Efficiency of trans-ethnic genome-wide meta-analysis and fine-mapping

Genetic control of DNA methylation is largely shared across European and East Asian populations

DNA methylation is an ideal trait to study the extent of the shared genetic control across ancestries, effectively providing hundreds of thousands of model molecular traits with large QTL effect sizes. We investigate cis DNAm QTLs in three European (n = 3701) and two East Asian (n = 2099) cohorts to quantify the similarities and differences in the genetic architecture across populations. We observe 80,394 associated mQTLs (62.2% of DNAm probes with significant mQTL) to be significant in both ancestries, while 28,925 mQTLs (22.4%) are identified in only a single ancestry. mQTL effect sizes are highly conserved across populations, with differences in mQTL discovery likely due to differences in allele frequency of associated variants and differing linkage disequilibrium between causal variants and assayed SNPs. This study highlights the overall similarity of genetic control across ancestries and the value of ancestral diversity in increasing the power to detect associations and enhancing fine mapping resolution.

Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study

AIMS/HYPOTHESIS

Type 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study.

METHODS

We conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci.

RESULTS

Four novel associations were identified (p < 5 × 10-9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis.

CONCLUSIONS/INTERPRETATION

Our findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations.

DATA AVAILABILITY

Full summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ).

Quantifying genetic heterogeneity between continental populations for human height and body mass index

Genome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_{g}$$\end{document}rg) or genome-wide significant SNPs (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_{{g\left( {GWS} \right)}}$$\end{document}rgGWS) for height and body mass index (BMI) in samples of European (EUR; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n = 49,839$$\end{document}n=49,839) and African (AFR; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n = 17,426$$\end{document}n=17,426) ancestry. The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{r}_{g}$$\end{document}r^g between EUR and AFR was 0.75 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{s}}.{\text{e}}. = 0.035$$\end{document}s.e.=0.035) for height and 0.68 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{s}}.{\text{e}}. = 0.062$$\end{document}s.e.=0.062) for BMI, and the corresponding \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{r}_{{g\left( {GWS} \right)}}$$\end{document}r^gGWS was 0.82 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{s}}.{\text{e}}. = 0.030$$\end{document}s.e.=0.030) for height and 0.87 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{s}}.{\text{e}}. = 0.064$$\end{document}s.e.=0.064) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{r}_{g}$$\end{document}r^g differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.

Genetic influences on susceptibility to rheumatoid arthritis in African-Americans

Large meta-analyses of rheumatoid arthritis (RA) susceptibility in European (EUR) and East Asian (EAS) populations have identified >100 RA risk loci, but genome-wide studies of RA in African-Americans (AAs) are absent. To address this disparity, we performed an analysis of 916 AA RA patients and 1392 controls and aggregated our data with genotyping data from >100 000 EUR and Asian RA patients and controls. We identified two novel risk loci that appear to be specific to AAs: GPC5 and RBFOX1 (PAA < 5 × 10-9). Most RA risk loci are shared across different ethnicities, but among discordant loci, we observed strong enrichment of variants having large effect sizes. We found strong evidence of effect concordance for only 3 of the 21 largest effect index variants in EURs. We used the trans-ethnic fine-mapping algorithm PAINTOR3 to prioritize risk variants in >90 RA risk loci. Addition of AA data to those of EUR and EAS descent enabled identification of seven novel high-confidence candidate pathogenic variants (defined by posterior probability > 0.8). In summary, our trans-ethnic analyses are the first to include AAs, identified several new RA risk loci and point to candidate pathogenic variants that may underlie this common autoimmune disease. These findings may lead to better ways to diagnose or stratify treatment approaches in RA.

Established and emerging strategies to crack the genetic code of obesity

Tremendous progress has been made in the genetic elucidation of obesity over the past two decades, driven largely by technological, methodological and organizational innovations. Current strategies for identifying obesity-predisposing loci/genes, including cytogenetics, linkage analysis, homozygosity mapping, admixture mapping, candidate gene studies, genome-wide association studies, custom genotyping arrays, whole-exome sequencing and targeted exome sequencing, have achieved differing levels of success, and the identified loci in aggregate explain only a modest fraction of the estimated heritability of obesity. This review outlines the successes and limitations of these approaches and proposes novel strategies, including the use of exceptionally large sample sizes, the study of diverse ethnic groups and deep phenotypes and the application of innovative methods and study designs, to identify the remaining obesity-predisposing genes. The use of both established and emerging strategies has the potential to crack the genetic code of obesity in the not-too-distant future. The resulting knowledge is likely to yield improvements in obesity prediction, prevention and care.

Ethnic and population differences in the genetic predisposition to human obesity

Obesity rates have escalated to the point of a global pandemic with varying prevalence across ethnic groups. These differences are partially explained by lifestyle factors in addition to genetic predisposition to obesity. This review provides a comprehensive examination of the ethnic differences in the genetic architecture of obesity. Using examples from evolution, heritability, admixture, monogenic and polygenic studies of obesity, we provide explanations for ethnic differences in the prevalence of obesity. The debate over definitions of race and ethnicity, the advantages and limitations of multi-ethnic studies and future directions of research are also discussed. Multi-ethnic studies have great potential to provide a better understanding of ethnic differences in the prevalence of obesity that may result in more targeted and personalized obesity treatments.

Dissecting the genetics of complex traits using summary association statistics

During the past decade, genome-wide association studies (GWAS) have been used to successfully identify tens of thousands of genetic variants associated with complex traits and diseases. These studies have produced extensive repositories of genetic variation and trait measurements across large numbers of individuals, providing tremendous opportunities for further analyses. However, privacy concerns and other logistical considerations often limit access to individual-level genetic data, motivating the development of methods that analyse summary association statistics. Here, we review recent progress on statistical methods that leverage summary association data to gain insights into the genetic basis of complex traits and diseases.

Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mechanisms

To gain insight into potential regulatory mechanisms through which the effects of variants at four established type 2 diabetes (T2D) susceptibility loci (CDKAL1, CDKN2A-B, IGF2BP2 and KCNQ1) are mediated, we undertook transancestral fine-mapping in 22 086 cases and 42 539 controls of East Asian, European, South Asian, African American and Mexican American descent. Through high-density imputation and conditional analyses, we identified seven distinct association signals at these four loci, each with allelic effects on T2D susceptibility that were homogenous across ancestry groups. By leveraging differences in the structure of linkage disequilibrium between diverse populations, and increased sample size, we localised the variants most likely to drive each distinct association signal. We demonstrated that integration of these genetic fine-mapping data with genomic annotation can highlight potential causal regulatory elements in T2D-relevant tissues. These analyses provide insight into the mechanisms through which T2D association signals are mediated, and suggest future routes to understanding the biology of specific disease susceptibility loci.

Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome

Considerable progress towards an understanding of complex diseases has been made in recent years due to the development of high-throughput genotyping technologies. Using microarrays that contain millions of single-nucleotide polymorphisms (SNPs), Genome Wide Association Studies (GWASs) have identified SNPs that are associated with many complex diseases or traits. For example, as of February 2015, 2111 association studies have identified 15,396 SNPs for various diseases and traits, with the number of identified SNP-disease/trait associations increasing rapidly in recent years. However, it has been difficult for researchers to understand disease risk from GWAS results. This is because most GWAS-identified SNPs are located in non-coding regions of the genome. It is important to consider that the GWAS-identified SNPs serve only as representatives for all SNPs in the same haplotype block, and it is equally likely that other SNPs in high linkage disequilibrium (LD) with the array-identified SNPs are causal for the disease. Because it was hoped that disease-associated coding variants would be identified if the true casual SNPs were known, investigators have expanded their analyses using LD calculation and fine-mapping. However, such analyses also identified risk-associated SNPs located in non-coding regions. Thus, the GWAS field has been left with the conundrum as to how a single-nucleotide change in a non-coding region could confer increased risk for a specific disease. One possible answer to this puzzle is that the variant SNPs cause changes in gene expression levels rather than causing changes in protein function. This review provides a description of (1) advances in genomic and epigenomic approaches that incorporate functional annotation of regulatory elements to prioritize the disease risk-associated SNPs that are located in non-coding regions of the genome for follow-up studies, (2) various computational tools that aid in identifying gene expression changes caused by the non-coding disease-associated SNPs, and (3) experimental approaches to identify target genes of, and study the biological phenotypes conferred by, non-coding disease-associated SNPs.

