014). The NF-κB pathway was the top enriched pathway when the 16 genes frequently deleted in more than 50% of the 33 cases were assessed for pathway enrichment. However, I was not able to assess the status of this pathway in patients versus controls because EBV-transformed LCLs are not an ideal model for studying NF-κB, given that EBV can activate this pathway and possibly mimic its native state in patients’ cells resulting in confounding factors that will result in misinterpretation of data (Ersing et al., 2013). Nevertheless, presence of immunological co-morbidities in more than half of the individuals where these parameters were assessed (e.g. by sinopulmonary screening), point to impaired immune system which is likely to be the result of impaired NF-κB pathway (as well as other immune-related pathways) as a key regulator of host defense and innate and adaptive immunity (Hetru and Hoffmann, 2009, Livolsi et al. , 2001, 101  Smith et al. , 2006). More importantly, this pathway is also strongly associated with nervous system, synaptic plasticity, neuronal signaling, learning skills, memory and behavior (Albensi and Mattson, 2000, Levenson et al. , 2004, Meffert et al. , 2003, Park and Youn, 2013). Identifying the specific pathway (e.g. NF-κB pathway) deregulated in the individuals is crucial for therapeutic purposes to improve at least some of their symptoms e.g. poor immune system (as described in the results section), overall health and quality of life. Future studies of mouse models of 2p15p16.1 deletion as well as patient induced-pluripotent neuronal cell lines could allow testing if the NF-κB pathway is affected in the individuals and whether a specific target can be identified for therapy.   The second common biological process for several genes from the 2p15p16.1 region involves genomic stability as dysfunction of 3 out of 4 candidate genes from 2p15p16.1 was reported to be associated with genomic instability (BCL11A (Huang et al., 2015); USP34, (Sy et al., 2013); and XPO1 (Forbes et al., 2015a)). Although, one tested aspect of genomic stability (i.e. the repair of IR-induced DNA breaks) in LCLs from patients with deletions was comparable to that seen in controls, the possibility remains that the 2p15p16.1 region is uniquely susceptible to breaks and rearrangements either due to the structure, sequence content or loss of one or many of the genes from the 2p15p16.1 region.  3.5.1.1 Other genes in the region My findings suggest that the abnormal phenotype caused by 2p15p16.1 deletions could be a result of a deletion of at least 1 of the 4 genes; however, I could not completely exclude the role of other coding or non-coding sequences from this region. Nine additional coding genes were deleted in more than 50% of cases. These include PUS10, PEX13, C2orf74, AHSA2, FAM161A, CCT4, COMMD1, KIAA1841, and PAPOLG, but I considered them less likely to be 102  the drivers of the phenotype, given that they tend to occur only in larger deletions and are accompanied with 1 or more of the 4 critical genes. Disease-causing mutations were noted in the HGMD for FANCL (Fanconi anemia), PEX13 (Zellweger syndrome), FAM161 (retinal abnormalities), and COMMD1 (elevated urinary copper), and all were reported to be autosomal recessive conditions. Moreover, only the autosomal recessive genes COMMD1, PEX13, and CCT4 were associated with neurodevelopmental abnormalities in mouse- or rat-knockout models (Lee et al., 2003, Maxwell et al. , 2003, van de Sluis et al. , 2007)); however, previous work in our lab excluded the presence of deleterious mutations in their intact alleles by exome sequencing. 3.5.2 2p15p16 duplications and mirror phenotype  The recent publication by Loviglio et al., (2016) suggested that duplications of 2p15p16.1 are associated with mirror phenotype for head size (macrocephaly) and increased BMI. This study reported that the average of head circumference, although significantly higher in duplication carriers (N=5) versus deletion carriers (N=20) (p=0.003), it was not significantly higher than in the general population (p=0.1203) (Loviglio et al., 2016). BMI in duplication carriers in this study does not reach a statistical significance when compared with the deletions (N=16) (p=0.096) and is comparable to the general population (p=0.409). I have performed overexpression analysis in zebrafish and reviewed the phenotype of cases with duplication from DECIPHER to further explore the possibility of the mirror phenotype. The overexpression of the 3 causative human genes, XPO1, REL, and BCL11A in zebrafish did not cause larger head size. This agrees with almost all of the duplications (six duplications that contain at least 1 of the candidate genes) that are reported on DECIPHER and 1 recently published case (chr2:60150427-61816209, hg19) (Mimouni-Bloch et al., 2015) that 103  encompassed these genes. Out of 6 cases with duplications involving at least 1 of the candidate genes from DECIPHER, only 1 had macrocephaly (ID no. 25833), and the single published case with the duplication had normal head size (Mimouni-Bloch et al., 2015). Therefore, my zebrafish and patient analysis data could not support the presence of mirror head or BMI phenotypes for the 2p15p16.1 microduplication. Similarly, the overall development and growth of the zebrafish body was similar in overexpressed fish embryos and controls, except for BCL11A. A dysmorphic development and failed proper dorsalization of fish embryos was noted only for BCL11A overexpression. The abnormal body phenotype observed for BCL11A overexpression in zebrafish is interesting since this is the first animal model report of its overexpression. High levels of BCL11A expression has been associated with different types of cancer (Jiang et al. , 2013, Khaled et al. , 2015, Yin et al. , 2009) due to acquired somatic amplifications of this gene but the phenotypic consequence of its germ-line duplication and overexpression in early developmental abnormalities is not yet understood. Dias and co-workers (2016) recently showed that loss-of-function of BCL11A in mouse models disrupts its normal transcriptional regulatory activity and leads to dysregulation of hundreds of genes enriched in ion channel activity and transport as well as several BAF complex genes involved in chromatin remodeling. My studies suggest that a very precise amount of this gene’s protein is required for normal functioning and any imbalance in its dosage (gain or loss) leads to developmental anomalies. 3.5.3 Unexplained cases and the role of regulatory elements where none of the critical genes are deleted  Although the deletion of any of the three candidate genes, XPO1, REL, and BCL11A may explain the similar developmental phenotypes in cases with deletions involving these genes, it 104  cannot explain the phenotypic similarity for cases that do not contain these genes. This included our case 2 and the case reported by Prontera et al., (2011), who did not have deletion of any of the 3 candidate genes. The role of the only two protein-coding genes involved in these deletions, FANCL and VRK2, in the syndrome was excluded as explained above after zebrafish knockdown and genotype assessment. Both of these cases shared similar phenotypic features to 2p15p16.1 microdeletion cases including cognitive impairment, DD, microcephaly, similar facial dysmorphism, and digital anomalies. The extent of the deletions in the two individuals are slightly different; 2.0Mb in case 2 and 3.5Mb in the published case (Prontera et al., 2011), but both deletions overlap and cover a large gene-desert region ~1Mb upstream of BCL11A previously reported to be enriched with enhancers/regulatory elements (Florisson et al., 2013). Deletion of these regulatory elements was proposed to be linked with BCL11A function due to their proximity to the BCL11A and supported by BCL11A’s dysfunction in the individual reported by Prontera et al., (2011) who had reduced BCL11A RNA expression and aberrant elevated levels of blood fetal hemoglobin (fHb) (Florisson et al., 2013) despite no deletion of this gene. Interestingly, our case 2 also had two small additional CNVs in the second intron of BCL11A and in a ~50Kb non-coding region upstream of BCL11A. Unfortunately, a hematologic assessment for this individual was not possible to determine the BCL11A dysfunction in erythroblasts and elevated blood fHb. In order to determine if there are important regulatory elements that are indirectly responsible for the syndrome’s phenotype, I performed detailed investigation of the non-coding deleted region in case 2 and Prontera et al., (2011). Identifying these regulatory elements is crucial for improved diagnosis of the unexplained cases and devising potential therapeutic strategies that can regulate the critical genes’ function indirectly and in a cell-specific manner 105  due to tissue-specificity of certain enhancers (Bauer et al., 2013). The next chapter describes my bioinformatics analysis of enhancers and regulatory elements involved in this region to decipher their importance in disease and phenotypic features observed in 2p15p16.1 microdeletion carriers.  3.6 Conclusion I conclude that the 2p15p16.1 region is genomically very complex, but the phenotype of patients with deletions of variable sizes strikingly similar. The phenotypic consequences of knocking down the 4 genes identified here have not, to our knowledge, been previously reported in zebrafish and highlight the possibility that multiple genes contribute to 2p15p16.1 deletion syndrome. XPO1 represents a particularly strong candidate, but the existence of patients without deletion of this gene indicates that there are other causes of the abnormal phenotype. These causes could include other potent developmental genes, their interaction and common pathway disruption, as well as the involvement of regulatory elements from this region. Presence of cases without deletion of the candidate genes yet similar phenotypic features reinforces the importance of regulatory elements in the region. I was intrigued with this possibility and performed, in the next chapter, a very fine molecular dissection of the 2p15p16.1 region to search for relevant regulatory elements in order to try and explain the abnormalities for the 2 unexplained cases who did not have deletions of any of the candidate genes.  106  Chapter 4: CHARACTERIZING TWO 2p15p16.1 MICRODELETIONS AND THEIR NON-CODING REGULATORY ELEMENT CONTENT 4.1 Overview In the previous chapter, I described two cases, one previously published (Prontera et al., 2011) and one from our cohort (case 2), with deletions not containing any of the 3 candidate genes (XPO1, REL and BCL11A). Nevertheless, these patients had similar phenotypic features to other 2p15p16.1 deletion cases, including ID, microcephaly, and unique craniofacial defects (for details refer to Table B.4; Appendix B)). The only 2 protein-coding genes, VRK2 and FANCL, involved in their deletions were not considered candidates for the phenotype based on no effect on development in zebrafish knockdown and report of deletions of these genes in physically-normal individuals. The deletions in these two patients cover a large (~2Mb) non-coding region and it was previously suggested that the deletion of enhancer regulatory elements from this region, regulating genes from the critical 2p15p16.1 deletion region and beyond, may be related to the abnormal phenotype (Florisson et al., 2013). Interestingly, the published case (Prontera et al., 2011) was also reported to have reduced expression of one of the candidate genes (BCL11A) in erythroblasts, even though this gene was not involved in the deletion (Funnell et al., 2015). Moreover, the patient had elevated levels of blood fHb which is negatively regulated by BCL11A. These findings indicated that BCL11A expression and function may have been indirectly affected by the deletion of distal regulatory elements/enhancers (Funnell et al., 2015). As described in the previous chapter, in our case 2, two additional smaller deletions outside of the large 2Mb deletion were identified mapping to intron 2 and ~50Kb upstream region of 107  BCL11A, further supporting the possibility of the involvement of this gene in the phenotype, despite its intact coding sequence. In this chapter I explored the presence and possible role of enhancers in the deleted non-coding regions in these two cases, overall, and specifically in relation to BCL11A. I also examined the regulatory sequence content of 2 additional small deletions found in our case 2 mapping to BCL11A’s second intron and its ~50Kb upstream region in terms of their relevance to the phenotype. 4.1.1 Enhancers Enhancers are short DNA fragments (typically 200-1500Kb in size) that regulate the expression of their target genes in an orientation-, location-, and distance-independent manner to the gene promoter but in a cell-type specific fashion (Pennacchio et al. , 2013, Schaffner, 2015). They bind to transcription factor (TF) elements required for gene expression and typically regulate the expression of genes in cis but can also bypass long-distances and regulate genes in trans (Pennacchio et al., 2013). This distant regulation of genes is believed to be facilitated by chromatin looping and spatial organization of DNA in the nucleus which allows two distant genomic loci to contact (Matharu and Ahituv, 2015). This can then facilitate bringing together the components of the TF complex bound to promoters and enhancers and allow proper gene expression (Kim et al. , 2015b, Matharu and Ahituv, 2015) (Figure 4.1).   108   Figure 4.1 Schematic diagram depicting chromatin looping and promoter-enhancer interaction. A) Linear genome shows a distal enhancer element (in green) several kilo- or mega-bases away from gene ‘A’ promoter. Both the gene promoter region as well as enhancer elements recruit a complex of transcription-factors (TFs) and proteins necessary for transcription. B) Chromatin looping in the 3-dimensional (3D) DNA organization brings the TF-complex-bound distal enhancer element(s) and gene ‘A’ promoter into close contact mediating the transcriptional regulation of gene ‘A’. This diagram was re-created from the information provided in Figure 2 from Matharu and Ahituv, (2015) and Figure 2 from Kim et al., (2015).   4.1.2 Locating enhancers  Developmentally-critical enhancers are found to be evolutionary conserved in sequence and synteny with respect to their target genes among different species, suggesting negative selection against their deleterious genetic variation (Clarke et al. , 2012, Emera et al. , 2016, Plessy et al. , 2005, Villar et al. , 2015, Yang et al. , 2015). This, however, does not mean that all functional enhancers are highly conserved in sequence as several functional enhancers have been reported with no sequence conservation but conserved function and synteny in distant species (Hardison, 2010, Yang et al., 2015).  Enhancers also share similar chromatin signatures in terms of DNA structure and accessibility, epigenetic marks, and enrichment in TF binding sites (Consortium, 2012). To 109  locate enhancers in the genome, several approaches have been carried out, with two of the main large-scale discovery efforts, VISTA enhancer browser and Encyclopedia of DNA elements (ENCODE) project briefly summarized below (Consortium, 2012, Visel et al., 2007).  4.1.2.1 VISTA enhancer browser  Pennachio et al. (2006) reported that inter-species comparative genome analysis and identification of non-coding sequences of extremely high conservation is an effective way to identify and locate functionally-important enhancers genome-wide. Their first assessment resulted in 167 candidate human enhancer elements with deep evolutionary conservation (Pennacchio et al. , 2006). Of these conserved enhancers, 45% showed in vivo activity and expression of a downstream reporter gene (LacZ) in transgenic mouse embryos (Pennacchio et al., 2006). This was done by incorporating the human orthologous enhancer sequences in vectors containing mouse promoters fused to LacZ reporter gene, followed by the microinjection of the vector in fertilized mouse eggs and LacZ staining of 11.5 day mouse embryos to visualize the location of LacZ expression, and in other words activity of enhancer, in vivo (Pennacchio et al., 2006, Visel et al., 2007). Enhancers with reproducible tissue-specific expression/activity pattern in at least 3 independent transgenic mouse embryos were then scored as ‘positive’ while no reproducible expression pattern as ‘negative’ (Pennacchio et al., 2006, Visel et al., 2007). However, the importance of negative enhancers were not fully ruled out due to their possible activity in earlier or later developmental stages than tested (11.5 day mouse embryos) (Pennacchio et al., 2006, Visel et al., 2007). The same group later reported a modified protocol to improve their prediction technique for discovery of enhancers and their spatial in vivo activity (i.e. tissue-of-expression), by isolating DNA sequences bound to a well-known enhancer binding protein ‘p300’, which allows to locate enhancers, from different tissues in mouse (forebrain, 110  midbrain, and limbs) using chromatin immunoprecipitation (ChIP) and massive parallel sequencing (Visel et al. , 2009a). The new approach resulted in 122 more candidate enhancer elements with improved prediction ability (80%) of tissue-of-expression in mouse embryos (Visel et al., 2009a).  These findings, therefore, suggested that tissue-specific enhancer elements can be predicted (with 50-80% success rate) based on their sequence conservation, their binding to ‘p300’ enhancer-specific proteins, as well as isolation of DNA from specific tissues (Pennacchio et al., 2006, Visel et al., 2009a, Visel et al., 2007). The data from these studies including the genomic location, tissue-specific expression positivity or negativity, and tissue-of-activity of enhancers are provided and continuously updated on VISTA enhancer browser (https://enhancer.lbl.gov) (Visel et al., 2007) and UCSC genome browser (https://genome.ucsc.edu) as more enhancers are found.  More than 2500 enhancer elements have been added to the VISTA enhancer browser so far, of which ~1400 have been confirmed to have positive and tissue-specific enhancer activity in vivo in mice. It is important to note, however, that although this database is useful to locate some enhancers in a given genomic region, it is not a reflection of all enhancers at a particular genomic region responsible for gene regulation as discovery of VISTA enhancers are limited to highly conserved, ‘p300’-bound sequences while there are many other enhancer elements that are bound to other TF complexes or have modest to no sequence conservation (Pennacchio et al., 2013) 4.1.2.2 Encyclopedia of DNA elements (ENCODE) project To identify enhancer elements and their cell-type specificity in our genome, ENCODE project recently carried out a comprehensive genome-wide analysis of enhancer-associated features such as open/accessible chromatin sites sensitive to endonuclease cleavage (known as 111  DNase I hypersensitive sites), and TF binding sites in 147 different cell-types (Consortium, 2012). So far, 399,124 regions with enhancer-like features have been identified which were also found to be enriched in trait-associated SNPs from Genome Wide Association Studies (GWASs) validating their functional importance (Consortium, 2012). This data generated by ENCODE has significantly improved the annotation of non-coding regions and identification of putative enhancer elements which are catalogued on ENCODE project’s website (https://www.encodeproject.org/) and UCSC genome browser (https://genome.ucsc.edu/ENCODE/). 