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Proc Natl Acad Sci U S A. 2019 May 28;116(22):10883-10888. doi: 10.1073/pnas.1814263116. Epub 2019 May 10.

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

Author information

1
Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892.
2
European Molecular Biology Laboratory, European Bioinformatics Institute, CB10 1SD Hinxton, United Kingdom.
3
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109.
4
Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109.
5
MRC Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, BS8 2BN Bristol, United Kingdom.
6
Department of Public Health Solutions, National Institute for Health and Welfare, FI-00271 Helsinki, Finland.
7
Rehabilitation Center, South Karelia Social and Health Care District EKSOTE, Fl-53130 Lappeenranta, Finland.
8
Institute of Biomedicine, School of Medicine, University of Eastern Finland, Fl-70211 Kuopio, Finland.
9
Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Fl-70211 Kuopio, Finland.
10
Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Fl-70100 Kuopio, Finland.
11
Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, FI-70210 Kuopio, Finland.
12
Department of Medicine, Kuopio University Hospital, FI-70210 Kuopio, Finland.
13
Department of Public Health, University of Helsinki, Fl-00014 Helsinki, Finland.
14
Saudi Diabetes Research Group, King Abdulaziz University, 21589 Jeddah, Saudi Arabia.
15
Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109.
16
Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109.
17
Department of Medicine, University of Helsinki and Helsinki University Central Hospital, FI-00029 Helsinki, Finland.
18
Minerva Foundation Institute for Medical Research, FI-00290 Helsinki, Finland.
19
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109; ljst@umich.edu birney@ebi.ac.uk collinsf@mail.nih.gov.
20
European Molecular Biology Laboratory, European Bioinformatics Institute, CB10 1SD Hinxton, United Kingdom; ljst@umich.edu birney@ebi.ac.uk collinsf@mail.nih.gov.
21
Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892; ljst@umich.edu birney@ebi.ac.uk collinsf@mail.nih.gov.

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.

KEYWORDS:

DNA methylation; eQTL; gene expression; mQTL; skeletal muscle

PMID:
31076557
DOI:
10.1073/pnas.1814263116
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Conflict of interest statement

Conflict of interest statement: D.J.G. and E.B. are members of the Human Induced Pluripotent Stem Cell Initiative and coauthors on a 2017 research article.

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