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Genome-wide polygenic score to predict chronic kidney disease across ancestries

Nature Medicine volume 28, pages 1412–1420 (2022)Cite this article

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Abstract

Chronic kidney disease (CKD) is a common complex condition associated with high morbidity and mortality. Polygenic prediction could enhance CKD screening and prevention; however, this approach has not been optimized for ancestrally diverse populations. By combining APOL1 risk genotypes with genome-wide association studies (GWAS) of kidney function, we designed, optimized and validated a genome-wide polygenic score (GPS) for CKD. The new GPS was tested in 15 independent cohorts, including 3 cohorts of European ancestry (n = 97,050), 6 cohorts of African ancestry (n = 14,544), 4 cohorts of Asian ancestry (n = 8,625) and 2 admixed Latinx cohorts (n = 3,625). We demonstrated score transferability with reproducible performance across all tested cohorts. The top 2% of the GPS was associated with nearly threefold increased risk of CKD across ancestries. In African ancestry cohorts, the APOL1 risk genotype and polygenic component of the GPS had additive effects on the risk of CKD.

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Fig. 1: Overview of the study design.
Fig. 2: Risk score distributions in five 1000 Genomes populations.
Fig. 3: Effects of the GPS for CKD.

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Data availability

The final formulation of the GPS for CKD along with the standardized metrics of performance have been deposited in the PGS catalog at https://www.pgscatalog.org/publication/PGP000269/. The UKBB genotype and phenotype data are available through the UKBB web portal at https://www.ukbiobank.ac.uk/. The eMERGE-III imputed genotype and phenotype data are available through the database of Genotypes and Phenotypes (dbGAP) under accession no. phs001584.v2.p2. The BioMe genotype datasets used in this study were generated by Regeneron and are not publicly available. However, the data will be made available for the purposes of replicating the results by contacting the corresponding author and through appropriate collaboration and/or data sharing agreements. The WPC and REGARDS imputed genotype and phenotype data are available through dbGAP under accession nos. phs000708.v1.p1 and phs002719.v1.p1, respectively. The GenHAT cohort is also available on dbGAP under accession no. phs002716.v1.p1. The HyperGEN cohort has been sequenced by the TOPMed consortium; WGS data along with phenotype data are available through dbGAP under accession no. phs001293.v3.p1. Minimum testing datasets with the GPS, CKD outcome, and a set of essential clinical covariates for each cohort are also available when consistent with the consent given by the participants and can be requested directly from the corresponding author with a 2–4-week response time frame. Because these datasets contain clinical data, access to them may require a data use agreement.

Code availability

The CKD phenotype software is available from the Phenotype Knowledge Database at https://phekb.org/phenotype/chronic-kidney-disease. The CKD GPS score equation is available through the PGS catalog at https://www.pgscatalog.org/publication/PGP000269/ and through our laboratory website at http://www.columbiamedicine.org/divisions/kiryluk/study_GPS_CKD.php.

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Acknowledgements

This work was funded by the National Human Genome Research Institute eMERGE-IV grant nos. 2U01HG008680-05, 1U01HG011167-01 and 1U01HG011176-01. Additional sources of funding included grant nos. UG3DK114926 (K.K.), RC2DK116690 (K.K.), R01LM013061 (C.W., K.K.), K25DK128563 (A.K.), UL1TR001873 (A.K., K.K.), R01HL151855 (J.B.M.) and UM1DK078616 (J.B.M.). The parent REGARDS study was supported by cooperative agreement no. U01NS041588 cofunded by the National Institute of Neurological Disorders and Stroke and the National Institute on Aging, the National Institutes of Health (NIH) and the Department of Health and Human Services. The HyperGEN (R01HL055673), GenHAT (R01HL123782) and WPC (R01HL092173, K24HL133373) studies were all supported by the NHLBI. Parts of this study were conducted using the UKBB resource under UKBB project no. 41849. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Author information

Authors and Affiliations

  1. Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA

    Atlas Khan, Ning Shang, Ali G. Gharavi & Krzysztof Kiryluk

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

    Michael C. Turchin, Noura S. Abul-Husn & Eimear E. Kenny

  3. Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA

    Amit Patki, Vinodh Srinivasasainagendra & Hemant K. Tiwari

  4. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Rajiv Nadukuru, Judy H. Cho & Girish Nadkarni

  5. Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA

    Alana C. Jones & Marguerite R. Irvin

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

    Edyta Malolepsza, Christopher Kachulis & Niall Lennon

  7. Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA

    Ozan Dikilitas, Iftikhar J. Kullo & Daniel J. Schaid

  8. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Elizabeth Karlson

  9. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Tian Ge & Jordan W. Smoller

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

    James B. Meigs

  11. Harvard Medical School, Boston, MA, USA

    James B. Meigs

  12. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA

    James B. Meigs

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

    Christoph Lange

  14. Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA

    David R. Crosslin

  15. Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, USA

    Gail P. Jarvik

  16. Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA

    Pavan K. Bhatraju

  17. Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Jacklyn N. Hellwege

  18. Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Paulette Chandler

  19. Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

    Laura Rasmussen Torvik

  20. Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, USA

    Alex Fedotov

  21. Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA

    Cong Liu, George Hripcsak & Chunhua Weng

  22. Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Noura S. Abul-Husn & Eimear E. Kenny

