Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations
oleh: Michael Elgart, Genevieve Lyons, Santiago Romero-Brufau, Nuzulul Kurniansyah, Jennifer A. Brody, Xiuqing Guo, Henry J. Lin, Laura Raffield, Yan Gao, Han Chen, Paul de Vries, Donald M. Lloyd-Jones, Leslie A. Lange, Gina M. Peloso, Myriam Fornage, Jerome I. Rotter, Stephen S. Rich, Alanna C. Morrison, Bruce M. Psaty, Daniel Levy, Susan Redline, the NHLBI’s Trans-Omics in Precision Medicine (TOPMed) Consortium, Tamar Sofer
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2022-08-01 |
Deskripsi
Combining a standard polygenic risk score (PRS) as a feature in a machine learning model increases the percentage variance explained for those traits, helping to account for non-linearities or interaction effects in genetics-based prediction models.