Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies

Meta-analysis is an increasingly popular tool for combining multiple different genome-wide association studies (GWASs) in a single aggregate analysis in order to identify associations with very small effect sizes. Because the data of a meta-analysis can be heterogeneous, referring to the differences in effect sizes between the collected studies, what is often done in the literature is to apply both the fixed-effects model (FE) under an assumption of the same effect size between studies and the random-effects model (RE) under an assumption of varying effect size between studies. However, surprisingly, RE gives less significant p values than FE at variants that actually show varying effect sizes between studies. This is ironic because RE is designed specifically for the case in which there is heterogeneity. As a result, usually, RE does not discover any associations that FE did not discover. In this paper, we show that the underlying reason for this phenomenon is that RE implicitly assumes a markedly conservative null-hypothesis model, and we present a new random-effects model that relaxes the conservative assumption. Unlike the traditional RE, the new method is shown to achieve higher statistical power than FE when there is heterogeneity, indicating that the new method has practical utility for discovering associations in the meta-analysis of GWASs.     

Meta-analysis of Correlated Traits via Summary Statistics from GWASs with an Application in Hypertension

Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple—even distinct—traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10⁻⁸) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10⁻⁷) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes.     

Analysis of family- and population-based samples in cohort genome-wide association studies

Cohort studies typically sample unrelated individuals from a population, although family members of index cases may also be recruited to investigate shared familial risk factors. Recruitment of family members may be incomplete or ancillary to the main cohort, resulting in a mixed sample of independent family units, including unrelated singletons and multiplex families. Multiple methods are available to perform genome-wide association (GWA) analysis of binary or continuous traits in families, but it is unclear whether methods known to perform well on ascertained pedigrees, sibships, or trios are appropriate in analysis of a mixed unrelated cohort and family sample. We present simulation studies based on Multi-Ethnic Study of Atherosclerosis (MESA) pedigree structures to compare the performance of several popular methods of GWA analysis for both quantitative and dichotomous traits in cohort studies. We evaluate approaches suitable for analysis of families, and combined the best performing methods with population-based samples either by meta-analysis, or by pooled analysis of family- and population-based samples (mega-analysis), comparing type 1 error and power. We further assess practical considerations, such as availability of software and ability to incorporate covariates in statistical modeling, and demonstrate our recommended approaches through quantitative and binary trait analysis of HDL cholesterol (HDL-C) in 2,553 MESA family- and population-based African-American samples. Our results suggest linear modeling approaches that accommodate family-induced phenotypic correlation (e.g., variance-component model for quantitative traits or generalized estimating equations for dichotomous traits) perform best in the context of combined family- and population-based cohort GWAS.     

Genetic Association Analysis of Complex Diseases Incorporating Intermediate Phenotype Information

Genetic researchers often collect disease related quantitative traits in addition to disease status because they are interested in understanding the pathophysiology of disease processes. In genome-wide association (GWA) studies, these quantitative phenotypes may be relevant to disease development and serve as intermediate phenotypes or they could be behavioral or other risk factors that predict disease risk. Statistical tests combining both disease status and quantitative risk factors should be more powerful than case-control studies, as the former incorporates more information about the disease. In this paper, we proposed a modified inverse-variance weighted meta-analysis method to combine disease status and quantitative intermediate phenotype information. The simulation results showed that when an intermediate phenotype was available, the inverse-variance weighted method had more power than did a case-control study of complex diseases, especially in identifying susceptibility loci having minor effects. We further applied this modified meta-analysis to a study of imputed lung cancer genotypes with smoking data in 1154 cases and 1137 matched controls. The most significant SNPs came from the CHRNA3-CHRNA5-CHRNB4 region on chromosome 15q24–25.1, which has been replicated in many other studies. Our results confirm that this CHRNA region is associated with both lung cancer development and smoking behavior. We also detected three significant SNPs—rs1800469, rs1982072, and rs2241714—in the promoter region of the TGFB1 gene on chromosome 19 (p = 1.46×10⁻⁵, 1.18×10⁻⁵, and 6.57×10⁻⁶, respectively). The SNP rs1800469 is reported to be associated with chronic obstructive pulmonary disease and lung cancer in cigarette smokers. The present study is the first GWA study to replicate this result. Signals in the 3q26 region were also identified in the meta-analysis. We demonstrate the intermediate phenotype can potentially enhance the power of complex disease association analysis and the modified meta-analysis method is robust to incorporate intermediate phenotype or other quantitative risk factor in the analysis.     

