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Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers

Abstract

Polygenic risk scores (PRSs) have shown promise in predicting susceptibility to common diseases1,2,3. We estimated their added value in clinical risk prediction of five common diseases, using large-scale biobank data (FinnGen; n = 135,300) and the FINRISK study with clinical risk factors to test genome-wide PRSs for coronary heart disease, type 2 diabetes, atrial fibrillation, breast cancer and prostate cancer. We evaluated the lifetime risk at different PRS levels, and the impact on disease onset and on prediction together with clinical risk scores. Compared to having an average PRS, having a high PRS contributed 21% to 38% higher lifetime risk, and 4 to 9 years earlier disease onset. PRSs improved model discrimination over age and sex in type 2 diabetes, atrial fibrillation, breast cancer and prostate cancer, and over clinical risk in type 2 diabetes, breast cancer and prostate cancer. In all diseases, PRSs improved reclassification over clinical thresholds, with the largest net reclassification improvements for early-onset coronary heart disease, atrial fibrillation and prostate cancer. This study provides evidence for the additional value of PRSs in clinical disease prediction. The practical applications of polygenic risk information for stratified screening or for guiding lifestyle and medical interventions in the clinical setting remain to be defined in further studies.

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Fig. 1: Adjusted survival curves from Cox proportional hazards models showing the cumulative risk of disease by PRS category in FinnGen (n = 135,300 individuals).
Fig. 2: The proportions of early- and late-onset cases with high clinical risk, high polygenic risk or neither.

Data availability

The FinnGen data may be accessed through Finnish Biobanks’ FinnBB portal (www.finbb.fi) and THL Biobank data may be accessed through THL Biobank (https://thl.fi/en/web/thl-biobank).

Code availability

The full genotyping and imputation protocol for FinnGen is described at https://doi.org/10.17504/protocols.io.nmndc5e.

References

  1. 1.

    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).

    CAS  Article  Google Scholar 

  2. 2.

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

    CAS  Article  Google Scholar 

  3. 3.

    Seibert, T. M. et al. Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts. Br. Med. J. 360, j5757 (2018).

    Article  Google Scholar 

  4. 4.

    Global Burden of Disease Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1789–1858 (2018).

    Article  Google Scholar 

  5. 5.

    Piepoli, M. F. et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur. Heart J. 37, 2315–2381 (2016).

    Article  Google Scholar 

  6. 6.

    Lindbohm, J. V. et al. 5-year versus risk-category-specific screening intervals for cardiovascular disease prevention: a cohort study. Lancet Public Health 4, e189–e199 (2019).

    Article  Google Scholar 

  7. 7.

    Brentnall, A. R., Cuzick, J., Buist, D. S. M. & Bowles, E. J. A. Long-term accuracy of breast cancer risk assessment combining classic risk factors and breast density. JAMA Oncol. 4, e180174 (2018).

    Article  Google Scholar 

  8. 8.

    Wilson, B. J. et al. Systematic review: family history in risk assessment for common diseases. Ann. Intern. Med. 151, 878–885 (2009).

    Article  Google Scholar 

  9. 9.

    Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    CAS  Article  Google Scholar 

  10. 10.

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

    CAS  Article  Google Scholar 

  11. 11.

    Choudhury, P. P. et al. Comparative validation of breast cancer risk prediction models and projections for future risk stratification. J. Natl Cancer Inst. 112, 278–285 (2020).

    Article  Google Scholar 

  12. 12.

    Timpson, N. J., Greenwood, C. M. T., Soranzo, N., Lawson, D. J. & Richards, J. B. Genetic architecture: the shape of the genetic contribution to human traits and disease. Nat. Rev. Genet. 19, 110–124 (2018).

    CAS  Article  Google Scholar 

  13. 13.

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

    CAS  Article  Google Scholar 

  14. 14.

    Abraham, G. et al. Genomic prediction of coronary heart disease. Eur. Heart J. 37, 3267–3278 (2016).

    CAS  Article  Google Scholar 

  15. 15.

    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  Google Scholar 

  16. 16.

    Alonso, A. et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J. Am. Heart Assoc. 2, e000102 (2013).

    Article  Google Scholar 

  17. 17.

    Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).

    CAS  Article  Google Scholar 

  18. 18.

    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  Google Scholar 

  19. 19.

    Lubitz, S. A. et al. Genetic risk prediction of atrial fibrillation. Circulation 135, 1311–1320 (2017).

    Article  Google Scholar 

  20. 20.

    Khera, A. V., Emdin, C. A. & Kathiresan, S. Genetic risk, lifestyle, and coronary artery disease. N. Engl. J. Med. 376, 1194–1195 (2017).

    PubMed  Google Scholar 

  21. 21.

    Natarajan, P. et al. Polygenic risk score identifies subgroup with higher burden of atherosclerosis and greater relative benefit from statin therapy in the primary prevention setting. Circulation 135, 2091–2101 (2017).

