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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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395509/
https://www.ncbi.nlm.nih.gov/pubmed/35995843
http://dx.doi.org/10.1038/s42003-022-03812-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395509/
https://www.ncbi.nlm.nih.gov/pubmed/35995843
http://dx.doi.org/10.1038/s42003-022-03812-z

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