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Genomic profiling in the placenta : toward a greater understanding of genetic variation contributing… Del Gobbo, Giulia Francesca 2021

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GENOMIC PROFILING IN THE PLACENTA: TOWARD A GREATER UNDERSTANDING OF GENETIC VARIATION CONTRIBUTING TO  PLACENTAL INSUFFICIENCY AND FETAL GROWTH RESTRICTION by  Giulia Francesca Del Gobbo  B.Sc., Queen’s University, 2015  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Medical Genetics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2021  © Giulia Francesca Del Gobbo, 2021 ii   The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Genomic profiling in the placenta: Toward a greater understanding of genetic variation contributing to placental insufficiency and fetal growth restriction  submitted by Giulia F. Del Gobbo in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Medical Genetics  Examining Committee: Dr. Wendy P. Robinson, Professor, Medical Genetics, UBC Supervisor  Dr. Angela Brooks-Wilson, Professor, Medical Genetics, UBC Supervisory Committee Member  Dr. Louis Lefebvre, Associate Professor, Medical Genetics, UBC University Examiner Dr. Catherine Garnis, Associate Professor, Surgery, UBC University Examiner Dr. Jacquetta Trasler, Professor, Pharmacology and Therapeutics, McGill University External Examiner  Additional Supervisory Committee Members: Dr. Carolyn Brown, Professor, Medical Genetics, UBC Supervisory Committee Member Dr. Evica Rajcan-Separovic, Clinical Professor, Pathology, UBC Supervisory Committee Member  iii  Abstract  Fetal growth restriction (FGR) is a common pregnancy complication in which the fetus does not grow to its genetic potential due to a pathological cause, which puts it at greater risk for morbidity and mortality in the perinatal period and poor health outcomes in childhood and adulthood. Although the etiology of FGR is diverse, insufficient function of the placenta underlies many cases, as the placenta is a crucial organ to support fetal growth and development and a healthy pregnancy. One of the few established genetic contributors to placental insufficiency and non-syndromic FGR is trisomy confined to the placenta. Beyond this, the contribution of smaller genomic imbalances (copy number variants) or common single nucleotide variants and their impact on gene regulation in the placenta, for example through DNA methylation, remains largely unexplored. In this thesis, I hypothesized that placental genomic imbalances, including aneuploidy and copy number variants (CNVs), and candidate single nucleotide variants in a gene relevant to DNA methylation (DNAme) are associated with poor fetal growth and/or altered placental DNAme. Using molecular-cytogenetic and microarray techniques, I assessed aneuploidy and CNVs in placentas from infants born small-for-gestational age (SGA) and adequately-grown controls. I found that confined placental mosaicism of autosomal aneuploidies or rare candidate CNVs involving genes related to placental function or growth were present in about 18% of SGA cases, and that CNV load was not associated with SGA. I also characterized a novel case of eight 2-4 Mb duplications confined to the placenta of an infant with FGR, in which the CNVs arose de novo in a cell in the trophoblast lineage. Finally, I studied two candidate single nucleotide polymorphisms in MTHFR, involved in the metabolic pathway that produces one-carbon units iv  for methylation reactions and purine synthesis. I found that these variants were not associated with altered placental DNAme, and that there was only a trend for increased risk of placental insufficiency complications of FGR and/or preeclampsia. Through these studies, I contributed to our understanding of genetic variation in the placenta and its association with FGR and placental insufficiency, and provided a foundation from which future studies can build.   v  Lay Summary  In ~10% of pregnancies, babies do not grow to their full potential and can suffer negative health consequences. Poor growth may occur when the placenta, the organ that transfers oxygen and nutrients to the baby, does not work efficiently. One cause of poor placental function and poor fetal growth is whole chromosome errors in the placenta, however deletions and duplications of smaller sections of the chromosomes or single-letter changes in the DNA are poorly studied. In this thesis, I studied these genetic changes in the placenta and found that in addition to chromosome errors, smaller deletions or duplications may also underlie poor growth of some babies. However, two single-letter changes in DNA thought to influence gene regulation did not do so in the placenta, nor did they impact pregnancy outcome. These studies improve our understanding of how the placental genome influences fetal growth and provide a foundation for future studies. vi  Preface  Parts of this dissertation have been published and include work performed by collaborators:  Chapter 1 Del Gobbo GF, Konwar C, and Robinson WP. The significance of the placental genome and methylome in fetal and maternal health. Human Genetics. 2020;139:1183-1196. © Springer-Verlag GmbH Germany, part of Springer Nature Parts of the section of the text that I authored and a modified version of Figure 1 is included in Chapter 1 with permission. I authored the section on genetic variation in the placenta, with the exception of the section on common genetic variation, and contributed to development of the content of the manuscript. Chaini Konwar and I contributed equally to the manuscript. All authors provided critical revisions of the final manuscript.  Chapter 2 Del Gobbo GF, Yin Y, Choufani S, Butcher EA, Wei J, Rajcan-Separovic E, Bos H, von Dadelszen P, Weksberg R, Robinson WP, and Yuen RKC. Genomic imbalances in the placenta are associated with poor fetal growth. Molecular Medicine. 2021;27:3. © Del Gobbo et al., 2021, under the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) The data, text, figures, and tables published in this article are included in Chapter 2. Samples were ascertained by Robinson lab research coordinators or collaborators, including Drs. Peter vii  von Dadelszen, Haley Bos, and Sylvie Langlois; or the Research Centre for Women’s and Infants’ Health BioBank at the Samuel Lunenfeld Research Institute and the Mount Sinai Hospital/University Health Network Department of Obstetrics & Gynaecology. Placental sampling and DNA extraction was performed by Robinson lab members including Ruby Jiang, Luana Avila, and Dr. Maria Peñaherrera, or the Research Centre for Women’s and Infants’ Health Biobank. CGH, MLPA, and parts of the microsatellite genotyping were performed by Ruby Jiang and Dr. Maria Peñaherrera. Dr. Sanaa Choufani prepared DNA samples from the Toronto cohort. The Center for Applied Genomics at the Hospital for Sick Children ran microarrays for both cohorts and microsatellite and qPCR testing for the Toronto cohort. Dr. John Wei performed initial CNV identification. Anita Yin and Emma Butcher contributed to the Toronto cohort CNV analysis. I contributed to the study design, prepared DNA for microarray, performed microsatellite genotyping, data analysis, result interpretation, generated all tables and figures, and wrote the manuscript. All authors provided critical revisions of the final manuscript.  Chapter 3 Del Gobbo GF, Yuan V, and Robinson WP. Confined placental mosaicism involving multiple de novo copy number variants associated with fetal growth restriction: A case report. American Journal of Medical Genetics Part A. 2021 [published online ahead of print] doi:10.1002/ajmg.a.62183. © Del Gobbo et al., 2021, under the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)  viii  The data, text, figures, and tables published in this article are included in Chapter 3. The case was ascertained through clinic recruitment by Kristal Louie. Ruby Jiang performed placental sampling, enzymatic separation of villi, and some of the DNA extraction. The Center for Applied Genomics at the Hospital for Sick Children ran the SNP array, and Dr. Maria Peñaherrera ran the methylation array, with assistance by Victor Yuan, Amy Inkster, Martin Wong, and me. Dr. Wendy Robinson and I conceived the study. Victor Yuan performed methylation array data processing, filtering, and normalization. I performed DNA extraction, microsatellite genotyping, data analysis, result interpretation, generated all tables and figures, and wrote the manuscript. All authors provided critical revisions of the final manuscript.  Chapter 4 Del Gobbo GF, Price EM, Hanna CW, and Robinson WP. No evidence for association of MTHFR 677C>T and 1298A>C variants with placental DNA methylation. Clinical Epigenetics. 2018;10:34-1.2018. © Del Gobbo et al., 2018, under the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/) The data, text, figures, and tables published in this article are included in Chapter 4. Samples were ascertained by Robinson lab research coordinators and collaborators, including Drs. Peter von Dadelszen, Hayley Bos, Sylvie Langlois, Deborah McFadden, and Margot Van Allen. Placental sampling and DNA extraction was performed by Robinson lab members, including Ruby Jiang, Luana Avila, and Drs. Maria Peñaherrera and E. Magda Price. Drs. E. Magda Price and Courtney Hanna performed parts of the MTHFR genotyping. Drs. Maria Peñaherrera and Courtney Hanna ran the methylation arrays. The Génome Québec Innovation Centre ran the ix  Sequenom iPlex Gold assays for ancestry informative marker genotyping. Dr. E. Magda Price performed the DNA methylation array analyses and generated a version of Figure 4.2 and Supplementary Figure 4.3. I performed part of the MTHFR genotyping, all methylation pyrosequencing assays, analysis of ancestry informative markers, genotyping data, and repetitive DNA methylation, generated all other figures and tables, and wrote the manuscript. All authors contributed to study design, analysis and interpretation of results, and provided critical revisions of the manuscript.  The work presented herein was approved by The University of British Columbia/Children’s and Women’s Health Centre of British Columbia Research Ethics board, certificates H17-01545, H04-70488, and H10-01028.  Chapters 2, 3, and 4 remain largely unchanged from their published versions, therefore I have retained the use of plural first person pronouns in these chapters. In the remainder of the dissertation, I use singular first person pronouns. x  Table of Contents  Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ...........................................................................................................................x List of Tables ................................................................................................................................xv List of Figures ............................................................................................................................. xvi List of Symbols .......................................................................................................................... xvii List of Abbreviations ............................................................................................................... xviii Acknowledgements .................................................................................................................... xxi Dedication .................................................................................................................................. xxii Chapter 1: Introduction ................................................................................................................1 1.1 Dissertation context and overview .................................................................................. 1 1.2 The placenta: A key mediator of in utero development and health ................................ 2 1.2.1 Functions of the human placenta ............................................................................ 2 1.2.2 Structure and development of the placenta ............................................................. 3 1.2.3 Placental insufficiency ............................................................................................ 5 1.3 Fetal growth restriction ................................................................................................... 7 1.3.1 Clinical definitions of fetal growth restriction ........................................................ 7 1.3.2 Causes and risk factors for fetal growth restriction ................................................ 9 1.3.2.1 Fetal factors ....................................................................................................... 10 1.3.2.2 Maternal environment ....................................................................................... 11 xi  1.3.2.3 Placental factors ................................................................................................ 13 1.4 Genetic variation associated with fetal growth restriction and placental insufficiency 13 1.4.1 Genetic variation: Aneuploidy, copy number variants, and sequence variants .... 13 1.4.2 Fetal aneuploidy & polyploidy are causes of fetal growth restriction .................. 16 1.4.3 Confined placental mosaicism .............................................................................. 17 1.4.3.1 Origins of trisomy confined to the placenta ...................................................... 17 1.4.3.2 Outcomes associated with CPM ....................................................................... 19 1.4.4 Rare genetic syndromes associated with fetal growth restriction ......................... 20 1.4.5 Common and inherited genetic variation associated with placental insufficiency 22 1.4.6 Placental copy number variation and pregnancy outcomes .................................. 27 1.5 DNA methylation and placental insufficiency .............................................................. 29 1.5.1 The placental methylome ...................................................................................... 30 1.5.2 Placental DNAme alterations in placental insufficiency complications ............... 31 1.6 Research objectives & hypothesis ................................................................................ 32 Chapter 2: Genomic imbalances in the placenta are associated with poor fetal growth ......34 2.1 Introduction ................................................................................................................... 34 2.2 Methods......................................................................................................................... 36 2.2.1 Research ethics approval....................................................................................... 36 2.2.2 Sample collection and cohort characteristics ........................................................ 36 2.2.2.1 Vancouver cohort .............................................................................................. 37 2.2.2.2 Toronto cohort .................................................................................................. 38 2.2.3 Aneuploidy screening and CPM follow-up .......................................................... 39 2.2.4 Microarray processing and CNV detection........................................................... 40 xii  2.2.5 Candidate CNVs ................................................................................................... 41 2.2.6 Placental-enhanced and imprinted genes .............................................................. 41 2.2.7 Functional pathway enrichment ............................................................................ 41 2.2.8 Statistical analyses ................................................................................................ 42 2.3 Results ........................................................................................................................... 42 2.3.1 Poor fetal growth is associated with placental aneuploidy ................................... 42 2.3.2 Load of CNVs does not differ between SGA and control placentas .................... 44 2.3.3 Candidate CNVs identified in SGA placentas ...................................................... 47 2.3.4 No difference in total, placental-enhanced, or imprinted genes involved in placental CNVs ..................................................................................................................... 48 2.3.5 No significantly enriched gene pathways in SGA CNVs ..................................... 49 2.4 Discussion ..................................................................................................................... 50 2.4.1 Strengths and limitations....................................................................................... 52 2.4.2 Research and clinical implications........................................................................ 53 2.4.3 Conclusions ........................................................................................................... 55 Chapter 3: Confined placental mosaicism of multiple de novo CNVs associated with fetal growth restriction: A case report ...............................................................................................56 3.1 Introduction ................................................................................................................... 56 3.2 Methods......................................................................................................................... 57 3.3 Results ........................................................................................................................... 58 3.4 Discussion ..................................................................................................................... 62 Chapter 4: No evidence for association of MTHFR 677C>T and 1298A>C variants with placental DNA methylation .........................................................................................................65 xiii  4.1 Introduction ................................................................................................................... 65 4.2 Methods......................................................................................................................... 68 4.2.1 Ethics approval and sample collection .................................................................. 68 4.2.2 Case characteristics ............................................................................................... 69 4.2.3 MTHFR genotyping .............................................................................................. 70 4.2.4 Population stratification ........................................................................................ 70 4.2.5 MTHFR genotype and DNAme ............................................................................ 71 4.2.6 Infinium HumanMethylation450 Beadchip (450k) array ..................................... 72 4.2.7 Repetitive DNA methylation ................................................................................ 73 4.2.8 Statistical analyses ................................................................................................ 74 4.3 Results ........................................................................................................................... 75 4.3.1 Analysis of ancestry informative markers identifies no significant population stratification .......................................................................................................................... 75 4.3.2 MTHFR genotypes are not significantly associated with placental insufficiency or neural tube defects ................................................................................................................ 77 4.3.3 MTHFR 677 and 1298 high-risk variants are not associated with altered genome-wide DNAme in the placenta ................................................................................................ 78 4.3.4 MTHFR 677 and 1298 high-risk variants not associated with altered site-specific DNAme in the placenta ......................................................................................................... 80 4.4 Discussion ..................................................................................................................... 82 Chapter 5: Discussion ..................................................................................................................92 5.1 Summary and significance of findings ......................................................................... 92 5.2 Strengths and limitations............................................................................................... 95 xiv  5.3 Future directions ......................................................................................................... 100 5.3.1 Advancing studies of copy number variation and mosaicism in the placenta .... 100 5.3.2 Broadening the scope and integrating placental ‘omics in genetic association studies of placental insufficiency complications ................................................................ 103 5.3.3 Considerations and opportunities for prenatal genetic screening ....................... 105 5.4 Conclusion .................................................................................................................. 107 References ...................................................................................................................................108 Appendices ..................................................................................................................................147 Appendix A Supplementary materials for Chapter 2 .............................................................. 147 A.1 Supplementary methods .......................................................................................... 147 A.2 Supplementary tables .............................................................................................. 152 A.3 Supplementary figures ............................................................................................ 164 Appendix B Supplementary materials for Chapter 3 .............................................................. 169 B.1 Supplementary methods .......................................................................................... 169 B.2 Supplementary tables .............................................................................................. 170 B.3 Supplementary figures ............................................................................................ 175 Appendix C Supplementary materials for Chapter 4 .............................................................. 176 C.1 Supplementary methods .......................................................................................... 176 C.2 Supplementary tables .............................................................................................. 178 C.3 Supplementary figures ............................................................................................ 181  xv  List of Tables  Table 1.1 Maternal factors associated with fetal growth restriction ............................................. 11 Table 1.2: Genes and common sequence variants associated with poor fetal growth or preeclampsia ................................................................................................................................. 24 Table 2.1: Study cohort clinical characteristics ............................................................................ 38 Table 2.2: Summary of findings from detection of placental aneuploidy .................................... 43 Table 2.3: Summary of load of CNVs in control and SGA placentas .......................................... 45 Table 2.4: Candidate CNVs with clinical relevance to SGA identified in study placentas .......... 47 Table 3.1: Eight large duplications present in a mosaic state in case PM324 placenta ................ 60 Table 4.1: Clinical characteristics of cases ................................................................................... 70 Table 4.2: Clinical characteristics of placental DNAme cases ..................................................... 72 Table 4.3: MTHFR 677TT and 1298CC genotypes in pregnancy complications......................... 78 Table 4.4: Genome-wide measures of altered DNAme in MTHFR high-risk and reference placentas ........................................................................................................................................ 80 Table 4.5: Literature assessing associations between MTHFR 677 or 1298 variants and altered DNAme in healthy tissues ............................................................................................................ 85  xvi  List of Figures  Figure 1.1: Structure of the placenta and chorionic villi ................................................................ 4 Figure 1.2: Confined placental mosaicism of trisomy from a trisomic zygote rescue.................. 18 Figure 2.1: Schematic of study design .......................................................................................... 36 Figure 2.2: Sizes of placental CNVs from control and SGA pregnancies .................................... 46 Figure 2.3: Total number of genes impacted by placental CNVs from control and SGA pregnancies ................................................................................................................................... 48 Figure 3.1: Estimated percentage of cells carrying the eight duplications in available samples from PM324 placenta and associated fetal membranes ................................................................ 61 Figure 4.1: Distribution of ancestry derived from MDS of AIM genotypes in control and pregnancy complication placentas ................................................................................................ 76 Figure 4.2: 450k array-wide differential DNAme in MTHFR high-risk 677 and high-risk 1298 placentas. ....................................................................................................................................... 81  xvii  List of Symbols  β DNA methylation beta value Δβ Difference in DNA methylation xviii  List of Abbreviations  1kGP   1000 Genomes Project 450k   Infinium HumanMethylation450 Beadchip 5-CH3-THF  5-methyltetrahydrofolate 5,10-CH2-THF 5,10-methylenetetrahydrofolate AIMs   Ancestry informative markers ASD   Autism spectrum disorder CGH   Comparative genomic hybridization CH3   Methyl group CpG   Cytosine-guanine dinucleotide CNV   Copy number variant CPM   Confined placental mosaicism CVS   Chorionic villus sample DMR   Differentially methylated region DNAme  DNA methylation DNMT  DNA methyltransferase EOPE   Early-onset preeclampsia EPIC   Infinium HumanMethylation EPIC Beadchip EVT   Extravillous trophoblast EWAS   Epigenome-wide association study eQTL   Expression-quantitative trait locus FDR   False-discovery rate xix  FGR   Fetal growth restriction GA   Gestational age GWAS  Genome-wide association study HLA-G  Human leukocyte antigen-G HWE   Hardy-Weinberg equilibrium ICM   Inner cell mass ICR   Imprinting control region LINE-1  Long interspersed nuclear element-1 LOPE   Late-onset preeclampsia mdnCNV  Multiple de novo copy number variants MDS   Multidimesnsional scaling MLPA   Multiplexed ligation-dependent probe amplification MTHFR  Methylene-tetrahydrofolate reductase mQTL   Methylation-quantitative trait locus nFGR   Normotensive fetal growth restriction NIPT   Non-invasive prenatal testing NTD   Neural tube defect OCM   One-carbon metabolism PE   Preeclampsia PlGF   Placental growth factor PMD   Partially-methylated domain PTB   Pre-term birth SAM   S-adenosyl methionine xx  sFLT-1  Soluble FMS-like tyrosine kinase-1 SGA   Small-for-gestational age SNP   Single nucleotide polymorphism SRS   Silver-Russell syndrome TE   Trophectoderm uNK   Uterine natural killer cell UPD   Uniparental disomy vil   Chorionic villi VEGF   Vascular endothelial growth factor VUS   Variant of uncertain significance XCI   X chromosome inactivation   xxi  Acknowledgements  Pursuing my interests through this doctoral research has been a great privilege, and I owe thanks to many individuals who have enabled this experience and supported my success.  I would like to sincerely thank my graduate supervisor, Dr. Wendy Robinson, for her invaluable mentorship and investment in my academic and personal growth. It has been a privilege to learn from you; thank you for believing in my potential and for providing me with the guidance and trust to develop as a researcher. Additionally, to my supervisory committee, Drs. Angela Brooks-Wilson, Carolyn Brown, and Evica Rajcan-Separovic, thank you for providing such thoughtful advice and feedback, and for supporting me throughout my studies. I owe particular thanks to Dr. Maria Peñaherrera for her encouragement, advice, and exemplary dedication that have been so important for my success; to Drs. Magda Price, Samantha Wilson, and Chaini Konwar for the guidance and assistance they provided both as exceptional mentors and friends; and to Victor Yuan and Amy Inkster for always being there to discuss ideas, troubleshoot problems, and share their experience. To all other Robinson lab members who have helped me and made my time in the lab so enjoyable: Ruby, Olivia, Irina, Johanna, Li Qing, Desmond, Elizabeth, Martin, Almas, Nikita, Icíar, and Emilie, thank you. Finally, to my parents, Luigi and Pamela, thank you for your unwavering love and support, and for always encouraging me to pursue my passion. To my sisters, Stefanie and Natalie, and all my other cheerleaders, thank you for keeping me smiling. Blake, I could not have made it this far without your support, patience and loving encouragement; thank you for always believing in me.  Thank you to the Canadian Institutes of Health Research and the BC Children’s Hospital Research Institute for the financial support for this work. xxii  Dedication   To my family. 1 Chapter 1: Introduction  Parts of this chapter have been previously published (see Preface for contribution details):  Del Gobbo GF, Konwar C, and Robinson WP. The significance of the placental genome and methylome in maternal and fetal health. Human Genetics. 2020;139:1183-1196.  1.1 Dissertation context and overview The placenta is a key regulator of fetal growth and development during the prenatal period.  It mediates nutrient, oxygen, and waste exchange between the mother and fetus, provides immune protection for the fetus, and synthesizes key hormones and growth factors to support the pregnancy and fetal development. Compromised placental function, as in the case of placental insufficiency, is therefore associated with pregnancy complications that can impact maternal and/or fetal health, including fetal growth restriction (FGR), which is associated with considerable health complications for the fetus in the perinatal period and beyond (1–9). The underlying causes of FGR and placental insufficiency in many cases are still not fully understood, however there is evidence that genetic variation contributes (10). This may act directly through altered gene dosage or function, or may influence gene regulation through epigenetic mechanisms such as DNA methylation in the placenta. Understanding how genetic variation in the placenta is associated with placental insufficiency and FGR is important to develop our understanding of this complex complication to improve detection and monitoring of pregnancies at risk, reduce the incidence of complications, and improve knowledge of associated long-term outcomes for medical counselling.    2 In this dissertation, I present studies of genetic variation in the human placenta and their association with fetal growth restriction and the placental epigenome in humans. In the introduction to this thesis, I provide an overview of i) placental function and development, ii) fetal growth restriction, iii) genetic variation associated with fetal growth restriction and placental insufficiency, and iv) the placental DNA methylome as it relates to placental insufficiency.  