A0769
Title: Improving understanding of complex diseases genetics with Bayesian sparse models and variational inference
Authors: Wenmin Zhang - Montreal Heart Institute (Canada) [presenting]
Abstract: Genome-wide association studies (GWAS) have discovered many associations between genetic variants and complex diseases. Yet, the interpretation of GWAS results, including identifying causal variants, understanding the interplay between traits, and characterizing disease heterogeneity, is complicated by linkage disequilibrium, as univariate regression models cannot account for correlation between variants. Additionally, the large number of genetic variants poses computational challenges and incurs a high burden of multiple testing. Two novel Bayesian sparse models and efficient variational inference algorithms are presented to address these challenges and facilitate the interpretation of GWAS results. The first method, SparsePro, integrates GWAS associations and functional annotations for prioritizing causal variants, demonstrating improved performance in simulations and identifying biologically relevant causal variants. The second method, SharePro, assesses whether two or more traits share the same genetic signals identified in GWAS. SharePro achieved improved power with a well-controlled false positive rate and identified biologically plausible colocalizations missed by other methods. SharePro could be further adapted for gene-environment interaction analysis by accounting for genetic effect heterogeneity and could effectively reduce multiple testing burdens. These new methods serve as valuable tools for improving the understanding of complex disease genetics.
