Subset scanning for multi-trait analysis using GWAS summary statistics

Abstract

Multi-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits, which heavily rely on domain knowledge. To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that agnostically searches and tests a subset of traits from a moderate or large number of traits (e.g., dozens to thousands) based on either individual-level or summary-level genetic data. We evaluated TraitScan using extensive simulations and found that it outperformed existing methods in terms of both testing power and trait selection when sparsity was low or modest. We then applied it to search for traits associated with Ewing Sarcoma, a rare bone tumor with peak onset in adolescence, among 706 traits in UK Biobank. Our analysis revealed a few promising traits worthy of further investigation, highlighting the use of TraitScan for more effective multi-trait analysis as biobanks emerge. Our algorithm is implemented in an R package TraitScan available at https://github.com/RuiCao34/TraitScan.