Article
Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.
Scientific reports - 30 May 2024
Hrytsenko Yana, Shea Benjamin, Elgart Michael, Kurniansyah Nuzulul, Lyons Genevieve, Morrison Alanna C, Carson April P, Haring Bernhard, Mitchell Braxton D, Psaty Bruce M, Jaeger Byron C, Gu C Charles, Kooperberg Charles, Levy Daniel, Lloyd-Jones Donald, Choi Eunhee, Brody Jennifer A, Smith Jennifer A, Rotter Jerome I, Moll Matthew, Fornage Myriam, Simon Noah, Castaldi Peter, Casanova Ramon, Chung Ren-Hua, Kaplan Robert, Loos Ruth J F, Kardia Sharon L R, Rich Stephen S, Redline Susan, Kelly Tanika, O'Connor Timothy, Zhao Wei, Kim Wonji, Guo Xiuqing, Ida Chen Yii-Der, Sofer Tamar
Abstract excerpt
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 demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus...
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