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Genome-wide meta-analyses identify multiple loci associated with smoking behavior


Consistent but indirect evidence has implicated genetic factors in smoking behavior1,2. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], β = 1.03, standard error (s.e.) = 0.053, P = 2.8 × 10−73). Two 10q25 SNPs (rs1329650[G], β = 0.367, s.e. = 0.059, P = 5.7 × 10−10; and rs1028936[A], β = 0.446, s.e. = 0.074, P = 1.3 × 10−9) and one 9q13 SNP in EGLN2 (rs3733829[G], β = 0.333, s.e. = 0.058, P = 1.0 × 10−8) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95% confidence interval (Cl) 1.04–1.08, P = 1.8 × 10−8). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95% Cl 1.08–1.18, P = 3.6 × 10−8) was significantly associated with smoking cessation.

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Figure 1: Genome-wide association results for the TAG Consortium.
Figure 2: Forest and regional plots of significant associations for CPD from meta-analyses of the TAG, Ox-GSK and ENGAGE consortia.
Figure 3: Forest and regional plots of significant associations for smoking behavior.


  1. Rose, R.J., Broms, U., Korhonen, T., Dick, D.M. & Kaprio, J. Genetics of Smoking Behavior. in Handbook of Behavior Genetics, 1 (ed. Kim, Y.-K.) 411–432 (Springer, New York, 2009).

  2. Li, M.D. Identifying susceptibility loci for nicotine dependence: 2008 update based on recent genome-wide linkage analyses. Hum. Genet. 123, 119–131 (2008).

    Article  CAS  Google Scholar 

  3. Thorgeirsson, T.E. et al. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature 452, 638–642 (2008).

    Article  CAS  Google Scholar 

  4. Fiore, M.C., Smith, S.S., Jorenby, D.E. & Baker, T.B. The effectiveness of the nicotine patch for smoking cessation. A meta-analysis. J. Am. Med. Assoc. 271, 1940–1947 (1994).

    Article  CAS  Google Scholar 

  5. Li, Y., Willer, C., Sanna, S. & Abecasis, G. Genotype imputation. Annu. Rev. Genomics Hum. Genet. 10, 387–406 (2009).

    Article  CAS  Google Scholar 

  6. de Bakker, P.I. et al. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum. Mol. Genet. 17, R122–R128 (2008).

    Article  CAS  Google Scholar 

  7. Kraft, P., Zeggini, E. & Ioannidis, J.P.A. Replication in genome-wide association studies. Stat. Sci. published online, doi:10.1214/09-STS290 (2010).

  8. Pereira, T.V., Patsopoulos, N.A., Salanti, G. & Ioannidis, J.P. Discovery properties of genome-wide association signals from cumulatively combined data sets. Am. J. Epidemiol. 170, 1197–1206 (2009).

    Article  Google Scholar 

  9. Ioannidis, J.P., Patsopoulos, N.A. & Evangelou, E. Heterogeneity in meta-analyses of genome-wide association investigations. PLoS One 2, e841 (2007).

    Article  Google Scholar 

  10. Pe'er, I. et al. Evaluating and improving power in whole-genome association studies using fixed marker sets. Nat. Genet. 38, 663–667 (2006).

    Article  CAS  Google Scholar 

  11. Pe'er, I., Yelensky, R., Altshuler, D. & Daly, M.J. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol. 32, 381–385 (2008).

    Article  Google Scholar 

  12. Thorgeirsson, T. et al. Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking behavior. Nat. Genet. 42, 448–453 (2010).

    Article  CAS  Google Scholar 

  13. Liu, J. et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat. Genet. 42, 436–440 (2010).

    Article  CAS  Google Scholar 

  14. Saccone, N.L. et al. Multiple distinct risk loci for nicotine dependence identified by dense coverage of the complete family of nicotinic receptor subunit (CHRN) genes. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 150B, 453–466 (2009).

    Article  CAS  Google Scholar 

  15. Nakajima, M. et al. Role of human cytochrome P4502A6 in C-oxidation of nicotine. Drug Metab. Dispos. 24, 1212–1217 (1996).

    CAS  PubMed  Google Scholar 

  16. Mwenifumbo, J.C. & Tyndale, R.F. Molecular genetics of nicotine metabolism. Handb. Exp. Pharmacol. 192, 235–259 (2009).

    Article  CAS  Google Scholar 

  17. Ray, R., Tyndale, R.F. & Lerman, C. Nicotine dependence pharmacogenetics: role of genetic variation in nicotine-metabolizing enzymes. J. Neurogenet. 23, 252–261 (2009).

    Article  CAS  Google Scholar 

  18. Zhang, L.I. & Poo, M.M. Electrical activity and development of neural circuits. Nat. Neurosci. 4 Suppl, 1207–1214 (2001).

    Article  CAS  Google Scholar 

  19. Levin, E.D., McClernon, F.J. & Rezvani, A.H. Nicotinic effects on cognitive function: behavioral characterization, pharmacological specification, and anatomic localization. Psychopharmacology (Berl.) 184, 523–539 (2006).

    Article  CAS  Google Scholar 

  20. Gratacòs, M. et al. Brain-derived neurotrophic factor Val66Met and psychiatric disorders: meta-analysis of case-control studies confirm association to substance-related disorders, eating disorders, and schizophrenia. Biol. Psychiatry 61, 911–922 (2007).

    Article  Google Scholar 

  21. Thorleifsson, G. et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41, 18–24 (2009).

    Article  CAS  Google Scholar 

  22. Zabetian, C.P. et al. A quantitative-trait analysis of human plasma-dopamine beta-hydroxylase activity: evidence for a major functional polymorphism at the DBH locus. Am. J. Hum. Genet. 68, 515–522 (2001).

    Article  CAS  Google Scholar 

  23. Hindorff, L.A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 9362–9367 (2009).

    Article  CAS  Google Scholar 

  24. Keskitalo, K. et al. Association of serum cotinine level with a cluster of three nicotinic acetylcholine receptor genes (CHRNA3/CHRNA5/CHRNB4) on chromosome 15. Hum. Mol. Genet. 18, 4007–4012 (2009).

    Article  CAS  Google Scholar 

  25. Pomerleau, O.F. et al. Genetic research on complex behaviors: an examination of attempts to identify genes for smoking. Nicotine Tob. Res. 9, 883–901 (2007).

    Article  CAS  Google Scholar 

  26. Lichtenstein, P. et al. The Swedish Twin Registry: a unique resource for clinical, epidemiological and genetic studies. J. Intern. Med. 252, 184–205 (2002).

    Article  CAS  Google Scholar 

  27. Furberg, H., Lichtenstein, P., Pedersen, N.L., Bulik, C. & Sullivan, P.F. Cigarettes and oral snuff use in Sweden: prevalence and transitions. Addiction. 10, 1509–1515 (2006).

    Article  Google Scholar 

  28. Kaprio, J., Pulkkinen, L. & Rose, R.J. Genetic and environmental factors in health-related behaviors: studies on Finnish twins and twin families. Twin Res. 5, 366–371 (2002).

    Article  Google Scholar 

  29. Kaprio, J. & Koskenvuo, M. Genetic and environmental factors in complex diseases: the older Finnish Twin Cohort. Twin Res. 5, 358–365 (2002).

    Article  Google Scholar 

  30. Centers for Disease Control and Prevention (CDC). Cigarette smoking among adults–United States, 2007. MMWR Morb. Mortal. Wkly. Rep. 57, 1221–1226 (2008); erratum 57, 1281 (2008).

  31. Furberg, H., Lichtenstein, P., Pedersen, N.L., Bulik, C. & Sullivan, P.F. Cigarettes and oral snuff use in Sweden: Prevalence and transitions. Addiction 101, 1509–1515 (2006).

    Article  Google Scholar 

  32. Frazer, K.A. et al. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

    Article  CAS  Google Scholar 

  33. Pritchard, J.K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  Google Scholar 

  35. Li, Y., Ding, J. & Abecasis, G.R. MACH 1.0: rapid haplotype reconstruction and missing genotype inference. Am. J. Hum. Genet. S79, 2290 (2006).

    Google Scholar 

  36. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    Article  CAS  Google Scholar 

  37. Servin, B. & Stephens, M. Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genet. 3, e114 (2007).

    Article  Google Scholar 

  38. Lin, D.Y. & Zeng, D. Proper analysis of secondary phenotype data in case-control association studies. Genet. Epidemiol. 33, 256–265 (2009).

    Article  CAS  Google Scholar 

  39. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).

    Article  CAS  Google Scholar 

  40. Centers for Disease Control and Prevention (CDC). Cigarette smoking among adults-United States, 2006. MMWR CDC Surveill. Summ. 56, 1157–1161 (2007).

  41. Dudbridge, F. & Gusnanto, A. Estimation of significance thresholds for genomewide association scans. Genet. Epidemiol. 32, 227–234 (2008).

    Article  Google Scholar 

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This work was funded by the University of North Carolina Lineberger Comprehensive Cancer Center University Cancer Research Fund Award and by US National Cancer Institute K07 CA118412 to H.F. Statistical analyses were carried out on the Genetic Cluster Computer (see URLs), which is supported by the Netherlands Scientific Organization (NWO 480-05-003). Acknowledgments for studies included in TAG are listed in the Supplementary Note.

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Authors and Affiliations



TAG: study conception, design, management: H.F., P.F.S., Y.K., J. Dackor; TAG Statistical Working Group: D.-Y.L., P.K., J.P.A.I., D.P., H.F., Y.K., J. Dackor, S.P.F., N.F., E.H.L., J.D.M., J.M.V., D.I.B., D.L., B.M.E., E.L.T., B. McKnight, P.F.S., D. Absher; TAG Phenotype Working Group: C. Lerman, J.K., H.H.M., L.M.T., J.A.-M., E.H.L., J.E.R., M.D.L., J.M.V., H.F., Y.K., J. Dackor, S.P.F., P.F.S., E.L.T.; data analysis: Y.K., D.M.A., F.G., E.H.L., J.D.M., J.M.V., A.U.J., L. Bernardinelli, S.R.P., S.-J.H., B.M.E., C. Ladenvall, J.R.B.P., T.T., E.L.T., J.C.B., G.L., S.W.; TAG Manuscript Writing Group: H.F., Y.K., J. Dackor, P.F.S., C. Lerman, M.D.L., J.K., J.A.-M., P.K. All authors reviewed and approved the final version of the manuscript. The corresponding authors had access to the full data set of summary results contributed by each study.

ARIC: study conception, design, management: E.B.; phenotype collection, data management: N.F.; sample processing and genotyping: N.F.; data analysis: Y.K., N.F.

Atherosclerosis Thrombosis and Vascular Biology Italian Study Group: study conception, design, management: L. Bernardinelli, P.M.M., P.A.M., D. Ardissino; phenotype collection, data management: F.M., L. Bernandinelli; data analysis: L. Bernandinelli.

ADVANCE: study conception, design, management: S.P.F., D. Absher, T.Q., C.I., T.L.A., J.W.K.; phenotype collection, data management: S.P.F., T.Q., C.I., T.L.A., J.W.K.; sample processing and genotyping: D. Absher, T.Q.; data analysis: S.P.F., D. Absher, T.L.A., J.W.K.

Baltimore Longitudinal Study of Aging: study conception, design, management: L. Ferrucci; phenotype collection, data management: L. Ferrucci; data analysis: T.T.

CHS: study conception, design, management: B.M.P., J.C.B., C.D.F.; phenotype collection, data management: B.M.P.; sample processing and genotyping: T.H., K.D.T.; data analysis: B.M.P., E.L.T., J.C.B., B. McKnight.

DGI: study conception, design, management: L.G.; phenotype collection, data management: P.A.; data analysis: P.A., C. Ladenvall.

FUSION: study conception, design, management: K.L.M., M.B.; phenotype collection, data management: H.M.S., J.T.; data analysis: H.M.S., A.U.J.

Framingham Heart Study: study conception, design, management: R.S.V., E.J.B., D.L.; phenotype collection, data management: S.R.P., R.S.V., S.-J.H., E.J.B., D.L.; data analysis: S.R.P., S.-J.H.

GAIN: study conception, design, management: D.F.L., P.V.G.; phenotype collection, data management: A.R.S., D.F.L., J. Duan, J.S., P.V.G.; sample processing and genotyping: J. Duan, P.V.G.; data analysis: A.R.S., D.F.L., J. Duan, J.S., P.V.G.

IARC/ARCAGE/Central European GWAS: phenotype collection, data management: D.Z., N.S.-D., J.L., P.R., E.F., D.M., V.B., L. Foretova, V.J., S. Benhamou, P.L., I.H., L.R., K.K., A.A., X.C., T.V.M., L. Barzan, C.C., R.L., D.I. Conway, A.Z., C.M.H., P.B.; sample processing and genotyping: J.D.M., M.L., P.B.; data analysis: E.H.L., J.D.M.

InCHIANTI: study conception, design, management: T.M.F., J.M.G., S. Bandinelli; phenotype collection, data management: Y.M.; data analysis: J.R.B.P.

MIGEN: study conception, design, management: R.E., V.S., O.M., C.J.O., D. Altshuler; phenotype collection, data management: G.L., S.M.S., R.E., V.S., B.F.V., O.M., S.K., C.J.O.; sample processing and genotyping: S.K., D. Altshuler; data analysis: G.L., B.F.V., D. Altshuler

NESDA: study conception, design, management: B.W.P., J.H.S.; phenotype collection, data management: B.W.P., J.H.S., N.V.; sample processing and genotyping: B.W.P., J.H.S.; data analysis: N.V.

NTR: study conception, design, management: D.I.B., G.W., E.J.C.d.G.; phenotype collection, data management: D.I.B., G.W., E.J.C.d.G., J.M.V.; sample processing and genotyping: D.I.B., G.W., E.J.C.d.G.; data analysis: J.M.V.

NHS: phenotype collection, data management: S.E.H., D.J.H., P.K., F.G.; sample processing and genotyping: S.J.C., S.E.H., D.J.H., P.K.; data analysis: S.J.C., F.G., P.K.

Rotterdam: study conception, design, management: A.H.; phenotype collection, data management: H.T., A.G.U.; sample processing and genotyping: H.T., A.G.U.; data analysis: H.T., A.G.U., S.W., C.M.v.D.

WGHS: study conception, design, management: B.M.E., G.P., D.I. Chasman, P.M.R.; phenotype collection, data management: B.M.E., G.P., D.I. Chasman, P.M.R.; sample processing and genotyping: G.P., D.I. Chasman; data analysis: B.M.E., G.P., D.I. Chasman.

Corresponding authors

Correspondence to Helena Furberg or Patrick F Sullivan.

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The author declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1–5, Supplementary Figures 1 and 2 and Supplementary Note (PDF 655 kb)

Supplementary Table 6

Association testing for CPD on chromosome 15, conditional on rs1051730 (XLS 46 kb)

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The Tobacco and Genetics Consortium. Genome-wide meta-analyses identify multiple loci associated with smoking behavior. Nat Genet 42, 441–447 (2010).

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