More than 800 million people suffer from kidney disease, yet the mechanism of kidney dysfunction is poorly understood, as gene-regulatory annotation of disease loci is highly incomplete. The epigenome could provide critical insight into disease understanding as it integrates genetic and environmental signals. Here we provided a comprehensive analysis of the genetic determinant of human kidney function by integrating kidney transcriptome, bulk and single cell epigenome. 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. This repository provides the dataset, software and custom code used in this study.
- eGFR_GWAS ~~~~~~~~Custom code for meta-analysis of eGFR GWAS.
- Kidney_meQTL ~~~~~~~~Custom code for mQTL mapping and analysis.
- Kidney_eQTL ~~~~~~~~Custom code for meta-analysis of kidney eQTL.
- Kidney_snATAC ~~~~~~~~Custom code for snATAC data processing and anlaysis.
- GWAS_heritability ~~~~~~~~Custom code for heritability estimation and analysis.
- Gene_prioritization ~~~~~~~~Custom code for kidney disease target gene prioritization.
- Figure_Code ~~~~~~~~Custom code for ploting main figures.
- eGFRcrea GWAS (1.5 million individuals): https://susztaklab.com/GWAS
- Human Kidney meQTL Atlas: http://www.susztaklab.com/Kidney_meQTL
- Human Kidney eQTL Atlas: http://www.susztaklab.com/Kidney_eQTL
- Human Kidney Open Chromatin Atlas: http://www.susztaklab.com/Human_snATAC
- Susztak Lab: https://www.med.upenn.edu/susztaklab
- Susztak Lab Kidney Biobank: http://www.susztaklab.com
- For any question, please contact us [email protected] and [email protected]
Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease.
Hongbo Liu, Tomohito Doke, Dong Guo, Xin Sheng, Ziyuan Ma, Joseph Park, Ha My T. Vy, Girish N. Nadkarni, Amin Abedini, Zhen Miao, Matthew Palmer, Benjamin F. Voight, Hongzhe Li, Christopher D. Brown, Marylyn D. Ritchie, Yan Shu and Katalin Susztak. Nature Genetics (2022)
https://www.nature.com/articles/s41588-022-01097-w