Role of Common Genetic Variation in Lone Atrial Fibrillation

Polygenic risk scores for the prediction of cardiometabolic disease

Cardiometabolic diseases contribute more to global morbidity and mortality than any other group of disorders. Polygenic risk scores (PRSs), the weighted summation of individually small-effect genetic variants, represent an advance in our ability to predict the development and complications of cardiometabolic diseases. This article reviews the evidence supporting the use of PRS in seven common cardiometabolic diseases: coronary artery disease (CAD), stroke, hypertension, heart failure and cardiomyopathies, obesity, atrial fibrillation (AF), and type 2 diabetes mellitus (T2DM). Data suggest that PRS for CAD, AF, and T2DM consistently improves prediction when incorporated into existing clinical risk tools. In other areas such as ischaemic stroke and hypertension, clinical application appears premature but emerging evidence suggests that the study of larger and more diverse populations coupled with more granular phenotyping will propel the translation of PRS into practical clinical prediction tools.

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

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.

Evaluating Polygenic Risk Scores in "Lone" Atrial Fibrillation

Background

Polygenic scores incorporating varying numbers of single nucleotide polymorphisms (SNPs) have been demonstrated to exert a prominent role in atrial fibrillation (AF). We sought to compare the relative discriminatory capacities of 2 previously validated polygenic scores in "lone" AF.

Methods

A total of 186 lone AF cases of European ancestry underwent SNP genotyping. A genome-wide polygenic score (GPS) and polygenic risk score (PRS) involving 6,730,541 and 1168 SNPs, respectively, were calculated for 186 cases and 423 controls of European ancestry from the 1000 Genomes (1KG) Project. The distribution of the polygenic scores was compared between the cases and controls and their discriminatory capacities were evaluated using receiver operating characteristic (ROC) curves.

Results

A total of 34.4% of patients with lone AF had GPS scores greater than the top 10th percentile of 1KG controls, corresponding to a 4.64-fold increased odds (95% confidence interval [CI], 2.99-7.18; P < 0.001) for AF. A PRS score in the top 10th percentile of 1KG controls was observed in 26.3% of cases, which equated to a 3.16-fold increased odds (95% CI, 2.01-4.98; P < 0.001) for AF. Comparison of C-statistics from ROC curves indicated improved discriminatory capacity of the GPS (0.76) relative to the PRS (0.70) (P = 0.002).

Conclusions

Our study evaluating 2 polygenic scores for AF suggests that the GPS, containing more than 6.7 million SNPs, exhibits an improved discriminatory capacity in lone AF compared with a PRS possessing 1168 SNPs. Our findings suggest that genetic risk scores for AF that maximally leverage genomic data may provide improved predictive power.