4.1.3 Linking enhancers to their target genes Despite progress in discovery of enhancer elements in the non-coding genomic regions, identification of their link to their target genes has been challenging (Sanyal et al. , 2012). Recent Chromosome Conformation Capture (3C) technology and its modified derivatives e.g. circular-3C (4C), 3C-carbon copy (5C), and Hi-C, have allowed to identify gene promoter-enhancer regions that physically contact/interact with each other in the 3D space but are far apart in the linear genome (Davies et al. , 2017). Below is a brief description of the above variations of the 3C technology. All 3C-based methods are designed to quantify the interaction between genomic loci and are based on cross-linking the DNA and fixating the interactions in the native genomic state (Davies et al., 2017). This is then followed by restriction-enzyme digestion of the genome into fragments and random ligation of fragments to quantify the proximal/interacting fragments (Davies et al., 2017). Nearby fragments are more likely to be ligated and quantification of their interaction can be performed in different ways. For example, 3C quantifies interaction between a single pair of genomic loci (e.g. a single promoter-enhancer interaction), 4C between one locus 112  and all genomic loci, 5C between all loci within a smaller ~1Mb genomic region, and Hi-C between all genomic loci genome-wide (Davies et al., 2017). All possible pairwise interactions between fragments are, nowadays, tested using high-throughput sequencing of ligated fragments (Davies et al., 2017). Teasing out and enriching only the Hi-C generated fragments that contain known promoter-sequence interactions is possible using a new developed technique called promoter capture-Hi-C (pcHi-C) (Mifsud et al. , 2015, Schoenfelder et al. , 2015). A recent study used pcHi-C in 17 different human primary hematopoietic cell types (e.g. erythroblasts) to identify regions that interact with 31,253 promoters and reported identification of novel promoter-enhancer interactions (data catalouged on http://promoter.bx.psu.edu/hi-c/) (Javierre et al. , 2016). However, these long-range interactions are reported to be highly cell-type specific (Javierre et al., 2016), and this type of analysis in neuronal cells is not yet available.  4.1.4 Enhancers and disease Increasing number of studies have identified that deletions of or mutations/SNPs in distal and intronic enhancer sequences cause gene misregulation and congenital malformations in patients (Corradin and Scacheri, 2014, Miguel-Escalada et al. , 2015, Sakabe et al. , 2012). Some of the recent examples include, craniofacial defects due to deletion of the enhancer regulating DLX5 and DLX6 (Brown et al. , 2010); forebrain and limb abnormalities due to deletions of and mutations in distal and intronic enhancers regulating SHH (Anderson et al. , 2014, Hill and Lettice, 2013, Jeong et al. , 2008, Sagai et al. , 2005, Visel et al. , 2009b); severe wrist bone deformities caused by deletion of enhancers regulating SHOX  (Gatta et al. , 2014); lethal lung developmental disorder caused by deletion of an intronic enhancer as well as a distal intergenic long non-coding RNA (lncRNA) functioning as an enhancer for FOXF1 (Szafranski et al. , 2014, Szafranski et al. , 2013b). These findings suggest that deletion of enhancers in the non-coding 113  intronic regions, intergenic regions, and non-coding RNA regions could alter the expression of their neighbouring genes without altering the gene’s copy number or sequence. It is also important to note that the affected genes are all reported to be haploinsufficient (Fukami et al. , 2016, Jeong et al., 2008, Szafranski et al., 2013b, Wang et al. , 2010) suggesting that heterozygous deletion of enhancers is sufficient to cause phenotype due to the dosage-sensitivity of their target genes.  Human GWASs and animal modeling reports have shown that these enhancer-involved deletions or SNPs typically affect the binding sites for important TFs responsible for the cis-regulation of the gene(s) (Bauer et al., 2013, Jeong et al., 2008, Meyer et al. , 2015, Palsson et al. , 2014). For instance, recently, an enhancer element in the second intron of BCL11A was shown to regulate the erythroid-specific expression and function of BCL11A in fHb regulation (Bauer et al., 2013). This enhancer was found to have a binding site for a key hematopoietic TF, GATA1, and SNPs in this TF binding site were frequently found in patients with dysregulated  BCL11A expression in erythroblasts and elevated levels of blood fHb but no other abnormal phenotypic features (Bauer et al., 2013, Canver et al. , 2015). Deletion of the orthologous copy of this enhancer in mouse, also, phenocopied the blood fHb level irregularities seen in humans while similarly presenting no other blood or physical abnormalities (Smith et al. , 2016) suggesting that the enhancer in the second intron of BCL11A is highly conserved and regulates the expression/function of BCL11A in an erythroid-specific manner (i.e. not responsible for BCL11A’s expression regulation in other cell-types/tissues) (Bauer et al., 2013, Canver et al., 2015, Smith et al., 2016).  In this chapter I analyzed the content, type, and possible role of regulatory sequences in the 2p15p16.1 region and explore the possibility that the deletion of enhancers in the two cases 114  with deletions in the 2p16.1 region not involving the 3 candidate genes, described in the previous chapter, is related to the abnormal phenotype.  4.2 Chapter goals My overall goal in this chapter was to identify the enhancers present in 2 deletion cases (one previously published case by Prontera et al., (2011) and the other our case 2) that did not include any of the 3 candidate genes described in the previous chapter. In addition, I explored their interaction with BCL11A, considering that this gene’s expression was altered in one of the cases, although the gene itself was intact (Funnell et al., 2015, Prontera et al., 2011). In order to achieve this I aimed to: 1) Identify the enhancers in the deletion regions and compare the enhancer content with other regions in chromosome 2 and genome-wide.  2) Determine the conservation of the deleted region and enhancers in comparison to the evolutionary distant species, zebrafish, as an indication of a preserved function. 3) Explore long-range interactions of enhancers in the deleted region with BCL11A’s promoter using bioinformatics  4.3 Materials and methods 4.3.1.1 Enhancer content in 2p16.1 deletions VISTA enhancer browser (https://enhancer.lbl.gov) was used to extract enhancer elements involved in the 2p16.1 region deleted in both cases (as well as in the two additional small deletions in our case 2). This browser provides a growing list of experimentally validated non-coding DNA regions with enhancer activity as confirmed in vivo in transgenic mice (Pennacchio et al., 2006, Visel et al., 2007).  115  I extracted the genomic location, activity/expression status (positive or negative), and tissue-of-activity of enhancers from VISTA enhancer browser and presented their distribution in the 2p16.1 region using UCSC genome browser (https://genome.ucsc.edu). At the time of this chapter preparation, 2582 in vivo tested elements were catalogued in VISTA genome-wide in humans, of which 1393 elements were confirmed to have positive enhancer activity based on tissue expression in at least 3 independently tested mouse embryos. This browser was also used to find and compare the distribution of all positive human-only enhancer elements in the human genome (total of 897) among different chromosomes. For extraction of brain- and neural-expressed enhancers, only those with expression patterns in the neural tube, cranial nerve, forebrain, midbrain, hindbrain, trigeminal V (ganglion, cranial), dorsal root ganglion, and neural crest–derived and facial mesenchyme were selected (total of 617), and the genomic distribution of these enhancers was assessed. The distribution of brain- and neural-expressed enhancers in each chromosome 2 band over the total number of enhancers on chromosome 2 was also determined (only chromosome bands with at least 4 enhancers were considered in this analysis). 4.3.1.2 Conservation of 2p16.1 deleted region and its enhancers in zebrafish The evolutionary conservation of the deleted 2p16.1 region in humans versus zebrafish in terms of preserved gene synteny for deleted genes VRK2, FANCL, long non-coding RNA gene FLJ30838, and non-deleted neighbouring gene, BCL11A, was identified by comparing human against zebrafish genome using Ensembl genome browser (http://www.ensembl.org). The zebrafish orthologue protein sequence homology for the 3 protein-coding genes in this region (BCL11A, VRK2 and FANCL) was checked and shown in the previous chapter. For the long non-coding RNA gene (FLJ30838), the zebrafish orthologue is identified as LOC103912006 on Ensembl genome browser. The cDNA nucleotide sequence were extracted from Ensembl 116  genome browser and aligned against the sequence of human counterpart using the Clustal Omega Multiple Sequence Alignment tool (EMBL-EBI; http://www.ebi.ac.uk/Tools/msa/clustalo/) (Sievers et al., 2011) to obtain percentage nucleotide homology and conservation for this gene. The percentage conservation of human VISTA enhancer elements in zebrafish was also determined by performing a BLAST search for each human enhancer element sequence against zebrafish genome. 4.3.1.3 Long-range interactions of BCL11A promoter with deleted 2p16.1 region enhancers Since BCL11A expression was reported to be reduced in the erythroblasts from one of the two patients with 2p16.1 deletion (Funnell et al., 2015, Prontera et al., 2011) it was proposed that the deletion of regulatory elements affects the 2p16.1 gene expression, although the gene is intact. Therefore, the long-range physical interaction of BCL11A promoter with distal enhancers was investigated using recently generated high-resolution promoter-enhancer interactions data in hematopoietic cell-lines (Javierre et al., 2016). To access this interaction data, 3D genome browser’s capture Hi-C function (http://promoter.bx.psu.edu/hi-c/chic.php) was used and erythroblast was used as the cell-type of interest. BCL11A was inserted as the target gene promoter. Only the interactions from the promoter region of BCL11A (chr2:60776977-60783318; hg19), rather than other gene promoters, were presented here and the interaction points were presented as chromosomal co-ordinates (hg19) and ranged from 277bp-7787bp in size.    117  4.4 Results 4.4.1 Summary of genomic and clinical findings in our 2 cases without the candidate genes Genomic: Our case 2 had a large ~2Mb deletion in the 2p16.1 region (chr2: 57606726-59619316, hg19) and two additional smaller deletions (<30Kb) in the same region (Figure 4.2) which were confirmed and breakpoints refined by QMPSF (as described in the methods section of the previous chapter and Table B.8; Appendix B). One deletion (~17.5Kb) involved the second intron of BCL11A (chr2:60700679-60719010, hg19) and the other (~27Kb) mapped to a non-coding region ~50Kb upstream of BCL11A (chr2: 60828557-60855688, hg19) (Figure 4.2).  The published case reported by Prontera et al., (2011) had an overlapping but slightly larger deletion (~3.5 Mb) (chr2:56853162-60380981, hg19) than the large deletion in our case 2 in the 2p16.1 region (Figure 4.2). The large 2p16.1 deletions in both patients was not found deleted in the healthy population (obtained from DGV) (Figure 4.2) suggesting that this region is intolerant to mega-base scale structural variation/deletion and supporting its functional relevance to development.         118   Figure 4.2 Deletions in both patients compared to CNVs found in healthy individuals in the same region. Note the paucity of CNVs, specifically deletions, in the healthy population for the large non-coding region downstream of BCL11A which is involved in the patients’ deletions. This non-coding region has an intergenic long non-coding RNA (lncRNA) gene (FLJ30838) which is involved in both deletions. Our case 2 also had two smaller deletions in the 2p16.1 region which is indicated by black arrows. They, also, were both involved in the non-coding regions: one mapped to the second intron of BCL11A, while the other mapped to the non-coding region ~50Kb upstream of BCL11A, shown by dashed lines. Red, deletions; blue, duplications.  The large deletions in both patients involved two protein-coding genes, FANCL and VRK2, which were ruled out to be causative (as explained in the previous chapter and in the introduction of this chapter). An intergenic lncRNA (also termed FLJ30838) was also involved in the non-coding region of both deletions (Figure 4.3). The transcriptome profile analysis for this lncRNA, which was previously performed and catalogued on the portal for the Genotype-119  Tissue Expression (GTEx) (https://www.gtexportal.org), suggested a higher and almost exclusive expression in the brain than other tissues (Figure 4.3).  Figure 4.3 Transcriptome profile for FLJ30838 long non-coding RNA gene involved in 2p16.1 deletions. The RNA expression profile identified by RNA sequencing and presented as Reads Per Kilobase of transcript per Million mapped (RPKM) reads in different brain regions, for this gene, is indicated in yellow and shows considerably higher levels of expression than other tissues. Some expression was also noted in testis, ovary, and brain pituitary gland while other tissues seem not to show substantial or any expression of this RNA gene. This transcriptome profile was obtained from the Genotype-Tissue Expression (GTEx) portal (https://www.gtexportal.org/home/).  Clinical: Both patients shared similar phenotypic features which included microcephaly, head shape abnormalities and similar unique craniofacial defects including ptosis, telecanthus, abnormal nasal root, large ears, and high, narrow palate, as well as digital/limb anomalies (for details refer to Table B.4; Appendix B). This suggested that the dysregulation of a common gene(s) may be the cause. As explained in the introduction, the hematological assessment of the published case reported by Prontera et al., (2011), suggested elevated levels of blood fHb which 120  indicated BCL11A was dysfunctional in the patient even though it was not involved in the deletion (Florisson et al., 2013). This was confirmed by the downregulation of BCL11A in the erythroblasts from the patient (Florisson et al., 2013). Therefore, it was concluded that the deletion of cis-regulatory enhancer elements responsible for distal regulation of BCL11A expression may indirectly have caused the characteristic phenotype (Florisson et al., 2013). Unfortunately, we did not have erythroblast cell-lines from our patient (case 2) nor were able to carry out a blood hematological assessment. Nevertheless, I speculated that BCL11A regulation may be also affected since the deletions in both patients overlapped in the same non-coding 2p16.1 region and shared similar phenotypic features.  4.4.2 Enhancer content in the 2p16.1 deleted region Although it was suggested that cis-regulatory enhancer elements in the non-coding region of the 2p16.1 deletion in the published case (Prontera et al., 2011) may be responsible for BCL11A dysregulation, no analysis of the enhancers and their link to BCL11A has been performed so far. Therefore, to identify if the 2p16.1 deleted region contains any enhancer elements, I carried out a bioinformatics-assisted analysis of the VISTA enhancers reported in this region using UCSC genome browser. The results of this analysis identified 37 enhancers, of which 15 were ‘positive’ (i.e. were confirmed to have tissue-specific in vivo activity/expression indicated with black bars on Figure 4.4) while the rest were ‘negative’ (i.e. did not show any or consistent tissue-specific activity/expression pattern in vivo in transgenic mice; indicated with grey bars on Figure 4.4). Table 4.1 summarizes the genomic location and tissue-of-expression of all 15 ‘positive’ enhancer elements. Of these 15 elements, 10 had brain/neural activity (indicated in bold in Table 4.1). Deletions from both individuals contained 6/10 brain/neural-active enhancer elements which were elements 1174, 1067, 975, 1119, 836, 394 (indicated in Table 121  4.1). Interestingly, two of these enhancer elements, 1174 and 1067, with dorsal root ganglion and limb expression were involved in the first and second intron of the lncRNA gene, FLJ30838, respectively (Figure 4.4). This indicated that not only this lncRNA has a high brain expression, but its intronic regions also contain important enhancers with roles in nervous system and limb development.  Figure 4.4 Enhancer elements in the deleted non-coding region of 2p16.1. A summary of all in vivo tested VISTA enhancer elements for this region (2p16.1) is indicated in the lower panel of the figure. Enhancers with a positive activity and expression in vivo in transgenic mice are shown with black bars while the grey bars indicate those with negative activity (or inconsistent expression pattern) in transgenic mice. Details of these enhancers in terms of their exact genomic location and tissue-of-expression are summarized in Table 4.1.     122   Table 4.1 Positive VISTA enhancer elements in the 2p16.1 region. Chromosome Position (bp) (hg19) element ID no. Expression Pattern (in vivo activity) location in mouse embryos  Commonly deleted in both patients chr2:58748340-58750140 1174 dorsal root ganglion  Yes chr2:58799729-58800607 1071 Ear  Yes chr2:58859997-58861674 1152 Limb  Yes chr2:58975738-58977115 1067 dorsal root ganglion, limb  Yes chr2:59102071-59103380 1199 Other  Yes chr2:59178992-59180242 1181 Heart  Yes chr2:59198905-59200529 393 Eye  Yes chr2:59304974-59306893 975 midbrain (mesencephalon)  Yes chr2:59476604-59477955 1119 neural tube, hindbrain (rhombencephalon) Yes chr2:59540640-59541937 836 facial mesenchyme Yes chr2:59746377-59746992 394 midbrain (mesencephalon) Yes chr2:60352514-60353602 779 midbrain (mesencephalon), forebrain No chr2:60441495-60442515 399 forebrain No chr2:60498057-60502013 1535 hindbrain (rhombencephalon) No chr2:60855056-60856888 1142 hindbrain (rhombencephalon) No Bold: Brain/neural-active enhancers     I then evaluated the enhancer content of the deleted region in relation to the rest of chromosome 2 and whole genome. The analysis showed that chromosome 2, which harbors the deletions in both patients, had the highest number of positive and brain/neural-active enhancers in the genome (104/897 and 68/617 enhancer elements, respectively (Figure 4.5, Part A)). Furthermore, brain/neural-active VISTA enhancers are not uniformly scattered along the chromosome 2 and they are enriched at 5 specific regions/cytobands along the chromosome 2. The second highest number of enhancers is in the deleted 2p16.1 region and contains ~25% of brain/neural-active enhancers.  123   Altogether, data suggested that the deleted non-coding 2p16.1 region has a high content of brain/neural-active enhancers in the human genome.   Figure 4.5 Distribution of enhancers among human chromosomes. A) Distribution of the total of 897 enhancers per chromosome. 104 positive enhancers and 68 brain/neural-expressed enhancers are on chromosome 2. B) Distribution of all 68 brain/neural-expressed enhancers on chromosome 2 cytobands. The 2p16.1 region deleted in both individuals involving 6/10 brain/neural-expressed enhancers is indicated with a red scale-bar. C) The percentage enrichment of top 5 enriched chromosome 2 cytobands with brain/neural-expressed enhancers. The top 5 enriched cytobands were identified based on if they harbored at least 4 brain/neural-expressed enhancers. Almost a quarter of brain/neural-expressed enhancers on chromosome 2 map to 2p16.1.    