B1120
Title: Modeling regulatory network topology improves genome-wide analyses of complex human traits
Authors: Xiang Zhu - The Pennsylvania State University (United States) [presenting]
Abstract: Genome-wide association studies (GWAS) in humans have catalogued many significant associations between genetic variants and complex traits. However, most of these findings have unclear biological significance, because they often have small effects and occur in non-coding regions. Integration of GWAS with gene regulatory networks addresses both issues by aggregating weak genetic signals within regulatory programs. We develop a Bayesian hierarchical modeling framework that integrates GWAS summary statistics with gene regulatory networks to infer genetic enrichments and associations simultaneously. We implement the method with an efficient variational inference algorithm that scales well with millions of genetic variants in the human genome. Our method improves upon existing approaches by explicitly modeling network topology to assess enrichments, and by automatically leveraging enrichments to identify associations. Applying this method to 18 human traits and 38 regulatory networks shows that genetic signals of complex traits are often enriched in interconnections specific to trait-relevant cell types or tissues. Prioritizing variants within enriched networks identifies known and previously undescribed trait-associated genes revealing biological and therapeutic insights.
