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

D Leland Taylor, Anne U Jackson, Narisu Narisu, Gibran Hemani, Michael R Erdos, Peter S Chines, Amy Swift, Jackie Idol, John P Didion, Ryan P Welch, Leena Kinnunen, Jouko Saramies, Timo A Lakka, Markku Laakso, Jaakko Tuomilehto, Stephen C J Parker, Heikki A Koistinen, George Davey Smith, Michael Boehnke, Laura J ScottEwan Birney, Francis S Collins

Research output: Contribution to journalArticle (Academic Journal)peer-review

84 Citations (Scopus)
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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.

Original languageEnglish
Pages (from-to)10883-10888
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number22
Early online date10 May 2019
Publication statusPublished - 28 May 2019


  • DNA methylation
  • EQTL
  • Gene expression
  • MQTL
  • Skeletal muscle


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