Integration of multiethnic fine-mapping and genomic annotation to prioritize candidate functional SNPs at prostate cancer susceptibility regions

Interpretation of biological mechanisms underlying genetic risk associations for prostate cancer is complicated by the relatively large number of risk variants (n = 100) and the thousands of surrogate SNPs in linkage disequilibrium. Here, we combined three distinct approaches: multiethnic fine-mapping, putative functional annotation (based upon epigenetic data and genome-encoded features), and expression quantitative trait loci (eQTL) analyses, in an attempt to reduce this complexity. We examined 67 risk regions using genotyping and imputation-based fine-mapping in populations of European (cases/controls: 8600/6946), African (cases/controls: 5327/5136), Japanese (cases/controls: 2563/4391) and Latino (cases/controls: 1034/1046) ancestry. Markers at 55 regions passed a region-specific significance threshold (P-value cutoff range: 3.9 × 10(-4)-5.6 × 10(-3)) and in 30 regions we identified markers that were more significantly associated with risk than the previously reported variants in the multiethnic sample. Novel secondary signals (P < 5.0 × 10(-6)) were also detected in two regions (rs13062436/3q21 and rs17181170/3p12). Among 666 variants in the 55 regions with P-values within one order of magnitude of the most-associated marker, 193 variants (29%) in 48 regions overlapped with epigenetic or other putative functional marks. In 11 of the 55 regions, cis-eQTLs were detected with nearby genes. For 12 of the 55 regions (22%), the most significant region-specific, prostate-cancer associated variant represented the strongest candidate functional variant based on our annotations; the number of regions increased to 20 (36%) and 27 (49%) when examining the 2 and 3 most significantly associated variants in each region, respectively. These results have prioritized subsets of candidate variants for downstream functional evaluation.

Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies

Localization of causal variants underlying known risk loci is one of the main research challenges following genome-wide association studies. Risk loci are typically dissected through fine-mapping experiments in trans-ethnic cohorts for leveraging the variability in the local genetic structure across populations. More recent works have shown that genomic functional annotations (i.e., localization of tissue-specific regulatory marks) can be integrated for increasing fine-mapping performance within single-population studies. Here, we introduce methods that integrate the strength of association between genotype and phenotype, the variability in the genetic backgrounds across populations, and the genomic map of tissue-specific functional elements to increase trans-ethnic fine-mapping accuracy. Through extensive simulations and empirical data, we have demonstrated that our approach increases fine-mapping resolution over existing methods. We analyzed empirical data from a large-scale trans-ethnic rheumatoid arthritis (RA) study and showed that the functional genetic architecture of RA is consistent across European and Asian ancestries. In these data, we used our proposed methods to reduce the average size of the 90% credible set from 29 variants per locus for standard non-integrative approaches to 22 variants.

Evaluation of transethnic fine mapping with population-specific and cosmopolitan imputation reference panels in diverse Asian populations

There has been limited success in identifying causal variants underlying association signals observed in genome-wide association studies (GWAS). The use of 1000 Genomes Project (1KGP) allows the imputation to estimate the genetic information at untyped variants. However, long stretches of high linkage disequilibrium within the genome prevent us from differentiating between causal variants and perfect surrogates, thus limiting our ability to identify causal variants. Transethnic strategies have been proposed as a possible solution to mitigate this. However, these studies generally rely on imputing genotypes from multiple ancestries from 1KGP but not against population-specific reference panels. Here, we perform the first transethnic fine-mapping study across three Asian cohorts from diverse ancestries at the loci implicated with eye and blood lipid traits, using population-specific reference panels that have been generated by whole-genome sequencing samples from the same ancestry groups. Our study outlines several challenges faced in a fine-mapping exercise where one simply aims to meta-analyse existing GWAS that have been imputed against reference haplotypes from the 1KGP.

Conditional entropy in variation-adjusted windows detects selection signatures associated with expression quantitative trait loci (eQTLs)

BACKGROUND

Over the past 50,000 years, shifts in human-environmental or human-human interactions shaped genetic differences within and among human populations, including variants under positive selection. Shaped by environmental factors, such variants influence the genetics of modern health, disease, and treatment outcome. Because evolutionary processes tend to act on gene regulation, we test whether regulatory variants are under positive selection. We introduce a new approach to enhance detection of genetic markers undergoing positive selection, using conditional entropy to capture recent local selection signals.

