Current Genome-wide Association Studies Research Articles

Identifying multiple causative genes at a single GWAS locus.

Genome-wide association studies (GWAS) are useful for nominating candidate genes, but typically are unable to establish disease causality or differentiate between the effects of variants in linkage disequilibrium (LD). Additionally, some GWAS loci might contain multiple causative variants or genes that contribute to the overall disease susceptibility at a single locus. However, the majority of current GWAS lack the statistical power to test whether multiple causative genes underlie the same locus, prompting us to adopt an alternative approach to testing multiple GWAS genes empirically. We used gene targeting in a disease-susceptible rat model of genetic hypertension to test all six genes at the Agtrap-Plod1 locus (Agtrap, Mthfr, Clcn6, Nppa, Nppb, and Plod1) for blood pressure (BP) and renal phenotypes. This revealed that the majority of genes at this locus (five out of six) can impact hypertension by modifying BP and renal phenotypes. Mutations of Nppa, Plod1, and Mthfr increased disease susceptibility, whereas Agtrap and Clcn6 mutations decreased hypertension risk. Reanalysis of the human AGTRAP-PLOD1 locus also implied that disease-associated haplotype blocks with polygenic effects were not only possible, but rather were highly plausible. Combined, these data demonstrate for the first time that multiple modifiers of hypertension can cosegregate at a single GWAS locus.

Genetic Variants on 3q21 and in the Sp8 Transcription Factor Gene (SP8) as Susceptibility Loci for Psychotic Disorders: A Genetic Association Study

BackgroundRecent genome-wide association studies (GWASs) investigating bipolar disorder (BD) have detected a number of susceptibility genes. These studies have also provided novel insight into shared genetic components between BD and schizophrenia (SCZ), two major psychotic disorders. To examine the replication of the risk variants for BD and the pleiotropic effect of the variants associated with BD, we conducted a genetic association study of single nucleotide polymorphisms (SNPs) that were selected based upon previous BD GWASs, which targeted psychotic disorders (BD and SCZ) in the Japanese population.MethodsForty-eight SNPs were selected based upon previous GWASs. A two-stage analysis was conducted using first-set screening (for all SNPs: BD = 1,012, SCZ = 1,032 and control = 993) and second-set replication samples (for significant SNPs in the screening analysis: BD = 821, SCZ = 1,808 and control = 2,149). We assessed allelic association between BD, SCZ, psychosis (BD+SCZ) and the SNPs selected for the analysis.ResultsEight SNPs revealed nominal association signals for all comparisons (Puncorrected<0.05). Among these SNPs, the top two SNPs (associated with psychosis: Pcorrected = 0.048 and 0.037 for rs2251219 and rs2709722, respectively) were further assessed in the second-set samples, and we replicated the signals from the initial screening analysis (associated with psychosis: Pcorrected = 0.0070 and 0.033 for rs2251219 and rs2709722, respectively). The meta-analysis between the current and previous GWAS results showed that rs2251219 in Polybromo1 (PBRM1) was significant on genome-wide association level (P = 5×10−8) only for BD (P = 9.4×10−9) and psychosis (P = 2.0×10−10). Although the association of rs2709722 in Sp8 transcription factor (SP8) was suggestive in the Asian population (P = 2.1×10−7 for psychosis), this signal weakened when the samples size was increased by including data from a Caucasian population (P = 4.3×10−3).ConclusionsWe found 3p21.1 (including PBRM1, strong linkage disequilibrium made it difficult to pinpoint the risk genes) and SP8 as risk loci for BD, SCZ and psychosis. Further replication studies will be required for conclusive results.

