Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association Statistics.

ABSTRACT: Emerging large-scale biobanks pairing genotype data with phenotype data present new opportunities to prioritize shared genetic associations across multiple phenotypes for molecular validation. Past research, by our group and others, has shown gene-level tests of association produce biologically interpretable characterization of the genetic architecture of a given phenotype. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for identifying shared genetic architecture among multiple phenotypes. The objective of WINGS is to identify groups of phenotypes, or "clusters," sharing a core set of genes enriched for mutations in cases. We validate WINGS using extensive simulation studies and then combine gene-level assoc

SUBMITTER: McGuirl MR 

PROVIDER: S-EPMC7268989 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature