Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Principles and methods for transferring polygenic risk scores across global populations

Nature Reviews Genetics (2023)Cite this article

Subjects

Abstract

Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Complex genetic ancestries and admixture using data from UCLA-ATLAS.
Fig. 2: Genetic factors that can influence PRS performance.
Fig. 3: Interplay between social, environmental and genetic determinants of health.
Fig. 4: Considerations for the assessment of PRS clinical utility.

References

  1. Kullo, I. J. et al. Polygenic scores in biomedical research. Nat. Rev. Genet. 23, 524–532 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Abdellaoui, A., Yengo, L., Verweij, K. J. H. & Visscher, P. M. 15 years of GWAS discovery: realizing the promise. Am. J. Hum. Genet. 110, 179–194 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019). This paper demonstrates that PRSs have limited generalizability across populations and emphasizes the importance of diversity to realize the full and equitable potential of PRSs.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Fatumo, S. et al. A roadmap to increase diversity in genomic studies. Nat. Med. 28, 243–250 (2022). This paper presents an updated ancestry tabulation for participants in GWAS catalogue and discusses strategies for increasing diversity in genomic studies.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zhou, W. et al. Global Biobank Meta-analysis Initiative: powering genetic discovery across human disease. Cell Genom. 2, 100192 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wang, Y., Tsuo, K., Kanai, M., Neale, B. M. & Martin, A. R. Challenges and opportunities for developing more generalizable polygenic risk scores. Annu. Rev. Biomed. Data Sci. 5, 293–320 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lewis, C. M. & Vassos, E. Polygenic risk scores: from research tools to clinical instruments. Genome Med. 12, 44 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  8. 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 

  9. 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 

  10. 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 

  11. Duncan, L. et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat. Commun. 10, 3328 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 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 

  13. Mars, N. et al. Genome-wide risk prediction of common diseases across ancestries in one million people. Cell Genom. 2, 100118 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Privé, F. et al. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. Am. J. Hum. Genet. 109, 373 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Ding, Y. et al. Polygenic scoring accuracy varies across the genetic ancestry continuum. Nature 618, 774–781 (2023). This paper shows that the prediction accuracy of PRSs decreases from individual to individual along the continuum of genetic ancestries.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cavazos, T. B. & Witte, J. S. Inclusion of variants discovered from diverse populations improves polygenic risk score transferability. HGG Adv. 2, 100017 (2021).

    CAS  PubMed  Google Scholar 

  17. Wientjes, Y. C. J. et al. Empirical and deterministic accuracies of across-population genomic prediction. Genet. Sel. Evol. 47, 5 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Pszczola, M., Strabel, T., Mulder, H. A. & Calus, M. P. L. Reliability of direct genomic values for animals with different relationships within and to the reference population. J. Dairy. Sci. 95, 389–400 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Wientjes, Y. C. J., Veerkamp, R. F. & Calus, M. P. L. The effect of linkage disequilibrium and family relationships on the reliability of genomic prediction. Genetics 193, 621–631 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Habier, D., Fernando, R. L. & Dekkers, J. C. M. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 2389–2397 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Yang, J., Zeng, J., Goddard, M. E., Wray, N. R. & Visscher, P. M. Concepts, estimation and interpretation of SNP-based heritability. Nat. Genet. 49, 1304–1310 (2017).

    Article  CAS  PubMed  Google Scholar 

  22. Daetwyler, H. D., Villanueva, B. & Woolliams, J. A. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3, e3395 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wang, Y. et al. Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations. Nat. Commun. 11, 3865 (2020). This paper theoretically and empirically investigates the impact of various genetic factors on the transferability of PRSs across populations.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhang, Y., Qi, G., Park, J.-H. & Chatterjee, N. Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits. Nat. Genet. 50, 1318–1326 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Chatterjee, N. et al. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat. Genet. 45, 400–405, 405e1–405e3 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ge, T., Chen, C.-Y., Neale, B. M., Sabuncu, M. R. & Smoller, J. W. Phenome-wide heritability analysis of the UK Biobank. PLoS Genet. 13, e1006711 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Mostafavi, H. et al. Variable prediction accuracy of polygenic scores within an ancestry group. eLife 9, e48376 (2020). This paper demonstrates that the predictive accuracy of PRSs can depend on sample characteristics such as age, sex and socioeconomic status even within a group that has relatively homogeneous genetic ancestries.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Shi, H. et al. Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data. Am. J. Hum. Genet. 106, 805–817 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Shi, H. et al. Population-specific causal disease effect sizes in functionally important regions impacted by selection. Nat. Commun. 12, 1098 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Lam, M. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat. Genet. 51, 1670–1678 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Hou, K. et al. Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nat. Genet. 55, 549–558 (2023).

    Article  CAS  PubMed  Google Scholar 

  33. Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514–518 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Chen, M.-H. et al. Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations. Cell 182, 1198–1213.e14 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. International HapMap Consortium. The International HapMap Project. Nature 426, 789–796 (2003).

    Article  Google Scholar 

  36. 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  Google Scholar 

  37. Zuk, O., Hechter, E., Sunyaev, S. R. & Lander, E. S. The mystery of missing heritability: genetic interactions create phantom heritability. Proc. Natl Acad. Sci. USA 109, 1193–1198 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zaidi, A. A. & Mathieson, I. Demographic history mediates the effect of stratification on polygenic scores. eLife 9, e61548 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Saitou, M., Dahl, A., Wang, Q. & Liu, X. Allele frequency differences of causal variants have a major impact on low cross-ancestry portability of PRS. Preprint at bioRxiv https://doi.org/10.1101/2022.10.21.22281371 (2022).

  40. Bitarello, B. D. & Mathieson, I. Polygenic scores for height in admixed populations. G3 10, 4027–4036 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zhang, H. et al. Novel methods for multi-ancestry polygenic prediction and their evaluations in 5.1 million individuals of diverse ancestry. Preprint at bioRxiv https://doi.org/10.1101/2022.03.24.485519 (2022).

  42. Digitale, J. C., Martin, J. N. & Glymour, M. M. Tutorial on directed acyclic graphs. J. Clin. Epidemiol. 142, 264–267 (2022).

    Article  PubMed  Google Scholar 

  43. Lipsky, A. M. & Greenland, S. Causal directed acyclic graphs. J. Am. Med. Assoc. 327, 1083–1084 (2022).

    Article  Google Scholar 

  44. Aschard, H., Vilhjálmsson, B. J., Joshi, A. D., Price, A. L. & Kraft, P. Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am. J. Hum. Genet. 96, 329–339 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Tennant, P. W. G. et al. Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. Int. J. Epidemiol. 50, 620–632 (2021).

    Article  PubMed  Google Scholar 

  46. Socrates, A. et al. Investigating the effects of genetic risk of schizophrenia on behavioural traits. NPJ Schizophr. 7, 2 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Peyrot, W. J. et al. Effect of polygenic risk scores on depression in childhood trauma. Br. J. Psychiatry 205, 113–119 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Peyrot, W. J. et al. Does childhood trauma moderate polygenic risk for depression? A meta-analysis of 5765 subjects from the psychiatric genomics consortium. Biol. Psychiatry 84, 138–147 (2018).

    Article  PubMed  Google Scholar 

  49. Dorans, K. S., Mills, K. T., Liu, Y. & He, J. Trends in prevalence and control of hypertension according to the 2017 American College of Cardiology/American Heart Association (ACC/AHA) guideline. J. Am. Heart Assoc. 7, e008888 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Centers for Disease Control and Prevention. Chronic kidney disease in the United States. CDC https://www.cdc.gov/kidneydisease/publications-resources/ckd-national-facts.html (2021).

  51. Chu, C. D. et al. Trends in chronic kidney disease care in the US by race and ethnicity, 2012–2019. JAMA Netw. Open. 4, e2127014 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2022. CA Cancer J. Clin. 72, 7–33 (2022).

    Article  PubMed  Google Scholar 

  53. Zavala, V. A. et al. Cancer health disparities in racial/ethnic minorities in the United States. Br. J. Cancer 124, 315–332 (2021).

