Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease (Data Set)

Summary statistic data of eGFRcrea GWAS, kidney eQTL and kidney mQTL data

Kidney mQTL mapping was performed based on genotype and DNA methylation data of kidney samples from 443 trans-ancestry individuals (79% are of European ancestry). The significance of the top associated variants per CpG was estimated by adaptive permutation in FastQTL using the covariates above and the setting “--permute 1000”. Beta distribution-adjusted empirical p-values from FastQTL were used to calculate q-values using Storey’s q method, and a false discovery rate (FDR) threshold of ≤0.01 was applied to identify CpGs with a significant mQTL. Totally, we identified 139,313 mCpGs and 13,771,378 significant SNP-mCpG pairs

eGFRcrea GWAS meta-analysis was performed in 1,508,659 trans-ancestry individuals (80% are of European ancestry) by integrating five GWAS studies. We identified 90,950 variants showing genome wide significant (p < 5E-8) association with eGFRcrea.

Kidney eQTL meta-analysis was performed in 686 trans-ancestry individuals (72% are of European ancestry) by integrating four eQTL studies. To define eGenes, we used the Storey approach to calculate q values for all associations for each gene. With significant q value (< 0.01), we identified 10,430 eGenes and 1,222,250 significant SNP-gene pairs.