![]() ![]() Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci. Charting a dynamic DNA methylation landscape of the human genome. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Regulatory genomic circuitry of human disease loci by integrative epigenomics. Stability and flexibility of epigenetic gene regulation in mammalian development. Quantifying genetic effects on disease mediated by assayed gene expression levels. Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments. An eQTL landscape of kidney tissue in human nephrotic syndrome. Genetic-variation-driven gene-expression changes highlight genes with important functions for kidney disease. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Renal compartment-specific genetic variation analyses identify new pathways in chronic kidney disease. Unravelling the complex genetics of common kidney diseases: from variants to mechanisms. Mapping eGFR loci to the renal transcriptome and phenome in the VA Million Veteran Program. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Multiple loci associated with indices of renal function and chronic kidney disease. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. ![]() GBD Chronic Kidney Disease Collaboration. Source data are provided with this paper. Drug–gene interactions were identified using the Drug Gene Interaction Database (DGIdb v.4.2.0, ) 45. GSE157079) 60 and mouse kidney single-cell RNA-seq data from the GEO (accession no. Mouse kidney snATAC-seq data were obtained from the GEO (accession no. Summary statistics for GWAS heritability analysis were obtained from the Alkes Price lab ( ) 37. There is no mechanism to obtain consent because kidney tissue was collected as medical discard and the samples were permanently deidentified. No consent was obtained to share individual-level genotype data for kidney samples. 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). The Integrative Genomics Viewer visualization of human kidney snATAC-seq is publicly available at. GSE115098, GSE173343, GSE172008 and GSE200547 and the Common Metabolic Diseases Genome Atlas ( ). The RNA-seq and human kidney snATAC-seq data have been deposited with the Gene Expression Omnibus (GEO) under accession nos. The GWAS summary statistics are also available at the GWAS Catalog (accession no. The data of eGFRcrea GWAS, kidney meQTLs and kidney eQTLs produced in the present study are publicly available online at the Susztaklab Kidney Biobank ( ) and figshare ( ) 91. 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. 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. We highlight key roles of proximal tubules and metabolism in kidney function regulation. We present a multi-stage prioritization strategy and prioritize target genes for 87% of kidney function loci. Heritability analysis reveals that methylation variation explains a larger fraction of heritability than gene expression. ![]() 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. In the present study, we define the genetic association with kidney function in 1.5 million individuals and identify 878 (126 new) loci. More than 800 million people suffer from kidney disease, yet the mechanism of kidney dysfunction is poorly understood. Nature Genetics volume 54, pages 950–962 ( 2022) Cite this article Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease ![]()
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