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Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores

Nature Genetics volume 54, pages 450–458 (2022)Cite this article

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Abstract

Polygenic risk scores suffer reduced accuracy in non-European populations, exacerbating health disparities. We propose PolyPred, a method that improves cross-population polygenic risk scores by combining two predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing linkage disequilibrium differences, and BOLT-LMM, a published predictor. When a large training sample is available in the non-European target population, we propose PolyPred+, which further incorporates the non-European training data. We applied PolyPred to 49 diseases/traits in four UK Biobank populations using UK Biobank British training data, and observed relative improvements versus BOLT-LMM ranging from +7% in south Asians to +32% in Africans, consistent with simulations. We applied PolyPred+ to 23 diseases/traits in UK Biobank east Asians using both UK Biobank British and Biobank Japan training data, and observed improvements of +24% versus BOLT-LMM and +12% versus PolyPred. Summary statistics-based analogs of PolyPred and PolyPred+ attained similar improvements.

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Fig. 1: Overview of PolyPred and PolyPred+.
Fig. 2: Recommendations for the application of PolyPred, PolyPred+ and related methods.
Fig. 3: Cross-population PRS results for simulated UK Biobank traits using in-sample LD.
Fig. 4: Cross-population PRS results for real UK Biobank traits.
Fig. 5: Cross-population PRS results for Biobank Japan and Uganda-APCDR traits.
Fig. 6: Cross-population PRS results for UK Biobank east Asians when incorporating both European and non-European training data.

Data availability

Access to the UK Biobank resource is available via application (http://www.ukbiobank.ac.uk). PRS coefficients generated in the present study are available for public download at http://data.broadinstitute.org/alkesgroup/polypred_results. Summary LD information of n = 337,000 British-ancestry UK Biobank individuals for 2,763 overlapping 3-Mb loci is available at https://data.broadinstitute.org/alkesgroup/UKBB_LD. Summary LD information of n = 50,000 UK Biobank individuals for SBayesR is available at https://zenodo.org/record/3350914. Summary LD information used by PRS-CS is available at https://github.com/getian107/PRScs. Baseline-LF v.2.2.UKB annotations and LD scores for UK Biobank SNPs are available at https://data.broadinstitute.org/alkesgroup/LDSCORE/baselineLF_v2.2.UKB.tar.gz. Source data are provided with this paper.

Code availability

PolyPred and PolyPred+ are provided as part of the open-source software package PolyFun, which is freely available at https://doi.org/10.5281/zenodo.6139679 (ref. 89) and https://github.com/omerwe/polyfun. BOLT-LMM is available at https://data.broadinstitute.org/alkesgroup/BOLT-LMM. SBayesR is available at https://cnsgenomics.com/software/gctb. PRS-CS is available at https://github.com/getian107/PRScs.

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Acknowledgements

We thank A. Schoech and C. Márquez-Luna for helpful discussions. This research was conducted using the UK Biobank resource under application no. 16549 and was funded by the National Institutes of Health (NIH; grant nos. U01 HG009379, U01 HG012009, R37 MH107649, R01 MH101244 and R01 HG006399). M.K. was supported by a Nakajima Foundation Fellowship and the Masason Foundation. W.J.P. was supported by an NWO Veni grant (no. 91619152). A.R.M. was supported by the National Institute of Mental Health (grant no. K99/R00MH117229). H.K.F. was supported by E. and W. Schmidt. A.V.K. was supported by grants (nos. 1K08HG010155 and 1U01HG011719) from the National Human Genome Research Institute and a sponsored research agreement from IBM Research. Y.O. was supported by JSPS KAKENHI (grant nos. 19H01021 and 20K21834) and AMED (grant nos. JP21km0405211, JP21ek0109413, JP21ek0410075, JP21gm4010006 and P21km0405217) and JST Moonshot R&D (grant nos. JPMJMS2021 and JPMJMS2024). Computational analyses were performed on the O2 High-Performance Compute Cluster at Harvard Medical School.

Author information

Author notes

  1. These authors contributed equally: Omer Weissbrod, Masahiro Kanai, Huwenbo Shi.

Authors and Affiliations

  1. Epidemiology Department, Harvard School of Public Health, Boston, MA, USA

    Omer Weissbrod, Huwenbo Shi, Steven Gazal, Wouter J. Peyrot & Alkes L. Price

  2. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Masahiro Kanai, Amit V. Khera, Alicia R. Martin, Hilary K. Finucane & Alkes L. Price

  3. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan

    Masahiro Kanai & Yukinori Okada

  4. OMNI Bioinformatics, San Francisco, CA, USA

    Huwenbo Shi

  5. Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Steven Gazal

  6. Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Steven Gazal

  7. Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands

    Wouter J. Peyrot

  8. Verve Therapeutics, Cambridge, MA, USA

    Amit V. Khera

  9. Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Yukinori Okada

  10. Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Hilary K. Finucane

  11. Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan

    Koichi Matsuda

  12. Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan

    Koichi Matsuda & Yoichiro Kamatani

  13. Division of Genetics, Institute of Medical Science, University of Tokyo, Tokyo, Japan

    Yuji Yamanashi

  14. Division of Clinical Genome Research, Institute of Medical Science, University of Tokyo, Tokyo, Japan

    Yoichi Furukawa

  15. Division of Molecular Pathology, IMSUT Hospital Department of Internal Medicine, Institute of Medical Science, University of Tokyo, Tokyo, Japan

