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A new method for multiancestry polygenic prediction improves performance across diverse populations

Nature Genetics volume 55, pages 1757–1768 (2023)Cite this article

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Abstract

Polygenic risk scores (PRSs) increasingly predict complex traits; however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRSs, using ancestry-specific genome-wide association study summary statistics from multiancestry training samples, integrating clumping and thresholding, empirical Bayes and superlearning. We evaluated CT-SLEB and nine alternative methods with large-scale simulated genome-wide association studies (~19 million common variants) and datasets from 23andMe, Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank, involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across 13 complex traits. Results demonstrated that CT-SLEB significantly improves PRS performance in non-European populations compared with simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offered insights into sample size requirements and SNP density effects on multiancestry risk prediction.

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Fig. 1: CT-SLEB workflow.
Fig. 2: Simulation results of various PRS methods in multiancestry settings.
Fig. 3: Comparison of CT-SLEB PRSs across different ancestries with single-ancestry EUR PRSs in the EUR population.
Fig. 4: Prediction performance of CT-SLEB PRS under varying SNP densities.
Fig. 5: Prediction accuracy of PRSs for heart metabolic disease burden and height in 23andMe, Inc. datasets.
Fig. 6: Prediction accuracy of five binary traits in 23andMe, Inc. datasets.
Fig. 7: Prediction accuracy of four blood lipid traits from the GLGC.
Fig. 8: Prediction accuracy of two traits from the AoU dataset.

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Data availability

Simulated genotype data for 600,000 subjects from 5 ancestries are at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/COXHAP. GWAS summary level statistics for five ancestries from GLGC are at: http://csg.sph.umich.edu/willer/public/glgc-lipids2021/results/ancestry_specific. GWAS summary statistics for three ancestries are from AoU at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/FAWEQK. The PRSs developed for six traits for GLGC and AoU have been released through the PGS Catalog (https://www.pgscatalog.org) with publication ID PGP000489 and score IDs PGS003767–PGS003848. The 23andMe GWAS summary statistics for the top 10,000 genetic markers associated with 3 traits (height, morning person and SBMN) across 5 diverse ancestries have been made available as Supplementary Data and are also available at: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/3NBNCV. The full GWAS summary statistics and the final PRSs for these three traits (height, morning person and SBMN) are available through 23andMe, Inc. to qualified researchers under an agreement with 23andMe, Inc. that protects the privacy of the 23andMe participants. Please visit research.23andme.com/dataset-access for more information and to apply for access to the data. The summary statistics for the four other traits used in the paper (any CVD, heart metabolic disease burden, depression and migraine) will not be made available because of 23andMe’s business requirements. Participants provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited institutional review board, Ethical & Independent Review Services.

Code availability

Simulation and data analyses code is available at GitHub (https://github.com/andrewhaoyu/multi_ethnic (ref. 66)). Software implementing CT-SLEB is available at GitHub (https://github.com/andrewhaoyu/CTSLEB (ref. 67)). The P + T method was implemented using R version 4.0.0 in conjunction with PLINK 1.9 available at https://www.cog-genomics.org/plink/1.9. Other methods and their corresponding repositories include: SCT and LDpred2 at https://github.com/privefl/bigsnpr, XPASS at https://github.com/YangLabHKUST/XPASS, PolyPred-S+ at https://github.com/omerwe/polyfun, PRS-CSx at https://github.com/getian107/PRScsx, and LDSC at https://github.com/bulik/ldsc. PLINK: https://www.cog-genomics.org/plink/1.9. Most of our statistical analyses were performed using the following R packages: ggplot2 v.3.3.3, dplyr v.1.0.4, data.table v.1.13.6, bigsnpr v.1.6.1, SuperLearner v.2.0.26, caret v.6.0.86, ranger v.0.12.1, glmnet v.4.1, RISCA v.1.01, XPASS v.0.1.0, xgboost v.1.7.5.1 and randomForest.

