Genetic polymorphisms in CYP2A6 are associated with a risk of cigarette smoking and predispose to smoking at younger ages

Gene 628 (2017) 205–210

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Gene journal homepage: www.elsevier.com/locate/gene

Research paper

Genetic polymorphisms in CYP2A6 are associated with a risk of cigarette smoking and predispose to smoking at younger ages

MARK

Gloria Pérez-Rubioa,1, Luis Alberto López-Floresa,1, Alejandra Ramírez-Venegasb, Valeri Noé-Díazb, Leonor García-Gómezb, Enrique Ambrocio-Ortiza, Candelaria Sánchez-Romerob, Rafael De Jesús Hernández-Zentenob, Raúl Humberto Sansoresc,⁎, Ramcés Falfán-Valenciaa,⁎⁎ a b c

Laboratorio HLA, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Mexico City, Mexico Departamento de Investigación en Tabaquismo y EPOC, Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas, Mexico City, Mexico Centro Respiratorio de México, Mexico City, Mexico

A R T I C L E I N F O

A B S T R A C T

Keywords: CYP2A6 Genetics of addiction Nicotine addiction Nicotine metabolism Mexican mestizo

Nicotine is the main component of cigarettes that causes addiction, which is considered a complex disease, and genetic factors have been proposed to be involved in the development of addiction. The CYP2A6 gene encodes the main enzyme responsible for nicotine metabolism. Depending on the study population, different genetic variants of CYP2A6 associated with cigarette smoking have been described. Therefore, we evaluated the possible association between SNPs in CYP2A6 with cigarette smoking and nicotine addiction-related variables in Mexican mestizo smokers. We performed a genetic association study comparing light smokers (LS, n = 349), heavy smokers (HS, n = 351) and never-smokers (NS, n = 394). SNPs rs1137115, rs4105144, rs1801272 and rs28399433 were genotyped in the CYP2A6 gene. We found that the A allele of rs1137115 (OR = 1.41) in exon 1 of CYP2A6 and the T allele of rs4105144 (OR = 1.32) in the 5′ UTR of the gene are associated with the risk of cigarette smoking (p < 0.05); rs1137115 affects the level of alternative splicing, resulting in a CYP2A6 isoform with low enzymatic activity, whereas rs4105144 is likely to be in a binding site for the transcription factor for glucocorticoids receptor (GR) and regulates the expression of CYP2A6. In addition, having a greater number of risk alleles (rs1137115 (A), rs4105144 (T) and rs28399433 (G)) is associated with a younger age at onset. The present study shows that in Mexican mestizos, the analyzed SNPs confer greater risk in terms of consumption and age of onset.

1. Introduction

involved in different neurotransmission pathways and those that have been implicated in the nicotine response; these include nicotinic cholinergic receptors and nicotine metabolic enzymes (Arinami et al., 2000; Verde Rello and Santiago Dorrego, 2013). The main enzyme implicated in nicotine metabolism is encoded by CYP2A6 (Hukkanen et al., 2005). The most abundant variants of this gene are single nucleotide polymorphisms (SNPs), which make CYP2A6 highly polymorphic and generate isoforms that vary in enzymatic activity; therefore, the nicotine concentration in the body varies individually. Smokers with certain CYP2A6 variants show different smoking behaviors from those who do not have these variants, which confirms the idea that smokers regulate their nicotine consumption to obtain the

Tobacco consumption is the principal preventable cause of mortality worldwide. A total of 80% of smokers worldwide are in developing countries such as Mexico, where tobacco consumption is still a major public health problem (World Health Organization, WHO | Tobacco, World Health Organization, 2016). Approximately 19.9% of Mexican adults are reported to have smoked at least 100 cigarettes in their lifetimes, and 11.8% are daily smokers (Gutiérrez et al., 2012). Nicotine is the main component in cigarettes that causes addiction, and at present, addiction is considered a disease that involves genetic components. Among these components are those that encode proteins

Abbreviations: cpd, cigarettes per day; HS, heavy smokers; LS, light smokers; NS, never-smokers; MAF, minor allele frequency.; SNP, single nucleotide polymorphisms ⁎ Correspondence to: R. H. Sansores, Centro Respiratorio de México, Tepepan 57, Toriello Guerra, Delegación Tlalpan, 14050 Ciudad de México, México. ⁎⁎ Correspondence to: R. Falfán-Valencia, Laboratorio HLA, Instituto Nacional de Enfermedades Respiratorias Ismael Cosio Villegas, Calzada de Tlalpan 4502, Sección XVI, Delegación Tlalpan, 14080 Ciudad de México, México. E-mail addresses: [email protected] (R.H. Sansores), [email protected] (R. Falfán-Valencia). 1 These authors contributed equally to this work. http://dx.doi.org/10.1016/j.gene.2017.07.051 Received 20 May 2017; Received in revised form 3 July 2017; Accepted 17 July 2017 Available online 20 July 2017 0378-1119/ © 2017 Published by Elsevier B.V.

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discrimination assay using 3 μL of DNA at 15 ng/μL concentration and TaqMan probes (Applied Biosystems Foster City CA, USA). In each template, we included 3 non-template controls, and 1% of the samples were genotyped in duplicate as an allele assignment control.

desired drug level in the body (Malaiyandi et al., 2005). CYP2A6 variability is distributed heterogeneously in populations worldwide; this distribution can explain the different metabolic responses to nicotine, which are tightly associated with nicotine addiction (López-Flores et al., 2017). Although some SNPs have been associated with nicotine addiction in various ethnic populations, in Hispanic populations, they are scarce. Our main objective was to determine an association of SNPs in CYP2A6 with smoking-related variables in Mexican Mestizo smokers.

