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

Hongbo Liu1,2,3, Tomohito Doke1,2,3, Dong Guo4, Xin Sheng1,2,3, Ziyuan Ma1,2,3, Joseph Park1,3,5, Ha My T. Vy6,7, Girish N. Nadkarni6,7,8,9, Amin Abedini1,2,3, Zhen Miao1,2,3, Matthew Palmer10, Benjamin F. Voight2,3,11,12, Hongzhe Li13, Christopher D. Brown3, Marylyn D. Ritchie3,5, Yan Shu4 and Katalin Susztak1,2,3 1Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA; 2Institute of Diabetes Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA; 3Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA; 4Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland at Baltimore, Baltimore, MD, USA; 5Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; 6Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; 7The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; 8The Hasso Plattner Institute of Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; 9The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA; 10Pathology and Laboratory Medicine at the Hospital of the University of Pennsylvania, Philadelphia, , USA; 11Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA; 12Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA; 13Department of Biostatistics, Epidemiology, and Informatics, and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

More than 800 million people suffer from kidney disease, yet the mechanism of kidney dysfunction is poorly understood. Here we define the genetic association with kidney function in 1.5 million individuals and identify 878 (126 novel) 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.