Abstract
Genes underneath signals from genome-wide association studies (GWAS) for kidney function are promising targets for functional studies, but prioritizing variants and genes is challenging. By GWAS meta-analysis for creatinine-based estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Genetics Consortium and UK Biobank (n = 1,201,909), we expand the number of eGFRcrea loci (424 loci, 201 novel; 9.8% eGFRcrea variance explained by 634 independent signal variants). Our increased sample size in fine-mapping (n = 1,004,040, European) more than doubles the number of signals with resolved fine-mapping (99% credible sets down to 1 variant for 44 signals, ≤5 variants for 138 signals). Cystatin-based eGFR and/or blood urea nitrogen association support 348 loci (n = 460,826 and 852,678, respectively). Our customizable tool for Gene PrioritiSation reveals 23 compelling genes including mechanistic insights and enables navigation through genes and variants likely relevant for kidney function in human to help select targets for experimental follow-up.
Highlights
To identify genetic variants associated with eGFRcrea, we conducted a linear mixed model-based GWAS19 of eGFRcrea in UK Biobank (UKB) (European ancestry, n = 436,581, Supplementary Data 1, imputed to Haplotype Reference Consortium[20] and UK10K panels21) and meta-analysed results with the CKD Genetics (CKDGen) Consortium data[7], for a total sample size of 1,201,909 individuals (Supplementary Fig. 1, “Methods”)
Our study demonstrates the impact of increased sample size on genome-wide association studies (GWAS) findings for eGFRcrea, and on substantially improved fine-mapping and alternative biomarker support
We aggregated comprehensive and systematic in silico follow-up results for the more than 5000 genes and 38,306 credible set variants underneath the identified loci into a Gene PriortiSation (GPS) tool to navigate through the abundance of evidence
Summary
GWAS meta-analysis identified 201 novel non-overlapping loci for eGFRcrea. To identify genetic variants associated with eGFRcrea, we conducted a linear mixed model-based GWAS19 of eGFRcrea in UKB (European ancestry, n = 436,581, Supplementary Data 1, imputed to Haplotype Reference Consortium[20] and UK10K panels21) and meta-analysed results with the CKDGen Consortium data (mostly European ancestry, n = 765,348, imputed to Haplotype Reference Consortium[20] or 1000 Genomes22)[7], for a total sample size of 1,201,909 individuals (Supplementary Fig. 1, “Methods”). Sensitivity meta-analyses restricting to individuals of European ancestry (n = 1,004,040) demonstrated similar association results for the 424 identified lead variants in European ancestry alone compared to the primary meta-analysis (Supplementary Data 2 and Supplementary Fig. 3). Two meta-analyses were undertaken for eGFRcrea; the primary meta-analysis (n = 1,201,930) showed 424 non-overlapping loci (201 novel and 223 known) at the significance level of P < 5 × 10−8; the second meta-analysis (n = 417,288) independently supported eGFRcrea association of 361 (of 424) lead variants (145/201 novel and 216/223 known) at onesidedP < 0.05. For example in the known UMOD/PDILT locus, we observed four independent signals, two novel and two previously described[7] (Supplementary Fig. 6). This suggests two new causal variants in this locus well-known for kidney function. We annotated all 38,306 credible set variants for being relevant for (i) functional consequence on the protein for variants within the gene
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