    Article  PubMed  Google Scholar 

  54. Marinac, C. R., Ghobrial, I. M., Birmann, B. M., Soiffer, J. & Rebbeck, T. R. Dissecting racial disparities in multiple myeloma. Blood Cancer J. 10, 19 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Daly, B. & Olopade, O. I. A perfect storm: how tumor biology, genomics, and health care delivery patterns collide to create a racial survival disparity in breast cancer and proposed interventions for change. CA Cancer J. Clin. 65, 221–238 (2015).

    Article  PubMed  Google Scholar 

  56. Carrot-Zhang, J. et al. Genetic ancestry contributes to somatic mutations in lung cancers from admixed Latin American populations. Cancer Discov. 11, 591–598 (2021).

    Article  CAS  PubMed  Google Scholar 

  57. Freedman, M. L. et al. Admixture mapping identifies 8q24 as a prostate cancer risk locus in African-American men. Proc. Natl Acad. Sci. USA 103, 14068–14073 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Fejerman, L. et al. Admixture mapping identifies a locus on 6q25 associated with breast cancer risk in US Latinas. Hum. Mol. Genet. 21, 1907–1917 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Gignoux, C. R. et al. An admixture mapping meta-analysis implicates genetic variation at 18q21 with asthma susceptibility in Latinos. J. Allergy Clin. Immunol. 143, 957–969 (2019).

    Article  CAS  PubMed  Google Scholar 

  60. Chi, C. et al. Admixture mapping reveals evidence of differential multiple sclerosis risk by genetic ancestry. PLoS Genet. 15, e1007808 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Tcheandjieu, C. et al. Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nat. Med. 28, 1679–1692 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Mahajan, A. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat. Genet. 54, 560–572 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Kelemen, M., Vigorito, E., Fachal, L., Anderson, C. A. & Wallace, C. ShaPRS: leveraging shared genetic effects across traits or ancestries improves accuracy of polygenic scores. Preprint at bioRxiv https://doi.org/10.1101/2021.12.10.21267272 (2021).

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

    Article  PubMed Central  Google Scholar 

  66. Choi, S. W. & O’Reilly, P. F. PRSice-2: polygenic risk score software for biobank-scale data. Gigascience 8, giz082 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Márquez-Luna, C., Loh, P.-R., South Asian Type 2 Diabetes (SAT2D) Consortium, SIGMA Type 2 Diabetes Consortium, & Price, A.L. Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet. Epidemiol. 41, 811–823 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Ruan, Y. et al. Improving polygenic prediction in ancestrally diverse populations. Nat. Genet. 54, 573–580 (2022). This paper introduces a Bayesian model that can integrate GWAS summary statistics from multiple populations to improve the predictive performance of PRSs across diverse populations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Weissbrod, O. et al. Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores. Nat. Genet. 54, 450–458 (2022). This paper leverages functionally informed fine-mapping to improve cross-population polygenic prediction.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Brown, B. C., Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye, C. J., Price, A. L. & Zaitlen, N. Transethnic genetic-correlation estimates from summary statistics. Am. J. Hum. Genet. 99, 76–88 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Coram, M. A., Fang, H., Candille, S. I., Assimes, T. L. & Tang, H. Leveraging multi-ethnic evidence for risk assessment of quantitative traits in minority populations. Am. J. Hum. Genet. 101, 638 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Cai, M. et al. A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits. Am. J. Hum. Genet. 108, 632–655 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Hoggart, C. et al. BridgePRS : a powerful trans-ancestry polygenic risk score method. Preprint at bioRxiv https://doi.org/10.1101/2023.02.17.528938 (2023).

  74. Tian, P. et al. Multiethnic polygenic risk prediction in diverse populations through transfer learning. Front. Genet. 13, 906965 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Mak, T. S. H., Porsch, R. M., Choi, S. W., Zhou, X. & Sham, P. C. Polygenic scores via penalized regression on summary statistics. Genet. Epidemiol. 41, 469–480 (2017).

    Article  PubMed  Google Scholar 

  76. Zhou, G., Chen, T. & Zhao, H. SDPRX: a statistical method for cross-population prediction of complex traits. Am. J. Hum. Genet. 110, 13–22 (2023).

