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

Hrytsenko, Y., Shea, B., Elgart, M., Kurniansyah, N., Lyons, G., Morrison, A. C., Carson, A. P., Haring, B., Mitchell, B. D., Psaty, B. M., Jaeger, B. C., Gu, C. C., Kooperberg, C., Levy, D., Lloyd-Jones, D., Choi, E., Brody, J. A., Smith, J. A., Rotter, J. I., … Sofer, T. (2024). Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-62945-9
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
Tamar Sofer
Affiliated Authors:
Eunhee Choi
Columbia Affiliation:
Subjects:
Machine Learning (MeSH)
Blood Pressure (MeSH)
Multifactorial Inheritance (MeSH)
Phenotype (MeSH)
Genome-Wide Association Study (MeSH)
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Publication Type:
Article
Unique ID:
10.1038/s41598-024-62945-9
PMID:
38816422
DOI:
10.1038/s41598-024-62945-9
Journal:
Scientific Reports
Publication Date:
Data Source:
PubMed
Source Link:
View PubMed Record
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Record Created: