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Improving polygenic prediction in ancestrally diverse populations

Nature Genetics volume 54, pages 573–580 (2022)Cite this article

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An Author Correction to this article was published on 04 July 2022

This article has been updated

Abstract

Polygenic risk scores (PRS) have attenuated cross-population predictive performance. As existing genome-wide association studies (GWAS) have been conducted predominantly in individuals of European descent, the limited transferability of PRS reduces their clinical value in non-European populations, and may exacerbate healthcare disparities. Recent efforts to level ancestry imbalance in genomic research have expanded the scale of non-European GWAS, although most remain underpowered. Here, we present a new PRS construction method, PRS-CSx, which improves cross-population polygenic prediction by integrating GWAS summary statistics from multiple populations. PRS-CSx couples genetic effects across populations via a shared continuous shrinkage (CS) prior, enabling more accurate effect size estimation by sharing information between summary statistics and leveraging linkage disequilibrium diversity across discovery samples, while inheriting computational efficiency and robustness from PRS-CS. We show that PRS-CSx outperforms alternative methods across traits with a wide range of genetic architectures, cross-population genetic overlaps and discovery GWAS sample sizes in simulations, and improves the prediction of quantitative traits and schizophrenia risk in non-European populations.

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Fig. 1: Overview of polygenic prediction methods.
Fig. 2: Prediction accuracy of single-discovery and multi-discovery polygenic prediction methods in simulations.
Fig. 3: Relative prediction accuracy for quantitative traits in each target population.
Fig. 4: Prediction accuracy of schizophrenia risk in EAS cohorts.

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Data availability

Publicly available data are available from the following sites: 1KG Phase 3 reference panels: https://mathgen.stats.ox.ac.uk/impute/1000GP_Phase3.html; Genetic map for each subpopulation: ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130507_omni_recombination_rates; UKBB summary statistics: http://www.nealelab.is/uk-biobank (‘GWAS round 2’ was used in this study); BBJ summary statistics were downloaded from PheWeb: https://pheweb.jp; PAGE summary statistics were downloaded from the GWAS Catalog: https://www.ebi.ac.uk/gwas/downloads/summary-statistics; PGC wave 2 schizophrenia GWAS (49 EUR cohorts): https://www.med.unc.edu/pgc/download-results/; leave-one-out schizophrenia EAS summary statistics are available upon request to the Schizophrenia Working Group of the PGC (https://www.med.unc.edu/pgc/pgc-workgroups/schizophrenia/). These leave-one-out summary statistics are under controlled access per the data use limitation imposed by compliance, participant consent and/or national laws. Application to access such data requires a short research proposal that will go through review and approval process of the PGC. This process takes 2 weeks. Individual-level schizophrenia data of East Asian ancestry are available upon application to the Stanley Global Asia Initiatives: SGAI@broadinstitute.org. These data must be under controlled access due to the data use limitation imposed by the compliance, participant consent and national laws. Application to access such data requires a short research proposal that will be reviewed by principal investigator of the constituent study and, if necessary, by the respective ethic committee. The principal investigator review process takes 2 weeks. TWB data used in this study contain protected health information and are thus under controlled access. Application to access such data can be made to the TWB (https://www.twbiobank.org.tw/new_web_en/). Posterior SNP effect size estimates generated by PRS-CSx for the traits examined in this work: https://github.com/getian107/PRScsx.

Code availability

The code used in this study is available from the following websites: PRS-CSx: https://github.com/getian107/PRScsx (https://doi.org/10.5281/zenodo.5893746); PRS-CS: https://github.com/getian107/PRScs (https://doi.org/10.5281/zenodo.5893748); LDpred2: https://privefl.github.io/bigsnpr/articles/LDpred2; PRSice-2: https://www.prsice.info; HAPGEN2: https://mathgen.stats.ox.ac.uk/genetics_software/hapgen/hapgen2.html; PLINK 1.9: https://www.cog-genomics.org/plink; PLINK 2.0: https://www.cog-genomics.org/plink/2.0/; LD score regression: https://github.com/bulik/ldsc; POPCORN: https://github.com/brielin/Popcorn; Interpolation of genetic maps: https://github.com/joepickrell/1000-genomes-genetic-maps; Population assignment: https://github.com/Annefeng/PBK-QC-pipeline.

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Acknowledgements

We thank B. Neale, M. Daly, R. Do and A. Bloemendal for helpful discussions. We thank the Neale Laboratory and BBJ for releasing the genome-wide association summary statistics from UKBB and BBJ. Individual-level phenotypes and genotypes for UKBB samples were obtained under application 32568. We thank the Schizophrenia Working Group of the PGC for providing the GWAS summary statistics for schizophrenia. T.G. is supported by National Institute on Aging (NIA) K99/R00AG054573, National Human Genome Research Institute (NHGRI) U01HG008685 and NHGRI U01HG011723. H.H. acknowledges supports from National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) K01DK114379, National Institute of Mental Health (NIMH) U01MH109539, Brain and Behavior Research Foundation Young Investigator Grant (28450), the Zhengxu and Ying He Foundation, and the Stanley Center for Psychiatric Research. L.H. and S.Q. are supported by Shanghai Municipal Science and Technology Major Project (2017SHZDZX01). A.R.M. is supported by NIMH K99/R00MH117229. A.S. is supported by NIMH P50MH094268. Y.A.F. is supported by the ‘National Taiwan University Higher Education Sprout Project (NTU-110L8810)’ within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. Y.F.L. is supported by the National Health Research Institutes (NP-109-PP-09), and the Ministry of Science and Technology (109-2314-B-400-017) of Taiwan.

Author information

Author notes

  1. These authors jointly supervised this work: Shengying Qin, Hailiang Huang, Tian Ge.

Authors and Affiliations

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

    Yunfeng Ruan, Yen-Chen Anne Feng, Max Lam, Zhenglin Guo, Ruize Liu, Alice Zheng, Alicia R. Martin, Hailiang Huang & Tian Ge

  2. Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China

    Yunfeng Ruan, Lin He & Shengying Qin

  3. Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan

    Yen-Feng Lin

  4. Department of Public Health and Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Yen-Feng Lin

  5. Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan

    Yen-Feng Lin

  6. Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Yen-Chen Anne Feng & Tian Ge

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

    Yen-Chen Anne Feng & Tian Ge

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

    Yen-Chen Anne Feng, Max Lam, Ruize Liu, Alicia R. Martin & Hailiang Huang

  9. Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan

    Yen-Chen Anne Feng

  10. Master of Public Health Program, National Taiwan University, Taipei, Taiwan

    Yen-Chen Anne Feng

  11. Biogen, Cambridge, MA, USA

    Chia-Yen Chen

  12. Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA

    Max Lam

  13. Research Division, Institute of Mental Health Singapore, Singapore, Singapore

    Max Lam

  14. Human Genetics, Genome Institute of Singapore, Singapore, Singapore

    Max Lam

  15. Departments of Psychiatry, Neuroscience, Biomedical Engineering, Genetic Medicine, and Mental Health, Johns Hopkins University School of Medicine and Bloomberg School of Public Health, Baltimore, MD, USA

    Akira Sawa

  16. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Alicia R. Martin & Hailiang Huang

