Open Collections

UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Mapping a new locus for non-syndromic strabismus with high-throughput genome analysis Ye, Xin 2014

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
24-ubc_2014_september_ye_xin.pdf [ 6.62MB ]
Metadata
JSON: 24-1.0167238.json
JSON-LD: 24-1.0167238-ld.json
RDF/XML (Pretty): 24-1.0167238-rdf.xml
RDF/JSON: 24-1.0167238-rdf.json
Turtle: 24-1.0167238-turtle.txt
N-Triples: 24-1.0167238-rdf-ntriples.txt
Original Record: 24-1.0167238-source.json
Full Text
24-1.0167238-fulltext.txt
Citation
24-1.0167238.ris

Full Text

MAPPING A NEW LOCUS FOR NON-SYNDROMIC STRABISMUS  WITH HIGH-THROUGHPUT GENOME ANALYSIS  by Xin Ye  B.Sc., The University of British Columbia, 2011  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Medical Genetics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  June 2014  © Xin Ye, 2014  ii Abstract Eye misalignment, called strabismus, occurs in up to 5% of individuals. While misalignment is frequently observed in rare complex syndromes, the majority of strabismus cases are non-syndromic. Over the past decade, genes and pathways associated with syndromic forms of strabismus have emerged, but the genes contributing to non-syndromic strabismus remain elusive. Non-syndromic strabismus is highly heterogeneous, and different loci have been inferred from previous genetics studies. Only a single strabismus locus, STBMS1, on chromosome 7 has been confirmed in more than one family, but the reported inheritance patterns of this locus with disease conflict and no specific variant has been proposed.  Here, I analyzed a large non-consanguineous family with multiple individuals affected by strabismus across seven generations.  The hypothesis is that a single variant is responsible for the non-syndromic strabismus in this particular family displaying dominant patterns of inheritance. Whole exome sequencing (WES) was performed to uncover large-blocks of variations within protein-coding regions of the genome shared by two affected distant relatives. In parallel, chromosome regions segregating with the strabismus phenotype in the family were identified using linkage analysis on 12 individuals. Linkage analysis identified one specific risk locus of high confidence. Based on the lack of protein-coding alterations in the locus, whole genome sequencing (WGS) was performed to find additional shared candidate causal variants. Combining the available information, a 10 Mb region on chromosome 14 was identified with high confidence that it was associated with strabismus, within which a set of potential regulatory sequence alterations have been highlighted for further study. This study represents the first identified locus for autosomal dominant, non-syndromic, strabismus. The project utilizes next-generation sequencing (NGS), linkage  iii analysis, and bioinformatic analyses to prioritize and select both coding and non-coding variants, demonstrating the effectiveness of combining NGS and classical genetic approaches. The research findings improve our understanding of strabismus genetics and defines multiple paths for future research, family-specific genetic testing for early diagnosis, and consequent preventive therapy.                    iv  Preface A version of chapter 1 has been published. (Ye, X.C., Pegado, V., Patel, M.S., and Wasserman W.W. (2014) Strabismus genetics across a spectrum of eye misalignment disorders. Clinical Genetics). I conducted literature research on strabismus and wrote the draft manuscript. V. Pegado and M.S. Patel provided guidance on clinical perspectives of strabismus and edited the manuscript. W.W. Wasserman supervised the research and extensively edited the manuscript. Dr. J. Horton (University of California, San Francisco), Dr. S. Narasimhan, and Dr. V. Pegado ascertained participants. Chromosomal microarray (CMA) testing for index was performed and analyzed by the Cytogenetic laboratory at Children’s and Women’s Health Center. DNA samples for next-generation sequencing (NGS) and microarray genotyping were prepared by M. Higginson, Z. Zong, and myself. The linkage analysis was a collaborative effort with the Hospital for Sick Children in Toronto (SickKids) through the Finding of Rare Disease Genes Canada (FORGE Canada) network and performed by N. Roslin. WES and WGS data analyses were performed by myself, with assistance from C. Shyr and other Wasserman lab members for computational support. The variant density plot script was programed by Dr. E. Nosova. Allele-specific PCR was designed and performed by myself, while standard PCR was designed and performed by X. Han. The collection of the samples for these studies was approved by the University of British Columbia Children’s & Women’s Research Ethics Board, approval number CW10-0317/H10-03215.  v Table of Contents  Abstract .................................................................................................................................... ii!Preface ..................................................................................................................................... iv!Table of Contents .................................................................................................................... v!List of Tables ........................................................................................................................ viii!List of Figures ......................................................................................................................... ix!List of Abbreviations ............................................................................................................. xi!Acknowledgements ............................................................................................................... xii!Chapter  1: Introduction ........................................................................................................ 1!1.1! Etiology and pathogenesis ........................................................................................... 3!1.2! Risk factors .................................................................................................................. 5!1.3! Family and twin studies ............................................................................................... 6!1.4! Genetic mechanisms .................................................................................................... 8!1.5! Linkage analysis ........................................................................................................... 8!1.6! Gene expression studies ............................................................................................. 10!1.7! Duane retraction syndrome ........................................................................................ 11!1.7.1! Chromosome 8q and type 1 DRS ........................................................................ 12!1.7.2! CHN1 and type 2 DRS ........................................................................................ 13!1.7.3! Type 3 DRS......................................................................................................... 13!1.7.4! Okihiro syndrome ............................................................................................... 14!1.8! Animal models ........................................................................................................... 15!1.8.1! Famous strabismic animals ................................................................................. 15! vi 1.8.2! Cats ..................................................................................................................... 15!1.9! Conclusion and future directions ............................................................................... 17!1.10! Research objectives .................................................................................................. 17!1.11! Study structure ......................................................................................................... 18!Chapter  2: Project Description ........................................................................................... 23!2.1! Introduction ................................................................................................................ 23!2.2! Materials and methods ............................................................................................... 25!2.2.1! Samples ............................................................................................................... 25!2.2.2! Phenotyping ........................................................................................................ 25!2.2.3! DNA preparation ................................................................................................. 26!2.2.4! Instrumentation ................................................................................................... 26!2.2.5! Next-generation sequencing data processing and variant calling ....................... 26!2.2.6! WES variant prioritization .................................................................................. 27!2.2.7! WES variant selection ......................................................................................... 27!2.2.8! Confirming variant presence ............................................................................... 28!2.2.9! WES variant density plot .................................................................................... 29!2.2.10! Linkage analysis ................................................................................................ 29!2.2.11! WGS variant prioritization ................................................................................ 31!2.2.12! WGS variant selection and confirmation .......................................................... 31!2.3! Results ........................................................................................................................ 32!2.3.1! The index case ..................................................................................................... 32!2.3.2! Pedigree ............................................................................................................... 32!2.3.3! Phenotyping ........................................................................................................ 33! vii 2.3.4! Whole exome sequencing analysis ..................................................................... 34!2.3.5! WES variant validation ....................................................................................... 35!2.3.6! WES variant density plot .................................................................................... 36!2.3.7! Linkage analysis .................................................................................................. 38!2.3.8! WGS analysis ...................................................................................................... 39!2.3.9! WGS variant prioritization .................................................................................. 40!2.4! Discussion .................................................................................................................. 42!Chapter  3: Future Directions .............................................................................................. 67!References .............................................................................................................................. 72!  viii List of Tables  Table 1.1    Selected syndromic strabismus associated genes and loci. .................................. 19!Table 2.1    Coverage and variants from whole exome sequencing (WES). .......................... 46!Table 2.2    Non-tolerant shared variants based on WESs from two distant relatives. ........... 46!Table 2.3    Selected syndromic strabismus associated loci. .................................................. 47!Table 2.4    Loci and corresponding hg19 coordinates associated with strabismus, either syndromic or non-syndromic. ................................................................................................. 49!Table 2.5    Number of shared and total variants and log2(ratio) for each chromosome in strabismic and control groups. ................................................................................................ 50!Table 2.6    Number of shared variants between different individuals on chr14:20,000,000-35,000,000............................................................................................................................... 51!Table 2.7    Sequencing data quality for whole genome sequencing (WGS). ........................ 52!Table 2.8   Coverage and variants of linked region from WGS with the BGI pipeline. ......... 52!Table 2.9   Coverage and variants of linked region from WGS with the in-house pipeline. .. 52!Table 2.10   Prioritized WGS variants from the linked region. .............................................. 53!  ix List of Figures Figure 1.1    A schematic representation of EOMs and nerve innervation with associated genes. ...................................................................................................................................... 20!Figure 1.2    Defects along the visual-motor axis can contribute to infantile esotropia ......... 21!Figure 1.3    Overview of the strabismus genetic study. ......................................................... 22!Figure 2.1    Pedigree for the subject family with non-syndromic strabismus. ...................... 54!Figure 2.2    Branch 1 of the subject family with non-syndromic strabismus. ....................... 55!Figure 2.3    Simplified branch 1 showing the genotyped individuals (with study ID) and ancestors required to link them. .............................................................................................. 56!Figure 2.4    Branch 2 of the subject family. ........................................................................... 57!Figure 2.5    Branch 3 of the subject family. ........................................................................... 58!Figure 2.6    Sanger sequence results of GTF2IRD2 variant for multiple individuals. .......... 59!Figure 2.7    Variant density plot of shared variants on chromosome 11. .............................. 60!Figure 2.8    Variant density plot of shared variants on chromosome 14. .............................. 61!Figure 2.9    Simulated maximum LOD scores under the alternative hypothesis of linkage, under a range of genetic models. ............................................................................................ 62!Figure 2.10 Screenshot of region chr14: 20,000,000 – 35,000,000 from UCSC genome browser. The linked region chr14: 22,779,843 – 32,908,192 is gene-poor. ........................... 63!Figure 2.11    Venn diagram of WGS analysis for 001, 013, and 014 based on the BGI pipeline. ................................................................................................................................... 64!Figure 2.12    Venn diagram of WGS analysis for 001, 013, and 014 based on the in-house pipeline. ................................................................................................................................... 65! x Figure 2.13    Single nucleotide variant chr14:28052726 A > C impacts binding score for FoxA1 transcription factor binding site. ................................................................................. 66!  xi List of Abbreviations  AS-PCR  allelic specific polymerase chain reaction CADD   Combined Annotation Dependent Depletion CFEOM  congenital fibrosis of extraocular muscles CMA   chromosomal microarray CNV   copy number variation DRS   Duane retraction syndrome EOM   extraocular muscles EVS   Exome Variant Server FANTOM5  functions annotation of the mammalian genome 5 FORGE Canada Finding of Rare Disease Genes Canada GSC   Michael Smith Genome Sciences Centre GWAS  genome wide association studies IBD   identical by descent IGV   Integrative Genomic Viewer MeSHOPs  Medical Subject Heading Overrepresentation Profiles NGS   next-generation sequencing PCR   polymerase chain reaction SickKids  Hospital for Sick Children in Toronto TFBS   transcription factor binding site TSS   transcription start site WES   whole exome sequencing WGS   whole genome sequencing   xii Acknowledgements I would like to acknowledge my supervisors, Drs. Millan Patel and Wyeth Wasserman, for giving me a chance to pursue my research interest, sharing their scientific enthusiasm, and supporting me through my graduate studies. I would also like to thank my committee, Drs. Anna Lehman and Colin Ross, for their advice and warm support. To all the current and past lab members who have helped me in one way or another, thank you for sharing your time generously. I owe a particular thanks to Dora who provided research management support, and Dr. Maja Tarailo-Gaovac who made key suggestions to improve the written thesis. To my MedGen peers, my gratitude for the suggestions and encouragement throughout my study. To my family, thank you for believing in me and supporting me. For financial support, my thanks to the University of British Columbia Medical Genetics Graduate Program and the Child & Family Research Institute.      1 Chapter  1: Introduction Eye misalignment, called strabismus occurs in up to 5% of individuals1. While misalignment is frequently observed in rare complex syndromes, the majority of strabismus cases are non-syndromic. Over the past decade, genes and pathways associated with syndromic forms of strabismus have emerged, but the genes contributing to non-syndromic strabismus remain elusive. Genetic testing for strabismus risk may allow for earlier diagnosis and treatment, as well as decreased frequency of surgery. We review human and model organism literature describing non-syndromic strabismus, including family, twin, linkage, and gene expression studies. Recent advances in the genetics of Duane retraction syndrome are considered, as relatives of those impacted show elevated familial rates of non-syndromic strabismus. As whole genome sequencing efforts are advancing for the discovery of the elusive strabismus genes, this overview is intended to support the interpretation of the new findings.  