RESULTS

We use conditional logistic regression to compare our Adjusted Haplotype Conditional Entropy (H|H) measure of positive selection to existing positive selection measures. H|H and existing measures were applied to published regulatory variants acting in cis (cis-eQTLs), with conditional logistic regression testing whether regulatory variants undergo stronger positive selection than the surrounding gene. These cis-eQTLs were drawn from six independent studies of genotype and RNA expression. The conditional logistic regression shows that, overall, H|H is substantially more powerful than existing positive-selection methods in identifying cis-eQTLs against other Single Nucleotide Polymorphisms (SNPs) in the same genes. When broken down by Gene Ontology, H|H predictions are particularly strong in some biological process categories, where regulatory variants are under strong positive selection compared to the bulk of the gene, distinct from those GO categories under overall positive selection. . However, cis-eQTLs in a second group of genes lack positive selection signatures detectable by H|H, consistent with ancient short haplotypes compared to the surrounding gene (for example, in innate immunity GO:0042742); under such other modes of selection, H|H would not be expected to be a strong predictor.. These conditional logistic regression models are adjusted for Minor allele frequency(MAF); otherwise, ascertainment bias is a huge factor in all eQTL data sets. Relationships between Gene Ontology categories, positive selection and eQTL specificity were replicated with H|H in a single larger data set. Our measure, Adjusted Haplotype Conditional Entropy (H|H), was essential in generating all of the results above because it: 1) is a stronger overall predictor for eQTLs than comparable existing approaches, and 2) shows low sequential auto-correlation, overcoming problems with convergence of these conditional regression statistical models.

CONCLUSIONS

Our new method, H|H, provides a consistently more robust signal associated with cis-eQTLs compared to existing methods. We interpret this to indicate that some cis-eQTLs are under positive selection compared to their surrounding genes. Conditional entropy indicative of a selective sweep is an especially strong predictor of eQTLs for genes in several biological processes of medical interest. Where conditional entropy is a weak or negative predictor of eQTLs, such as innate immune genes, this would be consistent with balancing selection acting on such eQTLs over long time periods. Different measures of selection may be needed for variant prioritization under other modes of evolutionary selection.

Characterizing the genetic differences between two distinct migrant groups from Indo-European and Dravidian speaking populations in India

Background

India is home to many ethnically and linguistically diverse populations. It is hypothesized that history of invasions by people from Persia and Central Asia, who are referred as Aryans in Hindu Holy Scriptures, had a defining role in shaping the Indian population canvas. A shift in spoken languages from Dravidian languages to Indo-European languages around 1500 B.C. is central to the Aryan Invasion Theory. Here we investigate the genetic differences between two sub-populations of India consisting of: (1) The Indo-European language speaking Gujarati Indians with genome-wide data from the International HapMap Project; and (2) the Dravidian language speaking Tamil Indians with genome-wide data from the Singapore Genome Variation Project.

Results

We implemented three population genetics measures to identify genomic regions that are significantly differentiated between the two Indian populations originating from the north and south of India. These measures singled out genomic regions with: (i) SNPs exhibiting significant variation in allele frequencies in the two Indian populations; and (ii) differential signals of positive natural selection as quantified by the integrated haplotype score (iHS) and cross-population extended haplotype homozygosity (XP-EHH). One of the regions that emerged spans the SLC24A5 gene that has been functionally shown to affect skin pigmentation, with a higher degree of genetic sharing between Gujarati Indians and Europeans.

Conclusions

Our finding points to a gene-flow from Europe to north India that provides an explanation for the lighter skin tones present in North Indians in comparison to South Indians.

A study assessing the association of glycated hemoglobin A1C (HbA1C) associated variants with HbA1C, chronic kidney disease and diabetic retinopathy in populations of Asian ancestry

Glycated hemoglobin A1C (HbA1C) level is used as a diagnostic marker for diabetes mellitus and a predictor of diabetes associated complications. Genome-wide association studies have identified genetic variants associated with HbA1C level. Most of these studies have been conducted in populations of European ancestry. Here we report the findings from a meta-analysis of genome-wide association studies of HbA1C levels in 6,682 non-diabetic subjects of Chinese, Malay and South Asian ancestries. We also sought to examine the associations between HbA1C associated SNPs and microvascular complications associated with diabetes mellitus, namely chronic kidney disease and retinopathy. A cluster of 6 SNPs on chromosome 17 showed an association with HbA1C which achieved genome-wide significance in the Malays but not in Chinese and Asian Indians. No other variants achieved genome-wide significance in the individual studies or in the meta-analysis. When we investigated the reproducibility of the findings that emerged from the European studies, six loci out of fifteen were found to be associated with HbA1C with effect sizes similar to those reported in the populations of European ancestry and P-value ≤ 0.05. No convincing associations with chronic kidney disease and retinopathy were identified in this study.

Mapping of immune-mediated disease genes

Genetic studies in immune-mediated diseases have yielded a large number of disease-associated loci. Here we review the progress being made in 12 such diseases, for which 199 independently associated non-HLA loci have been identified by genome-wide association studies since 2007. It is striking that many of the loci are not unique to a single disease but shared between different immune-mediated diseases. The challenge now is to understand how the unique and shared genetic factors can provide insight into the underlying disease biology. We annotated disease-associated variants using the Encyclopedia of DNA Elements (ENCODE) database and demonstrate that, of the predisposing disease variants, the majority have the potential to be regulatory. We also demonstrate that many of these variants affect the expression of nearby genes. Furthermore, we summarize results from the Immunochip, a custom array, which allows a detailed comparison between five of the diseases that have so far been analyzed using this platform.

High trans-ethnic replicability of GWAS results implies common causal variants

Genome-wide association studies (GWAS) have detected many disease associations. However, the reported variants tend to explain small fractions of risk, and there are doubts about issues such as the portability of findings over different ethnic groups or the relative roles of rare versus common variants in the genetic architecture of complex disease. Studying the degree of sharing of disease-associated variants across populations can help in solving these issues. We present a comprehensive survey of GWAS replicability across 28 diseases. Most loci and SNPs discovered in Europeans for these conditions have been extensively replicated using peoples of European and East Asian ancestry, while the replication with individuals of African ancestry is much less common. We found a strong and significant correlation of Odds Ratios across Europeans and East Asians, indicating that underlying causal variants are common and shared between the two ancestries. Moreover, SNPs that failed to replicate in East Asians map into genomic regions where Linkage Disequilibrium patterns differ significantly between populations. Finally, we observed that GWAS with larger sample sizes have detected variants with weaker effects rather than with lower frequencies. Our results indicate that most GWAS results are due to common variants. In addition, the sharing of disease alleles and the high correlation in their effect sizes suggest that most of the underlying causal variants are shared between Europeans and East Asians and that they tend to map close to the associated marker SNPs.

Describing and identifying the genetic variants that increase risk for complex diseases remains a central focus of human genetics and is fundamental for the emergent field of personalized medicine. Over the last six years, GWAS have revolutionized the field, discovering hundreds of disease loci. However, with only a handful of exceptions, the causal variants that generate the associations unveiled by GWAS have not been identified, and their frequency and degree of sharing across populations remains unknown. Here, we present a comprehensive comparison of GWAS results designed to try to understand the nature of causal variants. By examining the results of GWAS for 28 diseases that have been performed with peoples of European, East Asian, and African ancestries, we conclude that a large fraction of associations are caused by common causal variants that should map relatively close to the associated markers. Our results indicate that many of the disease risk variants discovered by GWAS are shared across Eurasians.

Comparison of the performance of two commercial genome-wide association study genotyping platforms in Han Chinese samples

Most genome-wide association studies to date have been performed in populations of European descent, but there is increasing interest in expanding these studies to other populations. The performance of genotyping chips in Asian populations is not well established. Therefore, we sought to test the performance of widely used fixed-marker, genome-wide association studies chips in the Han Chinese population. Non-HapMap Chinese samples (n = 396) were genotyped using the Illumina OmniExpress and Affymetrix 6.0 platforms, whereas a subset also were genotyped using the Immunochip. Genotyped markers from the Affymetrix 6.0 and Illumina OmniExpress were used for full genome imputation based on the HapMap 2 JPT+CHB (Japanese from Tokyo, Japan and Chinese from Beijing, China) reference panel. The concordance between markers genotypes for the three platforms was very high whether directly genotyped or genotyped and imputed single nucleotide polymorphisms (SNPs; >99.8% for directly genotyped and >99.5% for genotyped and imputed SNPs, respectively) were compared. The OmniExpress chip data enabled more SNPs to be imputed, particularly SNPs with minor allele frequency >5%. The OmniExpress chip achieved better coverage of HapMap SNPs than the Affymetrix 6.0 chip (73.6% vs. 65.9%, respectively, for minor allele frequency >5%). The Affymetrix 6.0 and Illumina OmniExpress chip have similar genotyping accuracy and provide similar accuracy of imputed SNPs. The OmniExpress chip however provides better coverage of Asian HapMap SNPs, although its coverage of HapMap SNPs is moderate.