FC033: Genome-Wide Association Meta-Analysis Identifies Novel Loci for Kidney Failure

Abstract

Abstract BACKGROUND AND AIMS The genetics of kidney function has been extensively studied, but since these analyses have been done mostly in populations with normal or mildly impaired kidney function, it is still unknown whether variants linked to kidney function also translate into genetic susceptibility for kidney failure. The primary aim of this study was to investigate the genetic background of kidney failure. METHOD We performed a meta-analysis of kidney failure genome-wide association studies, using kidney transplant recipients as cases (n = 6942) and donors as controls (n = 4788). Secondary disease-specific analyses were performed for kidney failure due to diabetes, IgA nephropathy, glomerulonephritis or polycystic kidney disease. Subsequently, we investigated genetic overlap with eGFR and urinary albumin-creatinine ratio (UACR) variability, based on publicly available CKDgen consortium meta-analysis data. RESULTS In the primary analysis, we found two suggestive hits for kidney failure: rs17046239 in GRM7 (P = 8.9 × 10−8), and rs9273431 in HLA-DQB1 (P = 5.3 × 10−8). In disease-specific analyses, we found three genome-wide (P < 5 × 10−8) significant hits for kidney failure due to diabetes: rs9273431 in HLA-DQB1 (P = 5.0 × 10−50), rs2476601 in PTPN22 (P = 2.9 × 10−13) and rs7110099 in INS-IGF2 (P = 4.4 × 10−9). Furthermore, we found suggestive hits for kidney failure due to glomerulonephritis (rs6531751, nearest gene: PDS5a, P = 9.3 × 10−8) or polycystic kidney disease (rs111857047 in PTPRD, P = 9.6 × 10−8). As follow-up analysis, we performed lookups for the identified SNPs in prior kidney function GWAS results of the CKDGen consortium. Our top hits for kidney failure due to diabetes were nominally significant (P < 0.05) in the creatinine-based estimated glomerular filtration rate (eGFRcrea) and/or the UACR results. The other hits were not significantly associated with either phenotype. Linkage disequilibrium score regression (LDSC) analysis did not reveal a significant genetic correlation (rG) between kidney failure and eGFRcrea or UACR. There was significant genetic overlap of type 2 diabetes with both kidney failure due to any cause (rG = 0.27; P = 0.0027) and kidney failure due to diabetes (rG = 0.57; P = 0.0002). In silico sequencing revealed that our diabetes-induced kidney failure hits had previously been associated with auto-immunity and type 1 diabetes; the other hits yielded no results. A UK Biobank phenome-wide association scan confirmed the associations of the three diabetes-specific kidney failure genetic hits with auto-immunity and diabetes phenotypes, and additionally linked them to thyroid function and blood cell counts. CONCLUSION We identified three genome-wide significant variants associated with kidney failure due to diabetes and four additional suggestive hits associated with cause-specific or overall kidney failure. The lack of strong genetic overlap with eGFR and UACR suggests that there is a separate genetic component that drives the risk of progression to kidney failure, independent of normal kidney function.