- Elgart, Michael;
- Lyons, Genevieve;
- Romero-Brufau, Santiago;
- Kurniansyah, Nuzulul;
- Brody, Jennifer A;
- Guo, Xiuqing;
- Lin, Henry J;
- Raffield, Laura;
- Gao, Yan;
- Chen, Han;
- de Vries, Paul;
- Lloyd-Jones, Donald M;
- Lange, Leslie A;
- Peloso, Gina M;
- Fornage, Myriam;
- Rotter, Jerome I;
- Rich, Stephen S;
- Morrison, Alanna C;
- Psaty, Bruce M;
- Levy, Daniel;
- Redline, Susan;
- Sofer, Tamar
Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22% for height, 27% for HDL cholesterol, 43% for body mass index, 50% for sleep duration, 58% for systolic blood pressure, 64% for total cholesterol, 66% for triglycerides, 77% for LDL cholesterol, and 100% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.