  17. Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Tian Ge

  18. Department of Psychiatry, Seoul National University Hospital, Seoul, Korea

    Yong Min Ahn, Kyooseob Ha & Se Hyun Kim

  19. Department of Biological Psychiatry and Neuroscience, Dokkyo Medical University School of Medicine, Mibu, Japan

    Kazufumi Akiyama

  20. Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan

    Makoto Arai, Yasue Horiuchi & Masanari Itokawa

  21. Department of Psychiatry, Sungkyunkwan University, Samsung Medical Center, Seoul, Korea

    Ji Hyun Baek & Kyung Sue Hong

  22. Department of Psychiatry, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan

    Wei J. Chen

  23. Department of Psychiatry, Chonbuk National University Medical School, Jeonbuk, Korea

    Young-Chul Chung

  24. Digital China Health Technologies Corp. Ltd., Beijing, China

    Gang Feng & Wenzhao Shi

  25. Department of Psychiatry, Shiga University of Medical Science, Shiga, Japan

    Kumiko Fujii & Yuji Ozeki

  26. Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, NY, USA

    Stephen J. Glatt

  27. Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA

    Stephen J. Glatt

  28. National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan

    Kotaro Hattori, Sayuri Ishiwata & Hiroshi Kunugi

  29. National Center of Neurology and Psychiatry, Tokyo, Japan

    Teruhiko Higuchi

  30. Department of Psychiatry, Kobe University Graduate School of Medicine, Kobe, Japan

    Akitoyo Hishimoto, Ikuo Otsuka & Ichiro Sora

  31. Department of Psychiatry, National Taiwan University, Taipei, Taiwan

    Hai-Gwo Hwu

  32. Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan

    Masashi Ikeda & Nakao Iwata

  33. Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon, Korea

    Eun-Jeong Joo

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

    Rene S. Kahn

  35. Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea

    Sung-Wan Kim

  36. Department of Psychiatry, Yonsei University College of Medicine, Seoul, Korea

    Se Joo Kim

  37. Department of Psychiatry, Institute of Biomedical Sciences, Tokushima University Graduate School, Tokushima, Japan

    Makoto Kinoshita, Shusuke Numata & Tetsuro Ohmori

  38. Department of Psychiatry, University of Indonesia, Jakarta, Indonesia

    Agung Kusumawardhani

  39. Institute of Mental Health, Singapore, Singapore

    Jimmy Lee & Kang Sim

  40. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    Jimmy Lee

  41. Department of Psychiatry, Pusan National University Hospital, Busan, Korea

    Byung Dae Lee

  42. Department of Psychiatry, Korea University College of Medicine, Seoul, Korea

    Heon-Jeong Lee

  43. Genome Institute of Singapore, A*STAR, Singapore, Singapore

    Jianjun Liu

  44. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Jianjun Liu

  45. Department of Psychiatry, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

    Xiancang Ma

  46. Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Korea

    Woojae Myung

  47. Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Ikuo Otsuka

  48. School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, Australia

    Sibylle G. Schwab

  49. Illawarra Health and Medical Research Institute, Wollongong, Australia

    Sibylle G. Schwab

  50. Department of Psychiatry, Dokkyo Medical University School of Medicine, Mibu, Japan

    Kazutaka Shimoda

  51. Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China

    Jinsong Tang

  52. Key Laboratory of Medical Neurobiology of Zhejiang Province, Hangzhou, Zhejiang, China

    Jinsong Tang

  53. Department of Psychiatry, the Second Xiangya Hospital, Central South University, Changsha, Hunan, China

    Jinsong Tang

  54. National Clinical Research Center on Mental Disorders, Changsha, Hunan, China

    Jinsong Tang

  55. Laboratory for Molecular Psychiatry, RIKEN Center for Brain Science, Wako, Japan

    Tomoko Toyota & Takeo Yoshikawa

  56. Department of Psychiatry, University of California San Diego, San Diego, CA, USA

    Ming Tsuang

  57. University of Western Australia, Perth, Australia

    Dieter B. Wildenauer

  58. Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Korea

    Hong-Hee Won

  59. Center for Translational Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China

    Feng Zhu

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  1. Yunfeng Ruan
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Consortia