Strabismus (eye misalignment) is one of the earliest recorded genetic disorders. More than 2400 years ago, Hippocrates observed ‘Children of parents having distorted eyes squint also for the most part’ 1. Strabismus can cause visual problems during development, including loss of binocular vision, amblyopia (‘lazy eye’), and abnormal retinal correspondence (shifting of the fixation point relative to the macula in one eye). Strabismus disrupts stereopsis, which impacts the performance of numerous practical tasks requiring the precise judgment of distance (e.g. driving) or depth (e.g. microscopy) 2. In addition to reduced visual function, strabismus is associated with psychosocial problems impacting self-image, interpersonal relationships, performance in school and employment 3. Children as  2 young as 5 years display a reduced tendency to interact with peers with noticeable strabismus 4,5. Strabismus negatively impacts employment rates and thus economic status 6. Strabismus surgery has positive impact on quality-adjusted life years (QALY), increasing QALY by 2.61, while being highly cost-effective ($1632/QALY) 7. While non-surgical intervention therapies (e.g. patching) in young children have not been similarly quantified, such practice is intended to reduce the need for surgical intervention. The prevalence of strabismus is 2–4% among Caucasians, 2.4% among Hispanic/Latinos, 2.5% among African-Americans, and 1% in East-Asians 8–11. Among Caucasians, esotropia (inward misalignment) is three times more common than exotropia, while exotropia predominates in Cameroon black (63% of cases) and Asian populations (more than 70% of cases) 12–15. Studies consistently report balanced distribution between genders 16–19. In most cases, non-syndromic strabismus is characterized by non-restrictive, non-paralytic ocular misalignment with the same magnitude in all directions of gaze, which is known as concomitant (comitant) strabismus. Incomitant strabismus is paralytic in origin and the angle of deviation varies in different directions. The occurrence of muscle paralysis can be determined by the broad H test, which is scored positive if one eye lags behind the other in at least one of the six positions of gaze 20. While the causes of non-syndromic strabismus are largely unknown, twin studies and family studies have demonstrated a substantial genetic contribution to strabismus 21. Although the heritability of strabismus has long been recognized, most advances at the level of specific genes have occurred during the past decade 8,12. Thus far, only a single non-syndromic strabismus locus on chromosome 7 has been confirmed to act in more than one family, and in those families the specific causal alterations have not been determined.  3 In this review, we summarize strabismus etiology and pathogenesis, genetic studies of non-syndromic strabismus and Duane retraction syndrome (DRS), as non-syndromic strabismus occurs at elevated rates in affected families 22, and describe model organism studies related to genetic forms of strabismus. 1.1 Etiology and pathogenesis The mechanisms underlying strabismus may involve one of several systems or tissues (Figure 1.1).  Past reports highlight the potential for disruptions in extraocular muscles (EOM), orbital connective tissues, cranial nerves, fusion centers, and the visual cortex 23. The position of the eye is determined by all the five components. Mechanical trauma, acquired inflammation or infiltration, and metabolic disorder can all lead to EOM myopathy and secondary strabismus. Abnormalities of either the location or stability of the connective tissue pulleys alter the direction of EOM pulling and contribute to both congenital and acquired strabismus. Congenital cranial dysinnervation disorders (CCDDs) have been associated with hypoplastic or misrouted motor nerves to EOMs, and additional cranial nerve abnormalities have been observed 23. Fusion centers include a convergent center at the rostral–dorsal midbrain and a divergence center that, based on acute onset of concomitant esotropia related to tumors, is likely situated in the hindbrain 24,25. Animal experiments show that abnormal early visual experience can lead to strabismus and cause changes in metabolic activity in the visual cortex 26. The age of onset distribution for strabismus is bimodal, with approximately 22% diagnosed before the age of 12 months and approximately 43% detected between 2 and 3 years of age. Non-accommodative strabismus was more common in the first group, while  4 accommodative strabismus was more common in the second group (where accommodative refers to strabismus arising with altered visual acuity) 27. Approximately 26% of first-degree relatives of patients with hypermetropic (far-sighted) accommodative esotropia were affected with strabismus 28, suggesting that individuals with inherited hypermetropia may be predisposed to strabismus. However, a recent study demonstrated that heritability of strabismus was independent of refractive error. Bivariate analysis indicated a phenotypic correlation of only 0.20 between refractive error and eso-deviation, including tropia (constant eye misalignment) and phoria (latent eye misalignment); in other words genetic contributions to strabismus and hypermetropia are largely independent 29. As indicated above, pathogenesis of infantile esotropia may result from defects spanning the visual-motor axis (Fig. 1.2) 30. Researchers have postulated about the relationship between strabismus and changes in the visual cortex. At the turn of the 20th century, Worth proposed that infantile esotropia was due to an inborn defect of fusion, as surgery on EOM could not reverse strabismus 31. Tychsen suggested that this fusion faculty was situated within the striate cortex, and specifically proposing that congenital defects would therefore be present in disparity-sensitive, binocular neurons 30. Using staining techniques, a paucity of such binocular connections was observed in both natural and induced strabismic monkeys while monocular connections remained. Electrophysiological measurement showed that loss of binocular responsiveness and disparity sensitivity was consistent with the reduced number of binocular connections 32. Hypotheses for strabismus mechanisms have been proposed which focus on the subcortical visual pathway, brainstem vergence motoneurons, the brainstem vestibule-ocular pathway, and cranial nerves 32. On the other end of the visual-motor axis, Chavasse proposed  5 a ‘motor’ hypothesis, suggesting that abnormal optical input, such as weakness of the EOM, may impede development of binocular fusion thus leading to strabismus. He argued that surgery in the very young age to restore eye alignment could rescue binocular vision 32,33. Clinical data showed that shorter durations of misalignment correlated with better stereopsis, implying that muscle abnormalities lead to poor stereopsis, not vice versa 33. Examination of strabismic EOM identifies some abnormalities. A 2012 magnetic resonance imaging study of 12 concomitant esotropes and 13 controls demonstrated rectus muscle enlargement. Cross sections of medial rectus muscle were up to 39% larger (p < 0.005), and those of lateral rectus muscle were up to 28% larger in the esotropic cases. Moreover, medial rectus contractility was 60% higher in exotropic individuals (p < 0.005) 34. It is inconclusive, however, whether the structural changes in EOMs are the cause of strabismus or merely reflect the adaptation to the change of motoneuron firing patterns, as observed in other skeletal muscle tissue 35. Schoeff et al. reasoned that the lack of evidence of EOM denervation or dysinnervation in non-syndromic strabismus suggested a visual cortex contribution 34. As live imaging technology advances, higher resolution examination may advance our understanding of the relative contribution of defects in muscle and nerves to the strabismus phenotype.  1.2 Risk factors Significant strabismus risk factors include retinopathy of prematurity, low birth weight, premature birth, and smoking during pregnancy. As our focus will remain on genetic risk, the interested reader may find additional information about the other factors in the systematic review by Maconachie et al. 19.    6 1.3 Family and twin studies Many early studies focused on the transmission of strabismus through families. However, findings varied in terms of heritability, inheritance mode, and the concordance of strabismic types 19. Surveys conducted between 1910 and 1950 indicated that hereditary factors ranged from 20% to 50% in families with esotropia 36. Schlossman and Priestley found that 47.5% of 158 patients with strabismus, 48.9% of 139 esotropes, and 36.8% of 19 exotropes belonged to families with two or more additional affected members. The authors suggested that the actual number might be larger since subtle alignment deviations could be missed 37. The highest reported familial incidence of strabismus was 65% 16,28. A longitudinal study found that 18% of 34 babies born in families with a parent affected by convergent (i.e. esotropia) strabismus developed constant or intermittent esotropia by 6 months 38. As the types of assessed relatives varied between studies and there was no consideration of environment, the precise genetic risk is unclear. Nevertheless, the figures were much higher than those in general population (approximately 5%), supporting a contribution of genetics to strabismus risk. The concordance of strabismus types varied across the studies. Families with a mixture of esotropia and exotropia phenotypes were reported 13,37. One study found that 80% of strabismus cases occurring in the same family were concordant 19. Another study reported 54% concordance within 39 studied families 13. As familial clustering of strabismus can reflect either a common genetic factor or an unrecognized environmental factor, twin studies are the key to quantify the relative genetic contribution. Twin studies of strabismus have reported higher concordance rates in monozygotic twins than dizygotic twins, suggesting a predominant genetic factor 19. Matsuo et al.’s twin study showed that strabismic subtypes of 67.3% of 49 pairs or sets were  7 concordant, and the concordance rate was higher in monozygosity (82.4%) than in multizygosity (47.6%) 39. Wilmer and Backus performed a meta analysis, reporting monozygosity and dizygosity concordances of 54% and 14%, respectively, in studies with systemic ascertainment; and 66% and 19%, respectively, without systematic ascertainment 40. This contradicted with Paul and Hardage’s 1994 study, but Wilmer and Backus observed that a translation error in the 1994 study led to an overestimation of dizygosity concordance 40,41. Podgor reported that the odds ratio for esotropia rose from 2.6 if a sibling from a preceding birth was affected to a ratio of 5.4 if a twin (or other multiple birth) was affected 21. Esotropia and exotropia have a strikingly different genetic risk profile. In the Podgor study, a striking odds ratio of 330 was reported for exotropia in cases of multiple birth with one affected twin, while single births had an extremely low odds ratio of 2.2, data most consistent with a strong multiple birth environmental impact on exotropia risk 21. A study with 1462 twins suggested that genetic heritability was specific to esotropia, reporting that heritability of eso-deviation was 64% while no heritability was detected for exo-deviation 29. Exotropia (75%) had higher observed concordance than esotropia (65.7%) in a Chinese twin study, which may reflect influence of both the multiple birth environmental influence on esotropia and potential ethnic differences in the genetic contribution to esotropia 19,42. A key consideration arises from twin studies. Wilmer and Backus raised the potential confounding contribution of phoria to the study of strabismus genetics. Phoria is a latent misalignment of the eyes that appears when fixation on a target is broken (which can be revealed with a cross-cover test). Wilmer and Backus observed that genetic factors were necessary for strabismus development but not for phoria development 40. Phoria cases have  8 been noted in families with strabismus, and a portion of strabismus genetics studies have included phoria as positive cases 13. Summarizing the above information, esotropia is most closely tied to heritable factors while exotropia has a stronger environmental component. Future studies should therefore be designed in a manner that controls for the environmental component, including multiple births. 1.4 Genetic mechanisms Dominant, recessive, and sex-linked inheritance patterns have been proposed for non-syndromic strabismus in family studies 19,37. In different families, Czellitzer reportedly suggested two recessive genes were responsible for strabismus, while Waardenburg proposed a model of a single autosomal gene 37,43. A study using quantitative measurement of sensory and motor function rejected the theories of Mendelian inheritance of strabismus as a single trait 14. The majority of studies have noted that simple Mendelian models cannot explain the complexity of strabismus inheritance patterns. There are multiple genetic mechanisms represented in the families described in the scientific literature. Furthermore, the high frequency of strabismus may confound family studies with some cases likely arising from environmental mechanisms. Without accurate categorization based on exquisite pathological characterization of the strabismus, and given the diversity of potential physical mechanisms, such conflicting results are not entirely unexpected. 1.5 Linkage analysis Parikh et al. identified the first concomitant strabismus locus on chromosome 7p22.1 (STBMS1) in a linkage analysis of a large family. Among seven initially assessed multiplex families with non-syndromic strabismus, one family showed a significant logarithm of the  9 odds (LOD) score on chromosome 7. Although the pedigree suggested an autosomal dominant inheritance pattern, the haplotype data was most consistent with an autosomal recessive model or a more complex model, such as the authors’ proposed semi-dominant inheritance model 44. The autosomal recessive inheritance model has been subject to discussion 12. The other six families in the original study were not consistent with the chromosome 7 loci contributing 44. In the subject family, eight of fourteen siblings were affected, and seven of these eight patients had hypermetropia of varying severity. Rice et al. examined 12 additional families, of which one was consistent with an STBMS1 role. Five affected family members had primary non-syndromic comitant esotropia while 21 examined family members were unaffected. In this second STBMS1 family, the pattern of inheritance best fits a dominant mode of inheritance 45. In combination the reports indicate that there is at least one non-syndromic strabismus associated genetic component at the STBMS1 locus. Elucidating the causal mutations in the two families may clarify the conflict between transmission models. The Ohtsuki group tried to identify comitant strabismus susceptibility loci through sib-pair analysis and non-parametric linkage analysis for multiple pedigrees. This initial 2003 attempt indicated multiple loci with low LOD scores 46. A 2008 report identified 4q28.3 and 7q31.2 loci as having significant evidence of linkage. After stratifying cases into esotrpoia and exotropia subgroups, they identified additional loci at 8q24.21 and 14q21.3, respectively47. A summary of reported candidate loci for comitant strabismus is presented in Table 1.1. Based on the range of findings, it appears likely that multiple genes are contributing to  10 familial forms of strabismus. Elucidating the specific genes remains a grand challenge for the field, but emerging genome sequencing tools may generate a new wave of insights. 1.6 Gene expression studies Experimental approaches to elucidate molecular mechanisms related to strabismus have been pursued. Microarray analysis showed that expression of 604 genes differ significantly between 100 strabismic EOM samples and 28 normal EOM samples. Together with polymerase chain reaction (PCR) experiments, three major conclusions were drawn. Collagen and collagen-related genes were upregulated; specific myosins, such as EOM-specific myosin (MYH13) and myosin heavy chain-1 (MYH1), and related contractile genes were downregulated; genes involved in energy balance, such as mitochondrion homeostasis or regulations of energy metabolism, were dysregulated in strabismic EOMs. The conclusions should be assessed with caution, since it was not specified which forms of strabismus were represented in the samples, although the authors suggested that the sample set may have a high portion of exotropia cases 48. In another study, expression levels of seven myogenesis-related genes in EOMs from 18 concomitant strabismus patients were compared against 12 samples from a single non-strabismic individual. Six of the genes had reduced expression levels, leading Zhu et al. to suggest that altered growth of muscles may be involved. However, it was unclear whether the patients had congenital strabismus nor the nature of the deviations involved 49. Furthermore, the two sample sets were collected in distinct ways (i.e. obtained from corrective surgery vs cadavers), which has been recognized to cause difficulty in the interpretation of gene expression studies 50,51.   