Details

Autor(en) / Beteiligte

• 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.

Titel

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

Ist Teil von

• Proceedings of the National Academy of Sciences - PNAS, 2019-05, Vol.116 (22), p.10883-10888

Ort / Verlag

United States: National Academy of Sciences

Erscheinungsjahr

2019

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.