Stanley Global Asia Initiatives

  • Yong Min Ahn
  • , Kazufumi Akiyama
  • , Makoto Arai
  • , Ji Hyun Baek
  • , Wei J. Chen
  • , Young-Chul Chung
  • , Gang Feng
  • , Kumiko Fujii
  • , Stephen J. Glatt
  • , Zhenglin Guo
  • , Kyooseob Ha
  • , Kotaro Hattori
  • , Teruhiko Higuchi
  • , Akitoyo Hishimoto
  • , Kyung Sue Hong
  • , Yasue Horiuchi
  • , Hailiang Huang
  • , Hai-Gwo Hwu
  • , Masashi Ikeda
  • , Sayuri Ishiwata
  • , Masanari Itokawa
  • , Nakao Iwata
  • , Eun-Jeong Joo
  • , Rene S. Kahn
  • , Sung-Wan Kim
  • , Se Joo Kim
  • , Se Hyun Kim
  • , Makoto Kinoshita
  • , Hiroshi Kunugi
  • , Agung Kusumawardhani
  • , Jimmy Lee
  • , Byung Dae Lee
  • , Heon-Jeong Lee
  • , Jianjun Liu
  • , Ruize Liu
  • , Xiancang Ma
  • , Woojae Myung
  • , Shusuke Numata
  • , Tetsuro Ohmori
  • , Ikuo Otsuka
  • , Yuji Ozeki
  • , Shengying Qin
  • , Yunfeng Ruan
  • , Akira Sawa
  • , Sibylle G. Schwab
  • , Wenzhao Shi
  • , Kazutaka Shimoda
  • , Kang Sim
  • , Ichiro Sora
  • , Jinsong Tang
  • , Tomoko Toyota
  • , Ming Tsuang
  • , Dieter B. Wildenauer
  • , Hong-Hee Won
  • , Takeo Yoshikawa
  • , Alice Zheng
  •  & Feng Zhu

Contributions

H.H. and T.G. designed the project; T.G. developed the statistical methods and programmed the code for PRS-CSx. Y.R. and T.G. conducted simulation studies. Y.R. and T.G performed the analysis in the UK Biobank; Y.-F.L. performed the analysis in the Taiwan Biobank. Y.R. performed the analysis in the schizophrenia cohorts. Y.-C.A.F. assigned the UKBB samples into superpopulation groups. C.-Y.C. provided critical suggestions for the study design. M.L. took part in the testing of the code and preprocessed schizophrenia East Asian cohorts. Z.G., L.H., A.S. and S.Q. contributed to the generation and preprocessing of schizophrenia East Asian data. Y.R., H.H. and T.G. wrote the manuscript; Y.-C.A.F., C.-Y.C. and A.R.M. provided critical revision for the manuscript. All the authors reviewed and approved the final version of the manuscript.

Corresponding authors

Correspondence to Shengying Qin, Hailiang Huang or Tian Ge.

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Extended data

Extended Data Fig. 1 Prediction accuracy of different polygenic prediction methods across different genetic architectures.

Phenotypes were simulated using 0.1%, 1% or 10% of randomly sampled causal variants (shared across populations), a cross-population genetic correlation of 0.7, and SNP heritability of 50%. PRS were trained using 100 K EUR samples and 20 K non-EUR (EAS or AFR) samples. Numerical results are reported in Supplementary Table 2.

Extended Data Fig. 2 Prediction accuracy of different polygenic prediction methods across different cross-population genetic correlations.

Phenotypes were simulated using 1% of randomly sampled causal variants (shared across populations), a cross-population genetic correlation of 0.4, 0.7 or 1.0, and SNP heritability of 50%. PRS were trained using 100 K EUR samples and 20 K non-EUR (EAS or AFR) samples. Numerical results are reported in Supplementary Table 3.

Extended Data Fig. 3 Prediction accuracy of different polygenic prediction methods across different discovery GWAS sample sizes.

Phenotypes were simulated using 1% of randomly sampled causal variants (shared across populations), a cross-population genetic correlation of 0.7, and SNP heritability of 50%. PRS were trained using 50 K EUR and 10 K non-EUR (EAS or AFR) samples, 100 K EUR and 20 K non-EUR samples, 200 K EUR and 40 K non-EUR samples, or 300 K EUR and 60 K non-EUR samples. Numerical results are reported in Supplementary Table 4.