11 1.7 Duane retraction syndrome While the focus of this review is the genetics of non-syndromic forms of strabismus, there are familial syndromes in which strabismus rates are elevated in otherwise non-syndromic family members. About 70% of DRS cases do not exhibit other congenital abnormalities, and approximately 20% of cases have a family history of strabismus 22,52. Overall DRS accounts for approximately 5% of strabismus cases 53. DRS is a congenital cranial dysinnervation disorder. Based on these observations, we include DRS in this review as we perceive an opportunity to find common causal genes between non-syndromic strabismus and DRS. Three types of DRS have been described based on clinical examination. In these studies, key attributes include abduction, movement of a body part away from the midline, and adduction, movement toward the midline. Type 1 DRS is characterized by marked limitation of abduction, type 2 DRS is characterized by marked limitation of adduction, and type 3 DRS is characterized by a combination of marked limitation of both 54. The majority (60%) of diagnosed DRS cases are female. Up to 60% of all cases are bilateral, and up to 80% of unilateral cases are left-sided 54,55. Wabbels et al. found predominant females cases (64%) and left eye involvement of unilateral cases (72%), whereas bilateral only accounted for 12% of cases 56. While most cases are sporadic, reports of familial DRS date back to 1896 57. Up to 10% of Duane anomalies are inherited in an autosomal dominant fashion 58. The connection between infantile esotropia and DRS are illustrated by recent studies. In the Strabismus Inheritance Study in Tasmania (SIST), a set of 133 families with infantile esotropia was recruited, of which multiple members were affected with DRS in two families. A separate set  12 of 40 families with at least one case of DRS were recruited, of which 21 had a familial history of ocular motility disorders but only two had multiple members affected by DRS 54. Linkage analysis had previously shown linkage between 8q12-13 and Duane syndrome. The SIST study confirmed a prior association of both DRS and infantile esotropia with partial trisomy 8 59,60. Combining this information, a gene-dosage mechanism was proposed 54. Separately, Khan et al. identified two susceptibility loci, 3p26.3-26.4 and 6q24.2-25.1 using multipoint linkage analysis in a consanguineous family with four affected children (one with DRS and three with non-syndromic esotropia) 22. 1.7.1 Chromosome 8q and type 1 DRS The focus on chromosome 8q in DRS studies has progressed to the search for a causal gene in the loci, but no clear single causal gene has been established. A de novo reciprocal balanced translocation t(6;8)(q26;q13) was identified in a patient with DRS. This patient had amblyopia and narrowing of palpebral fissures 61. The carboxypeptidase A6 (CPA6) gene at the previously identified DURS1 (DRS-1) locus on chromosome 8 was disrupted between the first two exons in this patient and was proposed as the causal gene 62. CPA6, a member of the M14 metallocarboxypeptidase family, is expressed in a limited number of tissues in mice, including the rectus muscle layer of the embryonic eye. In adult mouse, CPA6 was expressed in olfactory bulb and other parts of the brain 63. CPA6 knockdown using morpholino antisense oligos in zebrafish did not produce a phenotype, contradicting a dosage hypothesis 54,64. No pathogenic CPA6 mutations were identified in a set of 18 sporadic DRS patients (61). Two patients with microduplication of 8q12 displayed multiple congenital anomalies, including DRS 65,66. Studying a third patient with similar phenotype, including DRS, a recent study identified the minimal critical region at the loci of 1.2Mb, excluding CPA6. CHD7  13 duplication was suggested to be responsible for at least part of the features in resulting from the 8q12 duplication 67. Reported duplications and deletions in affected individuals do not overlap, suggesting either multiple contributing genes or a gene with distal regulatory regions might be responsible 68. Although the chromosome region 8q12-q13 has been linked to DRS1 in multiple cases, more study is required before a definite conclusion can be drawn about the causal gene. 1.7.2 CHN1 and type 2 DRS The CHN1 gene has been more clearly demonstrated to be a causal gene for DRS2. CHN1 is located on chromosome 2 and encodes two Rac-specific guanosine triphosphatase (GTPase)-activating alpha-2-chimerin isoforms. Miyake et al. identified seven heterozygous missense mutations in seven unrelated DRS2 families co-segregating with the affected haplotypes 69. These mutations were neither recorded in the single nucleotide polymorphism database nor observed on 788 control chromosomes. CHN1 mutations were present in 7 of 20 (35%) examined DRS families, while no CHN1 mutations were observed in 140 sporadic DRS patients 70. Predicted gain-of-function mutations in CHN1 were found in two families with type 2 DRS 71. Overexpression of wild-type alpha-2-chimerin in the chick embryonic oculomotor nucleus led to stalling of oculomotor nerve growth and the premature axon termination adjacent to the dorsal rectus muscle, supporting a functional role for CHN1 in DRS 69. 1.7.3 Type 3 DRS While loci have been established that account for a portion of type 1 and type 2 DRS, the genetic components of type 3 DRS are more elusive. It is possible that the type 3 DRS is more heterogeneous than the other two classes. In a thin-sectioned magnetic resonance  14 imaging (MRI) study, the abducens nerve was reliably observed in 60 eyes of 30 individuals from a control group. The abducens nerve on the affected eye was absent in 18 of 18 eyes from 16 patients with type 1 DRS, and in 2 of 2 eyes from type 2 DRS patients. The nerve was absent in only 3 of 5 eyes from five patients with type 3 DRS 72. The clinical heterogeneity in type 3 DRS may reflect genetic heterogeneity. 1.7.4 Okihiro syndrome In addition to the ocular anomalies of the basic form of DRS, Okihiro syndrome (also called Duane-radial ray syndrome) is associated with additional abnormalities affecting the upper limbs and, less commonly with renal anomalies and sensorineural hearing loss 73. Autopsy and MRI studies of Okihiro syndrome patients have revealed hypoplasia or absence of the sixth nerve nucleus (i.e. abducens nerve) on the affected side, the ipsilateral lateral rectus being innervated by branches of the oculomotor nerve 74,75. Mutations in the SALL4 zinc finger transcription factor gene were the first causal genetic alterations discovered for Okihiro syndrome patients 73. The discovery arose when Kohlhase et al. proposed that Okihiro syndrome might be due to mutations in a SALL gene family member based on phenotype overlap between Okihiro syndrome and Townes-Brocks syndrome, which is caused by mutations in the SALL1 gene. They successfully identified mutations in SALL4 gene from five of eight Okihiro families 76. Al-Baradie et al. identified a nonsense mutation in SALL4 gene in affected individuals originally reported by Okihiro et al. in 1977, as well as 2 additional families 77. The broader DRS phenotype is present in approximately 70% of SALL4 mutations carriers 78. A mouse model shows that Sall4 is regulated by Tbx5 transcription factor; both genes contribute to patterning and morphogenesis of the anterior forelimb and heart 79. This observation explains the shared  15 endophenotypes between Okihiro syndrome and Holt – Oram syndrome, which is associated with mutations in the TBX5 gene. Whole mount in situ hybridization analysis of Sall4 expression during mouse embryogenesis shows prominent expression in midbrain and branchial arches and suggests that a dosage reduction of Sall4 might disrupt abducens nerve development 78,79. 1.8 Animal models Although the genetic origins of strabismus remain to be fully deciphered, several animal models of the phenotype have been studied and may serve as resources in the search for causal genes. Most of the model animals described below are albinos, with pigmentation loss ranging from partial to complete. Visual abnormalities, including strabismus, have been linked with albinism in diverse mammals such as albino primates, white tigers, and albino cats (including Siamese cats) 80–83. 1.8.1 Famous strabismic animals Animals with cross-eyes have become popular images on the Internet. Joco, a cross-eyed lion at the Erfurt Zoo (Germany) is most likely to suffer from congenital strabismus. The cross-eyed opossum Heidi at the Leipzig Zoo (Germany) became a celebrity, but the condition was likely environmentally triggered. The causes of strabismus in animals vary, with only a portion deriving from genetic influence. Finding suitable animal models for the study of non-syndromic strabismus could accelerate research efforts. 1.8.2 Cats In Siamese cats, a temperature-sensitive mutated TYR gene encoding tyrosinase is expressed normally in cooler extremities, giving a darker color, while expression is reduced in warmer parts of the body, leading to poor pigmentation. Anatomical studies show that  16 axons of temporal retinal ganglion cells go to the opposite side of the brain instead of staying on the same side as observed in non-albino cats 84,85. The misrouting defects are also observed in albino mice and rabbits with TYR defects. Insertion of functional TYR genes into such albino mice and rabbits corrects for axon misrouting 86. Humans with ocular albinism also show abnormal decussation (crossing) of optic neurons, causing reduced or absent binocularity. This characteristic is associated with elevated prevalence of strabismus 87. Nevertheless, there is not yet convincing evidence that TYR mutations contribute to strabismus in humans. While the link between strabismus and axon misrouting is unknown, genes directly involved in optic chiasm development might be considered as candidates 88. The unusual axon wiring pattern observed in TYR defective albino animals raises concern that these animals may not be suitable models for human strabismus. Artificially induced strabismus models, such as those established by tenotomy (tendon lengthening) and by exposure to early abnormal visual experience, may be similarly ill-suited to study genetic influences on strasbismus. To evaluate the relevance of artificially induced strabismic cats, the ocular dominance distributions for cats with induced strabismus and natural strabismus were compared and found to be similar. Approximately 35% of cells were monocular in either strabismus group, but a statistically significant difference was noted with normal cats, which have 81% binocular cells 89. Work with the animal models continues, exemplified by a study which showed that early induced unilateral convergent strabismus in cats led to abnormal corpus callosum connection 90. Such experiments highlight how abnormal early visual experience impacts visual cortex development, but do not provide a clear path for using induced animal models to track down key genes. Thus the study of non-syndromic strabismus could benefit from efforts to identify additional eye misalignment animal models.   17 1.9 Conclusion and future directions The causal genes predisposing to non-syndromic forms of strabismus remain to be discovered. The combination of next-generation sequencing with both large-scale populations and targeted families may soon reveal critical genes and consequently confirm or expose critical molecular mechanisms. Genome-wide association studies have been reported to be underway, while exome sequencing family-specific studies of non-syndromic strabismus are likely to emerge soon 91,92. Aided by the background presented in this overview, the discovery of critical genes causing non-syndromic strabismus will allow earlier identification of individuals who are at high-risk and thus most likely to benefit from effective early intervention treatments. (This is the end of the publication.) 1.10 Research objectives  In order to improve our understanding of non-syndromic strabismus, I combined classical genetics and NGS approaches to identify variants that co-segregate with non-syndromic strabismus in a large North American family. Given the observed Mendelian autosomal dominant mode of inheritance in the family, I hypothesize that this familial strabismus is monogenic. Three major objectives of this study are: 1) To analyze variants affecting protein-coding regions that co-segregate with strabismus in a pair of third cousins using a WES approach. 2) To identify genomic region linked to strabismus through linkage analysis. 3) To investigate variants affecting non-protein coding regions within the linked region using WGS approach.   18 1.11 Study structure Family studies with well-defined phenotypes are crucial to advance the understanding of the genetics of non-syndromic strabismus. We identified a multi-generation strabismus family and took two parallel tracks: WES analysis and linkage analysis, which was a collaborative effort with the Hospital for Sick Children in Toronto (SickKids) through the Finding of Rare Disease Genes Canada (FORGE Canada) network. Both approaches led to the identification of the same region, but no promising protein-coding variant could be found in this region, which contains few exons (Figure 1.3). Next, we used WGS to scan the genome in a uniform manner and to generate additional candidate variants for the linked region. Lead candidates were selected through a combination of prioritization strategies and will be subjected to genetic validation in additional families and functional validation. 19 Table 1.1    Selected syndromic strabismus associated genes and loci. Loci Inheritance pattern Ethnicity Phenotype PMID 7p22.1(STBMS1) Recessive European Esotropia in infancy or childhood, 7 of 8 affected individuals had various degree of hypermetropia Parikh et al., 200344 7p22.1(STBMS1) Dominant Northern Irish Primary nonsyndromic comitant esotropia Rice et al., 200945 16p13.12-p12.3 Recessive Saudi Arabian Infantile esotropia and esotropic Duane retraction syndrome Khan et al., 201193 4q28.3 Dominant  Japanese Comitant strabismus  Shaaban et al., 200947 7q31.2 Recessive (Imprinting) Japanese Comitant strabismus  Shaaban et al., 200947  (Shaaban et al., 200994) 6q26 Imprinting Japanese Comitant strabismus  Shaaban et al., 200994 12q24.32 Imprinting Japanese Comitant strabismus  Shaaban et al., 200994 19q13.11 Imprinting Japanese Comitant strabismus  Shaaban et al., 200994     20  Figure 1.1    A schematic representation of EOMs and nerve innervation with associated genes.  21   Figure 1.2    Defects along the visual-motor axis can contribute to infantile esotropia             22                 Figure 1.3    Overview of the strabismus genetic study.Linked region(s) Variant density plots Shared, rare nonsynonymous variants Region(s) of interest Shared, rare variants Candidate variants for further validation No suitable candidate  23 Chapter  2: Project Description 2.1  Introduction Non-syndromic strabismus is heterogeneous.  The genetic mechanisms underlying the disorder remain largely unknown. Multiple loci scattered across many chromosomes, albeit often with weak evidence, have been associated with strabismus. The only locus confirmed in two independent families lies on chromosome 7 (STBMS1), but the families show conflicting inheritance patterns44,45. It is likely that most families with inherited forms of non-syndromic strabismus have family-specific variations. However, the discovery of each gene contributing to strabismus can be a great step forward in our understanding of the mechanisms underlying non-syndromic strabismus. The role of the gene/protein can be included to the known pathways, and the gene can be included in candidate gene analyses for other familial strabismus studies. We identified a family with multiple generations affected by strabismus, with the earliest reported case dating to 1849 (and documented by a photograph). The participation of this family provides a unique opportunity to investigate the genetic component of strabismus.  The research that follows seeks to identify the genetic cause of the strabismus phenotype in this particular family.  Previous studies suggest locus heterogeneity in non-syndromic strabismus and simple Mendelian models cannot explain the complexity of the inheritance patterns. Although a portion of non-syndromic strabismus cases might reflect a common disease-common variant model, it does not exclude the possibility of a Mendelian model in a certain family. A highly penetrant autosomal dominant inheritance pattern is rarely reported for non-syndromic strabismus. Based on the small number of families reporting such a  24 phenotype, we reason that the causal mutation is rare (occurring at a frequency of ≤ 1% in controls) in the subject family.  There are multiple technological approaches to the identification of causal genetic variants for family-based studies.  Linkage analysis is a traditional approach for family-based gene discovery, but it only detects broad regions that are likely to harbor variants with large effect size, which makes a great contribution to disease risk. Subsequent sequencing is required to identify the actual sequence variant(s)95. The advancement of the Human Genome Project and, more recently, NGS technology provides a powerful new means to explore the human genome. The development and maturation of bioinformatics tools makes annotation, organization, and interpretation of millions of variants an approachable problem for family-based studies. Successful WES studies on different diseases demonstrate its efficiency,96 and more than 180 distinct disease-causing genes have been identified thus far using the technology97. Although WES has successfully identified many disease-causing variants, the overall molecular diagnostic rate in large scale study is approximately 25%98. Protein-coding regions only comprise approximately 2% of the human genome.  The majority of the genome is missed using WES. At present WGS is less commonly used due to its inherent complexity and high cost, but it has been gaining an increasing popularity as the cost approaches WES and the interpretability increases. The WGS approach reveals more polymorphisms than WES, but we have neither a reliable means to filter these variants such as for coding mutations nor a cost-effective way to prove causation 97. As the technology and knowledge develop, new approaches will be established to improve the interpretability of WGS.  Within our study, we used all three approaches to identify the causal region for the family studied.  A specific 10 Mb region on chromosome 14 was identified that segregates  25 with the phenotype, within which more than 300 rare variants are present in our cases, of which 3 are highlighted based on a qualitative assessment of the available genome annotation data. 2.2 Materials and methods 2.2.1 Samples Based on the constructed pedigree and available contact information, a total of 18 family members were contacted initially, of which 13 gave written consent to participate in the research project. Eight participants reported early onset strabismus, and the other five reported no strabismus. Saliva and/or blood samples were collected from participants. Except for 012, who is married-in, all of the other 12 individuals were descendants of a common ancestor and were subjected to genotyping and linkage analyses. We subsequently recruited two additional strabismic members with full consent, 014 and 015, from two different branches of the family.   2.2.2 Phenotyping The phenotyping study examined 13 individuals: 8 participants from the genetic study and 5 only involved in the phenotyping study. 014 was examined by Dr. V. Pegado, 009, 011, and 013 were examined by Dr. S. Narasimhan. Dr. J. Horton ascertained/re-ascertained all except for 014. All participants were questioned about the age of onset (if applicable), ocular history, and medical history. Examination included visual acuity, pupils, eye movements, ocular alignment, stereopsis, slit lamp examination, fundus examination, and intraocular pressure.     26 2.2.3 DNA preparation At least 4mL blood sample or 6mL saliva samples were collected for one round of NGS, and at least 2mL saliva sample was collected from participants for genotyping. Blood samples were collected in a clinical setting while saliva samples were collected using Oragene-DNA (OG-500) saliva kit. DNA was extracted from blood samples using the Qiagen QIAsymphony SP instrument and the QIAsymphony DNA Midi Kit and from saliva samples with DNA Genotek prepIT-L2P sample preparation kit following protocol # PD-PR-015. All samples were confirmed to meet the quality requirements set by service providers. 7-10 µg DNA per sample at a concentration no less than 70 ng/µl was sent for NGS. 500 ng DNA per sample at a concentration of at least 50 ng/µl was sent for genotyping. 2.2.4 Instrumentation Chromosomal microarray analysis (CMA) was performed on Affymetrix- Cytoscan HD for index, 011. WES was performed via the Agilent SureSelect Human All Exon 38Mb kit and Illumina HiSEQ 2000 platform (Perkin Elmer). Linkage analysis was performed on HumanOmni2.5, using the Infinium LCG assay. WGS was performed on an Illumina HiSEQ 2000 platform (BGI America). 2.2.5 Next-generation sequencing data processing and variant calling  The genomic aligners, Bowtie (version 0.12.9 for WES and version 1.0.0 for WGS) and BWA (version 0.6.1 for WES and version 0.7.5a for WGS), were used to map the reads to the hg19 reference genome99,100. All stated coordinates are based on hg19 in this thesis. The Genome Analysis Toolkit (GATK) (version 1.0 for WES and version 2.8 for WGS) performed local re-alignment, which allowed for correcting some misalignment at the  27 extremity of reads 101. The same informatics pipeline was applied to both WES data and WGS data. SAMtools (versions 0.1.18 and 0.1.19 respectively) was applied to call variants from aligned WES and WGS reads 102. For comparison of two distinct informatics pipelines, WGS variant calls generated by the supplier (BGI) were also analyzed.   2.2.6 WES variant prioritization The SIFT software program was used to assign annotations to variants 103, focusing on non-synonymous coding variants. Custom computer programs (scripts) were used to extract results that correspond to an autosomal dominant model. Although strabismus incidence has been reported to be up to 5% in a population, the causes are a mix of environmental and genetic influences. Any single variant could only explain a portion of cases. Since no other family has been reported with such a clear autosomal dominant pattern of inheritance, the variant in the subject family may be private, which means the variant is restricted to the family. We thus focused on variants with a frequency not higher than 1% in dbSNP build 135, giving rise to the list of rare variants. We further prioritized variants that are predicted by SIFT to be damaging or cannot be predicted, resulting in the final list of candidate variants for consideration. 2.2.7 WES variant selection The list of rare variants was compared to pre-compiled lists of candidate genes from the research literature.  These candidate genes were identified through three approaches. First, Medical Subject Heading Overrepresentation Profiles (MeSHOPs) was used to identify genes arising in a significant number of articles annotated as strabismus-related in the Medline database 104. Second, literature review on both non-syndromic strabismus and  28 syndromic strabismus (such as CFEOM and DRS) was performed to construct a locus list. Loci were converted to chromosome coordinates based on hg19 (http://www.tallphil.co.uk/bioinformatics/cytobands/). Third, genes expressed in selected mouse brain fine structures from Allen Brain Atlas (http://mouse.brain-map.org/) were extracted, including ‘superior colliculus, sensory related’, ‘tegmental reticular nucleus’, ‘suprachiasmatic nucleus’, ‘subthalamic nucleus’, ‘substantia nigra, compact part’, ‘subiculum’, ‘pedunculopontine nucleus’, ‘lateral septal nucleus, caudal (daudodorsal part)’, ‘entorhinal area’, and ‘Edinger-Westphal nucleus’ 2.2.8 Confirming variant presence The Integrative Genomic Viewer (IGV) (version 2.0.34) was used to visualize read alignment and assess variant quality 105. For the leading candidate, allelic specific polymerase chain reaction (AS-PCR) was used to amplify the target variant 106,107. The online interfaces with default settings were used for the tools mentioned below. Pseudogenes were first identified by literature search and BLAT with assembly hg19 108, and flanking regions with distinctions between target and pseudogenes were selected by inspecting a MUSCLE alignment (https://www.ebi.ac.uk/Tools/msa/muscle/) 109. The following sequences were used as inputs: (GTF2IRD2) chr7: 74210484-74250981, (GTF2IRD2B) chr7:74508347-74548846, and (GTF2IRD2P1) chr7: 72656902-72685658. WASP, a web-based allele-specific PCR assay designing tool, was used to design initial pairs of primers 110, focusing on creating mismatch at the 3’ end of one primer, and Primer3 (http://primer3.sourceforge.net/) was used to optimize complementary primers. Finally, Beacon Designer (http://www.premierbiosoft.com/qOligo/Oligo.jsp?PID=1 ) was used to provide information of optimal reaction conditions. AS-PCR was used to amplify the target with the following  29 primers: 5’-GGGACACTCGGTGTCTGACA and 5’-AACTCCGTCTCAACAACAAC. The annealing temperature was optimized at 57°C. One African American, two Caucasian, and one Chinese DNA samples were used as controls. Final products were Sanger sequenced to confirm the presence of the predicted variant.  2.2.9 WES variant density plot The identification of identity by descent (IBD) regions often relies on the use of microarrays, which probe for sequence in a contiguous region instead of the punctate exons studied by WES.  In order to use WES data to identify candidate IBD regions in a pair of individuals, custom R-2.15 scripts were used to count the number of common variants for each chromosome.  The results were depicted in a variant density plot, with X-axis as the chromosome position. The upper half of a plot represents one individual while the lower half represents another individual. Each vertical line represents one variant. To overcome the punctate spatial distribution of WES, the combined set of variants from both individuals for the same chromosome was divided into 50 non-overlapping intervals, with each interval containing the same number of observed variants for a given chromosome.  We generated data for all 22 autosomes for the comparison between the two subjects and that of two anonymous controls (unaffected). The value of shared variants over total variants for each chromosome in each individual was generated, and logarithm (base 2) for the ratio of corresponding value was calculated. Variant clusters were defined as candidates for IBD regions.  2.2.10 Linkage analysis Linkage analysis was performed by our collaborator (Nicole Roslin, SickKids, Toronto ON). Multiple analyses were performed for quality control purpose, including but  30 not limited to call rate and relationship checking. Simulations were performed to determine the maximum possible LOD (logarithm of the odds) score for different model parameters under the alternative hypothesis (linkage). SLINK 3.02 was used to simulate pedigrees under dominant and recessive models with a range of disease allele frequencies and penetrance 111, and Merlin 1.1.2 was used to analyze these pedigrees under the same model 112. For a particular model, the maximum LOD score from the analysis of 1000 simulated pedigrees was declared the maximum LOD score.  Simulations were performed using q = 0.005, 0.01, and 0.02 for the dominant models, and 0.1, 0.16, and 0.2 for the recessive models (corresponding to estimated prevalence of 1%, 2.5% and 4% respectively). For autosomal dominant and recessive models, simulations were performed with penetrance of 50, 60, 70, 80, 90, 95 and 99%. An estimated phenocopy rate of 0.2% was used for all models. Multiple filters were applied to select a set of markers suitable for linkage analysis. First, SNPs on Y chromosome were excluded. Second, markers with alleles ambiguous for strand information (A/T and G/C variants) were removed to facilitate matching with HapMap data. Third, monomorphic markers were removed since they were uninformative for linkage under a dominant model. Fourth, only markers present in both this set and corresponding HapMap sets were retained. SNPs with minor allele frequency > 0.45 were retained for most linkage information. Lastly, only markers with pairwise r2 < 0.1 were kept to prevent inflation of multipoint LOD scores due to linkage disequilibrium between the SNPs 113.  Merlin 1.1.2 was used to perform multipoint linkage analysis under a dominant model with a disease allele frequency of 0.005 and penetrance of 0.2, 99 and 99% for 0, 1, 2 copies  31 of the disease-causing alleles, respectively. The parameters correspond to a disease prevalence of approximately 1%.  2.2.11 WGS variant prioritization Variants that fell within the candidate region identified by linkage analysis were selected. To ensure that the three samples used for WGS were consistent with the same IBD region, the numbers of variants in the linked region were examined for enrichment. Allele frequency was assessed using dbSNP build 137 and Exome Variant Server (EVS), and variants with a frequency higher than 1% were excluded. Heterozygous variants shared across the three samples were selected, and SnpEff (with hg19 database) was applied to annotate variants. The tools and parameters in the BGI computational analysis pipeline are largely unknown, but the BGI pipeline is expected to be different than the in-house pipeline. Therefore, lists of variants from these two pipelines were analyzed independently. 2.2.12 WGS variant selection and confirmation   Candidate variants were compared against an in-house database containing 110 exomes and against the Michael Smith Genome Sciences Centre (GSC) database, containing 1580 non-cancer genomes and 785 cancer genomes. Multiple annotation databases and corresponding bioinformatic tools were used to annotate variants, including functions annotation of the mammalian genome 5 (FANTOM5) database, JASPAR, Segway, RegulomeDB and Combined Annotation Dependent Depletion (CADD) 114–117,115. FANTOM5 database allows us to obtain expression profiles and functional annotation of cell-type-specific transcriptome, and we examined whether candidate variants overlapped with transcription start site (TSS) and enhancer118,119. JASPAR database stores matrix-based nucleotides profiles for transcription factors binding preference115. Segway provides  32 chromatin state annotations at a nucleotide resolution for both conserved and non-conserved sequences114. RegulomeDB utilizes experimentally measured evidence and computational processing evidence to provide functional assignment and was used to prioritize variants 120. CADD integrates many diverse annotations into a single C-score for SNVs or indels and was used to annotate the impact of variants117. Manual inspection via IGV (version 2.3.2) was used to evaluate the read quality and to judge the credibility of variants 105. Variants that were likely due to a sequencing error or alignment error were assigned to a lower priority category. Based on the qualitative assessment, top prioritized variants were confirmed to be present by standard PCR in WGS subjects.  2.3 Results 2.3.1 The index case An ophthalmologist referred the index patient to BC Children’s Hospital, where a preliminary determination of familial non-syndromic strabismus was proposed based on the family history and physical examination of the index and parents. Since the index exhibited additional minor characteristics beyond strabismus, a blood sample was obtained to prepare DNA for CMA analysis of potential copy number variation (CNV) and other structural alterations. The CMA results were unremarkable; the analysis did not reveal CNVs > 200 kb in size or any abnormally large runs of homozygosity (ROH). The 10 Mb locus on chromosome 14 mapped in this family was also examined on the array and no CNVs were detected.   2.3.2 Pedigree From the foundation of an extensive family genealogy project, we constructed a seven generation pedigree that contains 157 individuals, including deceased individuals.  33 Three major branches from the same common ancestor were traced (Figure 2.1). Most participants come from branch 1 (Figure 2.2 and Figure 2.3). In branch 1a and 1b, strabismus was reported across 4 consecutive generations according to family anecdotes and/or medical records. An even distribution of strabismus cases was observed between females (8 individuals) and males (7 individuals). An autosomal dominant model with high penetrance best matches the inheritance pattern. The index case was assigned the identifier 011 and is indicated with the identifier in all the figures. Late in the research project, subject 014 from branch 2 and subject 015 from branch 3 self-identified as having a strabismus phenotype were enrolled in the study (Figure 2.4 and Figure 2.5). Ophthalmic information is not yet fully available from these two emerging branches. 2.3.3 Phenotyping Thirteen individuals participated in clinical phenotype assessment sessions conducted by one of three vision experts, with twelve ascertained or re-ascertained on the same day by the same specialist for consistency.  Five individuals who participated in the phenotyping study but not the genetics analysis were confirmed to be unaffected by strabismus. The characteristics of strabismus were not uniform across the affected relatives in the family.  For several individuals, the original direction and amount of deviation can be difficult to ascertain retrospectively after multiple surgeries and/or development of other ocular conditions. The affected individuals could be grouped into two broad categories: esotropia and congenital superior oblique muscle palsy (fourth nerve palsy). Congenital superior oblique muscle palsy was reported in the re-ascertainment for 009 and 013 (neither of which had undergone surgery), while each was more generally classified as hypertropia in an initial  34 examination. Both esotropia and hypertropia were noted in 014 (this individual had undergone multiple surgeries).  2.3.4 Whole exome sequencing analysis Whole exome sequencing was performed on DNA samples from subjects 001 and 011 (i.e. the index), with over 50 million reads (>5x109 bp of raw sequence data) obtained for each.  Quality control measurements and the number of detected variations from the reference genome meeting the filtering criteria (see Methods) are listed in Table 2.1. The two distantly related individuals share 119 heterozygous non-synonymous variants as shown in the seventh column. As any causal variant for the dominant phenotype is expected to be rare in the general population, a maximum frequency threshold of 1% was applied, reducing the set of candidate variants to 60. By design, WES focuses on coding regions, as coding sequence alterations have until now been the most frequently reported causal mutation in genetics studies (albeit also the most deeply studied). We therefore prioritized the review of coding variants in our initial analysis. The eight variants (Table 2.2) selected by using SIFT were prioritized for further consideration (damaging variants and variants with unknown effect), and the other candidates were retained as lower priority, but still potentially important candidates.  We sought to determine if any of the prioritized eight candidate variants were located within genes previously connected to strabismus or strabismus-related tissues.  