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

• Type of Material: Article
• Authors: Monica Isgut, Georgia Institute of Technology; Jimeng Sun, University of Illinois; Arshed Quyyumi, Emory University; Greg Gibson, Georgia Institute of Technology
• Language: English
• Date: 2021-01-28
• Publisher: BMC
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s). 2021
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s13073-021-00828-8
• Title of Journal or Parent Work: Genome Medicine
• Volume: 13
• Issue: 1
• Start Page: 13
• End Page: 13
• Grant/Funding Information: This funding supported the study design, analysis, interpretation of data, and writing of the manuscript.; Monica Isgut was supported by a graduate teaching assistantship from the Georgia Institute of Technology (Georgia Tech) School of Biological Sciences and by NIH grant R56-HL138415 to Jimeng Sun while he was at Georgia Tech.
• Keywords: Polygenic risk scores; Risk assessment; Disease; Life Sciences & Biomedicine; Accuracy; Science & Technology; Ischemic heart disease; Algorithms; Genetics & Heredity; Myocardial infarction; Coronary artery disease; Validation; Cardiovascular risk
• Research Categories: Engineering, Biomedical; Computer Science; Health Sciences, Medicine and Surgery

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.