    Article  Google Scholar 

  22. 22.

    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).

    CAS  Article  Google Scholar 

  23. 23.

    McCarthy, M. I. Painting a new picture of personalised medicine for diabetes. Diabetologia 60, 793–799 (2017).

    Article  Google Scholar 

  24. 24.

    Bapat, A., Anderson, C. D., Ellinor, P. T. & Lubitz, S. A. Genomic basis of atrial fibrillation. Heart 104, 201–206 (2018).

    CAS  Article  Google Scholar 

  25. 25.

    Average Number of New Cases Per Year and Age-Specific Incidence Rates per 100,000 Females, UK (Cancer Research UK, accessed 4 April 2019); https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer

  26. 26.

    Cancer Incidence, Mortality, Prevalence and Survival in the Nordic Countries Version 8.2 (26.03.2019) (NORDCAN, accessed 4 April 2019); http://www-dep.iarc.fr/NORDCAN/

  27. 27.

    Maas, P. et al. Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the United States. JAMA Oncol. 2, 1295–1302 (2016).

    Article  Google Scholar 

  28. 28.

    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  Google Scholar 

  29. 29.

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

    CAS  Article  Google Scholar 

  30. 30.

    Borodulin, K. et al. Cohort Profile: The National FINRISK Study. Int. J. Epidemiol. 47, 696–696i (2018).

    Article  Google Scholar 

  31. 31.

    Grundy, S. M. et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J. Am. Coll. Cardiol. 73, 3168–3209 (2019).

    Article  Google Scholar 

  32. 32.

    American Diabetes Association 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2019. Diabetes Care 42, S13–S28 (2019).

    Article  Google Scholar 

  33. 33.

    Lindstrom, J. & Tuomilehto, J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 26, 725–731 (2003).

    Article  Google Scholar 

  34. 34.

    Terry, M. B. et al. 10-year performance of four models of breast cancer risk: a validation study. Lancet Oncol. 20, 504–517 (2019).

    Article  Google Scholar 

  35. 35.

    Kiiskinen, T. et al. Genomic prediction of alcohol-related morbidity and mortality. Transl. Psychiatry 10, 23 (2020).

    CAS  Article  Google Scholar 

  36. 36.

    Zhou, W. et al. Efficiently controlling for case–control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).

    CAS  Article  Google Scholar 

  37. 37.

    Schumacher, F. R. et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat. Genet. 50, 928–936 (2018).

    CAS  Article  Google Scholar 

  38. 38.

    Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).

    Article  Google Scholar 

  39. 39.

    Nielsen, J. B. et al. Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat. Genet. 50, 1234–1239 (2018).

    CAS  Article  Google Scholar 

  40. 40.

    Vilhjalmsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576–592 (2015).

    CAS  Article  Google Scholar 

  41. 41.

    May, S. & Hosmer, D. W. A simplified method of calculating an overall goodness-of-fit test for the Cox proportional hazards model. Lifetime Data Anal. 4, 109–120 (1998).

    CAS  Article  Google Scholar 

  42. 42.

    Royston, P. & Parmar, M. K. Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome. BMC Med. Res. Methodol. 13, 152 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

We thank S. Kivikko, H.-Y. Shen and U. Tuomainen for management assistance. The FINRISK analyses were conducted using the THL biobank permission for project BB2015_55.1. The FINRISK data used for the research were obtained from THL Biobank. For the Finnish Institute of Health and Welfare (THL)-driven FinnGen preparatory project (here called FinnGen), all patients and control subjects had provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, older cohorts were based on study-specific consents and later transferred to the THL Biobank after approval by Valvira, the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Valvira. The Ethics Review Board of the Hospital District of Helsinki and Uusimaa approved the FinnGen study protocol no. HUS/990/2017. The FinnGen preparatory project is approved by THL, approval number THL/2031/6.02.00/2017, amendments THL/341/6.02.00/2018, THL/2222/6.02.00/2018 and THL/283/6.02.00/2019. The following biobanks are acknowledged for collecting the FinnGen project samples: Auria Biobank (https://www.auria.fi/biopankki/en), THL Biobank (https://thl.fi/fi/web/thl-biopankki), Helsinki Biobank (https://www.terveyskyla.fi/helsinginbiopankki/en), Northern Finland Biobank Borealis (https://www.ppshp.fi/Tutkimus-ja-opetus/Biopankki), Finnish Clinical Biobank Tampere (https://www.tays.fi/en-US/Research_and_development/Finnish_Clinical_Biobank_Tampere), Biobank of Eastern Finland (https://ita-suomenbiopankki.fi/), Central Finland Biobank (https://www.ksshp.fi/fi-FI/Potilaalle/Biopankki), Finnish Red Cross Blood Service Biobank (https://www.bloodservice.fi/Research%20Projects/biobanking). We thank all study participants for their generous participation in FINRISK and FinnGen. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This work was supported by the Finnish Foundation for Cardiovascular Research (to S.R., V.S. and A.P.); Sigrid Jusélius Foundation (to S.R. and A.P.); University of Helsinki HiLIFE Fellow grants 2017–2020 (to S.R.); Academy of Finland Center of Excellence in Complex Disease Genetics (grant number 312062 to S.R., 312074 to A.P., 312075 to M.D.); Academy of Finland (grant number 285380 to S.R., 128650 to A.P.); The Finnish Innovation Fund Tekes (grant number 2273/31/2017 to E.W.); Horizon 2020 Research and Innovation Programme (grant number 667301 (COSYN) to A.P.); Ida Montin Foundation (to P.R.); Doctoral Programme in Population Health, University of Helsinki (to P.R.); and Emil Aaltonen Foundation (to P.R.). The FinnGen project is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and nine industry partners (AbbVie, AstraZeneca, Biogen, Celgene, Genentech, GSK, MSD, Pfizer and Sanofi). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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S.R. and N.M. conceived and designed the study. N.M. and P.R. carried out the statistical and computational analyses with advice from S.R., J.T.K., E.W., J.V.L., A.A.-O., M.D., V.S., B.M.N. and A.P. Quality control of the data was carried out by N.M., A.S.H., T.T.J.K., M.K., J.K. and P.P. The manuscript was written and revised by all of the co-authors. All co-authors have approved the final version of the manuscript.

