Risk factors affecting polygenic score performance across diverse cohorts

• Published in: eLife Vol. 12
• Main Authors: Hui, Daniel, Dudek, Scott, Kiryluk, Krzysztof, Walunas, Theresa L, Kullo, Iftikhar J, Wei, Wei-Qi, Tiwari, Hemant, Peterson, Josh F, Chung, Wendy K, Davis, Brittney H, Khan, Atlas, Kottyan, Leah C, Limdi, Nita A, Feng, Qiping, Puckelwartz, Megan J, Weng, Chunhua, Smith, Johanna L, Karlson, Elizabeth W, Jarvik, Gail P, Ritchie, Marylyn D
• Format: Journal Article
• Language: English
• Published: England eLife Sciences Publications Ltd 24.01.2025; eLife Sciences Publications, Ltd
• Subjects: Adult; African People - genetics; Age; Aged; Alcohol use; Biobanks; Blood pressure; Body Mass Index; Cohort Studies; Computational and Systems Biology; Datasets; Educational attainment; Epidemiology; Exercise; Female; Genetics and Genomics; gene–environment; Genome-Wide Association Study; Genotype & phenotype; GWAS; Health risks; Humans; Machine learning; Male; Middle Aged; Multifactorial Inheritance; polygenic scores; Regression analysis; Risk Factors; Socioeconomic factors; Socioeconomic status; White People - genetics; United Kingdom > UK; computational biology; systems biology; GWAS; genetics; polygenic scores; body mass index; genomics; human; gene–environment
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.88149

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.