Extended Data Fig. 4 Prediction accuracy of different polygenic prediction methods across different ratios of EUR vs. non-EUR GWAS sample sizes.

Phenotypes were simulated using 1% of randomly sampled causal variants (shared across populations), a cross-population genetic correlation of 0.7, and SNP heritability of 50%. PRS were trained using 120 K EUR samples without non-EUR samples, 100 K EUR and 20 K non-EUR (EAS or AFR) samples, 80 K EUR and 40 K non-EUR samples, or 60 K EUR and 60 K non-EUR samples. Numerical results are reported in Supplementary Table 5.

Extended Data Fig. 5 Prediction accuracy of different polygenic prediction methods across different SNP heritability.

Phenotypes were simulated using 1% of randomly sampled causal variants (shared across populations) and a cross-population genetic correlation of 0.7. SNP heritability was fixed at 50% in each population, 50% in the EUR population and 25% in the non-EUR population, or 25% in the EUR population and 50% in the non-EUR population. PRS were trained using 100 K EUR samples and 20 K non-EUR (EAS or AFR) samples. Numerical results are reported in Supplementary Table 6.

Extended Data Fig. 6 Prediction accuracy of different polygenic prediction methods across different proportions of shared causal variants between populations.

Phenotypes were simulated using 1% of randomly sampled causal variants. 100%, 70% or 40% of the causal variants were shared across populations. Shared causal variants had a cross-population genetic correlation of 0.7. SNP heritability was fixed at 50%. PRS were trained using 100 K EUR samples and 20 K non-EUR (EAS or AFR) samples. Numerical results are reported in Supplementary Table 7.

Extended Data Fig. 7 Prediction accuracy of different polygenic prediction methods when SNP effect sizes are minor allele frequency (MAF) and linkage disequilibrium (LD) dependent.

Phenotypes were simulated using 1% of randomly sampled causal variants (shared across populations), a cross-population genetic correlation of 0.7, and SNP heritability of 50%. SNP effect sizes were dependent on MAF and LD scores such that SNPs with lower MAF and located in lower LD regions tended to have larger effect sizes. PRS were trained using 100 K EUR samples and 20 K non-EUR (EAS or AFR) samples. Numerical results are reported in Supplementary Table 8.

Extended Data Fig. 8 Relative prediction accuracy for quantitative traits across target populations.

Relative prediction performance for single-discovery and multi-discovery PRS construction methods using discovery GWAS summary statistics a, from UKBB and BBJ, across 33 traits, in different UKBB target populations (EUR, EAS and AFR); b, from UKBB and BBJ, across 21 traits, in the Taiwan Biobank (TWB); c, from UKBB, BBJ and PAGE, across 14 traits, in different UKBB target populations (EUR, EAS and AFR). Each data point shows the relative increase of prediction performance, defined as R2/R2PRS-CS (UKBB)-EUR - 1, in which R2PRS-CS (UKBB)-EUR is the R2 of the trait in the EUR population using PRS-CS trained on the UKBB GWAS summary statistics. In UKBB target populations (panels a and c), R2 were averaged across 100 random splits of the target samples into validation and testing datasets. The crossbar indicates the median of the relative increase of predictive performance across the traits examined. ‘median N’ indicates the median sample size across the respective discovery GWAS.

Extended Data Fig. 9 Trace plots and autocorrelation functions (ACFs) for assessing the convergence and mixing of the Gibbs sampler used in PRS-CSx.

Left panels: Trace plots, after discarding the burn-in iterations and thinning the Markov chain by a factor of 5, for the posterior effects of rs7412 on low-density lipoprotein cholesterol when integrating UKBB, BBJ and PAGE GWAS summary statistics using PRS-CSx. Right panels: The autocorrelation functions (ACFs) for the traces shown on the left.

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Ruan, Y., Lin, YF., Feng, YC.A. et al. Improving polygenic prediction in ancestrally diverse populations. Nat Genet 54, 573–580 (2022). https://doi.org/10.1038/s41588-022-01054-7

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