A replication study for three nephrolithiasis loci at 5q35.3, 7p14.3 and 13q14.1 in the Japanese population

A previous genome-wide association study (GWAS) reported three novel nephrolithiasis-susceptibility loci at 5q35.3, 7p14.3 and 13q14.1. Here, we investigated the association of these loci with nephrolithiasis by using an independent Japanese sample set. We performed case-control association analysis using 601 patients with nephrolithiasis and 201 control subjects. We selected seven single-nucleotide polymorphisms (SNPs): rs12654812 and rs11746443 from 5q35.3 (RGS14-SLC34A1-PFN3-F12); rs12669187 and rs1000597 from 7p14.3 (INMT-FAM188B-AQP1); and rs7981733, rs1170155, and rs4142110 from 13q14.1 (DGKH (diacylglycerol kinase)), which were previously reported to be significantly associated with nephrolithiasis. rs12654812, rs12669187 and rs7981733 were significantly associated with nephrolithiasis after Bonferroni's correction (P=3.12 × 10(-3), odds ratio (OR)=1.43; P=6.40 × 10(-3), OR=1.57; and P=5.00 × 10(-3), OR=1.41, respectively). Meta-analysis of current and previous GWAS results indicated a significant association with nephrolithiasis (P=7.65 × 10(-15), 7.86 × 10(-14) and 1.06 × 10(-9), respectively). We observed a cumulative effect with these three SNPs; individuals with three or more risk alleles had a 5.9-fold higher risk for nephrolithiasis development than those with only one risk allele. Our findings elucidated the significance of genetic variation at these three loci in nephrolithiasis in the Japanese population.

EXclusion: Toward Integrating the X Chromosome in Genome-wide Association Analyses

The X chromosome lags behind autosomal chromosomes in genome-wide association study (GWAS) findings. Indeed, the X chromosome is commonly excluded from GWAS analyses despite being assayed on all current GWAS microarray platforms. This raises the question: why are so few hits reported on the X chromosome? This commentary aims to examine this question through review of the current X chromosome results in the National Human Genome Research Institute Catalog of Published Genome-Wide Association Studies (NHGRI GWAS Catalog). It will also investigate commonly cited reasons for exclusion of the X chromosome from GWAS and review the tools currently available for X chromosome analysis. It will conclude with recommendations for incorporating X chromosome analyses in future studies.

Genome-wide association analyses in Han Chinese identify two new susceptibility loci for amyotrophic lateral sclerosis

To identify susceptibility genes for amyotrophic lateral sclerosis (ALS), we conducted a genome-wide association study (GWAS) in 506 individuals with sporadic ALS and 1,859 controls of Han Chinese ancestry. Ninety top SNPs suggested by the current GWAS and 6 SNPs identified by previous GWAS were analyzed in an independent cohort of 706 individuals with ALS and 1,777 controls of Han Chinese ancestry. We discovered two new susceptibility loci for ALS at 1q32 (CAMK1G, rs6703183, Pcombined = 2.92 × 10(-8), odds ratio (OR) = 1.31) and 22p11 (CABIN1 and SUSD2, rs8141797, Pcombined = 2.35 × 10(-9), OR = 1.52). These two loci explain 12.48% of the overall variance in disease risk in the Han Chinese population. We found no association evidence for the previously reported loci in the Han Chinese population, suggesting genetic heterogeneity of disease susceptibility for ALS between ancestry groups. Our study identifies two new susceptibility loci and suggests new pathogenic mechanisms of ALS.

Properties and Modeling of GWAS when Complex Disease Risk Is Due to Non-Complementing, Deleterious Mutations in Genes of Large Effect

Current genome-wide association studies (GWAS) have high power to detect intermediate frequency SNPs making modest contributions to complex disease, but they are underpowered to detect rare alleles of large effect (RALE). This has led to speculation that the bulk of variation for most complex diseases is due to RALE. One concern with existing models of RALE is that they do not make explicit assumptions about the evolution of a phenotype and its molecular basis. Rather, much of the existing literature relies on arbitrary mapping of phenotypes onto genotypes obtained either from standard population-genetic simulation tools or from non-genetic models. We introduce a novel simulation of a 100-kilobase gene region, based on the standard definition of a gene, in which mutations are unconditionally deleterious, are continuously arising, have partially recessive and non-complementing effects on phenotype (analogous to what is widely observed for most Mendelian disorders), and are interspersed with neutral markers that can be genotyped. Genes evolving according to this model exhibit a characteristic GWAS signature consisting of an excess of marginally significant markers. Existing tests for an excess burden of rare alleles in cases have low power while a simple new statistic has high power to identify disease genes evolving under our model. The structure of linkage disequilibrium between causative mutations and significantly associated markers under our model differs fundamentally from that seen when rare causative markers are assumed to be neutral. Rather than tagging single haplotypes bearing a large number of rare causative alleles, we find that significant SNPs in a GWAS tend to tag single causative mutations of small effect relative to other mutations in the same gene. Our results emphasize the importance of evaluating the power to detect associations under models that are genetically and evolutionarily motivated.

