GEiPRS: a fast and powerful machine learning method for polygenic risk score prediction by leveraging genotype-environment interactions.

Abstract excerpt

Penalized regression methods are widely used for variant selection and polygenic risk score (PRS) analysis in disease genome-wide association studies (GWASs). However, the existing penalized regression-based PRS methods often neglect genotype-environment interaction (GEI) and struggles with high-dimensional GWAS data. To overcome these challenges, we propose a novel machine learning-based PRS method...

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