RT Journal Article
SR Electronic
T1 Modeling Regulatory Network Topology Improves Genome-Wide Analyses of Complex Human Traits
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.03.13.990010
DO 10.1101/2020.03.13.990010
A1 Zhu, Xiang
A1 Duren, Zhana
A1 Wong, Wing Hung
YR 2020
UL http://biorxiv.org/content/early/2020/12/08/2020.03.13.990010.abstract
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 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 StatementThe authors have declared no competing interest.