Genome-Wide Association Studies of Skeletal Phenotypes: What We Have Learned and Where We Are Headed

The primary goals of genome-wide association studies (GWAS) are to discover new molecular and biological pathways involved in the regulation of bone metabolism that can be leveraged for drug development. In addition, the identified genetic determinants may be used to enhance current risk factor profiles. There have been more than 40 published GWAS on skeletal phenotypes, predominantly focused on dual-energy x-ray absorptiometry-derived bone mineral density (BMD) of the hip and spine. Sixty-six BMD loci have been replicated across all the published GWAS, confirming the highly polygenic nature of BMD variation. Only seven of the 66 previously reported genes (LRP5, SOST, ESR1, TNFRSF11B, TNFRSF11A, TNFSF11, PTH) from candidate gene association studies have been confirmed by GWAS. Among 59 novel BMD GWAS loci that have not been reported by previous candidate gene association studies, some have been shown to be involved in key biological pathways involving the skeleton, particularly Wnt signaling (AXIN1, LRP5, CTNNB1, DKK1, FOXC2, HOXC6, LRP4, MEF2C, PTHLH, RSPO3, SFRP4, TGFBR3, WLS, WNT3, WNT4, WNT5B, WNT16), bone development: ossification (CLCN7, CSF1, MEF2C, MEPE, PKDCC, PTHLH, RUNX2, SOX6, SOX9, SPP1, SP7), mesenchymal-stem-cell differentiation (FAM3C, MEF2C, RUNX2, SOX4, SOX9, SP7), osteoclast differentiation (JAG1, RUNX2), and TGF-signaling (FOXL1, SPTBN1, TGFBR3). There are still 30 BMD GWAS loci without prior molecular or biological evidence of their involvement in skeletal phenotypes. Other skeletal phenotypes that either have been or are being studied include hip geometry, bone ultrasound, quantitative computed tomography, high-resolution peripheral quantitative computed tomography, biochemical markers, and fractures such as vertebral, nonvertebral, hip, and forearm. Although several challenges lie ahead as GWAS moves into the next generation, there are prospects of new discoveries in skeletal biology. This review integrates findings from previous GWAS and provides a roadmap for future directions building on current GWAS successes.

Power of single- vs. multi-marker tests of association.

Current genome-wide association studies still heavily rely on a single-marker strategy, in which each single nucleotide polymorphism (SNP) is tested individually for association with a phenotype. Although methods and software packages that consider multimarker models have become available, they have been slow to become widely adopted and their efficacy in real data analysis is often questioned. Based on conducting extensive simulations, here we endeavor to provide more insights into the performance of simple multimarker association tests as compared to single-marker tests. The results reveal the power advantage as well as disadvantage of the two- vs. the single-marker test. Power differentials depend on the correlation structure among tag SNPs, as well as that between tag SNPs and causal variants. A two-marker test has relatively better performance than single-marker tests when the correlation of the two adjacent markers is high. However, using HapMap data, two-marker tests tended to have a greater chance of being less powerful than single-marker tests, due to constraints on the number of actual possible haplotypes in the HapMap data. Yet, the average power difference was small whenever the one-marker test is more powerful, while there were many situations where the two-marker test can be much more powerful. These findings can be useful to guide analyses of future studies.

