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Genome-wide association meta-analysis of nicotine metabolism and cigarette consumption measures in smokers of European descent

Abstract

Smoking behaviors, including amount smoked, smoking cessation, and tobacco-related diseases, are altered by the rate of nicotine clearance. Nicotine clearance can be estimated using the nicotine metabolite ratio (NMR) (ratio of 3′hydroxycotinine/cotinine), but only in current smokers. Advancing the genomics of this highly heritable biomarker of CYP2A6, the main metabolic enzyme for nicotine, will also enable investigation of never and former smokers. We performed the largest genome-wide association study (GWAS) to date of the NMR in European ancestry current smokers (n = 5185), found 1255 genome-wide significant variants, and replicated the chromosome 19 locus. Fine-mapping of chromosome 19 revealed 13 putatively causal variants, with nine of these being highly putatively causal and mapping to CYP2A6, MAP3K10, ADCK4, and CYP2B6. We also identified a putatively causal variant on chromosome 4 mapping to TMPRSS11E and demonstrated an association between TMPRSS11E variation and a UGT2B17 activity phenotype. Together the 14 putatively causal SNPs explained ~38% of NMR variation, a substantial increase from the ~20 to 30% previously explained. Our additional GWASs of nicotine intake biomarkers showed that cotinine and smoking intensity (cotinine/cigarettes per day (CPD)) shared chromosome 19 and chromosome 4 loci with the NMR, and that cotinine and a more accurate biomarker, cotinine + 3′hydroxycotinine, shared a chromosome 15 locus near CHRNA5 with CPD and Pack-Years (i.e., cumulative exposure). Understanding the genetic factors influencing smoking-related traits facilitates epidemiological studies of smoking and disease, as well as assists in optimizing smoking cessation support, which in turn will reduce the enormous personal and societal costs associated with smoking.

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Fig. 1: Chromosomal location and annotation for all genome-wide significant SNPs for all six phenotypes.
Fig. 2: Associations in the meta-GWAS of the Nicotine Metabolite Ratio (NMR) were found in chromosome 4 and chromosome 19.
Fig. 3: Number and overlap of genome-wide significant SNP associations with the six phenotypes.

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References

  1. Benowitz NL. Nicotine addiction. N Engl J Med. 2010;362:2295–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Dempsey D, Tutka P, Jacob P, Allen F, Schoedel K, Tyndale RF, et al. Nicotine metabolite ratio as an index of cytochrome P450 2A6 metabolic activity. Clin Pharmacol Ther. 2004;76:64–72.

    Article  CAS  PubMed  Google Scholar 

  3. Loukola A, Buchwald J, Gupta R, Palviainen T, Hallfors J, Tikkanen E, et al. A genome-wide association study of a biomarker of nicotine metabolism. PLoS Genet. 2015;11:e1005498.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Chenoweth MJ, Tyndale RF. Pharmacogenetic optimization of smoking cessation treatment. Trends Pharmacol Sci. 2017;38(Jan):55–66.

    Article  CAS  PubMed  Google Scholar 

  5. Nakajima M, Yamamoto T, Nunoya K, Yokoi T, Nagashima K, Inoue K, et al. Role of human cytochrome P4502A6 in C-oxidation of nicotine. Drug Metab Dispos. 1996;24:1212–7.

    CAS  PubMed  Google Scholar 

  6. Nakajima M, Yamamoto T, Nunoya KI, Yokoi T, Nagashima K, Inoue K, et al. Characterization of CYP2A6 involved in 3’-hydroxylation of cotinine in human liver microsomes. J Pharmacol Exp Ther. 1996;277:1010–5.

    CAS  PubMed  Google Scholar 

  7. Lea RA, Dickson S, Benowitz NL. Within-subject variation of the salivary 3HC/COT ratio in regular daily smokers: prospects for estimating CYP2A6 enzyme activity in large-scale surveys of nicotine metabolic rate. J Anal Toxicol. 2006;30:386–9.

    Article  CAS  PubMed  Google Scholar 

  8. Mooney ME, Li ZZ, Murphy SE, Pentel PR, Le C, Hatsukami DK. Stability of the nicotine metabolite ratio in ad libitum and reducing smokers. Cancer Epidemiol Biomarkers Prev. 2008;17:1396–400.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. St Helen G, Jacob P 3rd, Benowitz NL. Stability of the nicotine metabolite ratio in smokers of progressively reduced nicotine content cigarettes. Nicotine Tob Res. 2013;15:1939–42.

    Article  CAS  Google Scholar 

  10. Rossini A, de Almeida Simao T, Albano RM, Pinto LF. CYP2A6 polymorphisms and risk for tobacco-related cancers. Pharmacogenomics. 2008;9:1737–52.