124  4.4.3 Conservation of deleted 2p16.1 region and enhancers in zebrafish  In order to further determine the possibility that the non-coding intergenic 2p16.1 region is of functional importance I analyzed its synteny and sequence conservation in zebrafish as an evolutionary distant species. Analysis of zebrafish genome, suggested a reverse-order but similar synteny for the large non-coding region found on chromosome 13 of zebrafish (a ~1 Mb region) (Figure 4.6), in relation to BCL11A’s orthologue bcl11aa. This non-coding region also had an ortholgous copy for the lncRNA in the human genome, identified as LOC103912006, with ~53% cDNA nucleotide homology to the human orthologue (FLJ30838), which together supported conservation of the non-coding region in zebrafish.   Figure 4.6 Interspecies comparison of gene and enhancer synteny and sequence conservation. Zebrafish displays a similar gene synteny to the human 2p16.1 region (chr2:58,273,777-60,780,633, hg19) with the bcl11aa orthologue adjacent to a long intergenic non-coding region with a lncRNA gene ‘LOC103912006’ followed by ‘fancl’ and ‘vrk2’ at a ~1Mb in size region on chromosome 13 (25.71Mb-26.66Mb). Interestingly, the lncRNA gene has 53% nucleotide sequence homology to the human counterpart cDNA. Positive human VISTA enhancer elements described in section 4.4.2 and listed in Table 4.1 that were conserved and also had an orthologue in zebrafish are shown by coloured bars which are as follows: green (element 1152 with limb activity), red (element 393 with eye activity), purple (element 975 with midbrain activity), black (element 1119 with neural tube/hindbrain activity), Orange (element 399 with forebrain activity). The exact chromosomal locations of these elements in zebrafish were: chr13:26506116-26505761 (element 1152); chr13:26321424-26321053 (element 393); chr13:26260507-26260188 (element 975); chr13:26195034-26194791 (element 1119); chr13:25919287-25918781 (element 399).     125  To test if the lncRNA orthologue in zebrafish and the non-coding region adjacent to it also contains conserved enhancers (as found in humans), the sequence of each human VISTA enhancer (described in section 4.4.2 and listed in Table 4.1) was compared against the zebrafish’s whole-genome using a BLAST search. Interestingly, data revealed that 5 positive human enhancer elements with brain and other tissue activities were also highly conserved (with 80-90% nucleotide homology) in zebrafish and located similarly in the non-coding region adjacent to the orthologue of BCL11A. These conserved human enhancers (which are also found in zebrafish) were elements 1152, 393, 975, 1119, and 399 active in limbs, eyes, midbrain, neural tube/hindbrain, and forebrain, respectively (Figure 4.6). Remarkably, all enhancers, except 399, mapped to the intronic region (first intron) of lncRNA orthologue in zebrafish (Figure 4.6). This suggests that these enhancers, except 399, involved in both deletions, could have key regulatory functions in development due to their deep conservation over 450 million years of evolution.    4.4.4 Long-range interactions between the BCL11A promoter and the 2p16.1 deleted region Presence of enhancer elements in the 2p16.1 region is interesting; however, the next question was whether they interact with BCL11A which may validate their role in the distal regulation of this gene. These enhancer elements are far apart in the linear genome from BCL11A but may come into a close contact in the 3D space by chromatin looping. To assess this, recently developed high-resolution technique of promoter capture Hi-C (pcHi-C) (Javierre et al., 2016, Mifsud et al., 2015) can be used which allows for identification of interactions between any gene promoter with other genomic regions. These interactions, however, are highly cell-type specific and since the enhancers described above are mostly brain/neural-specific, and the phenotype of the patients are related to neuro-development, the best cell-line to check these promoter-enhancer 126  interactions would be brain and neuronal cell-types. Unfortunately, pcHi-C data in brain tissue and neuronal cell-types is not yet available. The only available pcHi-C interaction data, so far, has been in 17 hematopoietic cell-types (Javierre et al., 2016) which can be accessed online (3D genome browser: http://promoter.bx.psu.edu/hi-c/).  Although, pcHi-C interactions in these hematopoietic cell-types may not be informative in identifying particular interactions of BCL11A promoter with brain/neural-active enhancers, they may still inform about the possible distal interactions between the BCL11A promoter with other putative enhancers in the non-coding region in erythroblasts. This is of interest considering that the expression of BCL11A as an inhibitor of fHb expression was altered in erythroblasts of the published 2p16.1 deletion patient, although the gene itself was intact (Funnell et al., 2015, Prontera et al., 2011).     pc-Hi-C interaction analysis identified five interactions between the BCL11A promoter (chr2:60776977-60783318, hg19) and other 2p16.1 genomic regions which are numbered and indicated in Figure 4.7. This included one interaction of BCL11A promoter with the second intron of BCL11A (interaction 1; chr2:60722184-60725012, hg19), three interactions of the promoter with the intergenic non-coding regions 0.25-1.5Mb downstream of BCL11A (interactions 2 [chr2:60292448-60298278, hg19]; 3 [chr2:59532987-59533605, hg19]; and 4 [chr2:58989627-58990688, hg19] ), and one promoter-promoter interaction (i.e. promoter of BCL11A with the promoter of FANCL -interaction 5 [chr2:58462621-58470407, hg19]).  127   Figure 4.7 Long-range interactions between BCL11A promoter and other genomic regions in erythroblasts. Five interactions (numbered above) were found in total between BCL11A promoter and different loci in the 2p16.1 region. Deletions are also indicated above interactions which show that only interactions 3, 4, and 5 were commonly disrupted by both deletion cases. Smaller deletions in our case 2 are also shown above. Note that interaction 1 is between BCL11A promoter and the second intron of BCL11A which happens to be adjacent to one of the small deletions in our case 2. BCL11A promoter is at chr2:60776977-60783318 (hg19) and the exact genomic co-ordinates for its interactions are as follows: interaction 1 (chr2:60722184-60725012, hg19); interaction 2 (chr2:60292448-60298278, hg19); interaction 3 (chr2:59532987-59533605, hg19); interaction 4 (chr2:58989627-58990688, hg19); interaction 5 (chr2:58462621-58470407, hg19).   Of these interactions, only 3 were commonly deleted in both patients which were interactions 3, 4, and 5 between BCL11A promoter and intergenic region (3), the second intron of the lncRNA (4), and the FANCL promoter (5), respectively (Figure 4.7). To further narrow-down the important interaction(s) from the 3 interactions, the small deletions reported in healthy 128  individuals on DGV was checked against these interaction regions. One of these interactions (interaction 3) were eliminated to be important since its interaction region (chr2:59532987-59533605, hg19) overlapped two very small reported deletions in healthy controls on DGV (data not shown).  Therefore, taken together, the second intron of the lncRNA (i.e. interaction 4), and the promoter region of FANCL (i.e. interaction 5) were shown to distally interact with the BCL11A promoter, at least in erythroblasts, and could serve as putative enhancers for BCL11A regulation. Their deletion may be the cause of downregulation of BCL11A in erythroblasts in the published case reported by Prontera et al., (2011) (Funnell et al., 2015) and common phenotypic features seen in our case 2 with overlapping large deletion.   In addition to the large deletion, our case 2 had two small deletions. The first deletion within the BCL11A’s second intron (~17.5Kb; chr2:60700679-60719010, hg19) interestingly mapped to previously reported regulatory GWAS SNPs, rs1427407 (at chr2: 60,718,043) and  rs7599488 (at chr2: 60,718,097), associated with fHb regulation (Basak et al., 2015, Mtatiro et al. , 2014) and schizophrenia (Basak et al., 2015, Goes et al. , 2015), respectively (Figure 4.8). This deletion was also adjacent to (4Kb away from) interaction 1 site (Figure 4.7). The second deletion upstream to BCL11A (~27Kb; chr2: 60828557-60855688; hg19) mapped to a positive VISTA enhancer (element 1142) with confirmed in vivo hindbrain activity (Figure 4.8). Therefore, the additional two small deletions within and peripheral to BCL11A gene sequence, in our case2, also posed as interesting candidates for disruption of additional enhancers regulating this gene. Based on these deletions and long-range interaction data, it can be deduced that BCL11A gene has multiple enhancer regions, in the upstream region, intronic region, and distal downstream intragenic region regulating its expression and function in different cell-types.  129   Figure 4.8 Characteristics of the two additional smaller deletions in our case 2. Our case 2 had two additional smaller deletions in the 2p16.1 region which mapped to the second intron of BCL11A and ~50Kb upstream of BCL11A and were ~17.5Kb (chr2:60700679-60719010, hg19) and ~27Kb (chr2: 60828557-60855688, hg19) in size, respectively. The red bars indicate the deletions. The positions of the two small CNVs (encircled) in relation to the large CNV are shown on the diagram. The intronic deletion mapped to 2 GWAS SNPs (indicated with green stars), rs1427407 (at chr2: 60,718,043) and rs7599488 (at chr2: 60,718,097), associated with fetal hemoglobin regulation and neurodevelopment, respectively. The other deletion mapped to a positive VISTA enhancer (element 1142) with confirmed in vivo hindbrain activity in transgenic mice. The thicker and thinner lines for the 2 small deletions indicate the array detected and qPCR refined breakpoints.  4.5 Discussion The traditional approach to determine the CNV causality is by identifying dosage-sensitive coding genes within the CNV (Rice and McLysaght, 2017). The importance of non-coding regulatory elements, such as enhancers, non-coding RNAs (e.g. lncRNAs, micro-RNAs) 130  involved in the CNVs, and their role in regulation of distant genes are only beginning to be explored (Li et al. , 2016, Zhang and Lupski, 2015). Recent analysis of CNVs in the healthy population has revealed that they are depleted in enhancers and ultra-conserved elements suggesting the strong selection against deleterious mutations in non-coding regulatory elements (Zarrei et al., 2015). As discussed in the introduction of this chapter, several pathogenic deletions have also been identified in the non-coding enhancer-containing regions adjacent to coding genes or within their intron(s) (Brown et al., 2010, Gatta et al., 2014, Szafranski et al., 2014, Szafranski et al., 2013b). In addition, GWASs have revealed that almost 90% of trait-associated SNPs fall in the non-coding regions while only the remaining ~10% are found in the protein-coding sequences (Farh et al. , 2015, Hindorff et al. , 2009). These findings altogether, suggests that a large number of genetic variants cause disease through disruption of the regulation of the protein-coding genes rather than the change in their copy number, coding sequence or protein structure (Hindorff et al., 2009, Kumar et al. , 2012, Pennisi, 2011). In the previous chapter, I explained that 2 out of 33 reported deletions in the 2p15p16.1 region had the typical microdeletion syndrome phenotypic features including microcephaly, unique facial dysmorphism and digital/limb anomalies but no deletion of the 3 candidate genes for the syndrome, XPO1, REL, and BCL11A. The role of the only two protein-coding genes in the 2 deletions, VRK2 and FANCL, to the characteristic phenotype was deemed less likely, based on presence of deletions of these genes in healthy individuals. The normal phenotypic features of these genes after knockdown of their orthologous copies in zebrafish further diminished their likelihood of being the sole cause for the phenotype. The possibility that the deletion of regulatory sequences could play a role in the phenotype was intriguing and supported by my findings of: a) high content of enhancers with 131  confirmed brain/neural activity in the deleted non-coding region; b) the depletion of large CNVs, mainly deletions, overlapping the deleted non-coding region in the healthy population; c) the deep conservation of this non-coding region and a subset of its enhancers in zebrafish; and d) presence of long-range physical interactions between the promoter of one of the candidate genes BCL11A with 2 specific loci (interactions 4 and 5, described in the results section) in this deleted non-coding region. Below, I list evidence from literature that these two interacting loci could have enhancer-like activity. a) Interaction 4: BCL11A promoter with lncRNA  This lncRNA is ~0.8Mb in size and downstream of BCL11A. The presence of an interaction between the BCL11A’s promoter with a locus in the second intron of this lncRNA in erythroblasts likely suggests the important role of this lncRNA in cis-regulation of BCL11A expression. The conservation of this lncRNA in sequence, enhancer involvement, and gene synteny in zebrafish as well as expression in brain suggests that its role in regulating BCL11A could be wider and beyond erythroblasts.  New emerging body of evidence suggests that a subset of lncRNAs are functional enhancer units that regulate the expression of their neighbouring genes and their disruption could lead to disease (Cech and Steitz, 2014, Li et al., 2016, Quinn and Chang, 2016). For instance, deletion of a lung-specific lncRNA was associated with lethal lung developmental disorder through disruption of its long-range chromatin interactions and regulation of its neighbouring gene, FOXF1 via TFs such as GLI2 (Szafranski et al. , 2013a). Moreover, recent evidence suggested that the CNVs of trait-associated intergenic lncRNAs is strongly associated with significant changes in the expression level of their nearby protein-coding gene(s) while the vice versa did not hold true suggesting a hierarchical regulation of genes by lncRNAs (Tan et al. , 2017). 132  A class of lncRNAs, known as activating ncRNAs, have been identified which have enhancer-like functions and activate gene expression of their neighbouring genes by mediating chromatin looping, and brining gene promoters and their cis-regulatory elements (TF complexes) in close proximity through a group of TFs and co-activators known as mediator complex (Lai et al. , 2013). siRNA-mediated knockdown of a subset of these enhancer-like lncRNAs reduced the expression level of their neighbouring genes while their enhancement lead to activation of their neighbouring genes in a non-cell-type specific and orientation independent manner (Orom et al. , 2010) suggesting their importance in regulation of their neighbouring genes in diverse cell-types.  The exact mechanism by which lncRNAs regulate the expression of their neighboring genes is yet unclear (Li et al., 2016). Contradictory findings regarding the role of lncRNA transcripts in gene regulation have been reported so far; see recent review (Li et al., 2016). Some studies have suggested that the RNA transcript itself is necessary for establishing and/or stabilizing chromatin looping and long-range interactions by guiding chromatin-remodeling complexes to specific genomic loci and mediating gene expression regulation (Khalil et al. , 2009, Krivega and Dean, 2012). Other studies suggested that processing e.g. splicing of the lncRNA transcription or alteration of the lncRNA’s intronic and promoter regions affects its enhancer activity (Engreitz et al. , 2016). Future studies will reveal the exact mechanism by which lncRNAs regulate the expression of genes in cis or trans (Li et al., 2016). b) Interaction 5: BCL11A promoter with FANCL promoter   It was also interesting to find an interaction of BCL11A promoter with another promoter which belonged to the FANCL gene. Recent genome-wide study of long-range interactions between gene promoters with other genomic regions suggested presence of 3 types of promoter interactions: a) intragenic interactions where promoter interacts with a gene internal (e.g. 133  intronic) region which accounted for 5% of interactions; b) extragenic interactions where promoter interacts with distal regulatory elements such as enhancers which accounted for 33% of interactions; c) intergenic interactions where promoter interacts with promoter of other proximal or distal genes which accounted for the majority of interactions, 42% (Li et al. , 2012). Using reporter luciferase gene assays, the enhancer activity of some gene promoters e.g. INTS1 was confirmed by placing their 500bp promoter region upstream of promoter of genes that typically interact with them (e.g. MAFK) or genes on a different chromosome that do not interact with them (e.g. CALM1) in MCF7 cell-lines (Li et al., 2012). The enhanced expression of target genes in both scenarios confirmed the enhancer potential of promoters (Li et al., 2012). By the same token, it can be speculated that FANCL promoter may have some enhancer function in regulating BCL11A transcriptional regulation.  The two interactions described above (4 and 5) were similarly affected in both individuals due to the deletion of the two BCL11A interaction sites, and could explains their similar phenotypic features. However, there were also differences in the deletions found in both individuals. For instance, the case reported by Prontera et al., (2011) had a larger deletion than our case 2 and involved more VISTA positive enhancers, elements 394 and 779 (both active in the midbrain), in the region and an additional BCL11A promoter interacting region (interaction 2) than our case 2. The importance of this interaction remains unknown but could point to a putative enhancer element. On the other hand, our case 2 had two additional small deletions: one deletion had SNPs associated with neuro-development and fHb regulation in the second intron of BCL11A (as indicated by GWASs) while the other contained a hindbrain-active enhancer in ~50Kb upstream region of BCL11A. It has previously been reported that enhancers often have additive effects in their gene regulation (Javierre et al., 2016); therefore, deletion of one or a 134  combination may lead to gene dysregulation in different manners such as abnormal expression in different cell-types or timing of expression. Therefore, the differences in the deletions found in the two patients and their enhancer involvement may be responsible for their slight phenotypic differences e.g. a more severe microcephaly in case 2 (OFC of <3rd percentile) versus a milder smaller head size in the case reported by Prontera et al., (2011) (OFC of <5th-10th percentile); or presence of certain neurological abnormalities in the case reported by Prontera et al., (2011) e.g. disturbed vision not found in our case 2. The drawback of my interaction analysis is that it was limited to erythroblast cell-lines due to lack of high-resolution interaction (pcHi-C) data in the brain and neural cell-types. Therefore, although the herein reported regulatory interactions may be different in the brain and neuronal context, due to the cell-type specificity of enhancers, the findings were still interesting as they have the potential to explain why BCL11A was dysregulated in erythroblasts of the published patient (Funnell et al., 2015, Prontera et al., 2011). My analysis of regulatory sequences in the CNV is a rare example of an attempt to understand the role of copy number changes without coding genes. This analysis is still hampered by the lack of in vivo experimental data to support the bioinformatically obtained findings and the complexity of the cell type/developmental stage and tissue-specific regulation of gene function. Nevertheless, such information is bound to be gathered in the future thus facilitating a better explanation of gene-empty CNVs in the phenotype.  4.6 Conclusion In conclusion, proper transcriptional regulation is mediated by dynamic interactions within the chromatin domains over the course of human development in a cell-type specific manner. These interactions can be of promoter-promoter or promoter-enhancer nature.  135  Structural variants such as the mega-base scale deletions at 2p16.1 region presented in this chapter can significantly impair the architecture and dynamic interactions between promoters and cis-regulatory elements (e.g. enhancers). Therefore, they can impact the spatiotemporal expression of key developmental haploinsufficient genes such as BCL11A indirectly and distally without altering their protein-coding sequence or copy number. Future CNV studies should not only consider the dosage of genes involved within the CNVs but also carefully assess the non-coding regulatory elements involved in the CNVs, the impact of CNVs on the genome’s architecture and looping mechanism, and their disruption on neighbouring genes involved outside of the CNV breakpoints. Available bioinformatics technologies and webtools such as VISTA enhancer data-mining, ENCODE annotated enhancer-associated features, interspecies conservation analysis of non-coding regions and enhancer elements using multiple sequence alignment databases e.g. BLAST and ENSEMBL, as well as pcHi-C interaction analysis for identification of physical interactions between promoters and cis-regulatory elements can significantly improve interpretation of CNVs that may have been misclassified as benign CNVs or CNVs of unknown clinical significance due to their depletion in pathogenic protein-coding genes.    136  Chapter 5: DISCUSSION 5.1 Overview Detection of submicroscopic CNVs by CMA has revolutionized genomics studies as these CNVs were previously left undetected by traditional cytogenetic techniques and karyotyping. Studying CNVs is a feasible way to identify candidate genes responsible for developmental disorders out of all genes in the whole-genome. Nonetheless, analysis of CNV effects on the function of their integral genes is not widely performed. The main goal of my PhD research project was, therefore, to characterize CNVs and their biological effects in both prenatal and postnatal developmental abnormalities in order to identify candidate developmental genes. I studied CNVs in miscarriages (arrested prenatal development) and a postnatal DD disorder known as 2p15p16.1 microdeletion syndrome. My approach was multi-faceted and included analysis of CNV gene content, function, dosage-change consequence in animal models, and biological pathway enrichment for CNV genes. This project resulted in identification of several candidate genes that may induce predisposition to embryonic lethality (e.g. TIMP2) by affecting the development of placenta but also maternal pregnancy-related tissue remodeling. Studying the postnatal developmental disorder, 2p15p16.1 microdeletion syndrome, and its associated CNVs from this chromosomal region, also, identified three candidate genes, XPO1, REL, and BCL11A as strong candidate causative genes for developmental anomalies in subjects with the syndrome. Finally, I explored the possible role of regulatory elements from 2p15p16.1 CNVs on one of the 3 candidate genes, BCL11A.  In this chapter, I will review the main findings, discuss their significance, describe the strength and limitations of the studies and outline future directions.  137  5.2 Review and significance In chapter 2, my analysis of rare and common CNVs detected in miscarriages showed that rare CNVs (not seen in controls) have a larger gene-density and higher percentage of genes causing embryonic lethality in mouse knockout models than common CNVs, suggesting that rare CNVs may be more likely to harbor candidate genes for embryonic/fetal developmental arrest. Determining candidate genes was, however, challenging because most of the CNVs with embryonic lethal genes were of unknown or parental origin. Also, the consequence of their heterozygous knockout or overexpression (as a true representation of how CNVs alter genes’ expression), could not be determined from the available homozygous knockout mouse studies described in the chapter.   As a pool, genes from rare CNVs were not enriched in biological pathways, but genes from common CNVs were enriched in immune-related pathways such as graft-vs.-host disease indicating that immune processes and maternal/fetal immune cross-talk are important for establishing the environment for successful embryo/fetal development. Recent genome-wide studies of CNVs in women with RPL in comparison to fertile controls have also identified enrichment of genes in immune-related pathways reinforcing their importance for healthy embryonic development. Overall, my analysis of rare and common CNVs and their genes in miscarriages can serve as an example for future studies when larger data-sets of CNVs become available in miscarriages and couples.   In chapter 3, I present evidence that in-depth characterization of a unique CNV can help pinpoint important causative developmental genes responsible for postnatal developmental abnormalities. I present de novo CNVs/deletions reported in the 2p15p16.1 genomic region detected in 33 subjects (25 published and 8 newly presented here) affected with similar 138  recognizable neuro-developmental phenotypic features such as ID, microcephaly, similar craniofacial defects, and body dysmorphologies. By careful analysis of the CNV breakpoints, gene content, gene deletion frequency, gene involvement in smallest deletions, gene haploinsufficiency scores, and known functions and mutation consequence of genes, I describe how candidate genes can be narrowed-down from CNVs containing multiple (>10) genes. Furthermore, using human protein atlas database with expression data in human brain, I showed that the 4 initially selected candidate genes, XPO1, BCL11A, REL, and USP34, are expressed in the brain and have reduced expression in patient lymphoblasts, but only 3 (XPO1, BCL11A, REL) have a knockdown phenotype in zebrafish comparable to the human developmental abnormalities (e.g. growth abnormalities, microcephaly, structural brain abnormalities). One of the genes, XPO1, appears to be the strongest candidate because its function to shuttle molecules from the nucleus to the cytoplasm was impaired in patient lymphoblasts. I confirmed these 3 candidates by showing moderate rescue of the phenotype, mainly head-size, by overexpression of human genes in knockdown zebrafish and demonstrated that overexpression of human genes in zebrafish does not cause mirror phenotypes e.g. macrocephaly when duplicated, as suggested but not characterized in-detail by a recent study. Overexpression of BCL11A only, interestingly, caused another abnormal phenotype of body development, but normal head size. Overall, in this chapter, for the first time, I present evidence using a multi-faceted analysis that XPO1, REL, and BCL11A are the best candidate genes out of >10 genes involved in the region for the phenotypic features seen in the individuals affected by 2p15p16.1 microdeletion syndrome. Finally, I demonstrate that these 3 genes and other genes from 2p15p16.1 CNV have a role in a common NF-κB pathway by using functional enrichment analysis and bioinformatics. NF-κB pathway has diverse roles and has widely been associated with both neuro-development and 139  immunoregulation. I conclude that the 2p15p16.1 microdeletion syndrome is due to deletion and dysfunction of 3 specific strong candidate genes but is also possibly due to the disturbed pathway they and other genes from the region participate in.  In chapter 4, I study 2p15p16.1 deletions from 2 cases that did not include any of the 3 candidate genes, and yet the patients had typical syndromic presentation. I looked for explanation by exploring the non-coding, regulatory content of these deletions and showed that: a) this region contains a large number of enhancer elements that are largely active in the brain and nervous-system, b) this region is under a tight evolutionary-constraint and is highly conserved in zebrafish as a distant species and c) BCL11A promoter physically interacts with and contacts the deleted region from these 2 cases in the native state, raising the possibility that the deletion disrupts this native interaction and impairs the regulation of  BCL11A expression in a cell-type specific manner. This, I suggest, could be the cause of similar phenotypic features in the deletion carrier individuals compared to individuals that carry deletion of BCL11A itself or other candidate genes.  5.3 Strengths and limitations  5.3.1 Strengths One of the main strengths of my study was the use of a multi-faceted approach including in vitro, in vivo and in silico evidence to characterize the CNVs and decipher the role and importance of their genes (and enhancer elements). This type of analysis allows for identification of candidate genes based on multiple lines of evidence increasing the confidence in their selection and classification as causative genes. Additionally, by identifying relevant interesting pathways for CNV genes, I presented how CNVs can have an effect beyond individual genes and affect a shared pathway(s) relevant to 140  the common phenotype. Studies have shown that identifying impaired pathways in patients can be used as a starting point for their alteration, and can lead to the improved diagnosis and therapy for ID which has long been perceived as a non-curable disease due to its irreversible damages.  My work is one of the rare examples of exploring the role of regulatory sequences from CNVs on the phenotype and neighbouring genes. I believe that it is important to analyze regulatory content of CNVs especially for gene-sparse CNVs as recent evidence has suggested that in addition to altering the dosage-sensitivity of their integral genes, CNVs tend to cause disease by altering the spatiotemporal expression of their neighbouring genes by modifying the chromatin architecture, physical genomic contacts, and promoter-enhancer interactions. More studies like this trying to characterize gene-empty CNVs will lead to better understanding of their role and reduction of the number of CNVs of unclear clinical significance. 5.3.2 Limitations I have realized that although zebrafish is an excellent model for some phenotypes (e.g. microcephaly and structural brain anomalies) it cannot be used to study biological processes unique to human. Therefore, although I have identified several genes of interest that are highly expressed in placenta in miscarriages, zebrafish as a non-placental model with an ex-vivo embryonic development was limited to study the consequence of their knockdown. Also, many of the miscarriage candidate genes that were embryonic-lethal in mouse knockouts had an unknown origin, and possibilities remain that they are not causative of miscarriage if they are transmitted from parents. In addition, I relied on available mouse knockout phenotypic data for the interpretation of CNV genes which, although, still valuable to gain insight about the function of CNV genes in relation to the disorder, are typically homozygous knockout models which are not a true representation of heterozygous CNVs and duplication CNVs that overexpress the gene, 141  and were predominant type of miscarriage CNVs. Therefore, it is not yet clear whether same lethal mouse knockout phenotypes can be obtained in humans if only one allele of CNV genes is lost or duplicated. I conclude that much more information is needed for interpretation of miscarriage CNVs. For example a database with CNVs from individuals with known reproductive history would be ideal, but is not yet available.  Finally, my investigations of regulatory elements was based on databases and a proof that the enhancers and regulatory sequences affect BCL11A would have to be obtained in vivo in animal models (e.g. zebrafish or mouse). Therefore, extension of animal modeling studies to regulatory elements is needed to categorically prove some of the findings in this project. 5.4 Future studies Recent development of the programmable genome-editing technology, CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein-9 nuclease  (Cas) 9); has allowed for creating accurate CNV modeling (Henao-Mejia et al. , 2016, Kraft et al. , 2015, Tai et al. , 2016), single-gene mutations (Henao-Mejia et al., 2016, Hwang et al. , 2013, Wang et al. , 2017a), as well as non-coding enhancer element mutations (Fulco et al. , 2016, Korkmaz et al. , 2016, Sanjana et al. , 2016) in vitro and in vivo by cutting the DNA and inducing or introducing structural variants, insertions and mutations at precisely-specified target sites.  This is interesting because gene expression can be manipulated at the DNA level (rather than RNA level) and exactly as it is altered in human patients (Hwang et al., 2013, Korkmaz et al., 2016). For instance, only one allele of the gene can be altered in sequence or copy number by mutations or CNVs (i.e. heterozygous variation) which is a closer representation of what takes place in human CNV carriers (Kraft et al., 2015, Paquet et al. , 2016, Tai et al., 2016, Wang et 142  al., 2017a). This was recently shown for CRISPR/Cas9-mediated heterozygous knockout of an autism gene, CHD8, in human control cell-lines which resulted in a similar transcriptome profile as patient cell-lines affected with autism (Wang et al., 2017a).   Moreover, modeling CNVs using CRISPR/Cas9 can be more informative about the local and global effects of CNVs at the chromatin level. For instance, modeling human CNVs from 2q35q36 region in orthologous regions in mice has shown that the CNV alters not only the level but also the tissue-of-expression of some CNV genes and neighbouring genes (Franke et al., 2016, Kraft et al., 2015, Lupianez et al., 2015, Lupianez et al., 2016). These CNV-mediated ectopic expressions are recently suggested to be caused by the effect of CNVs on the chromatin structure and promoter-enhancer interactions which can explain some of the phenotypic features in humans such as congenital digital anomalies including brachydactyly and polydactyly (Lupianez et al., 2015). Therefore, in order to fully understand the pathogenic effects of CNVs and their role in the phenotype, it is important to analyze their impact on the chromatin architecture as a whole rather than single-gene effect (Lupianez et al., 2016). Another future improvement could be the use of correct cell-lines corresponding to the affected tissues in humans, for instance, placental/trophoblast cells for miscarriages or neuronal cells for NDD-associated cases such as the 2p15p16.1 microdeletion syndrome described in this thesis. Although patient cell-lines such as lymphoblasts as well as animal models are still very useful tools to study the gene expression/function and identify novel candidate CNV genes for the disease, ideally, we aim to find the function of genes in the affected tissues in humans. This is difficult, however, mainly due to the fact that the affected tissues cannot be easily accessed, especially for NDD cases where brain and neuronal cells are the main affected tissues and cell-types. The advent of patient-derived induced pluripotent stem cell (iPSC) technology and its 143  subsequent differentiation into desired cell-types e.g. placental/trophoblast cells (Kojima et al. , 2017) or neuronal cells (Denham and Dottori, 2011) not only has improved functional studies for understanding gene functions and their variation e.g. CNV consequence in the desired cell-type but has also allowed for studying the effect of drugs on affected tissues (Zhang et al. , 2013) and experimenting potential future stem-cell transplant therapy for disorders (Cundiff and Anderson, 2011). iPSCs are generated by reprogramming patient dermal fibroblasts or other cell-types to pluripotent cells that resemble embryonic stem cells by induced overexpression of four TFs; OCT4, SOX2, KLF4 and c-MYC which can then be differentiated to other cell-types (Takahashi and Yamanaka, 2006). This technique, therefore, allows for an indirect way to obtain cell-type-of-interest from patients. For instance, recent studies of human iPSC generated neurons using skin or blood cells from children affected with NDDs and autism has informed us about the effect of gene-mutations on the neurons which included formation of fewer synapses, reduced soma size, defective spontaneous excitatory synaptic cross-talk when compared with neurons from unaffected controls (Kim et al. , 2014a).  Moreover, deriving neurons from iPSCs of patients carrying NDD-associated CNVs such as deletions in the 7q11.23 (Khattak et al. , 2015) and 15q11.2 (Das et al. , 2015) regions, have shown interesting disease-relevant gene-expression alterations including downregulation of several candidate NDD genes (involved inside and outside of CNVs) and neuron functional/morphological changes such as defective neuronal action-potentials for 7q11.23 deletions (Khattak et al., 2015) and dendritic morphology abnormalities for 15q11.2 deletions (Das et al., 2015) not seen in iPSC-derived neurons of controls.  Human iPSCs can also be used as a great in vitro model to generate CNVs by CRISPR/Cas9 technology and compare features to that of patient CNV carriers. This was 144  recently shown to be possible by generation of reciprocal NDD-associated CNVs such as 16p11.2 by CRISPR/Cas9 technology in human control iPSCs which were validated to carry heterozygous CNVs and present transcriptome profiles similar to cells (iPSCs and lymphoblasts) from patient CNV carriers (Tai et al., 2016). Therefore, application of CRISPR/Cas9 technology for genome-editing together with iPSC technology to obtain desired cell-types from the patients will undoubtedly improve our understanding of what goes wrong, genetically and phenotypically in the patients which is crucial before potential therapeutic interventions can be devised.    5.5 Conclusion The advancement of CMA technology and discovery of new CNVs throughout genome in different developmental disorders have significantly improved our understanding of genomic regions susceptible to pathogenicity. However, the precise pathogenic effect of a large number of CNVs is left unknown. Genotype-phenotype studies of patients carrying pathogenic CNVs can improve our understanding of the function of a large number of previously uncharacterized developmental genes. More multi-faceted studies of in silico, in vitro, and in vivo analysis of CNV effects on spatiotemporal expression of their genes and neighbouring genes and the phenotype are needed to decipher the exact pathomechanism of CNVs in the patients.  