1.2 The placenta: A key mediator of in utero development and health 1.2.1 Functions of the human placenta The placenta is a temporary extraembryonic organ that develops from the conceptus during pregnancy and is therefore genetically identical to the fetus (with exceptions discussed in section 1.4.3). The main function of the placenta is to support the development of the fetus during pregnancy, and it accomplishes this in a variety of ways. Firstly, it establishes a connection with the maternal uterus, separating maternal blood from the fetal circulation, and therefore is the interface for active and passive transfer of oxygen and nutrients from the mother and wastes to be excreted from the fetus (11). The placenta also produces many hormones, growth factors, and other proteins that act upon the mother, fetus, or the placenta itself to support the pregnancy and fetal development, including progesterone to facilitate maternal adaptations to pregnancy (12) and IGF-I and II, which are key for placental and fetal growth (13,14). Finally, the placenta protects the fetus from the maternal immune system and from potentially harmful pathogens and substances in the mother. To promote maternal tolerance to the semi-allogenic fetus, cells of the placenta secrete immunosuppressive factors and express non-classical human leukocyte antigen-G (HLA-G) to inhibit cell-killing actions of maternal immune cells at the maternal/fetal interface  3 (12,15). The placenta also protects the fetus by metabolizing certain harmful substances, including maternal glucocorticoids and a small number of pharmaceutical drugs (16,17); physically blocking the passage of certain pathogens through to the fetal circulation; and has an innate immune system that secretes antiviral and antimicrobial factors and can communicate with the maternal immune system (15).   1.2.2 Structure and development of the placenta The placenta is a discoid organ that directly interfaces with the maternal uterine lining and is connected to the fetus by the umbilical cord. The major functional units of the placenta are highly branched tree-like structures called chorionic villi (Figure 1.1). The chorionic villi contain capillaries and blood vessels, and are connected to the chorionic plate (fetal side of the placenta), where the vessels anastomose and connect to the fetus through the umbilical arteries and vein (18). In the villi, fetal vessels are surrounded by a stromal core (mesenchyme), and outer trophoblast cell layers (18) (Figure 1.1). The trophoblast cells include an outermost layer of multinucleated syncytiotrophoblast and an inner layer of mononucleated cytotrophoblast cells (18) (Figure 1.1). At their terminus, the villi are connected to the maternal uterine lining, the decidua. Surrounding the villi is the intervillous space, which is filled with maternal blood from the uterine spiral ateries (18). The outer syncytiotrophoblast is in direct contact with maternal blood in the intervillous space, and is thus the primary site of exchange between the maternal and fetal circulation. The human placenta is comprised of about 60-70 of these branched chorionic villus trees, divided by septae of maternal decidua into 20-40 multi-villus units, termed cotyledons (18) (Figure 1.1). The placental villi develop clonally, as molecular features indicative of clonality, such as X chromosome inactivation (XCI) skewing, in biopsies of  4 different depths within a villus are more highly correlated than biopsies of villi from different cotyledons, suggesting that each villus likely derives from just one or a few precursor cells (19).   Figure 1.1: Structure of the placenta and chorionic villi Schematic representation of the organization of the placenta (left), which is embedded in the maternal uterus. It is organized into branched chorionic villi that contain the fetal vasculature that will connect to the fetus via the umbilical vein and arteries. The villi are bathed by maternal blood contained in the intervillous space, delivered by the uterine spiral arteries. Representation of a chorionic villus (right), composed of the outer syncytiotrophoblast and inner cytotrophoblast cell layers, inner mesenchyme, and fetal blood vessels. The villus is anchored to the maternal decidua, and extravillous trophoblast cells migrate and invade the maternal decidua, where they interact with resident maternal immune cells such as the uterine natural killer cells (uNK), and remodel the uterine spiral arteries. (© 2019 Springer-Verlag GmbH Germany, part of Springer Nature; image adapted from Del Gobbo et al. (20)).  The first precursors to placental cells are derived early in development, approximately 6 days post-fertilization. At the blastocyst stage, two cell populations are present: the trophectoderm (TE) and the inner cell mass (ICM). The TE gives rise to the placental trophoblast: it will differentiate into cytotrophoblast cells, which may differentiate along the  5 villous or extravillous pathways (21). Villous cytotrophoblasts undergo cellular fusion to form the multinucleate syncytiotrophoblast (21) that provides the continuous outer cell layer of the placenta. Cytotrophoblasts may also differentiate into extravillous trophoblasts (EVTs), which invade the maternal decidua at the site of contact between the villus and decidua (Figure 1.1), where they remodel the maternal uterine lining, interact with maternal immune cells to modulate responses to pregnancy, and migrate into and remodel maternal spiral arteries, degrading the endothelial and smooth muscle layers to establish increased and continuous blood flow to the placenta during pregnancy (21).  The origins of the mesenchymal core of the placenta and the extraembryonic membranes, the chorion and amnion, are less clear, though all have contributions from ICM-derived cells. The ICM of the blastocyst differentiates into the hypoblast and the epiblast, from which the fetal cell precursors are derived. The mesenchymal core of the villi is derived from extraembryonic mesoderm, which is thought to be derived from cells of the epiblast, with contribution from the hypoblast (22). In combination with trophoblast cells, this extraembryonic mesoderm also contributes to the chorion, and with epiblast-derived cells, it contributes to the amnion (21,23).   1.2.3 Placental insufficiency  Placental insufficiency is the condition in which the placenta does not function adequately and does not deliver adequate oxygen and nutrients to the developing fetus. Given the crucial role of the placenta in supporting fetal development and pregnancy health, placental insufficiency can impact both maternal and fetal health, and is associated with pregnancy complications including fetal growth restriction (FGR), pre-eclampsia (PE), and pre-term birth  6 (PTB). Considering the placenta and in the study of such pregnancy complications is therefore imperative to better understanding their etiology. Placental insufficiency can be a result of deficient invasion and remodelling of the maternal uterine spiral arteries in early placentation (24,25). This may be due to insufficient numbers of EVTs invading the maternal spiral arteries, inadequate degradation of the endothelium and smooth muscle layer of the arteries by EVTs, or abnormal interactions between EVTs and maternal immune cells residing in the decidua or the spiral arteries (26). Supporting this, there is less smooth muscle reduction and fewer EVTs around uterine spiral arteries associated with FGR and PE compared to healthy pregnancies (27,28).  Insufficient spiral artery remodelling can result in reduced maternal blood flow to the placenta, as well as blood being delivered at a higher velocity and with greater pulsatility (29,30). This can induce oxidative stress in the placenta, resulting in increased apoptosis and a pro-inflammatory state (25,31,32). Additionally, the high-velocity and pulsatile blood may cause physical damage to the delicate chorionic villi, resulting in a reduced surface area for oxygen and nutrient exchange (25). Various placental pathologies are observed in PE and FGR that are associated with these changes, including increased syncytial knots (aggregates of apoptotic syncytiotrophoblast nuclei), villitis (inflammation of villi) of unknown etiology, and placental infarcts (33–35).      7 1.3 Fetal growth restriction Fetal growth restriction (FGR), also referred to as intrauterine growth restriction (IUGR), is a pregnancy complication in which the fetus does not grow to its genetic potential due to a pathological cause. Depending on the clinical definition employed, FGR occurs in about 10% of pregnancies, though this incidence varies between countries and populations globally (6,36). FGR is associated with morbidity and mortality in the neonatal period, as fetuses/infants with FGR are at increased risk for stillbirth, neonatal death, pre-term birth, and other complications associated with prematurity, including necrotizing enterocolitis and respiratory distress syndrome (1–6). FGR is also associated with long-term outcomes beyond the neonatal period, including increased risk for growth or neurodevelopmental delay in childhood (5,7), and cardiovascular disease, hypertension, and diabetes in adulthood (8,9). Because FGR is a complication that affects pregnancies all over the globe and has potentially far-reaching associated health complications, a better understanding of the disorder and how it arises is crucial to improving detection and outcomes in affected fetuses.  1.3.1 Clinical definitions of fetal growth restriction Defining FGR is challenging, as the clinical definition requires the identification of the cause of restricted growth, which is not always possible. Clinical definitions of FGR have therefore relied on identifying fetuses at greatest risk of poor outcomes and any additional indicators of an underlying pathology. The incidence of poor perinatal outcomes is significantly higher for infants born small-for-gestational age (SGA) (3,6), defined as a birth weight <10th percentile for gestational age, therefore SGA is often used as a surrogate for FGR. This has been extended to prenatal screening for FGR, with most societies using fetal growth measures <10th  8 percentile to identify fetuses at risk of FGR (37). This approach is adopted by the Society of Obstetricians and Gynecologists of Canada, which defines FGR as an estimated fetal weight or abdominal circumference <10th percentile for the gestational age (38).  Despite clinical evidence that adverse outcomes occur more frequently in pregnancies with poor fetal growth, fetal growth is naturally variable in the human population, therefore a subset of SGA infants are normally grown for their genetic potential and are small but healthy. In particular, it is the infants that are pathologically growth restricted that are at increased risk for poor outcomes, so differentiating the healthy from pathologically small is important. Given the even greater risk for poor outcomes, particularly stillbirth, in fetuses with birth weight <3rd percentile (39,40), certain experts recommend using this stricter cut-off for FGR (41), however this likely excludes a proportion of true FGR cases that present at higher growth centiles. Additional measures to define FGR include screening for evidence of placental insufficiency, which underlies many cases of FGR. For this reason, abnormal blood flow in the uterine artery and umbilical artery by Doppler ultrasound are commonly incorporated in the screening and diagnosis of FGR (37,41), as these metrics are suggestive of utero-placental impairment (42).  Certain sub-classifications of FGR also exist and can be reflective of pathological growth restriction. FGR may present as symmetric FGR, where the fetus is proportionally small, or asymmetric FGR, where the fetus’ head circumference is in the normal range and the rest of the body is small. Asymmetric FGR is thought to be suggestive of placental insufficiency, as this “brain sparing” is thought to be the result of preferential allocation of fetal blood flow toward the brain to ensure adequate delivery of oxygen and nutrients when faced with limited delivery of these resources from the placenta (43,44). Conversely, symmetric FGR may be indicative of a fetus that is small but adequately grown, or that is small due to intrinsic factors such as a genetic  9 syndrome or congenital infection (44). In particular, fetuses with asymmetric FGR have a higher risk of poor outcomes than those with symmetric FGR (45). FGR may also be sub-classified based on the gestational age at which it is identified. Early-onset FGR is a diagnosis of FGR prior to 32 weeks gestational age, whereas late-onset FGR is a diagnosis at or after 32 weeks (37,41). Early-onset FGR is less common, however it more frequently presents with maternal hypertension or PE, in addition to placental histological abnormalities suggestive of placental insufficiency (34,46).  In an attempt to focus on cases of pathologically poor growth for the studies presented in this thesis, I have defined FGR according to criteria utilized in the Robinson lab (47–51) of: birth weight <3rd percentile, or, birth weight <10th percentile with evidence suggestive of placental insufficiency, which may include: i) persistent unilateral or bilateral uterine artery notching at 22-25 weeks, or ii) absent or reversed end diastolic flow in the umbilical artery, or iii) oligohydramnios (amniotic fluid index <50 mm) (52). Birth weight centiles were calculated based on Canadian growth standards, specific to infant sex and gestational age at birth (53).  1.3.2 Causes and risk factors for fetal growth restriction Various factors can influence growth in utero, therefore FGR is a heterogeneous disease with a diverse etiology. As the fetus relies on a healthy in utero environment provided by the mother and the placenta, risk factors for FGR include not only fetal factors that have a direct influence on growth, but also various maternal and placental factors.   10 1.3.2.1 Fetal factors Fetal factors associated with FGR include multiple gestation, genetic syndromes, and congenital infections and defects. Multi-fetal pregnancies are commonly associated with FGR due to the requirement for resources from the mother to be shared among multiple fetuses and the risk of twin-twin transfusion syndrome in monochorionic twins (54); though estimates vary, approximately 10-50% of twin pregnancies may be complicated with FGR (55,56). Congenital infections including cytomegalovirus (CMV) and rubella, both acquired by vertical transmission from an infected mother, can have teratogenic effects on the fetus and are associated with FGR and congenital defects (57–59). In particular, CMV is estimated to be the most common congenital viral infection, and may be present in about 2% of SGA newborns (59,60). Additionally, a number of syndromes caused by genetic mutations are associated with FGR, including imbalances of the whole haploid portion of the genome (polyploidy), chromosome imbalances (aneuploidy), smaller imbalances of genetic material (copy number variants), and sequence mutations affecting single genes. These are discussed in further detail in Section 1.4. Individually, most of these syndromes are rare, however combined, they may account for about 10% of cases of isolated FGR at birth (10). Finally, congenital abnormalities in the fetus, including congenital heart defects and congenital diaphragmatic hernia, are also associated with an increased risk for FGR (61,62). Although the link between these pathologies is yet unclear, it is likely that they co-occur due to other factors, such as a genetic syndrome (63), rather than the congenital defect directly causing poor growth.      11 1.3.2.2 Maternal environment Fetal growth may be influenced by various maternal health conditions and environmental exposures (Table 1.1). Some of these may also be associated with underlying maternal genetic variation, therefore the maternal environment and genetic contributions to FGR are difficult to disentangle.  Table 1.1 Maternal factors associated with fetal growth restriction Exposure type Maternal risk factor Health condition    Diabetes mellitus Biometry: low BMI, low pre-pregnancy weight, low pregnancy weight     gain, obesity Autoimmune disorders: systemic lupus erythematosus, antiphospholipid     antibody syndrome Infection: HIV, malaria Hypertension: chronic or gestational Preeclampsia   Poor nutrition: malnutrition, Crohn’s disease Lung disease: asthma, chronic obstructive pulmonary disease Blood disorders: anaemia, sickle cell disease, thrombophilia Chemical/substance use Tobacco smoking Heavy alcohol use Recreational drugs: cocaine, marijuana, methamphetamine Pharmaceutical drugs: various, e.g. warfarin, antiepileptics,     antidepressants (SSRIs) Sociodemographic Primiparity Age <16 or >35 years Low socioeconomic status, lower education Ethnicity (Black/African American, Asian) Information compiled from references (6,64–81)   Several maternal health conditions that impact inflammation and immune regulation, utero-placental blood flow, and/or the availability or delivery of oxygen and nutrients to the fetus are associated with FGR (Table 1.1). The contribution of these conditions to cases of FGR are expected to vary depending on the population in question; in Western populations, maternal hypertension, obesity, and diabetes are more prevalent and may be associated with more cases,  12 whereas in low and middle-income countries, more cases of FGR may be linked to infectious pathogens, blood disorders such as sickle cell disease, or poor maternal nutrition. Maternal preeclampsia (PE), a multisystem hypertensive disorder of pregnancy, is a notable condition associated with FGR and SGA (6,67,68). PE is diagnosed in cases of maternal gestational hypertension accompanied by i) proteinuria, or ii) other organ dysfunction, or iii) uteroplacental dysfunction at or after 20 weeks gestation (82). PE can be subdivided into early- and late-onset based on diagnosis before or after 34 weeks gestation (83), and it is particularly early-onset PE (EOPE) that is associated with FGR (84). Rather than PE being a direct risk factor for FGR, both pregnancy complications are hypothesized to stem from placental insufficiency and are two possible presentations and severities of the same underlying pathology.  Various maternal lifestyle and sociodemographic factors are also associated with FGR (Table 1.1). Of chemical exposures, maternal tobacco use during pregnancy has been most consistently associated with FGR (65,67,74) and is a modifiable risk factor, as incidence of FGR decreases with earlier cessation of smoking during pregnancy (74). Maternal Black/African American and Asian ethnicity, low socioeconomic status, and fewer years of education are also associated with increased risk of FGR (6,67,68,81,85). These are likely confounded with other risk factors for FGR such as maternal health conditions, nutrition, smoking or drug/alcohol abuse, and may therefore not be risk factors in isolation. Despite this, genetic variation also may contribute to the differential risk for FGR between ethnicities, as differences in fetal growth persist between ethnic groups even under similar maternal socioeconomic and nutritional conditions (85,86). This is further supported by recent findings of a higher burden of genetic variants associated with low birth weight in individuals of African and East Asian ancestry (87).   13 1.3.2.3 Placental factors Adequate placental development and function is crucial to fetal growth, therefore defective placental function can be a major contributor to FGR. Evidence of placental insufficiency is present in a majority of cases of FGR and likely underlies most cases of FGR not explained by fetal congenital and genetic anomalies or infection (25). Additionally, many of the fetal genetic and maternal factors that increase risk for FGR also influence placental function, therefore the etiologies of FGR and placental insufficiency are intertwined. Placental abnormalities associated with FGR include placenta previa, the improper implantation and development of the placenta in the lower uterine segment (88), macroscopic placental thromboses and infarcts, microscopic lesions such as excessive fibrin deposition and villitis, as well as umbilical cord abnormalities (25). Additionally, chromosomal imbalances that are confined to the placenta, further described in Section 1.4.3, are associated with FGR.  1.4 Genetic variation associated with fetal growth restriction and placental insufficiency 1.4.1 Genetic variation: Aneuploidy, copy number variants, and sequence variants Aneuploidy is a deviation in the number of chromosomes from multiples of the haploid number (n=23), of which trisomy (extra chromosome) and monosomy (loss of a chromosome) are the most common. Aneuploidy arises spontaneously in humans due to non-disjunction, the improper separation of homologous chromosomes or sister chromatids, or anaphase lag, the delayed or lack of movement of a chromosome or chromatid to the spindle pole. These may occur during meiosis of parental gametes, resulting in an aneuploid zygote upon fertilization, or during mitosis in the early embryo.   14 Aneuploidy is common in early development, however its incidence decreases throughout development. About 20% of oocytes and 2-4% of sperm are estimated to be aneuploid (89–91), therefore at least 20% of conceptions may be aneuploid (89). This rate is higher with mothers of advanced age (>35 years), as maternal age is associated with increased rates of aneuploidy in oocytes and is the most significant risk factor for aneuploidy in humans (89–91). Mitotic errors in the cleavage stage of the embryo generate aneuploidy and mosaicism, where an individual carries two (or more) genetically different cell populations derived from the same zygote. At this stage, 50-85% of embryos carry an aneuploidy, mostly in mosaic form (92–97). By the blastocyst stage, the incidence of aneuploid embryos is reduced, suggesting arrested development of aneuploid embryos or growth advantages of euploid cells in mosaic embryos (96,98). Aneuploidy results in significantly imbalanced gene dosage, therefore the majority are lethal to the embryo. Aneuploidy is detected in approximately 5% of all clinically recognized pregnancies, and is the most common genetic cause of spontaneous abortion and birth defects in humans (89,99,100). Only monosomy X and, in rare cases, monosomy 21 are observed in clinically recognized pregnancies, therefore most autosomal monosomies are lethal prior to implantation. Autosomal trisomies are slightly more tolerated, however they most often result in spontaneous abortion. Only embryos with trisomies 13, 18, or 21 may survive until birth, though a large proportion spontaneously abort during pregnancy (101) and survival of embryos with trisomies 13 and 18 is associated with the presence of diploid cells in the placental trophoblast (102). In general, sex chromosome aneuploidies are less severe than autosomal aneuploidies. Although also observed in spontaneous abortions (especially 45,X), embryos with sex chromosome aneuploidies can survive to birth and are associated with milder phenotypes.  15 Whereas aneuploidy alters the number of copies of whole chromosomes, alterations of smaller portions of the chromosomes are also possible. A copy number variant (CNV) is a segment of DNA >50 bp that is present at a different copy number compared to a reference genome, typically as a duplication or deletion of DNA (103). CNVs are a common feature of the human genome, with up to 5-10% of the human genome contributing to CNVs and about 160 CNVs present in each individual (104,105). CNVs may increase or decrease the dosage of genes, interrupt sequences at breakpoints, or alter the regulatory landscape of affected regions. CNVs are well-established contributors to birth defects, developmental delay, autism spectrum disorder, and other psychiatric disorders (106–110). In particular, CNVs that are larger and rare in the general population are enriched in the genomes of patients affected by these disorders.  CNVs may be inherited from a parent or may arise de novo in the parental germ line or in early embryonic development. Typically, de novo CNVs are more detrimental as they have not been subjected to selection, and these are also enriched in clinical populations. CNVs can arise through non-allelic homologous recombination (NAHR), where recombination occurs between two large (>1 kb) highly homologous sequences (111). This commonly underlies recurrent CNVs that cause microdeletion and microduplication syndromes, as NAHR between the same low copy repeats or segmental duplications (sequences >1 kb with >90% homology present in at least two regions in the genome) give rise to the same or similar de novo CNVs in multiple unrelated individuals. CNVs may also arise through errors in replication due to short regions of microhomology, or by errors in non-homologous DNA repair (111).  In addition to large or small-scale genomic imbalances, small-scale variation in the genetic sequence may influence gene expression and protein production or function. Sequence variants in the genome comprise indels, which are small (<50 bp) insertions and/or deletions of  16 nucleotides, and single nucleotide variants (SNV), substitutions of a nucleotide that varies from that in the reference genome. These small variants are the most numerous in the genome, accounting for >99.9% of the variants in the average genome, however cumulatively they do not impact as many base pairs per genome as structural variants such as CNVs (105). The vast majority of sequence variants in the average genome are polymorphic (105), meaning they are common in the human population and are present in >1% of individuals. These variants are most often inherited, however rare variants may arise spontaneously as de novo mutations, typically due to errors in DNA replication or failure to repair non-replicative DNA damage or mutations.   1.4.2 Fetal aneuploidy & polyploidy are causes of fetal growth restriction  Large genomic imbalances in the fetus are significant contributors to FGR. In surviving embryos, autosomal trisomies 13, 18, and 21 cause Patau syndrome (112), Edwards syndrome (113), and Down syndrome (114), respectively. FGR is a common prenatal presentation of these syndromes, which are also characterized by post-natal growth delays, congenital abnormalities, and intellectual disability (101,115). With the exception of Down syndrome, most individuals do not survive beyond 1 year of age (101,115). Additionally, Turner syndrome (45,X) is associated with FGR and postnatal growth delays (101,115). Polyploidy is an imbalance in the whole haploid complement of chromosomes (n=23), most commonly presenting as triploidy (n=69). After trisomy, triploidy is the second most common cause of spontaneous miscarriages in humans, present in ~8% of miscarriages (116). Triploidy is associated with FGR, however phenotypes vary depending on the parental origin of the extra chromosome set. Digynic triploid (extra maternal complement) fetuses tend to be severely asymmetrically growth restricted and have congenital malformations, whereas diandric  17 triploid (extra paternal complement) fetuses may be normally grown or symmetrically growth restricted, also with congenital malformations (117). Given the significant imbalance of genetic material, most triploid embryos to not survive until birth, and those that do typically do not survive beyond a few days after birth. Combined with trisomy, these chromosomal imbalances contribute to approximately 20% of FGR detected prior to 26 weeks gestation (118,119).   1.4.3 Confined placental mosaicism Confined placental mosaicism (CPM), first described in the 1980s by Kalousek & Dill, occurs when chromosomal mosaicism is confined to the extraembryonic tissues, typically presenting as trisomy or mosaic trisomy in the placenta with a mainly diploid fetus (120). CPM is common in pregnancy: placental mosaicism is detected in approximately 1-2% of chorionic villus samples (CVS) at 10-12 weeks gestation (121–124), however the abnormality is confirmed in the amniotic fluid only 10% of the time (122,124). Additionally, even when non-mosaic trisomy is detected by CVS in an ongoing pregnancy, it is likely confined to the placenta when involving a non-viable trisomy, as the pregnancy would have ended in miscarriage if the fetus was significantly impacted.   1.4.3.1 Origins of trisomy confined to the placenta  Mosaic trisomic-diploid embryos may arise due to mitotic errors in a euploid zygote, generating a trisomic cell population, or by trisomic zygote rescue, where a mitotic chromosome segregation error results in the loss of one of the extra chromosomes in a trisomic zygote, generating a normal diploid cell population (Figure 1.2). Because one of the chromosomes in the  18 trisomy is randomly lost, uniparental disomy (UPD) in the diploid cell population, where both chromosomes in a pair come from the same parent, is a possible outcome 1/3 of the time.    Figure 1.2: Confined placental mosaicism of trisomy from a trisomic zygote rescue Fertilization with an aneuploid gamete results in a trisomic zygote (meiotic origin of trisomy). A chromosome segregation error in the early embryo generates a diploid cell due to loss of one of the extra chromosomes. If the diploid cells are present in the inner cell mass, particularly the epiblast, they may contribute to embryonic precursors, resulting in a diploid embryo and confinement of the trisomy to the extra-embryonic tissues.   CPM may be a common outcome of trisomic-diploid mosaic embryos. Compared to cells that will give rise to extraembryonic tissues, only a few cells of the blastocyst (present in the epiblast of the ICM) will contribute to the fetus proper, therefore mosaicism may be less likely to be present in fetal cell precursors. For trisomies that would otherwise be lethal to the fetus, if the trisomic cell population is restricted to extraembryonic precursor cells, the pregnancy may continue in some cases. The differential segregation of aneuploid and diploid cells to extraembryonic and embryonic precursors may occur by chance, however selection may also play a role, as a high incidence of skewed XCI in fetuses associated with trisomy CPM suggests  19 that the embryonic precursor pool is reduced (125), and this may be related to differential survival of aneuploid cells depending on whether they are present in the ICM or TE (126).  1.4.3.2 Outcomes associated with CPM Trisomy in the placenta may impact placental function and therefore pregnancy outcomes. Trisomy is associated with increased apoptosis and reduced proliferation of trophoblast in chorionic villi obtained from spontaneous abortions (127). Placentas from cases with trisomy 21 also show defective syncytiotrophoblast formation, which is associated with reduced secretion of hormones and signalling molecules such as human chorionic gonadotropin (hCG) (128). Although the fetus may not carry abnormal cells, CPM of trisomy is associated with placental insufficiency and poor outcomes in the fetus including FGR, stillbirth, pre-term birth, and congenital malformations (129–133). In particular, trisomy CPM may account for a significant proportion of cases of FGR, as approximately 10% of cases at birth have trisomy CPM (51,134,135). Trisomies 13 and 16 in the placenta are also associated with PE (136,137), though this is not a common feature of all trisomies. Despite increased risks for poor outcomes, trisomy CPM is also found in healthy uncomplicated pregnancies (138–141). Risk for poor outcomes associated with trisomy CPM depends on the proportion of abnormal cells in the placenta, their distribution among cell lineages, and the specific chromosome involved (142,143). CPM of mitotic origin is associated with lower levels of trisomy in the placenta, is typically restricted to either the trophoblast (type I CPM) or inner mesenchyme (type II CPM) lineages of the placenta, and is associated with lower incidence of poor outcomes (142–144). CPM from meiotic trisomies followed by trisomic zygote rescue are associated with high levels or full trisomy in the placenta and impact both the trophoblast and  20 mesenchyme of the villi (type III CPM) (142–144). These are associated with greater risk of poor outcomes, especially FGR (142–144). The origin of trisomy and the genes duplicated differ depending on the chromosome affected, and therefore outcomes associated with CPM are also chromosome-specific. For example, trisomy 16 CPM is nearly always of meiotic origin and is associated with high levels of trisomy in the placenta and more severe outcomes, including FGR, SGA, PE, PTB, and congenital abnormalities (129,136,142,143,145,146). Trisomy 8 CPM is commonly of mitotic origin and tends to be associated with normal outcomes (142,143,147). It mainly presents as type II CPM, presumably because trisomy 8 is not well tolerated when present in the trophoblast lineage (142,148).   