    Article  CAS  PubMed  Google Scholar 

  77. Zhao, Z., Fritsche, L. G., Smith, J. A., Mukherjee, B. & Lee, S. The construction of cross-population polygenic risk scores using transfer learning. Am. J. Hum. Genet. 109, 1998–2008 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Zhang, J. et al. An ensemble penalized regression method for multi-ancestry polygenic risk prediction. Preprint at bioRxiv https://doi.org/10.1101/2023.03.15.532652 (2023).

  79. Jin, J. et al. ME-Bayes SL: enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups. Preprint at bioRxiv https://doi.org/10.1101/2023.04.12.536510 (2023).

  80. Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A. & Smoller, J. W. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat. Commun. 10, 1776 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Xiao, J. et al. XPXP: improving polygenic prediction by cross-population and cross-phenotype analysis. Bioinformatics 38, 1947–1955 (2022).

    Article  CAS  PubMed  Google Scholar 

  82. Miao, J. et al. Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics. Nat. Commun. 14, 832 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Amariuta, T. et al. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat. Genet. 52, 1346–1354 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Ge, T. et al. Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations. Genome Med. 14, 70 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Zhao, Z. et al. PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics. Genome Biol. 22, 257 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  86. 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 

  87. Lloyd-Jones, L. R. et al. Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nat. Commun. 10, 5086 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  88. 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 

  89. Oetjens, M. T., Kelly, M. A., Sturm, A. C., Martin, C. L. & Ledbetter, D. H. Quantifying the polygenic contribution to variable expressivity in eleven rare genetic disorders. Nat. Commun. 10, 4897 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Dornbos, P. et al. A combined polygenic score of 21,293 rare and 22 common variants improves diabetes diagnosis based on hemoglobin A1C levels. Nat. Genet. 54, 1609–1614 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Lali, R. et al. Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories. Nat. Commun. 12, 5852 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Chen, C.-Y. et al. The impact of rare protein coding genetic variation on adult cognitive function. Nat. Genet. 55, 927–938 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Fiziev, P. P. et al. Rare penetrant mutations confer severe risk of common diseases. Science 380, eabo1131 (2023).

    Article  CAS  PubMed  Google Scholar 

  94. Weiner, D. J. et al. Polygenic architecture of rare coding variation across 394,783 exomes. Nature 614, 492–499 (2023).

    Article  CAS  PubMed  Google Scholar 

  95. Atkinson, E. G. et al. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat. Genet. 53, 195–204 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Marnetto, D. et al. Ancestry deconvolution and partial polygenic score can improve susceptibility predictions in recently admixed individuals. Nat. Commun. 11, 1628 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Sun, Q. et al. Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-specific effects via GAUDI. Preprint at bioRxiv https://doi.org/10.1101/2022.10.06.511219 (2022).

  98. Pain, O. et al. Evaluation of polygenic prediction methodology within a reference-standardized framework. PLoS Genet. 17, e1009021 (2021). This study establishes a reference-standardized framework for fair comparison of PRS construction methods.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Wang, Y. et al. Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology. Preprint at bioRxiv https://doi.org/10.1101/2022.12.29.522270 (2023).

  100. Shen, J. et al. Fine-mapping and credible set construction using a multi-population joint analysis of marginal summary statistics from genome-wide association studies. Preprint at bioRxiv https://doi.org/10.1101/2022.12.22.521659 (2022).

  101. Yuan, K. et al. Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases. Preprint at medRxiv https://doi.org/10.1101/2023.01.07.23284293 (2023).

  102. Harrell, F. E. Jr, Califf, R. M., Pryor, D. B., Lee, K. L. & Rosati, R. A. Evaluating the yield of medical tests. J. Am. Med. Assoc. 247, 2543–2546 (1982).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  104. Ho, W.-K. et al. European polygenic risk score for prediction of breast cancer shows similar performance in Asian women. Nat. Commun. 11, 3833 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Shieh, Y. et al. A polygenic risk score for breast cancer in US Latinas and Latin American women. J. Natl Cancer Inst. 112, 590–598 (2020).