2.6. Statistical analysis To describe the study population, we used the statistical software SPSS v.20.0 (IBM, New York, USA) in which we calculated the mean and standard deviation of each continuous quantitative variable and the percentage for the sex. All SNPs genotyped were evaluated in the control group (NS) using the Hardy-Weinberg test. Genetic association (by allele and genotype) was tested in a dominant model using Epidat version 3.1 (Xunta de Galicia, 2006). To identify SNPs associated with increase nicotine addiction, we compared HS vs. LS, and to associate SNPs with cigarette consumption, we compared HS vs. NS and LS vs. NS. In addition, we performed Pearson correlation analysis with the number of risk alleles and smoking-associated variables. Haplotype analysis was performed using Haploview version 4.2 (Barrett et al., 2005).

2. Methods 2.1. Participants We performed a case-control study and included ever cigarette smokers (n = 700) who had smoked for ≥ 10 years; they were recruited from the Smoking Cessation Support Clinic of the Department of Investigation in Tobacco Consumption and COPD at the Instituto Nacional de Enfermedades Respiratorias Ismael Cosío Villegas (INER) in Mexico City. The participants filled out a questionnaire on smoking history and were classified according to their self-reported cigarettes smoked per day (cpd) into light smokers (LS, 1–10 cpd, n = 349) and heavy smokers (HS, ≥ 20 cpd, n = 351). We also included a comparison group of healthy participants who were never-smokers (NS, n = 394). All participants were at least third-generation Mexican Mestizos (parents and grandparents born in Mexico), ≥ 30 years of age and had not been diagnosed with a psychiatric disorder. All participants filled out a questionnaire that included anthropometric data and history of inherited pathologies. The subjects voluntarily agreed to participate and signed an informed consent letter created specifically for this study. The protocol was approved by the INER science and research bioethics and biosecurity committees (protocol number B15-16).

3. Results 3.1. Demographic variables The demographic variables of the population study are shown in Table 1. There were statistically significant differences between age and sex; both variables were also reported to show significant differences in the National (Mexican) Addiction Survey: Tobacco, 2011 (Secretaría de Salud, 2011). The HS group had the highest age, and it included more men, subjects with a longer smoking history and a lower age of smoking onset than the LS group. The subjects were classified into five groups according to the zones of their birth states (Secretaría de Gobernación, 2013): Northwest (NW; Baja California, Baja California Sur, Chihuahua, Sinaloa and Sonora), Northeast (NE; Coahuila, Durango, Nuevo León, San Luis Potosí and Tamaulipas), West (WE; Aguascalientes, Colima, Guanajuato, Jalisco, Michoacán, Nayarit, Querétaro and Zacatecas), Central (CE; Ciudad de México, Estado de México, Guerrero, Hidalgo, Morelos, Puebla and Tlaxcala) and Southeast (SE; Campeche, Chiapas, Oaxaca, Quintana Roo, Tabasco, Veracruz and Yucatán). Most of the participants were from CE (83%), followed by WE and SE (8% each), and NE and NW had a minor proportion (< 1%).

2.2. Sample size calculation Sample size was calculated using Epi Info ver. 7 (CDC Epi Info, Epi Info™ 7, 2010) according to the following criteria: unpaired case-control study with 95% confidence level, 85% statistical power, case-control relation 1:1, minor allele frequency (MAF) of 10% and odds ratio (OR) = 2.0. Based on these criteria, we required at least 348 LS, HS and NS participants to obtain a statistical power > 80%. 2.3. DNA extraction

3.2. Analysis of allelic, genotypic and haplotype associations Obtained a 15-mL peripheral blood sample from each participant through venipuncture. Blood was collected in tubes with EDTA as an anticoagulant. DNA extraction was performed using a BDtract DNA isolation kit (Maxim Biotech, Inc. San Francisco, California, USA) and later was quantified with a NanoDrop 2000 (Thermo Scientific, DE, USA).

The included SNPs were in Hardy-Weinberg equilibrium; Fig. 2 presents the allelic frequencies reported in select populations from the 1000 Genomes Project and in our reference group (NS). In NS, rs28399433(G) had a different frequency from those of the other analyzed SNPs: it was higher in IBS, CEU, YRI and MXL but lower in CHB. rs1137115(A) had a similar frequency as MXL but was different from that of the other populations. rs1801272(A) had the highest frequency in IBS and CEU (~ 5%), followed by MXL (1.5%) and in our reference group (< 1%); it was not detected in CHB or YRI. Interestingly, rs4105144(T) had a MAF in our group but not in other reference populations, where rs4105144(C) is the minor allele. We did not include rs1801272 in subsequent tests because its low frequency did not have sufficient statistical power. Genotype and allele associations for each SNP were evaluated (Table 2). For the genotype associations, we used a dominant model, which assumes the same risk whether one or two copies of the risk allele are present. For rs1137115 (GA + AA vs. GG), OR = 1.51 (95% CI = 1.09–2.07) and for rs4105144 (CT + TT vs. CC), OR = 1.41 (95% CI = 1.03–1.92) when comparing the extreme phenotypes of HS vs. NS. In the allelic associations, the same two SNPs had statistically significant differences between their frequencies only for HS vs. NS

