Identifying causal variants at loci with multiple signals of association

Abstract

Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20-50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/.A LTHOUGH genome-wide association studies (GWAS) reproducibly identified thousands of risk loci (Hakonarson et al. 2007;Sladek et al. 2007;Zeggini et al. 2007; Yang et al. 2011a,b;Kottgen et al. 2013;Lu et al. 2013;Ripke et al. 2013), only a handful of causal genetic variants (i.e., variants that biologically alter disease risk) have been found (Altshuler et al. 2008;Manolio et al. 2008;McCarthy et al. 2008), thus prohibiting the mechanistic understanding of the genetic basis of common diseases. The linkage disequilibrium (LD) (Pritchard and Przeworski 2001;Reich et al. 2001) structure of the human genome has greatly benefited GWAS in interrogating only a subset of all variants to assay common variation across the genome. Unfortunately, LD hinders the identification of causal variants at risk loci in fine-mapping studies as at each locus, there are often tens to hundreds of variants tightly linked to the reported associated single-nucleotide polymorphism (SNP) (Malo et al. 2008;Maller et al. 2012;Yang et al. 2012). In a continued effort to identify causal variants, many finemapping studies that assess genetic variation at known GWAS risk loci are currently underway (Bauer et al. 2013;Coram et al. 2013;Diogo et al. 2013;Gong et al. 2013;Marigorta and Navarro 2013;Peters et al. 2013;Wu et al. 2013).Fine-mapping studies typically follow a two-step procedure. First, a statistical analysis of the association signal is performed to identify a minimum set of SNPs that can explain the signal. Second, the SNPs that ar...

10 Years of GWAS Discovery: Biology, Function, and Translation

“…The use of additional information, such as prior knowledge of the likely function of specific variants given their location and surrounding DNA motif(s), 139,140 could help to reduce the set of statistical candidates to a smaller number. This is already a fertile area of statistical and bioinformatic research 56,62,131,141,142 bringing together trait or disease GWAS results with those of tissue gene expression. More research on the resolution of fine-mapping is warranted, and this will be fueled by an expected increase in GWAS data on tissue-and cell-specific gene expression.…”

Fine Mapping Causal Variants with an Approximate Bayesian Method Using Marginal Test Statistics

“…The r-level confidence set (or r confidence set) was proposed in Hormozdiari et al (2014), where r is the probability that all causal SNPs are included in a selected SNP set. Specifically, for a SNP set with indexes I k ; the probability r is defined as…”

“…Different from the definition in Hormozdiari et al (2014), we do not include the null model M 0 in the above summation. When all SNPs are included in the set, the probability r is the posterior probability that there is at least 1 causal SNP in the region.…”

“…When all SNPs are included in the set, the probability r is the posterior probability that there is at least 1 causal SNP in the region. Because direct maximization of r by selecting k SNPs from p SNPs is computationally prohibitive, we follow the same stepwise selection as in Hormozdiari et al (2014). For each step, the SNP that increases r the most is selected.…”

“…There are different ways to set the model priors. For example, we can use a binomial prior for the number of causal SNPs, which assumes that the probability of each SNP being causal is independent and equal (Guan and Stephens 2011;Hormozdiari et al 2014 where #ðjM c jÞ is the total number of models with model size jM c j: We can also use a Beta-binomial distribution to introduce uncertainty on p; as discussed in Scott and Berger (2010). Different implications between the binomial prior and the Beta-binomial prior were also discussed and summarized in table 2 of Wilson et al (2010).…”