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Principles and methods for transferring polygenic risk scores across global populations

Nature Reviews Genetics volume 25, pages 8–25 (2024)Cite this article

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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.

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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.

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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
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  2. Nilanjan Chatterjee
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  3. Jibril Hirbo
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  4. Daniel J. Schaid
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  5. Iman Martin
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  6. Iftikhar J. Kullo
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  7. Eimear E. Kenny
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  8. Bogdan Pasaniuc
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  9. John S. Witte
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  10. Tian Ge
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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.

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Kachuri, L., Chatterjee, N., Hirbo, J. et al. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet 25, 8–25 (2024). https://doi.org/10.1038/s41576-023-00637-2

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