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We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS\n                    <jats:sub>BMI<\/jats:sub>\n                    ) 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\n                    <jats:sup>2<\/jats:sup>\n                    differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R\n                    <jats:sup>2<\/jats:sup>\n                    being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS\n                    <jats:sub>BMI<\/jats:sub>\n                    \u2013covariate interaction effects, modifying PGS\n                    <jats:sub>BMI<\/jats:sub>\n                    effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R\n                    <jats:sup>2<\/jats:sup>\n                    differences among strata and interaction effects \u2013 across all covariates, their main effects on BMI were correlated with their maximum R\n                    <jats:sup>2<\/jats:sup>\n                    differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS\n                    <jats:sub>BMI<\/jats:sub>\n                    individuals have highest R\n                    <jats:sup>2<\/jats:sup>\n                    and increase in PGS effect. Using quantile regression, we show the effect of PGS\n                    <jats:sub>BMI<\/jats:sub>\n                    increases as BMI itself increases, and that these differences in effects are directly related to differences in R\n                    <jats:sup>2<\/jats:sup>\n                    when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS\n                    <jats:sub>BMI<\/jats:sub>\n                    performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R\n                    <jats:sup>2<\/jats:sup>\n                    (mean 23%) across datasets. Finally, creating PGS\n                    <jats:sub>BMI<\/jats:sub>\n                    directly from GxAge genome-wide association studies effects increased relative R\n                    <jats:sup>2<\/jats:sup>\n                    by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS\n                    <jats:sub>BMI<\/jats:sub>\n                    performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.\n                  <\/jats:p>","DOI":"10.7554\/elife.88149","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T10:39:49Z","timestamp":1690799989000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.88149","source":"Crossref","is-referenced-by-count":9,"title":["Risk factors affecting polygenic score performance across diverse 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