2.4. Selecting SNPs We searched relevant publications from 2010 to 2015 in the National Center for Biotechnology Information (NCBI) database using the following keywords: “CYP2A6”, “nicotine metabolism”, “cigarette smoking”, “nicotine dependence”, “nicotine addiction” and “genetics”. Based on publications on genetic associations in Caucasian, Asian and African populations (Fig. 1), we identified the following SNPs in CYP2A6: rs1137115, rs1801272 and rs28399433; we also identified rs4105144 near the gene. 2.5. Genotyping Genotyping was performed using a real-time PCR (7300 Real-Time PCR system, Applied Biosystems, Foster City, CA, USA)-based allelic 206

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Fig. 1. Localization of selected SNPs in the study and their effect on CYP2A6. Gene location, mRNA, protein and effect of carrying risk alleles for each SNP evaluated.

cigarettes at earlier ages possess a greater number of risk alleles. In Fig. 3, we present the mean age of smoking onset according to the number of risk alleles of the smokers in our study.

Table 1 Demographic data of the study population. Variable

HS (n = 351)

LS (n = 349)

NS (n = 394)

p

Age (years) Sex (male)% cpd SI (packs/year) Years of smoking Age at onset (years)

53.4 ± 12.0 56.4 25.4 ± 10.9 38 ± 22.1 30.5 ± 11.6 17.1 ± 4.8

49.8 ± 12.4 51.6 6.5 ± 3.1 9 ± 6.4 25.7 ± 11.3 18.9 ± 5.3

50.7 ± 14.6 51.5

0.001 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01

4. Discussion The CYP2A6 gene encodes the enzyme responsible for > 80% of nicotine metabolism in smokers (Hukkanen et al., 2005), and it is highly polymorphic. For this reason, several haplotypes in the official nomenclature for CYP genes have been determined (Anon, 2014), and the number of alleles is given after the (*) symbol. In addition to SNPs, other types of mutations have been identified in CYP2A6, including gene duplications, gene deletions, gene conversions and copy number variations that determine the number of alleles (45 as of April 2017). In our study, minor alleles of rs1137115 and rs4105144 (A and T, respectively) are associated with cigarette consumption in a Mexican Mestizo population. Previously, rs1137115 was reported in Caucasians as being responsible for higher cigarette consumption among smokers (Anon, 2014). rs1137115(G > A) is a synonymous substitution located in the first exon. When the A allele is present, it is called CYP2A6*1A(51A), and it has been associated with low mRNA expression and slow nicotine metabolism (Bergen et al., 2015; Bloom et al., 2011); the presence of the risk allele results in an exonic splicing enhancer site, producing an isoform with reduced enzymatic activity (Bloom et al., 2011). On the other hand, rs4105144 (C) has been associated with lower cigarette consumption in European Caucasians and has high linkage disequilibrium (D′ = 1) with null alleles, including

Mean and standard deviation are shown for all variables, except for sex where we show percentage. cpd, cigarettes per day; SI, smoking index; HS, heavy smokers; LS, light smokers; NS, never-smokers.

(OR = 1.41 and OR = 1.32 for rs1137115(A) and rs4105144(T), respectively). rs28393433 was not associated with any variant. None of the three SNPs showed high linkage disequilibrium in our reference group. 3.3. Pearson correlation We included smokers who carried 0, 1, 2 or 3 risk alleles from among rs1137115(A), rs4105144(T) or rs28399433(G). The variables tested were those related to cigarette smoking and included years smoking, cpd and age of smoking onset. We obtained a significant Pearson correlation coefficient (ρ = −0.084; p = 0.049) for age of smoking onset, which shows a trend that individuals who consume

Fig. 2. Allele frequencies in different populations reported by1000 Genomes Project and our reference group (NS). Allele frequencies of polymorphisms evaluated among five principal populations.

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Yupik, who show high linkage disequilibrium (D′ = 1) between CYP2A6*1B, *7 and *8, but not in Caucasian populations, in which this effect has not been reported (Binnington et al., 2012). To date, there are no reports of an effect of rs4105144 in biological assays. This SNP is near the 5′ UTR of CYP2A6 (~ 2.7 kb distance), and it has been proposed as “tag SNP” for rs1801272, which is not applicable in our population. An in silico assay performed by the Encyclopedia of DNA elements (ENCODE) showed that this SNP is located in a DNaseI-hypersensitive region, which, depending to the allele carried, could affect both the regional structure and the expression of nearby genes (University of California Santa Cruz and Standford University, 2017). Additionally, this assay suggests that the region where rs4105144 is located, the GR transcription factor (glucocorticoids receptor), can be joined (University of California Santa Cruz and Standford University, 2017), which is particularly interesting because higher stress levels in smokers could be involved in the development of nicotine addiction (Vandenbergh and Scholomer, 2014); however, this should be demonstrated using a biological assay. Based on reports in the literature, we expect that rs1801272 and rs28399433 would be interesting; however, we did not find statistically significant differences in any variables. rs1801272(T > A; CYP2A6*2) results in a non-synonymous substitution (Leu160His) and encodes an inactive enzyme (Yamano et al., 1990). rs1801272(A) has been reported to be associated with higher cigarette consumption in Caucasian subjects with COPD (Siedlinski et al., 2012), but the MAF in our reference group (and in the entire population) was too low (A = 0.63%) compared to that in Europeans (A = 3.38%) and Caucasians from HapMap (A = 4.16%) (The International HapMap Consortium, 2007); therefore, based on the MAF obtained, we should consider increasing the sample size to obtain statistically reliable results. This SNP shows high linkage disequilibrium (D′ = 1) with rs4105144 in a Caucasian population (Thorgeirsson et al., 2010), but in our populations, there was no linkage disequilibrium between any SNPs analyzed. On the other hand, rs28399433 causes a substitution in the TATA box in the promoter (−48T > G; CYP2A6*9), partially decreasing the expression of the enzyme without modifying its structure (Pitarque et al., 2001). In a GWAS performed in Caucasians, this SNP was reported to be associated with a reduced amount of metabolized nicotine and was in high linkage disequilibrium (D′ = 1) with rs1801272; the authors estimated that the contribution to nicotine metabolism was greater for rs28399433 (4.05%) than for rs1801272 (0.30%) (Loukola et al., 2015). Another report in a Spanish population found an association between rs28399433 and higher nicotine addiction according to the Fagerström Test for Nicotine Dependence (Verde et al., 2011). However, in contrast to the global literature, rs28399433(T) (common allele) has been reported to be associated with smoking status and psychological nicotine dependence in Mexican families from Mexico City,

Table 2 Dominant model analysis for genotypes and alleles of SNPs analyzed in CYP2A6. SNP

HS (n = 351)

LS (n = 349)

NS (n = 394)

n

GF/ AF (%)

n

GF/ AF (%)

n

GF/ AF (%)

rs1137115 GG GA + AA G A

234 117 575 127

66.67 33.33 81.90 18.10

240 109 583 115

68.77 31.23 83.52 16.48

296 98 681 107

75.12 24.87 86.42 13.58

rs4105144 CC CT + TT C T

227 124 566 136

64.67 35.32 80.62 19.37

232 117 567 131

66.48 33.52 81.23 18.76

284 110 667 121

72.08 27.91 84.64 15.36

rs1801272 TT TA + AA T A

341 10 692 10

97.15 2.85 98.57 1.42

337 12 685 13

96.56 3.44 98.14 1.86

389 5 783 5

98.73 1.27 99.36 0.63

rs28399433 TT 254 TG + GG 97 T 596 G 106

72.36 27.63 84.90 15.09

242 107 578 120

69.34 30.66 82.80 17.19

277 117 664 124

70.30 29.69 84.26 15.73

pa

OR

CI 95%

0.0137

0.66 1.51 0.71 1.41

0.48–0.91 1.09–2.07 0.53–0.94 1.06–1.86

0.70 1.41 0.75 1.32

0.51–0.96 1.03–1.92 0.57–0.98 1.01–1.73

0.0204

0.0360 0.0476

HS, heavy smokers; LS, light smokers; NS, Never-smokers; GF, Genotypic frequency; AF, Allelic frequency. a p corrected, two-tailed X2 test, to compare HS vs. NS. NSIG: Not significant.

rs1801272(A) (also called CYP2A6*2) (Thorgeirsson et al., 2010). The same linkage disequilibrium has been reported in a Hungarian population (Fiatal et al., 2016). In the EPIC European cohort for lung cancer, rs4105144 was associated with higher cotinine (principal metabolite of nicotine) levels in genotype TT vs. CC (1.41 nmol/L and 1.19 nmol/L, respectively); however, there was no association in terms of cpd (Timofeeva et al., 2011). Our results show that the risk allele for rs4105144 is T (MAF = 15.36%), similar to Caucasians in HapMap (MAF = 26.27%) (National Center for Biotechnology Information, 2016), but for CEU, IBS, and other populations from the 1000 Genomes Project, the risk allele is C (A. 1000 Genomes Project Consortium et al., 2015) (Fig. 2). In addition, rs4105144(T) was not in linkage disequilibrium with the other SNPs analyzed, which highlights the importance of exploring genetic polymorphisms in populations with different genetics because the associations reported in certain populations will not necessarily be replicated in others. This phenomenon was reported in the Alaskan

Fig. 3. Correlation for smoking onset and the risk alleles number. Individuals who consumed cigarettes at earlier ages possess a greater number of risk alleles confirmed by a significant Pearson correlation coefficient.

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E.P., Kang, H.M., Korbel, J.O., Marchini, J.L., McCarthy, S., McVean, G.A., Abecasis, G.R., Gabriel, S.B., Gibbs, R.A., Green, E.D., Hurles, M.E., Knoppers, B.M., Korbel, J.O., Lander, E.S., Lee, C., Lehrach, H., Mardis, E.R., Marth, G.T., McVean, G.A., Nickerson, D.A., Schmidt, J.P., Sherry, S.T., Wang, J., Wilson, R.K., Gibbs, R.A., Boerwinkle, E., Doddapaneni, H., Han, Y., Korchina, V., Kovar, C., Lee, S., Muzny, D., Reid, J.G., Zhu, Y., Wang, J., Chang, Y., Feng, Q., Fang, X., Guo, X., Jian, M., Jiang, H., Jin, X., Lan, T., Li, G., Li, J., Li, Y., Liu, S., Liu, X., Lu, Y., Ma, X., Tang, M., Wang, B., Wang, G., Wu, H., Wu, R., Xu, X., Yin, Y., Zhang, D., Zhang, W., Zhao, J., Zhao, M., Zheng, X., Lander, E.S., Altshuler, D.M., Gabriel, S.B., Gupta, N., Gharani, N., Toji, L.H., Gerry, N.P., Resch, A.M., Flicek, P., Barker, J., Clarke, L., Gil, L., Hunt, S.E., Kelman, G., Kulesha, E., Leinonen, R., McLaren, W.M., Radhakrishnan, R., Roa, A., Smirnov, D., Smith, R.E., Streeter, I., Thormann, A., Toneva, I., Vaughan, B., ZhengBradley, X., Bentley, D.R., Grocock, R., Humphray, S., James, T., Kingsbury, Z., Lehrach, H., Sudbrak, R., Albrecht, M.W., Amstislavskiy, V.S., Borodina, T.A., Lienhard, M., Mertes, F., Sultan, M., Timmermann, B., Yaspo, M.-L., Mardis, E.R., Wilson, R.K., Fulton, L., Fulton, R., Sherry, S.T., Ananiev, V., Belaia, Z., Beloslyudtsev, D., Bouk, N., Chen, C., Church, D., Cohen, R., Cook, C., Garner, J., Hefferon, T., Kimelman, M., Liu, C., Lopez, J., Meric, P., O'Sullivan, C., Ostapchuk, Y., Phan, L., Ponomarov, S., Schneider, V., Shekhtman, E., Sirotkin, K., Slotta, D., Zhang, H., McVean, G.A., Durbin, R.M., Balasubramaniam, S., Burton, J., Danecek, P., Keane, T.M., Kolb-Kokocinski, A., McCarthy, S., Stalker, J., Quail, M., Schmidt, J.P., Davies, C.J., Gollub, J., Webster, T., Wong, B., Zhan, Y., Auton, A., Campbell, C.L., Kong, Y., Marcketta, A., Gibbs, R.A., Yu, F., Antunes, L., Bainbridge, M., Muzny, D., Sabo, A., Huang, Z., Wang, J., Coin, L.J.M., Fang, L., Guo, X., Jin, X., Li, G., Li, Q., Li, Y., Li, Z., Lin, H., Liu, B., Luo, R., Shao, H., Xie, Y., Ye, C., Yu, C., Zhang, F., Zheng, H., Zhu, H., Alkan, C., Dal, E., Kahveci, F., Marth, G.T., Garrison, E.P., Kural, D., Lee, W.-P., Fung Leong, W., Stromberg, M., Ward, A.N., Wu, J., Zhang, M., Daly, M.J., DePristo, M.A., Handsaker, R.E., Altshuler, D.M., Banks, E., Bhatia, G., del Angel, G., Gabriel, S.B., Genovese, G., Gupta, N., Li, H., Kashin, S., Lander, E.S., McCarroll, S.A., Nemesh, J.C., Poplin, R.E., Yoon, S.C., Lihm, J., Makarov, V., Clark, A.G., Gottipati, S., Keinan, A., Rodriguez-Flores, J.L., Korbel, J.O., Rausch, T., Fritz, M.H., Stütz, A.M., Flicek, P., Beal, K., Clarke, L., Datta, A., Herrero, J., McLaren, W.M., Ritchie, G.R.S., Smith, R.E., Zerbino, D., Zheng-Bradley, X., Sabeti, P.C., Shlyakhter, I., Schaffner, S.F., Vitti, J., Cooper, D.N., Ball, E.V., Stenson, P.D., Bentley, D.R., Barnes, B., Bauer, M., Keira Cheetham, R., Cox, A., Eberle, M., Humphray, S., Kahn, S., Murray, L., Peden, J., Shaw, R., Kenny, E.E., Batzer, M.A., Konkel, M.K., Walker, J.A., MacArthur, D.G., Lek, M., Sudbrak, R., Amstislavskiy, V.S., Herwig, R., Mardis, E.R., Ding, L., Koboldt, D.C., Larson, D., Ye, K., Gravel, S., Swaroop, A., Chew, E., Lappalainen, T., Erlich, Y., Gymrek, M., Frederick Willems, T., Simpson, J.T., Shriver, M.D., Rosenfeld, J.A., Bustamante, C.D., Montgomery, S.B., De La