Quantifying Missing Heritability at Known GWAS Loci

Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn''s Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.     

FLAGS: A Flexible and Adaptive Association Test for Gene Sets Using Summary Statistics

Genome-wide association studies (GWAS) have been widely used for identifying common variants associated with complex diseases. Despite remarkable success in uncovering many risk variants and providing novel insights into disease biology, genetic variants identified to date fail to explain the vast majority of the heritability for most complex diseases. One explanation is that there are still a large number of common variants that remain to be discovered, but their effect sizes are generally too small to be detected individually. Accordingly, gene set analysis of GWAS, which examines a group of functionally related genes, has been proposed as a complementary approach to single-marker analysis. Here, we propose a flexible and adaptive test for gene sets (FLAGS), using summary statistics. Extensive simulations showed that this method has an appropriate type I error rate and outperforms existing methods with increased power. As a proof of principle, through real data analyses of Crohn’s disease GWAS data and bipolar disorder GWAS meta-analysis results, we demonstrated the superior performance of FLAGS over several state-of-the-art association tests for gene sets. Our method allows for the more powerful application of gene set analysis to complex diseases, which will have broad use given that GWAS summary results are increasingly publicly available.     

A roadmap for the genetic analysis of renal aging

Several studies show evidence for the genetic basis of renal disease, which renders some individuals more prone than others to accelerated renal aging. Studying the genetics of renal aging can help us to identify genes involved in this process and to unravel the underlying pathways. First, this opinion article will give an overview of the phenotypes that can be observed in age‐related kidney disease. Accurate phenotyping is essential in performing genetic analysis. For kidney aging, this could include both functional and structural changes. Subsequently, this article reviews the studies that report on candidate genes associated with renal aging in humans and mice. Several loci or candidate genes have been found associated with kidney disease, but identification of the specific genetic variants involved has proven to be difficult. CUBN, UMOD, and SHROOM3 were identified by human GWAS as being associated with albuminuria, kidney function, and chronic kidney disease (CKD). These are promising examples of genes that could be involved in renal aging, and were further mechanistically evaluated in animal models. Eventually, we will provide approaches for performing genetic analysis. We should leverage the power of mouse models, as testing in humans is limited. Mouse and other animal models can be used to explain the underlying biological mechanisms of genes and loci identified by human GWAS. Furthermore, mouse models can be used to identify genetic variants associated with age‐associated histological changes, of which Far2, Wisp2, and Esrrg are examples. A new outbred mouse population with high genetic diversity will facilitate the identification of genes associated with renal aging by enabling high‐resolution genetic mapping while also allowing the control of environmental factors, and by enabling access to renal tissues at specific time points for histology, proteomics, and gene expression.     

Multiple common susceptibility variants near BMP pathway loci GREM1, BMP4, and BMP2 explain part of the missing heritability of colorectal cancer

Genome-wide association studies (GWAS) have identified 14 tagging single nucleotide polymorphisms (tagSNPs) that are associated with the risk of colorectal cancer (CRC), and several of these tagSNPs are near bone morphogenetic protein (BMP) pathway loci. The penalty of multiple testing implicit in GWAS increases the attraction of complementary approaches for disease gene discovery, including candidate gene- or pathway-based analyses. The strongest candidate loci for additional predisposition SNPs are arguably those already known both to have functional relevance and to be involved in disease risk. To investigate this proposition, we searched for novel CRC susceptibility variants close to the BMP pathway genes GREM1 (15q13.3), BMP4 (14q22.2), and BMP2 (20p12.3) using sample sets totalling 24,910 CRC cases and 26,275 controls. We identified new, independent CRC predisposition SNPs close to BMP4 (rs1957636, P = 3.93×10(-10)) and BMP2 (rs4813802, P = 4.65×10(-11)). Near GREM1, we found using fine-mapping that the previously-identified association between tagSNP rs4779584 and CRC actually resulted from two independent signals represented by rs16969681 (P = 5.33×10(-8)) and rs11632715 (P = 2.30×10(-10)). As low-penetrance predisposition variants become harder to identify-owing to small effect sizes and/or low risk allele frequencies-approaches based on informed candidate gene selection may become increasingly attractive. Our data emphasise that genetic fine-mapping studies can deconvolute associations that have arisen owing to independent correlation of a tagSNP with more than one functional SNP, thus explaining some of the apparently missing heritability of common diseases.     