This information will certainly be helpful in clinical diagnosis, counseling, management, as well as therapy for the patient families. Identifying the recurrence risk and the effect of CNVs on genes and biological pathways not only improves a more constructive advice and diagnosis to patient families but also allows them to be better managed by referral to appropriate specialists and improve their future decisions and planning e.g. improving the outcome of pregnancies by prenatal/preimplantation genetic diagnosis (PGD) of couples’ embryos or ameliorating certain 145  traits in their affected children by changing their nutritional supplementation and pharmacological intervention.  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CNV # Chromosome Cytoband Start (bp)  End (bp)  Size (bp)  Genes  Type (Loss/gain) - Origin Source study 1 1 q25.3 180913772 180962719 48947 STX6 KIAA1614 Loss - maternal A 2 2 p16.1 61113219 61149760 36541 REL Loss - unknown A 3 5 q12.1 60371269 60428901 57632 NDUFAF2 Loss - paternal A 4 6 q26 162206643 162351780 145137 PARK2 Loss - paternal A 5 7 p14.1 39504063 39681146 177083 POU6F2 YAE1D1 RALA LOC646999 Gain - maternal A 6 7 p14.3 33353930 33506525 152596 BBS9 Loss - maternal A NOT included (<1Kb size) 10 p15.3     116 WDR37 Gain - de novo A 7 13 q32.1 94077579 94079173 1594 GPR180 Gain - de novo A 8 14 q13.1 33636797 33664064 27267 NPAS3 Loss - paternal A 9 15 q22.1 58699828 58775581 75754 LIPC Loss - paternal A 10 17 p13.1 9446935 9655508 208573 USP43 WDR16 STX8 Gain - unknown A 11 17 q25.3 76869692 76955292 85601 LOC100653515 TIMP2 Gain - maternal A 12 19 q13.41 52976211 53458509 482298 ZNF816 ZNF578 ZNF600 ZNF320 ZNF808 ZNF321P ZNF701 ZNF83 ZNF28 ZNF137P ZNF611 ZNF468 Gain - paternal A 174   CNV # Chromosome Cytoband Start (bp)  End (bp)  Size (bp)  Genes  Type (Loss/gain) - Origin Source study  13 X p22.31* 6488521 8131951 1643430 MIR4767 VCX STS MIR651 HDHD1 PNPLA4 Gain - maternal A 14 X p22.2 13505178 13835312 330135 TCEANC EGFL6 GPM6B TRAPPC2 OFD1 RAB9A Gain - maternal A 15 X q28 151958361 152129458 171097 CETN2 NSDHL ZNF185 Gain - paternal A re-classified as a common CNV 16 q23.1 76695473 76850048 154575   Loss - maternal A re-classified as a common CNV 10 q21.3 68322419 68394611 72192 CTNNA3 Loss - maternal A re-classified as a common CNV 5 q23.3 129360220 129413588 53369 CHSY3 Loss - paternal A re-classified as a common CNV 11 p15.1 20485820 20603261 117441 PRMT3 Gain - paternal  A 16 17 p13.3 1549700 1552787 3087 RILP Loss - maternal B 17 X q25 126287592 127276011 988419 ACTRT1 Gain - unknown B 18 X q27.3 145029174 145189694 160520 MIR890 MIR888 MIR892A&B MIR891A&B Gain - maternal B   175   CNV # Chromosome Cytoband Start (bp)  End (bp)  Size (bp)  Genes  Type (Loss/gain) - Origin Source study  re-classified as a common CNV 9 p24.3 46587 548464 501877 C9orf66 FOXD4 KANK1 CBWD1 DOCK8 Gain - unknown B re-classified as a common CNV 1 p32.3-p32.2 55930462 56280909 350447   Loss - maternal B re-classified as a common CNV 11 p11.2 50414030 51372036 958006   Gain - de novo B re-classified as a common CNV 17 p11.2 18293171 18927546 634375 FBXW10 GRAP SLC5A10 TRIM16L USP32P2 FOXO3B TBC1D28 CCDC144B LOC339240 FLJ35934 TVP23B PRPSAP2 FAM83G LGALS9C KRT16P1 ZNF286B FAM106A Gain - paternal B re-classified as a common CNV 3 p26.2-p26.1 3899791 4168641 268850   Loss - maternal B re-classified as a common CNV XY p22.33 1736584 2060692 324108 ASMT Gain - paternal B re-classified as a common CNV 11 p15.4 2904010 2906824 2814 CDKN1C Gain - de novo C  176   CNV # Chromosome Cytoband Start (bp)  End (bp)  Size (bp)  Genes  Type (Loss/gain) - Origin Source study  19 2 p21 45063376 45186612 123236 SIX3 Gain - unknown C 20 7 q11.22 70356011 71708048 1352037 CALN1 WBSCR17 MIR3914-1 & -2 Gain - unknown C 21 7 p22.2 3347012 4017883 670871 SDK1 Loss - unknown C 22 10 q24.31 102777838 102897500 119662 PDZD7 SFXN3 KAZALD1 TLX1 TLX1NB Loss - unknown C 23 11 p11.12-q12.1 51432683 55710526 4277843 TRIM48 OR SPRYD5 Gain - unknown C 24 14 q24.1 68466776 68609006 142230 RAD51B Loss - unknown C 25 22 q13.33 50662989 51178264 515275 HDAC10 MAPK11 MAPK12 TUBGCP6 PLXNB2 FAM116B PPP6R2 SBF1 ADM2 LMF2 MIOX NCAPH2 SCO2 TYMP ODF3B CPT1B ARSA MAPK8IP2 LOC100144603 CHKB C22orf41 KLHDC7B SHANK3 ACR Gain - unknown C re-classified as a common CNV 13 q12.11 20285281 20405620 120339 PSPC1 ZMYM5 Gain - unknown C re-classified as a common CNV 7 q11.21 64691936 65070919 378983 ZNF92 Loss - unknown C 177   CNV # Chromosome Cytoband Start (bp)  End (bp)  Size (bp)  Genes  Type (Loss/gain) - Origin Source study  re-classified as a common CNV 8 q12.1 56776665 56922601 145936 LYN Gain - unknown C re-classified as a common CNV 1 p36.13 16840487 17231817 391330 MIR3675 MST1P2 MST1L ESPNP NBPF1 LOC729574 CROCCP2 Loss - unknown C re-classified as a common CNV X q22.2 103220412 103376914 156502 MIR1256 H2BFWT H2BFM LOC286437 TMSB15B SLC25A53 ZCCHC18 H2BFXP Gain - unknown C  Total: 25 CNVs containing 94 genes - note exclusion of highlighted (in red) small CNVs or those that found to be common after DGV re-evaluation. Source studies: A, Rajcan-Separovic et al. (2010 a,b); B, Robberecht et al. (2012); C, Viaggi et al. (2013). *This CNV was initially reported as common but here re-classified as a rare CNV due to lack of complete overlap with current DGV variants.      178   Table A.2 Common miscarriage CNVs identified in four high-resolution CMA studies (hg19 breakpoints). CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 1 1 p36.21 12917224 13142745 225521 PRAMEF5 PRAMEF10 PRAMEF8 PRAMEF4 PRAMEF6 PRAMEF7 PRAMEF2 PRAMEF22 PRAMEF23 Loss A 2 1 p36.13 16926547 17048932 122385 MIR3675 MST1P2 ESPNP NBPF1 CROCCP2 Loss A 3 1 p36.13 17012091 17214170 202079 MIR3675 MST1L ESPNP Loss A 4 1 q24.2 169206675 169256716 50041 NME7 Gain A 5 1 q31.3 196744521 196883539 139018 CFHR4 CFHR3 CFHR1 Loss A 6 1 q31.3 196744721 196799302 54581 CFHR3 CFHR1 Gain A 7 1 q31.3 196744721 196799302 54581 CFHR3 CFHR1 Loss A 8 1 q31.3 196744721 196799302 54581 CFHR3 CFHR1 Loss A 9 1 q44 248756015 248785703 29688 OR2T10 Loss A 10 1 q44 248756015 248785703 29688 OR2T10 Loss A 11 1 q44 248756015 248785703 29688 OR2T10 Loss A 12 1 q44 248756015 248785703 29688 OR2T10 Loss A 13 1 q44 248756015 248785703 29688 OR2T10 Loss A 14 1 q44 248756015 248785703 29688 OR2T10 Loss A 15 2 p25.3 306227 582057 275830   Gain A 16 2 p11.2 87375877 88005559 629682 MIR4435-2 MIR4435-1 LINC00152 Gain A 17 2 p11.2 89163862 91670343 2506481   Gain A 18 2 p11.2 89163862 89319978 156116   Gain A 19 2 p11.2 89163862 89319978 156116   Gain A 20 2 p11.2 89364272 91899802 2535530 LOC654342 Loss A 179  CNV # Chromosome Cytoband Start (bp) End (bp) CNV Size (bp) Genes Type (Loss/gain) - Origin unknown unless specified Source study 21 2 p11.2 89426448 90258461 832013   Loss A 22 2 p11.2 89493770 90258461 764691   Loss A 23 2 p11.2 89426448 90258461 832013   Loss A 24 2 p11.2 90017137 90234164 217027   Loss A 25 2 q37.3 242865720 243007500 141780   Loss A 26 2 q37.3 242865720 242976125 110405   Loss A 27 2 q37.3 242877027 242903095 26068   Loss A 28 2 q37.3 242930400 242976125 45725   Loss A 29 2 q37.3 242865720 243007500 141780   Loss A 30 2 q37.3 242865720 243007500 141780   Loss A 31 2 q37.3 242865920 243007359 141439   Loss A 32 3 q26.1 162514334 162619282 104948   Gain A 33 3 q26.1 162514334 162619282 104948   Gain A 34 3 q26.1 162514334 162619282 104948   Gain A 35 3 q26.1 162514334 162619282 104948   Loss A 36 3 q26.1 162514334 162619282 104948   Loss A 37 3 q26.1 162514334 162619282 104948   Loss A 38 3 q26.1 162514334 162619282 104948   Loss A 39 3 q26.1 162514334 162619282 104948   Gain A 40 3 q26.1 162514334 162619282 104948   Gain A 41 3 q26.1 162514534 162619141 104607   Gain A 42 3 q26.1 162514534 162619141 104607   Loss A 43 3 q26.1 162514534 162619141 104607   Gain A 44 3 q26.1 162514534 162619141 104607   Gain A 45 3 q26.1 162514534 162594653 80119   Gain A  180  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 46 3 q26.1 162514534 162619141 104607   Gain A 47 3 q26.1 162514334 162619282 104948   Loss A 48 3 q26.1 162556023 162619282 63259   Gain A 49 3 q29 195420386 195456687 36301 MUC20 MIR570 Gain A 50 3 q29 195348781 195457909 109128 MUC20 MIR570 SDHAP2 Loss A 51 4 p13 44565439 44631180 65741 YIPF7 Loss A 52 4 q13.2 69218415 69483419 265004 TMPRSS11E UGT2B17 Loss A 53 4 q13.2 69374940 69483419 108479 UGT2B17 Gain A 54 4 q13.2 69374940 69666179 291239 UGT2B15 UGT2B17 Loss A 55 4 q13.2 69374940 69483419 108479 UGT2B17 Loss A 56 4 q13.2 69374940 69483419 108479 UGT2B17 Gain A 57 4 q13.2 69374940 69483419 108479 UGT2B17 Loss A 58 4 q13.2 69374940 69483419 108479 UGT2B17 Gain A 59 4 q13.2 69375140 69483277 108137 UGT2B17 Loss A 60 4 q13.2 69402591 69483419 80828 UGT2B17 Loss A 61 4 q13.2 69402591 69483419 80828 UGT2B17 Loss A 62 4 q13.2 69402591 69483419 80828 UGT2B17 Loss A 63 4 q13.2 69402591 69483419 80828 UGT2B17 Loss A 64 4 q13.2 69402591 69483419 80828 UGT2B17 Loss A 65 4 q13.2 69402591 69483419 80828 UGT2B17 Loss A 66 4 q13.2 69402791 69483277 80486 UGT2B17 Loss A 67 4 q13.2 69402791 69483277 80486 UGT2B17 Loss A 68 4 q13.2 69402791 69483277 80486 UGT2B17 Loss A 69 4 q13.2 70153665 70203389 49724 UGT2B28 Loss A 70 4 q13.2 70153665 70203389 49724 UGT2B28 Loss A  181  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 71 4 q13.2 70153665 70203389 49724 UGT2B28 Loss A 72 4 q13.2 70153665 70203389 49724 UGT2B28 Loss A 73 4 q13.2 70153665 70203389 49724 UGT2B28 Loss A 74 4 q13.2 70153665 70203389 49724 UGT2B28 Loss A 75 4 q13.2 70153865 70203248 49383 UGT2B28 Loss A 76 4 q13.2 70153865 70203248 49383 UGT2B28 Loss A 77 4 q13.2 70166877 70203389 36512   Loss A 78 5 p15.33 692659 795944 103285 TPPP ZDHHC11 Gain A 79 5 p15.33 710494 895091 184597 BRD9 ZDHHC11 TRIP13 Loss A 80 5 p15.33 710494 773103 62609   Loss A 81 5 p15.33 758969 820616 61647 ZDHHC11 Loss A 82 5 p15.1 17603759 17652952 49194   Loss A 83 5 p14.3 21402739 21540419 137681 GUSBP1 Loss A 84 5 p13.2 36520666 36609496 88831 SLC1A3 Loss A 85 5 p13.1 41176485 41242981 66497 C6 Loss A 86 5 q23.3 129360220 129413588 53369 CHSY3 Loss - paternal A 87 6 p25.3 259328 376090 116763 DUSP22 Loss A 88 6 p25.3 259328 376090 116762 DUSP22 Gain A 89 6 p25.3 259528 375949 116421 DUSP22 Loss A 90 6 p25.3 259528 375949 116421 DUSP22 Loss A 91 6 p22.1 29102110 29152966 50856 OR2J2 Loss A 92 6 p21.31 34734166 34902536 168370 ANKS1A UHRF1BP1 SNRPC TAF11 Gain A 93 6 p21.32 32411957 32552156 140199 HLA-DRA HLA-DRB1 HLA-DRB5 HLA-DRB6 Loss A 94 6 p21.32 32450499 32611213 160714 HLA-DQA1 HLA-DRB1 HLA-DRB5 HLA-DRB6 Loss A 182  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes Type (Loss/gain) - Origin unknown unless specified Source study 95 6 p21.32 32450499 32565205 114706 HLA-DRB1 HLA-DRB5 HLA-DRB6 Loss A 96 6 p21.32 32450499 32552312 101813 HLA-DRB1 HLA-DRB5 HLA-DRB6 Loss A 97 6 p21.32 32450499 32525913 75414 HLA-DRB5 HLA-DRB6 Loss A 98 6 p21.32 32487224 32525913 38689 HLA-DRB5 HLA-DRB6 Loss A 99 6 p21.32 32487224 32552312 65088 HLA-DRB1 HLA-DRB5 HLA-DRB6 Gain A 100 6 p21.32 32487224 32552312 65088 HLA-DRB1 HLA-DRB5 HLA-DRB6 Gain A 101 6 p21.32 32487224 32552312 65088 HLA-DRB1 HLA-DRB5 HLA-DRB6 Loss A 102 6 p21.32 32487224 32552312 65088 HLA-DRB1 HLA-DRB5 HLA-DRB6 Gain A 103 6 p21.32 32487224 32552312 65088 HLA-DRB1 HLA-DRB5 HLA-DRB6 Loss A 104 6 p21.32 32487224 32525913 38689 HLA-DRB5 HLA-DRB6 Loss A 105 6 p21.32 32487224 32525913 38689 HLA-DRB5 HLA-DRB6 Loss A 106 6 p21.32 32487224 32525913 38689 HLA-DRB5 HLA-DRB6 Loss A 107 6 p21.32 32487224 32525913 38689 HLA-DRB5 HLA-DRB6 Loss A 108 6 p21.32 32487424 32552156 64732 HLA-DRB1 HLA-DRB5 HLA-DRB6 Loss A 109 6 q27 168371216 168581353 210137 HGC6.3 KIF25-AS1 KIF25 MLLT4 FRMD1 Gain A 110 6 q27 168371216 168558124 186908 HGC6.3 KIF25-AS1 KIF25 MLLT4 FRMD1 Gain A 111 6 q27 168371416 168525407 153991 HGC6.3 KIF25-AS1 KIF25 MLLT4 FRMD1 Gain A 112 7 q11.21 64566758 65176532 609774 ZNF92 LOC441242 INTS4L2 Loss A 113 7 q21.12 86394299 86493991 99692 GRM3 Loss A 114 7 q31.2 116194007 116228306 34299 CAV1 Gain - paternal  A 115 7 q34 142834447 142890809 56362 TAS2R39 PIP Loss A 116 7 q34 142834447 142890809 56362 TAS2R39 PIP Loss A 117 7 q34 142834447 142890809 56362 TAS2R39 PIP Loss A   183  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 118 7 q35 143883829 143952576 68747 RNU6-57 OR2A1 OR2A20P OR2A42 OR2A9P ARHGEF35 Gain A 119 8 p23.3 2071512 2134050 62538 MYOM2 Loss A 120 8 p23.1 7052986 7752727 699741 FAM66B ZNF705G DEFB4B SPAG11B DEFB104A DEFB4A DEFB105A DEFB106A DEFB107A LOC349196 USP17L1P DEFB103A FAM90A7P FAM90A10P DEFB107B DEFB104B DEFB106B DEFB105B DEFB103B DEFB109P1B USP17L4 SPAG11A Loss A 121 8 p23.1 7053186 8094773 1041587 FAM66B ZNF705G FAM66E ZNF705B DEFB4B MIR548I3 SPAG11B DEFB104A DEFB4A DEFB105A DEFB106A DEFB107A FAM86B3P LOC349196 USP17L8 USP17L1P DEFB103A FAM90A7P FAM90A10P DEFB107B DEFB104B DEFB106B DEFB105B DEFB103B DEFB109P1B USP17L4 USP17L3 SPAG11A Loss A   184   CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 122 8 p23.1 7053186 8094773 1041587 FAM66B ZNF705G FAM66E ZNF705B DEFB4B MIR548I3 SPAG11B DEFB104A DEFB4A DEFB105A DEFB106A DEFB107A FAM86B3P LOC349196 USP17L8 USP17L1P DEFB103A FAM90A7P FAM90A10P DEFB107B DEFB104B DEFB106B DEFB105B DEFB103B DEFB109P1B USP17L4 USP17L3 SPAG11A Loss A 123 8 p23.1 7274008 7752586 478578 DEFB4B SPAG11B DEFB104A DEFB4A DEFB105A DEFB106A DEFB107A DEFB103A FAM90A7P FAM90A10P DEFB107B DEFB104B DEFB106B DEFB105B DEFB103B SPAG11A Gain A 124 8 p23.1 10742247 10787935 45688 XKR6 Loss A 125 8 p22 15952011 16010296 58285 MSR1 Gain A 126 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Gain A 127 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Gain A 128 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Gain A 129 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Gain A 130 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Gain A 131 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Loss A  185   CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 132 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Gain A 133 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Gain A 134 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Gain A 135 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Gain A 136 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Gain A 137 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Loss A 138 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Gain A 139 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Loss A 140 8 p11.23 39237238 39380795 143557 ADAM3A ADAM5 Gain A 141 8 p11.23 39237438 39380654 143216 ADAM3A ADAM5 Loss A 142 8 p11.23 39237438 39380654 143216 ADAM3A ADAM5 Loss A 143 8 p11.23 39237438 39380654 143216 ADAM3A ADAM5 Gain A 144 8 p11.23 39237438 39380654 143216 ADAM3A ADAM5 Gain A 145 8 p11.23 39237438 39380654 143216 ADAM3A ADAM5 Gain A 146 8 p11.23 39237438 39380654 143216 ADAM3A ADAM5 Gain A 147 8 p11.23 39237438 39380654 143216 ADAM3A ADAM5 Loss A 148 8 p11.23 39258694 39380795 122101 ADAM3A ADAM5 Gain A 149 9 p21.1 28604083 28745187 141104 LINGO2 Loss A 150 9 p11.2 43505643 45529411 2023768 FAM27C SPATA31A6 LOC643648 CNTNAP3B Loss A 151 9 p11.2 43505643 43659653 154010 SPATA31A6 Loss A 152 9 p11.2 43505643 43659653 154010 SPATA31A6 Loss A 153 9 p11.2 43505643 44259605 753962 SPATA31A6 CNTNAP3B Gain A 154 9 p11.2 43590080 43659512 69432 SPATA31A6 Loss A 155 9 q32 116376646 116536569 159923   Loss A  186  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 156 10 p11.21 37414679 37483401 68722 ANKRD30A Loss A 157 10 q11.21 42607435 43103366 495932 ZNF37BP CCNYL2 LOC441666 ZNF33B LINC00839 Gain A 158 10 q11.22 46951037 48368749 1417712 FAM25B FAM25G ZNF488 CTSLP2 ANXA8L2 BMS1P2 FAM35DP FAM21B AGAP9 BMS1P6 LINC00842 FAM25C ANXA8 ANXA8L1 HNRNPA1P33 SYT15 GPRIN2 NPY4R LOC100996758 ANTXRL Gain A 159 10 q11.22 46951037 48368749 1417712 FAM25B FAM25G ZNF488 CTSLP2 ANXA8L2 BMS1P2 FAM35DP FAM21B AGAP9 BMS1P6 LINC00842 FAM25C ANXA8 ANXA8L1 HNRNPA1P33 SYT15 GPRIN2 NPY4R LOC100996758 ANTXRL Gain A 160 10 q11.22 46951037 48334607 1383570 FAM25B FAM25G CTSLP2 ANXA8L2 BMS1P2 FAM35DP FAM21B AGAP9 BMS1P6 LINC00842 FAM25C ANXA8 ANXA8L1 HNRNPA1P33 SYT15 GPRIN2 NPY4R LOC100996758 ANTXRL Gain A    187  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 161 10 q11.22 46951037 48334607 1383570 FAM25B FAM25G CTSLP2 ANXA8L2 BMS1P2 FAM35DP FAM21B AGAP9 BMS1P6 LINC00842 FAM25C ANXA8 ANXA8L1 HNRNPA1P33 SYT15 GPRIN2 NPY4R LOC100996758 ANTXRL Gain A 162 10 q11.22 46951237 48115525 1164288 FAM25B FAM25G ANXA8L2 BMS1P2 FAM35DP FAM21B AGAP9 BMS1P6 LINC00842 FAM25C ANXA8 ANXA8L1 HNRNPA1P33 SYT15 GPRIN2 NPY4R LOC100996758 ANTXRL Loss A 163 10 q11.22 46975957 48334607 1358650 FAM25B FAM25G CTSLP2 ANXA8L2 BMS1P2 FAM35DP FAM21B AGAP9 BMS1P6 LINC00842 FAM25C ANXA8 ANXA8L1 HNRNPA1P33 SYT15 GPRIN2 NPY4R LOC100996758 ANTXRL Gain A 164 10 q11.22 46975957 47148690 172733 NPY4R LOC100996758 LINC00842 HNRNPA1P33 GPRIN2 Gain A   188  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 165 10 q11.22 46976157 48115525 1139368 FAM25B FAM25G ANXA8L2 BMS1P2 FAM35DP FAM21B AGAP9 BMS1P6 LINC00842 FAM25C ANXA8 ANXA8L1 HNRNPA1P33 SYT15 GPRIN2 NPY4R LOC100996758 ANTXRL Loss A 166 10 q21.3 68322419 68394611 72192 CTNNA3 Loss - maternal A 167 11 p15.4 4983604 5011054 27450 MMP26 Loss A 168 11 p15.1 20485820 20603261 117441 PRMT3 Gain - paternal  A 169 11 p11.2 48063834 48495310 431476 OR4X2 OR4B1 OR4C3 OR4S1 OR4X1 OR4C45 PTPRJ Gain A 170 12 p13.31 8024155 8118177 94022 SLC2A14 SLC2A3 Gain A 171 12 p13.2 10583358 10602917 19559 KLRC1 KLRC2 Loss A 172 14 q11.1 19116954 20427383 1310430 POTEG POTEM OR4N2 OR4K2 OR4M1 OR4K5 OR4K1 OR4Q3 Loss A 173 14 q11.1 19434375 20480088 1045714 LOC101101776 OR4N2 OR4K2 POTEG LINC00516 OR4Q3 OR4M1 POTEM OR4K5 OR11H2 OR4K1 OR4K15 Gain A 174 14 q11.1 19434575 20414232 979657 LOC101101776 OR4N2 OR4K2 POTEG LINC00516 OR4Q3 OR4M1 POTEM OR4K5 OR11H2 OR4K1 Gain A   189  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 175 14 q11.1 19467177 20414232 947055 LOC101101776 OR4N2 OR4K2 POTEG LINC00516 OR4Q3 OR4M1 POTEM OR4K5 OR11H2 OR4K1 Loss A 176 14 q11.1 19554383 20414232 859849 LOC101101776 OR4N2 OR4K2 POTEG LINC00516 OR4Q3 OR4M1 POTEM OR4K5 OR11H2 OR4K1 Loss A 177 14 q11.1 19554183 20414373 860190 LOC101101776 OR4N2 OR4K2 POTEG LINC00516 OR4Q3 OR4M1 POTEM OR4K5 OR11H2 OR4K1 Loss A 178 14 q11.1 19572252 20435917 863665 LOC101101776 OR4N2 OR4K2 POTEG LINC00516 OR4Q3 OR4M1 POTEM OR4K5 OR11H2 OR4K1 Loss A 179 14 q11.1 19794361 20414373 620012 LOC101101776 OR4N2 OR4K2 LINC00516 OR4Q3 OR4M1 POTEM OR4K5 OR11H2 OR4K1 Loss A 180 14 q11.1 20146329 20414373 268044 OR4N2 OR4K2 OR4Q3 OR4M1 OR4K5 OR11H2 OR4K1 Loss A 181 14 q11.1 20203249 20414373 211124 OR4N2 OR4K2 OR4Q3 OR4M1 OR4K5 OR4K1 Loss A 182 14 q11.1 20203249 20414373 211124 OR4N2 OR4K2 OR4Q3 OR4M1 OR4K5 OR4K1 Loss A 183 14 q11.1 20203249 20414373 211124 OR4N2 OR4K2 OR4Q3 OR4M1 OR4K5 OR4K1 Loss A  190  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 184 14 q11.1 20203449 20414232 210783 OR4N2 OR4K2 OR4Q3 OR4M1 OR4K5 OR4K1 Loss A 185 14 q11.1 20217088 20414373 197285 OR4N2 OR4K2 OR4M1 OR4K5 OR4K1 Loss A 186 14 q11.1 20217088 20414373 197285 OR4N2 OR4K2 OR4M1 OR4K5 OR4K1 Loss A 187 14 q11.1 20217088 20427383 210295 OR4N2 OR4K2 OR4M1 OR4K5 OR4K1 Loss A 188 14 q11.2 21360044 21407736 47692 RNASE3 ECRP Loss - paternal A 189 14 q11.2 22272000 22996502 724502   Gain A 190 14 q11.2 22322390 22996502 674112   Gain A 191 14 q11.2 22342108 22952420 610312   Gain A 192 14 q11.2 22342308 22936054 593746   Gain A 193 14 q11.2 22342108 22936195 594087   Gain A 194 14 q11.2 22387218 23016739 629521   Gain A 195 14 q11.2 22387418 22936054 548636   Gain A 196 14 q11.2 22489704 22965064 475360   Gain A 197 14 q11.2 22489704 22936195 446491   Gain A 198 14 q11.2 22489904 22964922 475018   Gain A 199 14 q11.2 22505869 22936195 430326   Gain A 200 14 q11.2 22505869 22996502 490633   Gain A 