Hierarchical clustering of gene-level association statistics reveals shared and differential genetic architecture among traits in the UK Biobank

Genome-wide association (GWA) studies have generally focused on a single phenotype of interest. Emerging biobanks that pair genotype data from thousands of individuals with phenotype data using medical records or surveys enable testing for genetic associations in each phenotype assayed. However, methods for characterizing shared genetic architecture among multiple traits are lagging behind. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for characterizing shared and divergent genetic architecture among multiple phenotypes. The objective of WINGS (freely available at https://github.com/ramachandran-lab/PEGASUS-WINGS) is to identify groups of phenotypes, or \"clusters\", that share a core set of genes enriched for mutations in cases. We show in simulations that WINGS can reliably detect phenotype clusters across a range of percent shared architecture and number of phenotypes included. We then use the gene-level association test PEGASUS with WINGS to characterize shared genetic architecture among 87 case-control and seven quantitative phenotypes in 349,468 unrelated European-ancestry individuals from the UK Biobank. We identify 10 significant phenotype clusters that contain two to eight phenotypes. One significant cluster of seven immunological phenotypes is driven by seven genes; these genes have each been associated with two or more of those same phenotypes in past publications. WINGS offers a precise and efficient new application of Ward hierarchical clustering to generate hypotheses regarding shared genetic architecture among phenotypes in the biobank era.