Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle

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Taylor , D L , Jackson , A U , Narisu , N , Hemani , G , Erdos , M R , Chines , P S , Swift , A , Idol , J , Didion , J P , Welch , R P , Kinnunen , L , Saramies , J , Lakka , T A , Laakso , M , Tuomilehto , J , Parker , S C J , Koistinen , H A , Smith , G D , Boehnke , M , Scott , L J , Birney , E & Collins , F S 2019 , ' Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle ' , Proceedings of the National Academy of Sciences of the United States of America , vol. 116 , no. 22 , pp. 10883-10888 . https://doi.org/10.1073/pnas.1814263116

Title: Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle
Author: Taylor, D. Leland; Jackson, Anne U.; Narisu, Narisu; Hemani, Gibran; Erdos, Michael R.; Chines, Peter S.; Swift, Amy; Idol, Jackie; Didion, John P.; Welch, Ryan P.; Kinnunen, Leena; Saramies, Jouko; Lakka, Timo A.; Laakso, Markku; Tuomilehto, Jaakko; Parker, Stephen C. J.; Koistinen, Heikki A.; Smith, George Davey; Boehnke, Michael; Scott, Laura J.; Birney, Ewan; Collins, Francis S.
Contributor: University of Helsinki, HYKS erva
University of Helsinki, Department of Public Health
University of Helsinki, HUS Internal Medicine and Rehabilitation
Date: 2019-05-28
Language: eng
Number of pages: 6
Belongs to series: Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
URI: http://hdl.handle.net/10138/303970
Abstract: We integrate comeasured gene expression and DNA methylation (DNAme) in 265 human skeletal muscle biopsies from the FUSION study with >7 million genetic variants and eight physiological traits: height, waist, weight, waist-hip ratio, body mass index, fasting serum insulin, fasting plasma glucose, and type 2 diabetes. We find hundreds of genes and DNAme sites associated with fasting insulin, waist, and body mass index, as well as thousands of DNAme sites associated with gene expression (eQTM). We find that controlling for heterogeneity in tissue/muscle fiber type reduces the number of physiological trait associations, and that long-range eQTMs (>1 Mb) are reduced when controlling for tissue/muscle fiber type or latent factors. We map genetic regulators (quantitative trait loci; QTLs) of expression (eQTLs) and DNAme (mQTLs). Using Mendelian randomization (MR) and mediation techniques, we leverage these genetic maps to predict 213 causal relationships between expression and DNAme, approximately two-thirds of which predict methylation to causally influence expression. We use MR to integrate FUSION mQTLs, FUSION eQTLs, and GTEx eQTLs for 48 tissues with genetic associations for 534 diseases and quantitative traits. We identify hundreds of genes and thousands of DNAme sites that may drive the reported disease/quantitative trait genetic associations. We identify 300 gene expression MR associations that are present in both FUSION and GTEx skeletal muscle and that show stronger evidence of MR association in skeletal muscle than other tissues, which may partially reflect differences in power across tissues. As one example, we find that increased RXRA muscle expression may decrease lean tissue mass.
Subject: DNA methylation
gene expression
eQTL
mQTL
skeletal muscle
MENDELIAN RANDOMIZATION
GWAS
3111 Biomedicine
1184 Genetics, developmental biology, physiology
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