Clinical utility of polygenic risk scores for coronary artery disease

Abstract

Over the past decade, substantial progress has been made in the discovery of alleles contributing to the risk of coronary artery disease. In addition to providing causal insights into disease, these endeavours have yielded and enabled the refinement of polygenic risk scores. These scores can be used to predict incident coronary artery disease in multiple cohorts and indicate the clinical response to some preventive therapies in post hoc analyses of clinical trials. These observations and the widespread ability to calculate polygenic risk scores from direct-to-consumer and health-care-associated biobanks have raised many questions about responsible clinical adoption. In this Review, we describe technical and downstream considerations for the derivation and validation of polygenic risk scores and current evidence for their efficacy and safety. We discuss the implementation of these scores in clinical medicine for uses including risk prediction and screening algorithms for coronary artery disease, prioritization of patient subgroups that are likely to derive benefit from treatment, and efficient prospective clinical trial designs.

Fig. 1 ∣. The liability threshold model.

Risk for a given disease or binary outcome reaches a specific, unknown threshold (blue line) that, once surpassed, results in disease manifestation. Most polygenic risk score analyses model genetic risk based on the liability threshold model.

Fig. 2 ∣. Effect estimate for polygenic risk scores.

Effect estimates for individuals at the right tail (the top 1–5%, depending on the disease) of the polygenic risk score distribution are often equivalent to, or exceed, those of well-described monogenic mutations. In current clinical practice, the presence of a monogenic mutation can alter clinical decision making and treatment. APOB, apolipoprotein B-100; FH, familial hypercholesterolaemia; PCSK9, proprotein convertase subtilisin/kexin type 9.

Fig. 3 ∣. Event rates for clinical trials of coronary artery disease.

Enriching clinical trials for patients with elevated polygenic risk scores (solid blue line), based on observations of absolute and relative risk reduction, would allow the prespecified number of disease end points to be reached more rapidly and, therefore, with reduced costs relative to a standard clinical trial population (dotted blue line).

Fig. 4 ∣. Leveraging PRS for clinical trial design.

In an a priori trial design, individuals are chosen for participation based on their genotype prior to enrolment in an effort to enrich the trial for disease end points. In a post hoc design, individuals are stratified by genotype after completion of the study to delineate groups of individuals who are most likely to benefit, or derive the greatest benefit, from a therapy. In an adaptive trial design, heterogeneity observed at an interim analysis identifies individuals with a high PRS who are likely to derive the greatest benefit from a therapy. Following this, trial participants are selected based on genotype. CAD, coronary artery disease; PRS, polygenic risk scores.

Fig. 5 ∣. Prioritization of patients with high PRS for invasive therapy.

Clinical equipoise exists for the use of a ∣ coronary and b ∣carotid revascularization for specific patient populations. PRS could provide an opportunity to identifying patients likely to derive the greatest benefit from surgery or percutaneous intervention and alter the risk/benefit profile of surgery and anaesthesia. This strategy would require thorough evaluation in a RCT. CEA, carotid endarterectomy; PCI, percutaneous coronary intervention; PRS, polygenic risk scores. RCT, randomized controlled trial.