Corresponding author

Correspondence to Samuli Ripatti.

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Competing interests

A.P. is a member of the Pfizer Genetics Scientific Advisory Panel. V.S. has participated in a conference trip sponsored by Novo Nordisk and received an honorarium for participating in an advisory board meeting (unrelated to the present study). V.S. also has research collaboration with Bayer Ltd (unrelated to the present study). B.M.N. is a member of the scientific advisory board at Deep Genomics and a consultant for Camp4 Therapeutics, Takeda Pharmaceutical and Biogen.

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Peer review information Kate Gao was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Investigating goodness-of-fit by polygenic risk score deciles in FinnGen (n = 135,300). Y-axis represents the expected and observed risks in each decile.

Goodness-of-fit test for the Cox proportional hazards model with R package survMisc, following the methodology in May & Hosmer (2004).41 Error bars for the expected counts represent the 95% confidence intervals.

Extended Data Fig. 2 With any breast cancer as the outcome in FinnGen (n = 135,300), adjusted survival curves for estrogen receptor-specific polygenic risk scores (PRS).

The PRS for any breast cancer showed high correlation with the estrogen receptor-positive PRS (Pearson correlation r = 0.93) and moderate correlation with estrogen receptor-negative PRS (r = 0.54).

Extended Data Fig. 3 Difference in age at disease onset estimates across polygenic risk score categories in FinnGen (n = 135,300).

CHD = coronary heart disease, AF = atrial fibrillation or flutter, T2D = type 2 diabetes. The estimands are restricted mean survival times (RMST) for age at onset, and the error bars represent their 95% confidence intervals. Incident and prevalent cases included. All tests were two-tailed.

Extended Data Fig. 4 Difference in age at disease onset estimates by sex, across polygenic risk score categories (FinnGen, n = 135,300).

CHD = coronary heart disease, AF = atrial fibrillation or flutter, T2D = type 2 diabetes. The estimands are restricted mean survival times (RMST) for age at onset, and the error bars represent their 95% confidence intervals. Incident and prevalent cases included. All tests were two-tailed.

Extended Data Fig. 5 Correlation between polygenic and clinical risk.

PRS = polygenic risk score, CHD = coronary heart disease, AF = atrial fibrillation or flutter, T2D = type 2 diabetes. Sample size in FINRISK (CHD, n = 20,165; AF, n = 21,030; T2D n = 10,561) and in FinnGen (breast cancer, n = 37,841; prostate cancer n = 48,851), using respective incident disease cases and controls. Correlation was assessed with Pearson correlation.

Extended Data Fig. 6 Adjusted survival curves in FINRISK, showing cumulative risk of incident disease in by polygenic risk score (PRS) categories.

The FINRISK cohorts (total n = 21,813) comprised of 2,197 incident cases of CHD, 1,431 cases of AF, 2,516 cases of T2D, 404 cases of breast cancer, and 444 cases of prostate cancer. Only incident cases included.

Extended Data Fig. 7 Manhattan plot (A) and quantile-quantile plot (B) for the genome-wide association study (n = 408,961 from UK Biobank) for PheCode 411 (Ischemic heart disease) by Zhou et al.

For the GWAS, they used a generalized mixed model implemented in SAIGE.36 These summary statistics were used for constructing our polygenic risk score for coronary heart disease36.

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Mars, N., Koskela, J.T., Ripatti, P. 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). https://doi.org/10.1038/s41591-020-0800-0

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