Subset scanning for multi-trait analysis using GWAS summary statistics.

ABSTRACT:

Motivation

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.

Results

To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that identifies potential pleiotropic traits from a moderate or large number of traits (e.g. dozens to thousands) and tests the association between one genetic variant and the selected traits. TraitScan can handle either individual-level or summary-level GWAS data. We evaluated TraitScan using extensive simulations and found that it outperformed e

SUBMITTER: Cao R 

PROVIDER: S-EPMC11087659 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature