Abstract
Genome-wide association studies (GWAS) have cataloged 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. Here we develop a Bayesian framework that integrates GWAS summary statistics with regulatory networks to infer genetic enrichments and associations simultaneously. 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 new trait-associated genes revealing novel biological and therapeutic insights.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
We summarize our revisions by: (1) new simulation studies to illustrate the robustness of RSS-NET; (2) additional replication and comparative analyses of RSS-NET on real data; (3) expanded discussions of methodology intuition and implementation details. The new results are consistent with conclusions in our original manuscript, and the revised texts improve the readability of this work.
