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Ancestry-specific polygenic risk scores are risk enhancers for clinical cardiovascular disease assessments

Author

Listed:
  • George B. Busby

    (Allelica Inc)

  • Scott Kulm

    (Allelica Inc)

  • Alessandro Bolli

    (Allelica Inc)

  • Jen Kintzle

    (Allelica Inc)

  • Paolo Di Domenico

    (Allelica Inc)

  • Giordano Bottà

    (Allelica Inc)

Abstract

Clinical implementation of new prediction models requires evaluation of their utility in a broad range of intended use populations. Here we develop and validate ancestry-specific Polygenic Risk Scores (PRSs) for Coronary Artery Disease (CAD) using 29,389 individuals from diverse cohorts and genetic ancestry groups. The CAD PRSs outperform published scores with an average Odds Ratio per Standard Deviation of 1.57 (SD = 0.14) and identify between 12% and 24% of individuals with high genetic risk. Using this risk factor to reclassify borderline or intermediate 10 year Atherosclerotic Cardiovascular Disease (ASCVD) risk improves assessments for both CAD (Net Reclassification Improvement (NRI) = 13.14% (95% CI 9.23–17.06%)) and ASCVD (NRI = 10.70 (95% CI 7.35-14.05)) in an independent cohort of 9,691 individuals. Our analyses demonstrate that using PRSs as Risk Enhancers improves ASCVD risk assessments outlining an approach for guiding ASCVD prevention with genetic information.

Suggested Citation

  • George B. Busby & Scott Kulm & Alessandro Bolli & Jen Kintzle & Paolo Di Domenico & Giordano Bottà, 2023. "Ancestry-specific polygenic risk scores are risk enhancers for clinical cardiovascular disease assessments," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42897-w
    DOI: 10.1038/s41467-023-42897-w
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    References listed on IDEAS

    as
    1. Frank Dudbridge, 2013. "Power and Predictive Accuracy of Polygenic Risk Scores," PLOS Genetics, Public Library of Science, vol. 9(3), pages 1-17, March.
    2. Robert M. Maier & Zhihong Zhu & Sang Hong Lee & Maciej Trzaskowski & Douglas M. Ruderfer & Eli A. Stahl & Stephan Ripke & Naomi R. Wray & Jian Yang & Peter M. Visscher & Matthew R. Robinson, 2018. "Improving genetic prediction by leveraging genetic correlations among human diseases and traits," Nature Communications, Nature, vol. 9(1), pages 1-17, December.
    3. Clare Bycroft & Colin Freeman & Desislava Petkova & Gavin Band & Lloyd T. Elliott & Kevin Sharp & Allan Motyer & Damjan Vukcevic & Olivier Delaneau & Jared O’Connell & Adrian Cortes & Samantha Welsh &, 2018. "The UK Biobank resource with deep phenotyping and genomic data," Nature, Nature, vol. 562(7726), pages 203-209, October.
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