Vega, F.M., Byrnes, J.K., Carroll, A.W., DeGorter, M.K., Lacroute, P., Maples, B.K., Martin, A.R., Moreno-Estrada, A., Shringarpure, S.S., Zakharia, F., Halperin, E., Baran, Y., Lee, C., Cerveira, E., Hwang, J., Malhotra, A., Plewczynski, D., Radew, K., Romanovitch, M., Zhang, C., Hyland, F.C.L., Craig, D.W., Christoforides, A., Homer, N., Izatt, T., Kurdoglu, A.A., Sinari, S.A., Squire, K., Sherry, S.T., Xiao, C., Sebat, J., Antaki, D., Gujral, M., Noor, A., Ye, K., Burchard, E.G., Hernandez, R.D., Gignoux, C.R., Haussler, D., Katzman, S.J., James Kent, W., Howie, B., Ruiz-Linares, A., Dermitzakis, E.T., Devine, S.E., Abecasis, G.R., Min Kang, H., Kidd, J.M., Blackwell, T., Caron, S., Chen, W., Emery, S., Fritsche, L., Fuchsberger, C., Jun, G., Li, B., Lyons, R., Scheller, C., Sidore, C., Song, S., Sliwerska, E., Taliun, D., Tan, A., Welch, R., Kate Wing, M., Zhan, X., Awadalla, P., Hodgkinson, A., Li, Y., Shi, X., Quitadamo, A., Lunter, G., McVean, G.A., Marchini, J.L., Myers, S., Churchhouse, C., Delaneau, O., Gupta-Hinch, A., Kretzschmar, W., Iqbal, Z., Mathieson, I., Menelaou, A., Rimmer, A., Xifara, D.K., Oleksyk, T.K., Fu, Y., Liu, X., Xiong, M., Jorde, L., Witherspoon, D., Xing, J., Eichler, E.E., Browning, B.L., Browning, S.R., Hormozdiari, F., Sudmant, P.H., Khurana, E., Durbin, R.M., Hurles, M.E., Tyler-Smith, C., Albers, C.A., Ayub, Q., Balasubramaniam, S., Chen, Y., Colonna, V., Danecek, P., Jostins, L., Keane, T.M., McCarthy, S., Walter, K., Xue, Y., Gerstein, M.B., Abyzov, A., Balasubramanian, S., Chen, J., Clarke, D., Fu, Y., Harmanci, A.O., Jin, M., Lee, D., Liu, J., Jasmine Mu, X., Zhang, J., Zhang, Y., Li, Y., Luo, R., Zhu, H., Alkan, C., Dal, E., Kahveci, F., Marth, G.T., Garrison, E.P., Kural, D., Lee, W.-P., Ward, A.N., Wu, J., Zhang, M., McCarroll, S.A., Handsaker, R.E., Altshuler, D.M., Banks, E., del Angel, G., Genovese, G., Hartl, C., Li, H., Kashin, S., Nemesh, J.C., Shakir, K., Yoon, S.C., Lihm, J., Makarov, V., Degenhardt, J., Korbel, J.O., Fritz, M.H., Meiers, S., Raeder, B., Rausch, T., Stütz, A.M., Flicek, P., Paolo Casale, F., Clarke, L., Smith, R.E., Stegle, O., Zheng-Bradley, X., Bentley, D.R., Barnes, B., Keira Cheetham, R., Eberle, M., Humphray, S., Kahn, S., Murray, L., Shaw, R., Lameijer, E.-W., Batzer, M.A., Konkel, M.K., Walker, J.A., Ding, L., Hall, I., Ye, K., Lacroute, P., Lee, C., Cerveira, E., Malhotra, A., Hwang, J., Plewczynski, D., Radew, K., Romanovitch, M., Zhang, C., Craig, D.W., Homer, N., Church, D., Xiao, C., Sebat, J., Antaki, D., Bafna, V., Michaelson, J., Ye, K., Devine, S.E., Gardner, E.J., Abecasis, G.R., Kidd, J.M., Mills, R.E., Dayama, G., Emery, S., Jun, G., Shi, X., Quitadamo, A., Lunter, G., McVean, G.A., Chen, K., Fan, X., Chong, Z., Chen, T., Witherspoon, D., Xing, J., Eichler, E.E., Chaisson, M.J., Hormozdiari, F., Huddleston, J., Malig, M., Nelson, B.J., Sudmant, P.H., Parrish, N.F., Khurana, E., Hurles, M.E., Blackburne, B., Lindsay, S.J., Ning, Z., Walter, K., Zhang, Y., Gerstein, M.B., Abyzov, A., Chen, J., Clarke, D., Lam, H., Jasmine Mu, X., Sisu, C., Zhang, J., Zhang, Y., Gibbs, R.A., Yu, F., Bainbridge, M., Challis, D., Evani, U.S., Kovar, C., Lu, J., Muzny, D., Nagaswamy, U., Reid, J.G., Sabo, A., Yu, J., Guo, X., Li, W., Li, Y., Wu, R., Marth, G.T., Garrison, E.P., Fung Leong, W., Ward, A.N., del Angel, G., DePristo, M.A., Gabriel, S.B., Gupta, N., Hartl, C., Poplin, R.E., Clark, A.G., Rodriguez-Flores, J.L., Flicek, P., Clarke, L., Smith, R.E., Zheng-Bradley, X., MacArthur, D.G., Mardis, E.R., Fulton, R., Koboldt, D.C., Gravel, S., Bustamante, C.D., Craig, D.W., Christoforides, A., Homer, N., Izatt, T., Sherry, S.T., Xiao, C., Dermitzakis, E.T., Abecasis, G.R., Min Kang, H., McVean, G.A., Gerstein, M.B., Balasubramanian, S., Habegger, L., Yu, H., Flicek, P., Clarke, L., Cunningham, F., Dunham, I., Zerbino, D., Zheng-Bradley, X., Lage, K., Berg Jespersen, J., Horn, H., Montgomery, S.B., DeGorter, M.K., Khurana, E., Tyler-Smith,

