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Polygenic scores in biomedical research

Nature Reviews Genetics volume 23, pages 524–532 (2022)Cite this article

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Public health strategies aimed at disease prevention or early detection and intervention have the potential to advance human health worldwide. However, their success depends on the identification of risk factors that underlie disease burden in the general population. Genome-wide association studies (GWAS) have implicated thousands of single-nucleotide polymorphisms (SNPs) in common complex diseases or traits. By calculating a weighted sum of the number of trait-associated alleles harboured by an individual, a polygenic score (PGS), also called a polygenic risk score (PRS), can be constructed that reflects an individual’s estimated genetic predisposition for a given phenotype. Here, we ask six experts to give their opinions on the utility of these probabilistic tools, their strengths and limitations, and the remaining barriers that need to be overcome for their equitable use.

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References

  1. Wray, N. R., Kemper, K. E., Hayes, B. J., Goddard, M. E. & Visscher, P. M. Complex trait prediction from genome data: contrasting EBV in livestock to PRS in humans: genomic prediction. Genetics 211, 1131–1141 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Krapohl, E. et al. Multi-polygenic score approach to trait prediction. Mol. Psychiatry 23, 1368–1374 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Rodriguez, V. et al. Use of multiple polygenic risk scores for distinguishing schizophrenia-spectrum disorder and affective psychosis categories in a first-episode sample; the EU-GEI study. Psychol. Med. https://doi.org/10.1017/S0033291721005456 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Polygenic Risk Score Task Force of the International Common Disease Alliance. Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps. Nat. Med. 27, 1876–1884 (2021).

    Article  CAS  Google Scholar 

  5. Ritchie, S. C. et al. Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases. Nat. Metab. 3, 1476–1483 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Zheutlin, A. B. et al. Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four health care systems. Am. J. Psychiatry 176, 846–855 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Berg, J. J. et al. Reduced signal for polygenic adaptation of height in UK Biobank. eLife 8, e39725 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Sohail, M. et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife 8, e39702 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Novembre, J. & Barton, N. H. Tread lightly interpreting polygenic tests of selection. Genetics 208, 1351–1355 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Zhang, H. et al. Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses. Nat. Genet. 52, 572–581 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6, 361–369 (2018).

    Article  PubMed  Google Scholar 

  12. Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Mars, N. et al. Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat. Med. 26, 549–557 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Torkamani, A., Wineinger, N. E. & Topol, E. J. The personal and clinical utility of polygenic risk scores. Nat. Rev. Genet. 19, 581–590 (2018).

    Article  CAS  PubMed  Google Scholar 

  15. Meisner et al. Combined utility of 25 disease and risk factor polygenic risk scores for stratifying risk of all-cause mortality. Am. J. Hum. Genet. 107, 418–431 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Jukaranien et al. Genetic risk factors have substantial impact on healthy life years. Preprint at. medRxiv https://doi.org/10.1101/2022.01.25.22269831 (2002).

    Article  Google Scholar 

  17. Hoffmann, T. et al. Genome-wide association study of prostate-specific antigen levels identifies novel loci independent of prostate cancer. Nat. Commun. 8, 14248 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. International Schizophrenia, C. et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009).

    Article  CAS  Google Scholar 

  19. Pain, O. et al. Evaluation of polygenic prediction methodology within a reference-standardized framework. PLoS Genet. 17, e1009021 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wand, H. et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature 591, 211–219 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kannel, W. B., Dawber, T. R., Friedman, G. D., Glennon, W. E. & McNamara, P. M. Risk factors in coronary heart disease: the Framingham study. Ann. Int. Med. 61, 888–899 (1964).

    Article  CAS  PubMed  Google Scholar 

  22. Ding, Y. et al. Large uncertainty in individual PRS estimation impacts PRS-based risk stratification. Nat. Genet. 54, 30–39 (2022).

    Article  CAS  PubMed  Google Scholar 

  23. Khera, A. V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mega, J. L. et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet 385, 2264–2271 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mavaddat et al. Polygenic risk scores for breast cancer and breast cancer subtypes. Am. J. Hum. Genet. 104, 21–34 (2019).

    Article  CAS  PubMed  Google Scholar 

  26. Hudson et al. Prospective validation of breast cancer risk model integrating classical risk-factors and polygenic risk in 15 cohorts and six countries. Int. J. Epidemiol. 50, 1897–1911 (2021).

    Google Scholar 

  27. Gail et al. Weighing risks and benefits of tamoxifen treatment for preventing breast cancer. J. Natl Cancer Inst. 91, 1829–1846 (1999).

