Single- versus Multi-Ancestry Polygenic Risk Scores for CKD in Black Americans.


Journal

Journal of the American Society of Nephrology : JASN
ISSN: 1533-3450
Titre abrégé: J Am Soc Nephrol
Pays: United States
ID NLM: 9013836

Informations de publication

Date de publication:
29 Jul 2024
Historique:
received: 22 01 2024
accepted: 28 06 2024
medline: 29 7 2024
pubmed: 29 7 2024
entrez: 29 7 2024
Statut: aheadofprint

Résumé

Chronic kidney disease (CKD) is a risk factor for cardiovascular disease and early death. Recently, polygenic risk scores (PRS) have been developed to quantify risk for CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PRS development overall. Moreover, European-ancestry derived PRS demonstrate diminished predictive performance in African ancestry populations. This study aimed to develop a PRS for CKD in Black Americans. We obtained score weights from a meta-analysis of genome-wide association studies (GWAS) for estimated glomerular filtration rate (eGFR) in the Million Veteran Program (MVP) and Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study to develop an eGFR PRS. We optimized the PRS risk model in a cohort of Participants from the Hypertension Genetic Epidemiology Network (HyperGEN). Validation was performed in subsets of Black participants of the Trans-Omics in Precision Medicine (TOPMed) Consortium and Genetics of Hypertension Associated Treatment (GenHAT) Study. The prevalence of CKD-defined as stage 3 or higher-was associated with the PRS as a continuous predictor (OR[95% CI]:1.35[1.08,1.68]) and in a threshold-dependent manner. Further, including APOL1 risk status-a putative variant for CKD with higher prevalence among those of sub-Saharan African descent-improved the score's accuracy. PRS associations were robust to sensitivity analyses accounting for traditional CKD risk factors, as well as CKD classification based on prior eGFR equations. Compared with previously published PRS, the predictive performance of our PRS was comparable to a European-ancestry derived PRS for kidney traits. However, both single-ancestry PRS were less predictive than multi-ancestry derived PRS. In this study, we developed a PRS that was significantly associated with CKD with improved predictive accuracy when including APOL1 risk status. However, PRS generated from multi-ancestry populations outperformed single-ancestry PRS in our study.

Sections du résumé

BACKGROUND BACKGROUND
Chronic kidney disease (CKD) is a risk factor for cardiovascular disease and early death. Recently, polygenic risk scores (PRS) have been developed to quantify risk for CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PRS development overall. Moreover, European-ancestry derived PRS demonstrate diminished predictive performance in African ancestry populations.
METHODS METHODS
This study aimed to develop a PRS for CKD in Black Americans. We obtained score weights from a meta-analysis of genome-wide association studies (GWAS) for estimated glomerular filtration rate (eGFR) in the Million Veteran Program (MVP) and Reasons for Geographic and Racial Differences in Stroke (REGARDS) Study to develop an eGFR PRS. We optimized the PRS risk model in a cohort of Participants from the Hypertension Genetic Epidemiology Network (HyperGEN). Validation was performed in subsets of Black participants of the Trans-Omics in Precision Medicine (TOPMed) Consortium and Genetics of Hypertension Associated Treatment (GenHAT) Study.
RESULTS RESULTS
The prevalence of CKD-defined as stage 3 or higher-was associated with the PRS as a continuous predictor (OR[95% CI]:1.35[1.08,1.68]) and in a threshold-dependent manner. Further, including APOL1 risk status-a putative variant for CKD with higher prevalence among those of sub-Saharan African descent-improved the score's accuracy. PRS associations were robust to sensitivity analyses accounting for traditional CKD risk factors, as well as CKD classification based on prior eGFR equations. Compared with previously published PRS, the predictive performance of our PRS was comparable to a European-ancestry derived PRS for kidney traits. However, both single-ancestry PRS were less predictive than multi-ancestry derived PRS.
CONCLUSIONS CONCLUSIONS
In this study, we developed a PRS that was significantly associated with CKD with improved predictive accuracy when including APOL1 risk status. However, PRS generated from multi-ancestry populations outperformed single-ancestry PRS in our study.

Identifiants

pubmed: 39073889
doi: 10.1681/ASN.0000000000000437
pii: 00001751-990000000-00377
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NHLBI NIH HHS
ID : R35HL15466
Pays : United States
Organisme : NIDDK NIH HHS
ID : F31DK128990
Pays : United States
Organisme : NIDDK NIH HHS
ID : T32DK116672
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01HL136666
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01G011167
Pays : United States
Organisme : NHLBI NIH HHS
ID : K24HL133373
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01HL092173
Pays : United States
Organisme : NHLBI NIH HHS
ID : R35HL155466
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01HL136666
Pays : United States
Organisme : NHGRI NIH HHS
ID : U01HG011167
Pays : United States

Informations de copyright

Copyright © 2024 by the American Society of Nephrology.

Auteurs

Alana C Jones (AC)

Medical Scientist Training Program, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA.

Amit Patki (A)

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

Vinodh Srinivasasainagendra (V)

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

Hemant K Tiwari (HK)

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

Nicole D Armstrong (ND)

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

Ninad S Chaudhary (NS)

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

Nita A Limdi (NA)

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

Bertha A Hidalgo (BA)

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

Brittney Davis (B)

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

James J Cimino (JJ)

Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.

Atlas Khan (A)

Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, NY, USA.

Krzysztof Kiryluk (K)

Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, NY, USA.

Leslie A Lange (LA)

Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, CO, USA.

Ethan M Lange (EM)

Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, CO, USA.

Donna K Arnett (DK)

Office of the Provost, University of South Carolina, Columbia, SC, USA.

Bessie A Young (BA)

Division of Nephrology, University of Washington, Seattle, WA, USA.

Clarissa J Diamantidis (CJ)

Department of Medicine, Duke University School of Medicine, Durham, NC, USA.

Nora Franceschini (N)

Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Sylvia Wassertheil-Smoller (S)

Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, NY, USA.

Stephen S Rich (SS)

Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.

Jerome I Rotter (JI)

The Institute for Translational Genomic and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbort-UCLA Medical Center, Torrance, CA, USA.

Josyf C Mychaleckyj (JC)

Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.

Holly J Kramer (HJ)

Departments of Public Health Sciences and Medicine, Loyola University Medical Center, Taywood, IL, USA.

Yii-Der I Chen (YI)

The Institute for Translational Genomic and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbort-UCLA Medical Center, Torrance, CA, USA.

Bruce M Psaty (BM)

Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA.
Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.

Jennifer A Brody (JA)

Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.

Ian H de Boer (IH)

Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA.
Division of Nephrology, Department of Medicine, University of Washington, Seattle, WA, USA.

Nisha Bansal (N)

Division of Nephrology, Department of Medicine, University of Washington, Seattle, WA, USA.

Joshua C Bis (JC)

Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA.

Marguerite R Irvin (MR)

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

Classifications MeSH