Abstract
Genome-wide association studies (GWASs) have successfully identified loci of the human genome implicated in numerous complex traits. However, the limitations of this study design make it difficult to identify specific causal variants or biological mechanisms of association. We propose a novel method, AnnoRE, which uses GWAS summary statistics, local correlation structure among genotypes and functional annotation from external databases to prioritize the most plausible causal single-nucleotide polymorphisms (SNPs) in each trait-associated locus. Our proposed method improves upon previous fine-mapping approaches by estimating the effects of functional annotation from genome-wide summary statistics, allowing for the inclusion of many annotation categories. By implementing a multiple regression model with differential shrinkage via random effects, we avoid reductive assumptions on the number of causal SNPs per locus. Application of this method to a large GWAS meta-analysis of body mass index identified six loci with significant evidence in favor of one or more variants. In an additional 24 loci, one or two variants were strongly prioritized over others in the region. The use of functional annotation in genetic fine-mapping studies helps to distinguish between variants in high LD and to identify promising targets for follow-up studies.