Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.

ABSTRACT: Optimal use of genetics for precision medicine requires polygenic scores (PGS) that predict not just risk of disease, but also response to pharmaceutical or lifestyle interventions. These are detectable in observational datasets as PGS-by-exposure (PGS×E) interactions. Existing literature suggests that PGS based on interactions (iPGS) or variance effects (vPGS) may be more powerful than standard marginal PGS (mPGS) for the detection of PGS×E, but these have yet to be systematically compared. We describe a generalized pipeline for the development and comparison of different PGS types and apply it to detect genetic modification of the relationship between adiposity (measured by body mass index [BMI]) and a broad set of cardiometabolic risk factors (CRFs). Our applied analysis in the UK Bioba

SUBMITTER: Westerman KE 

PROVIDER: S-EPMC12036401 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature