Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease
Nature Genetics. 2022-06-16;54:950–962.
- More than 800 million people suffer from kidney disease, yet the mechanism of kidney dysfunction is poorly understood. In the present study, we define the genetic association with kidney function in 1.5 million individuals and identify 878 (126 new) loci. We map the genotype effect on the methylome in 443 kidneys, transcriptome in 686 samples and single-cell open chromatin in 57,229 kidney cells. Heritability analysis reveals that methylation variation explains a larger fraction of heritability than gene expression. We present a multi-stage prioritization strategy and prioritize target genes for 87% of kidney function loci. We highlight key roles of proximal tubules and metabolism in kidney function regulation. Furthermore, the causal role of SLC47A1 in kidney disease is defined in mice with genetic loss of Slc47a1 and in human individuals carrying loss-of-function variants. Our findings emphasize the key role of bulk and single-cell epigenomic information in translating genome-wide association studies into identifying causal genes, cellular origins and mechanisms of complex traits.
- Consortium data used in this publication
- The data of eGFRcrea GWAS, kidney meQTLs and kidney eQTLs produced in the present study are publicly available online at the Susztaklab Kidney Biobank (https://susztaklab.com/GWAS; https://susztaklab.com/Kidney_meQTL; https://susztaklab.com/Kidney_eQTL) and figshare (https://doi.org/10.6084/m9.figshare.15183495)91. The GWAS summary statistics are also available at the GWAS Catalog (accession no. GCST90100220). The RNA-seq and human kidney snATAC-seq data have been deposited with the Gene Expression Omnibus (GEO) under accession nos. GSE115098, GSE173343, GSE172008 and GSE200547 and the Common Metabolic Diseases Genome Atlas (https://cmdga.org/search/?type=Experiment&searchTerm=FNIH0000000). The Integrative Genomics Viewer visualization of human kidney snATAC-seq is publicly available at https://susztaklab.com/Human_snATAC. The summary statistics of five eGFRcrea GWAS datasets used for GWAS meta-analysis were obtained from consortium websites (download links provided in Supplementary Table 1). No consent was obtained to share individual-level genotype data for kidney samples. There is no mechanism to obtain consent because kidney tissue was collected as medical discard and the samples were permanently deidentified. Summary statistics for GWAS heritability analysis were obtained from the Alkes Price lab (https://alkesgroup.broadinstitute.org/LDSCORE/independent_sumstats)37. Mouse kidney snATAC-seq data were obtained from the GEO (accession no. GSE157079)60 and mouse kidney single-cell RNA-seq data from the GEO (accession no. GSE107585)56. Drug–gene interactions were identified using the Drug Gene Interaction Database (DGIdb v.4.2.0, https://www.dgidb.org)45. Source data are provided with this paper.