    Article  PubMed  Google Scholar 

  106. Du, Z. et al. Evaluating polygenic risk scores for breast cancer in women of African ancestry. J. Natl Cancer Inst. 113, 1168–1176 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  107. 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 

  108. 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 

  109. 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). This paper provides a comprehensive review of concepts and methods relevant for the development and evaluation of risk prediction models that incorporate genetic susceptibility factors.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Wang, M. et al. Validation of a genome-wide polygenic score for coronary artery disease in South Asians. J. Am. Coll. Cardiol. 76, 703–714 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Khera, A. V. et al. Whole-genome sequencing to characterize monogenic and polygenic contributions in patients hospitalized with early-onset myocardial infarction. Circulation 139, 1593–1602 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Khan, A. et al. Genome-wide polygenic score to predict chronic kidney disease across ancestries. Nat. Med. 28, 1412–1420 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Hurson, A. N. et al. Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries. Int. J. Epidemiol. 50, 1897–1911 (2022).

    Article  PubMed  Google Scholar 

  114. Leening, M. J. G., Vedder, M. M., Witteman, J. C. M., Pencina, M. J. & Steyerberg, E. W. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician’s guide. Ann. Intern. Med. 160, 122–131 (2014).

    Article  PubMed  Google Scholar 

  115. Kachuri, L. et al. Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction. Nat. Commun. 11, 6084 (2020). This paper quantifies the added predictive value of PRSs for 16 cancer types when added to models that contain extensive clinical and environmental risk factors.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Kerr, K. F. et al. Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology 25, 114–121 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Vickers, A. J. & Elkin, E. B. Decision curve analysis: a novel method for evaluating prediction models. Med. Decis. Mak. 26, 565–574 (2006).

    Article  Google Scholar 

  118. Pal Choudhury, 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  PubMed  Google Scholar 

  119. Pal Choudhury, P. et al. iCARE: an R package to build, validate and apply absolute risk models. PLoS ONE 15, e0228198 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. 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. 30, 339–348 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  121. Naret, O. et al. Improving polygenic prediction with genetically inferred ancestry. HGG Adv. 3, 100109 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Ding, Y. et al. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat. Genet. 54, 30–39 (2022). This paper estimates the variance of an individual’s PRS and highlights the importance of incorporating uncertainty into the interpretation of individual PRS estimates.

    Article  CAS  PubMed  Google Scholar 

  123. Chouldechova, A. & Roth, A. The frontiers of fairness in machine learning. Preprint at https://doi.org/10.48550/arXiv.1810.08810 (2018).

  124. Komiyama, J., Takeda, A., Honda, J. & Shimao, H. in Proc. 35th Int. Conf. Machine Learning Vol. 80 (eds Dy, J. & Krause, A.) 2737–2746 (PMLR, 2018).

  125. Agarwal, A., Dudik, M. & Wu, Z. S. in Proc. 36th Int. Conf. Machine Learning Vol. 97 (eds Chaudhuri, K. & Salakhutdinov, R.) 120–129 (PMLR, 2019).

  126. Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G. & Chin, M. H. Ensuring fairness in machine learning to advance health equity. Ann. Intern. Med. 169, 866–872 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  127. Kleinberg, J., Mullainathan, S. & Raghavan, M. Inherent trade-offs in the fair determination of risk scores. Preprint at https://doi.org/10.48550/arXiv.1609.05807 (2016).

  128. Oni-Orisan, A., Mavura, Y., Banda, Y., Thornton, T. A. & Sebro, R. Embracing genetic diversity to improve Black health. N. Engl. J. Med. 384, 1163–1167 (2021).

    Article  PubMed  Google Scholar 

  129. Lewis, A. C. F. et al. Getting genetic ancestry right for science and society. Science 376, 250–252 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Banda, Y. et al. Characterizing race/ethnicity and genetic ancestry for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) Cohort. Genetics 200, 1285–1295 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  131. Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294–305 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  132. Tillin, T. et al. Ethnicity and prediction of cardiovascular disease: performance of QRISK2 and Framingham scores in a U.K. tri-ethnic prospective cohort study (SABRE-Southall And Brent REvisited). Heart 100, 60–67 (2014).

    Article  PubMed  Google Scholar 

  133. Rodriguez, F. et al. Atherosclerotic cardiovascular disease risk prediction in disaggregated Asian and Hispanic subgroups using electronic health records. J. Am. Heart Assoc. 8, e011874 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  134. Aldrich, M. C. et al. Evaluation of USPSTF lung cancer screening guidelines among African american adult smokers. JAMA Oncol. 5, 1318–1324 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Pasquinelli, M. M. et al. Risk prediction model versus United States Preventive Services Task Force lung cancer screening eligibility criteria: reducing race disparities. J. Thorac. Oncol. 15, 1738–1747 (2020).

    Article  PubMed  Google Scholar 

  136. Mars, N. et al. Systematic comparison of family history and polygenic risk across 24 common diseases. Am. J. Hum. Genet. 109, 2152–2162 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Hujoel, M. L. A., Loh, P.-R., Neale, B. M. & Price, A. L. Incorporating family history of disease improves polygenic risk scores in diverse populations. Cell Genom. 2, 100152 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. 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 

  139. Pal Choudhury, P. et al. Comparative validation of the BOADICEA and Tyrer–Cuzick breast cancer risk models incorporating classical risk factors and polygenic risk in a population-based prospective cohort of women of European ancestry. Breast Cancer Res. 23, 22 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Lee, A. et al. Comprehensive epithelial tubo-ovarian cancer risk prediction model incorporating genetic and epidemiological risk factors. J. Med. Genet. 59, 632–643 (2022).

    Article  CAS  PubMed  Google Scholar 

  141. Riveros-Mckay, F. et al. Integrated polygenic tool substantially enhances coronary artery disease prediction. Circ. Genom. Precis. Med. 14, e003304 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. NIH. The ‘All of Us’ Research Program. N. Engl. J. Med. 381, 668–676 (2019).

    Article  Google Scholar 

  143. Choudhury, A. et al. High-depth African genomes inform human migration and health. Nature 586, 741–748 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Pereira, L., Mutesa, L., Tindana, P. & Ramsay, M. African genetic diversity and adaptation inform a precision medicine agenda. Nat. Rev. Genet. 22, 284–306 (2021).

    Article  CAS  PubMed  Google Scholar 

  145. Chapman, C. R. Ethical, legal, and social implications of genetic risk prediction for multifactorial disease: a narrative review identifying concerns about interpretation and use of polygenic scores. J. Community Genet. https://doi.org/10.1007/s12687-022-00625-9 (2022).

  146. Lemke, A. A. et al. Addressing underrepresentation in genomics research through community engagement. Am. J. Hum. Genet. 109, 1563–1571 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Wojcik, G. L. et al. Opportunities and challenges for the use of common controls in sequencing studies. Nat. Rev. Genet. 23, 665–679 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Bien, S. A. et al. Strategies for enriching variant coverage in candidate disease loci on a multiethnic genotyping array. PLoS ONE 11, e0167758 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  150. Kim, M. S., Patel, K. P., Teng, A. K., Berens, A. J. & Lachance, J. Genetic disease risks can be misestimated across global populations. Genome Biol. 19, 179 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  151. Martin, A. R. et al. Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations. Am. J. Hum. Genet. 108, 656–668 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Emde, A.-K. et al. Mid-pass whole genome sequencing enables biomedical genetic studies of diverse populations. BMC Genomics 22, 666 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Kim, M. S. et al. Testing the generalizability of ancestry-specific polygenic risk scores to predict prostate cancer in sub-Saharan Africa. Genome Biol. 23, 194 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  154. Borrell, L. N. et al. Race and genetic ancestry in medicine—a time for reckoning with racism. N. Engl. J. Med. 384, 474–480 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  155. Reales, G. & Wallace, C. Sharing GWAS summary statistics results in more citations. Commun. Biol. 6, 116 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  156. Wand, H. et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature 591, 211–219 (2021). This paper outlines a framework for systematic reporting of methods and results from PRS studies that is necessary to build a high-quality evidence base for informing PRS translational efforts.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. 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 

  158. Wang, Y. et al. Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts. Cell Genom. 3, 100241 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Linder, J. E. et al. Returning integrated genomic risk and clinical recommendations: the eMERGE study. Genet. Med. 25, 100006 (2023). This paper describes the ongoing prospective eMERGE study that returns integrated genetic risk assessment including monogenic risks, PRSs and family history to high-risk individuals for 11 conditions.

    Article  CAS  PubMed  Google Scholar 

  160. Lennon, N. J. et al. Selection, optimization, and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse populations. Preprint at bioRxiv https://doi.org/10.1101/2023.05.25.23290535 (2023).

