Genome-wide association scans identified CTNNBL1 as a novel gene for obesity.
Journal: 2008/July - Human Molecular Genetics
ISSN: 1460-2083
Abstract:
Obesity is a major public health problem with strong genetic determination; however, the genetic factors underlying obesity are largely unknown. In this study, we performed a genome-wide association scan for obesity by examining approximately 500 000 single-nucleotide polymorphisms (SNPs) in a sample of 1000 unrelated US Caucasians. We identified a novel gene, CTNNBL1, which has multiple SNPs associated with body mass index (BMI) and fat mass. The most significant SNP, rs6013029, achieved experiment-wise P-values of 2.69 x 10(-7) for BMI and of 4.99 x 10(-8) for fat mass, respectively. The SNP rs6013029 minor allele T confers an average increase in BMI and fat mass of 2.67 kg/m(2) and 5.96 kg, respectively, compared with the alternative allele G. We further genotyped the five most significant CTNNBL1 SNPs in a French case-control sample comprising 896 class III obese adults (BMI>> or = 40 kg/m(2)) and 2916 lean adults (BMI < 25 kg/m(2)). All five SNPs showed consistent associations with obesity (8.83 x 10(-3) < P < 6.96 x 10(-4)). Those subjects who were homozygous for the rs6013029 T allele had 1.42-fold increased odds of obesity compared with those without the T allele. The protein structure of CTNNBL1 is homologous to beta-catenin, a family of proteins containing armadillo repeats, suggesting similar biological functions. beta-Catenin is involved in the Wnt/beta-catenin-signaling pathway which appears to contribute to maintaining the undifferentiated state of pre-adipocytes by inhibiting adipogenic gene expression. Our study hence suggests a novel mechanism for the development of obesity, where CTNNBL1 may play an important role. Our study also provided supportive evidence for previously identified associations between obesity and INSIG2 and PFKP, but not FTO.
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Hum Mol Genet 17(12): 1803-1813

Genome-wide association scans identified <em>CTNNBL1</em> as a novel gene for obesity

+8 authors

Supplementary Material

[Supplementary Data]
School of Medicine, University of Missouri—Kansas City, Kansas City, MO 64108, USA
The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P. R. China
Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, P. R. China
Osteoporosis Research Center, Creighton University, Omaha, NE 68131, USA
Vanderbilt Microarray Shared Resource, Vanderbilt University, Nashville, TN 37232, USA
CNRS-8090-Institute of Biology, Pasteur Institute, Lille, France
Genomic Medicine, Hammersmith Hospital, Imperial College London, London, UK
To whom correspondence should be addressed at: Department of Basic Medical Science and Department of Orthopedic Surgery, University of Missouri—Kansas City, 2411 Holmes Street, Room M3-C03, Kansas City, MO 64108-2792, USA. Tel: +1 8162355354; Fax: +1 8162356517; Email: ude.ckmu@hgned
Received 2007 Aug 26; Accepted 2008 Mar 4.

Abstract

Obesity is a major public health problem with strong genetic determination; however, the genetic factors underlying obesity are largely unknown. In this study, we performed a genome-wide association scan for obesity by examining approximately 500 000 single-nucleotide polymorphisms (SNPs) in a sample of 1000 unrelated US Caucasians. We identified a novel gene, CTNNBL1, which has multiple SNPs associated with body mass index (BMI) and fat mass. The most significant SNP, rs6013029, achieved experiment-wise P-values of 2.69 × 10 for BMI and of 4.99 × 10 for fat mass, respectively. The SNP rs6013029 minor allele T confers an average increase in BMI and fat mass of 2.67 kg/m and 5.96 kg, respectively, compared with the alternative allele G. We further genotyped the five most significant CTNNBL1 SNPs in a French case–control sample comprising 896 class III obese adults (BMI ≥ 40 kg/m) and 2916 lean adults (BMI < 25 kg/m). All five SNPs showed consistent associations with obesity (8.83 × 10 < P < 6.96 × 10). Those subjects who were homozygous for the rs6013029 T allele had 1.42-fold increased odds of obesity compared with those without the T allele. The protein structure of CTNNBL1 is homologous to β-catenin, a family of proteins containing armadillo repeats, suggesting similar biological functions. β-Catenin is involved in the Wnt/β-catenin-signaling pathway which appears to contribute to maintaining the undifferentiated state of pre-adipocytes by inhibiting adipogenic gene expression. Our study hence suggests a novel mechanism for the development of obesity, where CTNNBL1 may play an important role. Our study also provided supportive evidence for previously identified associations between obesity and INSIG2 and PFKP, but not FTO.

Abstract

The second allele represents the minor allele of each locus.

