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

Atlas Khan, Michael C. Turchin, Amit Patki, Vinodh Srinivasasainagendra, Ning Shang, Rajiv Nadukuru, Alana C. Jones, Edyta Malolepsza, Ozan Dikilitas, Iftikhar J. Kullo, Daniel J. Schaid, Elizabeth Karlson, Tian Ge, James B. Meigs, Jordan W. Smoller, Christoph Lange, David R. Crosslin, Gail P. Jarvik, Pavan K. Bhatraju, Jacklyn N. HellwegePaulette Chandler, Laura Rasmussen Torvik, Alex Fedotov, Cong Liu, Christopher Kachulis, Niall Lennon, Noura S. Abul-Husn, Judy H. Cho, Iuliana Ionita-Laza, Ali G. Gharavi, Wendy K. Chung, George Hripcsak, Chunhua Weng, Girish Nadkarni, Marguerite R. Irvin, Hemant K. Tiwari, Eimear E. Kenny, Nita A. Limdi, Krzysztof Kiryluk

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1412-1420
Number of pages9
JournalNature Medicine
Issue number7
StatePublished - Jul 2022

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)


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