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Please use this identifier to cite or link to this item:
http://ir.nhri.org.tw/handle/3990099045/15874
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| Title: | Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores |
| Authors: | Hrytsenko, Y;Shea, B;Elgart, M;Kurniansyah, N;Lyons, G;Morrison, AC;Carson, AP;Haring, B;Mitchell, BD;Psaty, BM;Jaeger, BC;Gu, CC;Kooperberg, C;Levy, D;Lloyd-Jones, D;Choi, E;Brody, JA;Smith, JA;Rotter, JI;Moll, M;Fornage, M;Simon, N;Castaldi, P;Casanova, R;Chung, RH;Kaplan, R;Loos, RJF;Kardia, SLR;Rich, SS;Redline, S;Kelly, T;O'Connor, T;Zhao, W;Kim, W;Guo, X;Ida Chen, YD;Sofer, T |
| Contributors: | Institute of Population Health Sciences |
| 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 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. |
| Date: | 2024-05-30 |
| Relation: | Scientific Reports. 2024 May 30;14:Article number 12436. |
| Link to: | http://dx.doi.org/10.1038/s41598-024-62945-9 |
| JIF/Ranking 2023: | http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=2045-2322&DestApp=IC2JCR |
| Cited Times(WOS): | https://www.webofscience.com/wos/woscc/full-record/WOS:001236334900044 |
| Cited Times(Scopus): | https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85194998457 |
| Appears in Collections: | [鍾仁華] 期刊論文
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| PUB38816422.pdf | | 3266Kb | Adobe PDF | 97 | View/Open |
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