Genomic network analysis characterizes genetic architecture and identifies trait-specific biology.

Pervasive genetic overlap across human complex traits necessitates developing multivariate methods that can parse pleiotropic and trait-specific genetic signals. Here, we introduce Genomic Network Analysis (GNA), an analytic framework that applies the principles of network modelling to estimates of genetic overlap derived from genome-wide association study (GWAS) summary statistics. The result is a genomic network that describes the conditionally independent genetic associations between traits that remain when controlling for shared signal with the broader network of traits. Graph theory metrics provide added insight by formally quantifying the most important traits in the genomic network. GNA can discover additional trait-specific pathways by incorporating gene expression or genetic variants into the network to estimate their conditional associations with each trait. Extensive simulations establish GNA is well-powered for most GWAS. Application to a diverse set of traits demonstrate that GNA yields critical insight into the genetic architecture that demarcate genetically overlapping traits at varying levels of biological granularity.