posted on 2019-08-15, 14:41authored byAP Morris, TH Le, H Wu, A Akbarov, PJ van der Most, G Hemani, GD Smith, A Mahajan, KJ Gaulton, GN Nadkarni, A Valladares-Salgado, N Wacher-Rodarte, JC Mychaleckyj, ND Dueker, X Guo, Y Hai, J Haessler, Y Kamatani, AM Stilp, G Zhu, JP Cook, J Ärnlöv, SH Blanton, MH de Borst, EP Bottinger, TA Buchanan, S Cechova, FJ Charchar, P-L Chu, J Damman, J Eales, AG Gharavi, V Giedraitis, AC Heath, E Ipp, K Kiryluk, HJ Kramer, M Kubo, A Larsson, CM Lindgren, Y Lu, PAF Madden, GW Montgomery, GJ Papanicolaou, LJ Raffel, RL Sacco, E Sanchez, H Stark, J Sundstrom, KD Taylor, AH Xiang, A Zivkovic, L Lind, E Ingelsson, NG Martin, JB Whitfield, J Cai, CC Laurie, Y Okada, K Matsuda, C Kooperberg, Y-DI Chen, T Rundek, SS Rich, RJF Loos, EJ Parra, M Cruz, JI Rotter, H Snieder, M Tomaszewski, BD Humphreys, N Franceschini
Chronic kidney disease (CKD) affects ~10% of the global population, with considerable ethnic differences in prevalence and aetiology. We assemble genome-wide association studies of estimated glomerular filtration rate (eGFR), a measure of kidney function that defines CKD, in 312,468 individuals of diverse ancestry. We identify 127 distinct association signals with homogeneous effects on eGFR across ancestries and enrichment in genomic annotations including kidney-specific histone modifications. Fine-mapping reveals 40 high-confidence variants driving eGFR associations and highlights putative causal genes with cell-type specific expression in glomerulus, and in proximal and distal nephron. Mendelian randomisation supports causal effects of eGFR on overall and cause-specific CKD, kidney stone formation, diastolic blood pressure and hypertension. These results define novel molecular mechanisms and putative causal genes for eGFR, offering insight into clinical outcomes and routes to CKD treatment development.
Funding
T.H.L. is supported by the NIH (R01-DK-113632). G.H. is supported by the Wellcome Trust (208806/Z/17/Z). G.H. and G.D.S. work in the Medical Research Council Integrative Epidemiology Unit at the University of Bristol (MC_UU_00011/1). C.M.L. is supported by the Li Ka Shing Foundation, WT-SSI/John Fell funds, the Oxford NIHR Biomedical Research Centre, Widenlife and the NIH (5P50-HD-028138–27). R.L.S. and T.R. are supported by the NIH (R37-NS-029993 and U54-TR-002736) and the Evelyn F McKnight Brain Institute. H.S. and A.Z. are supported by the DFG (INST 208/664–1). M.T. is supported the British Heart Foundation (PG/17/35/33001) and Kidney Research UK (RP_017_20180302). N.F. is supported by the NIH (R01-MD-012765, R56-DK-104806, and R01-DK-117445-01A1). Additional funding and acknowledgements can be found in Supplementary Note 1.
Association summary statistics will be made available from: (i) the COGENTKidney Consortium component of the trans-ethnic meta-analysis; and (ii) the
trans-ethnic meta-analysis across the COGENT-Kidney Consortium, CKDGen
Consortium and Biobank Japan Project. Fine-mapping data for each distinct eGFR
signal will be made available, including the posterior probability of driving the
association for each SNV. These data will be made available via: (i) the University
of Liverpool Statistical Genetics and Pharmacogenomics Research Group website
(https://www.liverpool.ac.uk/translational-medicine/research/statistical-genetics/data-resources); and (ii) the dbGaP CHARGE Summary Results site92 with
accession number phs000930. The source data underlying Fig. 1 and Supplementary Figure 7 are provided as a Source Data file.