MAF calculated in the US Caucasian sample.

MAF reported for Caucasians in the public database of HapMap CEU.

FDR q values less than 0.05 are in bold.

Obtained using linear regression analyses.

The second allele represents the minor allele of each locus. Replication analyses compare genotype frequencies in obese and lean individuals using logistic regression under an additive model. The OR is the odds ratio of the risk allele.

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ACKNOWLEDGEMENTS

We wish to thank Dr Beverley Balkau and the DESIR consortium as well as Dr Claire Lévy-Marchal, who recruited a proportion of the French study lean controls. We thank Emmanuelle Durand, Jérôme Delplanque and Stefan Gaget for the technical support on replication SNP genotyping in the French case–control sample.

Conflict of Interest statement. None declared.

ACKNOWLEDGEMENTS

REFERENCES

REFERENCES

References

  • 1. Kopelman P.GObesity as a medical problem. Nature. 2000;404:635–643.[PubMed][Google Scholar]
  • 2. Ogden C.L., Carroll M.D., Curtin L.R., McDowell M.A., Tabak C.J., Flegal K.MPrevalence of overweight and obesity in the United States, 1999–2004. JAMA. 2006;295:1549–1555.[PubMed][Google Scholar]
  • 3. Bell C.G., Walley A.J., Froguel PThe genetics of human obesity. Nat. Rev. Genet. 2005;6:221–234.[PubMed][Google Scholar]
  • 4. Rankinen T., Zuberi A., Chagnon Y.C., Weisnagel S.J., Argyropoulos G., Walts B., Perusse L., Bouchard CThe human obesity gene map: the 2005 update. Obesity. 2006;14:529–644.[PubMed][Google Scholar]
  • 5. Hirschhorn J.N., Daly M.JGenome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 2005;6:95–108.[PubMed][Google Scholar]
  • 6. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447:661–678.
  • 7. Saxena R., Voight B.F., Lyssenko V., Burtt N.P., de Bakker P.I., Chen H., Roix J.J., Kathiresan S., Hirschhorn J.N., Daly M.J., et al Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316:1331–1336.[PubMed][Google Scholar]
  • 8. Scott L.J., Mohlke K.L., Bonnycastle L.L., Willer C.J., Li Y., Duren W.L., Erdos M.R., Stringham H.M., Chines P.S., Jackson A.U., et al A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316:1341–1345.[Google Scholar]
  • 9. Sladek R., Rocheleau G., Rung J., Dina C., Shen L., Serre D., Boutin P., Vincent D., Belisle A., Hadjadj S., et al A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007;445:881–885.[PubMed][Google Scholar]
  • 10. Duerr R.H., Taylor K.D., Brant S.R., Rioux J.D., Silverberg M.S., Daly M.J., Steinhart A.H., Abraham C., Regueiro M., Griffiths A., et al A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science. 2006;314:1461–1463.[Google Scholar]
  • 11. Hampe J., Franke A., Rosenstiel P., Till A., Teuber M., Huse K., Albrecht M., Mayr G., De L.V., Briggs J., et al A genome-wide association scan of nonsynonymous SNPs identifies a susceptibility variant for Crohn disease in ATG16L1. Nat. Genet. 2007;39:207–211.[PubMed][Google Scholar]
  • 12. Gudmundsson J., Sulem P., Manolescu A., Amundadottir L.T., Gudbjartsson D., Helgason A., Rafnar T., Bergthorsson J.T., Agnarsson B.A., Baker A., et al Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nat. Genet. 2007;39:631–637.[PubMed][Google Scholar]
  • 13. Yeager M., Orr N., Hayes R.B., Jacobs K.B., Kraft P., Wacholder S., Minichiello M.J., Fearnhead P., Yu K., Chatterjee N., et al Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat. Genet. 2007;39:645–649.[PubMed][Google Scholar]
  • 14. Herbert A., Gerry N.P., McQueen M.B., Heid I.M., Pfeufer A., Illig T., Wichmann H.E., Meitinger T., Hunter D., Hu F.B., et al A common genetic variant is associated with adult and childhood obesity. Science. 2006;312:279–283.[PubMed][Google Scholar]
  • 15. Lyon H.N., Emilsson V., Hinney A., Heid I.M., Lasky-Su J., Zhu X., Thorleifsson G., Gunnarsdottir S., Walters G.B., Thorsteinsdottir U., et al The association of a SNP upstream of INSIG2 with body mass index is reproduced in several but not all cohorts. PLoS Genet. 2007;3:e61.[Google Scholar]