1.4.4 Rare genetic syndromes associated with fetal growth restriction In addition to aneuploidy and polyploidy, several genetic syndromes caused by CNVs or sequence mutations are also associated with FGR (10). These include 7q11.23 microdeletion syndrome (Williams-Beuren syndrome), 4p- syndrome (Wolf-Hirschhorn syndrome), 22q11.2 microduplication syndrome, and Xp22.3 microdeletion syndrome (10,115). Monogenic disorders such as Cornelia de Lange syndrome, Rubinstein-Taybi syndrome, Fanconi anemia syndrome, and Bloom syndrome are also associated with FGR (10,115). In all of these cases, FGR is one of several clinical features of the syndrome (e.g. congenital defects, dysmorphic features), although it may be the only detectable presentation prior to or at birth. Individually, these syndromes are rare, therefore their contribution to cases of FGR at birth is low. Their cumulative contribution has not been thoroughly assessed, however recent efforts to profile CNVs in karyotypically normal FGR fetuses with or without congenital anomalies identifies a pathogenic CNV in 7-11%, or in 0-7% of cases FGR without congenital anomalies (149–153).  21 Imprinting disorders are also typically associated with altered growth, which may present prenatally and/or after birth. Imprinting is the phenomenon by which certain genes are expressed in a parent-of-origin-dependent manner. This is due to epigenetic regulation, typically by differential DNA methylation (DNAme, further explained in Section 1.5) at imprinting control regions (ICRs) on parental chromosomes. Imprinting disorders arise when this process is disrupted by i) UPD, ii) CNVs or sequence variants at ICRs and/or imprinted genes, or  iii) epigenetic changes that alter imprinting patterns (154). Imprinting disorders associated with FGR include Transient neonatal diabetes mellitus, Temple syndrome, and upd(20)mat (10).  Silver-Russell syndrome (SRS) is a notable rare genetic syndrome specifically characterized by pre- and postnatal growth retardation, in addition to distinct facial features and skeletal asymmetry (155). SRS has a diverse genetic etiology, associated with imprinted genes at 11p15.5 and chromosome 7. Between 40-60% of cases are associated with loss of DNAme at the paternal H19/IGF2 ICR (IC1) at 11p15.5, resulting in downregulated IGF2, usually uniquely expressed from the paternal allele (155,156). Downregulation of IGF2 in the fetus is clearly a candidate for FGR, however loss of expression in the placenta likely also contributes, as IGF2 normally promotes cytotrophoblast proliferation (157), and trophoblast-specific loss of Igf2 in mice causes placental and fetal growth restriction (14). Paternally-inherited mutations in IGF2 associated with a phenotype similar to that of SRS have also been reported (158). Some cases of SRS are also associated with upd(11)mat or maternal duplications at 11p15.5 that result in overexpression of the maternally-expressed gene CDKN1C (155). About 10% of SRS cases are associated with upd(7)mat, and additional cases have been reported with duplications of 7p and maternal segmental UPD of 7q, implicating imprinted genes on chromosome 7, including candidates GRB10, MEST, SGCE, and PEG10 (155,156,159).   22  1.4.5 Common and inherited genetic variation associated with placental insufficiency The associations between FGR and genetic syndromes are well established, however these syndromes underlie only a subset of isolated FGR cases at birth. Additional evidence exists for the contribution of genetic variation to FGR that is as yet unexplained by genetic syndromes. A past pregnancy or family history of FGR is a risk factor for FGR in the current pregnancy (160,161), suggesting a contribution of heritable factors. Common genetic variation has also been implicated in fetal growth, as up to 25-40% of variation in birth weight (162,163), and 66% of variation in fetal growth into the late third trimester of pregnancy (164) can be attributed to fetal genetic factors. Furthermore, maternal genetic variation is also estimated to contribute to 22% of variation in birth weight and fetal growth (163). Studies also support a contribution of inherited genetic variation in the development of PE, which shares a common association with placental insufficiency as FGR. A personal or family history of PE is associated with increased risk for PE, particularly EOPE (165), and heritability of PE is estimated to be around 55-60%, with approximately 30-35% attributable to maternal genetics and 20% to fetal (placental) genetics (166–168).  Genetic linkage studies have identified candidate genes associated with placental insufficiency mainly in the context of familial PE, as linkage analyses of FGR are lacking. These include genes relevant to placental function such as CORIN, which facilitates trophoblast invasion and spiral artery remodeling (169,170), ACVR2A, which encodes the activin receptor type-2A, involved in activin and inhibin signalling that mediates several processes in placentation and pregnancy (171–174), and STOX1, hypothesized to be important in recruitment of uterine natural killer cells (uNK) and monocytes by placental EVTs for successful interaction  23 at the maternal-placental interface (175,176), though this association has not been replicated in certain studies (177,178). All of these studies have focused on maternal genotypes, therefore the influence of placental variation in these genes on PE has not been established. Most studies to discover common genetic variants associated with FGR or PE have been case-control candidate association studies that focus on maternal and/or placental/fetal variants in genes with a known or hypothesized role in the etiology of these disorders. Mainly, these have been studies of single nucleotide polymorphisms (SNPs), which are SNVs that are present in >1% of the population. Although many studies report associations, few variants have been convincingly replicated in large populations and their genome-wide significance is not well established. Limited replication of these variants may be associated with small sample sizes used in discovery or replication cohorts, or due to use of different study populations. Nonetheless, many variants converge on a few key pathways for placental function. Table 1.2 summarizes variants associated with PE and FGR from candidate studies.  24 Table 1.2: Genes and common sequence variants associated with poor fetal growth or preeclampsia Pathophysiological process Gene Variant† Associated outcome Reference Thrombophilia ANXA5 M2 haplotype Mothers with M2 haplotype at higher risk of having SGA infant  (179)  MTHFR c.677C>T (rs1801133) Maternal, not fetal, TT genotype associated with PE (180) Placental, not maternal, TT genotype associated with PE (181,182) Maternal T allele associated with PE (meta-analysis) (183) Maternal TT genotype associated with FGR (184) c.1298A>C (rs1801131) Maternal CC genotype associated with PE (181) PAI-1 4G/5G indel Maternal 4G/4G genotype associated with FGR (184)   Maternal 4G/4G genotype associated with PE (185) F5 (factor V leiden) c.1691A>G Fetal G allele carriers associated with lower birth weight (186) Maternal G allele carriers associated with PE, no association with fetal variant (180) F2 (prothrombin) c.20210G>A Fetal A allele carriers associated with lower birth weight (186) Fetal A allele carriers associated with FGR, no association of maternal allele (187) Endothelial function, angiogenesis & vasoactivity ACE2 rs2074192 (C>T)  Fetal T allele associated with SGA, not maternal (188) AGT c.803T>C (rs699) Maternal C allele associated with PE (189) ANGPT1 rs2507800 (T>A) Maternal A allele associated with PE and SGA with abnormal uterine artery Doppler (190) EGF rs3756261 Maternal haplotypes that include the A allele associated with increased risk of PE and lower birth weight (191) REN rs5707 (A>C) Maternal and fetal C allele associated with PE (192) rs236864 (G>C), rs5707 (A>C),  rs5705 (T>G) CCT & TAT haplotype in fetus associated with PE (192) TSP1 c.2210A>G (rs2228262) Paternal and fetal G allele associated with SGA, particularly normotensive SGA (not associated with PE) (193) VEGFA rs3025039 (C>T) Maternal C allele associated with PE (194) Inflammation & immune regulation KIR  A haplotype  Maternal KIR AA (inhibitory) genotype in combination with a fetal HLA-C C2 allele associated with PE and FGR (195,196) HLA-C C2 allotype  25 Pathophysiological process Gene Variant† Associated outcome Reference Inflammation & immune regulation HLA-G Haplotypes with +14 bp insertion in exon 8 Insertion allele in the fetus, particularly when inherited from the father, associated with PE (197) G*0106, G*0101 haplotypes Fetal (placental) G*0106 carriers and G*0106/G*0101 heterozygotes associated with PE (198) IL4 rs2243250 (T>C)  Maternal T allele associated with PE (199) IL6 rs1548216 (G>C), rs2069843 (G>A), rs2069849 (C>T) Maternal variant CAT haplotype associated with SGA (200) IL10 -1082G>A,  -819C>T,  -592A>C Maternal -819 C allele, -592 A allele, and ACC haplotype associated with EOPE (201) KLRD1 rs3809214 (G>A), rs2302489 (A>T) Maternal haplotype of variant alleles associated with SGA in African Americans but not Europeans (200) TNF rs1800629 (G>A) Maternal A allele associated with PE (200,202) Growth IGF1 PCR1 CT repeat, D12S318 CA repeat Fetal genotype associated with SGA (203) IGF2 rs1003484 (C>T), rs3741211 (T>C), rs3741206 (T>G) Infants with paternal CTG haplotype associated with SGA (204) +3123A>G Fetal A allele associated with lower birth weight (205) IGF2R c.901C>G Fetal G allele associated with lower birth weight (205) GH1 -1A>T>C,  -3G>C CC alleles in infants associated with SGA (206) Placentation MMP2 -1306C>T Fetal T allele associated with FGR (207) SERPINA3 rs1884082 (G>T) Maternal T allele associated with FGR (208) rs4934 (A>G) Maternal G allele associated with PE (208) Cardiovascular & metabolic disease candidates INSR rs2059806 (G>A) Maternal AA genotype associated with PE (209) PLEKHG1 rs9478812 (G>A) Maternal A allele associated with PE  (210) † rsID used where possible, and reference and alternate alleles are included. If rsID not provided in publication, variant name from publication is used.  26 Numerous candidates studies have focused on variants associated with maternal diseases that predispose to FGR and PE (e.g. hypertension, cardiovascular disease, thrombophilia) (Table 1.2), with limited reproducibility. For example, maternal and fetal variants associated with inherited thrombophilia are associated with PE and FGR in some studies (180–184,186,187,211) but not others (212,213), and a meta-analysis found that a significant associations are likely due to publication bias (211). Additionally, several variants commonly associated with PE, including those in MTHFR, F5, AGT, and TNF, did not replicate in a large PE population (N=657) (214).  Several studies have also investigated variants in immune-related genes (Table 1.2), as proper interaction between decidual immune cells and placental trophoblasts is key to successful placentation. The interaction between maternal uNK cells and EVTs is mediated by binding of uNK KIR receptors to HLA-C on EVTs, and combinations of haplotypes in the maternal KIR gene and fetal HLA-C are associated with FGR and PE (195,196). Variants in placental HLA-G, involved in immunosuppression, have also been associated with PE and SGA (197,198). Additional studies of SNPs in genes involved in immune regulation and signalling have focused on maternal genotype (199–202), therefore further investigations of fetal/placental genotype are needed to further clarify their association with placental insufficiency. Compared to these previous candidate pathways, less attention has been paid to fetal or placental variants in genes involved in fetal growth or placentation (Table 1.2). Increased risk for FGR or SGA is associated with common SNPs in growth-related genes IGF1, IGF2, IGF2R, and GH1 (203–206); MMP2, which encodes a matrix-metalloprotease involved in implantation and early placental development (207); and FN1, involved in the extracellular matrix and secreted at high levels by the placenta in PE (215,216), though further studies to replicate these associations are needed.   27 Thus far, only one variant, a SNP in chromosome 13q12.3 near FLT1, has been identified and replicated in a large genome-wide association study (GWAS) of placental genotypes associated with PE (217,218). This gene encodes soluble FMS-like tyrosine-kinase-1 (sFLT-1), an antiangiogenic factor secreted by the syncytiotrophoblast that binds to placental growth factor (PlGF) and vascular endothelial growth factor (VEGF) in maternal circulation, and has previously been associated with endothelial damage in the mother and PE (219,220).  A large GWAS for FGR has not been performed yet, however GWAS have identified up to 60 birth weight-associated SNPs in the placenta/infant, including replicated associations with variants in or near ADCY5, CCNL1, and CDKAL1 (221–223) also associated with type II diabetes (224,225). The SNPs near ADCY5 and CCNL1 are also associated with moderately increased risk for SGA (221). Genome-wide significant SNPs from these studies also included variants near IGF family genes INS-IGF2, IGF1, IGF1R, and the placenta-specific gene PLAC1, which may have a role in PE (223,226). Additionally, a recent GWAS identified a SNP in SLIT2 associated with PTB, and placental expression of SLIT2 was correlated with birth weight (227).  1.4.6 Placental copy number variation and pregnancy outcomes Compared to extensive studies of common SNPs associated with pregnancy complications, studies of CNVs associated with pregnancy complications are lacking. Although the association between trisomy in the placenta and placental insufficiency has been known for some time, very few studies have investigated whether these smaller genomic imbalances in the placenta may also contribute to FGR and other complications. Copy number variation appears to be a fundamental feature of the polyploid (up to ~1,000 N) trophoblast giant cells of the mouse placenta (228,229), and although human placental EVTs can become up to 4N-8N as they  28 differentiate, such copy number variation is not apparent (230,231). A recent study of placentas from healthy and complicated pregnancies found that placentas harboured up to 3 times more CNVs than parental blood, and that placentas at term had a greater number of CNVs compared to those from the first trimester (232). Additionally, placentas from control pregnancies had a higher load of CNVs compared to those associated with complications of PE, SGA, and recurrent pregnancy loss (232,233). These findings led the authors to hypothesize that de novo copy number variants are important for healthy placental function (232,233). Conversely, another study found the opposite: that CNV load was greater in placentas from FGR and PE compared to healthy term pregnancies, and that it was positively correlated with severity of the pathology (234). Additional research is needed to establish the relationship between CNVs and pregnancy outcomes as the studies performed so far were small and gave inconsistent findings. Additionally, it is yet unknown if CPM of CNVs is a common occurrence as with trisomy CPM, or whether it may also contribute to pregnancy complications. At least one case of a submicroscopic CNV of clinical relevance detected by CVS and confined to placental tissue has been reported, and was associated with a healthy live-born male (235). Only a few other studies have investigated CNVs in placental tissue or products of conception, mainly focused on identifying pathogenic CNVs associated with stillbirth or miscarriage (236–238).       29 1.5 DNA methylation and placental insufficiency In addition to alterations in gene dosage or function, altered regulation of gene expression in the placenta may influence placentation and placental function, and may therefore contribute to complications of FGR and PE. One mechanism by which this may occur is through DNA methylation (DNAme), an epigenetic mark that is mitotically heritable and is involved in regulation of gene expression and cellular fate without altering the DNA sequence.  DNAme most commonly occurs as the addition of a methyl (CH3) group to the fifth carbon of a cytosine in the context of a cytosine-guanine dinucleotide (CpG). CpGs are not evenly distributed throughout the genome; overall, the genome is relatively deficient in CpGs, with the exception of some short regions of high CpG density, termed CpG islands (239). In somatic tissues, most CpGs are methylated, particularly those in repetitive elements and other repetitive DNA (e.g. satellite DNA), non-repetitive intergenic DNA, and exons of genes (240). CpG islands are associated with approximately 70% of gene promoters (241), and in contrast to the highly methylated CpGs in the rest of the genome, these tend to be lowly methylated (240). Although the relationship between DNAme and gene expression is complex, in general, DNAme at promoter CpG islands is associated with transcriptional repression (240). This may be due to DNAme interfering with the binding of transcription factors that activate gene expression, or resulting in the recruitment of methyl-binding proteins that mediate transcriptional silencing (240). Alternatively, DNAme may occur following transcriptional repression driven by other epigenetic factors (e.g. histone modifications) as a means to more stably repress transcription, thus reflecting rather than causing altered transcriptional states (242,243).   30 1.5.1 The placental methylome DNAme in the placenta is of interest to study in the context of complications of placental insufficiency. It may be reflective of altered transcriptional profiles associated with placental dysfunction, highlighting genes and genomic regions dysregulated in PE and FGR and implicated in pathogenesis. DNAme profiles differ between different cell types (244), therefore altered placental DNAme in PE and FGR may also indicate altered composition of cell types associated with these disorders. Finally, as an epigenetic mark, DNAme may be influenced by external environmental cues, and thus provides an avenue to assess the contribution of non-genetic variation to placental function and the etiology of FGR and PE.  The placenta, and particularly the trophoblast, which comprises the bulk of placental biopsies, exhibits a unique DNAme profile compared to somatic tissues. Overall, the placental genome is hypomethylated compared to somatic tissues (245). Placental repetitive elements are also hypomethylated, although the degree of hypomethylation depends on the repetitive element and its evolutionary age (246,247). Placental hypomethylation is attributed to large (>100 kb) regions of low-methylated DNA, termed partially-methylated domains (PMDs), that comprise up to 40% of the genome (248). Despite being overall hypomethylated, the placenta also exhibits tissue-specific hypermethylation at localized sites, particularly promoters of certain tumor suppressor genes (249). This profile of global hypomethylation and localized hypermethylation of tumor-suppressor gene promoters is similar to that in cancer, and may be reflective of the similar processes of tissue invasion and remodeling that occur in both healthy placentation and malignant cancer (249). Finally, the placenta also has a unique imprinting profile, with numerous placental-specific imprinted differentially-methylated regions (DMRs) (250,251).   31 1.5.2 Placental DNAme alterations in placental insufficiency complications Epigenome-wide association studies (EWAS) have consistently found differential placental DNAme associated with PE, especially EOPE (47,48,252–254). The specific CpGs that are differentially methylated are not consistent between studies, and the few studies to replicate their findings in an independent cohort find ~35% of differentially-methylated genes or CpGs replicate (47,254). EWAS for FGR have been less consistent in finding altered DNAme: a number of studies find differentially-methylated CpGs in the placenta associated with FGR (255–259), however others do not find any that reach genome-wide significance (47), suggesting that altered genome-wide methylation may not be a defining feature of FGR as it is for EOPE. Nonetheless, results from these placental EWAS of PE and FGR highlight differential DNAme in several overlapping and relevant pathways, such as: placental function, angiogenesis, cell adhesion, inflammation and immune regulation, and hypoxia response/oxidative stress. Studies of DNAme in candidate genes associated with FGR and PE have also found altered methylation at genes related to placental function and metabolism, fetal growth, and control of angiogenesis. Imprinted genes have been a particular focus of studies of FGR because of their association with growth and the importance of epigenetic regulation in their expression. Altered methylation at several ICRs and imprinted genes is associated with SGA (260); in particular, reduced DNAme at the H19/IGF2 ICR has been consistently associated with FGR (50,261–263). Candidate studies of DNAme in the leptin gene (LEP), important in endocrine signalling for fetal and placental growth, have found variable associations with FGR and EOPE (264–266). Altered DNAme at genes involved in regulation of cortisol signalling, known to impact fetal growth, have also been variably associated with FGR and PE (267,268). Additionally, altered DNAme at genes involved in placentation including MMP9, SYN1 and SYN2, involved in cellular fusion to  32 generate the syncytiotrophoblast, and VEGF and FLT1, involved in regulating angiogenesis and dysregulated in PE, are associated with PE or FGR (266,269–271). Finally, additional candidate studies have found altered DNAme at genes related to growth and energy homeostasis in the placenta and FGR (262,272).  1.6 Research objectives & hypothesis Much past research, including contributions from the Robinson lab, has established the association between trisomy CPM and placental insufficiency, FGR and other poor obstetric and perinatal outcomes (51,129,136,143,273). The contribution of smaller genomic imbalances in the placenta such as CNVs has not, however, been effectively studied in association with placental insufficiency and FGR, nor has the association between genetic variants and genome-wide DNA methylation in the context of placental insufficiency. In this dissertation I aimed to further assess how genetic variation in the placenta is associated with placental insufficiency and the placental methylome through studies of aneuploidy, copy number variants, and single nucleotide polymorphisms. I hypothesized that placental genomic imbalances, including aneuploidy and copy number variants, and candidate single nucleotide variants in a gene relevant to placental DNA methylation are associated with FGR and/or altered placental DNA methylation. To assess this, I performed: i) A study of aneuploidy and copy number variation in the placenta in association with small-for-gestational age (SGA), a common surrogate measure for FGR; ii) A study of the potential origin, placental distribution, and clinical relevance of multiple large de novo duplications found in the placenta from an infant with FGR;   33 iii) A study of the association between candidate SNPs in the MTHFR gene, involved in one-carbon metabolism and important for methylation reactions such as DNAme, and alterations to placental DNAme and risk of placental insufficiency complications.  These studies contribute further insights into the dynamics of genomic variation in the placenta and its influence on fetal growth, placental insufficiency, and the placental epigenome.   34 Chapter 2: Genomic imbalances in the placenta are associated with poor fetal growth  A version of this chapter has been published (see Preface for contribution details): Del Gobbo GF, Yin Y, Choufani S, Butcher EA, Wei J, Rajcan-Separovic E, Bos H, von Dadelszen P, Weksberg R, Robinson WP, and Yuen RKC. Genomic imbalances in the placenta are associated with poor fetal growth. Molecular Medicine. 2021;27:3.  2.1 Introduction Fetal growth restriction (FGR), where the fetus does not grow to its genetic potential, affects 5-12% of pregnancies in developed countries (36). FGR is associated with increased risk of perinatal, neonatal, pediatric and long-term adult health complications (1–9). Small-for-gestational-age (SGA, birth weight <10th percentile) is often used as a surrogate for FGR, however, a subset of SGA infants may be small but normally grown for their potential and thus otherwise healthy. In particular, pathologically growth-restricted infants are at increased risk for morbidity and mortality. Poor growth in utero is commonly attributed to placental insufficiency, however fetal infection or genetic abnormality, and maternal health or lifestyle factors may also play a role (25,274,275). Some of these factors (e.g. maternal smoking, infection, obesity) may also contribute to poor trophoblast development and function, thus the etiologies of FGR and placental insufficiency are complex and intertwined. A major known cause of placental insufficiency in a viable pregnancy is confined placental mosaicism (CPM), where some or most cells in the placenta are aneuploid, while the fetus has a predominantly normal diploid chromosome complement. CPM identified prenatally is associated with increased risk for FGR  35 and other pregnancy complications depending on the levels of abnormal cells and the chromosome(s) involved (143,276). Screening placentas postnatally has also confirmed an  association between CPM and FGR (134,135,138,277). We previously identified trisomy CPM in 4/43 FGR pregnancies, but none in 85 controls nor 18 cases associated with preeclampsia (PE) without FGR (51). Despite the evidence that large genomic imbalances in the placenta are associated with FGR, few studies have investigated the role of smaller genetic imbalances (<5-10 Mb), copy number variants (CNVs). To date, studies investigating CNVs associated with FGR have either not studied placental tissue (150,151) or had small sample sizes and found conflicting results (232,234).  In this study, we aimed to thoroughly evaluate the contribution of placental genomic imbalances to poor fetal growth. To this end, we assessed i) the incidence of large aneuploidies (>15 Mb) in 274 placentas from control and SGA pregnancies, and ii) the load, impact, and clinical relevance of placental CNVs (<15 Mb) to SGA in a subset of 114 euploid placentas. This is the largest study to date of its kind; it enhances our understanding of the underlying causes of placental dysfunction and poor fetal growth, and further establishes the importance of assessment of CPM in the clinic.      36 2.2 Methods 2.2.1 Research ethics approval Ethics approval for use of human research subjects in this study was obtained from the University of British Columbia/Children’s and Women’s Health Centre of British Columbia Research Ethics board (H17-01545) and from the Hospital for Sick Children (1000038847) and Mount Sinai Hospital (05-0038-E) Research Ethics boards. Informed written consent was obtained from all study participants.   2.2.2 Sample collection and cohort characteristics Figure 2.1 summarizes the study design and number of cases per cohort used at each analysis step of this study.   Figure 2.1: Schematic of study design Outline of study design, including methods and sample sizes used in both cohorts in this study. Genetic assessment methods are italicized. CGH, comparative genomic hybridization; MLPA, multiplexed ligation-dependent probe amplification   37 2.2.2.1 Vancouver cohort Placental samples for the Vancouver cohort were ascertained and processed as previously described (51) and include cases included in previous studies (47–49,51,278). Clinical information, including newborn sex and birth weight, gestational age at delivery, maternal age, and ethnicity were collected. Placental and maternal samples were processed and DNA was extracted as previously described (51).  This cohort (N=207) included 136 controls from uncomplicated pregnancies (no SGA, hypertension/PE, or known abnormal maternal serum screen results) and 71 cases of SGA (Table 2.1). Exclusion criteria were a prenatally-diagnosed chromosome abnormality or congenital anomaly in the fetus. SGA was defined as birth weight <10th percentile, adjusted for sex and gestational age at birth based on Canadian growth charts (53). The majority, 55/71 (77%) of SGA cases also met criteria for FGR, defined as birth weight <3rd percentile, or <10th percentile with additional findings suggestive of placental insufficiency, including i) persistent uterine artery notching at 22-25 weeks, ii) absent or reversed end diastolic velocity on umbilical artery Doppler, and/or iii) oligohydramnios (amniotic fluid index <50 mm). One FGR case had a birth weight >10th percentile but was diagnosed as FGR based on prenatal measurements of FGR and severe oligohydramnios. Preeclampsia (PE) was defined according to Canadian criteria (52) as previously described (278). A subset of SGA cases were associated with maternal PE (Table 2.1); the SGA cases associated with maternal PE delivered significantly earlier than those without (mean 33.2 weeks vs. 36.9 weeks, respectively; p<0.05, Mann-Whitney U-test), however birth weight did not differ (p>0.05, Student’s t-test). Following aneuploidy assessment, a subset of the euploid placentas were selected for further CNV profiling, including 24 control and 29 SGA cases, 90% of which fulfilled criteria for FGR (Figure 2.1, Table 2.1). These were  38 randomly selected after excluding cases or controls associated with a twin pregnancy (N=23), or known maternal smoking during pregnancy (N=3/108 respondents).   Table 2.1: Study cohort clinical characteristics Group N Gestational age  at birth (w),  mean (range) Maternal age  at birth (y), mean (range) Sex, N male (%) Birthweight  (S.D.), mean (range) Twins, N (%) PE, N (%) Vancouver cohort - Total samples Control 136 39.2 (30.1-41.9) 34.3 (23.8-45.8) 68 (50) 0.1 (-1.2 to 2.7) 11 (8) 0 (0) SGA 71 35.3 (23.6-41.7)* 35.2 (23.1-41.0) 34 (48) -1.9 (-3.6 to -1.2)* 12 (17) 31 (44)*     Subset of samples for CNV profiling  Control 24 39.3 (38.0-41.4) 34.8 (30.2-40.5) 13 (54) 0.01 (-1.1 to 2.2) 0 (0) 0 (0)  SGA 29 34.9 (24.0-40.6)* 34.4 (23.9-42.9) 18 (62) -1.9 (-3.0 to -0.6)* 0 (0) 11 (38)* Toronto cohort – Total samples Control 37 37.1 (27.3-41.0) 32.9 (21-43) 19 (51) 0.28 (-1.1 to 1.5) 0 (0) 0 (0) SGA 30 34.0 (27.1-38.6)* 35.1 (25-44) 9 (30) -2.2 (-3.5 to -1.2)* 5 (17)* 0 (0) *p<0.05, p-values calculated in comparison to respective control groups by Student’s t-test for maternal age and birth weight, Mann-Whitney U-test for gestational age, and Fisher’s exact test for all categorical variables. SGA, small-for-gestational age; PE, preeclampsia; N/A, not available.  2.2.2.2 Toronto cohort The Toronto cohort was ascertained and processed as part of a distinct study, and findings from the two cohorts were then subsequently compared. Placental samples were obtained as previously described (279). Clinical information including newborn sex, birth weight, and gestational age were collected for all cases. The original cohort included 99 samples, however following microarray quality filtering, 67 remained, including placentas from 37 control and 30 SGA pregnancies (Table 2.1, Figure 2.1). Definitions for control and SGA followed the same criteria as the Vancouver cohort, described above. Exclusion criteria were a prenatally-diagnosed chromosome abnormality or congenital anomaly in the fetus, CMV or toxoplasmosis infection,  39 or clinical amnionitis. Additionally, cases or controls were excluded if mothers were diagnosed with: i) preconceptional severe hypertension; ii) clinically significant thrombophilia; iii) advanced renal, heart or liver failure; iv) type I diabetes mellitus or gestational diabetes requiring treatment with insulin; or v) anemia and autoimmune disorders requiring therapy during pregnancy. Maternal PE was not present in any of the cases in this cohort (Table 2.1).  2.2.3 Aneuploidy screening and CPM follow-up Aneuploidy was detected using several methods in this study. In the Vancouver cohort, samples were assessed by comparative genomic hybridization (CGH), which can detect aneuploidies greater than 15 Mb, or by multiplexed ligation-dependent probe amplification (MLPA) of subtelomeric probes (SALSA MLPA Subtelomeres Mix, MRC-Holland, NL), designed to detect aneuploidies that extend to the ends of the chromosome (Figure 2.1). A subset of these samples (N=85 control and N=43 SGA), all screened by CGH, have been previously published (51); the current study is a retrospective re-assessment of aneuploidy in those cases, with additional samples collected. For more recent cases, MLPA was used to screen for aneuploidy because it is a reliable and cost-effective method to identify whole chromosome aneuploidies (monosomy and trisomy), as well as terminal duplications and deletions. In the Toronto cohort, aneuploidy was detected using CNV profiling by microarray (see below). All cases with an aneuploidy detected by any method were further assessed by microsatellite polymorphism genotyping of probes on the involved chromosome (Supplementary Methods). Aneuploidies identified by MLPA were also confirmed using CNV profiling by microarray to determine the extent of the alteration, particularly in cases where results suggested abnormalities restricted to one chromosome arm (see below and Supplementary Methods).  