Identifying causal variants by fine mapping across multiple studies

Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. We demonstrate the efficacy of MsCAVIAR in both a simulation study and a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL).Author summary: Genome-Wide Association Studies (GWAS) have successfully identified numerous genetic variants associated with a variety of complex traits in humans. However, most variants that are associated with traits do not actually cause those traits, but rather are correlated with the truly causal variants through Linkage Disequilibrium (LD). This problem is addressed by so-called “fine mapping” methods, which attempt to prioritize putative causal variants for functional follow-up studies. In this work, we propose a new method, MsCAVIAR, which improves fine mapping performance by leveraging data from multiple studies, such as GWAS of the same trait using individuals with different ethnic backgrounds (“trans-ethnic fine mapping”), while taking into account the possibility that causal variants may affect the trait more or less strongly in different studies. We show in simulations that our method reduces the number of variants needed for functional follow-up testing versus other methods, and we also demonstrate the efficacy of MsCAVIAR in a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL).