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  • Review Article
  • Published:

Clinical utility of polygenic risk scores for coronary artery disease

Nature Reviews Cardiology volume 19, pages 291–301 (2022)Cite this article

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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.

Key points

  • Genome-wide association studies demonstrate that multiple common genetic variants predispose individuals to coronary artery disease (CAD).

  • Polygenic risk scores (PRS) are singular, quantitative metrics for genetic susceptibility to a disease such as CAD.

  • The predictive performance of PRS for CAD is improved by incorporating evidence for association, linkage disequilibrium, anticipated functional impact and pleiotropy; trans-ancestry data improve the use of PRS in populations of diverse ancestry.

  • Post hoc analyses from completed clinical trials indicate that individuals with a high PRS for CAD derive the greatest relative and absolute benefit from LDL cholesterol-lowering strategies.

  • PRS for CAD could be used to identify individuals who would benefit from intensive lifestyle modification, imaging surveillance and early statin therapy.

  • PRS for CAD could be used to identify individuals at high risk for efficient clinical trial enrolment, and evidence of heterogeneous treatment benefit could be assessed through innovative trial designs.

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Fig. 1: The liability threshold model.
Fig. 2: Effect estimates for PRS.
Fig. 3: Event rates for clinical trials of coronary artery disease.
Fig. 4: Leveraging PRS for clinical trial design.
Fig. 5: Prioritization of patients with a high PRS for invasive therapy.

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Authors and Affiliations

  1. VA Palo Alto Healthcare System, Palo Alto, CA, USA

    Derek Klarin

  2. Division of Vascular Surgery, Stanford University School of Medicine, Palo Alto, CA, USA

    Derek Klarin

  3. Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Derek Klarin & Pradeep Natarajan

  4. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Pradeep Natarajan

  5. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    Pradeep Natarajan

Authors
  1. Derek Klarin
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  2. Pradeep Natarajan
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Both authors contributed substantially to all aspects of the article.

Corresponding author

Correspondence to Pradeep Natarajan.

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The authors declare no competing interests.

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Nature Reviews Cardiology thanks Guillaume Paré, Ali Torkamani and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

All Of Us: https://allofus.nih.gov/

eMERGE: https://emerge-network.org/

MVP: https://www.research.va.gov/mvp/

Polygenic Score Catalog: https://www.pgscatalog.org/

TOPMed: https://www.nhlbi.nih.gov/science/trans-omics-precision-medicine-topmed-program

Glossary

Genome-wide association studies

(GWAS). Studies that search, in an agnostic fashion, for allelic association with a particular phenotype by genotyping tag single-nucleotide polymorphisms across the entire genome.

Single-nucleotide polymorphism

(SNP). Specific substitution of a single nucleotide at a specific position in the genome.

Imputation

A technique that leverages the linkage disequilibrium between genotyped and ungenotyped variants to statistically infer missing genotypes using a reference panel of genotyped individuals.

Non-genotyped alleles

Single-nucleotide polymorphisms that can be inferred through statistical imputation but not directly observed on a genotype array.

Genome-wide significance

A level of statistical significance required to establish association for a common variant in genome-wide association studies (P = 5 × 10−8).

Linkage disequilibrium

(LD). The non-random association of alleles at two or more loci because of infrequent recombination events between them.

Least absolute shrinkage and selection operator

A modelling procedure for linear regression encouraging model sparsity.

Elastic net

Regularized regression method combining penalties of least absolute shrinkage and selection operator and ridge methods in a linear fashion.

Haplotypes

Combinations of alleles transmitted together on a single chromosome.

Pleiotropy

A single gene or variant yielding two or more apparently unrelated effects.

Whole-genome sequencing

Identification of all base pairs for an individual, with subsequent mapping of contiguous reads to a reference genome sequence (for next-generation sequencing).

Admixed populations

Populations in which previously diverged genetic lineages are mixed, for example, the AMR (admixed American) superpopulation in the 1,000 Genomes reference panel.

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Klarin, D., Natarajan, P. Clinical utility of polygenic risk scores for coronary artery disease. Nat Rev Cardiol 19, 291–301 (2022). https://doi.org/10.1038/s41569-021-00638-w

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