Improved power offered by a score test for linkage disequilibrium mapping of quantitative-trait loci by selective genotyping

Selective genotyping is used to increase efficiency in genetic association studies of quantitative traits by genotyping only those individuals who deviate from the population mean. However, selection distorts the conditional distribution of the trait given genotype, and such data sets are usually analyzed using case-control methods, quantitative analysis within selected groups, or a combination of both. We show that Hotelling's T(2) test, recently proposed for association studies of one or several tagging single-nucleotide polymorphisms in a prospective (i.e., trait given genotype) design, can also be applied to the retrospective (i.e., genotype given trait) selective-genotyping design, and we use simulation to demonstrate its improved power over existing methods.     

Density of common complex ocular traits in the aging eye: analysis of secondary traits in genome-wide association studies

Genetic association studies are identifying genetic risks for common complex ocular traits such as age-related macular degeneration (AMD). The subjects used for discovery of these loci have been largely from clinic-based, case-control studies. Typically, only the primary phenotype (e.g., AMD) being studied is systematically documented and other complex traits (e.g., affecting the eye) are largely ignored. The purpose of this study was to characterize these other or secondary complex ocular traits present in the cases and controls of clinic-based studies being used for genetic study of AMD. The records of 100 consecutive new patients (of any diagnosis) age 60 or older for which all traits affecting the eye had been recorded systematically were reviewed. The average patient had 3.5 distinct diagnoses. A subset of 10 complex traits was selected for further study because they were common and could be reliably diagnosed. The density of these 10 complex ocular traits increased by 0.017 log-traits/year (P = 0.03), ranging from a predicted 2.74 at age 60 to 4.45 at age 90. Trait-trait association was observed only between AMD and primary vitreomacular traction (P = 0.0009). Only 1% of subjects age 60 or older had no common complex traits affecting the eye. Extrapolations suggested that a study of 2000 similar subjects would have sufficient power to detect genetic association with an odds ratio of 2.0 or less for 4 of these 10 traits. In conclusion, the high prevalence of complex traits affecting the aging eye and the inherent biases in referral patterns leads to the potential for confounding by undocumented secondary traits within case-control studies. In addition to the primary trait, other common ocular phenotypes should be systematically documented in genetic association studies so that adjustments for potential trait-trait associations and other bias can be made and genetic risk variants identified in secondary analyses.     

The Aetiological Role of Human Papillomavirus in Oesophageal Squamous Cell Carcinoma: A Meta-Analysis

Background

The aetiological role of human papillomavirus (HPV) in oesophageal squamous cell carcinoma (OSCC) has been widely researched for more than three decades, with conflicting findings. In the absence of a large, adequately powered single case-control study, a meta-analysis of all available case-control studies is the most rigorous way of identifying any potential association between HPV and OSCC. We present the first global meta-analysis of case-control studies investigating the role of HPV in OSCC.

Methods

Case-control studies investigating OSCC tissue for presence of HPV DNA were identified. 21 case-control studies analyzing a total of 1223 cases and 1415 controls, met our inclusion criteria. HPV detection rates were tabulated for each study and all studies were assessed for quality. The random effects method was used to pool the odds ratios (OR).

Results

From all OSCC specimens included in this meta-analysis, 35% (426/1223) were positive for HPV DNA. The pooled OR for an HPV-OSCC association was 3.04 (95% CI 2.20 to 4.20). Meta-regression analysis did not find a significant association between OR and any of the quality domains. Influence analysis was non-significant for the effect of individual studies on the pooled estimate. Studies conducted in countries with low to medium OSCC incidence showed a stronger relationship (OR 4.65, 95% CI 2.47 to 8.76) than regions of high OSCC incidence (OR 2.65, 95% CI 1.80 to 3.91).

Conclusions

Uncertainty around the aetiological role of HPV in OSCC is due largely to the small number and scale of appropriately designed studies. Our meta-analysis of these studies suggests that HPV increases the risk of OSCC three-fold. This study provides the strongest evidence to date of an HPV-OSCC association. The importance of these findings is that prophylactic vaccination could be of public health benefit in prevention of OSCC in countries with high OSCC incidence.     

