Highly elevated polygenic risk scores are better predictors of myocardial infarction risk early in life than later

Abstract

Background Several polygenic risk scores (PRS) have been developed for cardiovascular risk prediction, but the additive value of including PRS together with conventional risk factors for risk prediction is questionable. This study assesses the clinical utility of including four PRS generated from 194, 46K, 1.5M, and 6M SNPs, along with conventional risk factors, to predict risk of ischemic heart disease (IHD), myocardial infarction (MI), and first MI event on or before age 50 (early MI). Methods A cross-validated logistic regression (LR) algorithm was trained either on ~ 440K European ancestry individuals from the UK Biobank (UKB), or the full UKB population, including as features different combinations of conventional established-at-birth risk factors (ancestry, sex) and risk factors that are non-fixed over an individual’s lifespan (age, BMI, hypertension, hyperlipidemia, diabetes, smoking, family history), with and without also including PRS. The algorithm was trained separately with IHD, MI, and early MI as prediction labels. Results When LR was trained using risk factors established-at-birth, adding the four PRS significantly improved the area under the curve (AUC) for IHD (0.62 to 0.67) and MI (0.67 to 0.73), as well as for early MI (0.70 to 0.79). When LR was trained using all risk factors, adding the four PRS only resulted in a significantly higher disease prevalence in the 98th and 99th percentiles of both the IHD and MI scores. Conclusions PRS improve cardiovascular risk stratification early in life when knowledge of later-life risk factors is unavailable. However, by middle age, when many risk factors are known, the improvement attributed to PRS is marginal for the general population.

The impact of age on genetic risk for common diseases

“…One aspect of genetic risk estimation that has not been fully explored is the role of age in modulating the effects of genetic risk. Several studies have shown that the prediction power of PRS varies across age groups in diseases including breast cancer [ 8 ], ischaemic heart disease [ 9 ] and prostate cancer [ 10 ]. Similarly, the standardised incidence ratio of breast cancer for BRCA1/2 mutation carriers also varies with age [ 11 ].…”

Genomic risk prediction of coronary artery disease in women with breast cancer: a prospective cohort study

“…However, we note that there was an almost significant interactive effect between being a past smoker and the PRS. Since PRS is known to be correlated to certain conventional CAD risk factors [ 10 , 30 ], it is plausible that some fraction of incident CAD risk explained by PRS may be dependent on smoking status. While this paper is not expressly predictive in nature, we note that the addition of PRS may not have provided additional risk discrimination on top of BMI and smoking because by middle age, the genes that compose this risk score may have already exerted their influence, and thus the PRS would not be expected to add discriminatory ability.…”

Machine learning applications for therapeutic tasks with genomics data

“…Isgut et al. 98 use logistic regression on polygenic risk scores to improve myocardial infarction risk prediction. Zhou et al.…”

“…Paré et al 97 use gradient boosted regression to approximate polygenic risk score for complex traits such as diabetes, height, and body mass index. Isgut et al 98 use logistic regression on polygenic risk scores to improve myocardial infarction risk prediction. Zhou et al 99 apply DNNs on the epigenomic features of both the coding and non-coding regions to predict gene expression for more than 200 tissue and cell types and later identify disease-causing SNPs.…”