    Article  CAS  PubMed  Google Scholar 

  11. Park SL, Murphy SE, Wilkens LR, Stram DO, Hecht SS, Le Marchand L. Association of CYP2A6 activity with lung cancer incidence in smokers: the multiethnic cohort study. Plos One. 2017;12:e0178435.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Yamamiya I, Yoshisue K, Ishii Y, Yamada H, Chiba M. Effect of CYP2A6 genetic polymorphism on the metabolic conversion of tegafur to 5-fluorouracil and its enantioselectivity. Drug Metab Dispos. 2014;42:1485–92.

    Article  PubMed  CAS  Google Scholar 

  13. Murai K, Yamazaki H, Nakagawa K, Kawai R, Kamataki T. Deactivation of anti-cancer drug letrozole to a carbinol metabolite by polymorphic cytochrome P450 2A6 in human liver microsomes. Xenobiotica. 2009;39:795–802.

    Article  CAS  PubMed  Google Scholar 

  14. Lerman C, Schnoll RA, Hawk LW, Cinciripini P, George TP, Wileyto EP, et al. Use of the nicotine metabolite ratio as a genetically informed biomarker of response to nicotine patch or varenicline for smoking cessation: a randomised, double-blind placebo-controlled trial. Lancet Resp Med. 2015;3:131–8.

    Article  CAS  Google Scholar 

  15. Vardavas CI, Filippidis FT, Agaku IT. Determinants and prevalence of e-cigarette use throughout the European Union: a secondary analysis of 26 566 youth and adults from 27 Countries. Tob Control. 2015;24:442–8.

    Article  PubMed  Google Scholar 

  16. Zhu AZX, Binnington MJ, Renner CC, Lanier AP, Hatsukami DK, Stepanov I, et al. Alaska Native smokers and smokeless tobacco users with slower CYP2A6 activity have lower tobacco consumption, lower tobacco-specific nitrosamine exposure and lower tobacco-specific nitrosamine bioactivation. Carcinogenesis. 2013;34:93–101.

    Article  PubMed  CAS  Google Scholar 

  17. Chenoweth MJ, Ware JJ, Zhu AZX, Cole CB, Cox LS, Nollen N, et al. Genome-wide association study of a nicotine metabolism biomarker in African American smokers: impact of chromosome 19 genetic influences. Addiction. 2018;113:509–23.

    Article  PubMed  Google Scholar 

  18. Baurley JW, Edlund CK, Pardamean CI, Conti DV, Krasnow R, Javitz HS. et al. Genome-wide association of the laboratory-based nicotine metabolite ratio in three ancestries. Nicotine Tob Res. 2016;18:1837–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Patel YM, Park SL, Han Y, Wilkens LR, Bickeboller H, Rosenberger A, et al. Novel association of genetic markers affecting CYP2A6 activity and lung cancer risk. Cancer Res. 2016;76(Oct):5768–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Pergadia ML, Glowinski AL, Wray NR, Agrawal A, Saccone SF, Loukola A, et al. A 3p26-3p25 genetic linkage finding for DSM-IV major depression in heavy smoking families. Am J Psychiatry. 2011;168(Aug):848–52.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zhu AZ, Renner CC, Hatsukami DK, Swan GE, Lerman C, Benowitz NL, et al. The ability of plasma cotinine to predict nicotine and carcinogen exposure is altered by differences in CYP2A6: the influence of genetics, race, and sex. Cancer Epidemiol Biomarkers Prev. 2013;22:708–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Park SL, Murphy SE, Wilkens LR, Stram DO, Hecht SS, Le Marchand L. Association of CYP2A6 activity with lung cancer incidence in smokers: the multiethnic cohort study. Plos One. 2017;12:e0178435.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. El-Boraie A, Taghavi T, Chenoweth MJ, Fukunaga K, Mushiroda T, Kubo M, et al. Evaluation of a weighted genetic risk score for the prediction of biomarkers of CYP2A6 activity. Addict Biol. 2020;25:e12741.

    Article  PubMed  Google Scholar 

  24. Vartiainen E, Seppala T, Lillsunde P, Puska P. Validation of self reported smoking by serum cotinine measurement in a community-based study. J Epidemiol Community Health. 2002;56:167–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Tanner JA, Novalen M, Jatlow P, Huestis MA, Murphy SE, Kaprio J, et al. Nicotine metabolite ratio (3-hydroxycotinine/cotinine) in plasma and urine by different analytical methods and laboratories: implications for clinical implementation. Cancer Epidemiol Biomarkers Prev. 2015;24:1239–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hornung RW, Reed LD. Estimation of average concentration in the presence of nondetectable values. Appl Occup Environ Hyg. 1990;5:46–51. 1990/01/01

    Article  CAS  Google Scholar 

  27. Aulchenko YS, Ripke S, Isaacs A, van Duijn CM. GenABEL: an R library for genome-wide association analysis. Bioinformatics. 2007;23:1294–6.

