A scalable adaptive quadratic kernel method for interpretable epistasis analysis in complex traits.

ABSTRACT: Our knowledge of the contribution of genetic interactions (epistasis) to variation in human complex traits remains limited, partly due to the lack of efficient, powerful, and interpretable algorithms to detect interactions. Recently proposed approaches for set-based association tests show promise in improving the power to detect epistasis by examining the aggregated effects of multiple variants. Nevertheless, these methods either do not scale to large Biobank data sets or lack interpretability. We propose QuadKAST, a scalable algorithm focused on testing pairwise interaction effects (quadratic effects) within small to medium-sized sets of genetic variants (window size ≤100) on a trait and provide quantified interpretation of these effects. Comprehensive simulations show that

SUBMITTER: Fu B 

PROVIDER: S-EPMC11529862 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature