Multi-polygenic score approach to trait prediction

TLDR

Polygenic scores aggregate thousands of trait‑associated DNA variants from GWASs to estimate individual genetic propensities and predict outcomes. This study applies a multi‑polygenic score (MPS) approach to boost predictive power by combining multiple discovery GWASs without assuming relationships among predictors. Using summary statistics from 81 well‑powered GWASs, the authors employed regularized regression with repeated cross‑validation to select and estimate contributions of 81 polygenic scores in a UK sample of 6,710 unrelated adolescents to predict educational achievement, BMI, and general cognitive ability. The MPS approach explained 10.9% of educational achievement, 4.8% of general cognitive ability, and 5.4% of BMI variance—1.1–1.6% higher than best single‑score models—and, with additional GWAS data, can be expanded to improve phenotype prediction, offering a valuable tool for research and potential clinical interventions in modest‑sample studies.

Abstract

A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWASs, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWASs of cognitive, medical and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body mass index (BMI) and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK representative sample of 6710 unrelated adolescents. The MPS approach predicted 10.9% variance in educational achievement, 4.8% in general cognitive ability and 5.4% in BMI in an independent test set, predicting 1.1%, 1.1%, and 1.6% more variance than the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene-environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.