Genome-wide Association Study of Liking for Several Types of Physical Activity in the UK Biobank and Two Replication Cohorts.
Journal
Medicine and science in sports and exercise
ISSN: 1530-0315
Titre abrégé: Med Sci Sports Exerc
Pays: United States
ID NLM: 8005433
Informations de publication
Date de publication:
01 08 2022
01 08 2022
Historique:
pubmed:
24
3
2022
medline:
19
7
2022
entrez:
23
3
2022
Statut:
ppublish
Résumé
A lack of physical activity (PA) is one of the most pressing health issues today. Our individual propensity for PA is influenced by genetic factors. Stated liking of different PA types may help capture additional and informative dimensions of PA behavior genetics. In over 157,000 individuals from the UK Biobank, we performed genome-wide association studies of five items assessing the liking of different PA types, plus an additional derived trait of overall PA-liking. We attempted to replicate significant associations in the Netherlands Twin Register (NTR) and TwinsUK. Additionally, polygenic scores (PGS) were trained in the UK Biobank for each PA-liking item and for self-reported PA behavior, and tested for association with PA in the NTR. We identified a total of 19 unique significant loci across all five PA-liking items and the overall PA-liking trait, and these showed strong directional consistency in the replication cohorts. Four of these loci were previously identified for PA behavior, including CADM2 , which was associated with three PA-liking items. The PA-liking items were genetically correlated with self-reported ( rg = 0.38-0.80) and accelerometer ( rg = 0.26-0.49) PA measures, and with a wide range of health-related traits. Each PA-liking PGS significantly predicted the same PA-liking item in NTR. The PGS of liking for going to the gym predicted PA behavior in the NTR ( r2 = 0.40%) nearly as well as a PGS based on self-reported PA behavior ( r2 = 0.42%). Combining the two PGS into a single model increased the r2 to 0.59%, suggesting that PA-liking captures distinct and relevant dimensions of PA behavior. We have identified the first loci associated with PA-liking and extended our understanding of the genetic basis of PA behavior.
Identifiants
pubmed: 35320144
doi: 10.1249/MSS.0000000000002907
pii: 00005768-202208000-00003
pmc: PMC9288543
mid: NIHMS1786165
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1252-1260Subventions
Organisme : NIMH NIH HHS
ID : RC2 MH089951
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH081802
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL136528
Pays : United States
Organisme : British Heart Foundation
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : P30 AG072980
Pays : United States
Organisme : Medical Research Council
ID : MR/M016560/1
Pays : United Kingdom
Organisme : NIDA NIH HHS
ID : R37 DA018673
Pays : United States
Organisme : NIMH NIH HHS
ID : U24 MH068457
Pays : United States
Organisme : Medical Research Council
ID : MC_UU_00007/10
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 212904/Z/18/Z
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : P30 AG019610
Pays : United States
Organisme : Medical Research Council
ID : MC_QA137853
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : R01 AG064587
Pays : United States
Organisme : NIA NIH HHS
ID : R56 AG067200
Pays : United States
Organisme : NIDA NIH HHS
ID : R01 DA018673
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK092127
Pays : United States
Organisme : Wellcome Trust
Pays : United Kingdom
Informations de copyright
Copyright © 2022 by the American College of Sports Medicine.
Références
Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet . 2012;380(9838):219–29.
Blair SN. Physical inactivity: the biggest public health problem of the 21st century. Br J Sports Med . 2009;43(1):1 LP–2.
van der Ploeg HP, Chey T, Korda RJ, Banks E, Bauman A. Sitting time and all-cause mortality risk in 222 497 Australian adults. Arch Intern Med . 2012;172(6):494–500.
Stubbe JH, Boomsma DI, Vink JM, et al. Genetic influences on exercise participation in 37.051 twin pairs from seven countries. PLoS One . 2006;1(1):e22.
den Hoed M, Brage S, Zhao JH, et al. Heritability of objectively assessed daily physical activity and sedentary behavior. Am J Clin Nutr . 2013;98(5):1317–25.
Franks PW, Ravussin E, Hanson RL, et al. Habitual physical activity in children: the role of genes and the environment. Am J Clin Nutr . 2005;82(4):901–8.
Aaltonen S, Ortega-Alonso A, Kujala UM, Kaprio J. A longitudinal study on genetic and environmental influences on leisure time physical activity in the Finnish twin cohort. Twin Res Hum Genet . 2010;13(5):475–81.
Klimentidis YC, Raichlen DA, Bea J, et al. Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE. Int J Obes (Lond) . 2018;42(6):1161–76.
Doherty A, Smith-Byrne K, Ferreira T, et al. GWAS identifies 14 loci for device-measured physical activity and sleep duration. Nat Commun . 2018;9(1):5257.
Qi G, Dutta D, Leroux A, et al. Genome-wide association studies of 27 accelerometry-derived physical activity measurements identified novel loci and genetic mechanisms. Genet Epidemiol . 2022;46(2):122–38.
Prince SA, Adamo KB, Hamel ME, Hardt J, Connor Gorber S, Tremblay M. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act . 2008;5:56.
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;380(9838):258–71.
Deci EL, Ryan RM. Intrinsic Motivation and Self-Determination in Human Behavior . New York: Plenum Press; 1985.
Ryan RM, Williams GC, Patrick H, Deci EL. Self-determination theory and physical activity: the dynamics of motivation in development and wellness. Hell J Psychol . 2009;6(2):107–24.
Teixeira PJ, Carraça EV, Markland D, Silva MN, Ryan RM. Exercise, physical activity, and self-determination theory: a systematic review. Int J Behav Nutr Phys Act . 2012;9:78.
Rogers H, Morris T, Moore M. A qualitative study of the achievement goals of recreational exercise participants. Qual Rep . 2008;13(4):706–34.
Aaltonen S, Kaprio J, Vuoksimaa E, Huppertz C, Kujala UM, Silventoinen K. Genetic architecture of motives for leisure-time physical activity: a twin study. Scand J Med Sci Sports . 2017;27(11):1431–41.
Vink JM, van Hooijdonk KJM, Willemsen G, Feskens EJM, Boomsma DI. Causes of variation in food preference in the Netherlands. Twin Res Hum Genet . 2020;23(4):195–203.
Sudlow C, Gallacher J, Allen N, 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 . 2015;12(3):e1001779.
Biobank U. Food preferences web questionnaire [Internet]. 2020. Available from: http://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/foodpref.pdf .
Duffy VB, Hayes JE, Sullivan BS, Faghri P. Surveying food and beverage liking: a tool for epidemiological studies to connect chemosensation with health outcomes. Ann N Y Acad Sci . 2009;1170:558–68.
Deglaire A, Méjean C, Castetbon K, et al. Development of a questionnaire to assay recalled liking for salt, sweet and fat. Food Qual Prefer . 2012;23(2):110–24.
Brunelle-Greene D. The Physical Activity Screening Tool and Liking of Physical Activity in Young Adults . CT, USA: University of Connecticut Storrs; 2017.
Hubert PA, Mahoney M, Huedo-Medina TB, Leahey TM, Duffy VB. Can assessing physical activity liking identify opportunities to promote physical activity engagement and healthy dietary behaviors? Nutrients . 2021;13(10):3366.
Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature . 2018;562(7726):203–9.
Ligthart L, van Beijsterveldt CEM, Kevenaar ST, et al. The Netherlands Twin Register: longitudinal research based on twin and twin-family designs. Twin Res Hum Genet . 2019;22(6):623–36.
Verdi S, Abbasian G, Bowyer RCE, et al. TwinsUK: the UK adult twin registry update. Twin Res Hum Genet . 2019;22(6):523–9.
Pallister T, Sharafi M, Lachance G, et al. Food preference patterns in a UK twin cohort. Twin Res Hum Genet . 2015;18(6):793–805.
Roederer M, Quaye L, Mangino M, et al. The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell . 2015;161(2):387–403.
Das S, Forer L, Schönherr S, et al. Next-generation genotype imputation service and methods. Nat Genet . 2016;48(10):1284–7.
Jiang L, Zheng Z, Qi T, et al. A resource-efficient tool for mixed model association analysis of large-scale data. Nat Genet . 2019;51(12):1749–55.
Bulik-Sullivan BK, Loh PR, Finucane HK, et al; Consortium SWG of the PG. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet . 2015;47(3):291–5.
Tsepilov YA, Freidin MB, Shadrina AS, et al. Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions. Commun Biol . 2020;3(1):329.
Timmers PRHJ, Wilson JF, Joshi PK, Deelen J. Multivariate genomic scan implicates novel loci and haem metabolism in human ageing. Nat Commun . 2020;11(1):3570.
Zheng J, Erzurumluoglu AM, Elsworth BL, 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 . 2017;33(2):272.
Finucane HK, Bulik-Sullivan B, Gusev A, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet . 2015;47(11):1228–35.
Vilhjálmsson BJ, Yang J, Finucane HK, et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet . 2015;97(4):576–92.
Speliotes EK, Willer CJ, Berndt SI, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet . 2010;42(11):937–48.
Boutwell B, Hinds D, Tielbeek J, Ong KK, Day FR, Perry JRB. Replication and characterization of CADM2 and MSRA genes on human behavior. Heliyon . 2017;3(7):e00349.
Day FR, Helgason H, Chasman DI, et al. Physical and neurobehavioral determinants of reproductive onset and success. Nat Genet . 2016;48(6):617–23.
Strawbridge RJ, Ward J, Cullen B, et al. Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort. Transl Psychiatry . 2018;8(1):39.
Watson HJ, Yilmaz Z, Thornton LM, et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat Genet . 2019;51(8):1207–14.
Fry A, Littlejohns TJ, Sudlow C, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol . 2017;186(9):1026–34.