Kidney Disease GWAS and QTL Summary Statistics from Trans-Ancestry Cohorts

Summary statistics from three genomic analyses for kidney disease research, created by Hongbo Liu. Kidney mQTL mapping was performed on 443 trans-ancestry individuals, identifying 139,313 mCpGs and 13,771,378 significant SNP-mCpG pairs. An eGFRcrea GWAS meta-analysis integrated five studies across 1,508,659 individuals, finding 90,950 genome-wide significant variants, while a kidney eQTL meta-analysis of 686 individuals identified 10,430 eGenes and 1,222,250 significant SNP-gene pairs.

Use Cases

• Prioritizing causal genetic variants for kidney function based on genome-wide significant associations from the eGFRcrea GWAS.
• Investigating epigenetic regulation in kidney tissue using the identified 139,313 methylation quantitative trait loci (mQTLs).
• Identifying gene expression regulators in the kidney through the 10,430 eGenes and their associated SNP-gene pairs from the eQTL analysis.
• Performing integrative multi-omics analyses to link genetic variants with methylation and gene expression changes relevant to kidney disease.

Strengths

• Large-scale trans-ancestry GWAS meta-analysis includes data from 1,508,659 individuals.
• Multi-omics integration provides summary statistics for genetic, methylation (mQTL), and expression (eQTL) associations.
• Statistical rigor is indicated by the use of permutation testing and a false discovery rate (FDR) threshold of ≤0.01.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.
• Data may reflect geographic bias inherent to the source studies, with cohorts predominantly of European ancestry (72-80%).

Provenance

Source

paperswithcode

Collection Method

Meta-analysis of multiple existing GWAS and QTL studies.

Geography

Trans-ancestry cohorts, predominantly of European descent.