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Böger, C.A.* ; Heid, I.M.

Chronic kidney disease: Novel insights from genome-wide association studies.

Kidney Blood Press. Res. 34, 225-234 (2011)

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Chronic kidney disease (CKD) is common, affecting about 10% of the general population, and causing significant morbidity and mortality. Apart from the risk conferred by traditional cardiovascular risk factors, there is a strong genetic component. The method of a genome-wide association study (GWAS) is a powerful hypothesis-free approach to unravel this component by association analyses of CKD with several million genetic variants distributed across the genome. Since the publication of the first GWAS in 2005, this method has led to the discovery of novel loci for numerous human common diseases and phenotypes. Here, we review the recent successes of meta-analyses of GWAS on renal phenotypes. UMOD, SHROOM3, STC1, LASS2, GCKR, ALMS1, TFDP2, DAB2, SLC34A1, VEGFA, PRKAG2, PIP5K1B, ATXN2/SH2B3, DACH1, UBE2Q2, and SLC7A9 were uncovered as loci associated with estimated glomerular filtration rate (eGFR) and CKD, and CUBN as a locus for albuminuria in cross-sectional data of general population studies. However, less than 1.5% of the total variance of eGFR and albuminuria is explained by the identified variants, and the relative risk for CKD is modified by at most 20% per locus. In African Americans, much of the risk for end-stage nondiabetic kidney disease is explained by common variants in the MYH9/APOL1 locus, and in individuals of European descent, variants in HLA-DQA1 and PLA(2)R1 implicate most of the risk for idiopathic membranous nephropathy. In contrast, genetic findings in the analysis of diabetic nephropathy are inconsistent. Uncovering variants explaining more of the genetically determined variability of kidney function is hampered by the multifactorial nature of CKD and different mechanisms involved in progressive CKD stages, and by the challenges in elucidating the role of low-frequency variants. Meta-analyses with larger sample sizes and analyses of longitudinal renal phenotypes using higher-resolution genotyping data are required to uncover novel loci associated with severe renal phenotypes.

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