PT  - JOURNAL ARTICLE
AU  - Xiang Zhu
AU  - Zhana Duren
AU  - Wing Hung Wong
TI  - Modeling Regulatory Network Topology Improves Genome-Wide Analyses of Complex Human Traits
AID  - 10.1101/2020.03.13.990010
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.03.13.990010
4099  - http://biorxiv.org/content/early/2020/03/14/2020.03.13.990010.short
4100  - http://biorxiv.org/content/early/2020/03/14/2020.03.13.990010.full
AB  - 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 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 networks specific to trait-relevant tissue or cell types. Prioritizing variants within enriched networks identifies known and new trait-associated genes revealing novel biological and therapeutic insights.