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Acknowledgements

We thank the research participants and employees of 23andMe, Inc. for making this work possible. We thank L. Noblin, M. J. Francis and E. Voeglein for helping with the research collaboration agreement with the Harvard T.H. Chan School of Public Health, Johns Hopkins Bloomberg School of Public Health and 23andMe, Inc. The analysis utilized the high-performance computation Biowulf cluster at the National Institutes of Health (NIH), USA, Faculty of Arts and Sciences Research Computing Cluster at Harvard University and the Joint High Performance Computing Exchange at Johns Hopkins Bloomberg School of Public Health. The UKBB data were obtained under UKBB resource application no. 17712. This work was funded by NIH grants: nos. K99 CA256513 to H.Z., R00 HG012223 to J.J., NHLBI 5T32HL007604-37 to Z.Y., R35-CA197449, U19-CA203654, R01-HL163560, U01-HG009088 and U01-HG012064 to X.L., R01 HG010480-01 to N.C. and U01HG011724 to N.C. The AoU Research Program is supported by the NIH, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA no.: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition, the AoU Research Program would not be possible without the partnership of its participants.

Author information

Authors and Affiliations

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

    Haoyu Zhang, Thomas U. Ahearn & Montserrat Garcia-Closas

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

    Haoyu Zhang, Tony Chen & Xihong Lin

  3. 23andMe, Inc., Sunnyvale, CA, USA

    Jianan Zhan, Jared O’Connell, Yunxuan Jiang, Stella Aslibekyan, Adam Auton, Elizabeth Babalola, Robert K. Bell, Jessica Bielenberg, Katarzyna Bryc, Emily Bullis, Daniella Coker, Gabriel Cuellar Partida, Devika Dhamija, Sayantan Das, Sarah L. Elson, Nicholas Eriksson, Teresa Filshtein, Alison Fitch, Kipper Fletez-Brant, Pierre Fontanillas, Will Freyman, Julie M. Granka, Karl Heilbron, Alejandro Hernandez, Barry Hicks, David A. Hinds, Ethan M. Jewett, Katelyn Kukar, Alan Kwong, Keng-Han Lin, Bianca A. Llamas, Maya Lowe, Jey C. McCreight, Matthew H. McIntyre, Steven J. Micheletti, Meghan E. Moreno, Priyanka Nandakumar, Dominique T. Nguyen, Elizabeth S. Noblin, Aaron A. Petrakovitz, G. David Poznik, Alexandra Reynoso, Morgan Schumacher, Anjali J. Shastri, Janie F. Shelton, Jingchunzi Shi, Suyash Shringarpure, Qiaojuan Jane Su, Susana A. Tat, Christophe Toukam Tchakouté, Vinh Tran, Joyce Y. Tung, Xin Wang, Wei Wang, Catherine H. Weldon, Peter Wilton, Corinna D. Wong & Bertram L. Koelsch

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

    Jin Jin, Jingning Zhang, Ruzhang Zhao & Nilanjan Chatterjee

  5. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Jin Jin

  6. Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA

    Wenxuan Lu

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

    Zhi Yu & Xihong Lin

  8. Booz Allen Hamilton Inc., McLean, VA, USA

    Dayne Okuhara

  9. Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK

    Montserrat Garcia-Closas

  10. Department of Statistics, Harvard University, Cambridge, MA, USA

    Xihong Lin

  11. Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Nilanjan Chatterjee

Authors
  1. Haoyu Zhang
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  2. Jianan Zhan
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  3. Jin Jin
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  4. Jingning Zhang
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  6. Ruzhang Zhao
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  7. Thomas U. Ahearn
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  8. Zhi Yu
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Consortia

23andMe Research Team

  • Stella Aslibekyan
  • , Adam Auton
  • , Elizabeth Babalola
  • , Robert K. Bell
  • , Jessica Bielenberg
  • , Katarzyna Bryc
  • , Emily Bullis
  • , Daniella Coker
  • , Gabriel Cuellar Partida
  • , Devika Dhamija
  • , Sayantan Das
  • , Sarah L. Elson
  • , Nicholas Eriksson
  • , Teresa Filshtein
  • , Alison Fitch
  • , Kipper Fletez-Brant
  • , Pierre Fontanillas
  • , Will Freyman
  • , Julie M. Granka
  • , Karl Heilbron
  • , Alejandro Hernandez
  • , Barry Hicks
  • , David A. Hinds
  • , Ethan M. Jewett
  • , Yunxuan Jiang
  • , Katelyn Kukar
  • , Alan Kwong
  • , Keng-Han Lin
  • , Bianca A. Llamas
  • , Maya Lowe
  • , Jey C. McCreight
  • , Matthew H. McIntyre
  • , Steven J. Micheletti
  • , Meghan E. Moreno
  • , Priyanka Nandakumar
  • , Dominique T. Nguyen
  • , Elizabeth S. Noblin
  • , Jared O’Connell
  • , Aaron A. Petrakovitz
  • , G. David Poznik
  • , Alexandra Reynoso
  • , Morgan Schumacher
  • , Anjali J. Shastri
  • , Janie F. Shelton
  • , Jingchunzi Shi
  • , Suyash Shringarpure
  • , Qiaojuan Jane Su
  • , Susana A. Tat
  • , Christophe Toukam Tchakouté
  • , Vinh Tran
  • , Joyce Y. Tung
  • , Xin Wang
  • , Wei Wang
  • , Catherine H. Weldon
  • , Peter Wilton
  •  & Corinna D. Wong

