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<br>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<br>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 &lt; 5E-8) association with eGFRcrea.<br>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 (&lt; 0.01), we identified 10,430 eGenes and 1,222,250 significant SNP-gene pairs.<br><br>