Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 117; no. 46; pp. 29013 - 29024
• Main Authors: Sheng, Xin, Qiu, Chengxiang, Liu, Hongbo, Gluck, Caroline, Hsu, Jesse Y., He, Jiang, Hsu, Chi-yuan, Sha, Daohang, Weir, Matthew R., Isakova, Tamara, Raj, Dominic, Rincon-Choles, Hernan, Feldman, Harold I., Townsend, Raymond, Li, Hongzhe, Susztak, Katalin
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 17.11.2020
• Subjects: Annotations; Apoptosis; Association analysis; Bayes Theorem; Bayesian analysis; Biological Sciences; Cell activation; Cohort Studies; Complement activation; Diabetes; Diabetes mellitus; Diabetes Mellitus - genetics; Diabetic Nephropathies - genetics; Diabetic Nephropathies - metabolism; Disease control; DNA Methylation; Epigenesis, Genetic; Epigenetics; Female; Gene Expression; Gene mapping; Genetic analysis; Genetic diversity; Genetic Predisposition to Disease; Genetic Variation; Genome-Wide Association Study; Genomes; Genomics; Genotype; Genotypes; Humans; Kidney diseases; Kidneys; Male; Metabolism; Methylation; Nucleotide sequence; Nucleotides; Pathogenesis; Phenotype; Phenotypes; Quantitative Trait Loci; Renal insufficiency; methylation quantitative trait loci (mQTL); chronic kidney disease; multitrait colocalization analysis (moloc); epigenetics; multiomics integration analysis
• DOI: 10.1073/pnas.2005905117
• ISSN: 0027-8424; 1091-6490

Poor metabolic control and host genetic predisposition are critical for diabetic kidney disease (DKD) development. The epigenome integrates information from sequence variations and metabolic alterations. Here, we performed a genome-wide methylome association analysis in 500 subjects with DKD from the Chronic Renal Insufficiency Cohort for DKD phenotypes, including glycemic control, albuminuria, kidney function, and kidney function decline. We show distinct methylation patterns associated with each phenotype. We define methylation variations that are associated with underlying nucleotide variations (methylation quantitative trait loci) and show that underlying genetic variations are important drivers of methylation changes. We implemented Bayesian multitrait colocalization analysis (moloc) and summary data-based Mendelian randomization to systematically annotate genomic regions that show association with kidney function, methylation, and gene expression. We prioritized 40 loci, where methylation and gene-expression changes likely mediate the genotype effect on kidney disease development. Functional annotation suggested the role of inflammation, specifically, apoptotic cell clearance and complement activation in kidney disease development. Our study defines methylation changes associated with DKD phenotypes, the key role of underlying genetic variations driving methylation variations, and prioritizes methylome and gene-expression changes that likely mediate the genotype effect on kidney disease pathogenesis.