Integration of biomarker polygenic risk score improves prediction of coronary heart disease in UK Biobank and FinnGen

Abstract Background In addition to age and sex, also smoking history and levels of blood pressure, cholesterol, lipoproteins and inflammation are established biomarkers for coronary heart disease (CHD). As standard polygenic risk scores (PRS) have recently proven successful for CHD prediction, it remains of high interest to determine how a combined PRS of biomarkers (BioPRS) constructed from statistically relevant biomarkers can further improve genetic prediction of CHD. Methods We developed CHDBioPRS, which combines BioPRS with PRS of CHD, via regularized regression in UK Biobank (UKB) training data ( n = 208,010). The resulting CHDBioPRS was tested on an independent UK Biobank subset ( n = 25,765) and on the FinnGen study ( n = 306,287). Results We observed a consistent pattern across all data sets where BioPRS was clearly predictive of CHD and improved standard PRS for CHD when the two were combined. In UKB test data, CHDPRS had a hazard ratio (HR) of 1.78 (95% confidence interval 1.67-1.91, area under the curve (AUC) 0.808) and CHDBioPRS had a HR of 1.88 (1.75-2.01, AUC 0.811) per one standard deviation of PRS. In FinnGen data, HR of CHDPRS was 1.57 (1.55-1.60, AUC 0.752) and HR of CHDBioPRS was 1.60 (1.58-1.62, AUC 0.755). We observed larger effects of CHDBioPRS in subsets of early onset cases with HR of 2.07 (1.85-2.32, AUC 0.790) in UKB test data and of 2.10 (2.04-2.16, AUC 0.791) in FinnGen. Results were similar when stratified by sex. Conclusions Integration of biomarker based BioPRS improved on the standard PRS for CHD and the gain was largest with early onset CHD cases. These findings highlight the benefit of enriching polygenic risk prediction of CHD with the genetics of associated biomarkers.