40 2.2.4 Microarray processing and CNV detection Placental DNA was assessed on the Infinium Omni2.5-8 BeadChip array (Illumina, USA) for the Vancouver cohort, and on the Affymetrix CytoScan HD array (ThermoFisher Scientific, USA) for the Toronto cohort (Figure 2.1) at The Centre for Applied Genomics, Toronto, Canada (280,281). In the Vancouver cohort, two DNA samples from different locations in each placenta were run on the array to assess the possibility of detecting mosaicism of CNVs by high-density microarray (Supplementary Methods). Following sample quality checks unique to each array type, all 54 Vancouver cases and 67/99 Toronto cases were available for analysis (Figure 2.1). CNVs were detected using in-house pipelines (280,281) applying 3-4 CNV-calling algorithms specific to each array platform (Supplementary Methods). Following CNV quality checks, high-confidence CNVs called by at least two algorithms with a minimum 50% reciprocal overlap, ≥5 probes, and ≥10 kb were kept for analysis. CNV boundaries were compared to the Database of Genomic Variants and in-house databases of CNVs in controls, and rare CNVs were defined as those present in <0.1% of controls and at least 50% of the length absent in controls. Given that CNV calls were only 64-92% concordant between technical replicates of placental DNA from the Vancouver cohort (Supplementary Methods, Supplementary Figure 2.1), mosaicism of CNVs was not investigated and the DNA sample with the higher microarray quality scores from each placenta was selected for CNV analyses. Ancestry was assessed using SNP genotypes by MDS clustering of identity-by-state distances in PLINK (282) (Supplementary Methods). The ancestry composition of both cohorts was comparable, as was the ancestry of SGA cases compared to controls within each cohort, with slightly more individuals of East Asian and/or South Asian ancestry in the SGA cases (Supplementary Table 2.1, Supplementary Figure 2.2).  41 2.2.5 Candidate CNVs CNVs with potential clinical relevance to SGA were prioritized based on: whether they were rare, ≥200 kb, overlap pathogenic or likely pathogenic CNVs in the DECIPHER or ClinVar databases, overlap genes with important roles in placental function or those that are reported to be differentially expressed or with variants associated with growth restriction. CNVs were categorized following American College of Medical Genetics guidelines (283). Candidate CNVs were confirmed and assessed for CPM using quantitative PCR (Supplementary Methods).  2.2.6 Placental-enhanced and imprinted genes A list of 356 genes with elevated expression in the placenta was downloaded from the Human Protein Atlas (284), including 78 with placental-specific elevated expression. A database of imprinted regions was curated from the OTAGO Imprinted Genes (285) and GeneImprint (286) databases, and reported placental imprinted differentially methylated regions (DMRs)(250,251) (Supplementary Table 2.2). Outer genomic boundaries were used to generate a consensus region for those genes associated with a placental imprinted DMR.  2.2.7 Functional pathway enrichment Enrichment of 2,191 GO and KEGG (287) pathways in genes with coding sequences impacted by rare CNVs in SGA was assessed using a generalized linear model with universal gene count correction in the cnvGSA R package. Sex and cohort (array) were included as covariates, and thresholds of 100-1,500 genes were used to limit pathways assessed. A false-discovery rate (FDR) of <0.1 was used to define significantly enriched (coefficient>0) or deficient (coefficient<0) pathways in SGA CNVs.  42 2.2.8 Statistical analyses Continuous variables were compared using the Student’s t-test or Mann-Whitney U test depending on whether the data was normally-distributed, as determined by the Shapiro-Wilk normality test. Categorical variables were compared by Fisher’s exact test. Bonferroni correction for multiple testing was used where applicable. Statistical power for comparing CNV load was assessed using the pwr package in R. Based on a previous report of a large effect size (d>0.95) in the difference in CNV load in control vs. SGA placentas (232), we assumed a slightly lower but still large effect size (d) of 0.8. Based on the minimum sample size in each group per cohort (N=24) and using an =0.05, our study had >80% power to detect significant differences in each cohort individually. Analyses were performed in R version 3.5.1 (288), and plots were generated using the ggplot2, ggbio, and ggpubr packages.   2.3 Results 2.3.1 Poor fetal growth is associated with placental aneuploidy Aneuploidy screening was performed in 207 placentas from the Vancouver cohort and 67 placentas passing microarray quality checks from the Toronto cohort. Amongst 173 control placentas, no cases of CPM or autosomal aneuploidy were detected. Two (1.1%) controls had constitutional abnormalities involving the sex chromosomes (Table 2.2), one of which only impacted Yqter. In contrast, amongst 101 SGA cases, 12 (11.9%) had a whole or partial autosomal trisomy present in the placenta (Table 2.2) (p=0.00017, OR=11.4, 95% CI=2.5-107.4; Fisher’s exact test). Placental autosomal aneuploidies were found both in cases of isolated SGA (N=9/70; 12.8%) and cases of SGA with maternal PE (N=3/31; 9%).     43 Table 2.2: Summary of findings from detection of placental aneuploidy Study Group (N) Balanced (M:F) Unbalanced (M:F) CGH/MLPA result Inferred karyotype CPM Control (173) 171 (86:85) 2 (1:1) Gain of X 47, XXX† No del(X/Yq) 46,XY,del(Yqter) Unk. SGA (101) 89 (39:50) 12 (4:8) Gain of 7  47,XX,+7/46,XX† Yes Gain of 7 47,XY,+7/46,XY† Yes Gain of 2 47,XX,+2/46,XX† Yes Gain of 13 47,XX,+13/46,XX† Yes dup(7q),del(Xp) 46,XX,der(X) t(X;7)(p22.2;q21.2)/46,XX Yes del(4q),dup(4p) 46,XY, der(4)del(4)(q34.2), dup(4)(p16.3p15.31)/46,XY Yes Gain of 10, Gain of X 48,XXX,+10/47,XXX Yes‡    N/A 47,XY,+2/46,XY Unk. N/A 47,XX,+i(15q)/46,XX Unk. N/A 47,XX,+16/46,XX Yes N/A 47,XX,+16/46,XX Yes N/A 47,XY,+16/46,XY Yes †Cases published in Robinson et al. 2010 (51); ‡Trisomy 10 is confined to the placenta (CPM), but trisomy X is constitutional. CGH, comparative genomic hybridization; CPM, confined placental mosaicism; MLPA, multiplexed ligation-dependent probe amplification; N/A, not available (cases were only screened by microarray); Unk., unknown/unable to confirm.  Of the cases with successful follow-up (10/12), all abnormalities in SGA placentas were determined to be CPM based on microsatellite genotyping (Table 2.2). Four of these cases were previously published19, however 8 are new and confirm the association between CPM and SGA. Of the 9 cases with available maternal DNA, uniparental disomy (UPD) was excluded in the diploid cell population from all but one previously-published case with CPM for trisomy 2 and probable upd(2)mat (51). The incidence of aneuploidy did not differ between cohorts (2/136 vs.  44 0/37 controls and 7/71 vs. 5/30 SGA in the Vancouver and Toronto cohorts, respectively). Overall, our cohorts had high maternal ages (Table 2.1). Among the SGA cases, maternal age tended to be higher in pregnancies associated with CPM than those without a placental aneuploidy (mean: 36.7 and 35.0 years, respectively; Supplementary Table 2.3), though this difference was not significant (p>0.05, Student’s t-test).  2.3.2 Load of CNVs does not differ between SGA and control placentas To explore the role of placental CNVs in in utero growth, 114 euploid placentas from control and SGA newborns were assessed using high-density microarrays (Figure 2.1). We found one SGA case (PM324) with mosaicism for 8 large 2-4 Mb duplications in the placenta (Supplementary Figure 2.3). As the combined level of aneuploidy exceeded 27 Mb, it was an outlier that was excluded from further comparisons so as to not bias results; we instead considered it as an additional case of placental segmental aneuploidy. Due to significant differences in load of CNVs between the different array platforms (Supplementary Table 2.4), we performed case-control comparisons within each cohort independently. We found no difference in total number or cumulative extent (bp) of CNVs per placenta, except for a greater cumulative bp of rare CNVs in SGA placentas in the Vancouver cohort (p=0.03, Mann-Whitney U test) (Table 2.3). When comparing these measures by gains and losses separately, there were also no significant differences (Table 2.3).    45 Table 2.3: Summary of load of CNVs in control and SGA placentas  Vancouver Cohort Toronto Cohort  Control (N=24)            SGA† (N=28) Control (N=37)      SGA (N=25) CNVs (N) 17 (11-25) 16 (9-27) 35 (20-57) 32 (22-52)     Gains     7 (1-11)       7 (1-14)      17 (9-38)     15 (8-36)     Losses     10 (4-20)     9 (3-17)     18 (9-33)     17 (10-29) Rare CNVs (N) 4 (1-10) 4 (2-10) 7 (1-29) 6 (1-19)     Gains     1 (0-6)     2 (0-6)     4 (0-22)     3 (0-16)     Losses     3 (0-7)     3 (0-9)     3 (0-12)     3 (0-6) Cumul. size (Mb) 1.22 (0.44-3.43) 1.59 (0.57-5.12) 3.20 (1.25-7.86) 3.17 (1.63-5.80)     Gains      0.57 (0.03-1.32)     0.86 (0.03-2.85)     2.36 (1.06-6.97)     2.05 (0.97-5.11)     Losses     0.65 (0.06-2.92)     0.72 (0.11-3.45)     0.84 (0.18-2.33)     1.12 (0.36-4.19) Cumul. size rare (kb) 219 (10-902) 327 (74-864)* 893 (38-3,652) 825 (19-3,329)     Gains     100 (0-781)     197 (0-819)     678 (0-3,460)     516 (0-2,937)     Losses     119 (0-485)     130 (0-517)     1,570 (0-5,776)     1,373 (0-4,516) †excludes outlier PM324. *p<0.05, Mann-Whitney U-test. All values reported as mean (range). Cumul., cumulative.  As larger CNVs are more likely to be impactful, we compared CNV size across all placentas in each group. In the Vancouver cohort, CNVs were larger in SGA placentas (p=0.002, Mann-Whitney U test; Supplementary Figure 2.4). When considering CNV gains and losses separately, only the losses were significantly larger (p=0.010, Mann-Whitney U test; Supplementary Figure 2.4). When separated by sex, the larger CNV sizes in SGA were significant only amongst females (Supplementary Figure 2.5). There were no significant differences between groups in the Toronto cohort. To further assess whether SGA placentas had a greater CNV load, we compared the number of gains or losses per placenta at size bins ranging from <15 kb to >3 Mb in all CNVs or only in rare CNVs between groups. There were no consistent differences between SGA and  46 control placentas. SGA placentas in the Vancouver cohort had fewer small losses (<15 kb, p=0.002; Mann-Whitney U-test), and those in the Toronto cohort had more large losses (500 kb-1 Mb, p=0.001; Mann-Whitney U-test). Both of these findings withstood multiple test corrections at a Bonferroni-corrected p-value threshold of p<0.005, but were not observed in rare CNVs (Figure 2.2).    Figure 2.2: Sizes of placental CNVs from control and SGA pregnancies Plots depict the mean number of CNVs per study group at different size bins in the Vancouver and Toronto cohorts, in all CNVs or exclusively rare CNVs, and separated by gains and losses. Overall, there is no consistent difference in the sizes of CNVs between SGA and control placentas. p-values calculated by Mann-Whitney U-test.     All<15kb15−30kb30−50kb50−100kb100−300kb300−500kb500kb−1Mb1−2Mb2−3Mb>3Mb01230123N CNVs (group mean)Rare <15kb15−30kb30−50kb50−100kb100−300kb300−500kb500kb−1Mb1−2Mb2−3Mb>3MbAll<15kb15−30kb30−50kb50−100kb100−300kb300−500kb500kb−1Mb1−2Mb2−3Mb>3Mb024024RareGainLoss<15kb15−30kb30−50kb50−100kb100−300kb300−500kb500kb−1Mb1−2Mb2−3Mb>3MbControl SGAVancouver Cohort Toronto Cohort****** p<0.005 47 2.3.3 Candidate CNVs identified in SGA placentas We next focused on rare CNVs ≥200 kb as these may be most likely to contribute to the SGA phenotype. There were 34 large rare CNVs present in SGA placentas and 53 in controls. CNVs with potential roles in placental function and/or fetal growth were identified in 5.7% (3/53) of SGA placentas but not in controls (0/61). The SGA cases carrying a candidate CNV were all isolated SGA without maternal PE. The 3 candidate CNVs were categorized as variants of uncertain significance (VUS)-likely pathogenic and impact the functionally relevant genes IHNBB, HSD11B2, CTCF, and CSMD3 (Table 2.4). These were confirmed by qPCR to be present in both placenta and cord blood, thus were not confined to the placenta.   Table 2.4: Candidate CNVs with clinical relevance to SGA identified in study placentas Case ID Sex Study group Genomic coordinates (hg19) Size (kb) CNV type Genes Category CPM 7665  Female SGA 2:121,092,278-121,914,455 822 Gain INHBB, GLI2 VUS-likely pathogenic No 6234  Female SGA 16:67,150,183-67,615,830 466 Loss HSD11B2, CTCF, 21 others VUS-likely pathogenic No 10506 Female SGA 8:112,947,262-116,124,691 3,177 Loss CSMD3 VUS-likely pathogenic No CPM, confined placental mosaicism; VUS, variant of uncertain significance.     48 2.3.4 No difference in total, placental-enhanced, or imprinted genes involved in placental CNVs To investigate the potential impact of CNVs, we compared the number of genes involved in CNVs per case. We found no differences in the Vancouver cohort, however there was a trend for more genes affected by losses in SGA placentas in the Toronto cohort (p=0.049, Mann-Whitney U-test; Figure 2.3). There were no significant differences when focusing on rare CNVs.    Figure 2.3: Total number of genes impacted by placental CNVs from control and SGA pregnancies The cumulative total of unique RefSeq genes impacted by CNVs for each case in the Toronto and Vancouver cohorts are shown, separated by all CNVs or exclusively rare CNVs, and by gains and losses. Toronto cohort SGA placentas had slightly more genes affected by losses than controls. A similar trend was found in Vancouver cohort, but the difference was not statistically significant. p-values calculated by Mann-Whitney U-test.    49 We did not find an enrichment of genes with enhanced placental expression in SGA CNVs, however there were more losses of placental-enhanced genes in controls in the Toronto cohort (p=0.02, Fisher’s exact test; Supplementary Table 2.5) that was not reproduced in the Vancouver cohort. Gains impacting ERVV-1 and ERVV-2, and CNVs impacting several PSG family genes, a region known to be copy number variable in the human population (289), were common in both cases and controls. We did not find any significant enrichment of imprinted regions in placental CNVs from SGA cases (Supplementary Table 2.6). Several common CNVs impacting imprinted regions were recurrent, including placental imprinted DMRs near SPRN and CYP2E1 (Supplementary Table 2.7). CNVs deemed as rare were also recurrent, including gains impacting KCNK9 and the DMR near PRMT2 (Supplementary Table 2.7). One rare CNV was present uniquely in a SGA case, arr[hg19] 22q11.21(19,931,668-19,980,300)x1, overlapping the placental-specific imprinted DMR and coding sequence of ARVCF. One other CNV resulted in a deletion of the INS gene in a control: arr[hg19] 11p15.5(2,170,670-2,199,458)x1 (Supplementary Table 2.7).  2.3.5 No significantly enriched gene pathways in SGA CNVs Out of 1,872 GO and KEGG pathways with genes involved in rare CNVs, we did not find any significantly enriched pathways in SGA CNVs (all FDR>0.4). 8 pathways were enriched at a nominal p<0.05, the top being “negative regulation of cell cycle” (p=0.031), and 7 were deficient (Supplementary Table 2.8). Investigating gains and losses separately, no enriched pathways were identified (all FDR>0.4). 10 pathways were enriched in SGA gains at a nominal p<0.05, the top being “regulation of cellular response to stress” (p=0.009), and three pathways were deficient in SGA gains (Supplementary Table 2.8).  50 2.4 Discussion In this study, we investigated the contribution of genomic imbalances in the placenta to poor fetal growth. In our otherwise low-risk population, we found that CPM involving trisomy or large segmental aneuploidy was present in 11.9% of SGA cases, or 12.7% when including the case with duplications totaling >27 Mb. Placental aneuploidy was present at similar rates in SGA whether or not maternal PE was also present (isolated SGA: 12.8%, SGA with PE: 9.7%), although a greater sample size is needed to accurately compare these incidences. The significant association of trisomy CPM with SGA/FGR confirms previous reports (51,134,135,138,277), however we have additionally identified cases of CPM of large segmental aneuploidies contributing to SGA, including a dup(7)(q21.2q36.3), del(X)(p22.2) likely deriving from a X;7 translocation event, and a case with dup(4)(p16.3p15.31), del(4)(q34.2). Although CPM can occur in healthy pregnancies (138–141), only non-mosaic aneuploidies affecting the sex chromosomes were identified in our controls.  While the incidence of placental aneuploidy associated with SGA in this study is comparable to past reports (51,134,135), it is expected to be population-dependent. The frequency of trisomy, and thus CPM, increases with advanced maternal age, which is also a risk factor for SGA. Indeed, we found that maternal age tended to be higher in SGA pregnancies with CPM (mean: 36.7 y) than those that were chromosomally-balanced (mean: 35.0 y). Conversely, CPM should contribute to fewer cases of SGA in populations with high rates of other risk factors for SGA, such as maternal smoking or poor nutrition (74,290). A higher CPM incidence is also expected using a stricter definition of FGR rather than SGA, e.g. fetal weight <3rd percentile or by using biomarkers like placental growth factor (PlGF) in maternal serum that are predictive of placental-mediated FGR (291). Although we could not measure maternal PlGF levels, our SGA  51 group was likely enriched for cases of pathological growth restriction as a large proportion of cases were <3rd percentile (68% Toronto cohort, 48% Vancouver cohort) and the majority of cases in the Vancouver cohort met criteria for FGR. Overall, we could not confirm previous reports finding decreased (232) or increased (234) load of CNVs in SGA placentas compared to controls. Small sample size may explain these discrepancies, as both past studies had <10 cases per group. With greater sample size and low incidence of other risk factors in our population, we were well poised to detect genetic contributors to SGA. Although we identified trends that suggest that some SGA placentas have an increased load of large CNVs, our findings did not support that placental CNVs commonly contribute to SGA. We also did not find significant differences in number of total or placental-expressed genes or imprinted regions in CNVs, which suggests that either these are not major drivers of poor fetal growth in our cohort or their effects are subtler than we had power to detect.  Nonetheless, a candidate VUS-likely pathogenic germline CNV was identified in 5.7% of SGA placentas in this study, all of which were SGA in the absence of maternal PE. This incidence is similar to past studies of prenatal samples, which identified pathogenic CNVs in 3-7% of cases of isolated FGR with normal karyotypes (149–151). Case 7665 has a duplication of INHBB, which encodes a subunit for the activin and inhibin proteins that play important roles in trophoblast growth and invasion (292,293), and altered mRNA or protein levels of these molecules are associated with miscarriage, severe PE, and FGR (174). Case 6234 has a deletion encompassing HSD11B2 and part of CTCF. HSD11B2 is highly expressed in placental trophoblast cells, and encodes 11β-HSD2, which regulates fetal exposure to maternal glucocorticoids (294). Reduced placental HSD11B2 expression or 11β-HSD2 protein levels has been associated with FGR (268,295–297), and patients with rare mutations in HSD11B2 have  52 significantly lower birth weight (298). CTCF is a highly-conserved transcription factor, and rare loss-of-function variants or deletions of the gene are associated with low birth weight, postnatal growth retardation, microcephaly and intellectual disability (299). Case 10506 had a 3 Mb deletion encompassing CSMD3, which is reported to be intolerant to loss-of-function variants (upper bound o/e=0.3 in gnomAD (300)), and Csmd3 knockout mice display lower body length and body fat (301).   2.4.1 Strengths and limitations This is the first study to our knowledge to characterize both aneuploidy and copy number variants in the placenta in association with poor fetal growth. It also contributes the largest sample evaluated for the association between placental CNVs and SGA to date. This CNV assessment was comprehensive, as we incorporated rigorous data processing following well-established pipelines, and several thorough lines of investigation to establish the copy number profile of the placenta in association with SGA, as well as potential clinical relevance of CNVs to poor fetal growth.  Due to the retrospective nature of this study, differences exist in clinical characteristics and methodologies between the cohorts and are a limitation of the study. Certain exclusion criteria used in the Toronto cohort were not available in the Vancouver cohort (e.g. infection during pregnancy), therefore we could not exclude such cases. Additionally, some cases of SGA in the Vancouver cohort were associated with maternal PE whereas all Toronto cohort cases were of isolated SGA. Aneuploidy screening methods used were also not equivalent, as MLPA cannot detect large interstitial duplications or deletions. Despite this, the Vancouver and Toronto cohorts had similar clinical characteristics (Table 2.1, Supplementary Table 2.1) and the methods to  53 screen for aneuploidy all accurately identify whole chromosome or chromosome arm abnormalities, therefore we combined the cohorts to improve our power to establish the contribution of placental aneuploidy to SGA. A limited number of placental biopsies were used to screen for aneuploidy in both cohorts, therefore it is likely that aneuploidies present at low levels or in a limited distribution in the placenta were missed. Unlike the aneuploidy assessment, we were unable to combine the two cohorts to study CNV load associated with SGA due to the significant differences between the high-density microarrays used for CNV detection. However even when assessed separately, each cohort had adequate power to identify differences at the large effect sizes described in previous reports (232,234), and testing the two cohorts independently gave us the opportunity to assess the reproducibility of our findings.   2.4.2 Research and clinical implications An appreciation for the association between placental aneuploidy and SGA/FGR is relevant for both research and clinical applications. For studies investigating the etiology of idiopathic SGA/FGR, excluding cases explained by CPM may increase the power of association studies. When identified prenatally, CPM may signify that the pregnancy is at increased risk for complications depending on the extent of the abnormality and the chromosome(s) involved. For example, CPM of trisomy 8 has low risk of complications (147), while that of trisomy 16 is associated with a high risk for FGR and PE (129,136,143,145). Additionally, there is an increased risk of UPD in the diploid cell population which can be associated with imprinting disorders; for example, upd(7)mat and upd(20)mat are associated with FGR and several long-term health complications (302,303). Reassuringly, follow-up studies of cases of CPM without  54 UPD suggest that most growth-restricted infants tend to have catch-up growth, normal neurodevelopment, and no global developmental delay (140,146,304,305). Identifying cases that were growth-restricted due to CPM can inform further long-term outcome studies, particularly in relation to specific trisomies, to improve our understanding of the developmental trajectories and risks for complications in affected infants, and address the clinical utility of screening for CPM and UPD in cases of FGR.  Our findings also provide evidence that CNVs impacting genes relevant to growth or placental function may contribute to idiopathic SGA. In contrast to findings of aneuploidy CPM, the CNVs identified in our study were germline alterations and may therefore have clinical implications beyond birth. Future studies profiling CNVs associated with SGA or FGR may add to ours and improve the annotation of CNVs found in cases of obstetric complications, for which information is largely absent in population databases. Given the widespread use of non-invasive methods to detect placental DNA in maternal blood and the development of methods to identify CNVs from these samples (306–308), the feasibility of identifying pathogenic CNVs prenatally is increasing. This will have relevant implications for both predicting pregnancies at risk of FGR and its associated complications and for post-natal counselling if CNVs are not confined to the placenta. Additional research on the incidence and impact of CNVs on obstetric outcomes is thus needed to assess the potential clinical utility of this information.     55 2.4.3 Conclusions Overall, we find consistent evidence that trisomy and segmental aneuploidy confined to the placenta are associated with a significant proportion of cases of poor fetal growth, and that rare germline CNVs overlapping genes of functional interest may also underlie a subset of idiopathic SGA cases. Together, these genomic imbalances may explain approximately 18% of SGA cases in our study population, and additional studies to evaluate the clinical utility of screening for these abnormalities are warranted. Increased placental CNV load may not commonly impact fetal growth, however studies with larger sample sizes may help elucidate whether subgroups of SGA/FGR are linked to placental CNV load.  56 Chapter 3: Confined placental mosaicism of multiple de novo CNVs associated with fetal growth restriction: A case report  A version of this chapter has been published (see Preface for contribution details): Del Gobbo GF, Yuan V, and Robinson WP. Confined placental mosaicism involving multiple de novo copy number variants associated with fetal growth restriction: A case report. American Journal of Medical Genetics Part A. 2021. [published online ahead of print] doi:10.1002/ajmg.a.62183.  3.1 Introduction Copy number variants (CNVs) are an important source of genetic variation in humans. The majority of CNVs are small; only about 3% of healthy adults carry a large rare CNV >1 Mb (103). This rate is higher in populations with congenital abnormalities, developmental delay, or neurodevelopmental disorders (309). The occurrence of several large rare CNVs in one individual is extremely rare even in clinical populations. Large chromosomal aberrations are common in early development (95), however abnormal embryos are typically not viable unless mosaicism with a normal cell population occurs and the abnormal cells are mainly restricted to extraembryonic tissues (310). This confined placental mosaicism (CPM) may impact placental function and lead to poor pregnancy outcomes like fetal growth restriction (FGR)(310). We report a novel case of CPM involving eight 2.4-3.9 Mb de novo duplications associated with FGR. We explore the potential of these CNVs to explain FGR and possible mechanisms of origin.     57 3.2 Methods Ethics approval was obtained from the University of British Columbia/Children’s and Women’s Health Centre of B.C. Research Ethics board (H17-01545). This case (PM324) was identified in Chapter 2 from a cohort of placentas from control and small-for-gestational age (SGA; birth weight <10th percentile) pregnancies profiled for CNVs using the Infinium Omni2.5-8 BeadChip array (Illumina, San Diego, USA). Due to case deidentification, minimal clinical data was available. Ascertainment was based on prenatal diagnosis of symmetric FGR of unknown cause (38). The mother was of normal BMI and did not smoke. A male infant was born at 40 weeks gestation with a birth weight of 2600 g (<1st percentile, adjusted for sex and gestational age (53)). The course of the pregnancy was otherwise normal. The placenta was <3rd percentile in weight, and histological exam showed mildly immature villi for the gestational age, but was otherwise unremarkable.  Samples of chorionic villi (vil), amnion, and chorion were obtained from four distinct placental cotelydons (sites 1-4), in addition to umbilical cord. Part of each sample of chorionic villi was enzymatically digested to produce samples enriched for the trophoblast and mesenchyme of the villi (51). In addition to the two samples (vil1, vil4) previously analyzed as part of the study in Chapter 2, DNA from vil2 and vil3 was screened for CNVs using the Omni2.5-8 array following the same methods (see Chapter 2, Section 2.2.4). Genotyping of microsatellite loci within the duplicated regions in all available tissues was used to confirm array findings, determine parental origin, and assess level of mosaicism (51) (Supplementary Methods; Supplementary Table 3.1). DNA from maternal blood was used to assess maternal genotype.  Imprinted genes, placental imprinted differentially methylated regions (DMRs), and genes with elevated placental expression were identified as described in Chapter 2, Section 2.2.6.  58 Coordinates of segmental duplications and repeat DNA were accessed from the genomicSuperDups and RepeatMasker tables from the UCSC Browser (311); fragile sites from the HumCFS database (312); and placental partially-methylated domains (PMDs), blocks of low-methylated DNA characteristic of the placental epigenome, as previously described (313). Enrichment of elements near breakpoints was assessed by permutation tests using the regioneR package in R, with 10,000 permutations selecting random non-overlapping regions of the same size in the genome. To determine potential alterations in DNA methylation (DNAme), DNA from vil1 and vil4 were assessed on the Infinium MethylationEPIC BeadChip (Illumina), along with chorionic villus samples from 19 healthy term pregnancies. Data was processed as described (313) and methylation beta (β) values were extracted for DNAme analysis.  3.3 Results In Chapter 2, we identified a placental chorionic villus sample (PM324 vil1) containing eight 2.4-3.9 Mb interstitial duplications in seven chromosomes (Table 3.1). Microarray assessment of three additional samples from the placenta (vil2-vil4; Figure 3.1a) suggested absence of these or any other large CNVs. Microsatellite genotyping of all extraembryonic samples confirmed that the proportion of cells containing each independent duplication were similar (Supplementary Table 3.2), therefore we concluded that they likely co-occurred in the same cells. Averaging estimates across all loci tested indicated that vil1 had ~60% abnormal cells, with the trophoblast more affected than the sample enriched for mesenchyme (72% and 22%, respectively; Figure 3.1b). Additionally, low levels of abnormal cells (<10%) were estimated in site 3, nearest to site 1 (Figure 3.1a,b). The amnion and umbilical cord, most similar  59 in developmental origin to fetal tissues, were unaffected, suggesting that the duplications were likely confined to the placenta (Figure 3.1b). One duplication involved the maternal chromosome, four involved paternal chromosomes, and three were uninformative for parental origin (Table 3.1). All of these findings suggested that these CNVs arose de novo in the embryo.  60 Table 3.1: Eight large duplications present in a mosaic state in case PM324 placenta Genomic coordinates (hg19) Cytogenetic band Size (Mb) Parental chromosome Genes (N) Genes of interest Chr1:200,478,352-204,413,297 1q32.1 3.93 Maternal 71 KISS1, REN, KDM5B Chr5: 169,133,115-172,752,205 5q35.1 3.62 Paternal 31  Chr6: 66,855,754-69,301,518 6q12 2.45 Unk. 0  Chr7: 65,791,671-69,249,095 7q11.21-q11.22 3.46 Unk. 15  Chr8: 92,757,374-96,311,905 8q21.3-q22.1 3.55 Unk. 29  Chr11: 43,851,111-47,385,923 11p11.2 3.53 Paternal 53 LARGE2 Chr11: 90,310,352-93,636,999 11q14.3-q21 3.33 Paternal 16 MTNR1B, VSTM5, PRDM11, MAPK8IP1 Chr17: 48,475,076-52,011,849 17q21.33-q22 3.54 Paternal 25  Unk., Unknown  61   Figure 3.1: Estimated percentage of cells carrying the eight duplications in available samples from PM324 placenta and associated fetal membranes a) Schematic of tissues sampled, including chorionic villi (cv), enzymatically separated trophoblast (tro) and mesenchyme (mes) from villi, chorion (ch), and amnion (am) from four distinct locations in the placenta (sites 1-4), and umbilical cord. Circles are not to scale. b) Mean percentage of abnormal cells in each sample calculated from all informative microsatellite loci tested within the duplications. Error bars indicate standard deviation.  Among the eight CNVs, >27.4 Mb was duplicated (Table 3.1). The CNVs were absent from population controls (103,314), and did not overlap known microduplication syndrome loci. One pathogenic and seven likely pathogenic duplications overlapped four of the CNVs (1q32.1, 5q35.1, 7q11.21q11.22, 11p11.2) by at least 50% (Supplementary Table 3.3) (315–317). Of the associated cases, only one with a likely pathogenic 1.17 Mb duplication in 5q35.1 showed evidence for poor growth (Supplementary Table 3.3). In total, 240 genes were involved in the  62 multiple duplications (Table 3.1), 40 of which were disease-associated in OMIM, and several were highly expressed in placenta (KISS1, REN, LARGE2, MNTR1B, and VSTM5). One duplication overlapped placental-specific imprinted DMRs near PRDM11 and MAPK8IP1 (Table 3.1). To explain the simultaneous occurrence of eight duplications, we searched for features that might be enriched around (<100 kb) the 16 CNV breakpoints. These were not associated with fragile sites, early- or late-replicating regions, or placental PMDs (p>0.05). There were no pairs of segmental duplications near CNV breakpoints, nor was there any enrichment of segmental duplications or Alu, LINE-1, or LTR repetitive elements (p>0.05).  To explore whether an unusual epigenetic profile may have contributed to genomic instability or impacted placental function, we compared DNAme in vil1 (containing CNVs) to vil4 (balanced) and 19 term controls. DNAme in vil1 was not distinct based on genome-wide principal components analysis, sample-sample pairwise correlations, overall methylation β-value distribution, nor DNAme of PMDs (Supplementary Figure 3.1). On average, DNAme in the duplications tended to be lower in vil1 compared to vil4 and term controls (Supplementary Figure 3.1c-d; Supplementary Table 3.4).  3.4 Discussion We describe the first example of multiple large (>1 Mb) de novo duplications identified in the placenta from an infant with FGR. The duplications were mosaic, impacted localized regions of the placenta, and involved both parental chromosomes, indicating a post-zygotic origin. As levels were highest in trophoblast, and enzymatically-separated mesenchyme retains up to 50% trophoblast cells (313), we presume the duplications are confined to the trophoblast. The  63 consistency of the level of mosaicism among duplications within individual samples suggests that they arose simultaneously in one cell early in development.  Chorionic villus trees grow clonally from a few precursors shortly after implantation into the maternal uterus (19,318). Because abnormal cells were present in two separate sampling sites, representing two different cotyledons, but absent from others, the mutational event must have occurred prior to primary villus formation. Because the abnormal cells are present in a relatively large patch of the placenta, we estimate that the mutational event most likely occurred in a trophectoderm cell after blastocyst formation, or shortly prior to blastocyst formation, with the abnormal cells being subsequently restricted to the trophectoderm. The apparent patchy distribution of mosaicism is expected given the placental tree structure, and does not allow inference of any selective growth advantage or disadvantage of the abnormal cells. The duplications may have impacted placental function and thereby fetal growth, as some relevant genes were duplicated, including KISS1, involved in trophoblast migration and angiogenesis and over-expressed in preeclampsia (319–321),  and REN, dysregulated in preeclampsia and involved in trophoblast proliferation (322). One paternal duplication involved polymorphic, maternal-imprinted placental DMRs associated with PRDM11 and MAPK8IP1 (251), and one duplication overlapped a likely pathogenic CNV in a patient with poor growth. Despite these lines of evidence, much of the placenta was chromosomally normal and it remains possible that other unidentified factors contributed to the severity of FGR in this case. The cause of this unusual multi-CNV event is unclear. Lack of evidence for large homologous sequences around CNV breakpoints argues against homologous recombination-based mechanisms. DNAme in vil1 containing the duplications was unaltered, although this does not exclude that epigenetic defects early in development may have been involved, as we tested  64 placental tissue after birth. Due to limited microarray probe density, we could not determine exact coordinates of the CNV breakpoints to perform sequence analysis to identify signatures of non-homologous, replication-based mechanisms of CNV origin. The occurrence of eight large duplications of consistent size is nonetheless remarkable, and there are few similar reports. Chromoanagenesis may generate multiple large CNVs, however the limited number and dispersal of the present duplications across several chromosomes does not fit with known molecular features of chromoanagenesis (323). Recently, the presence of 4-9 de novo CNVs, mainly duplications >100 kb, was reported in 5 of 60,000 individuals from a clinical population (324). These multiple de novo CNVs were associated with replication-based mechanisms, evidenced by short microhomologies and microhomeologies near breakpoints, and mosaicism was not observed (324). Another case of an SGA infant was reported with a placenta carrying 3 “partial trisomies”: a 22 Mb dup(6)(p22.3pter), a 5.8 Mb dup(9)(q34.13), and a 22 Mb dup(21)(q21.2qter), present in only one of five placenta biopsies (325). The alterations were all terminal, in contrast to the smaller interstitial duplications we identified.  This case is unique and relevant to the study of the diversity of genomic abnormalities in humans. Because mosaic abnormalities may persist in the placenta even when the fetus is normal, abnormalities such as this one, although rare, may be more prevalent in placental tissues. For example, this case was found amongst 54 SGA placentas screened for CNVs in Chapter 2. Future studies profiling CNVs and other genomic alterations in the placenta should consider testing multiple distinct regions to further explore such mosaicism. With increasing use of non-invasive testing to detect fetal genomic abnormalities from placental DNA in maternal blood, it is important to understand the diversity of genomic abnormalities in the placenta, how often they may be confined to the placenta, and their incidence in normal and uncomplicated pregnancies.  65 Chapter 4: No evidence for association of MTHFR 677C>T and 1298A>C variants with placental DNA methylation  A version of this chapter has been published (see Preface for contribution details): Del Gobbo GF, Price EM, Hanna CW, Robinson WP. No evidence for association of MTHFR 677C>T and 1298A>C variants with placental DNA methylation. Clinical Epigenetics. 2018;10:34-1.2018.  4.1 Introduction One-carbon metabolism (OCM) is a fundamental biochemical pathway that activates and transfers one-carbon units for purine synthesis and methylation of DNA, proteins, and lipids, making it important for processes such as DNA synthesis, cellular division, and proliferation. Both functional and dietary deficiencies are thought to contribute to altered OCM cycling. Several B vitamins act as substrates or cofactors for OCM, most notably vitamin B9 or folate, the transporter of methyl groups in OCM. Genetic variants in a central OCM enzyme, 5,10-methylenetetrahydrofolate reductase (MTHFR), have been heavily researched in association with human diseases, such as cardiovascular disease, pregnancy complications, and cancers (326–329). MTHFR catalyzes the irreversible reduction of 5,10-methylenetetrahydrofolate (5,10-CH2-THF) to 5-methyltetrahydrofolate (5-CH3-THF), which is subsequently used as the substrate for the conversion of homocysteine to methionine, catalyzed by the enzyme methionine reductase. Methionine is then used to synthesize S-adenosylmethionine (SAM), the universal methyl donor for methylation reactions, including DNA methylation (DNAme) catalyzed by DNA methyltransferases (DNMTs). As such, MTHFR is key to directing one-carbon units toward  66 DNAme reactions, which has motivated the investigation of alterations in DNAme as the mechanism underlying the association of genetic variants in MTHFR with various pathologies.  Two single-nucleotide polymorphisms (SNPs) in the MTHFR gene, 677C>T (rs1801133) and 1298A>C (rs1801131) result in reduced MTHFR function in vitro, particularly in the homozygous recessive state (330–334). These variants are common in the population; globally the variant allele frequencies are approximately 0.25-0.3 (dbSNP (335)), though frequencies vary between different populations. These variants have been associated with markers of altered OCM, such as increased levels of homocysteine and altered levels of blood folates (336–341), most consistently for the 677 variant. High-risk MTHFR genotypes (677TT and 1298CC) or variant alleles (677T and 1298C) have been found in association with a number of reproductive and developmental pathologies, including miscarriage (342,343) and neural tube defects (344–349). The 677T allele and 677TT genotype in mothers has also been associated with preeclampsia (PE), a maternal hypertensive disorder in pregnancy (181,183,350). An association between fetal (placental) MTHFR 677TT genotype and PE has been identified (182), though this is not as well studied as the maternal variants. Researchers have hypothesized that increased risk for pathology might be attributed to aberrant patterns in DNAme, resulting from altered OCM flux caused by these variant MTHFR enzymes (349,351,352). While several studies have investigated the association of the MTHFR 677C>T and 1298A>C variants with altered DNAme, results are inconsistent; some have reported associations between the high-risk homozygous MTHFR genotypes and/or folate levels and altered DNAme (353–358), whereas others find no association (359–362). As gene expression, DNAme patterns, and metabolic requirements are highly variable between tissues,  67 even these conflicting results ascertained in adult, non-pregnant blood, may not generalize to pregnancy complications. The placenta is a directly relevant tissue in which to study the interaction between MTHFR variants, altered DNAme and pregnancy complications. Due to the demand for DNA synthesis, cellular division, and proliferation by the growing fetus and placenta, the requirement for folate during pregnancy increases by approximately 5-10 times the level of non-pregnant women (363). High-affinity folate receptors on maternal-facing trophoblast cells allow the placenta to transport and concentrate folate from maternal blood up to three times within the placenta (364,365), ensuring the availability of this crucial nutrient during development. Consistent with studies in other tissues, the MTHFR 677T allele is associated with reduced MTHFR enzyme function in the placenta (366). If OCM flux is impaired and DNAme patterns are altered in the placenta due to reduced variant MTHFR function, this could have implications for placental function and thus increase risk for pregnancy complications. Aberrant DNAme at imprinted regions is known to have significant impact on placental development (reviewed in (367,368)). Genome-wide or imprinted gene-specific alterations in DNAme have been noted in placental insufficiency complications of fetal growth restriction (FGR) (50) and in early-onset PE (47,49,369). Additionally, the placenta is a tissue that may be more likely to exhibit altered DNAme in response to reduced MTHFR enzyme function. The placenta exhibits a high degree of within- and between-individual variability in DNAme (19,370), suggesting that it may be tolerant to changes in DNAme, allowing this organ to adapt to environmental conditions (19,370,371).   To date, no studies have investigated the association between DNAme in the placenta and the MTHFR 677TT and 1298CC high-risk variants. This may provide insight in to how the MTHFR variants have been previously associated with pregnancy complications, and potentially  68 help to resolve the currently conflicting literature investigating the association between these variants and DNAme in other tissues. In this study we evaluated whether fetal high-risk MTHFR genotypes were more prevalent in pregnancy complications of PE, FGR, and NTDs using 303 placental DNA samples. The DNAme patterns of 30 placentas were heavily profiled using both site-specific and genome-wide techniques, including the Infinium HumanMethylation450 array and repetitive DNA methylation, to understand the relationship between MTHFR 677TT and 1298CC high-risk genotypes and DNAme in the placenta.  4.2 Methods 4.2.1 Ethics approval and sample collection Ethics approval for this study was obtained from the University of British Columbia/Children’s Hospital and Women’s Health Centre of British Columbia Research Ethics Board (H04-70488, H10-01028). Placentas were collected from term deliveries at BC Women’s Hospital & Health Centre and from 2nd trimester stillbirths, elective terminations, and spontaneous abortions through the Embryo-Fetal Pathology laboratory. Cases with a prenatally-identified chromosomal abnormality were excluded. A minimum of two distinct sites were sampled from the fetal side of each placenta after fetal membranes (amnion and chorion) were removed. Samples were washed thoroughly with PBS to remove maternal blood. DNA was extracted by a standard salting-out procedure modified from Miller et al. (372) and quality evaluated using a Nanodrop ND-1000 (Thermo Scientific). One site from each placenta was selected at random for genotyping. As DNAme varies significantly within the placenta (19,370), DNA was combined in equal amounts from at least two sites to generate a more representative sample in which to evaluate placental DNAme.  69 4.2.2 Case characteristics A total of 303 placentas were screened for MTHFR 677 and 1298 polymorphisms. These included 179 placentas from uncomplicated pregnancies, 48 from pregnancies associated with preeclampsia (PE; 28 early-onset PE, 20 late-onset PE), 21 from pregnancies associated with fetal growth restriction in the absence of maternal hypertension (nFGR), and 55 from pregnancies with a fetal neural tube defect (NTD) (Table 4.1). PE was defined according to the Society of Obstetricians and Gynecologists of Canada criteria as pregnancies with i) gestational hypertension (BP>140/90mm Hg) and proteinuria (>300g/day) arising after 20 weeks gestation; or ii) pre-existing hypertension with superimposed gestational hypertension, proteinuria and/or one or more adverse maternal or fetal conditions, or iii) gestational hypertension without proteinuria, with one or more adverse maternal or fetal conditions (52). PE was subdivided into early-onset preeclampsia (EOPE), defined as a diagnosis of PE before 34 weeks gestation, and late-onset preeclampsia (LOPE), a diagnosis of PE after 34 weeks gestation (83). FGR commonly co-occurs with PE, and was defined as birth weight <3rd percentile accounting for both fetal sex and gestational age (GA) (53), or birth weight <10th percentile with additional clinical findings placental insufficiency such as uterine artery notching, absent or reversed end diastolic velocity on Doppler ultrasound, or oligohydramnios. Normotensive FGR (nFGR) was defined as unexplained FGR without the presence of maternal hypertension. NTDs were defined as a fetus diagnosed with spina bifida, anencephaly or encephalocele on ultrasound and/or fetal autopsy.      70 Table 4.1: Clinical characteristics of cases  N Sex; N male (% male) Gestational age (weeks); median (range) Maternal age (years); median (range) Control 179 85 (47%) 39.6 (19.4-41.9) 34.4 (23.8-42.7) EOPE 28 17 (61%) 32.7 (23.6-38.4)* 36.0 (19.7-42.9) LOPE 20 10 (50%) 38.5 (34.9-41.4)* 34.3 (26.1-41.5) nFGR 21 9 (42%) 36.2 (24.0-40.6)* 34.5 (26.1-42.8) NTD 55 28 (51%) 21.0 (16.7-23.7)* 30.4 (17.7-40.6)* *p<0.05, calculated in comparison to control group by Fisher’s Exact test for categorical variables and Mann-Whitney test for continuous variables. EOPE, early-onset preeclampsia; LOPE, late-onset preeclampsia; nFGR, normotensive fetal growth restriction; NTD, neural tube defect.  4.2.3 MTHFR genotyping Placental DNA was genotyped for the MTHFR 677 and 1298 polymorphisms using pyrosequencing. Primer sequences and reaction conditions can be found in Supplementary Table 4.1. 5 uL of PCR product was sequenced on a Pyromark Q96 MD Pyrosequencer (Qiagen) using standard protocols (373). A subset of the genotyping results from the NTD group (N=36) have been published elsewhere (374).  4.2.4 Population stratification Minor allele frequencies for the MTHFR 677 and 1298 SNPs vary significantly between different populations (105,375–377), as do the prevalence of NTDs, PE, and FGR (378). Frequencies of both high-risk MTHFR genotypes vary by ethnicity and geography, indicating that selective pressures have influenced its frequency (376,379). Therefore, prior to performing a genetic association analysis, we aimed to assess whether our pregnancy complication groups were matched for ancestry. Maternal self-reported ethnicity was available for only 67% of cases, and no information about the father’s ethnicity was available. We thus used a panel of 57 ancestry informative marker SNPs (AIMs) (380–382) that were developed to distinguish  71 between African, European, East Asian, and South Asian ancestry to infer ancestry of study samples and assess population stratification along three major axes of variation.  277 placental villus DNA samples were successfully genotyped at 53 AIMs using the Sequenom iPlex Gold platform by the Génome Québec Innovation Centre at McGill University, Montréal, Canada with a call rate >0.9 for SNPs and samples. Multidimensional scaling (MDS) with k=3 dimensions was performed in our study samples (N=277) in addition to individuals (N=2,157) from African, East Asian, European, and South Asian populations from the 1000 Genomes Project (1kGP)(105) using 50 of the AIMs genotypes that were available in both cohorts. This method allows 1kGP samples to be used as ancestry reference populations for our admixed population and has been previously used to identify ancestry outliers (383,384). The first three MDS coordinates were extracted for each sample and used to describe ancestry along a continuum rather than in discrete groups. We believe this better reflects ancestry in admixed populations such as that in Vancouver, as well as potentially better representing variation within an ancestry group. Further description of this method is included in Supplementary Methods.  4.2.5 MTHFR genotype and DNAme To assess the association of MTHFR genotype with DNAme, a subset of 30 control or mild pregnancy complication placentas were selected for in-depth DNAme profiling, hereafter referred to as the placental DNAme samples. None of these placentas had chromosomal abnormalities, as confirmed by MLPA in one or more sites per placenta. The effect of each MTHFR SNP was assessed independently by comparing 10 placentas with reference genotype at both MTHFR SNPs (677CC + 1298AA), 10 placentas with the 677TT high-risk genotype in combination with the reference 1298AA (termed “high-risk 677”), and 10 placentas with  72 1298CC high-risk genotype in combination with the reference 677CC genotype (termed “high-risk 1298”). The reference, high-risk 677 and high-risk 1298 groups were matched by sex, gestational age, birthweight, maternal reported ethnicity (Table 4.2).  As the high-risk genotypes are relatively rare in our population and we additionally excluded heterozygotes at either locus, some mild pregnancy complication cases (4 LOPE without FGR and 1 nFGR) were included to obtain sufficient numbers in each genotype group. We have previously found no evidence for altered placental DNAme associated with these phenotypes compared to controls (47).  Table 4.2: Clinical characteristics of placental DNAme cases  N Sex;  N male (%) Gestational age (weeks); median (range) Maternal ethnicity; N Caucasian (%)  Birth weight (s.d.); median (range) Reference  (677CC + 1298AA) 10 4 (40%) 39.0 (36.1-41.6) 8 (80%) 0.055 (-1.13-1.40) High-risk 677 (677TT + 1298AA) 10 4 (40%) 37.9 (34.6-40.3) 6 (60%) -0.13 (-2.97-0.70) High-risk 1298 (677CC + 1298CC) 10 5 (50%) 39.4 (38.6-40.7) 8 (80%) 0.005 (-1.61-2.20) p-values calculated in comparison to reference group by Fisher’s Exact test for categorical variables and Mann-Whitney test for continuous variables; SD, standard deviation.   4.2.6 Infinium HumanMethylation450 Beadchip (450k) array Combined placental DNA from the 30 placental DNAme samples described in Table 4.2 was purified using the Qiagen DNeasy Blood & Tissue kit (Qiagen) and 750 ng was bisulfite converted using the EZ DNA Methylation kit (Zymo Research) following the manufacturer’s protocols. Samples were processed following the Illumina Infinium HumanMethylation450 BeadChip protocol (385) and scanned using the Illumina HiScan 2000. Raw intensity was read into Illumina Genome Studio software 2011.1 and background normalization was applied. Data  73 processing was performed as described in Price and Robinson (386), including sample quality checks, probe filtering, data normalization and batch correction. This processing pipeline resulted in a final N=442,355 CpG sites from the 450k array for analysis.  4.2.7 Repetitive DNA methylation In addition to the 450k array, genome-wide DNAme was also assessed using repetitive Alu, LINE-1 and rDNA sequences (246) by pyrosequencing in the 30 placental DNAme samples. These sequences are dispersed throughout the genome, allowing DNAme to be measured at many sites using a single assay per repetitive sequence. DNAme at these three repetitive DNA sequences has been shown to be altered in association with different environmental or biological factors (387–391). 300ng of purified combined placental villi DNA was bisulfite converted using the EZ DNA Methylation-Gold Kit (Zymo Research) following the manufacturer’s protocol. Alu and LINE-1 elements were amplified using primer sets designed to complement the L1H and AluSx consensus sequences, respectively (392), and rDNA repeats were amplified using primers designed to target the rDNA promoter (393) (Supplementary Table 4.1). PCR products were sequenced on a Pyromark Q96 MD Pyrosequencer (Qiagen) using standard protocols (373). The DNAme status of each CpG dinucleotide (Alu N=3; LINE-1 N=4; rDNA N=26) was evaluated using the PyroQ CpG software (Biotage). For each assay, correlation of DNAme between CpGs was confirmed, and then an average DNAme was calculated across the CpGs within each assay in each sample.   74 4.2.8 Statistical analyses Statistical analyses were conducted in R statistical software (288). Deviation from Hardy-Weinberg equilibrium (HWE) at the two MTHFR SNPs in controls was assessed using an exact test for HWE. Differences in the distribution of ancestry MDS coordinate values between control and pregnancy complication groups (EOPE, LOPE, nFGR, NTD) were assessed using Kolmogrov-Smirnov tests. Association between frequency of MTHFR 677 TT and/or 1298 CC genotypes and pregnancy complications was assessed using a one-tailed Fisher’s exact test to test the hypothesis that there is a higher frequency of the high-risk genotypes in pregnancy complications compared to controls. For the placental DNAme samples, 450k array-wide average DNAme and percent outlier probes per sample were calculated as in Price et al. (374). Altered measures of genome-wide methylation, including 450k array-wide average, percent outlier probes, Alu, LINE-1 and rDNA methylation, were assessed using a Mann-Whitney test. All p-values from statistical tests involving multiple comparisons (ancestry MDS coordinate values, altered genome-wide DNAme measures) were corrected for multiple testing using the Bonferroni method.  450k array site-specific differential methylation was also assessed as in Price et al. (374). Briefly, a linear model with MTHFR group as the main effect and fetal sex and gestational age included as covariates was fit to every CpG on the array that passed quality checks and filtering (N=442,355). Differential methylation results were then extracted for the comparison of high-risk 677 to reference and for high-risk 1298 to reference. These comparisons were used to calculate group differences in DNAme (Δβ). Significant differentially methylated CpG sites were considered as those with a false discovery rate (FDR)<0.05 and Δβ≥0.05. Two dimension-reduction techniques were additionally used in the 450k array data: a differentially methylated  75 region (DMR) analysis, as in Price et al. (374), and an assessment of differential DNAme based on CpG density of the surrounding region. 450k probes were separated into four groups based on the CpG density: high density islands, island shores, intermediate density islands, and non-islands, defined as per Price et al. (394), and the unadjusted p-value distributions from the linear model were assessed in each CpG density group separately.  4.3 Results 4.3.1 Analysis of ancestry informative markers identifies no significant population stratification Prior to testing for the association of fetal MTHFR genotypes with NTDs or PE/FGR groups, we sought to confirm that these pregnancy complication groups were not confounded with ancestry, as the frequency of the MTHFR 677 and 1298 variants vary between different ancestry groups (375–377). Self-reported ethnicity was available from mothers for only 67% (203/303) of samples, which were predominately of European (self-reported Caucasian ethnicity) and East Asian ancestries. We thus described ancestry using coordinates 1, 2 and 3 obtained through MDS of genotypes at 50 AIMs (Supplementary Table 4.2) in 277 of our placental samples along with 2,157 samples from the 1000 Genomes Project (105) (1kGP) (Supplementary Figure 4.1). These 3 MDS coordinates were significantly different between the 4 major continental populations from 1kGP (Supplementary Figure 4.1). Furthermore, for those samples for which we had both maternal self-reported ethnicity in addition to AIMs (N=181), the 3 ancestry MDS coordinates were highly concordant with maternal self-reported ethnicity (Supplementary Figure 4.2). These findings suggest that this method is adequate to describe major patterns of genetic ancestry. There was no significant difference in the distribution of  76 ancestry MDS coordinate values 1, 2 or 3 between the NTD, PE, or nFGR pathology groups in comparison to controls (Figure 4.1). We thus concluded that our pathology groups do not show evidence of confounding by ancestry.   Figure 4.1: Distribution of ancestry derived from MDS of AIM genotypes in control and pregnancy complication placentas In N=25 EOPE, N=20 LOPE, N=18 nFGR, or N=53 NTD placentas, there were no significant differences in the distribution of ancestry MDS coordinate values compared to N=161 controls at either of the three ancestry MDS coordinates (Kolmogrov-Smirnov tests, Bonferroni-corrected p>0.05). This suggests that there is no population stratification by ancestry in the groups selected for this study. EOPE, early-onset preeclampsia; LOPE, late-onset preeclampsia; nFGR, normotensive fetal growth restriction; NTD, neural tube defect.    77 4.3.2 MTHFR genotypes are not significantly associated with placental insufficiency or neural tube defects To investigate whether the MTHFR 677TT and 1298CC genotypes were associated with PE, nFGR or NTD pathologies, we genotyped placentas at these two loci from 179 control, 28 EOPE, 20 LOPE, 21 nFGR, and 55 NTD pregnancies. Neither SNP deviated from HWE in controls (Supplementary Table 4.3). In our population of 303 placentas collected in Vancouver, Canada, the frequencies of the variant MTHFR 677T and 1298C alleles were 0.295 and 0.290, respectively. There was no significantly higher frequency of the high-risk MTHFR 677TT or 1298CC genotypes in EOPE, LOPE, nFGR or NTD cases compared to controls (Table 4.3). There was, however, a tendency for a higher frequency of MTHFR 677TT genotype in placentas from pregnancies complicated by placental insufficiency pathologies of PE or nFGR. When considered together (PE or nFGR; N=69), the higher frequency of MTHFR 677TT compared to controls was nominally significant (OR=2.53, p=0.048).    78 Table 4.3: MTHFR 677TT and 1298CC genotypes in pregnancy complications  N 677TT frequency (N) p-value† OR (95% CI) 1298CC frequency (N) p-value† OR (95% CI) Control 179 0.056 (10)  -- -- 0.101 (18) -- -- EOPE 28 0.107 (3) 0.249 2.02 (0.33-8.59) 0 (0) 1.00 0 LOPE 20 0.150 (3) 0.129 2.96 (0.48-13.1) 0.150 (3) 0.355 1.57 (0.27-6.27) nFGR 21 0.143 (3) 0.143 2.80 (0.45-12.3) 0.048 (1) 0.891 0.449 (0.01-3.16) NTD 55 0.091 (5) 0.260 1.69  (0.43-5.72) 0.073 (4) 0.809 0.70 (0.16-2.27) †p-values calculated by one-tailed Fisher’s Exact test. OR, odds ratio; CI, confidence intervals; EOPE, early-onset preeclampsia; LOPE, late-onset preeclampsia; nFGR, normotensive fetal growth restriction; NTD, neural tube defect.  4.3.3 MTHFR 677 and 1298 high-risk variants are not associated with altered genome-wide DNAme in the placenta Due to the central role that MTHFR plays in OCM, the high-risk MTHFR genotypes are often hypothesized to affect the cell’s ability to methylate DNA. We anticipated that such effects could potentially be more pronounced in the placenta due to its high demand for folate in pregnancy. We selected a subset of 30 placental samples with no, or mild pathology in which to profile DNAme using both genome-wide and site-specific approaches. The selected samples were of three MTHFR genotype groups 1) reference (N=10; MTHFR 677CC + 1298AA), 2) high-risk 677 (N=10; MTHFR 677TT + 1298CC); and 3) high-risk 1298CC (N=10; MTHFR 677CC + 1298CC). No cases with high-risk genotypes at both loci were available in our population to test.  79 First, these 30 placental DNAme samples were run on the 450k array, from which several measures of DNAme were obtained. Array-wide DNAme was calculated by taking the mean DNAme of 442,355 CpG sites in each sample. This array-wide measure of DNAme did not differ significantly between either of the high-risk MTHFR groups and the reference group (Table 4.4). Altered genome-wide DNAme might not be a characteristic of all carriers of the MTHFR variants, thus we also calculated the percentage of outlier CpG sites from the 450k array for each sample to identify individuals exhibiting outlying patterns of DNAme (395). Though there was no significant difference in outlier CpGs between the high-risk 677 and reference group (Table 4.4), there was a trend for more outlying CpG sites in the high-risk 1298 group than in the reference (Table 4.4, Bonferroni-corrected p=0.058). Next, the methylation of repetitive DNA sequences was assessed in the 30 placental DNAme samples. Repetitive DNAme assays target numerous sites in the genome that are not well covered by the 450k array, and thus give an additional measure of genome-wide DNAme. No significant alterations in the DNAme of Alu, LINE-1, or rDNA sequences were identified between either of the MTHFR high-risk genotype groups and the reference genotype group (Table 4.4). Slightly higher mean methylation was seen for the high-risk 677 group in all comparisons, though the range of values was similar. There was, however, a trend for decreased LINE-1 DNAme in the high-risk 1298 group compared to the reference group (nominal p=0.052), but this is not meaningful after correction for multiple comparisons. Overall, we find no conclusive evidence for altered genome-wide DNAme in association with the high-risk 677 or high-risk 1298 genotypes in the placenta using these DNAme measures.     80 Table 4.4: Genome-wide measures of altered DNAme in MTHFR high-risk and reference placentas  Array-wide average DNAme (β) Outlier array sites (%) Alu DNAme (%) LINE-1 DNAme (%) rDNAme (%) Reference (N=10) 0.407 (0.396-0.413)  0.661 (0.262-0.993) 18.9 (17.6-21.7) 52.8 (51.0-53.8) 19.4 (11.2-30.9) High-risk 677 (N=10) 0.405 (0.397-0.409) 0.851 (0.262-6.357) 20.1 (15.7-21.4) 53.5 (49.8-57.6) 20.0 (8.4-28.9) High-risk 1298 (N=10) 0.405 (0.395-0.413) 1.14 (0.501-3.37) 19.8 (17.5-21.6) 51.0 (46.5-55.0) 22.9 (10.2-28.9) All results reported as median (range); p-values calculated by Mann-Whitney test for the comparison of high-risk 677 or high-risk 1298 to the reference group with Bonferroni correction for multiple comparisons. β, beta-value; rDNA, ribosomal RNA genes.  