Dataset Information

Meta-analyses identify DNA methylation associated with kidney function and damage.

ABSTRACT: Chronic kidney disease is a major public health burden. Elevated urinary albumin-to-creatinine ratio is a measure of kidney damage, and used to diagnose and stage chronic kidney disease. To extend the knowledge on regulatory mechanisms related to kidney function and disease, we conducted a blood-based epigenome-wide association study for estimated glomerular filtration rate (n = 33,605) and urinary albumin-to-creatinine ratio (n = 15,068) and detected 69 and seven CpG sites where DNA methylation was associated with the respective trait. The majority of these findings showed directionally consistent associations with the respective clinical outcomes chronic kidney disease and moderately increased albuminuria. Associations of DNA methylation with kidney function, such as CpGs at JAZF1, PELI1 and CHD2 were validated in kidney tissue. Methylation at PHRF1, LDB2, CSRNP1 and IRF5 indicated causal effects on kidney function. Enrichment analyses revealed pathways related to hemostasis and blood cell migration for estimated glomerular filtration rate, and immune cell activation and response for urinary albumin-to-creatinineratio-associated CpGs.

SUBMITTER: Schlosser P

PROVIDER: S-EPMC8660832 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Meta-analyses identify DNA methylation associated with kidney function and damage.

Schlosser Pascal P Tin Adrienne A Matias-Garcia Pamela R PR Thio Chris H L CHL Joehanes Roby R Liu Hongbo H Weihs Antoine A Yu Zhi Z Hoppmann Anselm A Grundner-Culemann Franziska F Min Josine L JL Adeyemo Adebowale A AA Agyemang Charles C Ärnlöv Johan J Aziz Nasir A NA Baccarelli Andrea A Bochud Murielle M Brenner Hermann H Breteler Monique M B MMB Carmeli Cristian C Chaker Layal L Chambers John C JC Cole Shelley A SA Coresh Josef J Corre Tanguy T Correa Adolfo A Cox Simon R SR de Klein Niek N Delgado Graciela E GE Domingo-Relloso Arce A Eckardt Kai-Uwe KU Ekici Arif B AB Endlich Karlhans K Evans Kathryn L KL Floyd James S JS Fornage Myriam M Franke Lude L Fraszczyk Eliza E Gao Xu X Gào Xīn X Ghanbari Mohsen M Ghasemi Sahar S Gieger Christian C Greenland Philip P Grove Megan L ML Harris Sarah E SE Hemani Gibran G Henneman Peter P Herder Christian C Horvath Steve S Hou Lifang L Hurme Mikko A MA Hwang Shih-Jen SJ Jarvelin Marjo-Riitta MR Kardia Sharon L R SLR Kasela Silva S Kleber Marcus E ME Koenig Wolfgang W Kooner Jaspal S JS Kramer Holly H Kronenberg Florian F Kühnel Brigitte B Lehtimäki Terho T Lind Lars L Liu Dan D Liu Yongmei Y Lloyd-Jones Donald M DM Lohman Kurt K Lorkowski Stefan S Lu Ake T AT Marioni Riccardo E RE März Winfried W McCartney Daniel L DL Meeks Karlijn A C KAC Milani Lili L Mishra Pashupati P PP Nauck Matthias M Navas-Acien Ana A Nowak Christoph C Peters Annette A Prokisch Holger H Psaty Bruce M BM Raitakari Olli T OT Ratliff Scott M SM Reiner Alex P AP Rosas Sylvia E SE Schöttker Ben B Schwartz Joel J Sedaghat Sanaz S Smith Jennifer A JA Sotoodehnia Nona N Stocker Hannah R HR Stringhini Silvia S Sundström Johan J Swenson Brenton R BR Tellez-Plaza Maria M van Meurs Joyce B J JBJ van Vliet-Ostaptchouk Jana V JV Venema Andrea A Verweij Niek N Walker Rosie M RM Wielscher Matthias M Winkelmann Juliane J Wolffenbuttel Bruce H R BHR Zhao Wei W Zheng Yinan Y Loh Marie M Snieder Harold H Levy Daniel D Waldenberger Melanie M Susztak Katalin K Köttgen Anna A Teumer Alexander A

Nature communications 20211209 1

Chronic kidney disease is a major public health burden. Elevated urinary albumin-to-creatinine ratio is a measure of kidney damage, and used to diagnose and stage chronic kidney disease. To extend the knowledge on regulatory mechanisms related to kidney function and disease, we conducted a blood-based epigenome-wide association study for estimated glomerular filtration rate (n = 33,605) and urinary albumin-to-creatinine ratio (n = 15,068) and detected 69 and seven CpG sites where DNA methylation ...[more]

PMID: 34887417

Similar Datasets

Project description:A subset of individuals with type 1 diabetes will develop diabetic kidney disease (DKD). DKD is heritable and large-scale genome-wide association studies have begun to identify genetic factors that influence DKD. Complementary to genetic factors, we know that a person's epigenetic profile is also altered with DKD. This study reports analysis of DNA methylation, a major epigenetic feature, evaluating methylome-wide loci for association with DKD. Unique features (n = 485,577; 482,421 CpG probes) were evaluated in blood-derived DNA from carefully phenotyped White European individuals diagnosed with type 1 diabetes with (cases) or without (controls) DKD (n = 677 samples). Explicitly, 150 cases were compared to 100 controls using the 450K array, with subsequent analysis using data previously generated for a further 96 cases and 96 controls on the 27K array, and de novo methylation data generated for replication in 139 cases and 96 controls. Following stringent quality control, raw data were quantile normalized and beta values calculated to reflect the methylation status at each site. The difference in methylation status was evaluated between cases and controls; resultant P-values for array-based data were adjusted for multiple testing. Genes with significantly increased (hypermethylated) and/or decreased (hypomethylated) levels of DNA methylation were considered for biological relevance by functional enrichment analysis using KEGG pathways. Twenty-two loci demonstrated statistically significant fold changes associated with DKD and additional support for these associated loci was sought using independent samples derived from patients recruited with similar inclusion criteria. Markers associated with CCNL1 and ZNF187 genes are supported as differentially regulated loci (P < 10-8), with evidence also presented for AFF3, which has been identified from a meta-analysis and subsequent replication of genome-wide association studies. Further supporting evidence for differential gene expression in CCNL1 and ZNF187 is presented from kidney biopsy and blood-derived RNA in people with and without kidney disease from NephroSeq. Evidence confirming that methylation sites influence the development of DKD may aid risk prediction tools and stimulate research to identify epigenomic therapies which might be clinically useful for this disease.

Dataset Information

Meta-analyses identify DNA methylation associated with kidney function and damage.

Publications

Meta-analyses identify DNA methylation associated with kidney function and damage.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets