Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association

Abstract

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

Keywords: AHA Scientific Statements; atrial fibrillation; diabetes, type 2; genome-wide association studies; multifactorial inheritance; predictive genetic testing; venous thromboembolism.

Figure 1.. Development of a PRS.

A, Development of polygenic risk scores (PRSs). This typically involves attaining single nucleotide variant (SNV) effect sizes from a genome-wide association study (GWAS) and then adjusting these SNV effect sizes to account for linkage disequilibrium (LD). B, Training of the PRS. Typically, numerous PRSs are created per participant. Each PRS is then assessed through various association testing, and the most accurate PRS is collected. C, Validation of the PRS. The most accurate PRS is then validated in an independent cohort of participants.

Figure 2.. Predictive accuracy of polygenic risk scores when combined with clinical risk tools, compared to clinical risk tools alone.

A, Net reclassification index (NRI). Comparison of clinical risk tools with and without the integration of polygenic risk score (PRS).^, The clinical risk tool used atrial fibrillation was CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology model for atrial fibrillation) with a risk threshold of >5% over 5 years. Variables included in CHARGE-AF were age, height, weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking status, blood pressure–lowering medication, diabetes, heart failure, and history of myocardial infarction. The clinical tool for coronary artery disease was the American Heart Association/American College of Cardiology Pooled Cohort Equation with a 7.5% risk threshold over 10 years and included the following variables: age, diabetes, sex, race, smoking, total cholesterol, high-density lipoprotein (HDL), systolic blood pressure, and treatment for hypertension. The clinical tool for type 2 diabetes was the American Diabetes Association risk score, which had a 33% risk threshold over 10 years and included the following variables: age, sex, body mass index, history of stroke or coronary heart disease, parental history of diabetes, SBP, DBP, HDL, and triglycerides. All differences are statistically significant. B, Comparison of C statistics between clinical risk scores (same as stated in A) and a risk tool with a PRS integrated into the clinical risk tool. All differences are statistically significant.^,

Figure 3.. Predictive ability of polygenic risk scores for coronary artery disease.

A, Net reclassification index (NRI) comparing clinical risk tools and a risk tool with a polygenic risk score (PRS) integrated into the clinical risk tool for coronary artery disease across multiple ethnicities. African American includes Black Caribbean and Black African, and South Asian includes Indian, Bangladeshi, or Pakistani. Copyright © 2021 The Authors. Published by Elsevier Inc. Creative Commons CC-BY license. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this figure. B, NRI for the American Heart Association/American College of Cardiology Pooled Cohort Equations (AHA/ACC PCE) tool+PRS, clinical risk factors collectively as the AHA/ACC PCE tool, PRS, and individual clinical risk factors for coronary artery disease.^, BMI indicates body mass index. Copyright © 2018 The Authors. Creative Commons CC-BY license. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this figure.