4.3.4 MTHFR 677 and 1298 high-risk variants not associated with altered site-specific DNAme in the placenta The DNAme status of individual CpG sites targeted by the 450k array in association with the high-risk MTHFR genotype groups was next assessed. A linear model was fit to each CpG site to test for differential methylation by genotype group, including sex and gestational age at birth as covariates. None of the 442,355 CpG sites was differentially methylated at a FDR<0.05 Δβ>0.05 in either of the high-risk MTHFR genotype groups compared to the reference group (Figure 4.2).   81  Figure 4.2: 450k array-wide differential DNAme in MTHFR high-risk 677 and high-risk 1298 placentas.  Differential DNAme was determined using a linear model with MTHFR group as the main effect and fetal sex and gestational age as covariates. The magnitude of difference (Δβ) between high-risk and reference groups is depicted on the x-axis, and significance of the comparison (-log10(adjusted p-value)) is on the y-axis, for every CpG tested (N=442,355). A) Differential methylation between high-risk 677 and reference placentas. B) Differential methylation between high-risk 1298 and reference placentas. Neither comparison identified any CpG sites differentially methylated between the high-risk MTHFR placentas and reference placentas. FDR, false discovery rate.  Following this finding, two dimension-reduction techniques were utilized to explore whether identification of differences between MTHFR high-risk groups and controls in the 450k array data was limited due to small sample size or large number of test sites. Due to structural or functional differences, some genomic regions may be more vulnerable to the effects of a reduced ability to methylate DNA potentially caused by the presence of variant MTHFR enzymes. As such, 450k probes were separated into four groups based on CpG density of the surrounding region: high-density islands, island shores, intermediate-density islands, and non-islands.  82 Additionally, a differentially methylated region (DMR) finding tool was utilized to identify whether any DMRs existed between high-risk MTHFR genotype placentas and controls. Unadjusted p-value distributions did not show differential methylation at any of the four CpG density groups between MTHFR high-risk and reference placentas (Supplementary Figure 4.3), nor were any significant DMRs identified. Given these results, we conclude that large magnitude alterations in DNAme at CpG sites measured by the 450k array in the placenta are not commonly associated with high-risk 677 or 1298 MTHFR genotypes in our population.  4.4 Discussion Altered DNAme has been proposed as a mechanism through which MTHFR 677TT and 1298CC genotypes have been associated with pregnancy complications and other pathologies (349,351,352). In this study, we sought to investigate alterations in DNAme in association with MTHFR high-risk 677TT and 1298CC genotypes in the placenta, a crucial tissue for development of the fetus and a healthy pregnancy. Despite deeply profiling DNAme in N=10 high-risk 677, N=10 high-risk 1298, and N=10 reference placentas using a variety of measures, we did not identify evidence for altered placental genome-wide or site-specific DNAme in association with MTHFR high-risk genetic variants.  Given the fundamental involvement of OCM in activating and transporting methyl units, if the variant MTHFR alleles influence DNAme, this effect is predicted to be widespread and not gene-specific (396). By using the 450k array, we interrogated DNAme at over 440,000 sites in the placental genome, assessing specific CpG sites and also genome-wide trends. This array covers 99% of RefSeq genes and is widely dispersed across genomic features, and therefore can provide a reflection of genome-wide changes associated with specific genomic features or gene  83 regulation. No significant differences in the numerous measures of altered 450k array genome-wide or site-specific DNAme were identified, despite additionally utilizing dimension-reduction techniques to account for small sample size and large number of test sites. DNAme at repetitive DNA sequences, including Alu and LINE-1 repetitive elements and rDNA repeats, was also assessed, as they are not well covered by the array and they allow us to interrogate numerous locations in the genome in one pyrosequencing assay. The Alu and LINE-1 repetitive elements have previously been used as surrogate measures for genome-wide DNAme (246,397), and all three repetitive sequences have exhibited alterations in DNAme in certain pathologies and in response to environmental exposures (387–391). Though small sample size limited our power to detect significant differences in DNAme in this study, we aimed to mitigate this by deeply profiling the 30 placental DNAme samples using a variety of measures of altered DNAme to assess whether any differential methylation exists in association with the high-risk MTHFR genotypes. Our study cannot fully exclude that subtle differences in placental DNAme exist in association with high-risk MTHFR genotypes, or that a subset of at-risk placentas might show changes in DNAme while the groups as a whole did not. Despite this, the results from these numerous genome-wide assays reveal that at the very least, large magnitude and/or array-wide differential methylation does not commonly occur in association with high-risk MTHFR genotype in the placenta.  Our study is only the second to investigate the associations between MTHFR 677 and 1298 variants and altered DNAme using a genome-wide DNAme microarray platform, and the first to study this association in the placenta. Numerous studies have investigated altered DNAme in association with MTHFR 677 and/or 1298 variants using different measures of genome-wide DNAme and/or targeted gene DNAme, summarized in Table 4.5. Results from these various  84 studies, mainly in blood, are conflicting. Certain studies have found associations between MTHFR 677 or 1298 polymorphisms and altered DNAme; however, many do not find significant associations with altered DNAme, or only find altered DNAme in the presence of low levels of OCM nutrients (Table 4.5). Some of these inconsistencies may be explained by the use of different measures of altered DNAme (i.e. genome-wide, candidate site-specific, repetitive element DNAme) between studies, lack of multiple-test correction, use of different tissues, or inconsistencies in accounting for confounding variables. Nonetheless, the effect of the MTHFR 677 and 1298 variants on DNAme is clearly complex.    85 Table 4.5: Literature assessing associations between MTHFR 677 or 1298 variants and altered DNAme in healthy tissues Study Type of DNAme assessed: specific assay  Tissue Study Size† Results Studies finding associations with DNAme Stern et al. 2000 (353) Genome-wide: radiolabeled methyl group incorporation assay Blood 677CC: N=9 677TT: N=10 677TT associated with approximately 40% higher [3H]-methyl acceptance capacity than 677CC (p=0.04), reflecting global hypomethylation. Castro et al. 2004 (355)  Genome-wide: cytosine extension assay Blood 677CC/1298AA: N=17 677CT/1298AA: N=22 677TT/1298AA: N=9 677CT/1298AC: N=22 677CC/1298AC: N=20 677CC/1298CC: N=7 677TT associated with higher [3H]-dCTP relative incorporation compared to 677CC (p<0.05).  677TT/1298AA and 677CC/1298CC associated with higher relative incorporation than 677CC/1298AA (p<0.05). McKay et al. 2012 (352) Genome-wide: LUMA, Candidate sites (N=3): pyrosequencing Umbilical cord blood 677: N=160 1298: N=132  mother-infant pairs Maternal 677T allele associated with altered mean DNAme in IGF2 in infant cord blood (p=0.017); maternal 1298C allele associated with altered DNAme at one CpG in ZNT5 (p=0.012) in infant cord blood. No associations with genome-wide DNAme. van Mil et al. 2014 (398) Candidate sites (N=11): MassArray EpiTYPER Umbilical cord blood 677CC or CT: N=413 677TT: N=50  Maternal 677TT genotype associated with lower DNAme in infant blood at candidate CpG sites in NR3C1, DRD4, 5-HTT, IGF2DMR, H19, KCNQ1OT1, and MTHFR genes (p=0.03). Weiner et al. 2014 (357) Genome-wide: Methyl Flash Methylated DNA Quantification Kit Blood 677CC: N=40  677TT: N=40 677TT associated with significantly lower mean DNA methylation compared to 677CC (p=0.0034). Llanos et al. 2015 (358) LINE-1: pyrosequencing Female breast tissue 1298AA: N=73  1298AC or CC: N=45  1298C allele associated with lower LINE-1 methylation (OR 0.96; 95% CI 0.93-0.98). Song et al. 2016 (399) Genome-wide: Illumina 450k array Female breast tissue N=81 677T and 1298C alleles associated with differential methylation at 5 and 3 CpGs, respectively (unadjusted p-value<5.0x10-5). No sites reached significance at an adjusted p-value<0.05.     86 Study Type of DNAme assessed: specific assay  Tissue Study Size† Results Studies finding no association with DNAme Narayanan et al. 2004 (359) Genome-wide: radiolabeled methyl group incorporation assay Blood 677CC: N=90 677CT: N=84 677TT: N=25 / 1298AA: N=93  1298AC: N=77  1298CC: N=29  No altered DNAme in association with 677T or 1298C alleles. Jung et al. 2011 (400) Genome-wide: LC/MS Blood (Folic acid supplemented/placebo) 677CC: N=36/40 677CT: N=36/34 677TT: N=33/37 No altered DNAme between 3-year folic acid supplemented (0.8 mg/day) group and placebo group, and no difference in DNAme when stratified by MTHFR 677 genotype. Gomes et al. 2012 (360) Genome-wide: IMDQ kit Blood 677CC: N=72  677CT: N=39  677TT: N=12  No altered DNAme between MTHFR 677 genotype groups. Ono et al. 2012 (401) Genome-wide: LUMA Blood 677CC: N=112  677CT or TT: N=272 / 298AA: N=254  1298AC or CC: N=130  No altered DNAme in association with MTHFR 677 or 1298 variants. No interaction between genome-wide DNAme, folate intake, and MTHFR 677 or 1298 variants. Hanks et al. 2012 (402) Genome-wide: LC/MS, Candidate sites (N=7): pyrosequencing Colon  677CC: N=185  677CT: N=119  677TT: N=32  No difference in DNAme between MTHFR 677 genotype groups, even when accounting for folate biomarkers. No significant difference in DNAme at ESR1, MYOD1, IGF2, N33, MLH1, MGMT, APC genes by genotype group. de Arruda et al. 2013 (361) Genome-wide: IMDQ kit Oral epithelial cells 677CC: N=17  677CT: N=19  677TT: N=8  No difference in DNAme between MTHFR 677 genotype groups. Deroo et al. 2014 (403) LINE-1: pyrosequencing Blood N=646 women without breast cancer N=294 with breast cancer 677 or 1298 genotypes not associated with altered LINE-1 DNAme in women without breast cancer. Louie et al. 2016 (404) Candidate sites (N=3): bisulfite sequencing Sperm  677CC: N=21  677CT: N=19  677TT: N=4  677 genotype not associated with altered DNAme at MEST, H19, or IG-GTL2 imprinted differentially methylated regions.  87 Study Type of DNAme assessed: specific assay  Tissue Study Size† Results Wang et al. 2016 (362) Meta-analysis  11 studies  677: N=1147  1298: N=1053  No altered DNAme associated with 677T and 1298C alleles. Studies finding association with DNAme only with interaction with altered OCM nutrient status Friso et al. 2002 (354) Genome-wide: LC/MS Blood  677CC: N=187  677TT: N=105   677TT associated with approximately half the mean level of mCytosine than in 677CC group (p < 0.0001). This effect was driven by TT individuals with low folate status. Shelnut et al. 2004 (340) Genome-wide: radiolabeled methyl group incorporation assay and LC/MS Blood  677CC: N=22  677TT: N=19   No significant difference in DNAme between 677TT and 677CC groups. In response to 7-week folate repletion following 7-week folate depletion, significantly increased mean % change and raw change in DNAme in 677TT individuals (p=0.04 and 0.03 respectively). Friso et al. 2005 (356) Genome-wide: LC/MS Blood 677CC/1298AA: N=19 677TT/1298AA: N=72  677CC/1298CC: N=42  In presence of low folate, 1298AA associated with lower genome-wide DNAme compared to 1298AC or 1298CC genotypes (p=0.0001 and p=0.021, respectively), and 677TT/1298AA associated with lower DNAme compared to 677CC/1298AA (p<0.05) and 677CC/1298CC (p<0.0001).  In 677TT/1298AA individuals, DNAme significantly reduced in low folate vs high folate individuals (p<0.0001). Axume et al. 2007 (405) Genome-wide: cytosine extension assay Blood 677CC: N=14  677CT: N=12  677TT: N=17  677TT associated with lower DNAme compared to 677CC (p<0.05) after 7 weeks folate restriction followed by 7 week folate repletion treatment. La Merrill et al. 2012 (406) Genome-wide: LUMA Blood (pregnant women) 677CC: N=31 677CT or TT: N=164/ 1298AA: N=158 1298AC or CC: N=37  677T or 1298C alleles not associated with altered genome-wide DNAme, but vitamin B6 deficiency and presence of 677T allele associated with hypomethylation (p=0.02) Arabi et al. 2015 (407) Genome-wide: RRBS Candidate sites (N=6): pyrosequencing Sperm 677CC: N=13 677CT or TT: N=17 After 6 months of high dose folic acid supplementation, significant reduction in methylation in intergenic regions in 677CC men, whereas 677CT or TT men had significantly reduced methylation in promoters, exons, introns and intergenic regions (p<0.05) †Sample size given for each MTHFR SNP assessed in publication. If sample size of specific genotypes is not present, it was not reported in publication. Combined MTHFR 677/1298 genotypes are specified when available. LUMA: luminometric methylation assay; LINE-1: LINE-1 repetitive elements; LC/MS: liquid-chromatography tandem mass-spectrometry; IMDQ: imprint methylated DNA quantification.  88 Several studies reviewed in Table 4.5 suggest that altered DNAme in association with MTHFR 677 and 1298 variants might only be present under limited folate conditions (354,356). The presence of folate stabilizes the variant MTHFR 677 enzyme (408) and adequate folate attenuates the effects of high-risk MTHFR 677TT genotype on increased homocysteine (409,410). Due to the retrospective nature of the study, we were unable to assess folate concentrations in the placenta or maternal blood, and did not have complete information on maternal folic acid supplementation. Though folate status was unknown for the cases in this study, we assume that most of the women in our Canadian cohort were folate replete due to folic acid fortification in cereal and grain, increased literacy around healthy pregnancies, and high uptake of gestational monitoring. In a study of 368 pregnant women in Toronto, Canada, with similar demographics as our population in Vancouver, Plumptre et al. found that none of the women were folate deficient during pregnancy, even though 7% of women did not take folic acid supplements (411). It is possible that in the presence of adequate folate levels, the activity of the variant MTHFR 677 or 1298 enzymes in the placentas of our study were not diminished enough to result in a compromised OCM and altered DNAme. Despite this potential limitation, investigating alterations in placental DNAme in association with MTHFR variants in a folate-replete population under the hypothesis that this may increase risk for pregnancy complications is still warranted. Fortification of grain products with folic acid has not entirely reduced the incidence of NTDs in replete populations (412,413); in Canada, NTDs are the most common congenital abnormality (414). Additionally, pathologies such as PE and IUGR are also present at a high frequency in folate-replete populations, and associations between PE and MTHFR have been observed in such populations (181), indicating a mechanism for association with pathology beyond low folate/folic acid status.  89 In our study population, we found no significant association of NTDs, EOPE, LOPE or nFGR with high-risk 677TT or 1298CC placental genotypes, although there was a tendency to increased MTHFR 677TT in pregnancies affected by PE or FGR as a whole (OR 2.53, p=0.048). This trend is consistent with literature noting an increased risk for PE in association with the 677T allele in both maternal blood and in the placenta (181,182). In a recent meta-analysis of 52 different studies, with a combined total of 7,398 PE cases and 11,230 controls, Wu et al. identified a significantly increased risk for PE in association with the MTHFR 677T allele (183). However, in 1103 cases and 988 controls, no association between the MTHFR 1298C allele and PE was found (183). As for NTDs, our data was not suggestive of any association with fetal MTHFR 677TT or 1298CC genotype. Sample size limited our power to detect significant differences between study groups, however few studies have investigated associations between placental/fetal MTHFR variants and PE/IUGR and NTD pathologies in the Canadian population post-folic acid fortification, and the main focus of the current research was to assess altered placental DNAme in association with MTHFR high-risk genotypes. Larger studies in NTDs have identified increased risk in association with the 677 variant (349), but there is inconsistent evidence for an association with the 1298CC genotype (348,415).  Population stratification, the presence of systematic differences in allele frequencies between cases and controls, typically due to differences in ancestry, can be a limitation of genetic or epigenetic association studies. Specifically, false positive or negative results can be a consequence of failing to match study groups on this variable. To address this, we utilized an approach to assess population stratification in our study groups using three continuous variables of ancestry based on a multidimensional scaling (MDS) analysis of a panel of ancestry informative markers. This is similar to studies using MDS of genome-wide genotypes (i.e. from  90 a SNP array or DNA sequencing) in study samples combined with ancestry reference populations to identify ancestry outliers, select homogeneous groups, or infer ancestry (383,384,416), and to studies that include principal components or MDS coordinates highly associated with ancestry in statistical models to correct for ancestry (417,418). Other potential confounding factors for our study, such as maternal smoking status, diet, and medications taken during pregnancy, were not well-documented in all cases included in this study and thus could have resulted in heterogeneity between study groups that we were unable to account for in statistical modelling. Currently, the evidence supporting the relationship between MTHFR 677 or 1298 variant and pathology or altered DNAme is not conclusive enough for physicians to support implementing MTHFR genetic testing as a clinical practice (419). Despite this, MTHFR genotyping is available from 50 certified labs in the United States (419), and testing is widely promoted in the naturopathic field, where patients are told that a “faulty genotype” may explain a list of symptoms and diseases including “anxiousness, adrenal fatigue, brain fog, cervical dysplasia, increased risk of many cancers, low thyroid, leaky gut, high blood pressure, heart attacks, stroke, Alzheimer’s disease, diabetes, and miscarriages” (420). These patients are advised to take supplements containing “methyl folate” and “methyl B12” to increase methylation and decrease their risk of disease development (421). Our findings, coupled with variable results from other studies, suggest that these variants may not be of such strong concern in terms of DNAme, particularly in healthy individuals meeting folate requirements, however studies with larger sample sizes are required to validate this. At the very least, the negative results from our study suggest that if these variants have an effect on placental and thereby newborn health in Canada, it may not be through altered DNA methylation.  91 DNA methylation (DNAme) alterations have been proposed to be the link between MTHFR 677C>T and 1298A>C variants and increased risk for pregnancy complications. In this novel study of DNAme in human placentas of high-risk 677TT and 1298CC individuals, we did not find evidence of altered DNAme associated with these genotypes in numerous measures of genome-wide and CpG site-specific methylation. We conclude that widespread changes in DNAme do not occur in the placentas of MTHFR 677 and 1298 variant carriers in our folate-replete population. Further studies with larger sample sizes and/or in populations that are folate deficient may support or refute our results. The results from this study suggest that factors other than alterations in DNAme may contribute to the previously reported association between high-risk MTHFR genotypes and pathology.  92 Chapter 5: Discussion  This chapter is original and unpublished.  5.1 Summary and significance of findings In this dissertation, I investigated different forms of genetic variation in the placenta and their impact on FGR or the placental epigenome. I found that autosomal trisomy and segmental aneuploidy in the placenta, but not CNV load, are associated with SGA. I also found that a subset of SGA or FGR cases harboured rare CNVs involving placental or growth-related genes, or a molecular profile of multiple large de novo CNVs confined to the trophoblast. Additionally, I found that candidate SNPs in the MTHFR gene were not associated with altered placental DNAme, but placental MTHFR 677 TT genotype tended to be more prevalent in placental insufficiency complications of FGR and/or PE. The higher frequency of trisomy CPM in SGA cases in Chapter 2 confirms previous studies (51,134,135,138,277), however, I also identified segmental aneuploidy confined to the placenta in two cases where the infant was SGA. Despite a high incidence of segmental aneuploidy in human preimplantation embryos (94,422), this is rarely reported in studies of CPM in postnatal placentas (134,423) and there has not been specific follow-up of pregnancy outcomes in cases of unbalanced chromosomal rearrangements detected in chorionic villus samples (121,124,424). As a result, we know much less about the association between segmental aneuploidy and placental insufficiency and poor fetal growth as we do for trisomy. My findings highlight the need to study large genomic imbalances in the placenta to characterize alterations  93 of chromosomal segments that may lead to placental insufficiency, and reinforce the utility of screening for placental aneuploidy to identify cases of FGR or SGA that may be caused by CPM. The lack of difference in load of placental CNVs between SGA and controls is in contrast to previous studies finding either an increased (234) or decreased (232) load of CNVs in placentas associated with SGA or FGR compared to controls. Both were pilot studies with case and control groups of 10 or less; contradictory results between the studies and in comparison to my findings may therefore be due to chance due to small sample sizes or biological or technical variation in sampling or DNA quality between cases and controls. I found trends for greater load of CNVs in some SGA placentas, so it is also possible that differences are more subtle than those in past studies, or that CNV load may only be associated with a subset of cases of SGA, however further studies with larger sample sizes are needed to test this. By profiling CNVs in the placenta, I found that a subset of SGA cases were associated with rare CNVs of potential clinical relevance. These specific CNVs have not been previously reported in cases of FGR, and add to recent efforts to identify pathogenic CNVs in cases of isolated FGR (149–153).   In Chapter 3, I described a novel case of mosaicism of multiple large de novo duplications in a placenta from an infant with FGR. These duplications appeared to have arisen at the same time in a trophoblast precursor cell and are therefore confined to the placenta. No such cases of multiple isolated large rare CNVs in a mosaic state have been reported in the human genetics literature in a non-malignant tissue. Genomic rearrangements are a characteristic of cancer genomes, therefore the presence of multiple large CNVs is not uncommon in malignant tissue (425,426), however, these alterations typically accumulate over time (427). To my knowledge, an event of multiple CNVs arising at one time in cancer cells is also attributed to chromoanagenesis, which generates many alterations in one or a few chromosomes, rather than a  94 limited number of alterations dispersed across several chromosomes (323,428). This case is most similar to a recently described rare molecular phenotype in which patients carry multiple large de novo CNVs (mdnCNV) in several chromosomes, derived from replication mechanisms presumed to occur early in development (324). Though I could not test whether the CNVs were associated with replication errors, if this case is indeed an example of mdnCNV, it challenges the authors’ hypothesis that the event causing the duplications occurs in a very short period of time peri-implantation and does not present in a mosaic state (324). Furthermore, as is shown by cases of CPM involving trisomy or other chromosomal abnormalities, the placenta can tolerate most genetic abnormalities better than the fetus. Studying genetic profiles in the placenta may provide a unique opportunity to identify such novel and rare genetic phenotypes in humans. As studies of CNVs in the placenta are yet in their infancy, further studies are warranted to establish the incidence of this and other peculiar genomic profiles in placenta, discussed further below in Section 5.3.1. To further investigate genetic variation that may contribute to placental insufficiency, I studied the association between MTHFR 677C>T and 1298A>C SNPs and pregnancy complications or altered DNAme in the placenta. Despite many investigations of maternal genotype at these loci, this study is only one of two to study the placental genotype (182), and is the first to assess the association with FGR. In contrast to the previous study (182), the high-risk MTHFR genotypes were not associated with risk of PE (EOPE or LOPE), although I observed a trend for a higher frequency of the 677TT genotype in the larger combined group of placental insufficiency. An altered capacity to methylate DNA has been proposed as a mechanism for mediating the association of these variants with health complications, however I did not find altered genome-wide methylation in the placenta associated with the high-risk genotypes. There  95 is not agreement among studies assessing the influence of the MTHFR SNPs on genome-wide DNAme (Chapter 4, Table 4.5). This study is the first to test this in the placenta, and adds to the growing body of research finding no association with DNAme. This suggests that either altered DNAme in the placenta is more subtle than I had the power to detect, or that it may only occur when accompanied by folate deficiency, as some studies in blood have found (354,356). Additionally, in a recent experiment, researchers found that Mthfr-null mice do not have altered abundance of SAM nor altered DNAme compared to wild type mice (429). They also found that retention of one-carbon units in the folate cycle, which produces one-carbon units for purine synthesis, is important for prevention of NTDs (429), therefore it is also possible that the MTHFR SNPs are associated with complications through means other than altered DNAme.  5.2 Strengths and limitations A key lesson that I learned during my studies is that data integrity and careful analyses that consider the limitations of the data and technical and biological confounders are essential to produce robust results from which we can draw conclusions with confidence. Particularly in high-dimensional datasets, it may be easier to find significant associations that are exciting to publish when technical or biological confounders are not taken into account (386,430–432), however these results may not be robust or replicable, calling to question their biological relevance. Although I did not find significant associations that supported my hypothesis in several investigations in this thesis, I aimed to produce robust results by applying careful data quality checks to ensure analyses were based on sound data, and investigated multiple lines of inquiry to thoroughly test my hypotheses. For example, in Chapter 2, I compared CNVs between technical replicates and found that there was considerable technical variation in the CNV calls  96 based on microarray data, with an average of 20% of CNVs being discordant between replicates. As a result, I did not study mosaicism of CNVs as I could not differentiate whether discrepant CNV calls between two samples from the same placenta were due to technical variation or a biological signal of mosaicism without confirming each CNV. Inclusion of technical replicates in genomic studies is unfortunately not as common as it should be, however they are very useful to assess technical variation in genomic datasets and its potential influence on results and interpretations. This is important to consider when interpreting the reliability of results in published studies and when designing future studies. In addition to careful data checking and processing, I also thoroughly assessed my hypotheses. In Chapter 2, I investigated several ways in which the load of placental CNVs genome-wide or in specific genomic regions relevant for placental function or growth may differ between SGA and controls, while also applying appropriate multiple test corrections, to thoroughly test my hypothesis that placental CNV load is greater in SGA placentas. Additionally, in Chapter 4, I assessed differential DNAme using multiple methods to target different regions in the genome to thoroughly test whether DNAme was altered at a genome-wide or site-specific level in placentas carrying MTHFR high-risk genotypes.  Another important consideration when performing genetic association studies is population stratification, the presence of genetically distinct subgroups with varying allele frequencies, typically attributable to ancestry differences. Unaccounted for population stratification can significantly confound genetic association studies and lead to spurious findings and/or lack of replication between studies (430), therefore, I ensured that I tested for this in my studies. Prior to analyzing case-control genetic differences, I tested whether case and control populations had significantly different ancestry compositions using ancestry information derived from genome- 97 wide SNP genotypes (Chapter 2) or a panel of approximately 50 ancestry informative markers (Chapter 4). Additionally, rather than use this data to categorize individuals into ancestry groups, I applied dimension reduction to the SNP genotypes to describe an individuals’ ancestry along a continuum. Particularly for the urban populations of Vancouver and Toronto studied in this thesis, which are relatively diverse and admixed, using a measure of ancestry defined along a continuum may better represent individual variation and allow for a more accurate representation of ancestry than attempting to assign individuals into categories. In addition to performing careful analyses, the population that I studied in this thesis was also advantageous for performing genetic association studies. Because FGR can be quite heterogeneous, it may be challenging to study genetic variation associated with FGR in populations with high rates of other risk factors such as maternal smoking, diabetes, poor nutrition, etc. In the cohorts that I studied in this thesis, mothers are of mid to high socioeconomic status and there is a low incidence of maternal smoking, diabetes, and obesity. There was therefore a higher likelihood that cases of FGR were related to placental or genetic factors, which likely provided greater power to identify genetic variation associated with FGR. Additionally, the more stringent definition of FGR of birth weight <3rd percentile or <10th with evidence suggestive of placental insufficiency also likely enriched for cases of poor fetal growth due to a pathological cause. Although I used the broader definition of SGA in Chapter 2 to ensure that the two study cohorts were consistent, the majority of SGA cases in the Vancouver cohort met criteria for FGR, and the majority of cases in the Toronto cohort were <3rd percentile, therefore both were enriched for FGR cases based on this more stringent definition. Despite the benefit of this population, it also had some limitations. The ethics approvals for the studies herein allowed for a breadth of clinical information to be collected for most cases in  98 the cohort, however clinical information is not complete for all of the cases due to lack of availability or lack of maternal self-reporting. As such, I could not fully control for certain confounding factors with FGR such as maternal smoking, thrombophilia, or fetal congenital infections in our dataset. The SGA/FGR cases included in my studies may therefore not all be of unexplained etiology, and this heterogeneity could have limited the detection of genetic differences between cases and controls. Additionally, this also may have resulted in some differences between the Vancouver and Toronto cohorts in Chapter 2, as I could not apply the same exclusion criteria to the Vancouver cohort as was used to select the Toronto cohort cases. Small sample sizes were also a limitation in these studies. In Chapter 2, sample sizes in the Toronto and Vancouver cohorts were adequate to detect differences in CNV load at large effect sizes previously described (232), however they were not large enough to provide adequate power to detect more subtle differences in CNV load. Combining the two cohorts together to improve power to detect such subtle difference was unfortunately not feasible, as the CNV profiles differed between the cohorts and this could have led to false discoveries. In Chapter 4, sample sizes were also small both in the association study with and with DNAme, and may have limited my ability to detect differences. Although I tried to mediate this by including as many controls as possible in the pathology study, and by deeply profiling DNAme in the few samples available for the DNAme study, it remains possible that differences were more subtle than I had power to detect. Additionally, the study of PM324 in Chapter 3 essentially had an N=1, therefore I could not perform statistical comparisons with control placentas and had to rely on some more qualitative comparisons such as whether DNAme in this sample was an outlier.  Finally, the high-density microarrays used to assess genomic variation in all of the studies in this thesis also provided some limitations. Microarrays are a powerful tool for genomic  99 studies, as they provide a high-throughput assessment of genome-wide variation at a lower cost and with more user-friendly and less computationally-demanding analysis compared to genome sequencing technologies, however, the number and distribution of probes on these arrays limits the resolution and type of genomic information we can receive. CNV estimation depends on reliable measurements from multiple probes mapping to adjacent regions in the genome, therefore, the density of probe coverage for genomic regions limits the resolution to which CNVs can be identified and is why I only considered CNVs >10 kb in Chapter 2. Additionally, differences in probe density and distribution between different commercial microarrays resulted in different CNV profiles between the two cohorts in Chapter 2, which limited my ability to combine them to increase power. Microarrays can also only provide a snapshot of the genome;  for example, the probes on the Infinium 450k and EPIC arrays only measure 1.7% and 3% of the CpGs in the genome, and are skewed toward targeting CpGs in genes and CpG islands, providing less representation in intergenic and repetitive DNA, though the newer EPIC array has improved coverage in intergenic regulatory regions (433,434). To try to account for this, I complemented the microarray study with pyrosequencing assays to assess DNAme at repetitive DNA loci in Chapter 4, however many regions in the genome remain unaccounted for in this study. Finally, microarrays cannot tell us about genomic context of alterations, therefore I could not assess the chromosomal rearrangements of segmental aneuploidies in Chapter 2, nor the genomic context in which the eight de novo CNVs were present in case PM324 in Chapter 3.     100 5.3 Future directions 5.3.1 Advancing studies of copy number variation and mosaicism in the placenta Our understanding of CNVs associated with pregnancy complications is limited, however the studies in this thesis have laid the groundwork for future studies of placental CNVs associated with FGR or other pregnancy complications. Annotation of CNVs in isolated pregnancy complications is largely absent from databases of CNVs in clinical populations such as ClinVar or DECIPHER (315,316), therefore our ability to interpret pathogenicity of CNVs identified in cases of isolated SGA or FGR is limited to known genes or genomic regions mutated in syndromic FGR. Although on average 2.7% (range: 0-4%) of apparently isolated FGR cases may be explained by pathogenic variants (149–152), additional cases may be caused by CNVs of uncertain significance for which the evidence of a contribution to pregnancy complications is currently lacking. Future studies in additional populations are therefore needed to build a catalogue of CNVs associated with pregnancy complications to improve our ability to identify pathogenic CNVs that contribute to isolated FGR. Additionally, a history of prenatal complications is not considered in population control cohorts such as those in gnomAD or the Database of Genomic Variants (103,314), therefore CNVs that are common in the population may also be associated with FGR or other prenatal complications, and should also be considered in future studies. Finally, although we do not know the exact incidence, my study in Chapter 3 shows that CPM of CNVs is possible, therefore future studies should specifically consider CNVs in the placenta, as the genome of the placenta cannot be ignored when studying placental-mediated pathologies. Candidate CNVs identified in the placenta can then be tested in the fetus or infant to assess CPM, as a constitutional CNV could have implications for their future health.  101 Future studies would also benefit from considering biological sex (based on sex chromosome complement) during analysis. Sex biases in perinatal outcomes exist, with male fetuses at higher risk of FGR, stillbirth, PTB, and neonatal morbidity and mortality (435–438). Additionally, there is a bias toward more female fetuses associated with several trisomies (129,439,440), and based on this, researchers have hypothesized that female and male embryos have a different ability to adapt to and survive aneuploidy in early embryonic development, such that there is greater loss of male embryos in early pregnancy. Although I did not have power to fully address this in Chapter 2, I found that CNVs were significantly larger in SGA vs. control placentas in females but not males, which might reflect a differential association between CNV load and SGA based on sex. Such a difference is established in autism spectrum disorder (ASD), where females with ASD have a higher load of rare CNVs compared to males, suggesting that the genetic threshold for ASD may be higher in females compared to males (441). With increasing calls to consider sex as a biological variable in scientific studies (442,443) and an improved appreciation for sex differences in molecular features in the placenta (444–447), studying the interaction between placental CNVs and sex on risk for pregnancy complications will be an interesting avenue for future exploration.  The studies herein also highlight the need to consider the mosaic and clonal nature of the placenta in future genomic studies. Individual placental villus trees develop from a small pool of precursors (19,318), resulting in a patchiness of genomic features between samples from separate cotyledons (19,370,448). Sampling multiple regions in the term placenta is therefore needed to identify low-level mosaic abnormalities restricted to one or a few cotyledons. Cells carrying the multiple CNVs in PM324 were only detectable by microarray in one of four samples from this placenta, therefore this interesting case could have been missed had the positive villus sample  102 not been one of two samples randomly selected to study. Although it is recommended to pool multiple samples to gain a representative sample of the placenta for transcriptomic or epigenomic studies (370), testing multiple independent samples for genetic studies would enable better assessment of mosaicism, and these biological replicates could also be used to assess correlation and concordance in data within the same placenta. The clonality of the placenta is also important to consider when studying genetic variation earlier in development, as samples from small first trimester placentas will contain several independent chorionic villi, making detection of  mosaic alterations more challenging than in a sample of the same size from a term placenta that would contain just one or a few chorionic villi. This may influence inferences drawn about the load and rate of mutations in the placenta; for example, Kasak et al. reported a greater load of de novo CNVs in placentas of later gestational ages, leading them to hypothesize that acquisition of CNVs is important for healthy placental development and function (232). Although CNVs may certainly arise during placental development, a greater load of CNVs at later times in pregnancy may also be explained by an improved ability to detect mosaic alterations that have expanded clonally in a sample from a term placenta compared to one from the first or second trimester. Despite our understanding of chromosomal mosaicism in the placenta, little is known about the incidence and impact of mosaicism of smaller abnormalities such as CNVs and sequence mutations. Although I identified one case of placental mosaicism of CNVs in PM324, I was unable to profile mosaicism of CNVs between all biological replicates in the cohort of placentas in Chapter 2. Additional studies that profile placental genomes using multiple placental samples would therefore be useful to assess the frequency of mosaicism of CNVs in the placenta, identify additional cases of multiple de novo CNVs or other interesting genomic alterations, and  103 determine the influence of mosaic alterations on pregnancy complications. To do so, improvements in the reliability of CNV calls from high density microarrays are needed to minimize the chances that discrepant CNV calls are due to technical variation, or alternate methods such as whole genome sequencing could be used, which are more sensitive to detecting true CNVs (449). Assessing copy number variation at the cellular level may also help to improve the reliable detection of mosaic alterations in the placenta. Recently-developed single-cell sequencing technologies have enabled the characterization of extensive mosaicism of CNVs, including disease-relevant alterations, in tissues such as brain (450,451) and improved delineation of genetic heterogeneity and evolution of tumors (452,453). Although single-cell DNA sequencing has not yet been performed in placental tissue to my knowledge, several groups have studied placental gene expression by single-cell RNA sequencing (454–457), and novel tools to infer copy number profiles from single-cell RNA sequencing data could be applied to these publicly-available datasets (458,459). This would also provide a powerful resource to study copy number profiles among different cell types and lineages in the placenta and their relevance to placental-mediated complications, and is an avenue of research that could be applied today.  5.3.2 Broadening the scope and integrating placental ‘omics in genetic association studies of placental insufficiency complications Expanding genetic association studies beyond candidate studies to GWAS will be important to further our knowledge of common SNPs associated with placental insufficiency complications such as FGR. Candidate studies have thus far had limited success in producing robust and replicable results, and they rely on an a priori hypothesis for a gene’s association with placental insufficiency, therefore, they have a limited ability to provide novel insights into  104 disease pathogenesis. GWAS addresses this limitation, providing an unbiased assessment of genome-wide SNP variation associated with a trait. To date, only one large-scale GWAS have investigated placental/fetal variants associated with PE, and one with PTB (217,227), therefore additional studies are needed to identify variants associated with FGR. Because FGR is heterogeneous, future studies could focus on a more homogeneous population by excluding cases associated with other risk factors (e.g. CPM, maternal smoking) or incorporating evidence for placental insufficiency (e.g. abnormal umbilical artery Doppler, low maternal serum PlGF), as this may improve identification of SNPs associated with placental-mediated FGR. Conversely, studying a larger and more heterogeneous population of FGR would also be useful to increase sample size and allow the study of gene x environment interactions, as the association of variants with disease may be influenced by environmental variables, of which maternal exposures such as health complications or smoking would be of particular relevance to explore. Additionally, future studies should consider population diversity and assess whether associated variants may be population-specific or whether they are replicable across ancestries. All of this will require the development of large cohort studies with ample clinical information, or retrospective collection of such information in established birth cohorts, both of which are significant undertakings.  Rather than studying the placental genome in isolation, future genetic association studies could also benefit by incorporating additional types of placental ‘omics data to improve statistical power in smaller populations and enhance the detection of meaningful associations. Methods such as weighted correlation network analysis, where individual ‘omic datasets are condensed into co-regulated modules associated with an outcome and these modules are compared across data types (460,461), are a powerful tool to identify dysregulated pathways across multiple ‘omics levels and could provide insight into commonly dysregulated pathways in  105 placental insufficiency. Incorporating additional data types can also allow researchers focus on genetic variants that influence quantitative traits, termed quantitative trait loci (QTL). A considerable amount of variation in DNAme and gene expression among individuals is attributable to genetic variants, termed methylation-QTLs (mQTLs) and expression-QTLs (eQTLs) (462–466), which tend to be enriched in gene regulatory elements and over-represented in disease-associated loci (462–466). Only two studies have assessed genome-wide mQTLs and/or eQTLs in placenta, identifying approximately 1,000-3,000 eQTLs, and 4,000 mQTLs (467,468). Interestingly, GWAS significant loci associated with birth weight, childhood obesity, and childhood BMI are enriched in placental eQTLs (469), therefore these loci are of interest to study in association with other placental-associated traits. Performing genetic association studies on established placental mQTLs or eQTLs can help to hone in on genetic variants with potential impact on placental function, improving power to identify variants associated with placental insufficiency.   5.3.3 Considerations and opportunities for prenatal genetic screening In addition to gaining insight into genes and pathways necessary for adequate placental function and fetal growth, a goal of identifying genetic variation in the placenta associated with FGR and placental insufficiency is to enable better identification and management of FGR. Identifying individuals at risk of FGR and the underlying cause of pathological poor growth in those who develop FGR is relevant for clinical monitoring and counselling of future health, and will be important for development of additional studies to establish long-term outcomes that are specific to FGR of different origins.   106 With the increased adoption of non-invasive prenatal testing (NIPT), screening for placental genetic abnormalities is becoming increasingly possible. In NIPT, the “fetal” fraction of cell-free DNA circulating in maternal serum during pregnancy is analyzed to identify genetic abnormalities in the fetus, however this “fetal” fraction is mainly of placental origin (470). NIPT can therefore be useful not only to identify genetic abnormalities shared between the fetus and the placenta, but also abnormalities that are confined to the placenta. In Canada and the United States of America, as well as elsewhere in the world, NIPT is currently only used routinely in the clinic to detect common aneuploidies (trisomy 21, 13, 18 and sex chromosome aneuploidies) (471,472), however other autosomal trisomies can also be identified in 0.11-1.7% of cases depending on the population (473–477). Although these have low value to predict trisomy in the fetus, mainly owing to increased chances of CPM (473,475), identification of “rare” autosomal trisomies by NIPT may still be informative for identifying pregnancies at risk of complications associated with placental trisomy even if the fetus is euploid. As expected based on our knowledge of complications associated with placental trisomy, rare autosomal trisomies identified by NIPT are associated with miscarriage, FGR, congenital malformations, and intrauterine fetal demise, though normal pregnancy outcomes are also possible (474–477). Additionally, CNVs and sequence mutations can also be identified by NIPT (307,308,473,479–482). As our knowledge of common genetic variation associated with prenatal complications increases, NIPT may provide a valuable opportunity to screen for these variants and potentially improve identification of at-risk pregnancies.     107 5.4 Conclusion Compared to other common complex diseases, our understanding of genetic variation associated with placental insufficiency and its associated complications FGR and PE is quite minimal. Improving this understanding can not only assist in detection and management of at-risk pregnancies to improve fetal, neonatal, and lifelong outcomes, it will also enhance our understanding of requirements for healthy placental development and function. Through investigations of placental genetic variation and mosaicism at the level of whole chromosomes and submicroscopic CNVs, and studying single nucleotide variants and a potential link to placental DNAme, I have contributed to this understanding in this thesis. Future studies that explore the unique genetic profile of the placenta and study CNVs and sequence variants associated with placental insufficiency can build off of these findings to further characterize the role that genomic variation plays in the adequate function of this crucial organ.  108 References 1.  Bernstein IM, Horbar JD, Badger GJ, Ohlsson A, Golan A. Morbidity and mortality among very-low-birth-weight neonates with intrauterine growth restriction. Am J Obstet Gynecol. 2000;182(1):198–206.  2.  Lackman F, Capewell V, Richardson B, daSilva O, Gagnon R. The risks of spontaneous preterm delivery and perinatal mortality in relation to size at birth according to fetal versus neonatal growth standards. 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Prenat Diagn. 2018;38(3):210–8.     147 Appendices  Appendix A  Supplementary materials for Chapter 2 A.1 Supplementary methods Aneuploidy screening DNA from at least two distinct locations from each placenta from the Vancouver cohort were screened for aneuploidy using comparative genomic hybridization (CGH) as previously described (1), or multiplexed ligation- dependent probe amplification (MLPA) of subtelomeric probes on each chromosome (SALSA MLPA Subtelomeres Mix, MRC-Holland, NL). Cases with a suspected aneuploidy by MLPA were assessed using the Infinium Omni2.5-8 BeadChip array (Illumina, USA; see below). The extent of the aneuploidy was confirmed by inspecting probe intensities (LRR) and allele frequencies (BAF) of involved chromosomes and employing the cnvPartition algorithm in GenomeStudio 2.0 (Illumina). Aneuploidy screening in the Toronto cohort was performed alongside CNV profiling by high-density microarray (see below).  Confined placental mosaicism follow-up Mosaicism of aneuploidies was determined by genotyping microsatellite polymorphisms on involved chromosomes and comparing allelic ratios in all available tissues associated with the placenta (1). An aneuploidy was deemed to be confined to the placenta if it was not detectable in the amnion and/or umbilical cord for cases from the Vancouver cohort, or in cord blood for cases from the Toronto cohort. Maternal blood or decidual contamination of placental samples is not expected based on our sampling techniques, and this was confirmed by comparison to maternal alleles from blood or decidua where available. Confirmation and assessment of mosaicism of  148 CNVs was determined by quantitative PCR in placental and cord blood DNA. All mosaicism follow-up for the Toronto cohort samples was performed at The Centre for Applied Genomics, Hospital for Sick Children, Toronto, Canada.  Microarray processing and sample filtering Placental DNA from two distinct locations in the same placenta (biological replicates) were run on the Infinium Omni2.5-8 BeadChip array (Illumina, USA) for cases from the Vancouver cohort. 6 sets of technical replicates were also run to assess technical variability. For the Toronto cohort, one DNA sample from each placenta was run on the Affymetrix CytoScan HD array (ThermoFisher Scientific, USA). For two sets of monozygotic (MZ) twins in the Toronto cohort, DNA from one sample from each twin’s share of the same placenta were assessed, serving as biological replicates. All microarrays were processed at The Centre for Applied Genomics following established protocols (2,3). Poor quality samples were detected and removed if they had i) call rate < 0.97, ii) Log R Ratio (LRR) SD >0.3, or iii) waviness factor > 0.04 (Vancouver cohort); or if they failed Affymetrix default quality filters of "waviness_sd" or "SNPQC" (Toronto cohort). Significant maternal contamination in a villus sample would result in shifted allele frequencies and low quality scores, thus any affected sample would be filtered.  CNV detection and quality checks CNVs were detected using in-house pipelines (2,3), using CNV-calling algorithms designed for the different array platforms: iPattern (2), PennCNV (4), and QuantiSNP (5) for the Infinium array (Vancouver cohort); and iPattern, Nexus (6), Partek (7), and Chromosome Analysis Suite (ThermoFisher Scientific) for the Affymetrix array (Toronto cohort). Due to  149 limitations of the algorithms, large CNVs are often fragmented, therefore all large CNVs and all chromosomes with >1 Mb of CNVs in a sample were manually inspected by plotting the probe intensities (LRR) and allele frequencies (BAF). If a CNV was found to be fragmented, the calls were merged and boundaries confirmed by inspecting the LRR and BAF in the region. Samples with excessive CNV calls (> mean + 3 s.d.) were removed. Samples with an aneuploidy detected by CNV profiling in the Toronto cohort were excluded from further CNV analysis. High- confidence CNVs called by at least two algorithms with a minimum 50% reciprocal overlap, ≥5 probes, and ≥10 kb were kept for analysis. To assess the potential for detection of mosaicism of CNVs in the placenta, concordance in CNV calls between biological and technical replicates was assessed. Concordance between replicate A and B was defined as: 𝐶𝑜𝑛𝑐𝑜𝑟𝑑𝑎𝑛𝑐𝑒 =  𝑁 𝑜𝑣𝑒𝑟𝑙𝑎𝑝 𝐴𝐵𝑁 𝑜𝑣𝑒𝑟𝑙𝑎𝑝 𝐴𝐵 + 𝑁 𝑢𝑛𝑖𝑞𝑢𝑒 𝐴 + 𝑁 𝑢𝑛𝑖𝑞𝑢𝑒 𝐵 The range of concordance of CNVs between biological replicates was 0.35-0.94 (mean: 0.73) and between technical replicates was 0.64-0.92 (mean: 0.79) (Supplementary Figure 2.1). The majority (67%) of discordant CNV calls between technical replicates were deemed non-stringent in one replicate but stringent in the other. The rest were not detected by any algorithm in one replicate, but by at least two (stringent CNV) in the other. Given the lack of concordance between technical replicates, only one DNA sample per placenta was selected for further CNV studies. Additionally, one twin per MZ twin pair was also excluded from further studies to ensure a random sample.     150 Ancestry Assessment and Population Stratification Ancestry was assessed using SNP genotypes from the microarray experiments in PLINK (8) (Vancouver: v1.09, Toronto: v1.07) using the MDS clustering of identity-by-state distances independently in both cohorts. Probes mapping to the sex chromosomes, those with a call rate <0.95, or with a minor allele frequency <0.05 (Vancouver: 1,085,958; Toronto: 557,487) were removed. LD pruning was performed with a window size=50 kb, step size=5, and r2=0.25, and population stratification using MDS clustering of identity-by-state distances in PLINK was performed on the resulting in 163,089 and 115,465 tag SNPs for the Vancouver and Toronto cohorts, respectively. Values for the top 10 MDS coordinates were visualized in R and used to describe ancestry as a continuous variable. The top three MDS coordinates captured the majority of variation in ancestry between samples in both cohorts, separated the main ancestry groups into clusters (Supplementary Figure 2.2). Population stratification was tested using the Kolmogorov-Smirnov test between cases and controls for each of the top three coordinates. The Vancouver SGA group had significantly more individuals with East Asian ancestry (Coordinate 1 p=0.031), and the Toronto cohort had significantly more individuals with East Asian (Coordinate 1 p=0.009) and South Asian (Coordinate 2 p=0.030) ancestry in the SGA group (Supplementary Figure 2.2). Overall, the ancestry composition of the two cohorts was comparable (Supplementary Table 2.1).       151 References 1. Robinson WP, Penaherrera MS, Jiang R, et al. Assessing the role of placental trisomy in preeclampsia and intrauterine growth restriction. 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Analysis of a multifactor microarray study using Partek genomics solution. Methods Enzymol. 2006;411:256-270. 8. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population- based linkage analyses. Am J Hum Genet. 2007;81(3):559-575.    152 A.2 Supplementary tables Supplementary Table 2.1: Inferred ancestry of cases included in CNV studies   Vancouver cohort Toronto Cohort SGA Control % of total SGA Control % of total African 0 0 0% 3 3 9% East Asian 11 5 30% 7 2 13% European 12 15 51% 13 18 44% Latin American 1 0 2% 0 1 1% South Asian 3 2 9% 9 0 13% Mixed 2 2 8% 2 12 20%      153 Supplementary Table 2.2: Imprinted genes and placental imprinted differentially methylated regions (DMRs), curated from GeneImprint and OTAGO databases for imprinted genes, and placental imprinted DMRs reported in Hanna et al. 2016 (1) and Court et al. 2014 (2). Gene DMR Closest TSS Chr Start End Location Type Gene Source DMR Source TP73   1 3569084 3652765 Gene GI,OTAGO   RNU5D-1   1 45196727 45196842 Gene GI,OTAGO   DIRAS3 DIRAS3 1 68511645 68517314 Gene + DMR GI,OTAGO (1); (2) LRRTM1   2 80515483 80531874 Gene GI,OTAGO   GPR1-AS GPR1-AS 2 207040040 207082771 Gene+DMR GI,OTAGO (1); (2) ZDBF2 ZDBF2 2 207114583 207179148 Gene+DMR GI,OTAGO (2) NAP1L5 NAP1L5 4 89617066 89619386 Gene+DMR GI,OTAGO (1); (2) RHOBTB3 RHOBTB3 5 95049226 95160087 Gene+DMR GI,OTAGO (1) ERAP2   5 96211643 96255420 Gene GI   VTRNA2-1   5 135416160 135416286 Gene GI   FAM50B FAM50B 6 3848898 3851551 Gene+DMR GI,OTAGO (1); (2) ADTRP   6 11712287 11807279 Gene GI   TNDM   6 29640169 29644931 Gene GI   LIN28B LIN28B 6 105400985 105531207 Gene+DMR GI,OTAGO (1); (2) AIM1 AIM1 6 106959535 107018326 Gene+DMR GI,OTAGO (1); (2) PHACTR2   6 143857982 144152322 Gene OTAGO   PLAGL1 PLAGL1 6 144261437 144385735 Gene+DMR GI,OTAGO (1); (2) HYMAI   6 144324034 144329867 Gene GI,OTAGO   SLC22A2   6 160592093 160698670 Gene GI,OTAGO   SLC22A3   6 160769300 160876014 Gene GI,OTAGO   KIF25   6 168396921 168445769 Gene OTAGO   DDC   7 50526134 50633154 Gene GI   GRB10 GRB10 7 50657760 50861159 Gene+DMR GI,OTAGO (2) MAGI2   7 77646393 79082890 Gene GI   CALCR   7 93053799 93204042 Gene OTAGO   TFPI2   7 93514709 93520303 Gene GI,OTAGO   SGCE   7 94214542 94285521 Gene GI,OTAGO   PEG10 PEG10/SGCE 7 94285501 94299007 Gene+DMR GI,OTAGO (1); (2) PPP1R9A   7 94536514 94925727 Gene GI   DLX5   7 96649704 96654409 Gene GI   CPA4   7 129932974 129964020 Gene GI,OTAGO   MEST MEST/MESTIT1 7 130126012 130146133 Gene+DMR GI,OTAGO (1); (2) MESTIT1   7 130126898 130131013 Gene GI,OTAGO   COPG2IT1   7 130543453 130546900 Gene GI,OTAGO   COPG2   7 130146089 130353598 Gene OTAGO    154 Gene DMR Closest TSS Chr Start End Location Type Gene Source DMR Source KLF14   7 130417401 130418888 Gene GI,OTAGO   DLGAP2   8 1449532 1656642 Gene GI,OTAGO   ZFAT   8 135490031 135725292 Gene GI   ZFAT-AS1   8 135610314 135612932 Gene GI,OTAGO   KCNK9   8 140613081 140715299 Gene GI,OTAGO   PEG13   8 141104993 141110642 Gene GI   GLIS3 GLIS3 9 3824127 4348392 Gene+DMR GI,OTAGO (1); (2) INPP5F INPP5F 10 121577530 121588662 Gene+DMR GI,OTAGO (1); (2) H19 H19 Promoter/Enhancer 11 2016406 2024126 Gene+DMR GI,OTAGO (1); (2) IGF2 IGF2 DMR2 11 2150342 2170833 Gene+DMR GI,OTAGO (2) miR-483   11 2155364 2155439 Gene OTAGO   IGF2-AS   11 2161731 2169894 Gene GI,OTAGO   INS   11 2181009 2182571 Gene GI,OTAGO   KCNQ1   11 2465914 2870339 Gene GI,OTAGO   KCNQ1OT1 KCNQ1OT1 11 2629558 2722440 Gene+DMR GI,OTAGO (1); (2) KCNQ1DN   11 2891263 2893335 Gene GI,OTAGO   CDKN1C   11 2904443 2907111 Gene GI,OTAGO   SLC22A18   11 2920951 2946476 Gene GI,OTAGO   PHLDA2   11 2949503 2950685 Gene GI,OTAGO   OSBPL5   11 3108346 3187969 Gene GI   ZNF215   11 6947635 7005863 Gene OTAGO   WT1   11 32409321 32457176 Gene GI,OTAGO   WT1-AS   11 32457064 32480315 Gene GI,OTAGO   ANO1   11 69924408 70035634 Gene GI,OTAGO   ZC3H12C ZC3H12C 11 109963338 110042566 Gene+DMR GI (1); (2) NTM   11 131240373 132206716 Gene GI   RBP5   12 7276280 7281538 Gene GI   LRP1   12 57522276 57607134 Gene GI   WIF1   12 65444406 65515346 Gene OTAGO   ATP5F1EP2   13 28519343 28519710 Gene GI   RB1 RB1 13 48877887 49056122 Gene+DMR GI,OTAGO (2) LPAR6   13 48963707 49018840 Gene OTAGO   SMOC1   14 70320848 70499083 Gene GI   DLK1   14 101192042 101201539 Gene GI,OTAGO   MEG3 MEG3 14 101245747 101327368 Gene+DMR GI,OTAGO (1); (2) miR-337   14 101340830 101340922 Gene OTAGO   RTL1   14 101346992 101351184 Gene GI,OTAGO    155 Gene DMR Closest TSS Chr Start End Location Type Gene Source DMR Source MEG8   14 101361107 101402336 Gene GI,OTAGO   SNORD113-1   14 101391161 101391229 Gene GI   SNORD114-1   14 101416170 101416241 Gene GI   miR-134   14 101521024 101521096 Gene OTAGO   MKRN3 MKRN3 15 23807086 23873064 Gene+DMR GI,OTAGO (2) MAGEL2 MAGEL2 15 23888691 23894029 Gene+DMR GI,OTAGO (2) NDN NDN 15 23930565 23932759 Gene+DMR GI,OTAGO (2) NPAP1   15 24920541 24928593 Gene GI,OTAGO   SNRPN-SNURF SNRPN 15 24671872 25223870 Gene+DMR GI,OTAGO (1); (2) SNORD107   15 25227141 25227215 Gene GI   SNORD64   15 25230011 25230320 Gene GI,OTAGO   SNORD108   15 25232072 25232142 Gene GI,OTAGO   SNORD109A   15 25287065 25287575 Gene GI,OTAGO   SNORD116@   15 25296623 25351750 Gene GI,OTAGO   SNORD115@   15 25415870 25515005 Gene GI,OTAGO   SNORD115-48   15 25514930 25515005 Gene GI   SNORD109B   15 25523490 25523556 Gene GI,OTAGO   UBE3A   15 25582381 25684128 Gene GI,OTAGO   ATP10A   15 25922420 26110317 Gene GI   H73492   15 32554507 32554887 Gene OTAGO   IRAIN IGF1R 15 99189092 99409650 Gene+DMR GI,OTAGO (2) NAA60   16 3415099 3536960 Gene GI,OTAGO   ZNF597 ZNF597 16 3486104 3494155 Gene+DMR GI,OTAGO (1); (2) TP53   17 7565097 7590856 Gene GI   TCEB3C   18 44554573 44556449 Gene GI   DNMT1 DNMT1 19 10244021 10341962 Gene+DMR GI,OTAGO (1); (2) AXL   19 41725108 41767671 Gene OTAGO   ZNF331 ZNF331 19 54024235 54083523 Gene+DMR OTAGO (1); (2) C19MC C19MC 19 54150900 54265684 Gene+DMR GI,OTAGO (1); (2) NLRP2   19 55464498 55512510 Gene GI   ZIM2   19 57285920 57352097 Gene GI,OTAGO   PEG3 PEG3 19 57321445 57353128 Gene+DMR GI,OTAGO (1); (2) MIMT1   19 57352270 57359924 Gene GI,OTAGO   DGCR6   22 18893541 18901751 Gene GI   DGCR6L   22 20301799 20307603 Gene GI   CST1   20 23728190 23731905 Gene OTAGO   PSIMCT-1 PSIMCT-1 20 30134929 30136019 Gene+DMR GI,OTAGO (1); (2) BLCAP   20 36120874 36156333 Gene GI,OTAGO    156 Gene DMR Closest TSS Chr Start End Location Type Gene Source DMR Source NNAT NNAT 20 36147042 36152092 Gene+DMR GI,OTAGO (1); (2) DSCAM   21 41382926 42219065 Gene OTAGO   L3MBTL1 L3MBTL1 20 42136320 42170535 Gene+DMR GI,OTAGO (1); (2) SGK2   20 42187608 42216877 Gene GI   GDAP1L1   20 42875887 42909013 Gene GI   miR-296   20 57392670 57392749 Gene GI   miR-298   20 57393281 57393368 Gene GI   GNAS-AS1 GNAS-AS1 20 57393973 57426138 Gene+DMR GI (1); (2) GNAS GNAS DMR; GNAS DMR2; GNAS Ex1A 20 57413694 57486247 Gene+DMR GI,OTAGO (1); (2)   PPIEL 1 40024971 40026520 DMR   (1); (2)   HTR5A 7 154861569 154863381 DMR   (1); (2)   ERLIN2 8 37605517 37605978 DMR   (1); (2)   TRAPPC9 8 141107717 141111080 DMR   (1); (2)   NHP2L1 22 42077939 42078723 DMR   (1); (2)   WRB 21 40757510 40758276 DMR   (2)   MCCC1 3 182816738 182817626 DMR   (1); (2)   PDE4D 5 58334676 58335954 DMR   (1); (2)   AGBL3 7 134671024 134671987 DMR   (1); (2)   DCAF10 9 37800484 37801319 DMR   (1); (2)   FAM196A 10 128993810 128995192 DMR   (1); (2)   N4BP2L1 13 33001250 33002597 DMR   (1); (2)   RGMA 15 93614758 93616588 DMR   (1); (2)   FAM20A 17 66596999 66597500 DMR   (1); (2)   ZNF396 18 32956850 32957683 DMR   (1); (2)   THAP3 1 6684860 6685996 DMR   (1)   AKR7A3 1 19614429 19615702 DMR   (1)   C1orf216 1 36184400 36184863 DMR   (1)   EPHA10 1 38200920 38201123 DMR   (1)   ACOT11 1 54940382 54941170 DMR   (1)   PCSK9 1 55504848 55506512 DMR   (1)   IL12RB2 1 67772896 67773725 DMR   (1)   TNR 1 175568216 175568710 DMR   (1)   CACNA1E 1 181286640 181287967 DMR   (1)   G0S2 1 209848306 209849445 DMR   (1)   LINC00467 1 211589678 211590292 DMR   (1)   SLC30A3 2 27484942 27488313 DMR   (1)   C2orf91 2 42067938 42068648 DMR   (1)  157 Gene DMR Closest TSS Chr Start End Location Type Gene Source DMR Source   SIX2 2 45231226 45232888 DMR   (1)   DNAH7 2 196933266 196934154 DMR   (1)   SPHKAP 2 229045958 229046785 DMR   (1)   GADL1 3 30936070 30936531 DMR   (1)   C3orf62 3 49314155 49314920 DMR   (1)   GRM2 3 51740741 51741473 DMR   (1)   RPN1 3 128336483 128337044 DMR   (1)   RAB7A 3 128564782 128565090 DMR   (1)   FGF12 3 192126023 192127457 DMR   (1)   MFI2 3 196756621 196756875 DMR   (1)   JAKMIP1 4 6107021 6107791 DMR   (1)   GRID2 4 93226245 93227270 DMR   (1)   BANK1 4 102711702 102712397 DMR   (1)   FAM149A 4 187065417 187066505 DMR   (1)   SDHAP3 5 1594021 1595048 DMR   (1)   ZNF354C 5 178593785 178594990 DMR   (1)   CD83 6 14117480 14118415 DMR   (1)   RNF144B 6 18387077 18387809 DMR   (1)   C6orf47 6 31627653 31628935 DMR   (1)   MDGA1 6 37616410 37617124 DMR   (1)   MOCS1 6 39901897 39902693 DMR   (1)   PLG 6 161188022 161188822 DMR   (1)   SCIN 7 12609907 12610833 DMR   (1)   RAPGEF5 7 22122473 22123315 DMR   (1)   NPY 7 24323128 24325371 DMR   (1)   HECW1 7 43151828 43153950 DMR   (1)   HGF 7 81240257 81240667 DMR   (1)   NYAP1 7 100091181 100091786 DMR   (1)   EMID2 7 101006052 101006963 DMR   (1)   CCDC71L 7 106300098 106302548 DMR   (1)   KRBA1 7 149389444 149389941 DMR   (1)   DPP6 7 154585539 154586375 DMR   (1)   FDFT1 8 11659497 11660209 DMR   (1)   PTK2B 8 27182871 27183342 DMR   (1)   CHD7 8 61626185 61627281 DMR   (1)   PGM5P3-AS1 9 73568 73835 DMR   (1)   DNAJB5 9 34989434 34989605 DMR   (1)   EXD3 9 140301079 140302117 DMR   (1)  158 Gene DMR Closest TSS Chr Start End Location Type Gene Source DMR Source   PROSER2-AS1 10 11936672 11937255 DMR   (1)   ITGA8 10 15761192 15762312 DMR   (1)   PTCHD3 10 27702309 27703547 DMR   (1)   JMJD1C 10 65224441 65225999 DMR   (1)   FGF8 10 103534501 103536348 DMR   (1)   SPRN 10 135278717 135279147 DMR   (1)   CYP2E1 10 135341528 135343280 DMR   (1)   ART5 11 3662967 3663842 DMR   (1)   RNF141 11 10562070 10563302 DMR   (1)   NAV2 11 19366443 19368277 DMR   (1)   PRDM11 11 45201741 45202557 DMR   (1)   MAPK8IP1 11 45921134 45922184 DMR   (1)   GAL 11 68451396 68452097 DMR   (1)   C12orf5 12 4433587 4433983 DMR   (1)   ST8SIA1 12 22487219 22488465 DMR   (1)   TBC1D30 12 65218069 65218869 DMR   (1)   DLEU7 13 51417469 51418614 DMR   (1)   KLHL1 13 70680712 70683111 DMR   (1)   NRL 14 24563095 24564067 DMR   (1)   PTGDR 14 52734156 52736420 DMR   (1)   PACS2 14 105830606 105830859 DMR   (1)   SORD 15 45314789 45315642 DMR   (1)   DNM1P35 15 76030565 76031591 DMR   (1)   RASGRF1 15 79382548 79383980 DMR   (1)   LRRK1 15 101626335 101626824 DMR   (1)   PRR25 16 863240 863884 DMR   (1)   ZNF629 16 30816719 30817779 DMR   (1)   SIAH1 16 48399731 48400475 DMR   (1)   NDRG4 16 58534681 58535556 DMR   (1)   CMTM3 16 66637919 66639593 DMR   (1)   ZFP90 16 68572892 68573971 DMR   (1)   MLKL 16 74734230 74734885 DMR   (1)   CLEC3A 16 78079569 78080193 DMR   (1)   C17orf97 17 259426 260589 DMR   (1)   SEPT4 17 56609082 56609687 DMR   (1)   TNFRSF11A 18 60051870 60052464 DMR   (1)   MUM1 19 1324834 1325348 DMR   (1)   ZNF833P 19 11784246 11785337 DMR   (1)  159 Gene DMR Closest TSS Chr Start End Location Type Gene Source DMR Source   ZNF763 19 12075601 12076549 DMR   (1)   ANO8 19 17438249 17439339 DMR   (1)   C20orf160 20 30618874 30619244 DMR   (1)   LINC00657 20 34638489 34639686 DMR   (1)   CYP24A1 20 52789646 52791472 DMR   (1)   TAF4 20 60540388 60541082 DMR   (1)   TMPRSS3 21 42218551 42219853 DMR   (1)   PRMT2 21 48087452 48088150 DMR   (1)   ARVCF 22 19973978 19974866 DMR   (1)   PISD 22 32026380 32026975 DMR   (1)   KCTD17 22 37464839 37465279 DMR   (1) (1) Hanna CW, Penaherrera MS, Saadeh H, Andrews S, McFadden DE, Kelsey G, et al. Pervasive polymorphic imprinted methylation in the human placenta. Genome Res. 2016;26(6):756–67.; (2) Court F, Tayama C, Romanelli V, Martin-Trujillo A, Iglesias-Platas I, Okamura K, et al. Genome-wide parent-of-origin DNA methylation analysis reveals the intricacies of human imprinting and suggests a germline methylation-independent mechanism of establishment. Genome Res. 2014;24(4):554–69.; GI: Gene Imprint database; OTAGO: University of Otago catalogue of imprinted genes       Supplementary Table 2.3: Characteristics of SGA cases associated with aneuploid and euploid placentas Group Gestational age at birth (w), mean (range) Maternal age at birth (y), mean (range) Sex, N male (%) Birthweight (S.D.), mean (range) Twins, N (%) PE, N (%) Aneuploid (N=12) 34.2 (30.1-39.9) 36.7 (30.0-44.0) 4 (33) -2.14 (-2.97 to  -1.26) 1 (8) 3 (25) Euploid (N=89) 35.0 (23.6-41.7) 35.0 (23.1-44.0) 39 (44) -2.0 (-3.6 to  -1.2) 16 (18) 28 (31) All p>0.05, calculated by Student’s t-test for maternal age and birth weight, Mann-Whitney U-test for gestational age, and Fisher’s exact test for categorical variables.    160 Supplementary Table 2.4: CNV load differs between placentas assessed on different high-density microarray platforms   Infinium Omni2.5-8 (Vancouver cohort) Affymetrix CytoScan HD (Toronto cohort) Total CNVs 16.4 (9-27) 33.8 (20-57)* Gains:Losses (%) 43:57 49:51 Rare CNVs 4.4 (1-10) 6.9 (1-29)* Size of CNVs 87 kb (10 kb-1.6 Mb) 94 (10 kb-3.1 Mb)*    Size of gains 103 kb (10 kb-1 Mb) 137 kb (10 kb-2.6 Mb)*    Size of losses 74 kb (10 kb-1.6 Mb) 55 kb (10 kb-3.1 Mb) *p<0.05, calculated using Mann-Whitney U-test for continuous variables, and Fisher’s exact test for count data.; results reported as mean (range) unless otherwise specified   Supplementary Table 2.5: Number of genes known to have high expression in the placenta impacted by CNVs in SGA compared to control placentas.   Vancouver cohort Toronto cohort SGA Control OR p SGA Control OR p Gain Placental genes 20 10 1.28 0.57 3 9 0.62 0.56 Other genes 510 324     634 1172     Loss Placental genes 21 6 2.37 0.07 0 7 0 0.02 Other genes 300 202     563 639     p-values calculated by Fisher’s Exact test. OR, odds ratio.   Supplementary Table 2.6: Number of placental CNVs overlapping imprinted regions in SGA compared to control placentas.   Vancouver cohort Toronto cohort SGA Control OR p SGA Control OR p Gain Imprinted region 2 2 0.85 0.74 6 10 1.02 0.58 Other CNVs 195 166     370 631     Loss Imprinted region 1 4 0.24 0.97 0 2 0 0.52 Other CNVs 247 234     426 651     p-values calculated by Fisher’s Exact test. OR, odds ratio.  161 Supplementary Table 2.7: Placental CNVs overlapping imprinted genes and DMRs Case ID Group Location (chr:start-end) (hg19) Size (kb) Type CNV prevalence Imprinted region Placenta-specific imprinted DMR† Note 6234 SGA  6:168336617-168593509 256.9 Gain Common KIF25 gene   Coding sequence affected, potential brain-specific imprinting of KIF25 PM83 Control 7:100967084-101135490 168.4 Gain Common EMID2 DMR Yes   8303 Control 7:50664966-50751829 86.9 Gain Rare GRB10 gene & DMR   Coding sequence affected, DMR unaffected 8303 Control 8:140456265-140635721 179.4 Gain Rare KCNK9 gene   Coding sequence affected 7334 Control 8:140456265-140654581 198.3 Gain Rare KCNK9 gene   Coding sequence affected 4738 Control 10:135093373-135427143 333.7 Gain Common SPRN; CYP2E1 DMRs No; Yes   7665 SGA 10:135234127-135427143 193 Gain Common SPRN; CYP2E1 DMRs No; Yes   PM167 Control 10:135242873-135378450 135.7 Gain Common SPRN; CYP2E1 DMRs No; Yes   PM320 SGA 10:135242873-135378450 135.7 Gain Common SPRN; CYP2E1 DMRs No; Yes   PL86 SGA 10:135242873-135378802 135.9 Gain Common SPRN; CYP2E1 DMRs No; Yes   PM112 Control 10:135242873-135378802 135.9 Loss Common SPRN; CYP2E1 DMRs No; Yes   1792B SGA 10:135252179-135372310 120.1 Gain Common SPRN; CYP2E1 DMRs No; Yes   8262 Control 10:135265931-135427143 161.2 Gain Common SPRN; CYP2E1 DMRs No; Yes   3207 Control 10:27597613-27709398 111.8 Loss Common PTCHD3 DMR No   PM83 Control 10:27607059-27705855 98.8 Loss Common PTCHD3 DMR No   7334 Control 11:131638577-131689229 50.6 Gain Rare NTM gene   Coding sequence unaffected PM171 Control 11:2170670-2199458 28.7 Loss Rare INS gene, IGF2 gene & DMR   Coding sequence INS affected. Coding sequence and DMR IGF2 unaffected PM153 Control 15:25090405-25116541 26.1 Loss Common SNRPN gene & DMRs   Coding sequence unaffected, 1/5 DMRs affected 8385 Control 15:25420015-25436068 16 Loss Common SNORD115 cluster     8511 SGA 21:47902098-48097372 195.2 Gain Rare PRMT2 DMR Yes   8262 Control 21:47932298-48097372 165.1 Gain Rare PRMT2 DMR Yes   4738 Control 21:47958225-48097372 139.1 Gain Rare PRMT2 DMR Yes   PM132 SGA 22:19931668-19980300 48.6 Loss Rare ARVCF DMR Yes  †As described in Hanna et al. (2016); see Supplementary Table 2.2. TOR, Toronto cohort; VAN, Vancouver cohort.   162 Supplementary Table 2.8 Pathways nominally enriched (p<0.05) in rare placental CNVs in SGA compared to controls GO ID GO Pathway Name N Genes Enrichment Coefficient p FDR All CNVs GO:0045786 GO:NEGATIVE REGULATION OF CELL CYCLE 447 3.174 0.031 0.602 GO:0016491 GO:OXIDOREDUCTASE ACTIVITY 725 1.088 0.031 0.602 GO:2001056 GO:POSITIVE REGULATION OF CYSTEINE-TYPE ENDOPEPTIDASE ACTIVITY 122 3.161 0.032 0.602 GO:0043280 GO:POSITIVE REGULATION OF CYSTEINE-TYPE ENDOPEPTIDASE ACTIVITY INVOLVED IN APOPTOTIC PROCESS 113 2.978 0.044 0.602 GO:0010948 GO:NEGATIVE REGULATION OF CELL CYCLE PROCESS 219 2.953 0.045 0.602 GO:0009267 GO:CELLULAR RESPONSE TO STARVATION 113 3.769 0.047 0.602 GO:0010950 GO:POSITIVE REGULATION OF ENDOPEPTIDASE ACTIVITY 135 1.949 0.049 0.602 GO:0010952 GO:POSITIVE REGULATION OF PEPTIDASE ACTIVITY 149 1.949 0.049 0.602 GO:0051050 GO:POSITIVE REGULATION OF TRANSPORT 889 -0.813 0.027 0.602 GO:0032940 GO:SECRETION BY CELL 927 -0.921 0.036 0.602 GO:0051046 GO:REGULATION OF SECRETION 651 -1.061 0.039 0.602 GO:0000904 GO:CELL MORPHOGENESIS INVOLVED IN DIFFERENTIATION 619 -1.079 0.040 0.602 GO:0043270 GO:POSITIVE REGULATION OF ION TRANSPORT 204 -3.922 0.042 0.602 GO:0042391 GO:REGULATION OF MEMBRANE POTENTIAL 342 -1.327 0.043 0.602 GO:0090257 GO:REGULATION OF MUSCLE SYSTEM PROCESS 190 -2.200 0.049 0.602 Gains GO:0080135 GO:REGULATION OF CELLULAR RESPONSE TO STRESS 563 2.435 0.009 0.409 GO:0016491 GO:OXIDOREDUCTASE ACTIVITY 725 2.175 0.015 0.409 GO:0030162 GO:REGULATION OF PROTEOLYSIS 688 1.680 0.028 0.409 GO:0009607 GO:RESPONSE TO BIOTIC STIMULUS 839 2.383 0.030 0.409 GO:0042803 GO:PROTEIN HOMODIMERIZATION ACTIVITY 703 1.642 0.031 0.409 GO:0052547 GO:REGULATION OF PEPTIDASE ACTIVITY 386 1.614 0.035 0.409 GO:0052548 GO:REGULATION OF ENDOPEPTIDASE ACTIVITY 363 1.614 0.035 0.409 GO:0051726 GO:REGULATION OF CELL CYCLE 931 2.250 0.037 0.409 GO:0010564 GO:REGULATION OF CELL CYCLE PROCESS 555 2.753 0.038 0.409 GO:0045786 GO:NEGATIVE REGULATION OF CELL CYCLE 447 3.538 0.042 0.409  163 GO ID GO Pathway Name N Genes Enrichment Coefficient p FDR GO:0044463 GO:CELL PROJECTION PART 919 -1.137 0.038 0.420 GO:0051050 GO:POSITIVE REGULATION OF TRANSPORT 889 -0.926 0.041 0.409 GO:0099503 GO:SECRETORY VESICLE 453 -2.088 0.045 0.409 p-values calculated by generalized linear model with universal gene correction model with sex and cohort included as covariates in the cnvGSA R package; FDR, false-discovery rate 164 A.3 Supplementary figures  Supplementary Figure 2.1: Concordance in high-confidence CNV calls between biological or technical replicates of placental DNA. Values for biological or technical replicates from the Vancouver cohort are indicated as black dots, and biological replicates from each monozygotic twins’ share of the placenta from the Toronto cohort are indicated by white diamonds.  165  Supplementary Figure 2.2: Ancestry assessment in placental DNA from two Canadian cohorts. Ancestry in the Vancouver (A) and Toronto (B) cohorts are represented by the top 3 MDS coordinates. Population stratification between SGA and controls was assessed by comparison of MDS coordinates in the Vancouver (C) and the Toronto (D) cohorts independently. Vancouver cohort coordinate 1 is lower in SGA cases, indicating a greater number of individuals with East Asian ancestry. Toronto cohort coordinates 1 and 2 are different in SGA vs. controls, representing the higher number of individuals with East and South Asian ancestry in the SGA group. p-values calculated by Kolmogorov-Smirnov test. 166    Supplementary Figure 2.3: A case of placental mosaicism of large CNVs identified by SNP array. A) Genomic distribution of CNVs in PM324 placental site V1. Eight large duplications 2-4 Mb in size were identified in seven chromosomes, amounting to 27.4 Mb, in one of two DNA samples from the placenta. B) Illumina Genome Viewer image of both sites of placental DNA from PM324 showing the B Allele Frequency (BAF) and Log R Ratio (LRR) for the region containing one of the duplications at chr1:200,478,352-204,413,297 (hg19). Differences in BAF and LRR are indicative of a mosaic duplication in site V1 (top), which is absent from site V4 (bottom).  A) B)  167   Supplementary Figure 2.4: Comparison of sizes of all placental CNVs between SGA and controls in the Vancouver (A) and Toronto (B) cohorts. Vancouver cohort SGA placentas have significantly larger CNVs than controls. Separating by CNV type, this was only significant for losses. There were no significant differences between overall CNV sizes in the Toronto cohort SGA and control placentas. p-values calculated by Mann-Whitney U-test.   168   Supplementary Figure 2.5: Differences in CNV sizes between SGA and controls are present in placentas from females but not males in the Vancouver cohort. Density distributions of sizes of CNVs in male and female SGA compared to control placentas. Placentas from SGA females had significantly larger CNVs compared to female controls. In particular placentas from females with SGA had significantly larger losses than female controls. No significant differences were identified between sizes of CNVs in male SGA vs control placentas. p-values calculated by Mann-Whitney U test.    169  Appendix B  Supplementary materials for Chapter 3 B.1 Supplementary methods To confirm microarray findings, determine parental origin of the duplications, and assess level of mosaicism, genotyping of microsatellite repeats within duplications was performed in all available tissues from the placenta and in maternal blood. A minimum of two microsatellite loci were selected per duplication for analysis, with the exception of the duplication at 7q11.21-q11.22. Regions were amplified by PCR using a standard reaction including: 6.48 µL dH2O, 1 µL 10x PCR buffer (without MgCl2), 1 µL dNTPs (1.25 nM each), 0.4 µL MgCl2 (50 mM), 0.1 µL forward + reverse primer mixture (20 µM each), 0.02 µL Taq polymerase (5 U/µL), and 50 ng of genomic DNA. PCR cycling conditions were: 95° C for 2:00, [95° C for 0:30, custom annealing temperature for 0:45, 72° C for 1:30]x 35 cycles, 72° C for 7:00. Primer sequences and annealing temperatures are described in Supplementary Table 3.1. PCR products were visualized using an ABI Prism310 Genetic Analyzer (Applied Biosystems, Foster City, USA). The percentage of abnormal cells in each sample was estimated from loci with two clearly segregating alleles by determining allele dosage using the ratio of peak areas estimated by the GeneScan Analysis Software version 3.1.2 (Applied Biosystems). In total, 12/17 loci tested were informative for allele dosage (Supplementary Table 3.2). Parental origin was inferred by comparing alleles in placental-associated tissues (fetal genotype) to the maternal genotype. For 3/8 duplications, the fetal genotype was the same as the maternal genotype at tested loci, therefore parental origin could not be determined.    170 B.2 Supplementary tables Supplementary Table 3.1: Primer sequences and annealing temperatures for PCR amplification of microsatellite repeats  Locus Forward primer Reverse primer Annealing temp D1S1647 CTT GAA GCT TGG AAC CTT GA ACC AGC CTC CTA CAA CTC CT 62° C D1S2655 AGG GTC CCC AAA GAG CCT TC ATG GCA GCA CAT CCT GCT TC 62° C D5S429 TGT GTA CCA GCA TGG TTG AT CTA GTT TAA GGT TTG CCA GTT TTC 55° C D5S1960 GCG ACA GGG TGA GAT T GGT TTC ATC TGC CAA AGC 62° C D6S430 ACA ATG GTC TCA GCA TAG TTC C TCA GTG GTA GAC AAA CAC CTA CAG 55° C D6S1282 AAT CAC AGT TTC TTG CAG CC GAA AGT CTA GTG GTG ACA AAT ATC C 55° C D7S2503 CCA ATT CCA GGA AGG CTC TTG GTA ATA CTA CGT GCC AGG 65.5° C D8S1699 CAA CCT GAC CCT GCC A CAT GAT GTT CTA AGC ATA TCT GC 55° C D8S1988 CCT TTG GAC TCA GAC CAG AA TAG TCA GAG TCC TCA GAG AAA CA 55° C D8S270 ATT CAG AAC GAT GAG GAA GC AGA ATG GCA CAT AGT CTC GT 55° C D11S986 GAA GGA CTC GGC TCC AG GTA AGA GGA TGG TAG GAG GG 62° C D11S1385 CCG AGG CTA TTG CTG TTT TA AAC CTA CTG TGC TGC CAG TC 55° C D11S4174 GAT TAA ATG CCC ACT ATG TAG C GAT AGC TTT CCC AGA TGG TT 55° C D11S873 ATA ATG TAC TGT GAT AAA TGC T CCT GGT TTA GAA TAA TAC CT 55° C D11S1311 TGT TGA CTA AAT GAG TGT CAG ACC A CCT TGA GGG CAG GAA TAG TGT C 55° C D17S1820 CAT GAG GTC TTC CAG AAG G AAC ACA CTT GCT GAT GTG C 62° C D17S809 CAA AAA GGC AGA ATG CAG TA TCC AGA GTC AAA AAC ACA GG 62° C  171 Supplementary Table 3.2: Percent of cells carrying duplications based on microsatellite repeat genotyping studies Sample 1q32.1 5q35.1 7q11.21-q11.22 8q21.3-q22.1 11p11.2 11q14.3-q21 17q21.33-q22 Mean D1S1647 D1S2655 D5S429 D5S1960 D7S2503 D8S1699 D11S1385 D11S4174 D11S873 D11S1311 D17S1820 D17S809  vil1 60 35 80 50 60 100 75 40 55 60 50 60 60 tro1 75 55 100 60 80 90 60 60 80 70 60 70 72 mes1 30 20 30 25 15 35 20 10 25 20 15 20 22 ch1 0 0 0 0 0 0 0 0 0 0 0 0 0 am1 0 0 0 0 5 0 0 0 0 0 0 0 0 vil2 0 0 0 0 0 0 0 0 0 0 0 0 0 tro2 0 0 0 0 0 0 0 0 0 0 0 0 0 mes2 0 0 0 0 0 0 0 0 0 0 0 0 0 ch2 0 15 0 0 NA 0 0 0 0 0 0 0 1 vil3 5 NA 10 0 0 5 5 0 5 5 0 10 4 tro3 10 0 10 10 5 15 0 5 10 5 5 NA 7 mes3 5 0 10 5 5 0 0 5 10 0 0 5 4 ch3 0 5 0 0 5 0 0 0 0 0 0 0 1 am3 0 0 0 0 0 0 0 0 0 0 0 0 0 vil4 0 0 0 0 0 0 0 0 0 0 0 0 0 tro4 0 0 0 0 0 0 0 0 0 0 0 0 0 mes4 0 0 0 0 5 0 0 0 10 0 0 0 1 ch4 10 5 0 0 0 0 0 0 0 0 0 0 1 am4 0 0 0 0 0 5 0 0 0 0 0 0 0 cord 0 0 0 0 0 0 0 0 0 0 NA 0 0 Genotypes at D6S430, D6S1282, D8S1988, D8S270, and D11S986 were uninformative for allelic ratios (homozygous). NA, measurement not successful.    172 Supplementary Table 3.3: Pathogenic or likely pathogenic duplications from ClinVar, DECIPHER, or the literature that overlap PM324 duplications by at least 50% PM324 duplication coordinates† (size) Overlapping duplication coordinates† (size) Proportion overlap Classification Inheritance Patient phenotype Source Chr1:200,478,352-204,413,297 (3.93 Mb) Chr1:198,652,996-203,347,714 (4.69 Mb) 61% Likely pathogenic de novo Congenital diaphragmatic hernia (patient 353572) DECIPHER Chr1: 200,481,206-203,490,056 (3.01 Mb) 100% Likely pathogenic de novo Global developmental delay, behavioural problems, pervasive developmental disorder not otherwise specified, staring spells, headaches, paresthesias. No prenatal or perinatal complications, normal growth parameters. Olson et al. 2012‡ Chr1:200,544,702-204,050,423 (3.57 Mb) 100% Likely pathogenic unknown Developmental delay, myoclonic epilepsy, cognitive and motor difficulties, minor dysmorphic features. No perinatal complications, born large for gestational age (97th percentile), otherwise normal growth parameters Olson et al. 2012‡ Chr5: 169,133,115-172,752,205 (3.62 Mb) Chr5:170,526,977-171,694,974 (1.17 Mb) 100% Likely pathogenic de novo Proportionate short stature, intellectual disability, holoprosencephaly, ventricular septal defect, vesicoureteral reflux, facial asymmetry, upslanted palpebral fissure, synophrys, preaxial hand polydactyly, sandal gap, triphalangeal thumb (patient 400968) DECIPHER             173 PM324 duplication coordinates† (size) Overlapping duplication coordinates† (size) Proportion overlap Classification Inheritance Patient phenotype Source Chr7: 65,791,671-69,249,095 (3.46 Mb) Chr7:66,808,840-70,189,959 (3.38 Mb) 72% Likely pathogenic unknown N/A (patient 338259) DECIPHER Chr7:68,783,746-69,330,800 (547 kb) 85% Likely pathogenic de novo Global developmental delay, ataxia, seizures (patient 331481) DECIPHER Chr11: 43,851,111-47,385,923 (3.53 Mb) Chr11:45,449,325-46,180,545 (731 kb) 100% Likely pathogenic Inherited (paternal) Intellectual disability (patient 331490) DECIPHER Chr11:44,219,317-44,682,116 (462 kb) 100% Pathogenic unknown Abnormal facial shape, specific learning disability (allele RCV00053618) ClinVar †GRCh37/hg19; ‡ Olson HE, Shen Y, Poduri A, Gorman MP, Dies KA, Robbins M, et al. Micro-duplications of 1q32.1 associated with neurodevelopmental delay. Eur J Med Genet. 2012;55(2):145–50. 174 Supplementary Table 3.4: Mean methylation -values within duplications   1q32.1 5q35.1 6q12 7q11.21-q11.22 8q21.3-q22.1 11p11.2 11q14.3-q21 17q21.33-q22 Mean All CpGs (N) 2,150 1,468 63 329 832 1,942 446 882 1014   PM324 vil1 0.54 0.53 0.48 0.51 0.51 0.48 0.57 0.47 0.511   PM324 vil4 0.55 0.53 0.47 0.51 0.51 0.5 0.57 0.48 0.515   Term villi† 0.56  (0.54-0.57) 0.54  (0.52-0.56) 0.48  (0.39-0.53) 0.53  (0.50-0.54) 0.51  (0.48-0.53) 0.51  (0.49-0.53) 0.58  (0.55-0.59) 0.50  (0.46-0.51) 0.526 Promoter CpGs (N) 160 88 0 19 106 158 28 106 83.125   PM324 vil1 0.07 0.06 NA 0.03 0.07 0.07 0.04 0.11 0.064   PM324 vil4 0.11 0.07 NA 0.04 0.08 0.09 0.05 0.12 0.080   Term villi† 0.08  (0.07-0.09) 0.07  (0.06-0.08) NA 0.03  (0.02-0.04) 0.07  (0.06-0.08) 0.08  (0.07-0.10) 0.05  (0.04-0.06) 0.12  (0.11-0.13) 0.071 PMDs (N) 4 2 1 7 1 15 2 2 4.250   PM324 vil1 0.34 0.42 0.44 0.26 0.61 0.42 0.54 0.4 0.429   PM324 vil4 0.37 0.45 0.43 0.3 0.6 0.43 0.49 0.39 0.433   Term villi† 0.38  (0.34-0.42) 0.46  (0.42-0.51) 0.43  (0.35-0.48) 0.34  (0.24-0.40) 0.61  (0.58-0.63) 0.45  (0.41-0.50) 0.55  (0.45-0.63) 0.42  (0.33-0.48) 0.450 †values reported as mean (range) for N=19 term control villi; all other values reported as mean methylation  unless otherwise specified. PMDs, partially-methylated domains.   175 B.3 Supplementary figures   Supplementary Figure 3.1: Comparison of genome-wide methylation in PM324 vil1 site (pink) carrying CNVs to balanced site vil4 (green) and healthy term controls (grey). a) Principal component analysis of PM324 vil1 compared to term control villi. PM324 vil1 does not appear as an outlier in the top two PCs. b) Pairwise Spearman correlation (r) in genome-wide DNAme among PM324 vil1, vil4, and controls. PM324 vil1 does not have observably low correlation with other placental samples c) Density distribution of methylation -values, separated by CpGs in the PM324 vil1 duplication regions and the rest of the genome. Controls represent mean -value at each probe across all controls. d) Density distribution of mean methylation values at placental PMDs. PM324 vil1 has more lowly-methylated PMDs in duplicated regions, but does not show altered methylation at PMDs overall. 176 Appendix C  Supplementary materials for Chapter 4 C.1 Supplementary methods Ancestry can be a significant confounder in genetic association studies and is important to assess. While self-reported ethnicity is often used as a surrogate for genetic ancestry, it is not always available and may not always reflect genetic ancestry in admixed populations. In our study, maternal reported ethnicity was available for 67% of cases, with no information of paternal ethnicity. Due to the lack of complete information and the desire to assess fetal (placental) genetic ancestry rather than that of the mother, we chose to utilize a panel of 57 ancestry informative marker SNPs (AIMs) (1-3) to assess population stratification in our study. This panel was designed by Phillips et al. (1-3) to differentiate between European, South Asian, East Asian, and African ancestry. Rather than use these SNPs to categorize samples into discrete ancestry groups, we applied a method in which ancestry could be described as a continuous variable, which may better reflect admixture in our population from Vancouver, Canada.  DNA samples from 287 placental chorionic villi were genotyped at 55 AIM SNPs (2 assays were not designable) using the Sequenom iPlex Gold platform by the Génome Québec Innovation Centre at McGill University, Montreal, Canada. Samples with a call rate of <0.9 were excluded, and subsequent SNPs with a call rate of <0.9 were also excluded. This left 53 SNPs and 277 samples for ancestry assessment. In addition to our study samples, 2157 individuals from African (N=661), East Asian (N=504), European (N=503), and South Asian (N=489) populations from the 1000 Genomes Project (1kGP) Phase III (4) were added as ancestry reference populations. We were able to download genotypes for 50/53 SNPs from the 1000 Genomes Browser (NCBI). In both datasets, genotypes at the 50 AIM SNPs were re-coded as 0, 1, or 2 based on the minor allele in our genotyped placentas, where 0 represents homozygous  177 reference, 1 heterozygous and 2 homozygous alternative genotype. A pairwise Euclidean distance matrix was calculated using these genotypes in the combined placental and 1kGP dataset. Classical multidimensional scaling (MDS) was applied to this distance matrix to represent it in k=3 dimensions, as these three dimensions clustered the 1kGP populations by ancestry and were significantly different between these 4 1kGP ancestry populations (Supplementary Figure 4.1). We extracted the values from the first 3 coordinates from the MDS for the 277 study samples and utilized them to describe ancestry as a continuous measure to assess population stratification in the genetic association study in this article (Chapter 4, Figure 4.1). References 1. Phillips C, Salas A, Sanchez JJ, Fondevila M, Gomez-Tato A, Alvarez-Dios J, Calaza M, de Cal MC, Ballard D, Lareu MV, Carracedo A, SNPforID Consortium. Inferring ancestral origin using a single multiplex assay of ancestry-informative marker SNPs. Forensic Sci Int Genet. 2007;1(3-4):273-280.  2. Fondevila M, Phillips C, Santos C, Freire Aradas A, Vallone PM, Butler JM, Lareu MV, Carracedo A. Revision of the SNPforID 34-plex forensic ancestry test: Assay enhancements, standard reference sample genotypes and extended population studies. Forensic Sci Int Genet. 2013;7(1):63-74.  3. Phillips C, Freire Aradas A, Kriegel AK, Fondevila M, Bulbul O, Santos C, Serrulla Rech F, Perez Carceles MD, Carracedo A, Schneider PM, Lareu MV. Eurasiaplex: a forensic SNP assay for differentiating European and South Asian ancestries. Forensic Sci Int Genet. 2013;7(3):359-366.  4. 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, Abecasis GR. A global reference for human genetic variation. Nature. 2015;526(7571):68-74.  178  C.2 Supplementary tables Supplementary Table 4.1: PCR and pyrosequencing conditions.  MTHFR 677 MTHFR 1298 rDNA Alu LINE-1 Primer sequence (5’ to 3’)        Forward 5Biosg/CTC AAA GAA AAG CTG CGT GAT TCC AGC ATC ACT CAC TTT GTG AC 5Biosg/GTT TTG GGG TTG ATT AGA GG 5Biosg/TTT TTA TTA AAA ATA TAA AAA TTA GT TTT TGA GTT AGG TGT GGG ATA TA   Reverse TGT CAT CCC TAT TGG CAG GTT 5Biosg/CTT TGG GGA GCT GAA GGA CTA CTA AAA ACC CAA CCT CTC CAA C CCC AAA CTA AAA TAC AAT AA 5Biosg/AAA ATC AAA AAA TTC CCT TTC   Sequencing AAG CAC TTG AAG GAG AA AACAAA GAC TTC AAA GAC AC S1: GGG TTG ATT AGA GGG TT S2: TTT TGG GGA TAG GTG T S3: GGG GGA GGT ATA TTT TT AAT AAC TAA AAT TAC AAA C AGT TAG GTG TGG GAT ATA GT PCR reaction (µL)        dH2O 9.22 9.22 16.8 16.3 16.3   10x PCR buffer 1.5 1.5 2.5 2.5 2.5   1.25 nM dNTPs 2.4 2.4 4 4 4   10 µM F/R primers 0.6 0.6 0.5/0.5 0.5/0.5 0.5/0.5   5 U/µL Taq DNA        polymerase 0.18 0.18 0.2 0.2 0.2   DNA 50 ng 50 ng 15 ng (bsc) 30 ng (bsc) 30 ng (bsc) PCR cycling conditions        Step 1 95˚ C for 05:00 95˚ C for 05:00 95˚ C for 15:00 95˚ C for 15:00 95˚ C for 15:00   Step 2 95˚ C for 00:20 95˚ C for 00:20 95˚ C for 00:20 95˚ C for 01:30 95˚ C for 00:20   Step 3 57˚ C for 00:20 60˚ C for 00:20 54˚ C for 00:20 49˚ C for 01:00 50˚ C for 00:20   Step 4 72˚ C for 00:20 72˚ C for 00:20 72˚ C for 00:20 72˚ C for 01:20 72˚ C for 00:20   Step 5 Go to step 2, 49 times Go to step 2, 49 times Go to step 2, 40 times Go to step 2, 39 times Go to step 2, 44 times   Step 6 72˚ C for 05:00 72˚ C for 05:00 72˚ C for 05:00 72˚ C for 05:00 72˚ C for 05:00 PCR product sequenced  5 ul 5 ul 3 ul 12 ul 12 ul rDNA, ribosomal RNA genes; bsc, bisulfite converted 179 Supplementary Table 4.2: Global minor allele frequencies of 50 AIM SNPs used to assess ancestry.  AFR EAS EUR SAS rs39897 0.197 0.336 0.545 0.225 rs239031 0.683 0.043 0.012 0.004 rs722098 0.158 0.457 0.813 0.646 rs730570 0.241 0.238 0.841 0.539 rs734482 0.058 0.038 0.545 0.236 rs756913 0.076 0.004 0.500 0.146 rs773658 0.633 0.025 0.004 0.000 rs881929 0.356 0.885 0.388 0.145 rs896788 0.321 0.637 0.180 0.495 rs984038 0.449 0.402 0.392 0.698 rs1024116 0.266 0.093 0.538 0.309 rs1335873 0.921 0.344 0.289 0.228 rs1363345 0.532 0.172 0.638 0.313 rs1426654 0.074 0.012 0.997 0.685 rs1498444 0.074 0.249 0.436 0.336 rs1519654 0.082 0.000 0.500 0.157 rs1544656 0.388 0.127 0.803 0.357 rs1573020 0.477 0.045 0.000 0.009 rs1785864 0.130 0.388 0.582 0.293 rs1886510 0.095 0.156 0.525 0.182 rs1941411 0.125 0.003 0.701 0.169 rs1978806 0.513 0.000 0.004 0.012 rs2026721 0.548 0.002 0.053 0.022 rs2040411 0.170 0.699 0.331 0.528 rs2065160 0.454 0.782 0.101 0.121 rs2065982 0.077 0.705 0.055 0.224 rs2156208 0.707 0.059 0.542 0.187 rs2196051 0.033 0.001 0.705 0.189 rs2227203 0.034 0.079 0.436 0.231 rs2303798 0.687 0.204 0.014 0.185 rs2472304 0.033 0.164 0.599 0.156 rs2572307 0.547 0.000 0.004 0.000 rs2814778 0.964 0.000 0.006 0.000 rs2835133 0.609 0.299 0.059 0.153 rs3785181 0.099 0.622 0.088 0.134 rs3827760 0.003 0.873 0.011 0.013 rs5997008 0.523 0.022 0.007 0.040 rs6026972 0.073 0.045 0.588 0.272 rs7354930 0.086 0.000 0.344 0.047 rs7897550 0.067 0.410 0.278 0.263 rs9487258 0.285 0.112 0.739 0.389  180  AFR EAS EUR SAS rs9522149 0.057 0.012 0.763 0.213 rs10008492 0.017 0.001 0.566 0.065 rs10131666 0.181 0.139 0.605 0.327 rs10141763 0.465 0.460 0.044 0.299 rs10843344 0.021 0.125 0.340 0.239 rs10962599 0.023 0.000 0.726 0.173 rs12913832 0.028 0.002 0.636 0.071 rs16891982 0.036 0.006 0.938 0.059 rs17625895 0.054 0.004 0.482 0.112 Data retrieved from four major global ancestry groups from the 1000 Genomes Project (Phase III). AFR, African; EAS, East Asian; EUR, European; SAS, South Asian      Supplementary Table 4.3: MTHFR 677 and 1298 genotype counts and Hardy-Weinberg equilibrium in controls.  N 677CC 677CT 677TT   p 1298AA 1298AC 1298CC p Control 179 88 81 10 0.58 83 78 18 0.89 EOPE 28 13 12 3 - 15 13 0 - LOPE 20 13 4 3 - 12 5 3 - nFGR 21 9 9 3 - 13 7 1 - NTD 55 25 25 5 - 30 21 4 - p-values calculated using an exact test for HWE in the control group. HWE, Hardy-Weinberg equilibrium; EOPE, early-onset preeclampsia; LOPE, late-onset preeclampsia; nFGR, normotensive fetal growth restriction; NTD, neural tube defect  181 C.3 Supplementary figures  Supplementary Figure 4.1: Distribution of ancestry coordinates derived from MDS of 50 AIM SNP genotypes in N=2157 1000 Genomes Project (1kGP) samples and N=278 placental samples from the current study. A) 3D plot of the 3 MDS coordinates shows that the 1kGP samples cluster by superpopulations, representing 4 major ancestry groups (AFR, N=661; EAS, N=504; EUR, N=503; SAS, N=489). Placental study samples (black) cluster with the EUR or EAS ancestries, as expected based on available maternal-reported ethnicity, with some samples clustering between groups indicating mixed ancestry. B) Boxplot of distributions of MDS ancestry values within each of the four 1kGP superpopulations. Within each coordinate, the distributions of values between the four populations are all significantly different from one another (pair-wise Kolmogrov-Smirnov tests, Bonferroni-corrected p-value<0.05). AFR: African; EAS: East Asian; EUR: European; SAS: South Asian.  182   Supplementary Figure 4.2: Distribution of N=277 study samples along 3 MDS ancestry coordinates. Samples are coloured by maternal reported ethnicity, where available. The three ancestry values are highly concordant with maternal reported ethnicity, with evidence for mixed ancestry of placental (fetal) DNA samples that cluster between major ancestry clusters.     183  Supplementary Figure 4.3: Distribution of unadjusted p-values by CpG density between high-risk 677 or high-risk 1298 placentas compared to reference placentas. N=442,348 CpGs on the 450k array were separated into four CpG density categories, and the linear model (MTHFR group as the main effect and fetal sex and gestational age included as covariates) was re-run within each CpG density category. No CpG density category (high density island, island shore, intermediate density island, non-island) showed a trend for differential methylation by either MTHFR high-risk genotype group. 

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