201 14 q11.2 22520767 22952420 431653   Gain A 202 14 q11.2 22520767 22936195 415428   Gain A 203 14 q11.2 22565388 22965064 399676   Gain A 204 14 q32.33 106072462 106215634 143172 ELK2AP Loss A 205 14 q32.33 106342663 107214893 872230 LINC00226 LINC00221 ADAM6 KIAA0125 Gain A 191  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes Type (Loss/gain) - Origin unknown unless specified Source study 206 14 q32.33 106342663 106572247 229584 ADAM6 KIAA0125 Gain A 207 14 q32.33 106342663 106552084 209421 ADAM6 KIAA0125 Gain A 208 14 q32.33 106361476 106398339 36863 KIAA0125 Gain A 209 14 q32.33 106453638 106552084 98446   Gain A 210 14 q32.33 106453638 106552084 98446   Gain A 211 14 q32.33 106453638 106538480 84842   Gain A 212 14 q32.33 106572248 106622595 50347   Gain A 213 14 q32.33 106622595 107189890 567295 LINC00226 LINC00221 Gain A 214 14 q32.33 106653062 106876007 222945 LINC00226 Gain A 215 14 q32.33 106653062 106802023 148961 LINC00226 Gain A 216 14 q32.33 106653062 106762386 109324 LINC00226 Gain A 217 14 q32.33 106836146 107089189 253043 LINC00221 Gain A 218 15 q11.2 20408083 22669252 2261169 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3    Gain A 219 15 q11.2 20408083 22669252 2261169 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3    Gain A  192  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 220 15 q11.2 20408083 22698722 2290639 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3 MIR4509 Gain A 221 15 q11.2 20408083 22558897 2150814 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3    Gain A 222 15 q11.2 20408083 22558897 2150814 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3    Gain A 223 15 q11.2 20408083 22558897 2150814 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3    Gain A 224 15 q11.2 20408083 22558897 2150814 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3    Gain A  193  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 225 15 q11.2 20408083 22558897 2150814 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3    Gain A 226 15 q11.2 20408083 22558897 2150814 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3    Gain A 227 15 q11.2 20408083 22469523 2061440 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Loss A 228 15 q11.2 20408083 22378497 1970414 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2  Gain A 229 15 q11.2 20408083 20624622 216539 CHEK2P2 HERC2P3 Gain A 230 15 q11.2 20481502 22558897 2077395 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3    Loss A  194  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 231 15 q11.2 20481502 22558897 2077395 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3    Loss A 232 15 q11.2 20481502 22558897 2077395 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3    Loss A 233 15 q11.2 20481702 22558756 2077054 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3    Loss A 234 15 q11.2 20481702 22509254 2027552 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P    Loss A 235 15 q11.2 20549790 22558897 2009107 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P    Loss A  195  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 236 15 q11.2 20575446 22558897 1983451 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P    Loss A 237 15 q11.2 20588199 22285350 1697151 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 Loss A 238 15 q11.2 20604568 22558756 1954188 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P REREP3   Loss A 239 15 q11.2 20849110 22385025 1535915 NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 Loss A 240 15 q11.2 20935077 22210804 1275727 NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 Loss A 241 15 q11.2 20935077 22210804 1275727 NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 Loss A 242 15 q11.2 20935077 22210804 1275727 NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 Gain A 243 15 q11.2 20935077 22212114 1277037 NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 Gain A 244 15 q11.2 22285291 22558756 273465 LOC727924 OR4N4 OR4M2 OR4N3P REREP3  Gain A 196  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes Type (Loss/gain) - Origin unknown unless specified Source study 245 15 q11.2 22304396 22558897 254501 LOC727924 OR4N4 OR4M2 OR4N3P REREP3  Gain A 246 15 q11.2 22304396 22558897 254501 LOC727924 OR4N4 OR4M2 OR4N3P REREP3  Loss A 247 15 q11.2 22304396 22558897 254501 LOC727924 OR4N4 OR4M2 OR4N3P REREP3  Gain A 248 15 q11.2 22669052 23249681 580629 MIR4509 GOLGA8DP GOLGA6L1 TUBGCP5 CYFIP1 NIPA2 NIPA1 LOC283683 WHAMMP3 Gain A 249 15 q14 34694966 34785223 90257 GOLGA8A Loss A 250 15 q14 34723983 34785223 61240 GOLGA8A Loss A 251 15 q14 34735949 34785082 49133   Loss A 252 15 q14 34735749 34785223 49474   Loss A 253 15 q14 34735749 34785223 49474   Loss A 254 15 q15.3 43888727 43948487 59760 RNU6-28 CKMT1B CATSPER2 STRC Loss A 255 15 q22.31 66735365 66765272 29907 MAP2K1 Loss A 256 15 q22.31 66735365 66765272 29907 MAP2K1 Loss A 257 16 p11.2 31897383 34202147 2304764 RNU6-76 ZNF267 TP53TG3 SLC6A10P LOC390705 HERC2P4 LINC00273 TP53TG3C TP53TG3D TP53TG3B Loss A 258 16 p11.2 32051272 34625322 2574050 LOC100130700 RNU6-76 LOC146481 TP53TG3 LOC283914 SLC6A10P LOC390705 HERC2P4 UBE2MP1 LINC00273 TP53TG3C TP53TG3D TP53TG3B Loss A   197  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 259 16 p11.2 32051272 33610066 1558794 RNU6-76 TP53TG3 SLC6A10P LOC390705 HERC2P4 TP53TG3C TP53TG3D TP53TG3B Gain A 260 16 p11.2 32300741 33525616 1224875 RNU6-76 TP53TG3 SLC6A10P LOC390705 TP53TG3C TP53TG3B Loss A 261 16 p11.2 32470425 33610066 1139641 RNU6-76 TP53TG3 SLC6A10P LOC390705 TP53TG3C TP53TG3B Gain A 262 16 q23.1 76695473 76850048 154575   Loss - maternal A 263 17 p12 11466947 12185908 718961 MIR744 DNAH9 SHISA6 MAP2K4 ZNF18 Loss A 264 17 q11.2 28512692 28641878 129186 BLMH SLC6A4 NSRP1 Loss A 265 17 q12 34417106 34479494 62388 PIGW CCL4L2 TBC1D3B CCL3L3 CCL3 CCL3L1 CCL4 TBC1D3G TBC1D3H MYO19 ZNHIT3 CCL4L1 Loss A 266 17 q12 34437275 34479494 42219   Gain A 267 17 q12 34437475 34479353 41878   Gain A 268 17 q12 34437475 34479353 41878   Gain A 269 17 q12 34815327 34892076 76749 PIGW MYO19 ZNHIT3 Gain A 270 17 q21.31 44188241 44739075 550834 ARL17B KANSL1 LRRC37A2 NSF ARL17A KANSL1-AS1 NSFP1 LRRC37A Loss A 271 17 q21.31 44188441 44458174 269733 ARL17B KANSL1 ARL17A KANSL1-AS1 NSFP1 LRRC37A Loss A 272 17 q21.31 44188241 44351293 163052 KANSL1 KANSL1-AS1 Loss A 273 17 q21.31 44231746 44458174 226428 ARL17B KANSL1 ARL17A KANSL1-AS1 NSFP1 LRRC37A Gain A  198  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 274 17 q21.31 44458483 44738320 279837 LRRC37A2 NSF ARL17A NSFP1 Gain A 275 17 q21.31 44458483 44738320 279837 LRRC37A2 NSF ARL17A NSFP1 Gain A 276 17 q25.3 79849187 79910583 61396 ALYREF NOTUM MYADML2 NPB MAFG ANAPC11 SIRT7 PYCR1 PCYT2 MAFG-AS1 Gain A 277 18 q11.2 20482597 20529827 47230 MIR4741 RBBP8 Loss A 278 19 p13.2 12105497 12145146 39649 ZNF433 Loss A 279 19 q13.31 43260649 43545427 284778 LOC100289650 PSG8 PSG1 PSG6 PSG7 PSG11 PSG10P Loss A 280 19 q13.31 43294254 43842583 548329 LOC100289650 LOC284344 PSG1 PSG2 PSG4 PSG5 PSG6 PSG7 PSG9 PSG11 PSG10P Loss A 281 20 q13.33 60791524 60814501 22977 HRH3 OSBPL2 Gain A 282 22 q11.22 23056562 23228869 172307 MIR650 Gain A 283 22 q11.23 24351795 24382411 30616 GSTT1 LOC391322 Loss A 284 22 q11.23 24371005 24407190 36185 GSTT1 LOC391322 GSTTP2 Gain A 285 22 q11.23 24371205 24407049 35844 GSTT1 LOC391322 GSTTP2 Gain A 286 22 q11.23 25664418 25892394 227976 CRYBB2P1 IGLL3P LRP5L Loss A 287 22 q11.23 25664618 25892253 227635 CRYBB2P1 IGLL3P LRP5L Gain A 288 22 q13.1 39358912 39385639 26727 APOBEC3A_B APOBEC3A APOBEC3B Loss A 289 22 q13.1 39358912 39385639 26727 APOBEC3A_B APOBEC3A APOBEC3B Loss A 290 22 q13.1 39358912 39385639 26727 APOBEC3A_B APOBEC3A APOBEC3B Loss A  199  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified  Source study 291 22 q13.1 39358912 39385639 26727 APOBEC3A_B APOBEC3A APOBEC3B Loss A 292 22 q13.1 39358912 39385639 26727 APOBEC3A_B APOBEC3A APOBEC3B Loss A 293 X p22.33 2700316 2795271 94955 XG GYG2 Loss A 294 X p11.23 47862544 47969495 106951 SSX6 SPACA5 ZNF630 SPACA5B ZNF182 Gain A 295 X q22.2 103185926 103288204 102278 MIR1256 H2BFWT TMSB15B H2BFXP Gain A 296 X q22.2 103260143 103303380 43237 MIR1256 H2BFWT H2BFM Gain A 297 X q25 126504209 126634008 129799   Loss A 298 X q26.3 134800070 134910281 110211 CT45A3 CT45A4 CT45A1 CT45A2 Gain A 299 Y p11.2 3258261 4656559 1398298 TGIF2LY Loss A 300 Y p11.2 4212667 4293050 80383   Gain A 301 Y p11.2 4895443 6593068 1697625 TTTY21B TTTY1B TTTY2B TTTY8B TTTY7B TTTY23B TTTY7 TTTY21 TTTY23 TTTY1 TTTY2 TSPY2 PCDH11Y TTTY8 Loss A 302 Y p11.2 6794096 9148477 2354381 TTTY16 TTTY18 TTTY19 PRKY TTTY11 TTTY12 TBL1Y Loss A 303 Y p11.2 7679871 7694161 14290   Loss A 304 Y q11.21 15344853 15435110 90257 UTY Loss A 305 Y q11.221 16178320 16287170 108850   Gain A 306 Y q11.223 24095754 24507472 411718 RBMY2FP RBMY1F RBMY1J TTTY6B PRY2 TTTY5 TTTY6 PRY Loss A 307 Y q11.223 24371478 24514393 142915 RBMY2FP RBMY1F TTTY5 Gain A 200  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes Type (Loss/gain) - Origin unknown unless specified Source study 308 Y q11.223 26344619 28610118 2265499 CSPG4P1Y TTTY3 TTTY4 TTTY17A CDY1B GOLGA2P3Y BPY2B BPY2C TTTY3B TTTY4B TTTY4C TTTY17B TTTY17C DAZ3 DAZ2 DAZ4 GOLGA2P2Y BPY2 CDY1 Gain A 309 1 q21.2 149039120 149259380 220260 NBPF23 Gain - paternal B 310 1 p32.3-p32.2 55930462 56280909 350447   Loss - maternal B 311 2 p25.2 5834982 5848110 13128 SOX11 Gain - paternal B 312 2 p11.2 89427986 90247720 819734   Gain - maternal B 313 3 p26.2-p26.1 3899791 4168641 268850   Loss - maternal B 314 6 p25.1 4259170 4470485 211315   Loss - maternal B 315 6 q27 168336052 168596251 260199 HGC6.3 KIF25-AS1 KIF25 MLLT4 FRMD1 Gain - paternal B 316 7 q11.21 62020976 62438289 417313   Gain - de novo B 317 8 q24.23 137738675 137850011 111336   Loss - maternal B 318 9 p24.3 46587 548464 501877 C9orf66 FOXD4 KANK1 CBWD1 DOCK8 Gain B 319 10 q21.3 68754653 68922242 167589 CTNNA3 LRRTM3 Gain B 320 11 p11.12 50414030 51372036 958006   Gain - de novo B  201  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 321 11 p15.4 2904010 2906824 2814 CDKN1C Gain - de novo B 322 12 p11.21 31269300 31398147 128847   Gain - paternal B 323 12 q13.13 52700967 52759939 58972 KRT83 KRT85 KRT86 Gain - maternal B 324 14 q11.2 20213937 20425051 211114 OR4N2 OR4K2 OR4Q3 OR4M1 OR4K5 OR4K1 Gain B 325 14 q32.33 106326623 106963726 637103 LINC00226 LINC00221 ADAM6 KIAA0125 Gain - maternal B 326 14 q32.33 106350394 106863833 513439   Gain - paternal B 327 14 q32.33 106516402 106963726 447324   Gain - maternal B 328 15 q26.3 102025218 102274497 249279 TARSL2 PCSK6 TM2D3 Gain - paternal B 329 17 q12 34618594 34630969 12375 CCL3L3 CCL3L1 Loss B 330 17 q12 34618594 34630969 12375 CCL3L3 CCL3L1 Loss - maternal B 331 17 q21.31 44169808 44350293 180485 KANSL1 LOC644172 KANSL1-AS1 Gain - maternal B 332 17 p11.2 18293171 18927546 634375 FBXW10 GRAP SLC5A10 TRIM16L USP32P2 FOXO3B TBC1D28 CCDC144B LOC339240 FLJ35934 TVP23B PRPSAP2 FAM83G LGALS9C KRT16P1 ZNF286B FAM106A Gain - paternal B 333 XY p22.33 1736584 2060692 324108 ASMT Gain - paternal B  202  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 334 1 q21.2 149079747 149224043 144296 NBPF23 Gain C 335 1 q21.2 149079747 149224043 144296 NBPF23 Gain C 336 1 p36.13 16840487 17231817 391330 MIR3675 MST1P2 MST1L ESPNP NBPF1 LOC729574 CROCCP2 Loss C 337 2 p11.2 90012337 90234023 221686   Loss C 338 2 p11.2 90012337 90234023 221686   Loss C 339 2 p11.2 90012337 90234023 221686   Loss C 340 2 p11.2 90012337 91234023 221686   Loss C 341 2 q37.3 242886386 243007300 120914   Loss C 342 2 q37.3 242886386 243007300 120914   Loss C 343 3 q29 195340844 195459538 118694 MUC20 MIR570 SDHAP2 Gain C 344 3 q29 195340844 195459589 118694 MUC20 MIR570 SDHAP2 Gain C 345 6 p21.32 32485173 32604038 118865 HLA-DRB1 HLA-DRB5 HLA-DRB6 Loss C 346 6 p21.32 32485173 32604038 118865 HLA-DRB1 HLA-DRB5 HLA-DRB6 Loss C 347 7 q11.21 64691936 65070919 378983 ZNF92 Loss C 348 8 q12.1 56776665 56922601 145936 LYN Gain C 349 11 p11.12 50032746 50378802 346056 LOC441601 LOC646813 Gain C 350 13 q12.11 20285281 20405620 120339 PSPC1 ZMYM5 Gain C 351 13 q12.11 20285281 20405620 120339 PSPC1 ZMYM5 Gain C 352 14 q11.2 22387418 23016598 629180   Gain C 353 15 q11.1-q11.2 20102541 22486999 2384459 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Gain C  203   CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 354 15 q11.1-q11.2 20102541 22509254 2406713 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Gain C 355 15 q11.1-q11.2 20102541 22509254 2406713 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Gain C 356 15 q11.1-q11.2 20102541 22378143 2275603 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 Gain C 357 15 q11.1-q11.2 20481702 22509254 2027553 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Gain C 358 15 q11.1-q11.2 20481702 22318656 1836955 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924  Loss C 359 15 q11.2 20481702 22378143 1896442 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 Gain C  204  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 360 15 q11.2-q11.1 20481702 22509254 2027553 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Gain C 361 15 q11.1-q11.2 20481702 22509254 2027553 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Loss C 362 15 q11.1-q11.2 20481702 22486999 2005298 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Gain C 363 15 q11.1-q11.2 20481702 22509254 2027553 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Gain C 364 15 q11.1-q11.2 20481702 22509195 2027494 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Gain C 365 15 q11.1-q11.2 20481759 22432687 1950929 CHEK2P2 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Loss C  205  CNV # Chromosome  Cytoband Start (bp)  End (bp)  CNV Size (bp) Genes  Type (Loss/gain) - Origin unknown unless specified Source study 366 15 q11.1-q11.2 20575646 22509254 1933609 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Loss C 367 15 q11.2 20575646 22432687 1857042 HERC2P3 GOLGA6L6 GOLGA8CP NBEAP1 POTEB POTEB2 NF1P2 CT60 LOC646214 CXADRP2 LOC727924 OR4M2 OR4N4 OR4N3P     Loss C 368 15 q11.2 22318597 22509254 190658 LOC727924 OR4M2 OR4N4 OR4N3P  Gain C 369 15 q11.2 22318597 22509254 190658 LOC727924 OR4M2 OR4N4 OR4N3P  Loss C 370 X q22.2 103220412 103376914 156502 MIR1256 H2BFWT H2BFM LOC286437 TMSB15B SLC25A53 ZCCHC18 H2BFXP Gain C 371 X q22.2 103220412 103376914 156502 MIR1256 H2BFWT H2BFM LOC286437 TMSB15B SLC25A53 ZCCHC18 H2BFXP Gain C  Total: 371 CNVs containing 353 genes  Source studies: A, Rajcan-Separovic et al. (2010 a,b); B, Robberecht et al. (2012); C, Viaggi et al. (2013).   206  Table A.3 Common miscarriage CNV regions (CNVRs) identified in four high-resolution CMA studies. Merge of redundant individual common CNVs (hg19). CNVR # Chromosome  Cytoband Min-start (bp) Max-end (bp) CNVR size (bp) Source study 1 1 p36.21 12917224 13142745 225522 A 2 1 p36.13 16926547 17214170 287624 A 3 1 q24.2 169206675 169256716 50041 A 4 1 q31.3 196744521 196883539 139018 A 5 1 q44 248756015 248785703 29688 A 6 2 p25.3 306227 582057 275830 A 7 2 p11.2 87375877 88005559 629682 A 8 2 p11.2 89163862 91899802 2735941 A 9 2 q37.3 242865720 243007500 141780 A 10 3 q26.1 162514334 162619282 104949 A 11 3 q29 195348781 195457909 109129 A 12 4 p13 44565439 44631180 65741 A 13 4 q13.2 69218415 69666179 447765 A 14 4 q13.2 70153665 70203389 49724 A 15 5 p15.33 692659 895091 202432 A 16 5 p15.1 17603759 17652952 49193 A 17 5 p14.3 21402739 21540419 137680 A 18 5 p13.2 36520666 36609496 88830 A 19 5 p13.1 41176485 41242981 66496 A 20 5 q23.3 129360220 129413588 53369 A 21 6 p25.3 259328 376090 116762 A 22 6 p22.1 29102110 29152966 50856 A 23 6 p21.31 34734166 34902536 168370 A        207  CNVR # Chromosome  Cytoband Min-start (bp) Max-end (bp) CNVR size (bp) Source study 24 6 p21.32 32411957 32611213 199256 A 25 6 q27 168371216 168581353 210137 A 26 7 q11.21 64566758 65176532 609774 A 27 7 q21.12 86394299 86493991 99692 A 28 7 q31.2 116194007 116228306 34299 A 29 7 q34 142834447 142890809 56362 A 30 7 q35 143883829 143952576 68747 A 31 8 p23.3 2071512 2134050 62538 A 32 8 p23.1 7052986 8094773 1041788 A 33 8 p23.1 10742247 10787935 45688 A 34 8 p22 15952011 16010296 58285 A 35 8 p11.23 39237238 39380795 143557 A 36 9 p21.1 28604083 28745187 141104 A 37 9 p11.2 43505643 45529411 2023768 A 38 9 q32 116376646 116536569 159923 A 39 10 p11.21 37414679 37483401 68722 A 40 10 q11.21 42607435 43103366 495931 A 41 10 q11.22 46951037 48368749 1417713 A 42 10 q21.3 68322419 68394611 72192 A 43 11 p15.4 4983604 5011054 27450 A 44 11 p15.1 20485820 20603261 117441 A 45 11 p11.2 48063834 48495310 431476 A 46 12 p13.31 8024155 8118177 94022 A 47 12 p13.2 10583358 10602917 19559 A 48 14 q11.1 19116954 20414232 1297279 A 49 14 q11.2 21360044 21407736 47692 A  208  CNVR # Chromosome  Cytoband Min-start (bp) Max-end (bp) CNVR size (bp) Source study 50 14 q11.2 22272000 23016739 744739 A 51 14 q32.33 106072462 106215634 143172 A 52 14 q32.33 106342663 107214893 872230 A 53 15 q11.2 20408083 23249681 2841599 A 54 15 q14 34694966 34785223 90258 A 55 15 q15.3 43888727 43948487 59760 A 56 15 q22.31 66735365 66765272 29907 A 57 16 p11.2 31897383 34625322 2727940 A 58 16 q23.1 76695473 76850048 154575 A 59 17 p12 11466947 12185908 718961 A 60 17 q11.2 28512692 28641878 129186 A 61 17 q12 34417106 34892076 474970 A 62 17 q21.31 44188241 44739075 550834 A 63 17 q25.3 79849187 79910583 61396 A 64 18 q11.2 20482597 20529827 47230 A 65 19 p13.2 12105497 12145146 39649 A 66 19 q13.31 43260649 43842583 581934 A 67 20 q13.33 60791524 60814501 22977 A 68 22 q11.22 23056562 23228869 172307 A 69 22 q11.23 24351795 24407190 55396 A 70 22 q11.23 25664418 25892394 227976 A 71 22 q13.1 39358912 39385639 26727 A 72 X p22.33 2700316 2795271 94955 A 73 X p11.23 47862544 47969495 106951 A 74 X q22.2 103185926 103288204 102278 A 75 X q25 126504209 126634008 129799 A  209  CNVR # Chromosome  Cytoband Min-start (bp) Max-end (bp) CNVR size (bp) Source study 76 X q26.3 134800070 134910281 110211 A 77 Y p11.2 3258261 4656559 1398298 A 78 Y p11.2 4895443 6593068 1697625 A 79 Y p11.2 6794096 9148477 2354381 A 80 Y q11.21 15344853 15435110 90257 A 81 Y q11.221 16178320 16287170 108850 A 82 Y q11.223 24095754 24507472 411718 A 83 Y q11.223 26344619 28610118 2265499 A 84 1 q21.2 149039120 149259380 220260 B 85 1 p32.3-p32.2 55930462 56280909 350447 B 86 2 p25.2 5834982 5848110 13128 B 87 2 p11.2 89427986 90247720 819734 B 88 3 p26.2-p26.1 3899791 4168641 268850 B 89 6 p25.1 4259170 4470485 211315 B 90 6 q27 168336052 168596251 260199 B 91 7 q11.21 62020976 62438289 417313 B 92 8 q24.23 137738675 137850011 111336 B 93 9 p24.3 46587 548464 501877 B 94 10 q21.3 68754653 68922242 167589 B 95 11 p11.12 50414030 51372036 958006 B 96 11 p15.4 2904010 2906824 2814 B 97 12 p11.21 31269300 31398147 128847 B 98 12 q13.13 52700967 52759939 58972 B 99 14 q11.2 20213937 20425051 211114 B 100 14 q32.33 106326623 106963726 637103 B 101 15 q26.3 102025218 102274497 249279 B 210  CNVR # Chromosome  Cytoband Min-start (bp) Max-end (bp) CNVR size (bp) Source study 102 17 q12 34618594 34630969 12375 B 103 17 q21.31 44169808 44350293 180485 B 104 17 p11.2 18293171 18927546 634375 B 105 XY p22.33 1736584 2060692 324108 B 106 1 q21.2 149079747 149224043 144296 C 107 1 p36.13 16840487 17231817 391330 C 108 2 p11.2 90012337 90234023 221686 C 109 2 q37.3 242886386 243007300 120914 C 110 3 q29 195340844 195459538 118694 C 111 6 p21.32 32485173 32604038 118865 C 112 7 q11.21 64691936 65070919 378983 C 113 8 q12.1 56776665 56922601 145936 C 114 11 p11.12 50032746 50378802 346056 C 115 13 q12.11 20285281 20405620 120339 C 116 14 q11.2 22387418 23016598 629180 C 117 15 q11.1-q11.2 20102541 22509254 2406713 C 118 X q22.2 103220412 103376914 156502 C  Total: 118 CNVRs (redundant 353 common CNVs merged into CNVRs)  Source studies: A, Rajcan-Separovic et al. (2010 a,b); B, Robberecht et al. (2012); C, Viaggi et al. (2013).   