    Article  CAS  PubMed  Google Scholar 

  28. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44:821–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, 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(Jan):272–9.

    Article  CAS  PubMed  Google Scholar 

  30. Turner SD. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. https://www.biorxiv.org/content/10.1101/005165v1. 2014.

  31. Chenoweth MJ, Novalen M, Hawk LW Jr., Schnoll RA, George TP, Cinciripini PM, et al. Known and novel sources of variability in the nicotine metabolite ratio in a large sample of treatment-seeking smokers. Cancer Epidemiol Biomarkers Prev. 2014;23:1773–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. McKee SA, Weinberger AH. How can we use our knowledge of alcohol-tobacco interactions to reduce alcohol use? Annu Rev Clin Psychol. 2013;9:649–74.

    Article  PubMed  Google Scholar 

  33. Gubner NR, Kozar-Konieczna A, Szoltysek-Boldys I, Slodczyk-Mankowska E, Goniewicz J, Sobczak A, et al. Cessation of alcohol consumption decreases rate of nicotine metabolism in male alcohol-dependent smokers. Drug Alcohol Depend. 2016;163:157–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Benowitz NL, Hukkanen J, Jacob P, III. Nicotine chemistry, metabolism, kinetics and biomarkers. Handb Exp Pharmacol. 2009;192:29–60.

    Article  CAS  Google Scholar 

  35. Magi R, Morris AP. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics. 2010;11:288.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26:2336–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ware JJ, Chen XN, Vink J, Loukola A, Minica C, Pool R, et al. Genome-wide meta-analysis of cotinine levels in cigarette smokers identifies locus at 4q13.2. Sci Rep. 2016;6:20092.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Benner C, Spencer CCA, Havulinna AS, Salomaa V, Ripatti S, Pirinen M. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics. 2016;32(May):1493–501.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Benner C, Havulinna AS, Salomaa V, Ripatti S, Pirinen M. Refining fine-mapping: effect sizes and regional heritability. bioRxiv. 2018:318618. https://doi.org/10.1101/318618.

  41. Chen G, Blevins-Primeau AS, Dellinger RW, Muscat JE, Lazarus P. Glucuronidation of nicotine and cotinine by UGT2B10: loss of function by the UGT2B10 Codon 67 (Asp>Tyr) polymorphism. Cancer Res. 2007;67:9024–9.

    Article  CAS  PubMed  Google Scholar 

  42. Koga M, Ishiguro H, Yazaki S, Horiuchi Y, Arai M, Niizato K, et al. Involvement of SMARCA2/BRM in the SWI/SNF chromatin-remodeling complex in schizophrenia. Hum Mol Genet. 2009;18(Jul):2483–94.

    Article  CAS  PubMed  Google Scholar 

  43. Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019;51:237–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chen G, Giambrone NE, Lazarus P. Glucuronidation of trans-3’-hydroxycotinine by UGT2B17 and UGT2B10. Pharmacogenet Genomics. 2012;22:183–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL, et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell. 2016;167:1415–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Klarin D, Damrauer SM, Cho K, Sun YV, Teslovich TM, Honerlaw J, et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat Genet. 2018;50:1514–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Hoffmann TJ, Theusch E, Haldar T, Ranatunga DK, Jorgenson E, Medina MW, et al. A large electronic-health-record-based genome-wide study of serum lipids. Nat Genet. 2018;50:401–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zhu AZ, Zhou Q, Cox LS, Ahluwalia JS, Benowitz NL, Tyndale RF. Variation in trans-3’-hydroxycotinine glucuronidation does not alter the nicotine metabolite ratio or nicotine intake. Plos One. 2013;8:e70938.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Taghavi T, St, Helen G, Benowitz NL, Tyndale RF. Effect of UGT2B10, UGT2B17, FMO3, and OCT2 genetic variation on nicotine and cotinine pharmacokinetics and smoking in African Americans. Pharmacogenet Genomics. 2017;27:143–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. McDonagh EM, Wassenaar C, David SP, Tyndale RF, Altman RB, Whirl-Carrillo M, et al. PharmGKB summary: very important pharmacogene information for cytochrome P-450, family 2, subfamily A, polypeptide 6. Pharmacogenet Genom. 2012;22:695–708.