  • 16. Frayling T.M., Timpson N.J., Weedon M.N., Zeggini E., Freathy R.M., Lindgren C.M., Perry J.R., Elliott K.S., Lango H., Rayner N.W., et al A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316:889–894.[Google Scholar]
  • 17. Scuteri A., Sanna S., Chen W.M., Uda M., Albai G., Strait J., Najjar S., Nagaraja R., Orru M., Usala G., et al Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007;20:e115.[Google Scholar]
  • 18. Dina C., Meyre D., Gallina S., Durand E., Korner A., Jacobson P., Carlsson L.M., Kiess W., Vatin V., Lecoeur C., et al Variation in FTO contributes to childhood obesity and severe adult obesity. Nat. Genet. 2007;39:724–726.[PubMed][Google Scholar]
  • 19. The International HapMap Project. Nature. 2003;426:789–796.[PubMed]
  • 20. Frazer K.A., Ballinger D.G., Cox D.R., Hinds D.A., Stuve L.L., Gibbs R.A., Belmont J.W., Boudreau A., Hardenbol P., Leal S.M., et al A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007;449:851–861.[Google Scholar]
  • 21. Frazer K.A., Ballinger D.G., Cox D.R., Hinds D.A., Stuve L.L., Gibbs R.A., Belmont J.W., Boudreau A., Hardenbol P., Leal S.M., et al A second generation human haplotype map of over 3.1 million SNPs. Nature. 2007;449:851–861.[Google Scholar]
  • 22. Marchini J., Howie B., Myers S., McVean G., Donnelly PA new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 2007;39:906–913.[PubMed][Google Scholar]
  • 23. Gabriel S.B., Schaffner S.F., Nguyen H., Moore J.M., Roy J., Blumenstiel B., Higgins J., DeFelice M., Lochner A., Faggart M., et al The structure of haplotype blocks in the human genome. Science. 2002;296:2225–2229.[PubMed][Google Scholar]
  • 24. Barrett J.C., Fry B., Maller J., Daly M.JHaploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–265.[PubMed][Google Scholar]
  • 25. Purcell S., Cherny S.S., Sham P.CGenetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics. 2003;19:149–150.[PubMed][Google Scholar]
  • 26. Killeen P.RAn alternative to null-hypothesis significance tests. Psychol. Sci. 2005;16:345–353.[Google Scholar]
  • 27. Zhao L.J., Xiao P., Liu Y.J., Xiong D.H., Shen H., Recker R.R., Deng H.WA genome-wide linkage scan for quantitative trait loci underlying obesity related phenotypes in 434 Caucasian families. Hum. Genet. 2007;121:145–148.[PubMed][Google Scholar]
  • 28. Pritchard J.K., Rosenberg N.AUse of unlinked genetic markers to detect population stratification in association studies. Am. J. Hum. Genet. 1999;65:220–228.[Google Scholar]
  • 29. Price A.L., Patterson N.J., Plenge R.M., Weinblatt M.E., Shadick N.A., Reich DPrincipal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 2006;38:904–909.[PubMed][Google Scholar]
  • 30. Jabbour L., Welter J.F., Kollar J., Hering T.MSequence, gene structure, and expression pattern of CTNNBL1, a minor-class intron- containing gene—evidence for a role in apoptosis. Genomics. 2003;81:292–303.[PubMed][Google Scholar]
  • 31. Kikuchi ARegulation of beta-catenin signaling in the Wnt pathway. Biochem. Biophys. Res. Commun. 2000;268:243–248.[PubMed][Google Scholar]
  • 32. Moon R.T., Bowerman B., Boutros M., Perrimon NThe promise and perils of Wnt signaling through beta-catenin. Science. 2002;296:1644–1646.[PubMed][Google Scholar]
  • 33. Ross S.E., Hemati N., Longo K.A., Bennett C.N., Lucas P.C., Erickson R.L., MacDougald O.AInhibition of adipogenesis by Wnt signaling. Science. 2000;289:950–953.[PubMed][Google Scholar]
  • 34. Schmitz G., Langmann TMetabolic learning in the intestine: adaptation to nutrition and luminal factors. Horm. Metab. Res. 2006;38:452–454.[PubMed][Google Scholar]
  • 35. Grant S.F., Thorleifsson G., Reynisdottir I., Benediktsson R., Manolescu A., Sainz J., Helgason A., Stefansson H., Emilsson V., Helgadottir A., et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007;445:881–885.[PubMed]
  • 36. Freathy R.M., Weedon M.N., Bennett A., Hypponen E., Relton C.L., Knight B., Shields B., Parnell K.S., Groves C.J., Ring S.M., et al Type 2 diabetes TCF7L2 risk genotypes alter birth weight: a study of 24,053 individuals. Am. J. Hum. Genet. 2007;80:1150–1161.[Google Scholar]