Network analysis of genome-wide association studies for drug target prioritisation

Over the past decades, genome-wide association studies (GWAS) have led to a dramatic expansion of genetic variants implicated with human traits and diseases. These advances are expected to result in new drug targets but the identification of causal genes and the cell biology underlying human diseases from GWAS remains challenging. Here, we review protein interaction network-based methods to analyse GWAS data. These approaches can rank candidate drug targets at GWAS-associated loci or among interactors of disease genes without direct genetic support. These methods identify the cell biology affected in common across diseases, offering opportunities for drug repurposing, as well as be combined with expression data to identify focal tissues and cell types. Going forward, we expect that these methods will further improve from advances in the characterisation of context specific interaction networks and the joint analysis of rare and common genetic signals.     

Fine mapping of five loci associated with low-density lipoprotein cholesterol detects variants that double the explained heritability

Complex trait genome-wide association studies (GWAS) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants. Nevertheless, GWAS often leave much of the trait heritability unexplained. We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays. To assess this hypothesis, we re-examined 7 genes (APOE, APOC1, APOC2, SORT1, LDLR, APOB, and PCSK9) in 5 loci associated with low-density lipoprotein cholesterol (LDL-C) in multiple GWAS. For each gene, we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values. Next, we genotyped variants identified by us and by the 1000 Genomes Project (totaling 3,277 SNPs) in 5,524 volunteers. We found that in one locus (PCSK9) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci (PCSK9, APOE, and LDLR) there were additional variants independently associated with LDL-C, including a novel and rare LDLR variant that seems specific to Sardinians. Overall, this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3.1% to 6.5%. All association signals and the heritability estimates were successfully confirmed in a sample of ～10,000 Finnish and Norwegian individuals. Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci. Further, our results suggest that, as prelude to large-scale sequencing efforts, targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci.     

Mining the LIPG allelic spectrum reveals the contribution of rare and common regulatory variants to HDL cholesterol

Genome-wide association studies (GWAS) have successfully identified loci associated with quantitative traits, such as blood lipids. Deep resequencing studies are being utilized to catalogue the allelic spectrum at GWAS loci. The goal of these studies is to identify causative variants and missing heritability, including heritability due to low frequency and rare alleles with large phenotypic impact. Whereas rare variant efforts have primarily focused on nonsynonymous coding variants, we hypothesized that noncoding variants in these loci are also functionally important. Using the HDL-C gene LIPG as an example, we explored the effect of regulatory variants identified through resequencing of subjects at HDL-C extremes on gene expression, protein levels, and phenotype. Resequencing a portion of the LIPG promoter and 5' UTR in human subjects with extreme HDL-C, we identified several rare variants in individuals from both extremes. Luciferase reporter assays were used to measure the effect of these rare variants on LIPG expression. Variants conferring opposing effects on gene expression were enriched in opposite extremes of the phenotypic distribution. Minor alleles of a common regulatory haplotype and noncoding GWAS SNPs were associated with reduced plasma levels of the LIPG gene product endothelial lipase (EL), consistent with its role in HDL-C catabolism. Additionally, we found that a common nonfunctional coding variant associated with HDL-C (rs2000813) is in linkage disequilibrium with a 5' UTR variant (rs34474737) that decreases LIPG promoter activity. We attribute the gene regulatory role of rs34474737 to the observed association of the coding variant with plasma EL levels and HDL-C. Taken together, the findings show that both rare and common noncoding regulatory variants are important contributors to the allelic spectrum in complex trait loci.     

ezQTL: A Web Platform for Interactive Visualization and Colocalization of QTLs and GWAS Loci

Genome-wide association studies (GWAS) have identified thousands of genomic loci associated with complex diseases and traits, including cancer. The vast majority of common trait-associated variants identified via GWAS fall in non-coding regions of the genome, posing a challenge in elucidating the causal variants, genes, and mechanisms involved. Expression quantitative trait locus (eQTL) and other molecular QTL studies have been valuable resources in identifying candidate causal genes from GWAS loci through statistical colocalization methods. While QTL colocalization is becoming a standard analysis in post-GWAS investigation, an easy web tool for users to perform formal colocalization analyses with either user-provided or public GWAS and eQTL datasets has been lacking. Here, we present ezQTL, a web-based bioinformatic application to interactively visualize and analyze genetic association data such as GWAS loci and molecular QTLs under different linkage disequilibrium (LD) patterns (1000 Genomes Project, UK Biobank, or user-provided data). This application allows users to perform data quality control for variants matched between different datasets, LD visualization, and two-trait colocalization analyses using two state-of-the-art methodologies (eCAVIAR and HyPrColoc), including batch processing. ezQTL is a free and publicly available cross-platform web tool, which can be accessed online at https://analysistools.cancer.gov/ezqtl.     

