Evidence map›Paper›PMID 33651632›Full record

ArticleCirculation. Genomic and precision medicine2021

Integrated Polygenic Tool Substantially Enhances Coronary Artery Disease Prediction.

Fernando Riveros-Mckay, Michael E Weale, Rachel Moore, Saskia Selzam, Eva Krapohl, R Michael Sivley, William A Tarran, Peter Sørensen, Alexander S Lachapelle, Jonathan A Griffiths and 9 more

Erratum issuedAbstract read
In one paragraph

Article in Circulation. Genomic and precision medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 98 papers.

0numbers the graph read from it
0cells of the map it votes in
98citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

98 citing papers in PubMed.

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  13. The impact of collider bias on genetic prediction in psychotic disorders.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Article
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38 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Fernando Riveros-MckayGenomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
Michael E WealeGenomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
Rachel MooreGenomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
Saskia SelzamGenomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
Eva KrapohlGenomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
R Michael SivleyGenomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
William A TarranGenomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
Peter SørensenGenomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
Alexander S LachapelleGenomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
Jonathan A GriffithsGenomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
Ayden SaffariGenomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
John DeanfieldInstitute of Cardiovascular Sciences, University College London, United Kingdom (J.D.).
Chris C A SpencerGenomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
Julia Hippisley-CoxDepartment of Primary Care Health Sciences (J.H.-C.), University of Oxford, United Kingdom.
David J HunterNuffield Department of Population Health (D.J.H.), University of Oxford, United Kingdom.
Jack W O'SullivanDivision of Cardiology, Department of Medicine, Stanford University School of Medicine, CA (J.W.O., E.A.A.).
Euan A AshleyDivision of Cardiology, Department of Medicine, Stanford University School of Medicine, CA (J.W.O., E.A.A.).
Vincent Plagnol *Genomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).
Peter Donnelly *Genomics plc, Oxford, United Kingdom (F.R.-M., M.E.W., R.M., S.S., E.K., R.M.S., W.A.T., P.S., A.S.L., J.A.G., A.S., C.C.A.S., V.P., P.D.).

Funding

Medical Research Council MC_PC_17228Medical Research Council MC_QA137853
6 · The paper itself

Abstract

backgroundThere is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment.

methodsUsing the UK Biobank resource, we developed our own polygenic risk score for coronary artery disease (CAD). We used an additional 60 000 UK Biobank individuals to develop an integrated risk tool (IRT) that combined our polygenic risk score with established risk tools (either the American Heart Association/American College of Cardiology pooled cohort equations [PCE] or UK QRISK3), and we tested our IRT in an additional, independent set of 186 451 UK Biobank individuals.

resultsThe novel CAD polygenic risk score shows superior predictive power for CAD events, compared with other published polygenic risk scores, and is largely uncorrelated with PCE and QRISK3. When combined with PCE into an IRT, it has superior predictive accuracy. Overall, 10.4% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, compared with 4.4% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.9% (95% CI, 4.7-7.0). When individuals were stratified into age-by-sex subgroups, the improvement was larger for all subgroups (range, 8.3%-15.4%), with the best performance in 40- to 54-year-old men (15.4% [95% CI, 11.6-19.3]). Comparable results were found using a different risk tool (QRISK3) and also a broader definition of cardiovascular disease. Use of the IRT is estimated to avoid up to 12 000 deaths in the United States over a 5-year period.

conclusionsAn IRT that includes polygenic risk outperforms current risk stratification tools and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person's polygenic risk.

Indexed as

AdultAgedCoronary Artery DiseaseDatabases, GeneticFemaleGenetic Predisposition to DiseaseHumansIncidenceMaleMiddle AgedProportional Hazards ModelsRisk Factorscoronary artery diseasegenetic epidemiologygeneticsprimary preventionrisk factors

Identifiers

PMID33651632
PMCPMC8284388

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.