Integrating population genomics and medical genetics for understanding the genetic aetiology of eye traits

INTEGRATING POPULATION GENOMICS AND MEDICAL GENETICS FOR UNDERSTANDING THE GENETIC AETIOLOGY OF EYE TRAITS FAN QIAO (M.Sc. University of Minnesota) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSPHY SAW SWEE HOCK SCHOOL OF PUBLIC HEALTH NATIONAL UNIVERSITY OF SINGAPORE 2012 Acknowledgements I would like to express my sincerest gratitude to my supervisor, Prof. YikYing Teo, for his guidance, patience and encouraging high standards in my work through this study. He spent hours reviewing my original manuscripts, gave constructive feedback and made detailed corrections. His support has been invaluable for me to write this doctoral thesis. I am also deeply grateful to my supervisor, Prof. Seang-Mei Saw, for her continuous support, suggestions and providing research resources for me to accomplish my work. Her passion in research and the determination to slow the myopic progression in children has influenced me greatly. My sincere thanks also go to Dr. Yi-Ju Li, who encouraged me to move a step forward in my career and broadened my research experience. Her unflinching courage confronting ill health will inspire me for my whole life. I am also thankful to Dr. Ching-Yu Cheng. The conversations with Ching-Yu were always valuable for me to understand the clinical relevance of ocular diseases. My thanks are also due to Dr. Chiea-Chuen Khor for his prompt comments in reviewing my papers and the insight provided. I also wish to thank Dr. Liang Kee Goh for providing the infrastructure to support me at the beginning of this research, and Prof. Terri L Young and Prof. Tien-Yin Wong for their dedication along this project. During this research, I have worked with many collaborators for whom I have great regard. In particular, I am indebted to Dr. Veluchamy A. Barathi for performing gene and protein expression in ocular tissues. The discussion with her regarding the animal model of myopia was an interesting exploration. It is also my pleasure to acknowledge Dr. Akira Meguro and Dr. Isao Nakata for kindly sharing their data in the replication study and stimulating discussions. Many thanks go to my office-mates and colleagues, Zhou Xin, Chen Peng, Xiaoyu, Haiyang, Huijun, Rick, Queenie, Vivian, Chenwei and Wang Pei for their cheerful discussion and a source of inspiration. Finally, I would like to thank my family for their wholehearted support given to me - I owe everything to them. For me, the journey over the past several years has been more like a process of cultivation. The best way to express my gratitude is, without attachment to a self, to help others in my life. Tables of Contents SUMMARY………………………………………………………………… LIST OF TABLES……………………………………………… .8 LIST OF FIGURES………………………………………………… .9 CHAPTER INTRODUCTION .12 1.1 Statistical analysis of genome-wide association studies . 12 1.1.1 Linkage disequilibrium based association mapping 12 1.1.2 Study design and analytical strategy .13 1.1.2.1 Data quality control 13 1.1.2.2 Population structure .14 1.1.2.3 Study design .16 1.1.2.4 Multiple testing 17 1.1.3 Phenotype classification 18 1.1.3.1 Binary/quantitative traits 18 1.1.3.2 Paired eye measurements .19 1.1.4 Meta-analysis of genome-wide association studies 22 1.1.4.1 Imputation on genotyped data 22 1.1.4.2 Statistics in the meta-analysis 23 1.1.4.3 Statistical challenges in analyzing multi-ethnic populations 26 1.2 Recombination variation between populations 28 1.2.1 Recombination and genetic diversity 28 1.2.2 Variation in inter-population recombination .29 1.2.3 Current approaches of quantifying recombination differences .30 1.3 Refractive errors and the aetiology of myopia . 32 1.3.1 Types of refractive errors 33 1.3.1.1 Myopia, hyperopia and ocular biometrics 33 1.3.1.2 Astigmatism .34 1.3.2 Experimental animal myopia models 35 1.3.2.1 Deprivation myopia and inducing myopia .35 1.3.2.2 Emmetropisation and the role of scleral changes in eye growth 37 1.3.2.3 Peripheral refraction .37 1.3.3 Roles of environmental factors in controlling human refraction 38 1.3.4 Genetic basis of myopia 41 1.3.4.1 Familial aggregation and segregation .41 1.3.4.2 Estimates of heritability .43 1.3.5 Genetic loci associated with or linked to refractive errors 46 1.3.5.1 Myopic loci identified from genome-wide linkage studies 46 1.3.5.2 Candidate gene studies .50 1.3.5.3 Genome-wide association studies 57 1.3.6 Intervention to slow myopia progression 61 CHAPTER STUDY AIMS .65 CHAPTER GENETIC VARIANTS ON CHROMOSOME 1Q41 INFLUENCE OCULAR AXIAL LENGTH AND HIGH MYOPIA 67 3.1 Abstract 67 3.2 Background 68 3.3 Methods 70 3.3.1 Study cohorts 70 3.3.2 Data quality control 74 3.3.3 Statistical methods 77 3.3.4 Functional studies .78 3.3.4.1 Gene expression in human .78 3.3.4.2 Myopia-induced mouse model .79 3.4 Results 82 3.4.1 Datasets after quality control 82 3.4.2 Locus at chromosome 1q41 achieved genome-wide significance 83 3.4.3 Association with high myopia on the identified SNPs 84 3.4.4 Gene expression 85 3.5 Discussion . 