Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease

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...

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Veröffentlicht in:Nature genetics 2022-07, Vol.54 (7), p.950-962
Hauptverfasser: Liu, Hongbo, Doke, Tomohito, Guo, Dong, Sheng, Xin, Ma, Ziyuan, Park, Joseph, Vy, Ha My T., Nadkarni, Girish N., Abedini, Amin, Miao, Zhen, Palmer, Matthew, Voight, Benjamin F., Li, Hongzhe, Brown, Christopher D., Ritchie, Marylyn D., Shu, Yan, Susztak, Katalin
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Sprache:eng
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Zusammenfassung: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. Genome-wide analyses identify hundreds of loci associated with kidney function. Integrated analyses of expression, methylation and single-cell open chromatin and expression data derived from human kidney samples prioritize genes and mechanisms underlying renal disease.
ISSN:1061-4036
1546-1718
1546-1718
DOI:10.1038/s41588-022-01097-w