with a frequency for rs28399433(G) (16.4%) similar to that in our study (15.73%) (Svyryd et al., 2015). Our results show that individuals carrying more risk alleles begin smoking at younger ages, which is highly relevant, especially for youth populations at risk for smoking. CYP2A6 variants have been reported to be associated with cigarette consumption in adolescents (O'Loughlin et al., 2004; Schoedel et al., 2004). However, we propose that young smokers who carry slow-metabolism variants of CYP2A6 could have elevated nicotine levels in the body and, consequently, in the brain, due to higher retention and slower metabolism, producing a prolonged effect in neurotransmission pathways; this chronic exposure could lead to higher nicotine tolerance, which promotes increased cigarette consumption. This is the first report of an association between SNPs in CYP2A6 and nicotine addiction in unrelated Mexican Mestizo smokers. Our data are valuable because we include Mexican Mestizos from different regions of the country; nevertheless, the results are not exempt from limitations. The sample size is low for the results of rs1137115 and rs4105144; based on the a posteriori values obtained for MAF and OR, we will require at least 772 subjects in each study group to have statistical power of at least 80%. We were not able to analyze blood or urine cotinine levels to ascertain the self-reported cpd smoked by the subjects, as they are expensive tests for large sample sizes, as in our study. It will be desirable to analyze nicotine levels to evaluate smoking topography (puff number, puff volume, puff interval and puff duration), which modifies the nicotine dose that enters the body (Djordjevic et al., 2000; Strasser et al., 2011). We were not able to classify smokers according to their enzymatic activity (Dempsey et al., 2004) because the genotyped SNPs (except rs1801272) are not sufficient to assign them as wild-type allele or alleles determined by haplotypes; for example rs1137115(A) is present in CYP2A6*2, *14, *18B, *20, *21, *22, *28, *41, *42, *44 and *45, and rs28399433(G) is in CYP2A6*9, CYP2A6*13 and CYP2A6*15. 5. Conclusions In a Mexican Mestizo population, rs1137115(A) and rs4105144(T) in CYP2A6 are associated with cigarette consumption: on one hand, rs1137115(A) affects alternative splicing, producing an isoform with low enzymatic activity, and on the other hand, rs4105144(T) can regulate CYP2A6 expression via its location in a binding site of the GR transcription factor. The distribution of the analyzed SNPs differs from that in other ethnic populations. Finally, smokers carrying greater numbers of CYP2A6 risk alleles (rs1137115(A), rs4105144(T) y rs28399433(G)) are more likely to smoke at earlier ages. Our findings are useful for understanding the genotype-phenotype interactions of nicotine addiction and support personalized medicine for Mexican Mestizos in the treatment of smoking cessation. Funding This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors. Ethical conduct of research The participants were invited to participate in the present research study and were informed about the objective of the study. The participants then signed a letter of informed consent and were provided with an assurance-of-personal-data document. Both documents were approved by the research institute's Committee of Science and Bioethical Research (B15-16). References A. 1000 Genomes Project Consortium, Auton, A., Brooks, L.D., Durbin, R.M., Garrison,