  161. Mathieson, I. & Scally, A. What is ancestry? PLoS Genet. 16, e1008624 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  PubMed  Google Scholar 

  164. Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  165. Maples, B. K., Gravel, S., Kenny, E. E. & Bustamante, C. D. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. Am. J. Hum. Genet. 93, 278–288 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Li, N. & Stephens, M. Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics 165, 2213–2233 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Browning, S. R., Waples, R. K. & Browning, B. L. Fast, accurate local ancestry inference with FLARE. Am. J. Hum. Genet. 110, 326–335 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Price, A. L. et al. Sensitive detection of chromosomal segments of distinct ancestry in admixed populations. PLoS Genet. 5, e1000519 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  169. Salter-Townshend, M. & Myers, S. Fine-scale inference of ancestry segments without prior knowledge of admixing groups. Genetics 212, 869–889 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This Review was supported by the National Institutes of Health (NIH) for the Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium, with grant funding for the Coordinating Center (U01HG011697) and the study sites PREVENT (U01HG011710), CAPE (U01HG011715), CARDINAL (U01HG011717), FFAIRR-PRS (U01HG011719), EPIC-PRS (U01HG011720), D-PRISM (U01HG011723) and PRIMED-Cancer (U01CA261339). Additional funding was received from the NIH: R00CA246076 (to L.K.), R01HG010480 and U01CA249866 (to N.C.), R35GM140487 (to D.J.S.), R01CA241410 (to J.S.W.) and R01HG012354 (to T.G.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The authors thank Y. Ding and H. Zhang for their help with creating the figures in this Review.

Author information

Authors and Affiliations

  1. Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA

    Linda Kachuri & John S. Witte

  2. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

    Linda Kachuri & John S. Witte

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

    Nilanjan Chatterjee

  4. Department of Medicine Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Jibril Hirbo

  5. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA

    Jibril Hirbo

  6. Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA

    Daniel J. Schaid

  7. Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA

    Iman Martin

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

    Iftikhar J. Kullo

  9. Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eimear E. Kenny

  10. Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eimear E. Kenny

  11. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eimear E. Kenny

  12. Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Bogdan Pasaniuc

  13. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Bogdan Pasaniuc

  14. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Bogdan Pasaniuc

  15. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

    John S. Witte

  16. Department of Genetics, Stanford University, Stanford, CA, USA

    John S. Witte

  17. Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Tian Ge

  18. Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Tian Ge

  19. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Ying Wang & Tian Ge

  20. Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA

    Paul L. Auer

  21. Department of Biostatistics, University of Washington, Seattle, WA, USA

    Matthew P. Conomos

  22. Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    David V. Conti

  23. Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    David V. Conti

  24. Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA

    Yi Ding

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

    Ying Wang

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

    Ying Wang

  27. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Haoyu Zhang

  28. Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA

    Haoyu Zhang

  29. Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA

    Yuji Zhang

Authors
  1. Linda Kachuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nilanjan Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jibril Hirbo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel J. Schaid
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Iman Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Iftikhar J. Kullo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Eimear E. Kenny
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Bogdan Pasaniuc
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. John S. Witte
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Tian Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium Methods Working Group

  • Paul L. Auer
  • , Nilanjan Chatterjee
  • , Matthew P. Conomos
  • , David V. Conti
  • , Yi Ding
  • , Tian Ge
  • , Jibril Hirbo
  • , Linda Kachuri
  • , Eimear E. Kenny
  • , Iftikhar J. Kullo
  • , Iman Martin
  • , Bogdan Pasaniuc
  • , Daniel J. Schaid
  • , Ying Wang
  • , John S. Witte
  • , Haoyu Zhang
  •  & Yuji Zhang

Contributions

L.K., B.P., J.S.W. and T.G. conceptualized the Review. L.K., N.C., J.H. and T.G. drafted the manuscript with input from D.J.S., I.M., I.J.K., E.E.K., B.P. and J.S.W. All authors contributed to the literature search, synthesis and interpretation of findings, and reviewed and/or edited the manuscript.

Corresponding authors

Correspondence to John S. Witte or Tian Ge.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Genetics thanks Michael Inouye and the other, anonymous, reviewer for their contribution to the peer review of this work.

