Integration of Biomarker Polygenic Risk Score Improves Prediction of Coronary Heart Disease

Jake Lin; Nina Mars; Yu Fu; Pietari Ripatti; Tuomo Kiiskinen; FinnGen study; Taru Tukiainen; Samuli Ripatti; Matti Pirinen

doi:10.1016/j.jacbts.2023.07.006

Integration of Biomarker Polygenic Risk Score Improves Prediction of Coronary Heart Disease

JACC Basic Transl Sci. 2023 Oct 4;8(12):1489-1499. doi: 10.1016/j.jacbts.2023.07.006. eCollection 2023 Dec.

Authors

Jake Lin^{1

2}, Nina Mars¹, Yu Fu¹, Pietari Ripatti¹, Tuomo Kiiskinen¹; FinnGen study; Taru Tukiainen¹, Samuli Ripatti^{1

3

4

5}, Matti Pirinen^{1

3

6}

Affiliations

¹ Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
² Health Sciences, Faculty of Social Sciences, Tampere University, Tampere, Finland.
³ Department of Public Health, University of Helsinki, Helsinki, Finland.
⁴ Broad Institute of Massachusetts Institute of Technology, Harvard University, Cambridge, Massachusetts, USA.
⁵ Massachusetts General Hospital, Cambridge, Massachusetts, USA.
⁶ Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.

Abstract

There are several established biomarkers for coronary heart disease (CHD), including blood pressure, cholesterol, and lipoproteins. It is of high interest to determine how a combined polygenic risk score (PRS) of CHD-associated biomarkers (BioPRS) can further improve genetic prediction of CHD. We developed CHDBioPRS, combining BioPRS with PRS of CHD in the UK Biobank and tested it on FinnGen. We found that BioPRS was clearly predictive of CHD and that CHDBioPRS improved the standard CHD PRS. The largest effect was observed with early onset cases in FinnGen, with HRs above 2 per standard deviation of CHDBioPRS.

Keywords: GWAS; biomarkers; coronary heart disease; genomics; polygenic risk scores.