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

Yann C Klimentidis, David A Raichlen, Jennifer Bea, David O Garcia, Nathan E Wineinger, Lawrence J Mandarino, Gene E Alexander, Zhao Chen, Scott B Going, Yann C Klimentidis, David A Raichlen, Jennifer Bea, David O Garcia, Nathan E Wineinger, Lawrence J Mandarino, Gene E Alexander, Zhao Chen, Scott B Going

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.

Conflict of interest statement

Conflict of interest: The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Manhattan plot of GWAS for self-reported MVPA and VPA, strenuous sports or other exercises (abbreviated as SS or Other Exer.), and for accelerometer-based average accelerations and fraction of accelerations > 425 mg. Negative log10-transformed p-value for each SNP is plotted by chromosome and position (x-axis). The red horizontal line represents the threshold for genome-wide significant associations (p−9).
Figure 2
Figure 2
Genetic correlation of self-reported PA variables with other traits and diseases across the three statistical models employed. Traits/diseases shown are those that are in the top 10 of genetically correlated traits/diseases (according to p-value) for at least one of the 3 models. Traits/diseases are ordered from top to bottom in order of increasing p-value for Model 1. Horizontal position of bars corresponds to the genetic correlation (rg) between PA and the respective trait/disease. Error bars represent 95% confidence intervals for rg estimates. Bright green bars represent traits that showed a correlation with p-value <2.5 × 10−4, and light green bars represent traits with genetic correlation p<0.05. We excluded highly redundant traits (e.g. obesity, overweight) after leaving higher ranked one in.
Figure 3
Figure 3
Genetic correlation of accelerometry-based PA variables with other traits and diseases across the three statistical models employed. Traits/diseases shown are those that are in the top 10 of genetically correlated traits/diseases (according to p-value) for at least one of the 3 models. Traits/diseases are ordered from top to bottom in order of increasing p-value for Model 1. Horizontal position of bars corresponds to the genetic correlation (rg) between PA and the respective trait/disease. Error bars represent 95% confidence intervals for rg estimates. Bright green bars represent traits that showed a correlation with p-value <2.5 × 10−4, and light green bars represent traits with genetic correlation p<0.05. We excluded highly redundant traits (e.g. obesity, overweight) after leaving higher ranked one in.
Figure 4
Figure 4
Results of gene-based enrichment analysis for 30 general tissue types for PA-associated loci. Dashed line represents the Bonferroni-corrected significance threshold.