Evaluating rare variants under two-stage design

Current genome-wide association studies (GWAS) focusing on relatively common single-nucleotide polymorphisms (SNPs) usually adopt a cost-effective multi-staged design in which a proportion of the total samples are genotyped using a commercial SNP array with a reasonably good coverage of the whole genome at the initial stage, and a list of promising SNPs are further genotyped and evaluated on the remaining samples at the second stage. This staged design in principal can also be used for the study of rare genetic variants at the genome-wide scale, but the statistical methods developed for evaluating the relatively common SNPs under the staged design are not appropriate for rare variants due to the invalidity of large sample theorems. Here, we develop a new statistical framework that aims to evaluate rare variants under two-staged (or multi-staged) design. By extensive computer simulations, we evaluate the empirical type I error rate and power of the proposed procedures. A real example from two recent case-control rheumatoid arthritis genetic association studies is also used to demonstrate the performances of the proposed methods.

Abstract 1657: Genome-wide association study of lung cancer: Variation in TP63 gene confers the risk of lung adenocarcinoma

Abstract Lung cancer is the most common cause of death from cancer worldwide, and its incidence is increasing in East Asian and Western countries. Lung cancer comprises various types of histology that are often divided into two main types, non-small cell lung cancer and small cell lung cancer. Each type has different pathophysiological and clinical features, suggesting that their mechanisms of carcinogenesis differ. Adencarcinoma accounts for about 50% of all histological types of lung cancer, and its incidence is increasing in East Asian and Western countries. Because most of the current genome-wide association studies (GWAS) on lung cancer have been conducted in European populations with a full range of different histological types of lung cancer, GWAS that focus on individual histological groups with archived clinicopathological features are required. To identify genetic factors that modify the risk of lung adenocarcinoma, we conducted a genome-wide association study in a Japanese cohort, with replication in two independent studies in Japanese and Korean individuals, in a total of 2,098 lung adenocarcinoma cases and 11,048 controls. The combined analyses identified two susceptibility loci for lung adenocarcinoma: TERT (rs2736100, combined P = 2.91 × 10(-11), odds ratio (OR) = 1.27) and TP63 (rs10937405, combined P = 7.26 × 10(-12), OR = 1.31). When we analyzed the combinational combined effect of TP63 and TERT, we found that these two susceptibility genes showed a synergistic effect on the risk of lung adenocarcinoma (OR = 4.26). Fine mapping and re-sequencing of the region containing TP63 showed that a SNP (rs4488809) in intron 1 of TP63 showed the most significant association. Our results suggest that genetic variation in TP63 may influence susceptibility to lung adenocarcinoma in East Asian populations. Further functional studies are necessary to clarify the mechanisms by which TP63 influences susceptibility to lung adenocarcinoma. We will also report additional studies of TP63 on other ethnic populations as well as other histological types of lung cancer that will provide detailed information on the genetic etiology and heterogeneity of lung cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1657. doi:1538-7445.AM2012-1657

Power Comparison of Admixture Mapping and Direct Association Analysis in Genome‐Wide Association Studies

When dense markers are available, one can interrogate almost every common variant across the genome via imputation and single nucleotide polymorphism (SNP) test, which has become a routine in current genome-wide association studies (GWASs). As a complement, admixture mapping exploits the long-range linkage disequilibrium (LD) generated by admixture between genetically distinct ancestral populations. It is then questionable whether admixture mapping analysis is still necessary in detecting the disease associated variants in admixed populations. We argue that admixture mapping is able to reduce the burden of massive comparisons in GWASs; it therefore can be a powerful tool to locate the disease variants with substantial allele frequency differences between ancestral populations. In this report we studied a two-stage approach, where candidate regions are defined by conducting admixture mapping at stage 1, and single SNP association tests are followed at stage 2 within the candidate regions defined at stage 1. We first established the genome-wide significance levels corresponding to the criteria to define the candidate regions at stage 1 by simulations. We next compared the power of the two-stage approach with direct association analysis. Our simulations suggest that the two-stage approach can be more powerful than the standard genome-wide association analysis when the allele frequency difference of a causal variant in ancestral populations, is larger than 0.4. Our conclusion is consistent with a theoretical prediction by Risch and Tang ([2006] Am J Hum Genet 79:S254). Surprisingly, our study also suggests that power can be improved when we use less strict criteria to define the candidate regions at stage 1.

