Zhi Yu1,2,3, Jin Jin4, Adrienne Tin3,5, Anna Köttgen3,6, Bing Yu7, Jingsha Chen8, Aditya Surapaneni8, Linda Zhou8, Christie M Ballantyne9, Ron C Hoogeveen9, Dan E Arking10, Nilanjan Chatterjee11,4, Morgan E Grams11,3,8, Josef Coresh11,3,8. 1. Broad Institute of MIT and Harvard, Cambridge, Massachusetts zyu@broadinstitute.org. 2. Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts. 3. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 4. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 5. Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi. 6. Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Centre-University of Freiburg, Freiburg, Germany. 7. Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas. 8. Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland. 9. Department of Medicine, Baylor College of Medicine, Houston, Texas. 10. McKusick-Nathans Department of Genetic Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland. 11. Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland.
Abstract
BACKGROUND: Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (eGFR). The relationship between polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known. METHODS: We developed a genome-wide polygenic risk score (PRS) for eGFR by applying the LDpred algorithm to summary statistics generated from a multiethnic meta-analysis of CKDGen Consortium GWAS (n=765,348) and UK Biobank GWAS (90% of the cohort; n=451,508), followed by best-parameter selection using the remaining 10% of UK Biobank data (n=45,158). We then tested the association of the PRS in the Atherosclerosis Risk in Communities (ARIC) study (n=8866) with incident CKD, ESKD, kidney failure, and AKI. We also examined associations between the PRS and 4877 plasma proteins measured at middle age and older adulthood and evaluated mediation of PRS associations by eGFR. RESULTS: The developed PRS showed a significant association with all outcomes. Hazard ratios per 1 SD lower PRS ranged from 1.06 (95% CI, 1.01 to 1.11) to 1.33 (95% CI, 1.28 to 1.37). The PRS was significantly associated with 132 proteins at both time points. The strongest associations were with cystatin C, collagen α-1(XV) chain, and desmocollin-2. Most proteins were higher at lower kidney function, except for five proteins, including testican-2. Most correlations of the genetic PRS with proteins were mediated by eGFR. CONCLUSIONS: A PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases and broadly influences the plasma proteome, primarily mediated by eGFR.
BACKGROUND: Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (eGFR). The relationship between polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known. METHODS: We developed a genome-wide polygenic risk score (PRS) for eGFR by applying the LDpred algorithm to summary statistics generated from a multiethnic meta-analysis of CKDGen Consortium GWAS (n=765,348) and UK Biobank GWAS (90% of the cohort; n=451,508), followed by best-parameter selection using the remaining 10% of UK Biobank data (n=45,158). We then tested the association of the PRS in the Atherosclerosis Risk in Communities (ARIC) study (n=8866) with incident CKD, ESKD, kidney failure, and AKI. We also examined associations between the PRS and 4877 plasma proteins measured at middle age and older adulthood and evaluated mediation of PRS associations by eGFR. RESULTS: The developed PRS showed a significant association with all outcomes. Hazard ratios per 1 SD lower PRS ranged from 1.06 (95% CI, 1.01 to 1.11) to 1.33 (95% CI, 1.28 to 1.37). The PRS was significantly associated with 132 proteins at both time points. The strongest associations were with cystatin C, collagen α-1(XV) chain, and desmocollin-2. Most proteins were higher at lower kidney function, except for five proteins, including testican-2. Most correlations of the genetic PRS with proteins were mediated by eGFR. CONCLUSIONS: A PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases and broadly influences the plasma proteome, primarily mediated by eGFR.
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