基于高通量测序的全基因组关联研究策略

全基因组关联研究（Genome-wide association study, GWAS）是人类复杂疾病研究的重要组成部分之一,在群体水平检测全基因组范围的遗传变异与可观测性状间的遗传关联。传统的GWAS是以芯片（Array）技术获得高密度的遗传变异,尽管硕果累累,但也存在不少问题。如：所谓的“缺失的遗传力”,即利用关联分析检测达到全基因组水平显著的遗传变异位点只能解释小部分遗传力;在某些性状上不同研究的结果一致性较弱;显著关联的遗传变异位点的功能较难解释等。高通量测序技术,也称第二代测序（Next-generation sequencing, NGS）技术,可以快速、准确地产出高通量的变异位点数据,为解决以上问题提供了可行的方案。基于NGS技术的GWAS方法（NGS-GWAS）,可在一定程度上弥补传统GWAS的不足。文章对NGS-GWAS策略和方法进行了系统性调研,提出了目前较为可行的NGS-GWAS的实施策略和方法,并对NGS-GWAS如何应用于个体化医疗（Personalized medicine, PM）进行了展望。     

Identifying genetic variation affecting a complex trait in simulated data: a comparison of meta-analysis with pooled data analysis

We explored the power and consistency to detect linkage and association with meta-analysis and pooled data analysis using Genetic Analysis Workshop 14 simulated data. The first 10 replicates from Aipotu population were used. Significant linkage and association was found at all 4 regions containing the major loci for Kofendrerd Personality Disorder (KPD) using both combined analyses although no significant linkage and association was found at all these regions in a single replicate. The linkage results from both analyses are consistent in terms of the significance level of linkage test and the estimate of locus location. After correction for multiple-testing, significant associations were detected for the same 8 single-nucleotide polymorphisms (SNP) in both analyses. There were another 2 SNPs for which significant associations with KPD were found only by pooled data analysis. Our study showed that, under homogeneous condition, the results from meta-analysis and pooled data analysis are similar in both linkage and association studies and the loss of power is limited using meta-analysis. Thus, meta-analysis can provide an overall evaluation of linkage and association when the original raw data is not available for combining.     

Estimating kinship in admixed populations

Genome-wide association studies (GWASs) are commonly used for the mapping of genetic loci that influence complex traits. A problem that is often encountered in both population-based and family-based GWASs is that of identifying cryptic relatedness and population stratification because it is well known that failure to appropriately account for both pedigree and population structure can lead to spurious association. A number of methods have been proposed for identifying relatives in samples from homogeneous populations. A strong assumption of population homogeneity, however, is often untenable, and many GWASs include samples from structured populations. Here, we consider the problem of estimating relatedness in structured populations with admixed ancestry. We propose a method, REAP (relatedness estimation in admixed populations), for robust estimation of identity by descent (IBD)-sharing probabilities and kinship coefficients in admixed populations. REAP appropriately accounts for population structure and ancestry-related assortative mating by using individual-specific allele frequencies at SNPs that are calculated on the basis of ancestry derived from whole-genome analysis. In simulation studies with related individuals and admixture from highly divergent populations, we demonstrate that REAP gives accurate IBD-sharing probabilities and kinship coefficients. We apply REAP to the Mexican Americans in Los Angeles, California (MXL) population sample of release 3 of phase III of the International Haplotype Map Project; in this sample, we identify third- and fourth-degree relatives who have not previously been reported. We also apply REAP to the African American and Hispanic samples from the Women's Health Initiative SNP Health Association Resource (WHI-SHARe) study, in which hundreds of pairs of cryptically related individuals have been identified.     

Association of angiotensin-converting enzyme I/D polymorphism with heart failure: a meta-analysis

Heart failure (HF) is a complex clinical syndrome and is thought to have a genetic basis. Numerous case-control studies have investigated the association between heart failure and polymorphisms in candidate genes. Most studies focused on the angiotensin-converting enzyme insertion/deletion (ACE I/D) polymorphism, however, the results were inconsistent because of small studies and heterogeneous samples. The objective was to assess the association between the ACE I/D polymorphism and HF. We performed a meta-analysis of all case-control studies that evaluated the association between ACE I/D polymorphism and HF in humans. Studies were identified in the PUBMED and EMBASE databases, reviews, and reference lists of relevant articles. Two reviewers independently assessed the studies. Seventeen case-control studies with a total of 5576 participants were included in the meta-analysis, including 2453 cases with HF and 3123 controls. The heterogeneity between studies was significant. No association was found under all the four genetic models (D vs. I, DD vs. ID and II, DD and ID vs. II, DD vs. ID). Subgroup analyses for ischemic HF (IHF) and HF because of dilated cardiomyopathy (DHF) also showed no significant association between ACE I/D polymorphism and HF. No significant association between the ACE I/D polymorphism and risk of HF was found in this meta-analysis. The future studies should focus on large-scale prospective and case-control studies which designed to investigate gene-gene and gene-environment interactions to shed light on the genetics of HF.     

A Multi-SNP Locus-Association Method Reveals a Substantial Fraction of the Missing Heritability

There are many known examples of multiple semi-independent associations at individual loci; such associations might arise either because of true allelic heterogeneity or because of imperfect tagging of an unobserved causal variant. This phenomenon is of great importance in monogenic traits but has not yet been systematically investigated and quantified in complex-trait genome-wide association studies (GWASs). Here, we describe a multi-SNP association method that estimates the effect of loci harboring multiple association signals by using GWAS summary statistics. Applying the method to a large anthropometric GWAS meta-analysis (from the Genetic Investigation of Anthropometric Traits consortium study), we show that for height, body mass index (BMI), and waist-to-hip ratio (WHR), 3%, 2%, and 1%, respectively, of additional phenotypic variance can be explained on top of the previously reported 10% (height), 1.5% (BMI), and 1% (WHR). The method also permitted a substantial increase (by up to 50%) in the number of loci that replicate in a discovery-validation design. Specifically, we identified 74 loci at which the multi-SNP, a linear combination of SNPs, explains significantly more variance than does the best individual SNP. A detailed analysis of multi-SNPs shows that most of the additional variability explained is derived from SNPs that are not in linkage disequilibrium with the lead SNP, suggesting a major contribution of allelic heterogeneity to the missing heritability.     

Association between Glaucoma and Obstructive Sleep Apnea Syndrome: A Meta-Analysis and Systematic Review

BackgroundObstructive sleep apnea syndrome (OSAS) is a common disease that increases the risk of diabetes, heart disease, and stroke. However, studies of an association between OSAS and glaucoma neuropathy have reported controversial findings.ObjectiveThe main purpose of this study was to evaluate whether a significant association exists between OSAS and glaucoma by performing a meta-analysis of previous studies.MethodsA comprehensive literature search using the PubMed and Embase databases was performed to identify cross-sectional, case-control, and cohort studies related to the topic. We estimated a pooled odds ratio (OR) for the association between OSAS and glaucoma, by a fixed- or random-effects model.ResultsIn total, 16 studies with 2,278,832 participants met the inclusion criteria: one retrospective cohort study reported an adjusted hazard ratio of glaucoma of 1.67 (95% CI = 1.30–2.17). Using a fixed-effects model, the pooled OR of six case-control studies was 1.96 (95% CI = 1.37 2.80). A significant association was also identified in a meta-analysis of nine cross-sectional studies using a random-effects model, which showed a pooled OR of 1.41 (95% CI = 1.11 1.79). However, the reported pooled estimates for case control studies and cross-sectional studies were based on unadjusted ORs.ConclusionsOur results suggest that OSAS is associated with the prevalence of glaucoma. However, this result was based only on unadjusted estimates. Prospective cohort studies designed to take into consideration potential confounders, or examination of data from interventional trials to determine whether a reduction in OSAS status is associated with a reduced incidence of glaucoma, are needed to clarify whether OSAS is an independent risk factor for glaucoma.     

Diabetes Mellitus as a Risk Factor for Open-Angle Glaucoma: A Systematic Review and Meta-Analysis

Objective

To determine the association between diabetes mellitus (DM) and primary open-angle glaucoma (POAG).

Methods

This is a systematic review and meta-analysis of case-control and cohort studies. The literature search included two databases (PubMed and Embase) and the reference lists of the retrieved studies. Separate meta-analyses for case-control studies and cohort studies were conducted using random-effects models, with results reported as adjusted odds ratios (ORs) and relative risks (RRs), respectively.

Results

Thirteen studies—seven case-control studies and six population-based cohort studies—were included in this meta-analysis. The pooled RR of the association between DM and POAG based on the risk estimates of the six cohort studies was 1.40 (95% CI, 1.25–1.57). The pooled OR of the association between DM and POAG based on the risk estimates of the seven case-control studies was 1.49 (95% CI, 1.17–1.88). There was considerable heterogeneity among the case-control studies that reported an association between DM mellitus and POAG (P<0.001) and no significant heterogeneity among the cohort studies (P = 0.377). After omitting the case-control study that contributed significantly to the heterogeneity, the pooled OR for the association between DM and POAG was 1.35 (95% CI, 1.06–1.74).

Conclusions

Individuals with DM have an increased risk of developing POAG.     

Meta-analysis of Gene-Level Associations for Rare Variants Based on Single-Variant Statistics

Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying “causal” rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available.     

Inference of the Genetic Architecture Underlying BMI and Height with the Use of 20,240 Sibling Pairs

Evidence that complex traits are highly polygenic has been presented by population-based genome-wide association studies (GWASs) through the identification of many significant variants, as well as by family-based de novo sequencing studies indicating that several traits have a large mutational target size. Here, using a third study design, we show results consistent with extreme polygenicity for body mass index (BMI) and height. On a sample of 20,240 siblings (from 9,570 nuclear families), we used a within-family method to obtain narrow-sense heritability estimates of 0.42 (SE = 0.17, p = 0.01) and 0.69 (SE = 0.14, p = 6 × 10⁻⁷) for BMI and height, respectively, after adjusting for covariates. The genomic inflation factors from locus-specific linkage analysis were 1.69 (SE = 0.21, p = 0.04) for BMI and 2.18 (SE = 0.21, p = 2 × 10⁻¹⁰) for height. This inflation is free of confounding and congruent with polygenicity, consistent with observations of ever-increasing genomic-inflation factors from GWASs with large sample sizes, implying that those signals are due to true genetic signals across the genome rather than population stratification. We also demonstrate that the distribution of the observed test statistics is consistent with both rare and common variants underlying a polygenic architecture and that previous reports of linkage signals in complex traits are probably a consequence of polygenic architecture rather than the segregation of variants with large effects. The convergent empirical evidence from GWASs, de novo studies, and within-family segregation implies that family-based sequencing studies for complex traits require very large sample sizes because the effects of causal variants are small on average.     

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models

Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies.     

Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs

There is heightened interest in using next-generation sequencing technologies to identify rare variants that influence complex human diseases and traits. Meta-analysis is essential to this endeavor because large sample sizes are required for detecting associations with rare variants. In this article, we provide a comprehensive overview of statistical methods for meta-analysis of sequencing studies for discovering rare-variant associations. Specifically, we discuss the calculation of relevant summary statistics from participating studies, the construction of gene-level association tests, the choice of transformation for quantitative traits, the use of fixed-effects versus random-effects models, and the removal of shadow association signals through conditional analysis. We also show that meta-analysis based on properly calculated summary statistics is as powerful as joint analysis of individual-participant data. In addition, we demonstrate the performance of different meta-analysis methods by using both simulated and empirical data. We then compare four major software packages for meta-analysis of rare-variant associations—MASS, RAREMETAL, MetaSKAT, and seqMeta—in terms of the underlying statistical methodology, analysis pipeline, and software interface. Finally, we present PreMeta, a software interface that integrates the four meta-analysis packages and allows a consortium to combine otherwise incompatible summary statistics.     

A unified association analysis approach for family and unrelated samples correcting for stratification

There are two common designs for association mapping of complex diseases: case-control and family-based designs. A case-control sample is more powerful to detect genetic effects than a family-based sample that contains the same numbers of affected and unaffected persons, although additional markers may be required to control for spurious association. When family and unrelated samples are available, statistical analyses are often performed in the family and unrelated samples separately, conditioning on parental information for the former, thus resulting in reduced power. In this report, we propose a unified approach that can incorporate both family and case-control samples and, provided the additional markers are available, at the same time corrects for population stratification. We apply the principal components of a marker matrix to adjust for the effect of population stratification. This unified approach makes it unnecessary to perform a conditional analysis of the family data and is more powerful than the separate analyses of unrelated and family samples, or a meta-analysis performed by combining the results of the usual separate analyses. This property is demonstrated in both a variety of simulation models and empirical data. The proposed approach can be equally applied to the analysis of both qualitative and quantitative traits.     

Five years of GWAS discovery

The past five years have seen many scientific and biological discoveries made through the experimental design of genome-wide association studies (GWASs). These studies were aimed at detecting variants at genomic loci that are associated with complex traits in the population and, in particular, at detecting associations between common single-nucleotide polymorphisms (SNPs) and common diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. We start by giving a number of quotes from scientists and journalists about perceived problems with GWASs. We will then briefly give the history of GWASs and focus on the discoveries made through this experimental design, what those discoveries tell us and do not tell us about the genetics and biology of complex traits, and what immediate utility has come out of these studies. Rather than giving an exhaustive review of all reported findings for all diseases and other complex traits, we focus on the results for auto-immune diseases and metabolic diseases. We return to the perceived failure or disappointment about GWASs in the concluding section.