Evaluation of 19 susceptibility loci of breast cancer in women of African ancestry

Multiple breast cancer susceptibility loci have been identified in genome-wide association studies (GWAS) in populations of European and Asian ancestry using array chips optimized for populations of European ancestry. It is important to examine whether these loci are associated with breast cancer risk in women of African ancestry. We evaluated 25 single nucleotide polymorphisms (SNPs) at 19 loci in a pooled case-control study of breast cancer, which included 1509 cases and 1383 controls. Cases and controls were enrolled in Nigeria, Barbados and the USA; all women were of African ancestry. We found significant associations for three SNPs, which were in the same direction and of similar magnitude as those reported in previous fine-mapping studies in women of African ancestry. The allelic odds ratios were 1.24 [95% confidence interval (CI): 1.04-1.47; P = 0.018] for the rs2981578-G allele (10q26/FGFR2), 1.34 (95% CI: 1.10-1.63; P = 0.0035) for the rs9397435-G allele (6q25) and 1.12 (95% CI: 1.00-1.25; P = 0.04) for the rs3104793-C allele (16q12). Although a significant association was observed for an additional index SNP (rs3817198), it was in the opposite direction to prior GWAS studies. In conclusion, this study highlights the complexity of applying current GWAS findings across racial/ethnic groups, as none of GWAS-identified index SNPs could be replicated in women of African ancestry. Further fine-mapping studies in women of African ancestry will be needed to reveal additional and causal variants for breast cancer.

Identification of polymorphic inversions from genotypes

BackgroundPolymorphic inversions are a source of genetic variability with a direct impact on recombination frequencies. Given the difficulty of their experimental study, computational methods have been developed to infer their existence in a large number of individuals using genome-wide data of nucleotide variation. Methods based on haplotype tagging of known inversions attempt to classify individuals as having a normal or inverted allele. Other methods that measure differences between linkage disequilibrium attempt to identify regions with inversions but unable to classify subjects accurately, an essential requirement for association studies.ResultsWe present a novel method to both identify polymorphic inversions from genome-wide genotype data and classify individuals as containing a normal or inverted allele. Our method, a generalization of a published method for haplotype data [1], utilizes linkage between groups of SNPs to partition a set of individuals into normal and inverted subpopulations. We employ a sliding window scan to identify regions likely to have an inversion, and accumulation of evidence from neighboring SNPs is used to accurately determine the inversion status of each subject. Further, our approach detects inversions directly from genotype data, thus increasing its usability to current genome-wide association studies (GWAS).ConclusionsWe demonstrate the accuracy of our method to detect inversions and classify individuals on principled-simulated genotypes, produced by the evolution of an inversion event within a coalescent model [2]. We applied our method to real genotype data from HapMap Phase III to characterize the inversion status of two known inversions within the regions 17q21 and 8p23 across 1184 individuals. Finally, we scan the full genomes of the European Origin (CEU) and Yoruba (YRI) HapMap samples. We find population-based evidence for 9 out of 15 well-established autosomic inversions, and for 52 regions previously predicted by independent experimental methods in ten (9+1) individuals [3,4]. We provide efficient implementations of both genotype and haplotype methods as a unified R package inveRsion.

Weighted selective collapsing strategy for detecting rare and common variants in genetic association study

BackgroundGenome-wide association studies (GWAS) have been used successfully in detecting associations between common genetic variants and complex diseases. However, common SNPs detected by current GWAS only explain a small proportion of heritable variability. With the development of next-generation sequencing technologies, researchers find more and more evidence to support the role played by rare variants in heritable variability. However, rare and common variants are often studied separately. The objective of this paper is to develop a robust strategy to analyze association between complex traits and genetic regions using both common and rare variants.ResultsWe propose a weighted selective collapsing strategy for both candidate gene studies and genome-wide association scans. The strategy considers genetic information from both common and rare variants, selectively collapses all variants in a given region by a forward selection procedure, and uses an adaptive weight to favor more likely causal rare variants. Under this strategy, two tests are proposed. One test denoted by BwSC is sensitive to the directions of genetic effects, and it separates the deleterious and protective effects into two components. Another denoted by BwSCd is robust in the directions of genetic effects, and it considers the difference of the two components. In our simulation studies, BwSC achieves a higher power when the casual variants have the same genetic effect, while BwSCd is as powerful as several existing tests when a mixed genetic effect exists. Both of the proposed tests work well with and without the existence of genetic effects from common variants.ConclusionsTwo tests using a weighted selective collapsing strategy provide potentially powerful methods for association studies of sequencing data. The tests have a higher power when both common and rare variants contribute to the heritable variability and the effect of common variants is not strong enough to be detected by traditional methods. Our simulation studies have demonstrated a substantially higher power for both tests in all scenarios regardless whether the common SNPs are associated with the trait or not.

Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets

Current genome-wide association studies (GWAS) use commercial genotyping microarrays that can assay over a million single nucleotide polymorphisms (SNPs). The number of SNPs is further boosted by advanced statistical genotype-imputation algorithms and large SNP databases for reference human populations. The testing of a huge number of SNPs needs to be taken into account in the interpretation of statistical significance in such genome-wide studies, but this is complicated by the non-independence of SNPs because of linkage disequilibrium (LD). Several previous groups have proposed the use of the effective number of independent markers (M e) for the adjustment of multiple testing, but current methods of calculation for M e are limited in accuracy or computational speed. Here, we report a more robust and fast method to calculate M e. Applying this efficient method [implemented in a free software tool named Genetic type 1 error calculator (GEC)], we systematically examined the M e, and the corresponding p-value thresholds required to control the genome-wide type 1 error rate at 0.05, for 13 Illumina or Affymetrix genotyping arrays, as well as for HapMap Project and 1000 Genomes Project datasets which are widely used in genotype imputation as reference panels. Our results suggested the use of a p-value threshold of ~10−7 as the criterion for genome-wide significance for early commercial genotyping arrays, but slightly more stringent p-value thresholds ~5 × 10−8 for current or merged commercial genotyping arrays, ~10−8 for all common SNPs in the 1000 Genomes Project dataset and ~5 × 10−8 for the common SNPs only within genes.Electronic supplementary materialThe online version of this article (doi:10.1007/s00439-011-1118-2) contains supplementary material, which is available to authorized users.

Gene-based partial least-squares approaches for detecting rare variant associations with complex traits

Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis.

A novel bayesian graphical model for genome‐wide multi‐SNP association mapping

Most disease association mapping algorithms are based on hypothesis testing procedures that test one variant at a time. Those methods lose power when the disease mutations are jointly tagged by multiple variants, or when gene-gene interaction exist. Nearby variants are also correlated, for which procedures ignoring the dependence between variants will inevitably produce redundant results. With a large number of variants genotyped in current genome-wide disease association studies, simultaneous multivariant association mapping algorithms are strongly desired. We present a novel Bayesian method for automatic detection of multivariant joint association in genome-wide case-control studies. Our method has improved power and specificity over existing tools. We fit a joint probabilistic model to the entire data and identify disease variants simultaneously. The method dynamically accounts for the strong linkage disequilibrium (LD) between variants. As a result, only the primary disease variants will be identified, with all secondary associations due to LD effects filtered out. Our method better pinpoints the disease variants with improved resolution. The method is also computationally efficient for genome-wide studies. When applied to a real data set of inflammatory bowel disease (IBD) containing 401,473 variants in 4,720 individuals, our method detected all previously reported IBD loci in the same data, and recovered two missed loci. We further detected two novel interchromosome interactions. The first is between STAT3 and PARD6G, and the second is between DLG5 and an intergenic region at 5p14. We further validated the two interactions in an independent study.