86 CHAPTER GENOME-WIDE META-ANALYSIS OF FIVE ASIAN COHORTS IDENTIFIES PDGFRA AS A SUSCEPTIBILITY LOCUS FOR CORNEAL ASTIGMATISM . 103 4.1 Abstract 103 4.2 Background 104 4.3 Methods 106 4.3.1 Study cohorts 106 4.3.2 Data quality control 109 4.3.3 Statistical methods 113 4.4 Results 115 4.4.1 Datasets after quality control 115 4.4.2 Gene PDGFRA exhibiting genome-wide significance 116 4.5 Discussion . 117 CHAPTER GENOME-WIDE COMPARISON OF ESTIMATED RECOMBINATION RATES BETWEEN POPULATIONS . 130 5.1 Study summary 130 5.2 Methods 131 5.2.1 Development of recombination variation score 131 5.2.2 Simulation .134 5.2.3 Estimation of recombination rates 137 5.2.4 Simulation .138 5.2.5 SNP annotation, copy number variation and FST calculation 141 5.2.6 Quantification of variations in linkage disequilibrium .141 5.3 Results 143 5.3.1 Simulation studies on power and false positive rates 143 5.3.2 Application to HapMap and Singapore Genome Variation Project 145 5.3.3 Recombination variation and Linkage disequilibrium variation highly correlated 148 5.3.4 Regions with largest recombination variation less frequent in genes .149 5.4 Discussion . 149 CHAPTER CONCLUSION 181 6.1 Identified genetic variants associated with refractive errors . 181 6.2 Transferability of the genetic variants for refractive errors across populations 182 6.3 Statistical meta-analysis of GWAS in diverse populations 184 6.4 Missing heritability of myopia . 185 6.5 Recombination variations and implications in genetic association studies . 187 PUBLICATIONS . 190 REFERENCES . 191 Summary For complex human diseases, identifying the underlying genetic factors has previously primarily relied on either genome-wide linkage scans to narrow down the chromosomal regions that are linked to disease-causing genes or the candidate gene approach based on known mechanisms of disease pathogenesis. During the past few years, genome-wide association studies have emerged as popular tools to identify genetic variants underlying common and complex diseases, greatly advancing our understanding of the genetic architecture of human diseases. Refractive errors are complex ocular disorders, as the underlying causes are both genetic and environmental in origin. The need for continued research into the genetic aetiology of refractive errors is considerable, especially considering a mismatch between high heritability in twin studies and the paucity of evidence for associated genetic variation. This thesis seeks to address the potential roles of genetic factors involved in refractive errors. Through a meta-analysis of three genome-wide association scans on ocular biometry of axial length in Asians, we have determined that a genetic locus on chromosome 1q41 is associated with axial length and high myopia. In addition, our meta-analysis in five genome-wide association studies in Asians has revealed that genetic variants on chromosome 4q12 are associated with corneal astigmatism, exhibiting strong and consistent effects over Chinese, Malays and Indians. Inter-population variation in patterns of linkage disequilibrium, largely shaped by underlying homologous recombination, influences the transferability of genetic risk loci across different populations. Understanding the recombination variation provides the insight into fine-mapping of the functional polymorphisms by leveraging on the genetic diversity of different populations. This motivates an attempt to quantify the recombination variations between populations. For this purpose, a quantitative measure (varRecM) is proposed to evaluate the extent of inter-population differences in recombination rates. Our findings suggest that significant fine-scale differences exist in the recombination profiles of Europeans, Africans and East Asians. Regions that emerged with the strongest evidence harbour candidate genes for population-specific positive selection, and for genetic syndromes. List of Tables Table 1. Summary of analytic approaches for quantitative trait two-eye data in genome-wide association studies .21 Table 2. Myopia loci identified from genome-wide linkage studies .49 Table 3. Candidate genes studied for high myopia .54 Table 4. Genetic loci identified from genome-wide association studies .59 Table 5. Characteristics of study participants in the five Asian cohorts .92 Table 6. Top SNPs (Pmeta-value ≤ × 10-5) associated with AL from the meta3analysis in the three Asian cohorts .93 Table 7. Association between genetic variants at chromosome 1q41 and high myopia in the five Asian cohorts 94 Table 8. Characteristics of the participants in five studies . 128 Table 9. Top SNPs (P-value ≤ x 10 -6) identified from combined metaanalysis of five Asian population cohorts 129 Table 10. varRecM scores at top percentiles for pair-wise comparisons of the three HapMap populations between CEU and JPT + CHB, CEU and YRI, YRI and JPT + CHB 175 Table 11. The 20 strongest signals of varRecM scores in comparisons of HapMap populations 176 Table 12. The 20 strongest signals of varRecM score in comparison of populations of SGVP Chinese and Indians, and Chinese and HapMap East Asians .179 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156. 