To find genes associated with strabismus, we used a candidate approach. Using as a query the MeSH terms ’strabismus’, ‘esotropia’, ‘exotropia’, and ‘oculomotor muscles’, we ranked 30,251 gene and loci MeSH Over-representation Profiles (MeSHOPs) for similarity 104. Within this list, MeSHOPs for 23 genes and three loci had a statistical enrichment for one of the indicated  35 MeSH terms, however none overlap with the set of eight candidate genes arising from the exome sequencing.  Systemic literature review on syndromic forms of strabismus led to a list of associated loci (Table 2.3), which was combined with the set of non-syndromic loci (Table 1.1) to generate a final set of literature-based candidate regions (Table 2.4). Eighteen of 22 chromosomes have at least one locus linked to strabismus. The converted coordinate list does not overlap with the top eight variants on the final list.  We reasoned that genes expressed in anatomical structures associated with eye misalignment might have a higher chance to be causal. Therefore, we obtained expressed genes lists in a set of fine brain structures related to eye alignment in mouse from Allen Brain Atlas and compared to the final list. The eight genes on the final list did not express in any of the above structures.  2.3.5 WES variant validation Literature mining was performed individually for each of the eight candidate variants on the exome list.  The GTF2IRD2 variant chr7:74212518 G>A was selected for Sanger sequencing confirmation. GTF2IRD2 is adjacent to the William syndrome (WS) locus on chr7q, and the incidence of strabismus in WS is up to 78%121. This observation suggested a genetic link between strabismus and WS. WS is a rare neurodevelopmental disorder caused by hemizygous deletion at 7q11.23. At the telomeric end of the deletion lies three genes of the same transcription factor family (TFII-I family): GTF2I, GTF2IRD1, and GTF2IRD2122. Analysis of atypical deletion suggests that absence of GTF2IRD1 and GTF2I may be responsible for certain WS features such as craniofacial dysmorphology, hypersociability, and visuospatial deficits123,124. Disruption of GTF2IRD2 expression might contribute to WS  36 features. The frequency of chr7:74212518 G>A is 0.4% in Europeans, and both SIFT and PolyPhen predicted the P445S change to be damaging. Study of a skeletal-muscle-specific transgenic expression mouse model demonstrated that GTF2IRD2 has a role in determining muscle fiber type124.  At least two pseudogenes in the human genome share high sequence identity with GTF2IRD2, and standard PCR could not amplify the target region specifically. An AS-PCR-based approach was performed with samples from 001 and 011, and subsequent Sanger sequencing confirmed the presence of the variant in both individuals. G/G genotype was expected for unaffected individuals, while G/A or A/A genotype was expected for affected individuals. However, the GTF2IRD2 variant does not segregate with strabismus in all family members. Subject 008 does not have strabismus, but has a G/A genotype; subject 009 has strabismus and is an obligate carrier, but a G/G genotype was observed (Figure 2.6). To confirm the findings, new samples were collected and the experiments repeated, confirming the initial findings. We also found that 002 is homozygous for allele A. There is no consanguinity in this family, and chr7:74212518 G > A might be more common than reported in the population. Although GTF2IRD2 (chr7:74212518 G>A) was a promising candidate based on variant characteristics and literature, this gene was ruled out as the causal mutation for strabismus in this family by the follow-up individual genotyping. Further literature mining for the other seven variants did not lead to another lead candidate. 2.3.6 WES variant density plot To extract additional information from WES data, we generated variant density plots to identify potential IBD regions. Chromosome 11 (0.32) and 14 (0.49) show a higher log2  37 values compared to the rest (ranges from -0.61 to 0.29), suggesting that the two strabismic individuals share more variants in these two chromosomes than the control group and that potential IBD regions might reside on these chromosomes. Variant density plots for each chromosome in both groups were examined closely (Figure 2.7 and Figure 2.8). The left panel was based on data from two strabismic individuals, and the right panel was based on data from controls. For each plot, the upper half represents one individual while the lower half represents the other individual within the group. Three rows were created for each individual (row 1 to 6 from top to bottom): the innermost rows (3 and 4) represent all variants from the hg19 reference genome in each individual; the middle rows (2 and 5) display heterozygous variants in each individual; and the outermost rows (1 and 6) are identical and indicate positions of shared heterozygous positions between the two individuals. Two shared heterozygous variant clusters were observed: chr14:24,600,000 – 32,300,000 and chr11:48,200,000 - 56,000,000. Reviewing our variant call files revealed two shared rare variants (≤1% in the EVS and dbSNP databases) within these regions: two on chromosome 14 and zero on chromosome 11. One variant was protein coding change in G2E3 but predicted to have a neutral effect by SIFT, and the other was in an intron of STRN3. Literature review did not identify specific relevance of the genes. We expect a single corresponding region for the whole family due to the observed autosomal dominant mode of inheritance. Although the two individuals can share more than one region by chance, additional information from other relatives is expected to refine the candidate loci – thus linkage analysis was pursued.     38 2.3.7 Linkage analysis Samples from 12 individuals (8 affected; 4 unaffected) were genotyped using a high-density genotyping panel. A set of 17,779 SNPs was obtained after the SNP filtering step for linkage analysis. Simulations under the alternative hypothesis (linkage) generated a maximum simulated LOD score of 3.56, under an autosomal dominant model with minor allele frequency q = 0.005 and 99% penetrance. The LOD score curves did not change significantly with disease allele frequency, and the dominant models had consistently higher LOD scores than recessive models (Figure 2.9).  Based on the genotyping data from the subject family, the largest observed LOD score is 3.55, on chromosome 14, which is a striking score relative to the possible maximum (3.56) obtained from simulations. This is the only region with a LOD score higher than 3, and thus the only region for which rejection was made of the null hypothesis of independent assortment. The linked region on chromosome 14 spanned approximately 10Mb on the physical map and was bounded by the markers rs7146411 and rs1951187, corresponding to chr14: 22,779,843 – 32,908,192. This region identified by linkage analysis is a novel locus for non-syndromic strabismus. The pedigree suggested an autosomal dominant pattern of inheritance for this large multi-generation family, and the linkage analysis result supports this hypothesis. The LOD score under an autosomal dominant model of the linked region approaches the maximum LOD score as demonstrated by simulations. A single region has achieved a significant LOD score across the whole genome and is present in all affected individuals. Moreover, the linked region overlaps the chr14:24,600,000 – 32,300,000 region identified via WES variant density plots, further supporting its association with the familial strabismus. Displaying the  39 candidate region in the UCSC genome browser reveals a sparsely annotated zone consistent with a gene-poor region (Figure 2.10).  2.3.8 WGS analysis Whole genome sequencing was performed on three individuals with a specific focus on the candidate loci.  The three individuals, 001, 013, and 014, were selected to represent three distinct branches of the tree, and willingness to provide further samples.   Since 014 joined the study subsequent to the linkage analysis, we compared the number of shared variants between 014 and strabismic relatives to 014 and unrelated controls (Table 2.6). The upper two rows show that two unrelated individuals (C5 means control family 5 for example) share less than 14,500 variants. Since control families 3, 4, and 5 were collected to investigate a rare disease with unknown ancestral relationship, we did not compare between families. Individuals within the same control family are in each case a parent-offspring pair, thus sharing at least one allele across the entire genome. We extracted variants from the chr14:20,000,000 – 35,000,000 region for each individual. The parent-offspring control pairs exhibit 17,000 to 20,400 variants in total within the loci, and numbers of shared variants between all pairs of strabismic individuals are within this range while the numbers of shared variants between unrelated individuals are within 13,300 to 14,100 (Table 2.6). This observation suggests that 014 shares the linked region.  More than 1 billion clean reads were processed for each of 001, 013, and 014 using two pipelines, and the number of called variants are presented in Table 2.8 and Table 2.9. We focused on the rare variants within the candidate region shared across the three distant relatives, considering each bioinformatics processing pipeline separately. From the BGI pipeline, the three individuals share 671 rare heterozygous variants (Figure 2.11 and Table  40 2.8). For variants based on the in-house pipeline, approximately 14% of variants were left after the 1% filter. About 1,200 variants were homozygous, and the rest were heterozygous. The three individuals share 378 rare heterozygous variants (Figure 2.12 and Table 2.9). Combing the BGI list and the in-house list and de-prioritizing variants with low read quality with IGV, a list of 675 variants was subjected to further analysis.  The combined list of candidate variants was compared against a database of NGS variants maintained at the Michael Smith Genome Sciences Centre (GSC). No ophthalmological annotations are available from GSC, but variations arising in this small collection and within our family would suggest that the variation is not rare or the sequencing technologies and bioinformatics processing are producing systematic false positives. De-prioritizing variants that were present more than 25 times in a combination of our in-house exome database and the GSC left 332 candidate variants in the rare variant list.  2.3.9 WGS variant prioritization Since the linked region is gene-poor, we opted to perform variant prioritization using SnpEff rather than the more commonly used SIFT and PolyPhen2 software.  SnpEff generates annotation for different isoforms of a gene and informs the location of non-coding variants in terms of nearby gene. Five of 332 variants map to exons and only one is predicted to be a non-synonymous-coding change.  We used different methods to annotate variants with regulatory information. The FANTOM5 projected generated detailed annotation of promoter and enhancer regions using a high-throughput version of the cap analysis gene expression (CAGE) technique. No candidate variant overlaps with reported TSS, and no high quality variant overlaps with FANTOM5 enhancers119 .  Comparison against the VISTA enhancer database also did not  41 reveal matches (http://enhancer.lbl.gov/). One variant was found to overlap with a reported JASPAR transcription factor binding sites (TFBS). chr14:28052726 A > C falls on a FoxA1 TFBS, and it is predicted to have a high impact on the TFBS binding capacity (ranked the top 89% of all possible single nucleotide substitutions) (Figure 2.13). It was reported twice in GSC normal samples and once in a GSC cancer sample. Next, we used less direct regulatory information from ENCODE to identify potential variants. The ENCODE project provides comprehensive information for chromatin properties in multiple human cell lines, and many different tools have been developed to utilize the information. Segway labeled chr14:29247628 TAAACAAACAA > TAAACAA as a candidate repressor region, due to the presence of H3 trimethyl-lysine 27 (H3K27me3) mark in H1 human embryonic stem cell lines. H3K27me3 is associated with inactive gene, and it signals bivalent promoter with K4me3 in embryonic stem cells. Inspection through IGV revealed an AAAC deletion within C14orf23. A small region surrounding this variant is highly conserved in the genome, suggesting that it is under evolutionary selection and that a change may have a functional impact. The chr14:29247628 TAAACAAACAA > TAAACAA deletion was reported in one normal sample from GSC. Another variant suggested by Segway was chr14:32547404 A > T, which was labeled as flanking a TSS. A strong H3K27Ac peak can be observed on 7 cell lines from ENCODE data through UCSC genome browser, which suggests an active regulatory element. This variant falls in an intron of ARHGAP5, which belongs to the same protein family as CHN1 that is associated with Duane retraction syndrome type 2. Moreover, an alternative TSS of ARHGAP5 is reported in the FANTOM5 data in close proximity to the variant. Only one normal sample from GSC carries chr14:32547404 A > T.  42 Additional annotation tools were used to integrate information to evaluate the three selected variants. Briefly, chr14:28052726 A > C has a RegulomeDB score of 5 and a C-score of 6.274. chr14:29247628 TAAACAAACAA > TAAACAA has a RegulomeDB score of 5 and a C-score of 14.65. chr14:32547404 A > T has a RegulomeDB score of 2b and a C-score of 14.12. The scoring scheme can be found in respective references117,120. These three variants were confirmed through Sanger sequencing to be present in the three subjects and are summarized in Table 2.10.   2.4 Discussion This study identified a family with non-syndromic strabismus, and the pedigree shows a Mendelian autosomal dominant inheritance pattern. Two distant relatives, 001 and 011, were subjected to WES, and two potential IBD regions were identified through the process. Linkage analysis was performed in collaboration with the FORGE network, and analysis with 12 descendants from a common ancestor revealed a single significantly linked region on chromosome 14 with a maximum LOD score of 3.55. This linked region overlaps with one of the two shared IBD regions from WES, supporting its segregation with the familial strabismus. WGS provided a comprehensive inventory of variants in the linked region for three affected individuals. A list of 332 candidate variants was obtained after multiple filters, including 327 variants in non-coding regions and 5 in coding regions. Through multi-level assessment, 3 candidate variants were selected for Sanger sequencing and the genotype was confirmed in 001, 013, and 014. However, causality of specific variants remains to be proven by additional validation.   43 As a whole, strabismus is a complex disease, and previous studies have showed locus heterogeneity, but a subset of cases might better fit a Mendelian disease model. This observation supports the idea that rare variants can be the driving forces of common diseases and more similar to Mendelian diseases than is postulated by the common disease-common variant model125. Our linked region is the best-identified locus for non-syndromic strabismus, and both pedigree and molecular evidence supports an autosomal dominant inheritance model. The striking pedigree and enthusiastic participants greatly contribute to this study. A genealogy project initiated by a family member collected ample information regarding the family history, including some descriptions of eye problems, fuels the genetic project. The emerging technology, NGS and microarray, allows us to perform genetic research in an efficient and cost-effective way. Applications of NGS have already successfully identified many genes for Mendelian diseases, for undiagnosed childhood genetic diseases, and for predisposition to common complex diseases 126. Given the small sample sizes for both cases and controls in many NGS studies, it is common to see a focus on coding variants. For example, WES is based on the assumption that mutations on coding regions are more likely to influence human phenotype and contribute to high penetrant diseases on a nucleotide for nucleotide basis 126. Due to the paucity of protein coding alterations within the candidate loci, we performed WGS to seek potential regulatory sequence disrupting variants.  The nature of regulatory mutations can be large-scale structural alterations such as a copy number change, or small alterations impacting either non-coding RNA change or a cis-regulatory disruption such as a TFBS. A structural variation can be either unbalanced  (copy number variants) or balanced, such as inversions or reciprocal translations, and the change  44 can be defined to span greater than one kilobase 127. CMA did not detect an alteration in a cytogenetic level of resolution, but smaller scale alterations below the ~50 kilobase threshold might be missed by the CMA procedure. Non-coding RNA can also play a role in human diseases. Based on annotation derived from GENCODE v7 and ENCODE, individuals contain approximately 200 non-coding RNA variants, of which 4-17 are < 0.5% and appear to be selected against128. Nevertheless, there is no evidence for the involvement of non-coding RNA in this case.  Different defects along the visual-motor axis might contribute to strabismus, and all three variants seem to contribute to strabismus with a neuronal origin. Chr14:28052726 A > C falls on a FoxA1 TFBS, which lies 1.2 Mb away from FOXG1 and 0.9 Mb away from NOVA1. FoxA1 is a pioneer factor that differentially binds to distant enhancer, leading to cell type-specific changes in chromatin structure, and collaborates with other specific transcription factors129. The TFBS variant might contribute to strabismus by altering FoxA1 recruitment and the downstream expression of a gene in the linked region.  The linked region is associated with Rett-like phenotypes, and FOXG1 (forkhead box G1) has been implicated in multiple clinical features. A feature in FOXG1-related encephalopathy is the high prevalence of strabismus, which is not observed in other Rett Syndrome (RTT) patients, and FOXG1 syndrome is an autosomal dominant disorder 130. FOXG1 contains only one exon (exon 1) and encodes a winged-helix transcriptional repressor, which is critical in forebrain development. Four alternative transcripts for additional exons (exon 2 to 5) are identified in fetal brain 130. Point mutations of FOXG1, deletion and duplication of region including FOXG1 have all shown similar phenotypes, indicating a dosage sensitivity effect 131. Deletions excluding FOXG1 also lead to Rett-like  45 phenotypes, suggesting a cis-acting regulatory element hypothesis. A minimal region of a silencer (chr14: 29,875,671 – 30,303,082  hg19) has been identified to locate 0.6 Mb from FOXG1 coding sequence, supporting the hypothesis 132. These observations suggest that FOXG1 is regulated by multiple distal genetic elements, and FoxA1 TFBS might be one of these elements and underlies the familial strabismus. One variant (chr14:29247628 TAAACAAACAA > TAAACAA) falls within C14orf23 intron, which is a gene with unknown function. This position is highly conserved through evolution, forming a conserved island and likely undergoing a positive natural selection. C14orf23 is 2.5 Kb away from FOXG1 and its deletion is implicated in facial dysmorphism in a case of Rett Syndrome (RTT) 133. This variant could also have an impact on FOXG1 expression due to its close proximity.  chr14:32547404 falls within an intron of ARHGAP5. A clear H3K27Ac peak implicates an active regulatory element may be situated in the region. ARHGAP5 and CHN1 (ARHGAP2) belong to the same protein family, and CHN1 is associated with Duane retraction syndrome type 2. ARHGAP5 encodes Rho GTPase activating protein 5 that negatively regulates Rho GTPases and mediate cytoskeleton changes. Similar to CHN1, ARHGAP5 might also responsible to a neural development pathway and contribute to strabismus.  The current study identifies a linked region of non-syndromic strabismus with high confidence and proposes three candidate variants, but the exact causal mutation remains elusive. Future work will be discussed in Chapter 3. 46 Table 2.1    Coverage and variants from whole exome sequencing (WES). Bowtie + BWA Reads processed Percentage of reads aligned (with good quality) Mean of coverage (exons) Total variants Coding variants (not synonymous) Shared heterozygous variants <= 1% shared heterozygous variants FORGE 336_001  50,655,069 84.4% 27.42 96889 770 119 60 FORGE 336_011  50,576,080 88.6% 28.47 99068 758 119 60  Table 2.2    Non-tolerant shared variants based on WESs from two distant relatives. * Warning! Low confidence. Chr  Coordinate Ref  Alt  Amino acid Gene symbol Predicted impact Allele frequency 19 58385748 G A A337V ZNF814 DAMAGING 0 7 74212518 G A P445S GTF2IRD2 DAMAGING 0.000449 9 43091410 T C E805G ANKRD20A3 DAMAGING NA 9 69423844 C G Q714E ANKRD20A4 DAMAGING NA 6 32557449 G A S24F HLA-DRB1 DAMAGING *  0 1 144148856 A T K94M RP3-377D14.1(NBPF9) DAMAGING * NA 17 45567593 ATCTCTC ATCTC *105* MRPL45P2 N/A NA 8 8887542 TAACAACA TAACA *350* ERI1 N/A NA     47 Table 2.3    Selected syndromic strabismus associated loci. CFEOM: congenital fibrosis of extraocular muscles DRS: Duane retraction syndrome  PEOA: Progressive external ophthalmoplegia, autosomal dominant Loci Gene Inheritance pattern Phenotype PMID 16q24.3  TUBB3 Dominant CFEOM1, CFEOM3A Tischfield et al., 2010134 12q12  KIF21A Dominant CFEOM1, CREOM3B Yamada et al., 2003135 11q13  ARIX (PHOX2A) Recessive CFEOM2 Nakano et al., 2001136 13q12.11  Dominant CFEOM3C Aubourg et al., 2005137 8q12-13  Dominant DRS1 Pizzuti et al., 200261 & Amouroux et al., 201267 2q31.1 CHN1 Dominant DRS2 Miyake et al., 200869 3q23 FOXL2 De novo DRS, blepharophimosis-ptosis-epicanthus inversus syndrome (BPES) Vincent et al., 2005138 16q12.1 SALL1 N/A DRS, Townes-Brocks syndrome (TBS) Barry et al., 2008139 20q13.2  SALL4 Dominant Okihiro syndrome (Duane radial ray syndrome) Kohlhase et al., 200276 & Al-Baradie et al. 200277 22q11.2-22qter  De novo DRS, cat-eye syndrome Gómez-Lado et al., 2006140 5p13.3-13.2  De novo DRS, type I Chiari malformation Bayrakli et al., 2010141 4q27-31  De novo DRS1, mild learning difficulties Chew et al., 199517 1q42.13-43  De novo DRS, febrile convulsions, dysmorphic Kato et al., 2007142 2q13,  10q24.2-26.3, 20q13.12,  22q11.11-q11.22  De novo DRS, other systemic abnormalities Smith et al., 2010143 13q12.2-13  Dominant Moebius syndrome Slee et al., 1991144 15q25 POLG Dominant PEOA1 Van Goethem et al., 2001145 & Lamantea et al., 2002146  48 Loci Gene Inheritance pattern Phenotype PMID 4q35 ANT1 Dominant PEOA2 Kaukonen et al., 2000147 10q24 C10orf2 Dominant PEOA3 Suomalainen et al., 1995148, Li et al., 1999149 & Spelbrink et al., 2001150 17q23 POLG2 Dominant PEOA4 Longley et al., 2006151 & Young et al., 2011152 8q23 RRM2B Dominant PEOA5 Tyynismaa et al., 2009153 & Fratter et al., 2011154 10q21 DNA2 Dominant PEOA6 Ronchi et al,, 2013155 11q24.2 ROBO3 Recessive Gaze palsy, horizontal, with progressive scoliosis Jen et al., 2004156 7p15.2 HOXA1 Recessive Athabaskan brainstem dysgenesis syndrome, Bosley-Salih-Alorainy syndrome Tischfield et al., 2005157 & Bosley et al., 2008158            49 Table 2.4    Loci and corresponding hg19 coordinates associated with strabismus, either syndromic or non-syndromic. Chromosome  p arm  q arm  Coordinates 1   q42.13-q43  227000000-243700000 2   q13, q31-q32.1  110200000-114400000, 169700000-189400000 3   q21-q22,q23  121900000-142800000 4   q27-q31,q35  120800000-155600000, 183200000-191154276 5 p13.3-13.2    33800000-33800000 6   q26  161000000-164500000 7 p15, p22.1  q31.2  20900000-28800000, 4500000-7300000, 114600000-117400000 8   q12-q13, q23, q24.21  55500000-73900000, 106200000-117700000, 127300000-131500000 9       10   q21.3-q22.1, q24-q26.3  64500000-74900000, 97000000-135534747 11   q13, q23, q24.2 63400000-77100000, 110400000-121200000, 123900000-127800000 12   q12, q24.32  38200000-46400000, 125900000-129300000 13   q12.2-q13  27800000-40100000 14       15   q25  78300000-89100000 16 p13.2-p12.3  q12.1, q24.3 7900000-21200000, 47000000-52600000, 88700000-90354753 17   q23 57600000-62600000 18       19   q13.11  32400000-35500000 20   q13.12-q13.2  42100000-55000000 21       22   q11.1-11.22 14700000-23500000     50 Table 2.5    Number of shared and total variants and log2(ratio) for each chromosome in strabismic and control groups. Strab S – number of shared variants between two strabismic individuals; Strab T – number of total variants from two strabismic individuals; Strab S/T – number of shared variants over number of total variants for two strabismic individuals;  Control S – number of shared variants between two control individuals; Control T – number of total variants from two control individuals; Control S/T – number of shared variants over number of total variants for two control individuals; Chromosome Strab S Strab T Strab S/T Control S Control T Control S/T  Log2[(Strab S/T)/(Control S/T)] chr22 135 3057 0.0442 230 3407 0.0675 -0.612 chr1 834 15045 0.0554 1249 16224 0.0770 -0.474 chr20 164 3845 0.0427 221 4122 0.0536 -0.330 chr21 191 3494 0.0547 257 3822 0.0672 -0.299 chr6 526 9269 0.0567 652 9627 0.0677 -0.255 chr4 334 7707 0.0433 428 8537 0.0501 -0.210 chr8 283 5674 0.0499 348 6129 0.0568 -0.187 chr12 455 7340 0.0620 552 7946 0.0695 -0.164 chr16 379 6356 0.0596 443 6670 0.0664 -0.156 chr15 305 5032 0.0606 368 5512 0.0668 -0.139 chr3 488 8572 0.0569 570 9322 0.0611 -0.103 chr5 503 7122 0.0706 553 7414 0.0746 -0.079 chr19 540 7887 0.0685 566 8126 0.0697 -0.025 chr17 491 7370 0.0666 518 7783 0.0666 0.001 chr2 618 10653 0.0580 674 11856 0.0568 0.029 chr7 478 7212 0.0663 504 7934 0.0635 0.061 chr13 167 3966 0.0421 171 4272 0.0400 0.073 chr9 370 6423 0.0576 346 6981 0.0496 0.217 chr10 488 7644 0.0638 444 8148 0.0545 0.228 chr18 188 3135 0.0600 172 3375 0.0510 0.235 chr11 510 8043 0.0634 445 8784 0.0507 0.324 chr14 316 4525 0.0698 234 4709 0.0497 0.491    51 Table 2.6    Number of shared variants between different individuals on chr14:20,000,000-35,000,000. C3-1: first member of control family 3; C3-2: second member of control family 3;  C4-1: first member of control family 4; C4-2: second member of control family 4;  C5-1: first member of control family 5; C5-2: second member of control family 5.  Comparison*pair*(unrelated)* 013*&*C472* 013*&*C471* 013*&*C571* 013*&*C372* 013*&*C371* 013*&*C572*Number'of'shared'variants' 13316' 13501' 13668' 13809' 13899' 14057'Comparison*pair*(IBD)* C571*&*C572* 001*&*014* 001*&*013* 013*&*014* C471*&*C472* C371*&*C372*Number'of'shared'variants' 17107' 18459' 18957' 19499' 20162' 20396' 52 Table 2.7    Sequencing data quality for whole genome sequencing (WGS). BGI pipeline Clean Reads  Percentage of reads aligned Average of coverage (exons) 001 1,249,733,158 96.2% 38.86 013  1,058,300,000 95.46% 37.27 014 1,069,350,000 96.13% 38.46  Table 2.8   Coverage and variants of linked region from WGS with the BGI pipeline. Bowtie + BWA Number clean reads in the region Mean of coverage (chr14:200,000,000-350,000,000) Total variants Variants with a frequency of 1% (dbSNP) Rare heterozygous variants Rare shared heterozygous variants 001  6,456,829 41.14 26,046 4,727 3,142 671 013  5,874,158 38.75 26,689 4,948 3,352 671 014 5,794,351 38.25 25,836 4,748 3,077 671  Table 2.9   Coverage and variants of linked region from WGS with the in-house pipeline. Bowtie + BWA Number of clean reads in the region Mean of coverage (chr14:200,000,000-350,000,000)  Total variants Variants with a frequency of 1% (dbSNP) Rare heterozygous variants Rare shared heterozygous variants 001  5,814,112 38.76 21,516 3,057 1,849 378 013  5,480,743 36.53 21,886 3,057 1,880 378 014 5,431,398 36.21 21,551 2,950 1,756 378      53 Table 2.10   Prioritized WGS variants from the linked region. Variant Number of times seen in 2365 GSC samples  Feature RegulomeDB score CADD score Gene of interest  chr14: 28052726 A > C 3 potential FoxA1 TFBS 5 6.274 1.2 Mb away from FOXG1 chr14:29247628 TAAACAAACAA > TAAACAA 1 H3K27me3 mark 5 14.65 2.5 Kb away from FOXG1 chr14:32547404 A > T 1 H3K27Ac peak 2b 14.12 ARHGAP5   54 died at birthdied at birth004007 005006003002001009013008 012010 011??015died atbirth? ? ? ? ? ? ? ? ? ? ? ? ? ?014     Figure 2.1    Pedigree for the subject family with non-syndromic strabismus.        Branch 2 Branch 1 Branch 3  55        Figure 2.2    Branch 1 of the subject family with non-syndromic strabismus.     died at birthdied at birth004007 005006003002001009013008 012010 011??Branch 1a Branch 1b  56 died at birth004007 005006003002001009013008 012010 011            Figure 2.3    Simplified branch 1 showing the genotyped individuals (with study ID) and ancestors required to link them.      57     Figure 2.4    Branch 2 of the subject family.  died atbirth? ? ? ? ? ? ? ? ? ? ? ? ? ?014 58    Figure 2.5    Branch 3 of the subject family.   015 59   Subject 011  Subject 008 Expected: A/G  Expected: G/G  Observed: A/G  Observed: A/G               Unrelated Control       Subject 009 Expected: G/G       Expected: A/G Observed: G/G       Observed: G/G     Figure 2.6    Sanger sequence results of GTF2IRD2 variant for multiple individuals.    60             Figure 2.7    Variant density plot of shared variants on chromosome 11. The left panel is based on data from both strabismic individuals, and the right panel is based on data from two unrelated controls. The region (chr11: 48,200,000-56,000,000) that is enriched with shared variants is boxed.   ●●Position of the variationExistance of the variation1233507639981902635343514050134235565655641695976921699370850117065541453233818175874189783022069985725348916303460523411371738232457444012634729820148611651499837695560734556954846586790146063716462098735639909966502510666469032678149797017156771650241738016627698742182641364870091759012255395564259102491287104967227110649829113802601116708020118185346119469425122601619124043782125851055129816991134160839first samplefirst sample heteroshared variationssecond samplesecond sample heteroshared variations 61                Figure 2.8    Variant density plot of shared variants on chromosome 14. The left panel is based on data from both strabismic individuals, and the right panel is based on data from two unrelated controls. The region (chr14: 24,600,000-32,300,000) that is enriched with shared variants is boxed.  ● ●Position of the variationExistance of the variation190101802008511420444588209169582150041122038562232453012385432424679877253466363118883333178479352449503656424239623533437695465008536350862792518927265275538555847466580973976002281362028395644968726531894369066311704802157211715673924736748747077586204377176610781403298128884686753305889348569071422091948043927404499372454694776221958784089677561399938024100947641101593081103412652104608068106083044107178653first samplefirst sample heteroshared variationssecond samplesecond sample heteroshared variations 62  Figure 2.9    Simulated maximum LOD scores under the alternative hypothesis of linkage, under a range of genetic models.      ! 7!recessive!models!with!a!range!of!disease!allele!frequencies!and!penetrances!using!SLINK!3.02!(Schaffer,!et!al.,!2011),!and!analyzed!under!the!same!model!using!Merlin!1.1.2!(Abecasis,!et!al.,!2002).!!The!phenocopy!rate!was!fixed!at!0.2%!for!all!models.!!The!maximum!LOD!score!obtained!from!the!analysis!of!1000!simulated!pedigrees!was!declared!the!maximum!LOD!score!for!any!particular!model.!! The!disease!prevalence!of!nonHsyndromic!strabismus!has!been!estimated!to!be!1!to!4%.!!Assuming!a!fully!penetrant!dominant!model,!this!corresponds!to!a!disease!allele!frequency!(q)!of!0.005!to!0.02.!!In!order!to!judge!the!sensitivity!of!the!results!to!q,!simulations!were!performed!using!q=0.005!(1%!prevalence),!0.01!(2.5%!prevalence),!and!0.02!(4%!prevalence)!for!the!dominant!models,!and!0.1,!0.16!and!0.2!for!the!recessive!models!(corresponding!to!1%,!2.5%!and!4%!prevalence,!respectively).!!For!both!dominant!and!recessive!models,!simulations!were!performed!using!penetrances!of!50,!60,!70,!80,!90,!95!and!99%.!! The!maximum!simulated!LOD!score!was!3.56,!under!an!autosomal!dominant!model!with!q=0.005!and!99%!penetrance.!!The!LOD!score!curves!did!not!differ!much!when!the!disease!allele!frequency!was!changed!(Figure!5).!!Dominant!models!had!consistently!higher!LOD!scores!than!recessive!models.!1 11 11 1150 60 70 80 90 1000.00.51.01.52.02.53.03.5Simulat d maximum LOD cores, FORGE 336PenetranceMaximum simulated LOD2 2222 223 3333 334 44 44 4 45 55 55 5 56 66 66 6 6123456dom, q=0.005dom, q=0.01dom, q=0.02rec, q=0.1rec, q=0.16rec, q=0.2Thu May  2 10:05:38 2013/Users/nroslin/Projects/Forge2013/Forge336/12Sim/Full/sim.pdf!Figure'5.''Simulated$maxi um$LOD$scores$under$the$alternative$hypothesis$of$linkage,$under$a$range$of$genetic$models.'SNP$filtering$After!removing!SNPs!that!had!possible!quality!issues,!genotypes!for!more!than!2!million!SNPs!remained.!!This!set!of!markers!was!reduced!in!order!to!end!up!with!a!set!suitable!for!linkage!analysis.!!First,!only!SNPs!on!chromosomes!1!to!22!and!X!were!kept.!!Next,!markers!that!had!alleles!ambiguous!for!strand!information!(A/T!and!G/C!variants)!were!removed,!in!order!to!facilitate!matching!with!HapMap!data.!!Markers!that!were!monomorphic!in!the!observed!data!were!also!removed,!since!they!will!be!uninformative!for!linkage!under!a!dominant!model!when!both!affected!and!unaffected!individuals!have!been!genotyped.!!At!this!point,!approximately!1!million!markers!remained.!!Markers!that!were!present!in!both!this!set!and!the!CEU! 63   Figure 2.10 Screenshot of region chr14: 20,000,000 – 35,000,000 from UCSC genome browser. The linked region chr14: 22,779,843 – 32,908,192 is gene-poor. Scalechr14:RefSeq GenesSequencesSNPsHuman mRNAsSpliced ESTsDNase ClustersChimpGorillaOrangutanGibbonRhesusCrab-eating_macaqueBaboonGreen_monkeyMarmosetSquirrel_monkeyBushbabyChinese_tree_shrewSquirrelLesser_Egyptian_jerboaPrairie_voleChinese_hamsterGolden_hamsterMouseRatNaked_mole-ratGuinea_pigChinchillaBrush-tailed_ratRabbitPikaPigAlpacaBactrian_camelDolphinKiller_whaleTibetan_antelopeCowSheepDomestic_goatHorseWhite_rhinocerosCatDogFerret_PandaPacific_walrusWeddell_sealBlack_flying-foxMegabatDavid’s_myotis_(bat)MicrobatBig_brown_batHedgehogShrewStar-nosed_moleElephantCape_elephant_shrewManateeCape_golden_moleTenrecAardvarkArmadilloOpossumTasmanian_devilWallabyPlatypusSaker_falconPeregrine_falconCollared_flycatcherWhite-throated_sparrowMedium_ground_finchZebra_finchTibetan_ground_jayBudgerigarParrotScarlet_macawRock_pigeonMallard_duckChickenTurkeyAmerican_alligatorGreen_seaturtlePainted_turtleChinese_softshell_turtleSpiny_softshell_turtleLizardX_tropicalisCoelacanthTetraodonFuguYellowbelly_pufferfishNile_tilapiaPrincess_of_BurundiBurton’s_mouthbreederZebra_mbunaPundamilia_nyerereiMedakaSouthern_platyfishSticklebackAtlantic_codZebrafishMexican_tetra_(cavefish)Spotted_garLampreyCommon SNPs(138)All SNPs(138)RepeatMasker5 Mb hg1921,000,000 22,000,000 23,000,000 24,000,000 25,000,000 26,000,000 27,000,000 28,000,000 29,000,000 30,000,000 31,000,000 32,000,000 33,000,000 34,000,000UCSC Genes (RefSeq, GenBank, CCDS, Rfam, tRNAs & Comparative Genomics)RefSeq GenesPublications: Sequences in Scientific ArticlesHuman mRNAs from GenBankHuman ESTs That Have Been SplicedH3K27Ac Mark (Often Found Near Active Regulatory Elements) on 7 cell lines from ENCODEDigital DNaseI Hypersensitivity Clusters in 125 cell types from ENCODETranscription Factor ChIP-seq from ENCODE100 vertebrates Basewise Conservation by PhyloPMultiz Alignments of 100 VertebratesSimple Nucleotide Polymorphisms (dbSNP 138) Found in >= 1% of SamplesSimple Nucleotide Polymorphisms (dbSNP 138)Repeating Elements by RepeatMaskerPOTEMP712POR11H2OR4Q3OR4M1OR4N2OR4K2OR4K5OR4K1OR4K15OR4K14OR4K13OR4L1OR4K17OR4N5OR11G2OR11H6OR11H4TTC5CCNB1IP1SNORA79SNORD126RPPH1PARP2TEP1KLHL33OSGEPAPEX1TMEM55BPNPRNASE10RNASE9RNASE11RNASE12AX747992TRNA_ProTRNA_LeuTRNA_ProTRNA_ThrTRNA_LeuTRNA_ThrTRNA_ProOR6S1TRNA_TyrTRNA_TyrTRNA_TyrTRNA_TyrTRNA_PseudoTRNA_ThrTRNA_TyrTRNA_ProANGRNASE4EDDM3AEDDM3BRNASE6RNASE1RNASE3ECRPRNASE2METTL17SLC39A2NDRG2TPPP2RNASE13RNASE7RNASE8ARHGEF40ZNF219TMEM253OR5AU1LINC00641HNRNPCRPGRIP1SUPT16HCHD8SNORD9SNORD8Metazoa_SRPRAB2BTOX4METTL3SALL2OR10G3D21847OR10G2TRA@OR4E2TRATRATCRVA15TCRAV5.1aTCRAV alpha 18/J alpha F/C alphaTCRATRAV12-1TCRAAV1S4A1N1TTCR-alphaAV2S1A1TRA@AV8S2hADV14S1Receptor V-alpha regionTCR-alphaTCRATCRATRAV20TCRATRDTCR-alphaTCRAVN1TCRATCRAAV4S1V alpha immunoglobulinTCRhADV38S2av27s1TCR- alpha V 33.1TCR-alphaAK093552AK125397TCRDV2hDV103S1TCRAADV21S1A1NTCRATCRATCRADAD1ABHD4OXA1LSLC7A7MRPL52MMP14LRP10REM2RBM23PRMT5TRNAHAUS4AJUBAC14orf93PSMB5PSMB11CDH24ACIN1C14orf119BC153822CEBPESLC7A8U6HOMEZPPP1R3EBCL2L2-PABPN1SLC22A17EFSIL25CMTM5MYH6MIR208AMYH7MIR208BNGDNZFHX2THTPAAP1G2AX747770JPH4DHRS2BX161431DHRS4-AS1DHRS4DHRS4L2LRRC16BCPNE6NRLNRLPCK2DCAF11FITM1PSME1EMC9PSME2RNF31IRF9REC8IPO4TM9SF1TSSK4NEDD8-MDP1GMPR2TINF2TGM1RABGGTAHP08474DHRS1NOP9CIDEBLTB4R2LTB4RADCY4RIPK3NFATC4NYNRINCBLN3KHNYNSDR39U1AK056368CMA1CTSGGZMHGZMBSTXBP6Mir_548NOVA1MIR4307BC148262LINC00645DD413682FOXG1C14orf23BC034423MIR548AIPRKD1BC062469BC062469U6G2E3SCFD1COCHLOC100506071STRN3MIR624AP4S1HECTD1HEATR5ABC041327DTD2GPR33NUBPLTRNA_GluARHGAP5-AS1ARHGAP5Mir_684AKAP6JA429831BC039319NPAS3EGLN3SPTSSAEAPPzoom in to <= 10,000,000 bases to view itemszoom in to <= 10,000,000 bases to view itemszoom in to <= 10,000,000 bases to view itemsLayered H3K27Ac100 _0 _100 Vert. Cons4.88 _-4.5 _0 - 64     Figure 2.11    Venn diagram of WGS analysis for 001, 013, and 014 based on the BGI pipeline.   S"001%%%%%S"014%S"013% 65    Figure 2.12    Venn diagram of WGS analysis for 001, 013, and 014 based on the in-house pipeline.     S"001%%%%%S"014%S"013% 66               Figure 2.13    Single nucleotide variant chr14:28052726 A > C impacts binding score for FoxA1 transcription factor binding site.  67 Chapter  3: Future Directions The recruitment of additional family members and, ideally additional families with similar genetic transmission patterns, is critical to validate a variant as the causal mutation. Two sets of distant relatives are currently being recruited for participation in order to refine the linked region. The recruiting process can be challenging, but we have seen increased interest in the family as the project progresses. As the number of participants grows and more phenotypic information is collected, it will be interesting to calculate penetrance based on a Bayesian method 159,160. In addition, we have recently identified a three-generation family with genetic strabismus and similar clinical description as in our subject family; enrolment of the family is underway. By publishing our initial findings, we expect to draw attention from clinicians and find additional families with the same transmission pattern. The same linked region might be identified in a proportion of these families, especially families with matching phenotypes. Such findings could support our finding and facilitate a greater understanding of the genetic mechanism underlying non-syndromic strabismus.  Another approach is to analyze locus transmission in parent-child pairs (affected and controls) from many distinct families. Statistical evidence can be collected to show that the identified linked region is involved in a subset of cases with a genetic component. Data generated by existing genome-wide association studies (GWAS) for strabismus can be reprocessed for such statistical analysis. Moreover, samples from GWAS collection can be subjected to sequencing or targeted position analysis to obtain the ratio of variants across strabismus and control populations. One strabismus GWAS has been reportedly underway led by a group at Harvard University, although no publications have appeared with results91,92.  68 Contradictory to its name, whole genome sequencing does not cover the complete genome (approximately 98% coverage was generated for the three WGS datasets in this study). Missing regions typically include difficult to sequence regions with high GC content or regions that can be mapped to more than one location and thus discarded in the alignment process. Among the sequenced regions, some are made up of simple repeats. One study has shown that variants in poorly covered regions (due to the repetitive nature of the sequences) can be causal161. As sequencing capacities and alignment tools improve, we may discover changes in the linked region that we are not able to detect today.  The sensitivity of variant calling is low when the coverage is shallow. High coverage is required to determine individual genotypes with high confidence126. Many called variants are not reliable when the depth of coverage is below 50-fold. Currently, the clinical standard is 100-fold, and this has been shown to improve calling accuracy. Ideally, we would like to obtain uniform 100-fold depth of coverage for the linked region. Alternatively, we could combine WGS raw data from 001, 013, and 014 to increase the variant detection sensitivity, but an increased false positive rate might be observed.  While we have initiated this project without substantial budget, additional NGS across family members may be pursued as the cost of NGS decreases. As de novo genome assembly approaches mature, we expect to re-process the WGS data in order to search for small-scale structural changes. Alternatively, software may emerge to allow high-quality detection of structural changes based on alignment methods. Detailed phenotyping is essential to study a heterogeneous condition like strabismus since multiple forms and different underlying genetic elements may be involved. We need to ascertain if participants have other forms of strabismus and refine phenotyping. As we  69 expand our collaboration network, we might develop a non-invasive characterization strategy with the help from ophthalmologists, such as MRI imaging of the muscle and nerve. A better understanding of the cause of the eye misalignment, muscular vs. neuronal, will allow us to prioritize genes of interest. We will then focus on a smaller profile and examine the impact of specific variants.  As I highlighted in the introduction, there is no established animal model to study non-syndromic strabismus. One challenge is the difficulty to characterize strabismus in animals. Researchers use similar criteria to measure eye misalignment in predators, such as cats and monkey. However, the measurement is more difficult in prey animals, for which eyes are not on the same plane and are more widely spread to maximize the horizontal vision to avoid predators. It is unclear whether models such as mice and zebrafish can exhibit strabismus-related phenotypes. If not, what alternative phenotypes should we look for in these model organisms when a strabismus-causing mutation is introduced? This is a crucial question to ask before we delve into validation. Without a clear predictable phenotype, we cannot test our hypothesis that the selected mutation is sufficient for the development of strabismus.  A more approachable method is the development of functional analysis for each variant in appropriate human cell lines. The CRISPR-Cas9 system is able to introduce changes in any region in the genome, including regulatory regions. This system has drawn a lot of attention due to its high efficacy and ease of operation. The lentiCRISPR system allows simultaneous transduction of Cas9 and a single-guide RNA (sgRNA), and the targeting specificity is high162. Modified Cas9 can have very specific function and provide us an even more effective way to incorporate the variant to a cell line, if it is known to express the gene  70 of concern. We can then examine whether a specific variant leads to a change at the expression level. Although the link between the expression profile and strabismus phenotype remains unclear, it is one important step for the project before we reach out to establish animal model. As we briefly discussed in the introduction, non-syndromic strabismus may have a strong environmental component. External forces, such as trauma, can also lead to strabismus development. Environmental factors, such as maternal smoking and decreasing socioeconomic status, can also contribute to strabismus development163–165. In the subject family, the pedigree strongly suggests a high penetrance autosomal dominant condition, but certain requisite carriers do not report an eye phenotype. Giving the various expressivities across family members, common genetic and/or environmental modifiers might be involved. Future investigation may explore the potential correlates with the phenotype in the family, or across a broader group if additional families are identified.   In conclusion, this project builds upon a unique chance to understand the genetics of non-syndromic strabismus.  While additional work will be required to bridge candidate variants to the phenotype, the linkage mapping to the specific segment of chromosome 14 will allow the global community to pursue several exciting lines of research. Based only on the results obtained in this thesis, it should be possible to assess the contribution of the region to genetic transmission of strabismus. Genotyping the region across the entire family should provide a clear measure of penetrance.  And further characterization of the family may reveal specific structural properties in tissues related to strabismus.   The validation of a variant is not the end of this exciting project, which sets out to solve the 2400-year-old mystery; instead it is the beginning of a long journey to understand  71 vision and its related molecular network in the modern time. Ultimately, we will develop a precise understanding of the root cause of strabismus, at least in this family, and begin to contemplate improved strabismus treatment.                     72 References 1. Lorenz, B. Genetics of isolated and syndromic strabismus: facts and perspectives. Strabismus 10, 147–156 (2002). 2. Mazyn, L. I. N., Lenoir, M., Montagne, G. & Savelsbergh, G. J. P. The contribution of stereo vision to one-handed catching. Exp. Brain Res. Exp. Hirnforsch. Expérimentation Cérébrale 157, 383–390 (2004). 3. Satterfield, D., Keltner, J. L. & Morrison, T. L. Psychosocial aspects of strabismus study. Arch. Ophthalmol. 111, 1100–1105 (1993). 4. Lukman, H. & Choong, Y. F. Strabismus-related prejudice in 5-6-year-old children. Br. J. Ophthalmol. 95, 1031–1032 (2011). 5. Lukman, H. et al. Negative social reaction to strabismus in school children ages 8-12 years. J. AAPOS Off. Publ. Am. Assoc. Pediatr. Ophthalmol. Strabismus Am. Assoc. Pediatr. Ophthalmol. Strabismus 15, 238–240 (2011). 6. Mojon-Azzi, S. M. & Mojon, D. S. Strabismus and employment: the opinion of headhunters. Acta Ophthalmol. (Copenh.) 87, 784–788 (2009). 7. Beauchamp, C. L. et al. The Cost Utility of Strabismus Surgery in Adults. J. Am. Assoc. Pediatr. Ophthalmol. Strabismus 10, 394–399 (2006). 8. Engle, E. C. The genetic basis of complex strabismus. Pediatr. Res. 59, 343–348 (2006). 9. Multi-ethnic Pediatric Eye Disease Study Group. Prevalence of Amblyopia and Strabismus in African American and Hispanic Children Ages 6 to 72 Months: The Multi-ethnic Pediatric Eye Disease Study. Ophthalmology 115, 1229–1236.e1 (2008). 10. Chia, A. et al. Prevalence of Amblyopia and Strabismus in Young Singaporean Chinese Children. Invest. Ophthalmol. Vis. Sci. 51, 3411–3417 (2010). 11. Matsuo, T. & Matsuo, C. The Prevalence of Strabismus and Amblyopia in Japanese Elementary School Children. Ophthalmic Epidemiol. 12, 31–36 (2005). 12. Engle, E. C. Genetic basis of congenital strabismus. Arch. Ophthalmol. 125, 189–195 (2007). 13. Ferreira, R. da C., Oelrich, F. & Bateman, B. Genetic aspects of strabismus. Arq. Bras. Oftalmol. 65, 171–175 (2002). 14. Mash, A. J. & Spivey, B. E. Genetic aspects of strabismus. Doc. Ophthalmol. 34, 285–291 (1973). 15. Tinley, C. & Grötte, R. Comitant horizontal strabismus in South African black and mixed race children--a clinic-based study. Ophthalmic Epidemiol. 19, 89–94 (2012). 16. Dufier, J. L., Briard, M. L., Bonaiti, C., Frezal, J. & Saraux, H. Inheritance in the Etiology of Convergent Squint. Ophthalmologica 179, 225–234 (1979). 17. Chew, E. et al. Risk factors for esotropia and exotropia. Arch. Ophthalmol. 112, 1349–1355 (1994). 18. Horta-Santini, J. M., Vergara, C., Colón-Casasnovas, J. E. & Izquierdo, N. J. Strabismus surgery at the Puerto Rico Medical Center: a brief report. P. R. Health Sci. J. 30, 203–205 (2011). 19. Maconachie, G. D. E., Gottlob, I. & McLean, R. J. Risk Factors and Genetics in Common Comitant Strabismus: A Systematic Review of the Literature. JAMA Ophthalmol. 1–8 (2013). doi:10.1001/jamaophthalmol.2013.4001  73 20. Grosvenor, T. & Grosvenor, T. P. in Prim. Care Optom. 244 (Elsevier Health Sciences, 2007). 21. Podgor, M. J., Remaley, N. A. & Chew, E. Associations between siblings for esotropia and exotropia. Arch. Ophthalmol. 114, 739–744 (1996). 22. Khan, A. O. et al. Infantile esotropia could be oligogenic and allelic with Duane retraction syndrome. Mol. Vis. 17, 1997–2002 (2011). 23. Demer, J. L. in Pediatr. Ophthalmol. Neuro-Ophthalmol. Genet. pp 59–75 (Springer Berlin Heidelberg, 2010). at <http://link.springer.com.ezproxy.library.ubc.ca/chapter/10.1007/978-3-540-85851-5_6> 24. Williams A & Hoyt CS. ACute comitant esotropia in children with brain tumors. Arch. Ophthalmol. 107, 376–378 (1989). 25. Lee, J.-M., Kim, S.-H., Lee, J.-I., Ryou, J.-Y. & Kim, S.-Y. Acute comitant esotropia in a child with a cerebellar tumor. Korean J. Ophthalmol. KJO 23, 228–231 (2009). 26. Adams, D. L., Economides, J. R., Sincich, L. C. & Horton, J. C. Cortical metabolic activity matches the pattern of visual suppression in strabismus. J. Neurosci. Off. J. Soc. Neurosci. 33, 3752–3759 (2013). 27. Graham, P. A. Epidemiology of strabismus. Br. J. Ophthalmol. 58, 224–231 (1974). 28. Ziakas, N. G., Woodruff, G., Smith, L. K. & Thompson, J. R. A study of heredity as a risk factor in strabismus. Eye Lond. Engl. 16, 519–521 (2002). 29. Sanfilippo, P. G. et al. Heritability of strabismus: genetic influence is specific to eso-deviation and independent of refractive error. Twin Res. Hum. Genet. Off. J. Int. Soc. Twin Stud. 15, 624–630 (2012). 30. Tychsen, L. in Clin. Strabismus Manag. Princ. Surg. Tech. (Saunders, 1999). 31. Worth, C. A. Squint!: its causes, pathology and treatment. (Philadelphia!: Blakiston, 1903). at <http://archive.org/details/squintitscausesp00wortrich> 32. Tychsen, L. in Pediatr. Ophthalmol. Neuro-Ophthalmol. Genet. (Lorenz, B. & Brodsky, M. C.) 41–57 (Springer Berlin Heidelberg, 2010). at <http://link.springer.com/chapter/10.1007/978-3-540-85851-5_5> 33. Tychsen, L. Can Ophthalmologists Repair the Brain in Infantile Esotropia? Early Surgery, Stereopsis, Monofixation Syndrome, and the Legacy of Marshall Parks. J. Am. Assoc. Pediatr. Ophthalmol. Strabismus 9, 510–521 (2005). 34. Schoeff, K., Chaudhuri, Z. & Demer, J. L. Functional magnetic resonance imaging of horizontal rectus muscles in esotropia. J. AAPOS Off. Publ. Am. Assoc. Pediatr. Ophthalmol. Strabismus Am. Assoc. Pediatr. Ophthalmol. Strabismus 17, 16–21 (2013). 35. Lennerstrand, G. Strabismus and eye muscle function. Acta Ophthalmol. Scand. 85, 711–723 (2007). 36. Maumenee, I. H. et al. Inheritance of congenital esotropia. Trans. Am. Ophthalmol. Soc. 84, 85–93 (1986). 37. Schlossman, A. & Priestley, B. S. Role of heredity in etiology and treatment of strabismus. AMA Arch. Ophthalmol. 47, 1–20 (1952). 38. Aurell, E. & Norrsell, K. A longitudinal study of children with a family history of strabismus: factors determining the incidence of strabismus. Br. J. Ophthalmol. 74, 589–594 (1990).  74 39. Matsuo, T., Hayashi, M., Fujiwara, H., Yamane, T. & Ohtsuki, H. Concordance of strabismic phenotypes in monozygotic versus multizygotic twins and other multiple births. Jpn. J. Ophthalmol. 46, 59–64 (2002). 40. Wilmer, J. B. & Backus, B. T. Genetic and environmental contributions to strabismus and phoria: evidence from twins. Vision Res. 49, 2485–2493 (2009). 41. Paul, T. O. & Hardage, L. K. The heritability of strabismus. Ophthalmic Genet. 15, 1–18 (1994). 42. Wei, N. F. [Genetic factors of concomitant strabismus]. Zhonghua Yan Ke Za Zhi Chin. J. Ophthalmol. 23, 282–283 (1987). 43. Waardenburg, P. J. Squint and heredity. Doc. Ophthalmol. Proc. Ser. 7-8, 422–494 (1954). 44. Parikh, V. et al. A strabismus susceptibility locus on chromosome 7p. Proc. Natl. Acad. Sci. U. S. A. 100, 12283–12288 (2003). 45. Rice, A. et al. Replication of the recessive STBMS1 locus but with dominant inheritance. Invest. Ophthalmol. Vis. Sci. 50, 3210–3217 (2009). 46. Fujiwara, H. et al. Genome-wide search for strabismus susceptibility loci. Acta Med. Okayama 57, 109–116 (2003). 47. Shaaban, S. et al. Chromosomes 4q28.3 and 7q31.2 as new susceptibility loci for comitant strabismus. Invest. Ophthalmol. Vis. Sci. 50, 654–661 (2009). 48. Altick, A. L., Feng, C.-Y., Schlauch, K., Johnson, L. A. & von Bartheld, C. S. Differences in gene expression between strabismic and normal human extraocular muscles. Invest. Ophthalmol. Vis. Sci. 53, 5168–5177 (2012). 49. Zhu, Y. et al. Abnormal expression of seven myogenesis-related genes in extraocular muscles of patients with concomitant strabismus. Mol. Med. Rep. (2012). doi:10.3892/mmr.2012.1149 50. Sanoudou, D. et al. Transcriptional profile of postmortem skeletal muscle. Physiol. Genomics 16, 222–228 (2004). 51. Franz, H. et al. Systematic analysis of gene expression in human brains before and after death. Genome Biol. 6, R112 (2005). 52. Andrews, C. V., Hunter, D. G. & Engle, E. C. in GeneReviewsTM (Pagon, R. A. et al.) (University of Washington, Seattle, 1993). at <http://www.ncbi.nlm.nih.gov/books/NBK1190/> 53. Appukuttan, B. et al. Localization of a gene for Duane retraction syndrome to chromosome 2q31. Am. J. Hum. Genet. 65, 1639–1646 (1999). 54. Connell, B. J. et al. Are Duane syndrome and infantile esotropia allelic? Ophthalmic Genet. 25, 189–198 (2004). 55. Marshman, W. E. et al. Congenital anomalies in patients with Duane retraction syndrome and their relatives. J. AAPOS Off. Publ. Am. Assoc. Pediatr. Ophthalmol. Strabismus Am. Assoc. Pediatr. Ophthalmol. Strabismus 4, 106–109 (2000). 56. Wabbels, B. K., Lorenz, B. & Kohlhase, J. No evidence of SALL4-mutations in isolated sporadic duane retraction ‘syndrome’ (DURS). Am. J. Med. Genet. A. 131, 216–218 (2004). 57. Sevel, D. & Kassar, B. S. Bilateral Duane syndrome. Occurrence in three successive generations. Arch. Ophthalmol. 91, 492–494 (1974). 58. Gutowski, N. J. Duane’s syndrome. Eur. J. Neurol. 7, 145–149 (2000).  75 59. Anwar, S., Bradshaw, K. & Vivian, A. J. Ophthalmic manifestations of trisomy 8 mosaic syndrome. Ophthalmic Genet. 19, 81–86 (1998). 60. Fineman, R. M. et al. Complete and partial trisomy of different segments of chromosome 8: case reports and review. Clin. Genet. 16, 390–398 (1979). 61. Pizzuti, A. et al. A peptidase gene in chromosome 8q is disrupted by a balanced translocation in a duane syndrome patient. Invest. Ophthalmol. Vis. Sci. 43, 3609–3612 (2002). 62. Vincent, C. et al. A proposed new contiguous gene syndrome on 8q consists of Branchio-Oto-Renal (BOR) syndrome, Duane syndrome, a dominant form of hydrocephalus and trapeze aplasia; implications for the mapping of the BOR gene. Hum. Mol. Genet. 3, 1859–1866 (1994). 63. Lyons, P. J., Callaway, M. B. & Fricker, L. D. Characterization of carboxypeptidase A6, an extracellular matrix peptidase. J. Biol. Chem. 283, 7054–7063 (2008). 64. Lyons, P. J., Ma, L., Baker, R. & Fricker, L. D. Carboxypeptidase A6 in zebrafish development and implications for VIth cranial nerve pathfinding. PloS One 5, e12967 (2010). 65. Lehman, A. M. et al. A characteristic syndrome associated with microduplication of 8q12, inclusive of CHD7. Eur. J. Med. Genet. 52, 436–439 (2009). 66. Monfort, S. et al. Detection of known and novel genomic rearrangements by array based comparative genomic hybridisation: deletion of ZNF533 and duplication of CHARGE syndrome genes. J. Med. Genet. 45, 432–437 (2008). 67. Amouroux, C. et al. Duplication 8q12: confirmation of a novel recognizable phenotype with duane retraction syndrome and developmental delay. Eur. J. Hum. Genet. EJHG 20, 580–583 (2012). 68. Baris, H. N. et al. Complex cytogenetic rearrangements at the DURS1 locus in syndromic Duane retraction syndrome. Clin. Case Rep. n/a–n/a (2013). doi:10.1002/ccr3.11 69. Miyake, N. et al. Human CHN1 mutations hyperactivate alpha2-chimaerin and cause Duane’s retraction syndrome. Science 321, 839–843 (2008). 70. Miyake, N. et al. CHN1 mutations are not a common cause of sporadic Duane’s retraction syndrome. Am. J. Med. Genet. A. 152A, 215–217 (2010). 71. Chan, W.-M., Miyake, N., Zhu-Tam, L., Andrews, C. & Engle, E. C. Two novel CHN1 mutations in 2 families with Duane retraction syndrome. Arch. Ophthalmol. 129, 649–652 (2011). 72. Kim, J. H. & Hwang, J.-M. Presence of the abducens nerve according to the type of Duane’s retraction syndrome. Ophthalmology 112, 109–113 (2005). 73. Michaelides, M. & Moore, A. T. The genetics of strabismus. J. Med. Genet. 41, 641–646 (2004). 74. Demer, J. L., Clark, R. A., Lim, K. H. & Engle, E. C. Magnetic resonance imaging of innervational and extraocular muscle abnormalities in Duane-radial ray syndrome. Invest. Ophthalmol. Vis. Sci. 48, 5505–5511 (2007). 75. Miller, N. R., Kiel, S. M., Green, W. R. & Clark, A. W. Unilateral Duane’s retraction syndrome (Type 1). Arch. Ophthalmol. 100, 1468–1472 (1982). 76. Kohlhase, J. et al. Okihiro syndrome is caused by SALL4 mutations. Hum. Mol. Genet. 11, 2979–2987 (2002).  76 77. Al-Baradie, R. et al. Duane radial ray syndrome (Okihiro syndrome) maps to 20q13 and results from mutations in SALL4, a new member of the SAL family. Am. J. Hum. Genet. 71, 1195–1199 (2002). 78. Kohlhase, J. et al. SALL4 mutations in Okihiro syndrome (Duane-radial ray syndrome), acro-renal-ocular syndrome, and related disorders. Hum. Mutat. 26, 176–183 (2005). 79. Koshiba-Takeuchi, K. et al. Cooperative and antagonistic interactions between Sall4 and Tbx5 pattern the mouse limb and heart. Nat. Genet. 38, 175–183 (2006). 80. Webb, A. A. & Cullen, C. L. Coat color and coat color pattern-related neurologic and neuro-ophthalmic diseases. Can. Vet. J. 51, 653–657 (2010). 81. Guillery, R. W. et al. Abnormal central visual pathways in the brain of an albino green monkey (Cercopithecus aethiops). J. Comp. Neurol. 226, 165–183 (1984). 82. Guillery, R. W. & Kaas, J. H. Genetic abnormality of the visual pathways in a ‘white’ tiger. Science 180, 1287–1289 (1973). 83. Bernays, M. E. & Smith, R. I. Convergent strabismus in a white Bengal tiger. Aust. Vet. J. 77, 152–155 (1999). 84. Rengstorff, R. H. Strabismus measurements in the Siamese cat. Am. J. Optom. Physiol. Opt. 53, 643–646 (1976). 85. Kaas, J. H. Serendipity and the Siamese cat: the discovery that genes for coat and eye pigment affect the brain. ILAR J. Natl. Res. Counc. Inst. Lab. Anim. Resour. 46, 357–363 (2005). 86. Jeffery, G., Brem, G. & Montoliu, L. Correction of retinal abnormalities found in albinism by introduction of a functional tyrosinase gene in transgenic mice and rabbits. Brain Res. Dev. Brain Res. 99, 95–102 (1997). 87. Biswas, S. & Lloyd, I. C. Oculocutaneous albinism. Arch. Dis. Child. 80, 565–569 (1999). 88. Burdon, K. P. et al. Investigation of albinism genes in congenital esotropia. Mol. Vis. 9, 710–714 (2003). 89. Grünau, M. W. von & Rauschecker, J. P. Natural strabismus in non-siamese cats: Lack of binocularity in the striate cortex. Exp. Brain Res. 52, 307–310 (1983). 90. Bui Quoc, E. et al. Asymmetrical interhemispheric connections develop in cat visual cortex after early unilateral convergent strabismus: anatomy, physiology, and mechanisms. Front. Neuroanat. 5, 68 (2011). 91. National Eye Institute Workshop to Identify Gaps, Needs, and Opportunities in Ophthalmic Genetics [NEI Strategic Planning]. at <http://www.nei.nih.gov/strategicplanning/ophthalmic.asp> 92. Andrew, C., Mackinnon, S., Hunter, D. . & Engle, E. C. GENETICS OF COMITANT STRABISMUS: A STUDY BASED AT CHILDREN’S HOSPITAL BOSTON. at <http://www.ashg.org/2009meeting/abstracts/fulltext/f10870.htm> 93. Khan, A. O. et al. Potential linkage of different phenotypic forms of childhood strabismus to a recessive susceptibility locus (16p13.12-p12.3). Mol. Vis. 17, 971–976 (2011). 94. Shaaban, S., Matsuo, T., Strauch, K. & Ohtsuki, H. Investigation of parent-of-origin effect in comitant strabismus using MOD score analysis. Mol. Vis. 15, 1351–1358 (2009). 95. Bailey-Wilson, J. E. & Wilson, A. F. Linkage analysis in the next-generation sequencing era. Hum. Hered. 72, 228–236 (2011).  77 96. Koboldt, D. C., Steinberg, K. M., Larson, D. E., Wilson, R. K. & Mardis, E. R. The next-generation sequencing revolution and its impact on genomics. Cell 155, 27–38 (2013). 97. Boycott, K. M., Vanstone, M. R., Bulman, D. E. & MacKenzie, A. E. Rare-disease genetics in the era of next-generation sequencing: discovery to translation. Nat. Rev. Genet. 14, 681–691 (2013). 98. Yang, Y. et al. Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N. Engl. J. Med. 369, 1502–1511 (2013). 99. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009). 100. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinforma. Oxf. Engl. 25, 1754–1760 (2009). 101. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010). 102. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinforma. Oxf. Engl. 25, 2078–2079 (2009). 103. Kumar, P., Henikoff, S. & Ng, P. C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009). 104. Cheung, W. A., Ouellette, B. F. F. & Wasserman, W. W. Quantitative biomedical annotation using medical subject heading over-representation profiles (MeSHOPs). BMC Bioinformatics 13, 249 (2012). 105. Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011). 106. Gaudet, M., Fara, A.-G., Beritognolo, I. & Sabatti, M. in Single Nucleotide Polymorph. (Komar, A. A.) 415–424 (Humana Press, 2009). at <http://link.springer.com/protocol/10.1007/978-1-60327-411-1_26> 107. Liu, J. et al. An improved allele-specific PCR primer design method for SNP marker analysis and its application. Plant Methods 8, 34 (2012). 108. Kent, W. J. BLAT--the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002). 109. Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5, 113 (2004). 110. Wangkumhang, P. et al. WASP: a Web-based Allele-Specific PCR assay designing tool for detecting SNPs and mutations. BMC Genomics 8, 275 (2007). 111. Schäffer, A. A., Lemire, M., Ott, J., Lathrop, G. M. & Weeks, D. E. Coordinated conditional simulation with SLINK and SUP of many markers linked or associated to a trait in large pedigrees. Hum. Hered. 71, 126–134 (2011). 112. Abecasis, G. R., Cherny, S. S., Cookson, W. O. & Cardon, L. R. Merlin--rapid analysis of dense genetic maps using sparse gene flow trees. Nat. Genet. 30, 97–101 (2002). 113. Huang, Q., Shete, S. & Amos, C. I. Ignoring linkage disequilibrium among tightly linked markers induces false-positive evidence of linkage for affected sib pair analysis. Am. J. Hum. Genet. 75, 1106–1112 (2004). 114. Hoffman, M. M. et al. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat. Methods 9, 473–476 (2012).  78 115. Mathelier, A. et al. JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res. 42, D142–147 (2014). 116. Khurana, E. et al. Integrative annotation of variants from 1092 humans: application to cancer genomics. Science 342, 1235587 (2013). 117. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014). 118. FANTOM Consortium and the RIKEN PMI and CLST (DGT). A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014). 119. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014). 120. Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22, 1790–1797 (2012). 121. Kapp, M. E., von Noorden, G. K. & Jenkins, R. Strabismus in Williams syndrome. Am. J. Ophthalmol. 119, 355–360 (1995). 122. Makeyev, A. V. et al. GTF2IRD2 is located in the Williams-Beuren syndrome critical region 7q11.23 and encodes a protein with two TFII-I-like helix-loop-helix repeats. Proc. Natl. Acad. Sci. U. S. A. 101, 11052–11057 (2004). 123. Hirota, H. et al. Williams syndrome deficits in visual spatial processing linked to GTF2IRD1 and GTF2I on chromosome 7q11.23. Genet. Med. Off. J. Am. Coll. Med. Genet. 5, 311–321 (2003). 124. Palmer, S. J. et al. GTF2IRD2 from the Williams-Beuren critical region encodes a mobile-element-derived fusion protein that antagonizes the action of its related family members. J. Cell Sci. 125, 5040–5050 (2012). 125. Cirulli, E. T. & Goldstein, D. B. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat. Rev. Genet. 11, 415–425 (2010). 126. Goldstein, D. B. et al. Sequencing studies in human genetics: design and interpretation. Nat. Rev. Genet. 14, 460–470 (2013). 127. Stankiewicz, P. & Lupski, J. R. Structural variation in the human genome and its role in disease. Annu. Rev. Med. 61, 437–455 (2010). 128. 1000 Genomes Project Consortium et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012). 129. Lupien, M. et al. FoxA1 translates epigenetic signatures into enhancer-driven lineage-specific transcription. Cell 132, 958–970 (2008). 130. Florian, C., Bahi-Buisson, N. & Bienvenu, T. FOXG1-Related Disorders: From Clinical Description to Molecular Genetics. Mol. Syndromol. (2011). doi:10.1159/000327329 131. Takagi, M. et al. A 2.0 Mb microdeletion in proximal chromosome 14q12, involving regulatory elements of FOXG1, with the coding region of FOXG1 being unaffected, results in severe developmental delay, microcephaly, and hypoplasia of the corpus callosum. Eur. J. Med. Genet. 56, 526–528 (2013). 132. Allou, L. et al. 14q12 and severe Rett-like phenotypes: new clinical insights and physical mapping of FOXG1-regulatory elements. Eur. J. Hum. Genet. 20, 1216–1223 (2012). 133. Kumakura, A., Takahashi, S., Okajima, K. & Hata, D. A haploinsufficiency of FOXG1 identified in a boy with congenital variant of Rett syndrome. Brain Dev. doi:10.1016/j.braindev.2013.09.006  79 134. Tischfield, M. A. et al. Human TUBB3 mutations perturb microtubule dynamics, kinesin interactions, and axon guidance. Cell 140, 74–87 (2010). 135. Yamada, K. et al. Heterozygous mutations of the kinesin KIF21A in congenital fibrosis of the extraocular muscles type 1 (CFEOM1). Nat. Genet. 35, 318–321 (2003). 136. Nakano, M. et al. Homozygous mutations in ARIX(PHOX2A) result in congenital fibrosis of the extraocular muscles type 2. Nat. Genet. 29, 315–320 (2001). 137. Aubourg, P. et al. Assignment of a new congenital fibrosis of extraocular muscles type 3 (CFEOM3) locus, FEOM4, based on a balanced translocation t(2;13) (q37.3;q12.11) and identification of candidate genes. J. Med. Genet. 42, 253–259 (2005). 138. Vincent, A. L., Watkins, W. J., Sloan, B. H. & Shelling, A. N. Blepharophimosis and bilateral Duane syndrome associated with a FOXL2 mutation. Clin. Genet. 68, 520–523 (2005). 139. Barry, J. S. & Reddy, M. A. The association of an epibulbar dermoid and Duane syndrome in a patient with a SALL1 mutation (Townes-Brocks Syndrome). Ophthalmic Genet. 29, 177–180 (2008). 140. Gómez-Lado, C., Eirís, J., Martínez-Yriarte, J. M., Blanco, O. & Castro-Gago, M. Duane’s syndrome and 22 marker chromosome: a possible cat-eye syndrome. Acta Paediatr. Oslo Nor. 1992 95, 1510–1511 (2006). 141. Bayrakli, F. et al. Heterozygous 5p13.3-13.2 deletion in a patient with type I Chiari malformation and bilateral Duane retraction syndrome. Clin. Genet. 77, 499–502 (2010). 142. Kato, Z., Yamagishi, A. & Kondo, N. Interstitial deletion of 1q42.13-q43 with Duane retraction syndrome. J. AAPOS Off. Publ. Am. Assoc. Pediatr. Ophthalmol. Strabismus Am. Assoc. Pediatr. Ophthalmol. Strabismus 11, 62–64 (2007). 143. Smith, S. B. & Traboulsi, E. I. Duane syndrome in the setting of chromosomal duplications. Am. J. Ophthalmol. 150, 932–938 (2010). 144. Slee, J. J., Smart, R. D. & Viljoen, D. L. Deletion of chromosome 13 in Moebius syndrome. J. Med. Genet. 28, 413–414 (1991). 145. Van Goethem, G., Dermaut, B., Löfgren, A., Martin, J. J. & Van Broeckhoven, C. Mutation of POLG is associated with progressive external ophthalmoplegia characterized by mtDNA deletions. Nat. Genet. 28, 211–212 (2001). 146. Lamantea, E. et al. Mutations of mitochondrial DNA polymerase gammaA are a frequent cause of autosomal dominant or recessive progressive external ophthalmoplegia. Ann. Neurol. 52, 211–219 (2002). 147. Kaukonen, J. et al. Role of adenine nucleotide translocator 1 in mtDNA maintenance. Science 289, 782–785 (2000). 148. Suomalainen, A. et al. An autosomal locus predisposing to deletions of mitochondrial DNA. Nat. Genet. 9, 146–151 (1995). 149. Li, F. Y. et al. Mapping of autosomal dominant progressive external ophthalmoplegia to a 7-cM critical region on 10q24. Neurology 53, 1265–1271 (1999). 150. Spelbrink, J. N. et al. Human mitochondrial DNA deletions associated with mutations in the gene encoding Twinkle, a phage T7 gene 4-like protein localized in mitochondria. Nat. Genet. 28, 223–231 (2001). 151. Longley, M. J. et al. Mutant POLG2 disrupts DNA polymerase gamma subunits and causes progressive external ophthalmoplegia. Am. J. Hum. Genet. 78, 1026–1034 (2006).  80 152. Young, M. J. et al. Biochemical analysis of human POLG2 variants associated with mitochondrial disease. Hum. Mol. Genet. 20, 3052–3066 (2011). 153. Tyynismaa, H. et al. A heterozygous truncating mutation in RRM2B causes autosomal-dominant progressive external ophthalmoplegia with multiple mtDNA deletions. Am. J. Hum. Genet. 85, 290–295 (2009). 154. Fratter, C. et al. RRM2B mutations are frequent in familial PEO with multiple mtDNA deletions. Neurology 76, 2032–2034 (2011). 155. Ronchi, D. et al. Mutations in DNA2 link progressive myopathy to mitochondrial DNA instability. Am. J. Hum. Genet. 92, 293–300 (2013). 156. Jen, J. C. et al. Mutations in a human ROBO gene disrupt hindbrain axon pathway crossing and morphogenesis. Science 304, 1509–1513 (2004). 157. Tischfield, M. A. et al. Homozygous HOXA1 mutations disrupt human brainstem, inner ear, cardiovascular and cognitive development. Nat. Genet. 37, 1035–1037 (2005). 158. Bosley, T. M. et al. The clinical spectrum of homozygous HOXA1 mutations. Am. J. Med. Genet. A. 146A, 1235–1240 (2008). 159. Rogatko, A., Pereira, C. A. & Frota-Pessoa, O. A Bayesian method for the estimation of penetrance: application to mandibulofacial and frontonasal dysostoses. Am. J. Med. Genet. 24, 231–246 (1986). 160. Otto, P. A. & Horimoto, A. R. V. R. Penetrance rate estimation in autosomal dominant conditions. Genet. Mol. Biol. 35, 583–588 (2012). 161. Kirby, A. et al. Mutations causing medullary cystic kidney disease type 1 lie in a large VNTR in MUC1 missed by massively parallel sequencing. Nat. Genet. 45, 299–303 (2013). 162. Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014). 163. Hakim, R. B. & Tielsch, J. M. Maternal cigarette smoking during pregnancy. A risk factor for childhood strabismus. Arch. Ophthalmol. 110, 1459–1462 (1992). 164. Cotter, S. A. et al. Risk factors associated with childhood strabismus: the multi-ethnic pediatric eye disease and Baltimore pediatric eye disease studies. Ophthalmology 118, 2251–2261 (2011). 165. Pathai, S., Cumberland, P. M. & Rahi, J. S. Prevalence of and early-life influences on childhood strabismus: findings from the Millennium Cohort Study. Arch. Pediatr. Adolesc. Med. 164, 250–257 (2010).   

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
https://iiif.library.ubc.ca/presentation/dsp.24.1-0167238/manifest

Comment

Related Items