Replication of epistatic DNA loci in two case-control GWAS studies using OPE algorithm

Background One of the limiting factors of current genome-wide association studies (GWAS) is the inability of current methods to comprehensively examine SNP interactions for a reasonable sized dataset. It is hypothesised that this limitation is one of the reasons that GWAS studies have not been able to have a greater impact [1,2]. Many current methods for handling interactions are computationally expensive and do not scale to entire studies. Those methods that do scale often achieve this by pruning their datasets in some manner. This is commonly done by considering only those SNPs that show strong marginal effects, despite the fact that a strongly interacting pair may consist of SNPs with low effects individually.

Temporal Trends in Results Availability from Genome-Wide Association Studies

The application of genome-wide association study (GWAS) approaches for the study of genetic determinants of common diseases has propelled human genetics forward, resulting in a surfeit of genomic data. With the accompanying level of widespread collaboration and sharing of data, access to this body of valuable genomic data and the application of novel analytic approaches beyond the level of first GWAS scans is yielding additional insights, both in terms of new genetic discoveries and important general biological findings [1]. However, recent work shows that standard statistical approaches can be applied to aggregate genome-wide association results that place individual research participants at increased risks for misuse related to privacy and confidentiality. We define “misuse” as analysis efforts aimed at exposing individual research participants' information, including revealing disease status, predicted future likelihood or past presence of other traits, or attempts to link another DNA result with a participant, for example, to determine presence or absence in a research cohort, ancestry, and relatedness (e.g., paternity/non-paternity). Thus, there is the small but theoretically possible risk of later legal or discriminatory actions that were originally unforeseen by investigators and would likely be unwanted and unexpected by the research participants [2]–[7]. At this time the risks to research participant identification generally exist when there is access to (at least) a moderate number of genetic variant results that include both statistics (regression coefficients or two-sided p-values) and cohort-specific population allele frequencies [2]–[5], [7]. To date, scientific discussion about these potential risks has focused largely on theoretical scenarios and the related ethical and policy responses [2]–[8]. Initial publications [2], [3] resulted in significant policy shifts and reduction in the open access to GWAS results by the creation of controlled access repositories for results (e.g., for the Wellcome Trust Case Control Consortium [WTCCC] results and Framingham Heart Study [FHS] SHARe 100K GWAS results), but the literature contains no systematic assessment of the extent of current GWAS results availability, temporal trends in availability, or the number of studies that remain at a potentially unacceptable level of risk.

A Genome-Wide Association Study in Europeans and South Asians Identifies 5 New Loci for Coronary Artery Disease

### Study Hypothesis Recently, genome-wide association studies (GWAS) have identified several common variants associated with increased risk of coronary artery disease (CAD) and myocardial infarction (MI) by 10% to 30%. The authors state that these loci explain only a small proportion of the predicted genetic risk and that all of the current loci for CAD and MI by GWAS have been discovered in European populations. The authors hypothesized that discovery of new susceptibility loci of smaller effect sizes would be aided by conducting much larger studies in addition to an emphasis on early-onset CAD and clearly defined clinical end points. Therefore, they assembled the Coronary Artery Disease (C4D) Genetics Consortium.1 ### How Was the Hypothesis Tested? The authors performed a meta-analysis of 4 large GWAS of CAD, 2 of European ancestry (PROCARDIS and HPS) and 2 of South Asian ancestry (PROMIS and LOLLIPOP), with ≈575 000 genotyped single-nucleotide polymorphisms (SNPs) in a discovery data set comprising 15 420 individuals with CAD (cases) (8424 Europeans and 6996 South Asians) and 15 062 control subjects. They observed little evidence for ancestry-specific associations, supporting the use of combined analyses. Furthermore, the authors state that because all individuals were genotyped on the same platform (whole-genome Illumina BeadChips), a meta-analysis of actual genotypes rather than imputed data enabled analysis of low-frequency variants (1% to 5%), which have been excluded from GWAS either because of sample size or because imputation has been required to combine data from different genotyping platforms. After the meta-analysis, they selected 59 SNPs from 50 loci that showed potential new associations from the meta-analysis of the European and South Asian studies (41 SNPs; P <1.0×10−4), the European-only meta-analysis (8 SNPs; P <3.0×10−5), the South …