    Takayuki Morisaki

  16. Department of Cancer Biology, Institute of Medical Science, University of Tokyo, Tokyo, Japan

    Yoshinori Murakami

  17. Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan

    Yoichiro Kamatani

  18. Department of Public Policy, Institute of Medical Science, University of Tokyo, Tokyo, Japan

    Kaori Muto & Akiko Nagai

  19. Department of Urology, Iwate Medical University, Iwate, Japan

    Wataru Obara

  20. Department of Internal Medicine and Rheumatology, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Ken Yamaji

  21. Department of Respiratory Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Kazuhisa Takahashi

  22. Division of Pharmacology, Department of Biomedical Science, Nihon University School of Medicine, Tokyo, Japan

    Satoshi Asai

  23. Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan

    Satoshi Asai & Yasuo Takahashi

  24. Tokushukai Group, Tokyo, Japan

    Takao Suzuki & Nobuaki Sinozaki

  25. Department of Hematology, Nippon Medical School, Tokyo, Japan

    Hiroki Yamaguchi

  26. Department of Bioregulation, Nippon Medical School, Kawasaki, Japan

    Shiro Minami

  27. Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan

    Shigeo Murayama

  28. Fukujuji Hospital, Japan Anti-Tuberculosis Association, Tokyo, Japan

    Kozo Yoshimori

  29. Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan

    Satoshi Nagayama

  30. Center for Clinical Research and Advanced Medicine, Shiga University of Medical Science, Shiga, Japan

    Daisuke Obata

  31. Department of General Thoracic Surgery, Osaka International Cancer Institute, Osaka, Japan

    Masahiko Higashiyama

  32. Iizuka Hospital, Fukuoka, Japan

    Akihide Masumoto

  33. National Hospital Organization, Osaka National Hospital, Osaka, Japan

    Yukihiro Koretsune

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  1. Omer Weissbrod
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  10. Alkes L. Price
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Consortia

The Biobank Japan Project

  • Koichi Matsuda
  • , Yuji Yamanashi
  • , Yoichi Furukawa
  • , Takayuki Morisaki
  • , Yoshinori Murakami
  • , Yoichiro Kamatani
  • , Kaori Muto
  • , Akiko Nagai
  • , Wataru Obara
  • , Ken Yamaji
  • , Kazuhisa Takahashi
  • , Satoshi Asai
  • , Yasuo Takahashi
  • , Takao Suzuki
  • , Nobuaki Sinozaki
  • , Hiroki Yamaguchi
  • , Shiro Minami
  • , Shigeo Murayama
  • , Kozo Yoshimori
  • , Satoshi Nagayama
  • , Daisuke Obata
  • , Masahiko Higashiyama
  • , Akihide Masumoto
  •  & Yukihiro Koretsune

Contributions

O.W., M.K., H.S. and A.L.P. designed the study. O.W., M.K., H.S. and S.G. analyzed the data. O.W., M.K., H.S. and A.L.P. wrote the manuscript with assistance from S.G., W.J.P., A.V.K, Y.O., A.R.M. and H.K.F.

Corresponding authors

Correspondence to Omer Weissbrod or Alkes L. Price.

Ethics declarations

Competing interests

O.W. is an employee and holds equity in Eleven Therapeutics. H.S. is an employee of Genentech and holds stock in Roche. A.V.K. is an employee and holds equity in Verve Therapeutics, and has served as a scientific advisor to Sanofi, Amgen, Maze Therapeutics, Navitor Pharmaceuticals, Sarepta Therapeutics, Novartis, Silence Therapeutics, Korro Bio, Veritas International, Color Health, Third Rock Ventures, Foresite Labs and Columbia University (NIH); A.V.K. received speaking fees from Illumina, MedGenome, Amgen and the Novartis Institute for Biomedical Research, and also received a sponsored research agreement from IBM Research. All other authors declare no competing interests.

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Nature Genetics thanks Marylyn Ritchie and Vincent Plagnol for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Cross-population PRS results for real UK Biobank traits, using summary statistics from a meta-analysis of many cohorts.

We report average prediction accuracy (relative-R2, but computed with respect to PRS-CS instead of BOLT-LMM; see main text), meta-analyzed across 4 well-powered, approximately independent traits, for PRS trained in European Network for Genetic and Genomic Epidemiology (ENGAGE) samples (average N = 61,365) and applied to four UK Biobank populations. Target population sample sizes are indicated in parentheses; PolyPred and its summary statistic-based analogues used 500 additional training samples from each target population to estimate mixing weights. Asterisks above each bar denote statistical significance of the difference vs. PRS-CS, with red asterisks denoting a disadvantage (*P < 0.05; **P < 0.001). P-values were computed using a two-sided Wald test and were not adjusted for multiple comparisons. Errors bars denote standard errors. Numerical results, results for all 4 traits analyzed, absolute prediction accuracies (R2), and P-values of relative improvements vs. PRS-CS are reported in Supplementary Table 5 and Supplementary Table 8.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1–11 and Note.

Reporting Summary.

Peer Review File.

Supplementary Tables

Supplementary Tables 1–11.

Source data

Source Data Fig. 3

Source data for Fig. 3.

Source Data Fig. 4

Source data for Fig. 4.

Source Data Fig. 5

Source data for Fig. 5.

Source Data Fig. 6

Source data for Fig. 6.

Source Data Extended Data Fig. 1

Source data for Extended Data Fig. 1.

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Weissbrod, O., Kanai, M., Shi, H. et al. Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores. Nat Genet 54, 450–458 (2022). https://doi.org/10.1038/s41588-022-01036-9

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