Genome-wide polygenic score withAPOL1risk genotypes predicts chronic kidney disease across major continental ancestries
Authors: Khan Atlas , Turchin Michael C. , Patki Amit , Srinivasasainagendra Vinodh , Shang Ning , Nadukuru Rajiv , Jones Alana C. , Malolepsza Edyta , Dikilitas Ozan , Kullo Iftikhar J. , Schaid Daniel J. , Karlson Elizabeth , Ge Tian , Meigs James B. , Smoller Jordan W. , Lange Christoph , Crosslin David R. , Jarvik Gail , Bhatraju Pavan , Hellwege Jacklyn N. , Chandler Paulette , Torvik Laura Rasmussen , Fedotov Alex , Liu Cong , Kachulis Christopher , Abul-Husn Noura S. , Cho Judy H. , Ionita-Laza Iuliana , Gharavi Ali G. , Chung Wendy K. , Hripcsak George , Weng Chunhua , Nadkarni Girish , Irvin Marguerite R. , Tiwari Hemant K. , Kenny Eimear E. , Limdi Nita A. , Kiryluk Krzysztof 
Published: October 2021

Introduction

Chronic kidney disease (CKD) is a common complex condition associated with significant morbidity and mortality in the US and worldwide. Early detection is critical for effective prevention of kidney disease progression. Polygenic prediction of CKD could enhance screening and prevention of kidney disease progression, but this approach has not been optimized for risk prediction in ancestrally diverse populations.

Methods

We developed and validated a genome-wide polygenic score (GPS) for CKD defined by estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2using common variant association statistics from GWAS for eGFR combined with information onAPOL1risk genotypes. The score was designed to ensure transferability across major continental ancestries, genotyping platforms, imputation panels, and phenotyping strategies, and was tested following ClinGen guidelines. The polygenic component of the score was developed and optimized using 28,047 cases and 251,772 controls (70% of UK Biobank participants of European ancestry), while the weights forAPOL1effects were derived based on UK Biobank participants of African ancestry (967 cases and 6,191 controls). We tested the performance of the score in 15 independent testing cohorts, including 3 cohorts of European ancestry (total 23,364 cases and 117,883 controls), 6 cohorts of African ancestry (4,268 cases and 10,276 controls), 4 cohorts of Asian ancestry (1,030 cases and 9,896 controls), and 2 Hispanic/Latinx cohorts (1,492 cases and 2,984 controls).

Results

We demonstrated the risk score transferability with reproducible performance across all independent testing cohorts. In the meta-analyses, disease odds ratios per standard deviation of the score were estimated at 1.49 (95%CI: 1.47-1.50, P<1.0E-300) for European, 1.32 (95%CI: 1.26-1.38, P=1.8E-33) for African, 1.59 (95%CI: 1.52-1.67, P=1.3E-30) for Asian, and 1.42 (95%CI: 1.33-1.51, P=4.1E-14) for Latinx cohorts. The top 2% cutoff of the GPS was associated with nearly 3-fold increased risk of CKD across all major ancestral groups, the degree of risk that is equivalent to a positive family history of kidney disease. In African-ancestry cohorts,APOL1risk genotype and the polygenic risk components of the GPS had additive effects on the risk of CKD with no significant interactions. We also observed that individuals of African ancestry had a significantly higher polygenic risk score for CKD compared to other populations, even without accounting forAPOL1variants.

Conclusions

By combiningAPOL1risk genotypes with the available GWAS for renal function, we designed, optimized, and validated a GPS predictive of CKD across four major continental ancestries. With the upper tail of the GPS distribution associated with disease risk equivalent to a positive family history, this score could be used for clinically meaningful risk stratification.