A0953
Title: Beyond guilty by association at scale: Searching for causal variants on the basis of genome-wide summary statistics
Authors: Zihuai He - Stanford University (United States) [presenting]
Abstract: Understanding the causal genetic architecture of complex phenotypes is essential for future research into disease mechanisms and potential therapies. The aim is to present a novel framework for genome-wide detection of sets of variants that carry non-redundant information on the phenotypes and are therefore more likely to be causal in a biological sense. Crucially, the framework requires only summary statistics obtained from standard genome-wide marginal association testing. The described approach, implemented in open-source software, is also computationally efficient, requiring less than 15 minutes on a single CPU to perform genome-wide analysis. Through extensive genome-wide simulation studies, it is shown that the method can substantially outperform usual two-stage marginal association testing and fine-mapping procedures in precision and recall. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD), 82 loci associated with AD are identified, including 37 additional loci missed by conventional GWAS pipeline. The identified putative causal variants achieve state-of-the-art agreement with massively parallel reporter assays and CRISPR-Cas9 experiments.
