Controversial association results for INSIG2 on body mass index may be explained by interactions with age and with MC4R

European Journal of Human Genetics

Volumn 22, Issue 10, 2014, Pages 1217-1224

  Malzahn, Dörthe ^a   Müller Nurasyid, Martina ^b,c,d   Heid, Iris M ^d,e   Wichmann, H Erich ^b,c,d   Bickeböller, Heike ^a  

^a UNIVERSITY MEDICAL CENTER GÖTTINGEN   (Germany)

^b LUDWIG MAXIMILIANS UNIVERSITY   (Germany)

^c UNIVERSITY OF MUNICH   (Germany)

^d INSTITUTE OF EPIDEMIOLOGY   (Germany)

^e UNIVERSITY OF REGENSBURG   (Germany)

Author keywords

age dependence; body mass; cohort studies; genetic epistasis; longitudinal studies; obesity

Indexed keywords

ADIPOGENESIS; ADIPOSE TISSUE; ADULT; AGE; AGED; APPETITE; ARTICLE; BODY MASS; COHORT ANALYSIS; FEMALE; GENE; GENE FUNCTION; GENE INTERACTION; GENE LOCUS; GENE REPLICATION; GENETIC ASSOCIATION; GENETIC VARIABILITY; HUMAN; INSULIN INDUCED GENE 2; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; MALE; MELANOCORTIN 4 RECEPTOR GENE; MIDDLE AGED; OBESITY; POPULATION RESEARCH; PRIORITY JOURNAL; SINGLE NUCLEOTIDE POLYMORPHISM;

EID: 84907401055     PISSN: 10184813     EISSN: 14765438     Source Type: Journal    
DOI: 10.1038/ejhg.2014.3     Document Type: Article

References (42)

1. Wilborn C, Beckham J, Campbell B et al: Obesity: prevalence, theories, medical consequences, management, and research directions. J Int Soc Sports Nutr 2005; 2: 4-31.
2. Bell CG, Walley AJ, Froguel P: The genetics of human obesity. Nat Rev Genet 2005; 6: 221-234.
3. Herbert A, Gerry NP, McQueen MB et al: A common genetic variant is associated with adult and childhood obesity. Science 2006; 312: 279-283.
4. Geller F, Reichwald K, Dempfle A et al: Melanocartin-4 receptor gene variant I103 is negatively associated with obesity. Am J Hum Genet 2004; 74: 572-581.
5. Krapivner S, Popov S, Chernogubova E et al: Insulin-induced gene 2 involvement in human adipocyte metabolism and body weight regulation. J Clin Endocrinol Metab 2008; 93: 1995-2001.
6. Xiang Z, Litherland SA, Sorensen NB et al: Pharmacological characterization of 40 human melanocortin-4 receptor polymorphisms with the endogenous proopiomelanocortin-derived agonists and the agouti-related protein (AGRP) antagonist. Biochemistry 2006; 45: 7277-7288.
7. Deng HW, Deng H, Liu YJ et al: A genomewide linkage scan for quantitative-trait loci for obesity phenotypes. Am J Hum Genet 2002; 70: 1138-1151.
8. Lyon HN, Emilsson V, Hinney A et al: The association of a SNP upstream of INSIG2 with body mass index is reproduced in several but not all cohorts [electronic article]. PLoS Genet 2007; 3: e61.
9. Dina C, Meyre D, Samson C et al: Comment on 'a common genetic variant is associated with adult and childhood obesity'. Science 2007; 315: 187b.
10. Loos R.J.F., Barroso I, O'Rahilly S, Wareham NJ: Comment on: a common genetic variant is associated with adult and childhood obesity. Science 2007; 315: 187c.
11. Rosskopf D, Bornhorst A, Rimmbach C et al: Comment on: a common genetic variant is associated with adult and childhood obesity. Science 2007; 315: 187d.
12. Boes E, Kollerits B, Heid IM et al: INSIG2 polymorphism is neither associated with BMI nor with phenotypes of lipoprotein metabolism. Obesity 2008; 16: 827-833.
13. Smith AJP, Cooper JA, Li LK, Humphries SE: INSIG2 gene polymorphism is not associated with obesity in Caucasian, Afro-Caribbean and Indian subjects. Int J Obes 2007; 31: 1753-1755.
14. Kumar J, Sunkishala RR, Karthikeyan G, Sengupta S: The common genetic variant upstream of INISG2 gene is not associated with obesity in Indian population. Clin Genet 2007; 71: 415-418.
15. Feng Y, Dong H, Xiang Q et al: Lack of association between rs7566605 and obesity in a Chinese population. Hum Genet 2006; 120: 743-745.
16. Bressler J, Fornage M, Hanis CL et al: The INSIG2 rs7566605 genetic variant does not play a major role in obesity in a sample of 24 722 individuals from four cohorts. BMC Med Genet 2009; 10: 56.
17. Andreasen CH, Mogensen MS, Borch-Johnsen K et al: Non-replication of genome-wide based associations between common variants in INSIG2 and PFKP and obesity in studies of 18 014 Danes. PLoS One 2008; 3: e2872.
18. Heid IM, Huth C, Loos RJF et al: Meta-analysis of the INSIG2 association with obesity including 74 345 individuals: does heterogeneity of estimates relate to study design? [electronic article]. PLoS Genet 2009; 5: e1000694.
19. Liu G, Zhu H, Dong Y, Podolsky RH, Treiber FA, Snieder H: Influence of common variants in FTO and near INSIG2 and MC4R on growth curves for adiposity in African-and European-American youth. Eur J Epidemiol 2011; 26: 463-473.
20. Fornage M, Papanicolaou G, Lewis CE, Boerwinkle E, Siscovick DS: Common INSIG2 polymorphisms are associated with age-related changes in body size and high-density lipoprotein cholesterol from young adulthood to middle age. Metabolism 2010; 59: 1084-1091.
21. Wu AC, Gillman MW, Taveras EM, Litonjua AA: INSIG2 is associated with lower gain in weight-for-length between birth and age 6 month. Clin Med Pediatrics 2009; 3: 33-37.
22. Gauderman JW, Macgregor S, Briollais L et al: Longitudinal data analysis in pedigree studies. Genet Epidemiol 2003; 25: S18-S28.
23. Lasky-Su J, Lyon HN, Emilsson V et al: On the replication of genetic associations: Timing can be everything! Am J Hum Genet 2008; 82: 849-858.
24. Goldstein JL, DeBose-Boyd RA, Brown MS: Protein sensors for membrane sterols. Cell 2006; 124: 35-46.
25. Engelking LJ, Liang G, Hammer RE et al: Schoenheimer effect explained-feedback regulation of cholesterol synthesis in mice mediated by Insig proteins. J Clin Invest 2005; 115: 2489-2498.
26. Talbert ME, Langefeld CD, Ziegler JT, Haffner SM, Norris JM, Bowden DW: INSIG2 SNPs associated with obesity and glucose homeostasis traits in hispanics: the IRAS family study. Obesity 2009; 17: 1554-1562.
27. Friedman JM: Modern science versus the stigma of obesity. Nat Med 2004; 10: 563-569.
28. Dempfle A, Hinney A, Heinzel-Gutenbrunner M et al: Large quantitative effect of melanocortin-4 receptor gene mutations on body mass index. J Med Genet 2004; 41: 795-800.
29. Hinney A, Bettecken T, Tarnow P et al: Prevalence, spectrum, and functional characterization of melanocortin-4 receptor gene mutations in a representative population-based sample and obese adulds from Germany. J Clin Endocrinol Metab 2006; 91: 1761-1769.
30. Heid IM, Vollmert C, Hinney A et al: Association of the 103I MC4R allele with decreased body mass in 7 937 participants of two population based surveys. J Med Genet 2005; 42: e21.
31. Guo Y, Lanktree MB, Taylor KC et al: Gene-centric meta-analyses of 108 912 individuals confirm known body mass index loci and reveal three novel signals. Hum Mol Genet 2013; 22: 184-201.
32. Young EH, Wareham NJ, Farooqi S et al: The V103I polymorphism of the MC4R gene and obesity: population based studies and meta-analysis of 29 563 individuals. Int J Obes 2007; 31: 1437-1441.
33. Stutzmann F, Vatin V, Cauchi S et al: Non-synonymous polymorphisms in melanocortin-4 receptor protect against obesity: the two facets of a Janus obesity gene. Hum Mol Genet 2007; 16: 1837-1844.
34. Heid IM, Vollmert C, Kronenberg F et al: Association of the MC4R V103I polymorphism with the metabolic syndrome: the KORA study. Obesity 2008; 16: 369-376.
35. Brönner G, Sattler AM, Hinney A et al: The 103I variant of the melanocortin 4 receptor is associated with low serum triglyceride levels. J Clin Endocrinol Metab 2006; 91: 535-538.
36. Löwel H, Dö ring A, Schneider A, Heier M, Thorand B, Meisinger C: The MONICA Augsburg surveys-basis for prospective cohort studies. Gesundheitswesen 2005; 67(Suppl 1): S13-S18.
37. Cupples LA, Heard-Costa N, Lee M, Atwood LD: Genetics analysis workshop 16 problem 2: the Framingham Heart Study data. BMC Proc 2009; 3(Suppl 7): S3.
38. Strug L, Sun L, Corey M: The genetics of cross-sectional and longitudinal body mass index [electronic article]. BMC Genet 2003; 4(Suppl I): S14.
39. Kraja AT, Corbett J, Ping A et al: Rheumatoid arthritis, item response theory, Blom transformation, and mixed models [electronic article]. BMC Proc 2007; 1(Suppl 1): S116.
40. Laird NM, Ware JH: Random-effects models for longitudinal data. Biometrics 1982; 38: 963-974.
41. Malzahn D, Balavarca Y, Lozano JP, Bickebö ller H: Tests for candidate-gene interaction for longitudinal quantitative traits measured in a large cohort. BMC Proc 2009; 3(Suppl 7): S80.
42. Malzahn D, Schillert A, Mü ller M, Bickebö ller H: The longitudinal nonparametric test as a new tool to explore gene-gene and gene-time effects in cohorts. Genet Epidemiol 2010; 34: 469-478.