    Article  CAS  Google Scholar 

  52. Tanner JA, Tyndale RF. Variation in CYP2A6 activity and personalized medicine. J Pers Med. 2017;7:18.

    Article  PubMed Central  Google Scholar 

  53. Checkoway H, Powers K, Smith-Weller T, Franklin GM, Longstreth WT, Swanson PD. Parkinson’s disease risks associated with cigarette smoking, alcohol consumption, and caffeine intake. Am J Epidemiol. 2002;155:732–8.

    Article  PubMed  Google Scholar 

  54. Milberger S, Biederman J, Faraone SV, Chen L, Jones J. ADHD is associated with early initiation of cigarette smoking in children and adolescents. J Am Acad Child Psy. 1997;36:37–44.

    Article  CAS  Google Scholar 

  55. Diaz FJ, James D, Botts S, Maw L, Susce MT, de Leon J. Tobacco smoking behaviors in bipolar disorder: a comparison of the general population, schizophrenia, and major depression. Bipolar Disord. 2009;11:154–65.

    Article  PubMed  Google Scholar 

  56. Wassenaar CA, Dong Q, Wei QY, Amos CI, Spitz MR, Tyndale RF. Relationship between CYP2A6 and CHRNA5-CHRNA3-CHRNB4 variation and smoking behaviors and lung cancer risk. J Natl Cancer I. 2011;103:1342–6.

    Article  CAS  Google Scholar 

  57. Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, et al. Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet. 2010;42:436–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Fowler CD, Lu Q, Johnson PM, Marks MJ, Kenny PJ. Habenular alpha5 nicotinic receptor subunit signalling controls nicotine intake. Nature. 2011;471:597–601.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Minica CC, Mbarek H, Pool R, Dolan CV, Boomsma DI, Vink JM. Pathways to smoking behaviours: biological insights from the Tobacco and Genetics Consortium meta-analysis. Mol Psychiatry. 2017;22:82–8.

    Article  CAS  PubMed  Google Scholar 

  60. Wilkinson L. Exact and approximate area-proportional circular Venn and Euler diagrams. IEEE Trans Vis Comput Graph. 2012;18:321–31.

    Article  PubMed  Google Scholar 

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Acknowledgements

This work is supported by the NIH (PGRN grant DA020830 and CA197461), CIHR (FDN-154294 and PJY-159710), a Canada Research Chair, Wellcome Trust Sanger Institute, Broad Institute, European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007, grant agreement number 201413, NIAAA (AA-12502/AA-00145/AA-09203/AA15416/K02AA018755), the Academy of Finland (100499/205585/118555/141054/264146/308248/312073/288509/312076/285380/312062/286284/134309/126925/121584/124282/129378/117787/41071/265240/263278), Finnish Foundation for Cardiovascular Research, Sigrid Juselius Foundation, University of Helsinki HiLIFE Fellow grant, Social Insurance Institution of Finland, Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001), Juho Vainio Foundation, Paavo Nurmi Foundation, Finnish Cultural Foundation, Tampere Tuberculosis Foundation, Emil Aaltonen Foundation, Yrjö Jahnsson Foundation, Signe and Ane Gyllenberg Foundation, Diabetes Research Foundation of Finnish Diabetes Association, EU Horizon 2020 (755320), European Research Council (742927), Tampere University Hospital Supporting Foundation, Doctoral Program in Population Health (University of Helsinki), Finnish Research Foundation of the Pulmonary Diseases, Biomedicum Helsinki Foundation, and the Cancer Foundation Finland. We thank Dr Aino Kankaanpää, Maria Novalen, Leanne McNeill, and Tabatha Goncalves for laboratory work.

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JB, MJC, TP, MP, NGM, JK, AL, and RFT contributed to the project design and data analysis plan. TK, SR, PAFM, TL, OR, VS, RJR, TPG, CL, NGM, JK, and RFT contributed to the collection and/or analysis of original cohort data. JB, MJC, TP, GZ, CB, and SG performed data analysis. JB, MJC, MP, JK, AL, and RFT interpreted the data. JB, MJC, MP, NGM, JK, AL, and RFT wrote the paper. All authors read and approved the final manuscript.

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Correspondence to Rachel F. Tyndale.

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TK has consulted for Pfizer Finland. VS attended a conference trip sponsored by Novo Nordisk, received an advisory board meeting honorarium, and collaborates with Bayer ltd. RFT has consulted for Quinn Emmanual and Ethismos.

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Buchwald, J., Chenoweth, M.J., Palviainen, T. et al. Genome-wide association meta-analysis of nicotine metabolism and cigarette consumption measures in smokers of European descent. Mol Psychiatry 26, 2212–2223 (2021). https://doi.org/10.1038/s41380-020-0702-z

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