ArticleNature communications2026
Modeling nonlinear and interaction effects of spatiotemporal and nongenetic factors improves prediction for complex traits.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Adjusting for nongenetic factors improves genetic association testing and polygenic scores, yet most studies rely on linear adjustments for limited covariate sets. Location and time covariates can proxy environmental exposures but are rarely included, and linear adjustments cannot capture their nonlinear effects or interactions. We adopt a null model approach where an auxiliary nonlinear model predicts phenotypes from covariates alone. This prediction is then included as an additional covariate in downstream analysis. Using 16 phenotypes in the UK Biobank, we show gradient boosted decision tree nulls including spatiotemporal features improve covariate modeling. Incorporating these nonlinear spatiotemporal covariate predictions improves polygenic prediction for all phenotypes (median 7.3% R
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