Contributions

H.Z. and N.C. conceived the project. H.Z., J. Zhan, J.J., J. Zhang, W.L. and R.Z. carried out all data analyses with supervision from N.C. J.Z., J.O.C. and Y.J. ran GWASs for training data from 23andMe Inc. with supervision from B.L.K. R.Z. ran GWASs for training data from AoU with supervision from N.C. and H.Z. H.Z., T.C. and D.O. developed the software and online resources for data sharing. H.Z., J. Zhan, J.J., J. Zhang, W.L., R.Z. and N.C. drafted the manuscript. X.L., M.G.C. and T.U.A. provided comments. All authors reviewed and approved the final version of the manuscript.

Corresponding authors

Correspondence to Haoyu Zhang or Nilanjan Chatterjee.

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Competing interests

J.Z., J.O., Y.J., S.A., A.A., E.B., R.K.B., J.B., K.B., E.B., D.C., G.C.P., D.D., S.D., S.L.E., N.E., T.F., A.F., K.F.B., P.F., W.F., J.M.G., K.H., A.H., B.H., D.A.H., E.M.J., K.K., A.K., K.H.L., B.A.L., M.L., J.C.M., M.H.M., S.J.M., M.E.M., P.N., D.T.N., E.S.N., A.A.P., G.D.P., A.R., M.S., A.J.S., J.F.S., J.S., S.S., Q.J.S., S.A.T., C.T.T., V.T., J.Y.T., X.W., W.W., C.H.W., P.W., C.D.W. and B.L.K. are employed by and hold stock or stock options in 23andMe, Inc. The remaining authors declare no competing interests.

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Nature Genetics thanks Shing Wan Choi, Bjarni Vilhjálmsson and the other, anonymous, reviewer(s) 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 CT-SLEB detailed flowchart.

The method contains three major steps: 1. Two-dimensional clumping and thresholding; 2. Empirical-Bayes procedure for utilizing genetic correlations of effect sizes across populations; 3. Super-learning model for combining PRSs under different tuning parameters. The tuning dataset is used to train the super learning model. The final prediction performance is evaluated based on an independent validation dataset. For continuous traits, the prediction is evaluated using R2 obtained from the linear regression between outcome and PRS after adjusting for covariates (Methods). For binary traits, the prediction is evaluated using the area under the ROC curve (AUC).

Extended Data Fig. 2 Performance of CT-SLEB with different tuning and validation sample sizes.

The total tuning and validation sample size is set as 2000, 5000, 100,000 and 200,000 with half for tuning and half for validation. Analyses are conducted in the multiancestry setting under a strong negative selection model. The training sample size for the AFR population is 15,000. The training sample size for EUR is 100,000. The sample size for the tuning dataset and validation for each population is fixed at 10,000, respectively. Common SNP heritability is assumed to be 0.4 across all populations and effect-size correlation is assumed to be 0.8 across populations. The causal SNPs proportion is varied across 0.01 (top panel), 0.001 (medium panel), or 5×10−4 (bottom panel). The final prediction R2 is reported as the average of ten independent simulation replicates.

Supplementary information

Supplementary information

Supplementary Figs. 1–22 and Note.

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Supplementary Table 1

Supplementary Tables 1–11.

Supplementary Data 1

The 23andMe GWAS summary statistics for the top 10,000 genetic markers associated with three traits (height, morning person and SBMN) across five diverse ancestries.

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Zhang, H., Zhan, J., Jin, J. et al. A new method for multiancestry polygenic prediction improves performance across diverse populations. Nat Genet 55, 1757–1768 (2023). https://doi.org/10.1038/s41588-023-01501-z

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