Author:
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
AbstractWe 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 a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model’s performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.
Funder
National Heart, Lung, and Blood Institute
Publisher
Springer Science and Business Media LLC