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Abstract: TH-PO048

Polygenic Prediction of Estimated Glomerular Filtration Rate in Individuals of African Descent and from the Americas

Session Information

Category: Augmented Intelligence, Digital Health, and Data Science

  • 300 Augmented Intelligence, Digital Health, and Data Science


  • Kramer, Holly J., Division of Nephrology and Hypertension, Loyola University Chicago, Maywood, Illinois, United States
  • Bentley, Amy, Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States
  • Hughes, Odessica Nicole, The University of Manchester, Manchester, United Kingdom
  • Nadkarni, Girish N., Mount Sinai Health System, New York, New York, United States
  • Mychaleckyj, Josyf, University of Virginia, Charlottesville, Virginia, United States
  • Morris, Andrew, The University of Manchester, Manchester, United Kingdom
  • Franceschini, Nora, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States

Group or Team Name

  • COGENT-Kidney Consortium.

Chronic kidney disease more often impacts individuals of African descent (AFR) or those from origin from the Americas (AMS), who are under-represented in genome-wide association studies (GWAS). Polygenic scores have been proposed for disease risk prediction. However, polygenic scores developed from GWAS of individuals of European ancestry have limited transferability into other populations, reflecting differences in allele frequencies and linkage disequilibrium across populations. Previous studies have demonstrated their performance is poor for estimated glomerular filtration rate (eGFR) in AFR and AMS populations. Here, we investigate the predictive power of polygenic scores for eGFR developed in AFR and AMS for African American and Hispanic/Latino individuals.


We used summary data from eGFR GWAS meta-analyses and the Bayesian Regression and Continuous Shrinkage Priors (PRS-CS) approach to train polygenic scores in: (i) AFR + AMS (66,825 individuals); (ii) AFR-specific (40,001 individuals); and (iii) AMS-specific (26,824 individuals). We compared the performance of the AFR + AMS, and AFR- or AMS-specific polygenic scores in two additional test samples with GWAS data of 8,270 African Americans from the Women’s Health Initiative (WHI-AA) and 8,291 Hispanics/Latinos from the BioMe Biobank (BIOME-HA). We assessed the strength of the association and the variance explained of the three polygenic scores in each validation studies.


When applied to WHI-AA, the AFR polygenic score outperformed the AMS score and explained 12.5% of the eGFR variance, while the AFR + AMS polygenic score had the best performance, explaining 13.5% of the eGFR variance. For scores applied to BIOME-HA, the best performance was for the AFR + AMS score, which explained 5.2% of the eGFR variance, compared to the AMS score that explained 2.4%. All polygenic scores were significantly associated with eGFR in the validation datasets.


In this largest aggregated GWAS of AFR and AMS individuals, we showed increased predictive power offered by a multi-population polygenic score for eGFR in both African Americans and Hispanics/Latinos. This study has shown the largest variance explained by a polygenic score in AFR, enhancing resources for disease prediction in this population.


  • NIDDK Support