    Article  CAS  PubMed  Google Scholar 

  28. Widén, E. et al. How communicating polygenic and clinical risk for atherosclerotic cardiovascular disease impacts health behavior: an observational follow-up study. Circ. Genom. Precis. Med. https://doi.org/10.1161/CIRCGEN.121.003459 (2022).

  29. Inouye, M. et al. Genomic risk prediction of coronary artery disease in 480,000 adults: implications for primary prevention. J. Am. Coll. Cardiol. 72, 1883–1893 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Lee, A. et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet. Med. 21, 1708–1718 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Fatumo, S. et al. A roadmap to increase diversity in genomic studies. Nat. Med. 28, 243–250 (2022).

    Article  CAS  PubMed  Google Scholar 

  33. Lambert, S. A., Abraham, G. & Inouye, M. Towards clinical utility of polygenic risk scores. Hum. Mol. Genet 28, R133–R142 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Fahed, A. C. et al. Polygenic background modifies penetrance of monogenic variants for tier 1 genomic conditions. Nat. Commun. 11, 3635 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kuchenbaecker, K. B. et al. Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers. J. Natl Cancer Inst. 109, djw302 (2017).

    Article  PubMed Central  CAS  Google Scholar 

  36. Carver, T. et al. CanRisk Tool–a web interface for the prediction of breast and ovarian cancer risk and the likelihood of carrying genetic pathogenic variants. Cancer Epidemiol. Biomark. Prev. 30, 469–473 (2021).

    Article  CAS  Google Scholar 

  37. Brigden, T. et al. Implementing polygenic scores for cardiovascular disease into NHS health checks, PHG Foundation https://www.phgfoundation.org/report/prs-implementation-and-delivery (2021).

  38. Kullo, I. J. et al. Incorporating a genetic risk score into coronary heart disease risk estimates: effect on low-density lipoprotein cholesterol levels (the MI-GENES Clinical Trial). Circulation 133, 1181–1188 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kullo, I. J., Jarvik, G. P., Manolio, T. A., Williams, M. S. & Roden, D. M. Leveraging the electronic health record to implement genomic medicine. Genet. Med. 15, 270–271 (2013).

    Article  PubMed  Google Scholar 

  40. Chang, E. T. et al. Reliability of self-reported family history of cancer in a large case-control study of lymphoma. J. Natl Cancer Inst. 98, 61–68 (2006).

    Article  PubMed  Google Scholar 

  41. Peto, J. et al. Prevalence of BRCA1 and BRCA2 gene mutations in patients with early-onset breast cancer. J. Natl Cancer Inst. 91, 943–949 (1999).

    Article  CAS  PubMed  Google Scholar 

  42. Mars, N. et al. The role of polygenic risk and susceptibility genes in breast cancer over the course of life. Nat. Commun. 11, 6383 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Dixon, P., Keeney, E., Taylor, J. C., Wordsworth, S. & Martin, R. M. Can polygenic risk scores contribute to cost-effective cancer screening? A systematic review. Preprint at. medRxiv https://doi.org/10.1101/2021.11.26.21266911 (2021).

  44. Turley, P. et al. Problems with using polygenic scores to select embryos. N. Engl. J. Med. 385, 78–86 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Karavani, E. et al. Screening human embryos for polygenic traits has limited utility. Cell 179, 1424–1435.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chatterjee, N., Shi, J. & García-Closas, M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 17, 392–406 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ding, K. & Kullo, I. J. Evolutionary genetics of coronary heart disease. Circulation 119, 459–467 (2009).

    Article  PubMed  Google Scholar 

  48. Goff, D. C. Jr. et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 129, S49–S73 (2014).

    Article  PubMed  Google Scholar 

  49. Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Dikilitas, O. et al. Predictive utility of polygenic risk scores for coronary heart disease in three major racial and ethnic groups. Am. J. Hum. Genet. 106, 707–716 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ruan, Y. & et al. Improving polygenic prediction in ancestrally diverse populations. Preprint at. medRxiv https://doi.org/10.1101/2020.12.27.20248738 (2021).

  52. Graham et al. The power of genetic diversity in genome-wide association studies of lipids. Nature 600, 675–679 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Forzano, F. et al. The use of polygenic risk scores in pre-implantation genetic testing: an unproven, unethical practice. Eur. J. Hum. Genet. https://doi.org/10.1038/s41431-021-01000-x (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Powell, K. The broken promise that undermines human genome research. Nature 590, 198–201 (2021).

    Article  CAS  PubMed  Google Scholar 

  55. Lambert, S. A. et al. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat. Genet. 53, 420–425 (2021).

    Article  CAS  PubMed  Google Scholar 

  56. Pain, O., Gillett, A. C., Austin, J. C., Folkersen, L. & Lewis, C. M. A tool for translating polygenic scores onto the absolute scale using summary statistics. Eur. J. Hum. Genet. https://doi.org/10.1038/s41431-021-01028-z (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Folkersen, L. et al. Impute.me: an open-source, non-profit tool for using data from direct-to-consumer genetic testing to calculate and interpret polygenic risk scores. Front. Genet. 11, 578 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Safarova, M. S., Ackerman, M. J. & Kullo, I. J. A call for training programmes in cardiovascular genomics. Nat. Rev. Cardiol. 18, 539–540 (2021).

    Article  PubMed  Google Scholar 

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Acknowledgements

I.J.K. is funded by NIH grants HG-006379, HG-011710 and HL-70710. A.R.M. is supported by funding from the NIH (R00MH117229).

Author information

Authors and Affiliations

  1. Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA

    Iftikhar J. Kullo

  2. Social, Genetic and Developmental Psychiatry Centre & Department of Medical & Molecular, King’s College London, London, UK

    Cathryn M. Lewis

  3. Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    Michael Inouye

  4. Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia

    Michael Inouye

  5. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Alicia R. Martin & Samuli Ripatti

  6. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Alicia R. Martin & Samuli Ripatti

  7. Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland

    Samuli Ripatti

  8. Department of Public Health, University of Helsinki, Helsinki, Finland

    Samuli Ripatti

  9. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Nilanjan Chatterjee

Authors
  1. Iftikhar J. Kullo
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  2. Cathryn M. Lewis
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  3. Michael Inouye
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  4. Alicia R. Martin
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Contributions

Iftikhar J. Kullo is a Professor of Cardiovascular Medicine, at Mayo Clinic, Rochester, Minnesota, USA. His research laboratory focuses on the genetic epidemiology of coronary heart disease and implementation of genomic medicine. He is a Principal Investigator in the National Human Genome Research Institute’s eMERGE and PRIMED Networks and serves on the US National Advisory Council on Human Genome Research.

Cathryn M. Lewis is Professor of Genetic Epidemiology and Statistics at King’s College London, UK, where she leads the Social, Genetic and Developmental Psychiatry Centre. She co-chairs the Psychiatric Genomics Consortium Major Depressive Disorder Working Group and leads the Biomarkers and Genomics theme in the NIHR Maudsley Biomedical Research Centre, performing translational research to establish the evidence base for genomics in a clinical setting.

Michael Inouye is a computational biologist who has been analysing human genome data for more than 20 years. He is a Professor and Director of Research at the University of Cambridge, UK, Munz Chair of Cardiovascular Prediction and Prevention at the Baker Heart and Diabetes Institute and Director of the Cambridge Baker Systems Genomics Initiative.

Alicia R. Martin is a population and statistical geneticist. Her research examines the role of human history in shaping global genetic and phenotypic diversity. To ensure that vast Eurocentric study biases do not exacerbate health disparities, she is developing statistical methods, genomics resources and research capacity for diverse and under-represented populations.

Samuli Ripatti is Professor and Vice Director at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki, and chair of the Academy of Finland’s Centre of Excellence in Complex Disease Genetics. His research group studies genetic variation and its effects on common disease risks and management. His research uses cardiometabolic diseases and cancers as models to learn about disease mechanisms and genome-based strategies for prevention and prognosis.

Nilanjan Chatterjee is a Bloomberg Distinguished Professor at Johns Hopkins University, USA, and was previously the Chief of the Biostatistics Branch at the National Cancer Institute. He is known for his research on sample size requirements for polygenic prediction, methods for building polygenic scores (PGS) and integration of PGS with non-genetic risk factors.

Corresponding authors

Correspondence to Iftikhar J. Kullo, Cathryn M. Lewis, Michael Inouye, Alicia R. Martin, Samuli Ripatti or Nilanjan Chatterjee.

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Competing interests

C.M.L. is a member of the Scientific Advisory Board for Myriad Neuroscience. A.R.M. has consulted for 23andMe and Illumina and received speaker fees from Genentech, Pfizer and Illumina. The other contributors declare no competing interests.

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Related links

Clinical Genome Resource (ClinGen) Complex Disease Working Group: https://www.clinicalgenome.org/working-groups/complex-disease/

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

Global Biobank Meta-analysis Initiative: www.globalbiobankmeta.org

Heart study: https://www.hra.nhs.uk/planning-and-improving-research/application-summaries/research-summaries/the-heart-study-and-version-10

International Consortium for Integrative Genomics Prediction: www.interveneproject.eu

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

PRIMED consortium: https://primedconsortium.org/

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Kullo, I.J., Lewis, C.M., Inouye, M. et al. Polygenic scores in biomedical research. Nat Rev Genet 23, 524–532 (2022). https://doi.org/10.1038/s41576-022-00470-z

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