211   Appendix B  Supplementary Tables and Figure for Chapter 3 Table B.1 Genomic details of 2p15p16.1 microdeletions in 33 cases.  Case Chr Start End Size Gender Inheritance Platform Additional reported genomic findingsFlorisson et al. 2013 (1) 2 55,616,146 62,362,249 6,746,103 min del Male de novo , parenta l  origin ND Affymetrix 250K Nsp1 SNP array, fine mapping by qPCRNone reported55,580,038 62,416,010 6,835,972 balRajcan-Separovic et al. 2007 (2) 2 55,627,639 63,519,476 7,891,837 Male de novo , paternal allele deleted Affymetrix Genome-Wide Human SNP Array 6.0 (*) Maternal  del  at 16p13.11 (1.1 Mb)  Case_1 2 55,676,099 65,250,541 9,574,442 Male de novo , parenta l  origin ND Affymetrix CytoScan 750K Array, hg19 No additional  pathogenic CNVs  ReportedProntera et al. 2011 2 56,853,162 60,380,981 3,527,819 Female de novo , parenta l  origin ND Affymetrix Genome-Wide Human SNP Array 6.0 Paracentric invers ion of chromosome 7 and an apparently ba lanced trans location between chromosome 1 and 7, involving the inverted chromosome 7 [46,XX,der(7)inv(7)(q21.1q32.1)t(1;7)(q23q32.1)]Rajcan-Separovic et al. 2007 (1) 2 56,919,993 63,032,165 6,112,172 Female de novo , parenta l  a l lele NI Affymetrix Genome-Wide Human SNP Array 6.0 (*) Paternal  del  at Xp22.31 (1.4 Mb)Case_2 2 57,606,726 59,619,316 2,012,590 Male de novo , parenta l  origin ND Affymetrix CytoScan 750K Array, hg19 de novo  12p11.21-q11 del  (6.5 Mb)**, de novo 2p16.1 BCL11A intronic del  (17 Kb) , 2p16.1 intergenic del   (22.55 Kb)de Leeuw et al. 2008 2 58,216,217 61,667,426 3,451,209 min del Male de novo , parenta l  origin ND Fine mapping by qPCR in Florisson paper (*) Mosaic del  2p15-16.1 (20/30 cel l s  observed with deletion)58,024,114 61,873,699 3,849,585 balFlorisson et al. 2013 (2) 2 58,714,795 65,392,528 6,677,733 min del Female de novo , parenta l  origin ND Affymetrix 250K Nsp1 SNP array, fine mapping by qPCRNone reported58,685,038 65,440,018 6,754,980 balCase_3 2 59,017,244 64,379,673 5,362,429 Male de novo , paternal allele deleted Affymetrix Cytogenetics  Whole-Genome 2.7M ArrayNo additional  pathogenic CNVs  ReportedFelix  et al. 2010 2 59,139,200 62,488,871 3,349,671 Female de novo , paternal allele deleted Affymetrix Genome-Wide Human SNP Array 6.0 None reported Liang et al. 2009 2 59,241,620 62,385,716 3,144,096 Female de novo , paternal allele deleted Agi lent Human Genome CGH Microarray Ki t 105A Polymorphic invers ion of chr 9Balci et al. 2015/Basak et al. 2015 (3)2 59,958,420 60,834,298 875,878 min del Female de novo, parenta l  origin ND Agi lent 180K ol igonucleotide array None reported59,939,228 60,856,351 917,123 balBasak et al. 2015 (2) 2 60,029,857 61,059,383 1,029,526 NA de novo , parenta l  origin ND Human CytoSNP-12 BeadChips  (I l lumina) None reportedJorgez et al. 2014 (4) 2 60,066,496 66,376,496 6,310,000 Male de novo , parenta l  origin ND SignatureChipOS v3.0 None reportedOttolini et al. 2015 2 60,118,706 61,800,462 1,681,756 Female de novo , parenta l  origin ND Not mentioned None reportedPiccione et al. 2012  (2) 2 60,257,496 62,762,496 2,505,000 Male de novo , parenta l  origin ND Agi lent Human Genome CGH 244A array de novo  Xq28 del  (29 kb)Piccione et al. 2012 (1) 2 60,603,496 61,246,496 643,000 Female de novo , paternal allele deleted Agi lent Human Genome CGH 44K array Paternal  6q12 del  (930 Kb)212   Our 8 new cases are highlighted in gray. Genomic data are based on hg19 assembly. (Florisson et al., 2013)- Breakpoints calculated based on qPCR data in Table 1 and Table SI in original paper. NI - non informative; ND - not determined * Higher-resolution array platforms used to reassess and update the breakpoints for these cases. Only the corrected/updated breakpoints are mentioned for these cases. ** Case 2 also had a secondary CNV of 6.5Mb (de novo deletion of 12q pericentromeric region). The coding part of this CNV was 1.5Mb and included 11 reference genes and 4 OMIM genes, only one of which was bioinformatically predicted to be haploinsufficient (DNM1L). The OMIM genes were associated with phenotypes not noted in the patient namely lethal encephalopathy due to defective mitochondrial and peroxisomal fission (DNM1L), Charcot-Marie-Tooth disease type 4H (FGD4), arrhythmogenic right ventricular cardiomyopathy (PKP2), myopathy, lactic acidosis, and sideroblastic anemia (YARS2). The role of these genes based on the phenotypes seen in case 2 is not likely but cannot be excluded. Case Chr Start End Size Gender Inheritance Platform Additional reported genomic findingsCase_4 2 60,650,589 61,621,631 971,042 Female de novo , parenta l  origin ND Affymetrix CytoScan 750K Array, hg19 No additional  pathogenic CNVs  ReportedHucthagowder et al. 2012 2 60,672,255 63,144,695 2,472,440 Female de novo , parenta l  origin ND Affymetrix Genome-Wide Human SNP Array 6.0 None reportedPeter et al. 2014 2 60,689,299 60,830,491 141,192 min del Male de novo , parenta l  origin ND EmArray Cyto6000 v.2 (Emmory Univers i ty) 2q13 del  (343 Kb) and 6p25.3 del  (80 Kb), unknown origin203,000 reportedHancarova et al. 2013b / Basak et al. 2015 (1)2 60,689,977 61,127,979 438,002 Female de novo , paternal allele deleted I l lumina Human CYtoSNP-12 BeadChip array, fine mapping of proximal  bpt by MLPALarge run of homozygos i ty (ROH) on proximal  s ide of 2p deletion, extends  to ~61,809,113 bp (Supplementa l  Figure S.I), negative testing for  fragi le X and Rett syndromes  (MECP2) Jorgez et al. 2014 (5) 2 61,056,496 65,656,496 4,600,000 Male de novo , parenta l  origin ND SignatureChipOS v2.0 (135K) None reportedCase_5 2 61,060,687 65,653,379 4,592,692 Male de novo , parenta l  origin ND Signature Genomics  SignatureChipOSTM No additional  pathogenic CNVs  ReportedJorgez et al. 2014 (3) 2 61,126,496 63,516,496 2,390,000 Male de novo , parenta l  origin ND SignatureChipOS v1.0 (105K) None reportedChabchoub et al.  2008 2 61,203,258 61,786,583 583,325 Male de novo , parenta l  origin ND Affymetrix CytoScan 750K Array, hg19* None Reported, Negative testing  for Marfan syndrome (FBN1) and Wi l l iams-Beuren (ELN)Case_6 2 61,438,499 61,797,959 359,460 Male de novo , parenta l  origin ND Affymetrix Genome-Wide Human SNP Array 6.0 No additional  pathogenic CNVs  ReportedShimojima et al. 2015 (1) 2 61,495,220 61,733,075 237,855 Male de novo , parenta l  origin ND Agi lent 60 K Human Genome CGH Microarray None reportedFannemel et al. 2014 2 61,500,346 61,733,075 232,729 Male de novo , parenta l  origin ND Agi lent 180K SurePrint G3 Human CGH Fragi le s i te at  12q13.2 (benign variant), maternal  9p24.3 dup (493,399 Kb), maternal  9p24.3 dup (408,965 Kb), paternal  17q25.3 dup (656,833 Kb)Jorgez et al. 2014 (6) 2 61,566,496 64,316,496 2,750,000 Male de novo , parenta l  origin ND Exon targeted 180K Ol igo array (BCM V.8) None reportedCase_7 2 61,585,906 64,253,124 2,667,218 Male de novo , maternal allele deleted Affymetrix Cytogenetics  Whole-Genome 2.7M Array No additional  pathogenic CNVs  ReportedShimojima et al. 2015 (2) 2 61,618,699 65,142,743 3,524,044 Female undetermined Agi lent 60 K Human Genome CGH Microarray None reportedRonzoni et al. 2015 2 61,659,957 61,762,873 102,916 Male de novo , parenta l  origin ND Agi lent 180K None reportedCase_8 2 61,739,766 62,534,498 794,732 Male de novo , maternal allele deleted Affymetrix Cytogenetics  Whole-Genome 2.7M Array No additional  pathogenic CNVs  Reported213  Table B.2 Sequence of morpholinos used to knockdown the genes of interest in zebrafish. Human gene Zebrafish orthologue (Zv9)  Type of MO : (Sequence (5'→ 3')) Target site XPO1 xpo1a SB: (TCAGAAACTGTGGAAGAAGCCAGAA) i8-e9 boundary   xpo1b SB: (AAAATGATCTTACTTACCTGTGGCC) e1-i1 boundary USP34 usp34 SB: (GCCTTAATGAGTGCTACTGACCTCT) e2-i2 boundary REL rel SB: (AAAGATTGAACTTACGCAATGGCCC) e4-i4 boundary (did not work)  rel SB: (GTATAAATACTCGTACTCACCATCC)  e1-i1 boundary (did not work)   rel TB: (TTGAGAGGGATTGCACATCCATAAC) translation initiation site BCL11A bcl11aa SB: (TGTCTTTACTTACGCGAAAAATCCC) e1-i1 boundary   bcl11ab SB: (AGAACTTTCCGGTAACTTACGCGAA) e1-i1 boundary VRK2 vrk2 SB: (AAGTCAATCTCATACCTTGTTCCGT) e4-i4 boundary FANCL fancl SB: (CCACTTGATTTATTGTACCTGCTTC) e3-i3 boundary  MM-control SB: (TCACAAACTCTCGAACAACCCAGAA) No binding site   p53 TB: (GCGCCATTGCTTTGCAAGAATTG) translation initiation site  Abbreviations: MO = morpholino; SB = Splice-blocking; TB = Translation-blocking; i: intron; e: exon; MM = gene mismatch         214  Table B.3 Sequence of PCR primers used to confirm the knockdown of the genes of interest in zebrafish. Zebrafish gene Forward primer (Sequence (5'-> 3')) Reverse primer (Sequence (5'→ 3')) PCR targeted region (amplicon size) Annealing temperature  xpo1a CAGAATGCCCCTCTGGTTCA  TAGCAGCATGTAGTGCAGGG e8-e11 (420bp) 57.0 °C  xpo1b GACAATGTTAGCCGACCACG  TGCGACTTGCACCCACAATA e1-e6 (464bp) 56.0 °C  rel* - - - -  usp34 ATGTGTGAGAACTGCGCCGA AAAGCAGCGTAGACCCGTTT e1-e3 (489bp) 52.9 °C  bcl11aa ATCTTCCCTGCGCCATCTTT GCCATTGCACTGCTTCCTTT 5' UTR-e2 (363bp) 55.0 °C  bcl11ab CCATGAAGCCCAACACAAGC ATGTCGGCCAAAGGAAACCT 5' UTR-e2 (353bp) 53.5 °C  fancl TGAGCTGCTTGCTGATGAGA ACTGACATCCTGGTTGGCTC e1-e5 (297bp) 53.0 °C  vrk2 GTCCCTGTTCGAGATGATGCT GTACACCCAATTGTAGCACGC e2-e5 (300bp) 57.0 °C  Actb2 (b-actin2)** GCAGAAGGAGATCACATCCCTGGC CATTGCCGTCACCTTCACCGTTC e4-e5 (322bp) 52.0 °C  e, exon; UTR, Untranslated region.    *rel knockdown was not successful with two independently designed splice-blocking morpholinos, therefore translation-blocking morpholino was used and knockdown was confirmed with  western-blotting. **primer sequences for Actb2 were previously published (Casadei et al. , 2011) and were used as the housekeeping gene control.      215  Table B.4 Detailed phenotypes of all 33 cases with 2p15p16.1 microdeletion syndrome.  Florisson et al. 2013 (1)Rajcan-Separovic et al. 2007 (2)Case_1Prontera et al. 2011Rajcan-Separovic et al. 2007 (1)Case_2de Leeuw et al. 2008Florisson et al. 2013 (2)Case_3Felix et al. 2010Liang et al. 2009Balci et al. 2015/Basak et al. 2015 (3)Basak et al. 2015 (2)Jorgez et al. 2014 (4)Ottolini et al. 2015Piccione et al. 2012 (2)Piccione et al. 2012 (1)Case_4Hucthagowder et al. 2012Peter et al. 2014Hancarova et al. 2013b / Basak et al. 2015 (1)Jorgez et al. 2014 (5)Case_5Jorgez et al. 2014 (3)Chabchoub et al. 2008Case_6Shimojima et al. 2015 (1)Fannemel et al. 2014Jorgez et al. 2014 (6)Case_7Shimojima et al. 2015 (2)Ronzoni et al. 2015Case_8Total (#)Total (%)Age at last reported examination (years.months)4 6 4 9 8 1.832 13 3 4 4.6 3 6 1.911 0.7 41.10.2 11 14 16 16 9 16 5.11 3 21 14 12 18 14 126  (median)Sex M M M F F M M F M F F F NR M F M F F F M F M M M M M M M M M F M M M:61%; F: 35%Deletion Size (Mb)6.74617.89189.57443.52786.11222.01263.45126.67775.36243.34973.14410.87581.02956.31001.68182.50500.64300.97102.47240.20300.43804.60004.59272.39000.58330.35950.23780.23272.75002.66723.52400.10290.79472.7 (median)1. Growth  Phenotype 30 90.91Prenatal Intrauterine growth retardation  –  +  +  +  –  –  +  +  +  +  +  –  +  –  +  +  –  +  –  – 12 36.36Postnatal growth retardation  +  +  +  –  –  +  +  +  +  +  +  +  –  +  + 12 36.36Feeding problems  –  +  +  +  +  +  +  –  +  +  –  +  +  +  +  +  +  +  +  +  +  –  +  + 20 60.61Height centi le <3rd (‡), 3-10 (+)  – ‡  – ‡  –  – ‡  –  +  – ‡  –  – ‡ ‡  –  – ‡  – ‡  –  –  – ‡ ‡ ‡  –  – 12 36.36Weight centi le <3rd (‡), 3-10(+)  –  + ‡  –  –  – ‡ ‡  –  – ‡ ‡  –  – ‡ ‡  – ‡ ‡ ‡  –  – ‡ ‡ ‡  –  – 14 42.42OCF centile (Microcelphaly)<3rd (‡); 5th-10th (+) ‡ ‡ ‡  + ‡ ‡ ‡ ‡ ‡ ‡ ‡  –°  + ‡ ‡ ‡  +  – ‡ ‡ ‡ ‡ ‡  –° ‡ ‡  +  –° ‡ ‡  –#  –° 26 78.792. CNS anomalies 33 100.00Intellectual disability (ID)  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  + 28 84.85Developmental delay (DD)  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  + 33 100.00Delayed language skills  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  + 25 75.76AUTISM/autistic features  +  +  –  +  +  –  +  +  –  –  –  +  –  –  –  –  + 8 24.24Attention deficit behaviour  +  +  +  –  –  –  –  +  +  –  +  +  –  –  –  –  –   + 8 24.24Other abnormal behaviour  +  –  –  –  –  +  +  +  +  +  –  +  +  +  +  –  + 11 33.33Hypotonia  +  +  +  +  +  +  +  +  +  +  +  +  –  +  +  +  +  +  + 18 54.55Spasticity legs  –  +  –  +  +  –  –  +  +  –  +  –  +  – 7 21.21Other  +  +  +  + 4 12.12Structure brain abnormalities (neuroimaging)  +  +  +  –  +  –  –  –  +  +  +  –  +  –  –  +  –  –  + 10 30.30Cortical dysplasia on cranial  +  +  +  –  +  –  –  –  +  –  +  –  –  –  – 6 18.18Seizure  +  –  –  –  +  –  –  –  +  +  +  –  –  –  – 5 15.15Abnormal EEG  +  –  –  +  –  –  +  +  – 4 12.12Vision-Strabismus  +  +  +  –  +  –  +  –  +  –  +  –  +  +  +  +  –  + 12 36.36Vision-Optic nerve hypoplasia  –  +  –  –  +  –  +  –  –  –  +  –  –  –  –  –  –  –  –  –  – 4 12.12Vision-Disturbed vision  –  +  –  +  +  –  +  –  –  +   –  –  +  +  –  –  +  –  +  –  –  – 9 27.27Hearing loss  –  +  –  –  –  –  –  +  –  –  –  –  +  –  –  –  –  –  –  –  –  +  –  – 4 12.12Other  +  +  +  –  +  +  +  –  +  +  +  + 10 30.30Neuromotor deficitsNeurostructural (Brain) abnormalitiesNeurological abnormalitiesGeneral informationPostnatalMeasurement Cognitive deficitBehaviour features216   Florisson et al. 2013 (1)Rajcan-Separovic et al. 2007 (2)Case_1Prontera et al. 2011Rajcan-Separovic et al. 2007 (1)Case_2de Leeuw et al. 2008Florisson et al. 2013 (2)Case_3Felix et al. 2010Liang et al. 2009Balci et al. 2015/Basak et al. 2015 (3)Basak et al. 2015 (2)Jorgez et al. 2014 (4)Ottolini et al. 2015Piccione et al. 2012 (2)Piccione et al. 2012 (1)Case_4Hucthagowder et al. 2012Peter et al. 2014Hancarova et al. 2013b / Basak et al. 2015 (1)Jorgez et al. 2014 (5)Case_5Jorgez et al. 2014 (3)Chabchoub et al. 2008Case_6Shimojima et al. 2015 (1)Fannemel et al. 2014Jorgez et al. 2014 (6)Case_7Shimojima et al. 2015 (2)Ronzoni et al. 2015Case_8Total (#)Total (%)3. Craniofacial abnormalities 32 96.97Bitemporal narrowing  +  +  +  +  +  +  +  +  –  +  +  –  –  +  –  +  +  +  + 15 45.45Receding short forehead  –  +  +  –  +  +  +/-  +  +  –  +  –  –  –  –  –  + 8 24.24Metopic prominence or Metopic craniosynostosis  +  +  +  +  +  +  +  –  +  +  –  + 10 30.30Other head shape abnormality  +  +  +  +  +  +  +  +  +  +  +  –  –  +  +  +  –  +  +  +  + 18 54.55Epicanthal folds  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  +  –  –  –  +  +  +  + 20 60.61Short palpebral fissures  +  +  +  +  +  +  –  +  +  +  +  –  –  –  +  –  +  –  – 12 36.36Downslanting palpebral fissure  –  +  +  –  +  +  +  –  +  –  –  +  +  –  +  –  +  +  +  +  +  +  –  +  + 17 51.52Ptosis  +  +  +  +  +  +  +  +  +  +  +  +  –  –  –  –  +  +  +  +   –  +  +  –  – 18 54.55Telecanthus  +  +  +  +  +  +  +  +  +  +  +  +  –  +  +  +  +  +  –  +  –  +  + 20 60.61Hypertelorism  +  +  +  +  +  +  +  +  –  +  +  –  +  +  +  +  + 15 45.45Bilateral tear duct obstruction or absence  +  –  + 2 6.06Long, straight eyelashes  –  +  +  –  +  +  +  +  +  +  –  +  +  +  +  –  –  +  +  – 14 42.42Long, thin eyebrows  –  +  +  –  –  –  +  –  +  –  –  – 4 12.12Synophrys  +  +  –  +  +  + 5 15.15Abnormal nasal root (Broad/high or other)  +  +  +  +  +  +  +  +  +  +  +  +  +  –  –  +  +  +  +  +  +  +  +  + 22 66.67Prominent nasal tip  –  +  +  +  +  –  –  –  +  +  –  +  +  –  +  +  –  –  +  +  + 13 39.39Large ears  +  +  +  +  +  +  +  –  –  +  –  –  –  –  –  –  –  +  +  –  –  –  – 10 30.30dysplastic ears  +  +  +  –  –  +  –  +  +  +  +  +  –  +  +  +  +  + 14 42.42low set ears  –  +  +  +  –  +  +  +  –  –  + 7 21.21Smooth, long philtrum  +  +  +  +  +  +  +  –  +  +  +  +  +  +  +  +  +  –  +  +  +  – 19 57.58Smooth upper vermillion border  –  +  +  +  +  –  +  –  –  –  –  +  +  +  +  +  +  + 12 36.36Protruding tongue  –  + 1 3.03Everted lower lip  +  +  +  +  +  +  –  +  –  +  –  +  +  +  +  –  +  –  + 14 42.42Thin upper lip  +  +  +  –  +  +  +  –  –  –  +  –  + 8 24.24High, narrow palate (or palate abnormality)  –  +  +  +  +  +  +  –  –  –  –  +  –  +  +  +  +  +  +  –  +  +  +  + 17 51.52Retrognathia  +  +  –  –  +  –  +  +  +  –  +  +  –  –  –  –  +  +  +  +  + 13 39.39Maxillary(+), small chin (‡)  +  + ‡  +  +  + ‡  + 8 24.24Cleft lip/palate  +  + 2 6.064. Ectodermal abnormalities 24 72.73Teeth Dental (malocclusion)  +  +  +  +  +  +  + 7 21.21Hair Hair  +  +  +  +  +  + 6 18.18Skin Skin  –  +  +  + 3 9.09Frequent upper respiratory infections  –  –  –  –  +  +  –  –  –  –  +  +  –  –  +  –  +  –  +  +  +  –  + 10 30.30Frequent ear infections  –  –  +  +  +  –  +  +  +  +  + 8 24.24Hematologic parameters**  +  +++  +  +  +  +  +++  +  +  ++ 10 30.30Immunologic parameters  +  +  + 3 9.09EyesNoseMouthImmune/hematoEarsHead217   Our 8 new cases are highlighted in gray. +, feature present; -, feature absent; +/-, borderline; –°, indicates the case without microcephaly but having other head shape abnormalities; –#, indicates the case without microcephaly but having abnormal brain; blank, Not assessed or not mentioned; NR, not reported. **For this item, +, indicates abnormal fetal hemoglobin; ++, indicates other abnormal hematologic feature(s); +++, indicates abnormal fetal hemoglobin and other hematologic feature(s)     Florisson et al. 2013 (1)Rajcan-Separovic et al. 2007 (2)Case_1Prontera et al. 2011Rajcan-Separovic et al. 2007 (1)Case_2de Leeuw et al. 2008Florisson et al. 2013 (2)Case_3Felix et al. 2010Liang et al. 2009Balci et al. 2015/Basak et al. 2015 (3)Basak et al. 2015 (2)Jorgez et al. 2014 (4)Ottolini et al. 2015Piccione et al. 2012 (2)Piccione et al. 2012 (1)Case_4Hucthagowder et al. 2012Peter et al. 2014Hancarova et al. 2013b / Basak et al. 2015 (1)Jorgez et al. 2014 (5)Case_5Jorgez et al. 2014 (3)Chabchoub et al. 2008Case_6Shimojima et al. 2015 (1)Fannemel et al. 2014Jorgez et al. 2014 (6)Case_7Shimojima et al. 2015 (2)Ronzoni et al. 2015Case_8Total (#)Total (%)5. Thoracic abnormalities 24 72.73Widened internipple distance  –  +  –  +  +  –  +  –  +  –  –  +  –  +  +  –  –  +  –  – 9 27.27Extra nipple  –  +  –  –  –  –  –  –  –  –  –  –  –  –  +  – 2 6.06Thoracic abnormalities (pectus, hernia, refulx)  +  +  –  –  +  +  +  +  +  +  +  + 10 30.30Curvature of the spine  +  +  +  +  –  +  +  +  –  +  +  + 10 30.30Cardiac defect  –  –  –  –  –  –  +  +  +  +  –  –  +  –  – 5 15.15Laryngomalasia  –  +  –  –  –  –  –  –  –  –  –  –  +  – 2 6.06Stomach  +  –  +  +  +  +  +  +  + 8 24.246. Genital abnormalites 11 33.33Testes  +  –  –  +  –  –  +  +  +  +  + 7 21.21Endocrine  +  –  –  –  +  – 2 6.06Hypogonadism  –  +  –  –  –  –  –  –  –  +  –  –  –  –  –  –  – 2 6.06Other genital abnormality  –  +  +  +  +  +  +  –  – 6 18.187. Urinary system abnormalities 7 21.21Hydronephrosis  –  +  –  +  –  +  –  –  –  –  –  –  +  –  –  –  –  – 4 12.12Kidney  –  +  –  +  –  –  +  –  +  –  –  – 4 12.12Other urinary system  –  –  +  +  +  –  – 3 9.098. Digital abnormalities 25 75.76Camptodactyly  +  +  +  –  +  –  –  +  +  +  –  +  +  –  +  –  –  +  –  –  –  +  + 13 39.39Metatarsus adductus  –  +  –  +  +  –  –  –  +  +  –  –  +  –  –  –  – 6 18.18Other digital anomalies  +  +  +  +  +  –  +  –  +  +  +  +  +  +  +  +  +  + 16 48.48Phenotype frequencies 22 53 44 36 46 16 35 18 24 23 23 15 12 19 20 26 16 17 33 4 34 18 7 30 21 33 21 26 17 35 24 9 19218  Table B.5 Gene contents of all 33 cases with 2p15p16.1 microdeletion syndrome.  Florisson et al. 2013 (1)Rajcan-Separovic et al. 2007 (2)Case_1Prontera et al. 2011Rajcan-Separovic et al. 2007 (1)Case_2de Leeuw et al. 2008Florisson et al. 2013 (2)Case_3Felix et al. 2010Liang et al. 2009Balci et al. 2015 (basak et al.  patient 3)Basak et al. 2015 (2)Jorgez et al. 2014 (4)Ottolini et al. 2015Piccione et al. 2012 (2)Piccione et al. 2012 (1)Case_4Hucthagowder et al. 2012Peter et al. 2014Hancarova et al. 2013b / Basak et al. 2015 (1)Jorgez et al. 2014 (5)Case_5Jorgez et al. 2014 (3)Chabchoub et al. 2008Case_6Shimojima et al. 2015 (1)Fannemel et al. 2014Jorgez et al. 2014 (6)Case_7Shimojima et al. 2015 (2)Ronzoni et al. 2015Case_8Gene Deleted (Total#)Total (%)HI Score*% HI Score (from Decipher)CCDC88A mRNA + + 2 6.1 45.90% 19.33CCDC104 mRNA + + + 3 9.1 87.80%SMEK2 mRNA + + + 3 9.1 5.00% 6.41PNPT1 mRNA + + + 3 9.1 41.50% 13.4EFEMP1 mRNA + + + 3 9.1 53.00% 11.3MIR217 ncRNA + + + 3 9.1MIR216A ncRNA + + + 3 9.1MIR216B ncRNA + + + 3 9.1CCDC85A mRNA + + + 3 9.1 72.90% 28.38VRK2 mRNA + + + + + + + 7 21.2 67.40% 19.11FANCL mRNA + + + + + + + 7 21.2 35.50% 9.14LINC01122 ncRNA + + + + + + + + + + + 11 33.3AC007131.1 ncRNA + + + + + + + + + + + 11 33.3MIR4432 ncRNA + + + + + + + + + + + + + + + 15 45.5BCL11A mRNA + + + + + + + + + + + + + + + + + + + 19 57.6 12.40% 0.61PAPOLG mRNA + + + + + + + + + + + + + + + + + 17 51.5 22.70% 23.11LINC01185 ncRNA + + + + + + + + + + + + + + + + + + 18 54.5REL mRNA + + + + + + + + + + + + + + + + + + + 19 57.6 5.50% 0.91PUS10 mRNA + + + + + + + + + + + + + + + + + + + 19 57.6 36.60% 39.93PEX13 mRNA + + + + + + + + + + + + + + + + + + + 19 57.6 46.40% 67.6KIAA1841 mRNA + + + + + + + + + + + + + + + + + + 18 54.5 59.10% 28.06LOC339803 ncRNA + + + + + + + + + + + + + + + + + + 18 54.5C2orf74 mRNA + + + + + + + + + + + + + + + + + + 18 54.5 90.87AHSA2 mRNA + + + + + + + + + + + + + + + + + + 18 54.5 92.60% 53.59USP34 mRNA + + + + + + + + + + + + + + + + + + + + + + + + + 25 75.8 6.50% 12.35SNORA70B ncRNA + + + + + + + + + + + + + + + + + + + + + + + 23 69.7XPO1 mRNA + + + + (+) + + + + + + + + + + + + + + + + + + + + 24 72.7 0.50% 7.74FAM161A mRNA + + + + + + + + + + + + + + + + + + 18 54.5 75.75CCT4 mRNA + + + + + + + + + + + + + + + + + + 18 54.5 8.10% 5.4COMMD1 mRNA + + + + + + + + + + + + + + + + + + 18 54.5 74.70% 20.88Gene ID           219   (+), Gene possibly included based on data, not included in count; + (in red color), Gene disrupted by the breakpoint; ncRNA, non-coding RNA gene; mRNA, protein coding messenger RNA gene. Our 8 new cases are highlighted in gray; Genes deleted in >50% of all cases are shown in bold. *From Huang et al. (2010) Supplementary Table S2. Florisson et al. 2013 (1)Rajcan-Separovic et al. 2007 (2)Case_1Prontera et al. 2011Rajcan-Separovic et al. 2007 (1)Case_2de Leeuw et al. 2008Florisson et al. 2013 (2)Case_3Felix et al. 2010Liang et al. 2009Balci et al. 2015 (basak et al.  patient 3)Basak et al. 2015 (2)Jorgez et al. 2014 (4)Ottolini et al. 2015Piccione et al. 2012 (2)Piccione et al. 2012 (1)Case_4Hucthagowder et al. 2012Peter et al. 2014Hancarova et al. 2013b / Basak et al. 2015 (1)Jorgez et al. 2014 (5)Case_5Jorgez et al. 2014 (3)Chabchoub et al. 2008Case_6Shimojima et al. 2015 (1)Fannemel et al. 2014Jorgez et al. 2014 (6)Case_7Shimojima et al. 2015 (2)Ronzoni et al. 2015Case_8Gene Deleted (Total#)Total (%)HI Score*% HI Score (from Decipher)B3GNT2 mRNA + + + + + + + + + + + + + + + + 16 48.5 36.11MIR5192 ncRNA + + + + + + + + + + + + + + + + 16 48.5TMEM17 mRNA + + + + + + + + + + + + + + 14 42.4 37.90% 32.65EHBP1 mRNA + + + + + + + + + + + + + 13 39.4 43.60% 2.88AC009501.4 ncRNA + + + + + + + + + + + 11 33.3OTX1 mRNA + + + + + + + + + + + 11 33.3 63.70% 7.55DBIL5P2 ncRNA + + + + + + + + + + + 11 33.3WDPCP mRNA + + + + + + + + + + + 11 33.3 24.69MDH1 mRNA + + + + + + + + + 9 27.3 53.40% 5.43UGP2 mRNA + + + + + + + + + 9 27.3 1.70% 6.89VPS54 mRNA + + + + + + + + + 9 27.3 41.60% 7.35PELI1 mRNA + + + + + + + 7 21.2 6.20% 7.26LINC00309 ncRNA + + + + + + 6 18.2LGALSL mRNA + + + + + + 6 18.2 25.04AFTPH mRNA + + + + + + 6 18.2 17.80% 20.39MIR4434 ncRNA + + + + + + 6 18.2AC007365.1 ncRNA + + + + + + 6 18.2SERTAD2 mRNA + + + + + + 6 18.2 7.80% 27.45AC007880.1 ncRNA + + + + + + 6 18.2AC007386.2 ncRNA + + + + + + 6 18.2SLC1A4 mRNA + + + + + 5 15.2 69.00% 39.21CEP68 mRNA + + + + 4 12.1 92.90% 83.58RAB1A mRNA + + + + 4 12.1 10.70% 3.32ACTR2 mRNA + + + 3 9.1 31.10% 8.79SPRED2 mRNA + + + 3 9.1 18.80% 17.57220  Table B.6 A summary of known functions and mutations of genes deleted in >50% of cases and their animal knockout model reports.   Function (PubMed ID)  Other variants Animal knockout model (PubMed ID) Gene Main biological role(s)  Brain/neuronal-related function(s)  No. of reported mutations in HGMD [phenotypes (Reference)] Mouse (MGI; Reference) Zebrafish Other (Reference) XPO1 Encodes an exportin protein (XPO1/CRM1) that mediates export of ~200 leucine-rich nuclear export signal (NES)-bearing proteins and of RNAs from nucleus to cytoplasm (Fukuda et al. , 1997). It is also involved in mitotic microtubule spindle (kinetochore) regulation and assembly (Forbes et al., 2015a, Forbes et al. , 2015b). Nucleocytoplasmic shuttling mediated by XPO1 is  reported for  molecules relevant for synaptogenesis  (Simon-Areces et al., 2013) and neuronal positioning in brain (Honda and Nakajima, 2006). These proteins normally function in cytoplasm. _ _ _ Drosophila:  knockdown results in nuclear accumulation of expanded polyglutamine (polyQ) protein aggregates leading to neural cell toxicity and neurodegenerative diseases (Chan et al., 2011). REL Encodes c-Rel protein which is a proto-oncogene and a member of the NF-kB family of transcription factors . It acts as a crucial regulator of B and T cell function (Gilmore and Gerondakis, 2011). c-Rel is known for its neuroprotective and anti-apoptotic role in NF-kB pathway (Lanzillotta et al., 2015, Sarnico et al., 2009b) and is required for hippocampal long-term synaptic plasticity and memory formation (Salles et al., 2014). _ Abnormal immune-system morphology including decreased level of B-cells/T-cells, abnormal levels of IgG/IgM/IgE/macrophages and cytokine secretion; abnormal DNA replication and cell cycle; increased neuron apoptosis; abnormal skeleton morphology premature death (Lanzillotta et al., 2015) _ _    221    Function (PubMed ID)  Other variants Animal knockout model (PubMed ID) Gene Main biological role(s)  Brain/neuronal-related function(s)  No. of reported mutations in HGMD,  phenotypes (Reference) Mouse (MGI or Pubmed Reference) Zebrafish Other (Reference) USP34 Encodes a deubiquitinating enzyme which acts as the regulator of axin stability and Wnt/beta-catenin signaling pathway (Lui et al., 2011) as well as the negative regulator of NF-kB pathway (Engel et al., 2014, Poalas et al., 2013). It also functions as promoter of genome stability by regulating ubiquitin signaling at DNA double-strand breaks (Sy et al., 2013). Unknown 1, Congenital heart disease (Zaidi et al., 2013) Early lethality in mice (prior to 4 weeks of age). Surviving mice grew poorly and exhibited numerous neurological abnormalities (Brommage et al., 2014). _ Drosophila: knock down results in early lethality (Tsou et al., 2012). BCL11A Encodes a zinc-finger protein which functions as a myeloid and B-cell proto-oncogene. It is associated with leukemogenesis and hematopiesis and has been identified as a negative regulator of fetal hemoglobin (Basak et al., 2015, Chaouch et al. , 2016, Funnell et al., 2015, Guda et al. , 2015). BCL11A have recently been associated with proper migration of cortical projection neurons and neurodevelopment (Basak et al., 2015, Funnell et al., 2015, Wiegreffe et al., 2015)(Dias et al., 2016) 2, Increased fetal hemoglobin levels (Bauer et al., 2013), Autism (Iossifov et al., 2012) Abnormal immune-system morphology including decreased level of B-cells/T-cells/leukocytes ,abnormal humoural immune response ; abnormal hematopoiesis, erythropoiesis and decreased hemoglobin content; Abnormal neuronal/axon morphology and/or differentiation, complete neonatal lethality _ _ PAPOLG Encodes a member of the Poly(A) polymerase family which mediate the post-transcriptional addition of poly(A) tail to the 3' end of mRNA precursors and several small RNAs. Unknown _ _ _ _  222    Function (PubMed ID)  Other variants Animal knockout model (PubMed ID) Gene Main biological role(s)  Brain/neuronal-related function(s)  No. of reported mutations in HGMD,  phenotypes (Reference) Mouse (MGI or Pubmed Reference) Zebrafish Other (Reference) PEX13 Encodes a peroxisomal membrane protein which has an important role in peroxisomal biogenesis (Krause et al., 2013). _ 10, Zellweger syndrome, autosomal recessive (Al-Dirbashi et al., 2009, Ebberink et al., 2011, Krause et al., 2006, Krause et al., 2013, Shimozawa et al., 1999, Suzuki et al., 2001, Xiong et al., 2015); Neonatal adrenoleukodystrophy, autosomal recessive (Shimozawa et al., 1999) abnormal/thin cerebellum and cerebral cortex development, postnatal growth retardation and decresead body weight , hypotonia, impaired coordination, impaired reflex and limb grasping , neuronal abnormalities including astrocytosis, gliosis, and neuron degeneration, premature death and complete neonatal lethality No major phenotypic abnormalities due to ineffective knockdown (Krysko et al. , 2010) _ PUS10 Encodes a pseudouridylate synthase which catalyzes the isomerization of uridine to pseudouridine of RNAs (Kamalampeta et al. , 2013). Also, act as RNA chaperones, assisting with proper tRNA folding and assembly (Kamalampeta et al., 2013, Roovers et al. , 2006). Unknown _ _ _ _ KIAA1841 Encodes an uncharacterized protein KIAA1841 Unknown _ _ _ _     223    Function (PubMed ID)  Other variants Animal knockout model (PubMed ID) Gene Main biological role(s)  Brain/neuronal-related function(s)  No. of reported mutations in HGMD,  phenotypes (Reference) Mouse (MGI or Pubmed Reference) Zebrafish Other (Reference) C2orf74 Encodes an uncharacterized protein, chromosome 2 open reading frame 74. Unknown _ _ No orthologue _ AHSA2 Encodes a protein acting as a co-chaperone of heat shock protein 90 (Hsp90) and is associated with ATPase activator activity. Unknown _ decreased total retina thickness No orthologue _ FAM161A Encodes a protein with restricted activity to mature photoreceptors in the retina. Unknown 11, Retinitis pigmentosa, autosomal recessive (Bandah-Rozenfeld et al., 2010, Langmann et al., 2010, O'Sullivan et al., 2012, Wang et al., 2014); Retinal dystrophy (Carmichael et al., 2013); Retinal degeneration (Maranhao et al., 2015) abnormal vision/retinal photoreceptor morphology and their degeneration; microgliosis; abnormal cilium morphology (Karlstetter et al. , 2014) _ _ CCT4 Encodes a molecular chaperone which facilitates proper folding of proteins upon ATP hydrolysis e.g. actin and tubulin (Llorca et al. , 1999, Sergeeva et al. , 2013). _ _ _ _ Rat: Mutation in Cct4 is associated with recessive hereditary sensory neuropathy (Lee et al., 2003, Sergeeva et al., 2014) and cutaneous nerve degeneration (Hsu et al. , 2004)    224    Function (PubMed ID)  Other variants Animal knockout model (PubMed ID) Gene Main biological role(s)  Brain/neuronal-related function(s)  No. of reported mutations in HGMD,  phenotypes (Reference) Mouse (MGI or Pubmed Reference) Zebrafish Other (Reference) COMMD1 Encodes a regulator of copper homeostasis, sodium uptake, and NF-kB signaling (Bartuzi et al. , 2013). It acts as the negative regulator of NF-kB activity by promoting ubiquitination and the subsequent proteasomal degradation of NF-kB role-player, RELA (Burstein et al. , 2005, Thoms et al. , 2010). Diseases associated with COMMD1 include Wilson disease and copper toxicosis (autosomal recessive disorders) characterized by accumulation of copper in the liver (Vonk et al. , 2011). Change in the expression of COMMD1 affects brain metal-ion homeostasis by influencing intracellular zinc concentrations due to abnormal copper levels. This is believed to affect autism-associated pathways at excitatory synapses (Baecker et al. , 2014). COMMD1 is also known as a regulator of misfolded protein aggegation involved in neurodegenerative disorders e.g. Amyotrophic Lateral Sclerosis (ALS) and Parkinson's (Vonk et al. , 2014). Heterozygous deletion  of COMMD1 has no phenotype, but homozygous deletion associated with high functioning autism (Levy et al., 2011) 1, Elevated urinary copper (Gupta et al. , 2010) abnormal brain development, embryonic growth retardation and decreased size, abnormal embryo turning, abnormal allantois morphology, abnormal placenta vasculature, complete prenatal lethality (van de Sluis et al., 2007), increased liver copper level (Vonk et al., 2011) _ Dog: Increased liver copper level and hepatitis (Favier et al. , 2015, Favier et al. , 2012) VRK2* Encodes a serine/threonine protein kinase that modulates several signaling pathways including p53 signaling pathway and tumor cell growth (Blanco et al. , 2006). It acts as the negative-regulator of apoptosis (Monsalve et al. , 2013). VRK2 acts as a modulator of accumulation of misfolded polyglutamine (polyQ) aggregation and toxicity in early stages of neurodegenerative disease progression by negative regulation of the chaperonin (CCT4) stability (Kim et al. , 2014b) through inhibiting USP25 deubiquitinase (Kim et al. , 2015a).     225    Function (PubMed ID)  Other variants Animal knockout model (PubMed ID) Gene Main biological role(s)  Brain/neuronal-related function(s)  No. of reported mutations in HGMD,  phenotypes (Reference) Mouse (MGI or Pubmed Reference) Zebrafish Other (Reference) FANCL* Encodes a ubiquitin ligase that ubiquitinates FANCD2 which is an essential step in the Fanconia anemia pathway of DNA repair (Singh et al. , 2009). _ 11, Fanconi anemia, autosomal recessive (Ali et al., 2009, Ameziane et al., 2012, Chandrasekharappa et al., 2013, Grunert et al., 2014, Meetei et al., 2003, Vetro et al., 2015)  abnormal male/female reproductive system and infertility; decreased embryo weight and complete prenatal/embryonic lethality (Agoulnik et al. , 2002) Mutations in fancl in zebrafish leads to sex reversal by Tp53-mediated germ cell apoptosis, but no major phenotypic abnormalities are observed (Rodriguez-Mari et al. , 2010, Rodriguez-Mari and Postlethwait, 2011). Drosophila: Knockdown of FANCL orthologue results in viable and fertile flies with specifc hypersensitivity to cross-linking agents and hence DNA repair deficiency (Marek and Bale, 2006).    HGMD: The Human Gene Mutation Database - a catalogue of human gene mutation studies with their reported phenotypic features (Accessed at: http://www.hgmd.cf.ac.uk/ac/index.php) MGI: Mouse Genome informatics database - a catalogue of mouse gene knockout studies with their reported phenotypic features (Accessed at: http://www.informatics.jax.org/) *These genes were not deleted in >50% of cases but were involved in isolation in two cases (case 2 and Prontera et al., 2011).     226   Table B.7 Enriched pathways for the 16 most commonly deleted genes in the 2p15p16.1 microdeletion region. Pathway Name  #Gene EntrezGene Statistics Canonical NF-kappaB pathway 2 XPO1, REL C=35;O=2;E=0.01;R=164.29;rawP=6.67e-05;adjP=0.0020 Endogenous TLR signaling 2 XPO1, REL C=57;O=2;E=0.02;R=100.88;rawP=0.0002;adjP=0.0020 CD40/CD40L signaling 2 XPO1, REL C=58;O=2;E=0.02;R=99.14;rawP=0.0002;adjP=0.0020 IL23-mediated signaling events 2 XPO1, REL C=66;O=2;E=0.02;R=87.13;rawP=0.0002;adjP=0.0020 Aurora A signaling 2 XPO1, REL C=64;O=2;E=0.02;R=89.85;rawP=0.0002;adjP=0.0020 IL2 signaling events mediated by PI3K 2 XPO1, REL C=67;O=2;E=0.02;R=85.82;rawP=0.0002;adjP=0.0020 Signaling events regulated by Ret tyrosine kinase 2 XPO1, REL C=69;O=2;E=0.02;R=83.34;rawP=0.0003;adjP=0.0026 IL12-mediated signaling events 2 XPO1, REL C=113;O=2;E=0.04;R=50.89;rawP=0.0007;adjP=0.0033 IL2-mediated signaling events 2 XPO1, REL C=115;O=2;E=0.04;R=50.00;rawP=0.0007;adjP=0.0033 LPA receptor mediated events 2 XPO1, REL C=100;O=2;E=0.03;R=57.50;rawP=0.0005;adjP=0.0033  15/16 genes were recognized by Webgestalt. This table lists the top 10 pathways for the WebGestalt pathway commons enrichment analysis. The first column lists the pathway, the second and third columns indicate the number and gene identification for the genes involved, and the last row contains the statistics from the enrichment analysis: number of reference genes in the category (C), number of observed genes in the gene set and also in the category (O), expected number in the category (E), Ratio of enrichment (R), p value from hypergeometric test (rawP), and p value adjusted by the multiple test adjustment (adjP).    227  Table B.8 QMPSF ratios for primers a-e for two small deletions (see page 45 for primer sequences). primer a)        Gene Name Size Case 2 14-04 14-05 2p15 XPO1_Ex7 152 1.034317 0.981081 0.994294 17q12 HNF1B_Ex3 165 1.10292 0.995137 1.071725 1q21.1 GJA5_Ex1 185 1.093466 0.968953 1.012583 16p11.2 SEZ6L2_Ex5 202 0.94858 0.876942 0.921564 2p15 KIAA1841_Ex6 223 1.058526 0.877835 0.931544 17q12 AATF_Ex3 292 1.155748 1.064816 1.118445 10p11.23 Bambi_Ex3 156 0.873655 0.979123 0.942249 10p12.1 ABI1_Ex9 172 1.067021 1.040605 0.950808 2p16 BCL11A intron 196 0.549311 1.148827 1.041663 1q21.1 CHD1L_Ex18 213 0.951816 1.068891 0.939432  primer b)        Gene Name Size Case 2 14-04 14-05 2p15 XPO1_Ex7 152 1.15 1.07 1.07 17q12 HNF1B_Ex3 165 0.98 0.96 0.97 1q21.1 GJA5_Ex1 185 1.06 1.00 0.98 16p11.2 SEZ6L2_Ex5 202 1.04 0.87 0.94 2p15 KIAA1841_Ex6 223 1.00 0.99 1.01 17q12 AATF_Ex3 292 1.00 1.01 1.04 10p11.23 Bambi_Ex3 156 0.79 1.00 1.00 10p12.1 ABI1_Ex9 172 0.96 1.07 0.98 2p16 BCL11A intron-extention 205 0.59 1.11 1.05 1q21.1 CHD1L_Ex18 213 0.92 1.07 1.02  228  primer c)        Gene Name Size Case 2 14-04 14-05 2p15 XPO1_Ex7 152 1.28 1.06 1.03 17q12 HNF1B_Ex3 165 1.06 0.96 0.92 1q21.1 GJA5_Ex1 185 1.22 1.02 0.95 16p11.2 SEZ6L2_Ex5 202 0.90 0.93 0.91 2p15 KIAA1841_Ex6 223 1.10 1.00 0.95 17q12 AATF_Ex3 292 1.17 0.99 0.98 10p11.23 Bambi_Ex3 156 0.77 0.89 0.97 10p12.1 ABI1_Ex9 172 0.90 1.01 1.03 2p16 BCL11A intergenic enhancer 204 0.51 1.05 1.18 1q21.1 CHD1L_Ex18 213 0.93 1.03 1.07  primer d)        Gene Name Size Case 2 14-04 14-05 2p15 XPO1_Ex7 152 1.18 1.03 0.98 17q12 HNF1B_Ex3 165 0.99 0.92 0.90 1q21.1 GJA5_Ex1 185 1.12 0.98 0.95 16p11.2 SEZ6L2_Ex5 202 0.88 0.83 0.86 2p15 KIAA1841_Ex6 223 0.95 0.99 0.96 17q12 AATF_Ex3 292 1.09 0.98 0.95 10p11.23 Bambi_Ex3 156 0.90 0.94 0.99 10p12.1 ABI1_Ex9 172 1.07 1.03 1.10 1q21.1 CHD1L_Ex18 213 1.11 1.06 1.11 2p16 BCL11A intergenic-upstream 224 0.65 1.19 1.22   229  primer e)        Gene Name Size Case 2 14-04 14-05 2p15 XPO1_Ex7 152 1.14 1.03 1.05 17q12 HNF1B_Ex3 165 0.92 0.92 0.97 1q21.1 GJA5_Ex1 185 1.07 0.98 1.02 16p11.2 SEZ6L2_Ex5 202 0.82 0.87 0.89 2p15 KIAA1841_Ex6 223 0.95 0.96 0.97 17q12 AATF_Ex3 292 1.04 0.97 1.03 10p11.23 Bambi_Ex3 156 0.84 0.95 0.93 10p12.1 ABI1_Ex9 172 0.97 1.07 0.98 2p16 BCL11A intergenic-downstream 193 1.04 1.13 1.06 1q21.1 CHD1L_Ex18 213 1.02 1.07 1.00  14-4 and 14-5 are controls, additional genes are included in the panel for ratio establishment. Value less than 0.7 is considered a deletion.   230   Figure B.1 Morpholino titration analysis for 2p15p16.1 zebrafish orthologous genes. Dose-response curve of xpo1a, xpo1b, rel, usp34, bcl11aa, bcl11ab, vrk2, and fancl morpholinos (MOs) were obtained by injection of 1-2 cell stage wild-type zebrafish embryos with increasing amounts of MOs (5ng, 7.5ng, and 10ng). The head and body size/structure of injected fish were compared with control embryos (injected with 10ng of gene-mismatch MOs) and the % of embryos with normal, affected, or dead phenotypes were scored at 3 days post-fertilisation (dpf).   

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