  • 37. Dina C., Meyre D., Samson C., Tichet J., Marre M., Jouret B., Charles M.A., Balkau B., Froguel PComment on ‘A common genetic variant is associated with adult and childhood obesity. Science. 2007;315:187.[PubMed][Google Scholar]
  • 38. Loos R.J., Barroso I., O'Rahilly S., Wareham N.JComment on ‘A common genetic variant is associated with adult and childhood obesity. Science. 2007;315:187.[Google Scholar]
  • 39. Hall D.H., Rahman T., Avery P.J., Keavney BINSIG-2 promoter polymorphism and obesity related phenotypes: association study in 1428 members of 248 families. BMC Med. Genet. 2006;7:83.[Google Scholar]
  • 40. Rosskopf D., Bornhorst A., Rimmbach C., Schwahn C., Kayser A., Kruger A., Tessmann G., Geissler I., Kroemer H.K., Volzke HComment on ‘A common genetic variant is associated with adult and childhood obesity. Science. 2007;315:187.[PubMed][Google Scholar]
  • 41. Colhoun H.M., McKeigue P.M., Davey S.GProblems of reporting genetic associations with complex outcomes. Lancet. 2003;361:865–872.[PubMed][Google Scholar]
  • 42. Ioannidis J.P., Patsopoulos N.A., Evangelou EHeterogeneity in meta-analyses of genome-wide association investigations. PLoS ONE. 2007;2:e841.[Google Scholar]
  • 43. Evangelou E., Maraganore D.M., Ioannidis J.PMeta-analysis in genome-wide association datasets: strategies and application in Parkinson disease. PLoS ONE. 2007;2:e196.[Google Scholar]
  • 44. Chanock S.J., Manolio T., Boehnke M., Boerwinkle E., Hunter D.J., Thomas G., Hirschhorn J.N., Abecasis G., Altshuler D., Bailey-Wilson J.E., et al Replicating genotype-phenotype associations. Nature. 2007;447:655–660.[PubMed][Google Scholar]
  • 45. Wacholder S., Chanock S., Garcia-Closas M., El Ghormli L., Rothman NAssessing the probability that a positive report is false: an approach for molecular epidemiology studies. J. Natl Cancer Inst. 2004;96:434–442.[PubMed][Google Scholar]
  • 46. Wakefield JA Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am. J. Hum. Genet. 2007;81:208–227.[Google Scholar]
  • 47. Deng H.WPopulation admixture may appear to mask, change or reverse genetic effects of genes underlying complex traits. Genetics. 2001;159:1319–1323.[Google Scholar]
  • 48. Deng H.W., Deng H., Liu Y.J., Liu Y.Z., Xu F.H., Shen H., Conway T., Li J.L., Huang Q.Y., Davies K.M., Recker R.RA genomewide linkage scan for quantitative-trait loci for obesity phenotypes. Am. J. Hum. Genet. 2002;70:1138–1151.[Google Scholar]
  • 49. Bray B.A., Bouchard C., James W.P. Marcel Dekker, NY: 1997. [PubMed]
  • 50. Ahn S.J., Costa J., Emanuel J.RPicoGreen quantitation of DNA: effective evaluation of samples pre- or post-PCR. Nucleic Acids Res. 1996;24:2623–2625.[Google Scholar]
  • 51. Singer V.L., Jones L.J., Yue S.T., Haugland R.PCharacterization of PicoGreen reagent and development of a fluorescence-based solution assay for double-stranded DNA quantitation. Anal. Biochem. 1997;249:228–238.[PubMed][Google Scholar]
  • 52. Di X., Matsuzaki H., Webster T.A., Hubbell E., Liu G., Dong S., Bartell D., Huang J., Chiles R., Yang G., et al Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays. Bioinformatics. 2005;21:1958–1963.[PubMed][Google Scholar]
  • 53. Rabbee N., Speed T.PA genotype calling algorithm for affymetrix SNP arrays. Bioinformatics. 2006;22:7–12.[PubMed][Google Scholar]
  • 54. Yuan H.Y., Chiou J.J., Tseng W.H., Liu C.H., Liu C.K., Lin Y.J., Wang H.H., Yao A., Chen Y.T., Hsu C.NFASTSNP: an always up-to-date and extendable service for SNP function analysis and prioritization. Nucleic Acids Res. 2006;34:W635–W641.[Google Scholar]
  • 55. Benjamini YControlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B. 1995;57:289–300.[PubMed][Google Scholar]
  • 56. Storey J.D., Tibshirani RStatistical significance for genomewide studies. Proc. Natl Acad. Sci. USA. 2003;100:9440–9445.[Google Scholar]
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