References

    1. Fiuza-Luces C, Garatachea N, Berger NA, Lucia A. Exercise is the real polypill. Physiol. 2013
    1. US Surgeon General. Physical Activity and Health: A Report of the Surgeon General. S/N 017-023-00196-5. 1996
    1. Blair SN. Physical inactivity: the biggest public health problem of the 21st century. Br J Sports Med [Internet] 2009 Jan 9;43(1):1 LP-2.
    1. Kaplan GA, Strawbridge WJ, Cohen RD, Hungerford LR. Natural history of leisure-time physical activity and its correlates: associations with mortality from all causes and cardiovascular disease over 28 years. Am J Epidemiol. 1996
    1. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJF, Martin BW. Correlates of physical activity: Why are some people physically active and others not? Lancet. 2012:258–71.
    1. Trost SG, Owen N, Bauman AE, Sallis JF, Brown W. Correlates of adults’ participation in physical activity: review and update. Med Sci Sport Exerc. 2002
    1. den Hoed M, Brage S, Zhao JH, Westgate K, Nessa A, Ekelund U, et al. Heritability of objectively assessed daily physical activity and sedentary behavior. Am J Clin Nutr. 2013:1317–25.
    1. Gielen M, Westerterp-Plantenga MS, Bouwman FG, Joosen AMCP, Vlietinck R, Derom C, et al. Heritability and genetic etiology of habitual physical activity: a twin study with objective measures. [Internet] Genes Nutr. 2014:415.
    1. Stubbe JH, Boomsma DI, Vink JM, Cornes BK, Martin NG, Skytthe A, et al. Genetic influences on exercise participation in 37.051 twin pairs from seven countries. PLoS One. 2006
    1. Joosen AMCP, Gielen M, Vlietinck R, Westerterp KR. Genetic analysis of physical activity in twins. Am J Clin Nutr United States. 2005 Dec;82(6):1253–9.
    1. Pérusse L, Tremblay A, Leblanc C, Bouchard C. Genetic and environmental influences on level of habitual physical activity and exercise participation. Am J Epidemiol. 1989
    1. Lauderdale DS, Fabsitz R, Meyer JM, Sholinsky P, Ramakrishnan V, Goldberg J. Familial determinants of moderate and intense physical activity: a twin study. Med Sci Sports Exerc United States. 1997 Aug;29(8):1062–8.
    1. Kaprio J, Koskenvuo M, Sarna S. Cigarette smoking, use of alcohol, and leisure-time physical activity among same-sexed adult male twins. Prog Clin Biol Res United States. 1981;69(Pt C):37–46.
    1. Thompson PD, Tsongalis GJ, Ordovas JM, Seip RL, Bilbie C, Miles M, et al. Angiotensin-converting enzyme genotype and adherence to aerobic exercise training. Prev Cardiol. 2006
    1. Herring MP, Sailors MH, Bray MS. Genetic factors in exercise adoption, adherence and obesity. Obes Rev England. 2014 Jan;15(1):29–39.
    1. Wilkinson AV, Gabriel KP, Wang J, Bondy ML, Dong Q, Wu X, et al. Sensation-seeking genes and physical activity in youth. Genes, Brain Behav. 2013
    1. Caldwell Hooper AE, Bryan AD, Hagger MS. What keeps a body moving? The brain-derived neurotrophic factor val66met polymorphism and intrinsic motivation to exercise in humans. J Behav Med United States. 2014 Dec;37(6):1180–92.
    1. Lightfoot JT. Current understanding of the genetic basis for physical activity. J Nutr United States. 2011 Mar;141(3):526–30.
    1. De Moor MHM, Liu Y-J, Boomsma DI, Li J, Hamilton JJ, Hottenga J-J, et al. Genome-wide association study of exercise behavior in Dutch and American adults. Med Sci Sports Exerc United States. 2009 Oct;41(10):1887–95.
    1. Kim J, Min H, Oh S, Kim Y, Lee AH, Park T. Joint identification of genetic variants for physical activity in Korean population. Int J Mol Sci. 2014;15(7):12407–21.
    1. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med Public Library of Science. 2015;12(3)
    1. The Aric Investigators. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am J Epidemiol [Internet] 1989;129(4):687–702.
    1. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc [Internet] 2003 Aug;35(8):1381–95. [cited 2013 Aug, 6]
    1. Ipaq Sci M, Med S, Hardcastle S, Blake N, Hagger MS, et al. Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire (IPAQ) - Short Form. J Am Diet Assoc. 2002
    1. Ekelund U, Sepp H, Brage S, Becker W, Jakes R, Hennings M, et al. Criterion-related validity of the last 7-day, short form of the International Physical Activity Questionnaire in Swedish adults. Public Health Nutr. 2006
    1. American Heart Association. American Heart Association Recommendations for Physical Activity in Adults [Internet] 2016 [cited 2003 Aug 20]. Available from: .
    1. Doherty A, Jackson D, Hammerla N, Plötz T, Olivier P, Granat MH, et al. Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study. In: Buchowski M, editor. PLoS One [Internet] 2. Vol. 12. Public Library of Science; 2017. Feb 1, p. e0169649. [cited 2017 Apr, 17]
    1. Hildebrand M, Van Hees VT, Hansen BH, Ekelund U. Age group comparability of raw accelerometer output from wrist-and hip-worn monitors. Med Sci Sports Exerc. 2014
    1. Baecke JA, Burema J, Frijters JE. A short questionnaire for the measurement of habitual physical activity in epidemiological studies. Am J Clin Nutr. 1982;36(5):936–42.
    1. Folsom AR, Arnett DK, Hutchinson RG, Liao F, Clegg LX, Cooper LS. Physical activity and incidence of coronary heart disease in middle-aged women and men. Med Sci Sport Exerc. 1997;29(7):901–9.
    1. Richardson MT, Ainsworth BE, Wu HC, Jacobs DRJ, Leon AS. Ability of the Atherosclerosis Risk in Communities (ARIC)/Baecke Questionnaire to assess leisure-time physical activity. Int J Epidemiol England. 1995 Aug;24(4):685–93.
    1. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. Genome-wide genetic data on ~500,000 UK Biobank participants. bioRxiv [Internet] 2017 Jul 20;
    1. O’Connell J, Sharp K, Shrine N, Wain L, Hall I, Tobin M, et al. Haplotype estimation for biobank-scale data sets. Nat Genet. 2016
    1. Consortium GP. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, et al. An integrated map of genetic variation from 1,092 human genomes. Nature [Internet] Nature Publishing Group. 2012;491(7422):56–65.
    1. McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016
    1. UK10K Consortium. The UK10K project identifies rare variants in health and disease. Nature. 2015
    1. Galinsky KJ, Bhatia G, Loh PR, Georgiev S, Mukherjee S, Patterson NJ, et al. Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia. Am J Hum Genet. 2016
    1. Howie B, Marchini J, Stephens M, Chakravarti A. Genotype Imputation with Thousands of Genomes. G3 GenesGenomesGenetics [Internet] Genetics Society of America. 2011;1(6):457–70.
    1. Townsend P. J Soc Policy [Internet] 2. Vol. 16. Cambridge University Press; 1987. Deprivation; pp. 125–46. 2009/01/01.
    1. Smith GD, Whitley E, Dorling D, Gunnell D. Area based measures of social and economic circumstances: cause specific mortality patterns depend on the choice of index. J Epidemiol Community Health. 2001
    1. Loh P-R, Tucker G, Bulik-Sullivan BK, Vilhjalmsson BJ, Finucane HK, Salem RM, et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet United States. 2015 Mar;47(3):284–90.
    1. Loh P-R, Kichaev G, Gazal S, Schoech AP, Price AL. bioRxiv [Internet] Cold Spring Harbor Laboratory; 2017. Mixed model association for biobank-scale data sets.
    1. Klarin D, Zhu QM, Emdin CA, Chaffin M, Horner S, McMillan BJ, et al. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease. Nat Genet United States. 2017 Sep;49(9):1392–7.
    1. Klarin D, Emdin CA, Natarajan P, Conrad MF, Kathiresan S. Circ Cardiovasc Genet [Internet] 2 Vol. 10. American Heart Association Inc; 2017. Genetic Analysis of Venous Thromboembolism in UK Biobank Identifies the ZFPM2 Locus and Implicates Obesity as a Causal Risk FactorCLINICAL PERSPECTIVE.
    1. Fadista J, Manning AK, Florez JC, Groop L. The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants. Eur J Hum Genet. 2016;24:1202–5.
    1. Yaghootkar H, Bancks MP, Jones SE, McDaid A, Beaumont R, Donnelly L, et al. Quantifying the extent to which index event biases influence large genetic association studies. Hum Mol Genet England. 2017 Mar;26(5):1018–30.
    1. Saunders AM, Strittmatter WJ, Schmechel D, St George-Hyslop PH, Pericak-Vance MA, Joo SH, et al. Association of apolipoprotein E allele ε4 with late-onset familial and sporadic alzheimer’s disease. Neurology. 1993
    1. Lahoz C, Schaefer EJ, Cupples LA, Wilson PW, Levy D, Osgood D, et al. Apolipoprotein E genotype and cardiovascular disease in the Framingham Heart Study. Atherosclerosis Ireland. 2001 Feb;154(3):529–37.
    1. Eichner JE, Dunn ST, Perveen G, Thompson DM, Stewart KE, Stroehla BC. Apolipoprotein E polymorphism and cardiovascular disease: a HuGE review. Am J Epidemiol United States. 2002 Mar;155(6):487–95.
    1. Tingley D, Yamamoto T, Hirose K, Keele L, Imai K. Mediation: R Package for Causal Mediation Analysis. J Stat Softw [Internet] 2014;59(5):1–38.
    1. Team RDC. R: A language and environment for statistical computing. 2011
    1. Chyou PH. A simple and robust way of concluding meta-analysis results using reported P values, standardized effect sizes, or other statistics. Clin Med Res. 2012:219–23.
    1. Watanabe K, Taskesen E, van Bochoven A, Posthuma D. FUMA: Functional mapping and annotation of genetic associations. bioRxiv. 2017 Feb
    1. Consortium TGte. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Sci [Internet] 2015 May 8;348(6235):648–60.
    1. de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: Generalized Gene-Set Analysis of GWAS Data. In: Tang H, editor. PLOS Comput Biol [Internet] 4. Vol. 11. Public Library of Science; 2015. Apr 17, p. e1004219. [cited 2018 Mar 5]
    1. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics [Internet] 2017;33(2):272.
    1. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236–41.
    1. Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J, Consortium SWG of the PG et al. Nat Genet [Internet] 3. Vol. 47. Nature Publishing Group a division of Macmillan Publishers Limited; 2015. Mar, LD Score regression distinguishes confounding from polygenicity in genome-wide association studies; pp. 291–5. All Rights Reserved.
    1. Elliott L, Sharp K, Alfaro-Almagro F, Douaud G, Miller K, Marchini J, et al. bioRxiv [Internet] Cold Spring Harbor Laboratory; 2017. The genetic basis of human brain structure and function: 1,262 genome-wide associations found from 3,144 GWAS of multimodal brain imaging phenotypes from 9,707 UK Biobank participants.
    1. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Nature [Internet] 7538. Vol. 518. Nature Publishing Group a division of Macmillan Publishers Limited; 2015. Feb 12, Genetic studies of body mass index yield new insights for obesity biology; pp. 197–206. All Rights Reserved.
    1. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Nat Genet [Internet] Vol. 42. Metabolism Initiative and Program in Medical and Population Genetics, Broad Institute; Cambridge, Massachusetts, USA: 2010. Nov, Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index; pp. 937–48. 1546-1718 (Electronic)
    1. Boutwell B, Hinds D, Tielbeek J, Ong KK, Day FR, Perry JRB. Replication and characterization of CADM2 and MSRA genes on human behavior. Heliyon England. 2017 Jul;3(7):e00349.
    1. Day FR, Helgason H, Chasman DI, Rose LM, Loh P-R, Scott RA, et al. Nat Genet [Internet] 6. Vol. 48. Nature Publishing Group a division of Macmillan Publishers Limited; 2016. Jun, Physical and neurobehavioral determinants of reproductive onset and success; pp. 617–23. All Rights Reserved.
    1. Strawbridge RJ, Ward J, Cullen B, Tunbridge EM, Hartz S, Bierut L, et al. Genome-wide analysis of risk-taking behaviour and cross-disorder genetic correlations in 116,255 individuals from the UK Biobank cohort. bioRxiv [Internet] 2017 Aug 16;
    1. Clarke T-K, Adams MJ, Davies G, Howard DM, Hall LS, Padmanabhan S, et al. Genome-wide association study of alcohol consumption and genetic overlap with other health-related traits in UK Biobank (N=112 117) Mol Psychiatry England. 2017 Oct;22(10):1376–84.
    1. Ibrahim-Verbaas CA, Bressler J, Debette S, Schuur M, Smith AV, Bis JC, et al. Mol Psychiatry. Macmillan Publishers Limited; 2016. GWAS for executive function and processing speed suggests involvement of the CADM2 gene [Internet] pp. 189–97.
    1. Yan X, Wang Z, Schmidt V, Gauert A, Willnow TE, Heinig M, et al. Mol Metab [Internet] Elsevier GmbH; 2017. Cadm2 regulates body weight and energy homeostasis in mice; pp. 1–9.
    1. Pericak-Vance MA, Bebout JL, Gaskell PC, Jr, Yamaoka LH, Hung WY, Alberts MJ, et al. Linkage studies in familial Alzheimer disease: evidence for chromosome 19 linkage. Am J Hum Genet. 1991
    1. Thompson PD, Tsongalis GJ, Seip RL, Bilbie C, Miles M, Zoeller R, et al. Apolipoprotein e genotype and changes in serum lipids and maximal oxygen uptake with exercise training. Metabolism [Internet] 2004 Feb;53(2):193–202. [cited 2017 Aug 16]
    1. Dudbridge F, Fletcher O. Gene-environment dependence creates spurious gene-environment interaction. Am J Hum Genet United States. 2014 Sep;95(3):301–7.
    1. Lee H, Ash GI, Angelopoulos TJ, Gordon PM, Moyna NM, Visich PS, et al. Obesity-Related Genetic Variants and their Associations with Physical Activity [Internet] Sport Med Open. 2015:34.
    1. Richmond RC, Davey Smith G, Ness AR, den Hoed M, McMahon G, Timpson NJ. Assessing Causality in the Association between Child Adiposity and Physical Activity Levels: A Mendelian Randomization Analysis. PLoS Med. 2014
    1. Day FR, Loh P-R, Scott RA, Ong KK, Perry JR. Am J Hum Genet [Internet] 2. Vol. 98. Elsevier; 2016. Feb 4, A Robust Example of Collider Bias in a Genetic Association Study; pp. 392–3.
    1. Aschard H, Vilhjalmsson BJ, Joshi AD, Price AL, Kraft P. Adjusting for heritable covariates can bias effect estimates in genome-wide association studies. Am J Hum Genet United States. 2015 Feb;96(2):329–39.
    1. Vink JM, Boomsma DI, Medland SE, de Moor MHM, Stubbe JH, Cornes BK, et al. Variance components models for physical activity with age as modifier: a comparative twin study in seven countries. Twin Res Hum Genet England. 2011 Feb;14(1):25–34.
    1. Shungin D, Winkler T, Croteau-Chonka D, Ferreira T, Mägi R, Lakka T, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518(7538):187–96.
    1. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet United States. 2011 Sep;43(10):977–83.
    1. Biological insights from 108 schizophrenia-associated genetic loci. Nature England. 2014 Jul;511(7510):421–7.
    1. Sniekers S, Stringer S, Watanabe K, Jansen PR, Coleman JRI, Krapohl E, et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat Genet United States. 2017 Jul;49(7):1107–12.
    1. Jones SE, Tyrrell J, Wood AR, Beaumont RN, Ruth KS, Tuke MA, et al. Genome-Wide Association Analyses in 128,266 Individuals Identifies New Morningness and Sleep Duration Loci. PLoS Genet United States. 2016 Aug;12(8):e1006125.

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