U-statistics in genetic association studies

Many common human diseases are complex and are expected to be highly heterogeneous, with multiple causative loci and multiple rare and common variants at some of the causative loci contributing to the risk of these diseases. Data from the genome-wide association studies (GWAS) and metadata such as known gene functions and pathways provide the possibility of identifying genetic variants, genes and pathways that are associated with complex phenotypes. Single-marker-based tests have been very successful in identifying thousands of genetic variants for hundreds of complex phenotypes. However, these variants only explain very small percentages of the heritabilities. To account for the locus- and allelic-heterogeneity, gene-based and pathway-based tests can be very useful in the next stage of the analysis of GWAS data. U-statistics, which summarize the genomic similarity between pair of individuals and link the genomic similarity to phenotype similarity, have proved to be very useful for testing the associations between a set of single nucleotide polymorphisms and the phenotypes. Compared to single marker analysis, the advantages afforded by the U-statistics-based methods is large when the number of markers involved is large. We review several formulations of U-statistics in genetic association studies and point out the links of these statistics with other similarity-based tests of genetic association. Finally, potential application of U-statistics in analysis of the next-generation sequencing data and rare variants association studies are discussed.     

Promise and pitfalls of the Immunochip

Genomewide association studies (GWAS) have proven a powerful hypothesis-free method to identify common disease-associated variants. Even quite large GWAS, however, have only at best identified moderate proportions of the genetic variants contributing to disease heritability. To provide cost-effective genotyping of common and rare variants to map the remaining heritability and to fine-map established loci, the Immunochip Consortium has developed a 200,000 SNP chip that has been produced in very large numbers for a fraction of the cost of GWAS chips. This chip provides a powerful tool for immunogenetics gene mapping.     

Genetic analysis of biological pathway data through genomic randomization

Genome Wide Association Studies (GWAS) are a standard approach for large-scale common variation characterization and for identification of single loci predisposing to disease. However, due to issues of moderate sample sizes and particularly multiple testing correction, many variants of smaller effect size are not detected within a single allele analysis framework. Thus, small main effects and potential epistatic effects are not consistently observed in GWAS using standard analytical approaches that consider only single SNP alleles. Here, we propose unique methodology that aggregates variants of interest (for example, genes in a biological pathway) using GWAS results. Multiple testing and type I error concerns are minimized using empirical genomic randomization to estimate significance. Randomization corrects for common pathway-based analysis biases, such as SNP coverage and density, linkage disequilibrium, gene size and pathway size. Pathway Analysis by Randomization Incorporating Structure (PARIS) applies this randomization and in doing so directly accounts for linkage disequilibrium effects. PARIS is independent of association analysis method and is thus applicable to GWAS datasets of all study designs. Using the KEGG database as an example, we apply PARIS to the publicly available Autism Genetic Resource Exchange GWAS dataset, revealing pathways with a significant enrichment of positive association results.     

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.     

Use of Genome-Wide Association Studies for Cancer Research and Drug Repositioning

Although genome-wide association studies have identified many risk loci associated with colorectal cancer, the molecular basis of these associations are still unclear. We aimed to infer biological insights and highlight candidate genes of interest within GWAS risk loci. We used an in silico pipeline based on functional annotation, quantitative trait loci mapping of cis-acting gene, PubMed text-mining, protein-protein interaction studies, genetic overlaps with cancer somatic mutations and knockout mouse phenotypes, and functional enrichment analysis to prioritize the candidate genes at the colorectal cancer risk loci. Based on these analyses, we observed that these genes were the targets of approved therapies for colorectal cancer, and suggested that drugs approved for other indications may be repurposed for the treatment of colorectal cancer. This study highlights the use of publicly available data as a cost effective solution to derive biological insights, and provides an empirical evidence that the molecular basis of colorectal cancer can provide important leads for the discovery of new drugs.     

The New Perspectives on Genetic Studies of Type 2 Diabetes and Thyroid Diseases

Recently, genome-wide association studies (GWAS) have led to the discovery of hundreds of susceptibility loci that are associated with complex metabolic diseases, such as type 2 diabetes and hyperthyroidism. The majority of the susceptibility loci are common across different races or populations; while some of them show ethnicity-specific distribution. Though the abundant novel susceptibility loci identified by GWAS have provided insight into biology through the discovery of new genes or pathways that were previously not known, most of them are in introns and the associated variants cumulatively explain only a small fraction of total heritability. Here we reviewed the genetic studies on the metabolic disorders, mainly type 2 diabetes and hyperthyroidism, including candidate genes-based findings and more recently the GWAS discovery; we also included the clinical relevance of these novel loci and the gene-environmental interactions. Finally, we discussed the future direction about the genetic study on the exploring of the pathogenesis of the metabolic diseases.     

Network-based Analysis of Genome Wide Association Data Provides Novel Candidate Genes for Lipid and Lipoprotein Traits

Genome wide association studies (GWAS) identify susceptibility loci for complex traits, but do not identify particular genes of interest. Integration of functional and network information may help in overcoming this limitation and identifying new susceptibility loci. Using GWAS and comorbidity data, we present a network-based approach to predict candidate genes for lipid and lipoprotein traits. We apply a prediction pipeline incorporating interactome, co-expression, and comorbidity data to Global Lipids Genetics Consortium (GLGC) GWAS for four traits of interest, identifying phenotypically coherent modules. These modules provide insights regarding gene involvement in complex phenotypes with multiple susceptibility alleles and low effect sizes. To experimentally test our predictions, we selected four candidate genes and genotyped representative SNPs in the Malmö Diet and Cancer Cardiovascular Cohort. We found significant associations with LDL-C and total-cholesterol levels for a synonymous SNP (rs234706) in the cystathionine beta-synthase (CBS) gene (p = 1 × 10⁻⁵ and adjusted-p = 0.013, respectively). Further, liver samples taken from 206 patients revealed that patients with the minor allele of rs234706 had significant dysregulation of CBS (p = 0.04). Despite the known biological role of CBS in lipid metabolism, SNPs within the locus have not yet been identified in GWAS of lipoprotein traits. Thus, the GWAS-based Comorbidity Module (GCM) approach identifies candidate genes missed by GWAS studies, serving as a broadly applicable tool for the investigation of other complex disease phenotypes.Genome wide association studies (GWAS)¹ meta-analyses have pinpointed a number of new gene regions contributing to multifactorial diseases. GWAS typically find limited numbers of loci that contribute modestly to complex phenotypes (1), and GLGC meta-analysis of GWAS data has reached the limit of what can be expected (2) without the use of alternative strategies. Given that susceptibility loci for complex traits are unlikely to be randomly distributed in the genome (3), we might expect that the genes associated with a disease will be more likely to be present within the same pathways or functional groupings. In published cases, pathway based GWAS analysis provides an alternative approach to the dissection of complex disease traits (4, 5). In addition, nominal GWAS p values superimposed upon the human molecular network have been used to identify genes associated with multiple sclerosis (6), and the disease association protein–protein link evaluator (DAPPLE) has been used to find significant interactions among proteins encoded by genes in loci associated with other particular diseases (7). Other approaches incorporate heterogeneous molecular data such as linkage studies, cross species conservation measures, gene expression data and protein–protein interactions to better understand GWAS results (8, 9). Integrating molecular network information, pathway analyses, and GWAS data thus holds promise for identifying new susceptibility loci and improving the identification of relevant candidate genes.If a gene is involved in a specific functional process or disease, its molecular network neighbors might also be suspected to have some role (3). In line with this “local” hypothesis, proteins involved in the same disease show a high propensity to interact (10) or cluster together (11) with each other. Interactions between variations in multiple genes, each with strong or modest effects, perturbing the same pathways or modules, may govern complex traits (3, 6). The molecular triangulation (MT) algorithm can be applied to rank seed genes according to their common disease associated neighbors, assigning closer and more connected neighbors higher values (12). Interactions between modestly associated MT genes may be indicative of coherent disease pathways or of genes conferring susceptibility to disease in a coordinated manner. The jActiveModule method (13) combines seed gene scores with biologically relevant interactions to identify network modules where perturbations causative of disease are more likely to reside. Lastly, although not yet implemented at the module level, phenotypic coherence between interacting pairs of genes has been quantified using the combination of molecular level gene to disease relationships and Medicare comorbidity data (14, 15).We believe that GWAS significant SNPs and variants representing potential candidate genes can use the above strategies to reveal more about the missing heritability of complex phenotypes. The most important risk factors for coronary artery disease (CAD) include serum concentrations of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG). We present a GWAS-based meta-analysis Comorbid Module (GCM) approach that uses significant (p < 5 × 10⁻⁸) GWAS signals for these four traits in the context of molecular networks to prioritize modules of disease-associated candidate genes. We evaluate our approach experimentally through allelic association and genotyping within the Malmö Diet and Cancer Cardiovascular Cohort (MDC-CC) for SNPs representing top candidate genes.