Article | Published:

Genetics and Epigenetics

Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE

International Journal of Obesityvolume 42pages11611176 (2018) | Download Citation

Abstract

Background/objectives

Physical activity (PA) protects against a wide range of diseases. Habitual PA appears to be heritable, motivating the search for specific genetic variants that may inform efforts to promote PA and target the best type of PA for each individual.

Subjects/methods

We used data from the UK Biobank to perform the largest genome-wide association study of PA to date, using three measures based on self-report (nmax = 377,234) and two measures based on wrist-worn accelerometry data (nmax = 91,084). We examined genetic correlations of PA with other traits and diseases, as well as tissue-specific gene expression patterns. With data from the Atherosclerosis Risk in Communities (ARIC; n = 8,556) study, we performed a meta-analysis of our top hits for moderate-to-vigorous PA (MVPA).

Results

We identified ten loci across all PA measures that were significant in both a basic and a fully adjusted model (p < 5 × 10−9). Upon meta-analysis of the nine top hits for MVPA with results from ARIC, eight were genome-wide significant. Interestingly, among these, the rs429358 variant in the APOE gene was the most strongly associated with MVPA, whereby the allele associated with higher Alzheimer’s risk was associated with greater MVPA. However, we were not able to rule out possible selection bias underlying this result. Variants in CADM2, a gene previously implicated in obesity, risk-taking behavior and other traits, were found to be associated with habitual PA. We also identified three loci consistently associated (p < 5 × 10−5) with PA across both self-report and accelerometry, including CADM2. We found genetic correlations of PA with educational attainment, chronotype, psychiatric traits, and obesity-related traits. Tissue enrichment analyses implicate the brain and pituitary gland as locations where PA-associated loci may exert their actions.

Conclusions

These results provide new insight into the genetic basis of habitual PA, and the genetic links connecting PA with other traits and diseases.

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Acknowledgements

This research was conducted using the UK Biobank Resource under Application Number 15678. We thank the participants and organizers of the UK Biobank. We also thank the participants and organizers of the ARIC study. Data from ARIC was obtained from dbGaP through accession number phs000280.v2.p1. The authors would like to acknowledge support from the National Institute of Diabetes and Digestive and Kidney Diseases grant (K01DK095032), the National Institute on Aging (AG019610), the State of Arizona and Arizona Department of Health Services (ADHS), and the McKnight Brain Research Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Atherosclerosis Risk in Communities: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). Funding for GENEVA was provided by National Human Genome Research Institute grant U01HG004402 (E. Boerwinkle). The authors thank the staff and participants of the ARIC study for their important contributions. LDHUB Acknowledgements: We gratefully acknowledge all the studies and databases that made GWAS summary data available: ADIPOGen (Adiponectin genetics consortium), C4D (Coronary Artery Disease Genetics Consortium), CARDIoGRAM (Coronary ARtery DIsease Genome wide Replication and Meta-analysis), CKDGen (Chronic Kidney Disease Genetics consortium), dbGAP (database of Genotypes and Phenotypes), DIAGRAM (DIAbetes Genetics Replication And Meta-analysis), ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis), EAGLE (EArly Genetics & Lifecourse Epidemiology Eczema Consortium, excluding 23andMe), EGG (Early Growth Genetics Consortium), GABRIEL (A Multidisciplinary Study to Identify the Genetic and Environmental Causes of Asthma in the European Community), GCAN (Genetic Consortium for Anorexia Nervosa), GEFOS (GEnetic Factors for OSteoporosis Consortium), GIANT (Genetic Investigation of ANthropometric Traits), GIS (Genetics of Iron Status consortium), GLGC (Global Lipids Genetics Consortium), GPC (Genetics of Personality Consortium), GUGC (Global Urate and Gout consortium), HaemGen (haemotological and platelet traits genetics consortium), HRgene (Heart Rate consortium), IIBDGC (International Inflammatory Bowel Disease Genetics Consortium), ILCCO (International Lung Cancer Consortium), IMSGC (International Multiple Sclerosis Genetic Consortium), MAGIC (Meta-Analyses of Glucose and Insulin-related traits Consortium), MESA (Multi-Ethnic Study of Atherosclerosis), PGC (Psychiatric Genomics Consortium), Project MinE consortium, ReproGen (Reproductive Genetics Consortium), SSGAC (Social Science Genetics Association Consortium) and TAG (Tobacco and Genetics Consortium), TRICL (Transdisciplinary Research in Cancer of the Lung consortium), UK Biobank. We gratefully acknowledge the contributions of Alkes Price (the systemic lupus erythematosus GWAS and primary biliary cirrhosis GWAS) and Johannes Kettunen (lipids metabolites GWAS).

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Affiliations

  1. Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA

    • Yann C. Klimentidis
    •  & Zhao Chen
  2. School of Anthropology, University of Arizona, Tucson, AZ, USA

    • David A. Raichlen
  3. Department of Medicine, University of Arizona, Tucson, AZ, USA

    • Jennifer Bea
  4. Department of Nutritional Sciences, University of Arizona, Tucson, AZ, USA

    • Jennifer Bea
    •  & Scott B. Going
  5. Department of Health Promotion Sciences, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA

    • David O. Garcia
  6. Scripps Translational Science Institute, La Jolla, CA, USA

    • Nathan E. Wineinger
  7. Center for Disparities in Diabetes, Obesity and Metabolism, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Arizona, Tucson, AZ, USA

    • Lawrence J. Mandarino
  8. Departments of Psychology and Psychiatry, Neuroscience and Physiological Sciences Interdisciplinary Programs, BIO5 Institute, and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA

    • Gene E. Alexander
  9. Arizona Alzheimer’s Consortium, Phoenix, AZ, USA

    • Gene E. Alexander

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The authors declare that they have no conflict of interest.

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Correspondence to Yann C. Klimentidis.

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https://doi.org/10.1038/s41366-018-0120-3