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

    Noura S. Abul-Husn

  24. Department of Biostatistics, Columbia University, New York, NY, USA

    Iuliana Ionita-Laza

  25. Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA

    Wendy K. Chung

  26. Division of General Internal Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Eimear E. Kenny

  27. Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA

    Nita A. Limdi

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  1. Atlas Khan
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  2. Michael C. Turchin
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  28. Judy H. Cho
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  29. Iuliana Ionita-Laza
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Contributions

A.K. and K.K. conceptualized the study. A.K., K.K., M.C.T., A.P., V.S., R.N., A.C.J., E.M., C.K., N.L., I.I.L., T.G., M.R.I., H.K.T. and E.E.K. devised the methodology and carried out the genetic data analysis. N.S., C.Li., G.H., C.W. and G.N. carried out the e-phenotyping. O.D., I.J.K., D.J.S., E.K., J.B.M., J.W.S., C.La., D.R.C., G.P.J., P.K.B., J.N.H., P.C., L.R.T., A.G.G., W.K.C., G.H. and C.W. provided the eMERGE-III data contributions. G.N., J.H.C., N.S.A-H. and E.E.K. provided the BioMe data contributions. M.R.I., H.K.T. and N.A.L. provided the UAB data contributions. A.K. and A.F. managed the project. K.K. provided overall supervision. A.K. and K.K. wrote the original manuscript draft. A.K., N.S., E.M., A.G.G., T.G., J.B.M., D.R.C., J.N.H., I.I.L., G.H., M.R.I., H.K.T., E.E.K., N.A.L. and K.K. reviewed and edited the manuscript.

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Correspondence to Krzysztof Kiryluk.

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Extended data

Extended Data Fig. 1 Distribution of risk allele frequencies (RAF) and their effect sizes for the variants included in the GPS.

Distribution of risk allele frequencies (RAF) and their effect sizes for the variants included in the GPS (a) comparison of RAF distributions for the risk variants included in the CKD GPS demonstrates higher frequency of rare (RAF < 0.01) and common (RAF > 0.99) risk alleles in African compared to European genomes (based on 1000 G reference populations); this may be explained by the exclusion of variants with MAF < 0.01 in European discovery GWAS; (b) highly skewed effect size (weight) distribution for the variants included in the GPS for CKD; (c) Distribution of RAF difference (AFR-EUR) demonstrating higher average frequency of risk alleles in African genomes (mean RAF difference = 0.002) and a slight rightward shift of the RAF difference distribution from the expected mean of 0; (d) Mean RAF difference (AFR-EUR) as a function of effect size binned into three categories (high, intermediate, and low) based on the observed distribution of effects sizes in panel b, demonstrating that the risk alleles with larger effect size have higher average frequency in African compared to European genomes. EUR: European (N = 503) and AFR: African (N = 661). The bars represent 95% confidence intervals around the mean RAF difference estimate for each bin; two-sided P-values were calculated using t-test.

Extended Data Fig. 2 Risk score distributions in eMERGE-III (N = 22,453) and UKBB (N = 77,584) validation datasets.

Risk score distributions in eMERGE-III (N = 22,453) and UKBB (N = 77,584) validation datasets: (a) the distribution of raw polygenic score without APOL1 in UKBB by ancestry; (b) the distribution of ancestry-adjusted polygenic score (method 1: mean-adjusted) in UKBB by ancestry; (c) the distribution of ancestry-adjusted polygenic score (method 2: mean and variance-adjusted) in UKBB by ancestry. Panels (d), (e) and (f) show the same analyses for the eMERGE-III dataset, respectively.

Extended Data Fig. 3 Final GPS calibration analysis in eMERGE-III cohorts combined (N = 22,453).

Final GPS calibration analysis in eMERGE-III cohorts combined (N = 22,453): predicted risk (X-axis) as a function of the observed risk (Y-axis) in the multiethnic eMERGE-III dataset after ancestry adjustment with (a) method 1 and (b) method 2. The bars represent 95% confidence intervals.

Extended Data Fig. 4 Distributions of the raw (non-standardized) genome-wide polygenic score (GPS) by Yu et al. in the eMERGE-III validation datasets by ancestry.

Distributions of the raw (non-standardized) genome-wide polygenic score (GPS) by Yu et al. in the eMERGE-III validation datasets by ancestry.

Extended Data Fig. 5 PCA projections of the study participants from the UKBB (top) and eMERGE-III (bottom) against the 1000 G reference populations.

PCA projections of the study participants from the UKBB (top) and eMERGE-III (bottom) against the 1000 G reference populations: (a) UKBB (N = 77,584) and (b) eMERGE-III (N = 22,453) participants plotted against the reference 1000 G populations (N = 2,504); (b, e) plotted by self-reported race/ethnicity; and (c, f) plotted by final ancestry group assignment. X-axis: PC1; Y-axis: PC2; AFR: African; AMR: Admixed American; EAS: East Asian; EUR: European; and SAS: South Asian.

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Khan, A., Turchin, M.C., Patki, A. et al. Genome-wide polygenic score to predict chronic kidney disease across ancestries. Nat Med 28, 1412–1420 (2022). https://doi.org/10.1038/s41591-022-01869-1

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