A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.

ABSTRACT: Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and furt

SUBMITTER: Li X 

PROVIDER: S-EPMC11981678 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature