Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations

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...

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Bibliographic Details
Published in:	Communications biology Vol. 5; no. 1; pp. 856 - 12
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 22.08.2022 Nature Publishing Group Nature Portfolio
Subjects:	45/43 631/114/1305 631/208/205/2138 Biology Biomedical and Life Sciences Blood pressure Body mass index Cholesterol Genetic Predisposition to Disease Genome-Wide Association Study High density lipoprotein Humans Learning algorithms Life Sciences Low density lipoprotein Machine Learning Minority & ethnic groups Multifactorial Inheritance Phenotypes Polygenic inheritance Polymorphism, Single Nucleotide Prediction models Single-nucleotide polymorphism Triglycerides
ISSN:	2399-3642, 2399-3642
Online Access:	Get full text
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