Additional information

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

Related links

BridgePRS: https://github.com/clivehoggart/BridgePRS

CT-SLEB: https://github.com/andrewhaoyu/CTSLEB

ME-Bayes SL: https://github.com/Jin93/MEBayesSL

PolyPred-S+/PolyPred-P+: https://github.com/omerwe/polyfun

PROSPER: https://github.com/Jingning-Zhang/PROSPER

PRS-CSx(-auto): https://github.com/getian107/PRScsx

SDPRX: https://github.com/eldronzhou/SDPRX

ShaPRS: https://github.com/mkelcb/shaprs

TL-Multi: https://github.com/mxxptian/TLMulti

TL-PRS/MTL-PRS: https://github.com/ZhangchenZhao/TLPRS

X-Wing: https://github.com/qlu-lab/X-Wing

XP-BLUP: https://github.com/tanglab/XP-BLUP

XPASS( + ): https://github.com/YangLabHKUST/XPASS

XPXP: https://github.com/YangLabHKUST/XPXP

Glossary

Absolute risk

The probability that a person or group of individuals who are free of a certain disease at a given point in time will develop that disease over a certain time period. Absolute risks are typically expressed as proportions from 0 to 100%.

Admixture

The process by which two or more previously separated populations come into contact, often through migration, generating a descendant population with a mixed mosaic of genetic material.

Admixture mapping

An approach that consists of inferring local genetic ancestry and testing for association between local ancestry segments derived from different ancestral populations and the phenotype.

Area under the receiver operating characteristic curve

(AUC). The ability of a model to discriminate between diseased and disease-free individuals is calculated as the AUC, which compares the true positive rate (sensitivity) with the false positive rate (1 – specificity). An AUC of 0.50 indicates that the classification accuracy of a model is equal to chance; an AUC of 1.0 indicates perfect discrimination.

Clumping

A procedure that iteratively selects the variant with the lowest P-value within a specified window from genome-wide association study (GWAS) results and removes nearby variants that are correlated with the selected variants above a specific linkage disequilibrium (LD) threshold.

Genetic architecture

The genetic basis of a trait described by the number, frequency and magnitude of effect size of genetic variants contributing to its heritability.

Genetic correlation

The correlation between the genetic influences on two traits, or the proportion of variance that two traits share due to genetics.

Haplotype

A cluster of polymorphisms or alleles that typically reside near each other on a chromosome and tend to be inherited together.

Linkage disequilibrium

(LD). Non-random association of alleles at different genetic loci, often measured as the square of the correlation coefficient between two alleles. LD is, on average, lower in African populations compared with European and Asian populations.

Meta-analysis

Statistical analysis that combines results from multiple studies.

Net reclassification indices

Metrics that measure the extent to which a new model improves classification as compared with an old model, calculated as the difference between the proportion of individuals who are correctly reclassified and the proportion of individuals who are incorrectly reclassified.

P-value thresholding

A procedure that selects the genetic variants whose P-value is below a threshold in a genome-wide association study (GWAS).

Polygenic risk scores

(PRSs; also known as genetic risk scores). Single values that quantify an individual’s genetic predisposition to a discrete health outcome, calculated as a sum of alleles weighted by effect sizes corresponding to a relative magnitude of association.

Polygenic scores

Single values that quantify an individual’s genetic predisposition calculated as a sum of trait-associated alleles weighted by their additive, per-allele effect sizes, typically derived from genome-wide association studies (GWAS).

Population structure

The presence of multiple genetically distinct subpopulations that differ in their allele frequencies and mean phenotypic values. Not accounting for this structure can lead to spurious associations in genome-wide association studies (GWAS) and polygenic risk score (PRS) analyses.

Relative risk

The probability that a certain health outcome will occur in a person or group of individuals relative to the probability that this event will occur in a reference population. Relative risks are typically expressed as ratios, with 1.0 indicating no difference between the comparison groups.

Risk stratification

The process of classifying and ordering individuals according to their specific risk estimates.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kachuri, L., Chatterjee, N., Hirbo, J. et al. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet (2023). https://doi.org/10.1038/s41576-023-00637-2

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41576-023-00637-2

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing