Abstract
Background:
Meta-analysis of case–control genome-wide association studies (GWAS) for early onset and morbid obesity identified four variants in/near the PRL, PTER, MAF and NPC1 genes.
Objective:
We aimed to validate association of these variants with obesity-related traits in population-based samples.
Design:
Genotypes and anthropometric traits were available in up to 31 083 adults from the Fenland, EPIC-Norfolk, Whitehall II, Ely and Hertfordshire studies and in 2042 children and adolescents from the European Youth Heart Study. In each study, we tested associations of rs4712652 (near-PRL), rs10508503 (near-PTER), rs1424233 (near-MAF) and rs1805081 (NPC1), or proxy variants (r2>0.8), with the odds of being overweight and obese, as well as with body mass index (BMI), percentage body fat (%BF) and waist circumference (WC). Associations were adjusted for sex, age and age2 in adults and for sex, age, age group, country and maturity in children and adolescents. Summary statistics were combined using fixed effects meta-analysis methods.
Results:
We had 80% power to detect odds ratios of 1.046 to 1.092 for overweight and 1.067 to 1.136 for obesity. Variants near PRL, PTER and MAF were not associated with the odds of being overweight or obese, or with BMI, %BF or WC after meta-analysis (P>0.15). The NPC1 variant rs1805081 showed some evidence of association with %BF (β=0.013 s.d./allele, P=0.040), but not with any of the remaining obesity-related traits (P>0.3).
Conclusion:
Overall, these variants, which were identified in a GWAS for early onset and morbid obesity, do not seem to influence obesity-related traits in the general population.
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References
Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L, Steinthorsdottir V et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat genet 2010; 42: 949–960.
Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat genet 2010; 42: 937–948.
Meyre D, Delplanque J, Chevre JC, Lecoeur C, Lobbens S, Gallina S et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat genet 2009; 41: 157–159.
Day N, Oakes S, Luben R, Khaw KT, Bingham S, Welch A et al. EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br J Cancer 1999; 80 (Suppl 1): 95–103.
Dykes J, Brunner EJ, Martikainen PT, Wardle J . Socioeconomic gradient in body size and obesity among women: the role of dietary restraint, disinhibition and hunger in the Whitehall II study. Int J Obes Relat Metab Disord 2004; 28: 262–268.
Ekelund U, Brage S, Franks PW, Hennings S, Emms S, Wareham NJ . Physical activity energy expenditure predicts progression toward the metabolic syndrome independently of aerobic fitness in middle-aged healthy Caucasians: the Medical Research Council Ely Study. Diabetes Care 2005; 28: 1195–1200.
Marmot M, Brunner E . Cohort Profile: the Whitehall II study. Int J Epidemiol 2005; 34: 251–256.
Riddoch C, Edwards D, Page A, Froberg K, Andersen A, Wedderkopp N et al. The European youth heart study-cardiovascular disease risk factors in children: rationale, aims, study design, and validation of methods. J Phys Act Health 2005; 2: 115–129.
Rolfe Ede L, Loos RJ, Druet C, Stolk RP, Ekelund U, Griffin SJ et al. Association between birth weight and visceral fat in adults. Am J Clin Nutr 2010; 92: 347–352.
Syddall HE, Aihie Sayer A, Dennison EM, Martin HJ, Barker DJ, Cooper C . Cohort profile: the Hertfordshire cohort study. Int J Epidemiol 2005; 34: 1234–1242.
Syddall HE, Simmonds SJ, Martin HJ, Watson C, Dennison EM, Cooper C et al. Cohort profile: The Hertfordshire Ageing Study (HAS). Inter J Epidemiol 2010; 39: 36–43.
Vimaleswaran KS, Li S, Zhao JH, Luan J, Bingham SA, Khaw KT et al. Physical activity attenuates the body mass index-increasing influence of genetic variation in the FTO gene. Am J Clin Nutr 2009; 90: 425–428.
Europe Co. The Eurofit Test Battery. Council of Europe: Strasburg, France, 1988.
Cole T, Bellizzi M, Flegal K, Dietz W . Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000; 320: 1240–1243.
Finucane FM, Sharp SJ, Purslow LR, Horton K, Horton J, Savage DB et al. The effects of aerobic exercise on metabolic risk, insulin sensitivity and intrahepatic lipid in healthy older people from the Hertfordshire Cohort study: a randomised controlled trial. Diabetologia 2010; 53: 624–631.
Kilpelainen TO, Zillikens MC, Stancakova A, Finucane FM, Ried JS, Langenberg C et al. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nat Genet 2011; 43: 753–760.
Durnin JV, Womersley J . Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr 1974; 32: 77–97.
Slaughter MH, Lohman TG, Boileau RA, Horswill CA, Stillman RJ, Van Loan MD et al. Skinfold equations for estimation of body fatness in children and youth. Hum Biol 1988; 60: 709–723.
Lohman T, Roche A, Martorell R . Anthropometric Standardization Reference Manual. Human Kinetics Books: Champaign: IL, 1991.
LaForgia J, Dollman J, Dale MJ, Withers RT, Hill AM . Validation of DXA body composition estimates in obese men and women. Obesity 2009; 17: 821–826.
Sopher AB, Thornton JC, Wang J, Pierson Jr RN, Heymsfield SB, Horlick M . Measurement of percentage of body fat in 411 children and adolescents: a comparison of dual-energy X-ray absorptiometry with a four-compartment model. Pediatrics 2004; 113: 1285–1290.
Sun SS, Chumlea WC, Heymsfield SB, Lukaski HC, Schoeller D, Friedl K et al. Development of bioelectrical impedance analysis prediction equations for body composition with the use of a multicomponent model for use in epidemiologic surveys. AmJ Clin Nutr 2003; 77: 331–340.
Tanner J . Growth at Adolescence, 2nd edn Blackwell: Oxford, 1962.
Li S, Zhao JH, Luan J, Luben RN, Rodwell SA, Khaw KT et al. Cumulative effects and predictive value of common obesity-susceptibility variants identified by genome-wide association studies. Am J Clin Nutr 2010; 91: 184–190.
Nilsson L, Olsson AH, Isomaa B, Groop L, Billig H, Ling C . A common variant near the PRL gene is associated with increased adiposity in males. Mol Genet Metab 2010; 102: 78–81.
Sandholt CH, Vestmar MA, Bille DS, Borglykke A, Almind K, Hansen L et al. Studies of metabolic phenotypic correlates of 15 obesity associated gene variants. PLoS One 2011; 6: e23531.
Willer CJ, Li Y, Abecasis GR . METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010; 26: 2190–2191.
Millard EE, Gale SE, Dudley N, Zhang J, Schaffer JE, Ory DS . The sterol-sensing domain of the Niemann-Pick C1 (NPC1) protein regulates trafficking of low density lipoprotein cholesterol. J Biol Chem 2005; 280: 28581–28590.
Garver WS, Jelinek D, Francis GA, Murphy BD . The Niemann-Pick C1 gene is downregulated by feedback inhibition of the SREBP pathway in human fibroblasts. J Lipid Res 2008; 49: 1090–1102.
Gevry N, Schoonjans K, Guay F, Murphy BD . Cholesterol supply and SREBPs modulate transcription of the Niemann-Pick C-1 gene in steroidogenic tissues. J Lipid Res 2008; 49: 1024–1033.
Jelinek D, Heidenreich RA, Erickson RP, Garver WS . Decreased Npc1 gene dosage in mice is associated with weight gain. Obesity (Silver Spring, Md) 2010; 18: 1457–1459.
Uronen RL, Lundmark P, Orho-Melander M, Jauhiainen M, Larsson K, Siegbahn A et al. Niemann-Pick C1 modulates hepatic triglyceride metabolism and its genetic variation contributes to serum triglyceride levels. Arterioscler Thromb Vasc Biol 2010; 30: 1614–1620.
Acknowledgements
The authors would like to thank the study teams, who collected the data used in these analyses. We also acknowledge the volunteers, who gave their time to take part in the individual studies. The Fenland study was supported by the Wellcome Trust; the Medical Research Council; the Support for Science Funding programme; and CamStrad. The EPIC Norfolk Study was supported by Cancer Research United Kingdom; and the Medical Research Council. The Whitehall II Study was supported by grants from the Medical Research Council (MRC); the British Heart Foundation; the United Kingdom Health and Safety Executive; the United Kingdom Department of Health; the US National Heart, Lung, and Blood Institute (Grant HL36310); the US National Institute on Aging (Grant AG13196); the US Agency for Health Care Policy and Research (Grant HS06516); and the John D and Catherine T MacArthur Foundation Research Networks on Successful Midlife Development and Socio-economic Status and Health. The MRC Ely Study was supported by the Medical Research Council; and the Wellcome Trust. The Hertfordshire Study was supported by the Medical Research Council UK; and the University of Southampton UK. The EYHS was supported by grants from The Danish Heart Foundation; The Danish Medical Research Council Health Foundation; The Danish Council for Sports Research; The Foundation in Memory of Asta Florida Bolding Renée Andersen; The Faculty of Health Sciences, University of Southern Denmark; and The Estonian Science Foundation (Grant 3277, 5209).
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den Hoed, M., Luan, J., Langenberg, C. et al. Evaluation of common genetic variants identified by GWAS for early onset and morbid obesity in population-based samples. Int J Obes 37, 191–196 (2013). https://doi.org/10.1038/ijo.2012.34
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DOI: https://doi.org/10.1038/ijo.2012.34
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