Article
Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.
Communications biology - 22 Aug 2022
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
Abstract excerpt
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...
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