157. 158. 159. Gwiazda, J., Deng, L., Dias, L. & Marsh-Tootle, W. Association of education and occupation with myopia in COMET parents. Optom Vis Sci 88, 1045-53 (2011). Gwiazda, J.E. et al. Accommodation and related risk factors associated with myopia progression and their interaction with treatment in COMET children. Invest Ophthalmol Vis Sci 45, 2143-51 (2004). 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Curr Opin Genet Dev 16, 565-72 (2006). 213 [...]... regions of largest varRecM scores with overlapping signals of positive selection 161 Figure 24 Plots of the top 20 regions of the varRecM scores for the comparison between samples of HapMap CEU and JPT+CHB 164 Figure 25 Plots of the top 20 regions of the varRecM scores for the comparison between samples of HapMap CEU and YRI 166 Figure 26 Plots of the top 20 regions of the varRecM scores for. .. for the comparison between samples of HapMap JPT+CHB and YRI 168 Figure 27 Plots of the top 20 regions of the varRecM scores for the comparison between samples of SGVP CHS and INS 170 Figure 28 Plots of the top 20 regions of the varRecM scores for the comparison between samples of SGVP CHS and HapMap JPT+CHB .172 10 Figure 29 Scatter plot of varLD score versus varRecM score among HapMap and SGVP... coalescent theory101 The resolution of these maps thus depends on the density of the SNPs in these databases, and typically yields a resolution in the order of kilobases Such LD-based estimates of recombination rate are sex-averaged over tens of thousands of generations, and are likely to be influenced by the locus-specific demographic forces102,103 Despite the potential limitations, these estimated rates of. .. mainly through the elongation of the vitreous chamber, is equivalent to a myopic shift of -2.00 to -3.00 D without corresponding changes in the optical power of the cornea and lens In contrast, the differences in lens thickness and corneal curvature (CC) by comparing myopic to emmetropic subjects are minimal114 Therefore, the control of the AL and excessive elongation of the eyes is crucial for achieving... physical distance of less than 100kb in only one population but not the other108 Using different definitions can alter the number and positions of the detected 31 hotspots, and simply querying whether hotspots overlap between populations may neglect vital information on the local recombination profile 1.3 Refractive errors and the aetiology of myopia Refractive errors broadly comprise two types of ocular abnormalities:... scenarios the traits might be moderately or weakly correlated between two eyes41 Neither the use of data from one eye nor an average from both eyes is appropriate due to the negligence of phenotypic dissimilarity A wide array of statistical approaches has emerged recently for the detection of the pleiotropic genetic factors contributing to multiple correlated traits, which could also be applied to two -eye. .. as the benchmark for evaluating the fidelity of the association signal at each marker9 Notably, the Bonferroni correction is simple but conservative, as assuming the independence of one million genetic variants and all tests conducted without considering the intermarker correlation Replication is thus considered as the gold standard for GWAS publications16 Currently, the identification of candidate genetic. .. project5 In the simple scenario, an association study 12 compares the frequency of alleles or genotypes for a particular variant between the cases and controls The current design of GWAS relies on genetic correlations between the genotyped markers and underlying functional polymorphisms, named LD-mapping LD is the non-random association of alleles at two or more loci The amount of LD depends on the difference... for the assessment of the complete array of genetic variants (most of which are un-typed) Step-by-step guidelines and techniques for performing imputation-based genome-wide meta-analysis was reviewed by de Bakker and colleagues58 The development of several imputation methods for inferring the genotypes of untyped markers has provided a solution for this problem (for a review, see59) The basic idea behind... the imputation is more accurate in high-LD regions60 Second, the level of genetic similarity of the study population to the reference panels affects the utility of the haplotypes copied from the reference samples in imputing genotypes in the study populations Imputation accuracy based on HapMap reference panels is highest in European populations, which are closely related to the HapMap CEU panel, and . INTEGRATING POPULATION GENOMICS AND MEDICAL GENETICS FOR UNDERSTANDING THE GENETIC AETIOLOGY OF EYE TRAITS FAN QIAO (M.Sc. University of Minnesota) A THESIS. regions of the varRecM scores for the comparison between samples of HapMap CEU and YRI 166 Figure 26. Plots of the top 20 regions of the varRecM scores for the comparison between samples of HapMap. JPT+CHB and YRI 168 Figure 27. Plots of the top 20 regions of the varRecM scores for the comparison between samples of SGVP CHS and INS 170 Figure 28. Plots of the top 20 regions of the varRecM