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Rank concordance of polygenic indices

Nature Human Behaviour volume 7, pages 802–811 (2023)Cite this article

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Abstract

Polygenic indices (PGIs) are increasingly used to identify individuals at risk of developing disease and are advocated as screening tools for personalized medicine and education. Here we empirically assess rank concordance between PGIs created with different construction methods and discovery samples, focusing on cardiovascular disease and educational attainment. We find Spearman rank correlations between 0.17 and 0.93 for cardiovascular disease, and 0.40 and 0.83 for educational attainment, indicating highly unstable rankings across different PGIs for the same trait. Potential consequences for personalized medicine and gene–environment (G × E) interplay are illustrated using data from the UK Biobank. Simulations show how rank discordance mainly derives from a limited discovery sample size and reveal a tight link between the explained variance of a PGI and its ranking precision. We conclude that PGI-based ranking is highly dependent on PGI choice, such that current PGIs do not have the desired precision to be used routinely for personalized intervention.

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Fig. 1: Concordance across six PGIs for EA (N = 39,296).
Fig. 2: Concordance across six PGIs for CVD (N = 39,296).
Fig. 3: Venn diagram depicting the overlap in individuals ranked in the top quintiles of five CVD PGIs (N = 4,061).
Fig. 4: Results of ordinary least squares regressions explaining years of education by the EA PGI, year of birth (YoB) and the interaction between the EA PGI and YoB in the subsample of siblings of the UKB (N = 38,049). The figure visualizes the regression coefficient of the interaction term between the EA PGI and YoB.
Fig. 5: Rank concordance in deciles of the PGI distributions for varying levels of the genetic correlation between the samples (rows) and of the explained fraction of the SNP-based heritability (columns).
Fig. 6: The predictive performance of a PGI as function of the explained fraction of the SNP-based heritability and the genetic correlation between the discovery samples and the prediction sample.

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Data availability

Individual-level genotype and phenotype data are available by application via the UKB website (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access). The genome-wide summary statistics from 23andMe can be obtained by completing the 23andMe publication dataset access request form at https://research.23andme.com/dataset-access/. The genome wide summary statistics from CARDIoGRAM are available at http://www.cardiogramplusc4d.org/data-downloads/, file ‘CARDIoGRAMplusC4D 1000 Genomes-based GWAS - Additive’. The authors declare that the results supporting the findings of this study are available within the paper and its supplementary information files.

Code availability

Code for analyses and figures is available in a GitHub repository at https://github.com/DilnozaM/Rank-Concordance-of-PGI.

References

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Chabris, C. F., Lee, J. J., Cesarini, D., Benjamin, D. J. & Laibson, D. I. The fourth law of behavior genetics. Curr. Dir. Psychol. Sci. 24, 304–312 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet 9, 1003348 (2013).

    Article  Google Scholar 

  4. The International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nat. Lett. 460, 748–752 (2009).

    Article  Google Scholar 

  5. 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 

  6. 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 

  7. 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 

  8. Kumar, A. et al. Whole-genome risk prediction of common diseases in human preimplantation embryos. Nat. Med. 28, 513–516 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Turley, P. et al. Problems with using polygenic scores to select embryos. N. Engl. J. Med. 385, 79–85 (2021).

    Article  Google Scholar 

  10. Johnston, J. & Matthews, L. J. Polygenic embryo testing: understated ethics, unclear utility. Nat. Med. 28, 446–448 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Von Stumm, S. & Plomin, R. Using DNA to predict intelligence. Intelligence 86, 101530 (2021).

    Article  Google Scholar 

  12. Shero, J. et al. The practical utility of genetic screening in school settings. NPJ Sci. Learn. 6, 1–10 (2021).

    Article  Google Scholar 

  13. Biroli, P. et al. The economics and econometrics of gene-environment interplay. arXiv https://doi.org/10.48550/arXiv.2203.00729 (2022).

  14. Pereira, R. D., van Kippersluis, H. & Rietveld, C. A. The interplay between maternal smoking and genes in offspring birth weight. J. Hum. Resour. https://doi.org/10.1101/2020.10.30.20222844 (2022).

    Article  Google Scholar 

  15. Barcellos, S. H., Carvalho, L. S. & Turley, P. Education can reduce health differences related to genetic risk of obesity. Proc. Natl Acad. Sci. USA 115, E9765–E9772 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Slob, E. A. W. & Rietveld, C. A. Genetic predispositions moderate the effectiveness of tobacco excise taxes. PLoS ONE 16, e0259210 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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 

  18. 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 

  19. Becker, J. et al. Resource profile and user guide of the Polygenic Index Repository. Nat. Hum. Behav. 5, 1744–1758 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Schultz, L. M. et al. Stability of polygenic scores across discovery genome-wide association studies. HGG Advances. 3, 100091 (2022)

  21. Mills, M. C., Barban, N. & Tropf, F. C. An Introduction to Statistical Genetic Data Analysis (Cambridge MIT Press, 2020).

  22. Aragam, K. G. et al. Limitations of contemporary guidelines for managing patients at high genetic risk of coronary artery disease. J. Am. Coll. Cardiol. 75, 2769–2780 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 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 

  24. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Nikpay, M. et al. A comprehensive 1,000 genomes-based genome-wide association meta-analysis of coronary artery disease. Nat. Genet. 47, 1121–1130 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 1–16 (2015).

    Article  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ni, G. et al. A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts. Biol. Psychiatry. 90, 611–620 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Conley, D., Laidley, T. M., Boardman, J. D. & Domingue, B. W. Changing polygenic penetrance on phenotypes in the 20th century among adults in the US population. Sci. Rep. 6, 6–10 (2016).

    Article  Google Scholar 

  32. Yengo, L., Yang, J. & Visscher, P. M. Expectation of the intercept from bivariate LD Score regression in the presence of population stratification. Preprint at bioRxiv https://doi.org/10.1101/310565 (2018).

  33. Adhyaru, B. B. & Jacobson, T. A. Safety and efficacy of statin therapy. Nat. Rev. Cardiol. 15, 757–769 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. 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: executive summary. J. Am. Coll. Cardiol. 73, 3168–3209 (2019).

    Article  PubMed  Google Scholar 

  35. Goff, D. C. 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. J. Am. Coll. Cardiol. 63, 2935–2959 (2014).

    Article  PubMed  Google Scholar 

  36. Van Kippersluis, H. et al. Overcoming attenuation bias in regressions using polygenic indices: a comparison of approaches. Preprint at bioRxiv https://doi.org/10.1101/2021.04.09.439157 (2022).

  37. Witte, J. S., Visscher, P. M. & Wray, N. R. The contribution of genetic variants to disease depends on the ruler. Nat. Rev. Genet. 5, 765–776 (2014).

    Article  Google Scholar 

  38. De Vlaming, R. Genetic-nuture and assortative-mating-effects simulator. GitHub https://github.com/devlaming/gnames (2022).

  39. Bulik-Sullivan, B. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. De Vlaming, R. et al. Meta-GWAS Accuracy and Power (MetaGAP) calculator shows that hiding heritability is partially due to imperfect genetic correlations across studies. PLoS Genet 13, e1006495 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Choi, S. W., Mak, T. S.-H. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15, 2759–2772 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ware, E. B. et al. Heterogeneity in polygenic scores for common human traits. Preprint at bioRxiv https://doi.org/10.1101/106062 (2017).

  43. Pain, O. et al. Evaluation of polygenic prediction methodology within a reference-standardized framework. PLoS Genet. 17, 1–22 (2021).

    Article  Google Scholar 

  44. Clifton, L., Collister, J. A., Liu, X., Littlejohn, T. J. & Hunter, D. J. Assessing agreement between different polygenic risk scores in the UK Biobank. Sci. Rep. 12, 12812 (2022).

  45. Sun, J. et al. Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction. Nat. Commun. 12, 1–9 (2021).

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  47. Glasziou, P. P., Irwig, L., Heritier, S., Simes, R. J. & Tonkin, A. Monitoring cholesterol levels: measurement error or true change? Ann. Intern. Med. 148, 656–661 (2008).

    Article  PubMed  Google Scholar 

  48. Jiang, L. et al. A resource-efficient tool for mixed model association analysis of large-scale data. Nat. Genet. 51, 1749–1755 (2019).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

UK Biobank has obtained ethical approval from the National Research Ethics Committee (11/NW/0382). This research has been conducted using the UK Biobank Resource under application number 41382. The authors gratefully acknowledge funding from NORFACE through the Dynamic of Inequality across the Life Course (DIAL) programme (GEIGHEI 462-16-100). Research reported in this publication was also supported by the National Institute on Aging of the National Institutes of Health under Award R56AG058726. S.F.W.M. gratefully acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement (GENIO 101019584). C.A.R. and S.v.H. gratefully acknowledge funding from the European Research Council (GEPSI 946647; DONNI 851725). We are grateful for A. Okbay and employees and research participants of the 23andMe, Inc. cohort for sharing GWAS summary statistics for EA, and we thank P. Biroli, T. Galama, E. Slob and R. de Vlaming for insightful comments. This work made use of the Dutch national e-infrastructure with the support of the SURF Cooperative using grant EINF-1107. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands

    Dilnoza Muslimova, Rita Dias Pereira, Hans van Kippersluis, Cornelius A. Rietveld & S. Fleur W. Meddens

  2. Tinbergen Institute, Amsterdam, the Netherlands

    Dilnoza Muslimova, Rita Dias Pereira, Stephanie von Hinke, Hans van Kippersluis & Cornelius A. Rietveld

  3. School of Economics, University of Bristol, Bristol, UK

    Stephanie von Hinke

  4. Erasmus University Rotterdam Institute for Behaviour and Biology, Erasmus University Rotterdam, Rotterdam, the Netherlands

    Cornelius A. Rietveld

  5. Statistics Netherlands, The Hague, the Netherlands

    S. Fleur W. Meddens

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Contributions

All authors designed and oversaw the study. S.F.W.M. and D.M. conducted the GWAS in UKB and the meta-analyses with other GWAS summary statistics, constructed the PGIs and prepared the illustrative applications. R.D.P. performed the G × E analyses. H.v.K. and C.A.R. conducted the simulations. C.A.R. and S.v.H. assisted with the empirical analyses. All authors contributed to preparing and critically reviewing the manuscript and the supplementary file.

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Correspondence to Dilnoza Muslimova.

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Muslimova, D., Dias Pereira, R., von Hinke, S. et al. Rank concordance of polygenic indices. Nat Hum Behav 7, 802–811 (2023). https://doi.org/10.1038/s41562-023-01544-6

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