Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores

Abstract We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on dem...

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Bibliographic Details
Main Authors: Yana Hrytsenko, Benjamin Shea, Michael Elgart, Nuzulul Kurniansyah, Genevieve Lyons, Alanna C. Morrison, April P. Carson, Bernhard Haring, Braxton D. Mitchell, Bruce M. Psaty, Byron C. Jaeger, C. Charles Gu, Charles Kooperberg, Daniel Levy, Donald Lloyd-Jones, Eunhee Choi, Jennifer A. Brody, Jennifer A. Smith, Jerome I. Rotter, Matthew Moll, Myriam Fornage, Noah Simon, Peter Castaldi, Ramon Casanova, Ren-Hua Chung, Robert Kaplan, Ruth J. F. Loos, Sharon L. R. Kardia, Stephen S. Rich, Susan Redline, Tanika Kelly, Timothy O’Connor, Wei Zhao, Wonji Kim, Xiuqing Guo, Yii-Der Ida Chen, The Trans-Omics in Precision Medicine Consortium, Tamar Sofer
Format: Article
Language:English
Published: Nature Portfolio 2024-05-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-024-62945-9

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https://doi.org/10.1038/s41598-024-62945-9

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