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variation for smoking behaviors and nicotine dependence. Clin. Pharmacol. Ther. 77, 145–158. http://dx.doi.org/10.1016/j.clpt.2004.10.011. National Center for Biotechnology Information, 2016. Reference SNP (refSNP) Cluster Report: rs4105144, dbSNP. Short Genet. Var. O'Loughlin, J., Paradis, G., Kim, W., DiFranza, J., Meshefedjian, G., McMillan-Davey, E., Wong, S., Hanley, J., Tyndale, R.F., 2004. Genetically decreased CYP2A6 and the risk of tobacco dependence: a prospective study of novice smokers. Tob. Control. 13, 422–428. http://dx.doi.org/10.1136/tc.2003.007070. Pitarque, M., von Richter, O., Oke, B., Berkkan, H., Oscarson, M., Ingelman-Sundberg, M., 2001. Identification of a single nucleotide polymorphism in the TATA box of the CYP2A6 gene: impairment of its promoter activity. Biochem. Biophys. Res. Commun. 284, 455–460. http://dx.doi.org/10.1006/bbrc.2001.4990. Schoedel, K.A., Hoffmann, E.B., Rao, Y., Sellers, E.M., Tyndale, R.F., 2004. Ethnic variation in CYP2A6 and association of genetically slow nicotine metabolism and smoking in adult Caucasians. Pharmacogenetics 14, 615–626. http://dx.doi.org/10. 1097/00008571-200409000-00006. Secretaría de Gobernación, 2013. Diario Oficial de la Federación. II Sesión extraordinaria del Consejo Nacional de Seguridad Pública, México, D. F. Secretaría de Salud, 2011. Encuesta Nacional de Adicciones 2011: Reporte de Tabaco. Siedlinski, M., Cho, M.H., Bakke, P., Gulsvik, A., Lomas, D.A., 2012. Genome-wide association study of smoking behaviors in COPD patients. Thorax 66, 894–902. http:// dx.doi.org/10.1136/thoraxjnl-2011-200154.Genome-wide. Strasser, A.A., Benowitz, N.L., Pinto, A.G., Tang, K.Z., Hecht, S.S., Carmella, S.G., Tyndale, R.F., Lerman, C.E., 2011. Nicotine metabolite ratio predicts smoking topography and carcinogen biomarker level. Cancer Epidemiol. Biomark. Prev. 20, 234–238. http://dx.doi.org/10.1158/1055-9965.EPI-10-0674. Svyryd, Y., Ramírez-Venegas, A., Sánchez-Hernández, B., Aguayo-Gómez, A., LunaMuñoz, L., Arteaga-Vázquez, J., Regalado-Pineda, J., Mutchinick, O.M., 2015. Genetic risk determinants for cigarette smoking dependence in mexican mestizo families. Nicotine Tob. Res. 18, 620–625. http://dx.doi.org/10.1093/ntr/ntv213. The International HapMap Consortium, 2007. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861. http://dx.doi.org/10.1038/ nature06258. Thorgeirsson, T.E., Gudbjartsson, D.F., Surakka, I., Vink, J.M., Amin, N., Geller, F., Sulem, P., Rafnar, T., Esko, T., Walter, S., Gieger, C., Rawal, R., Mangino, M., Prokopenko, I., Mägi, R., Keskitalo, K., Gudjonsdottir, I.H., Gretarsdottir, S., Stefansson, H., Thompson, J.R., Aulchenko, Y.S., Nelis, M., Aben, K.K., den Heijer, M., Dirksen, A., Ashraf, H., Soranzo, N., Valdes, A.M., Steves, C., Uitterlinden, A.G., Hofman, A., Tönjes, A., Kovacs, P., Hottenga, J.J., Willemsen, G., Vogelzangs, N., Döring, A., Dahmen, N., Nitz, B., Pergadia, M.L., Saez, B., De Diego, V., Lezcano, V., Garcia-Prats, M.D., Ripatti, S., Perola, M., Kettunen, J., Hartikainen, A.-L., Pouta, A., Laitinen, J., Isohanni, M., Huei-Yi, S., Allen, M., Krestyaninova, M., Hall, A.S., Jones, G.T., van Rij, A.M., Mueller, T., Dieplinger, B., Haltmayer, M., Jonsson, S., Matthiasson, S.E., Oskarsson, H., Tyrfingsson, T., Kiemeney, L.A., Mayordomo, J.I., Lindholt, J.S., Pedersen, J.H., Franklin, W.A., Wolf, H., Montgomery, G.W., Heath, A.C., Martin, N.G., Madden, P.A.F., Giegling, I., Rujescu, D., Järvelin, M.-R., Salomaa, V., Stumvoll, M., Spector, T.D., Wichmann, H.-E., Metspalu, A., Samani, N.J., Penninx, B.W., Oostra, B.A., Boomsma, D.I., Tiemeier, H., van Duijn, C.M., Kaprio, J., Gulcher, J.R., McCarthy, M.I., Peltonen, L., Thorsteinsdottir, U., Stefansson, K., 2010. Sequence variants at CHRNB3-CHRNA6 and CYP2A6 affect smoking behavior. Nat. Genet. 42, 448–453. http://dx.doi.org/10.1038/ng.573. Timofeeva, M.N., McKay, J.D., Smith, G.D., Johansson, M., Byrnes, G.B., Chabrier, A., Relton, C., Ueland, P.M., Vollset, S.E., Midttun, Ø., Nygård, O., Slimani, N., Romieu, I., Clavel-Chapelon, F., Boutron-Ruault, M.C., Fagherazzi, G., Kaaks, R., Teucher, B., Boeing, H., Weikert, C., Bueno-de-Mesquita, H.B., Van Gils, C., Peeters, P.H.M., Agudo, A., Barricarte, A., Huerta, J.M., Rodríguez, L., Sánchez, M.J., Larrañaga, N., Khaw, K.T., Wareham, N., Allen, N.E., Travis, R.C., Gallo, V., Norat, T., Krogh, V., Masala, G., Panico, S., Sacerdote, C., Tumino, R., Trichopoulou, A., Lagiou, P., Trichopoulos, D., Rasmuson, T., Hallmans, G., Riboli, E., Vineis, P., Brennan, P., 2011. Genetic polymorphisms in 15q25 and 19q13 loci, cotinine levels, and risk of lung cancer in EPIC. Cancer Epidemiol. Biomark. Prev. 20, 2250–2261. http://dx.doi. org/10.1158/1055-9965.EPI-11-0496. University of California Santa Cruz and Standford University, 2017University of California Santa Cruz and Standford Universit. ENCODE: Encyclopedia of DNA Elements. Vandenbergh, D.J., Scholomer, G.L., 2014. Finding genomic function for genetic associations in nicotine addiction research: the ENCODE Project's role in future pharmacogenomic analysis. Pharmacol. Biochem. Behav. 1, 34–44. http://dx.doi.org/10. 1097/OPX.0b013e3182540562.The. Verde Rello, Z., Santiago Dorrego, C., 2013. Gene genetics of the tobacco smoking. Med. Clin. 140, 66–67. http://dx.doi.org/10.1016/j.medcli.2012.09.008. Verde, Z., Santiago, C., Rodríguez González-Moro, J.M., de Lucas Ramos, P., López Martín, S., Bandrés, F., Lucia, A., Gómez-Gallego, F., 2011. “Smoking genes”: a genetic association study. PLoS One 6, 3–8. http://dx.doi.org/10.1371/journal.pone. 0026668. World Health Organization, WHO | Tobacco, World Health Organization, 2016World Health Organization, WHO | Tobacco, World Health Organ. http://www.who.int/ mediacentre/factsheets/fs339/en/ (accessed April 11, 2017). Xunta de Galicia, 2006. Organización Panamericana de la Salud, EPIDAT. Versión 3.1. Yamano, S., Tatsuno, J., Gonzalez, F.J., 1990. The CYP2A3 gene product catalyzes coumarin 7-hydroxylation in human liver microsomes. Biochemistry 29, 1322–1329.

C., Chen, Y., Colonna, V., Xue, Y., Gerstein, M.B., Balasubramanian, S., Fu, Y., Kim, D., Auton, A., Marcketta, A., Desalle, R., Narechania, A., Wilson Sayres, M.A., Garrison, E.P., Handsaker, R.E., Kashin, S., McCarroll, S.A., Rodriguez-Flores, J.L., Flicek, P., Clarke, L., Zheng-Bradley, X., Erlich, Y., Gymrek, M., Frederick Willems, T., Bustamante, C.D., Mendez, F.L., David Poznik, G., Underhill, P.A., Lee, C., Cerveira, E., Malhotra, A., Romanovitch, M., Zhang, C., Abecasis, G.R., Coin, L., Shao, H., Mittelman, D., Tyler-Smith, C., Ayub, Q., Banerjee, R., Cerezo, M., Chen, Y., Fitzgerald, T.W., Louzada, S., Massaia, A., McCarthy, S., Ritchie, G.R., Xue, Y., Yang, F., Gibbs, R.A., Kovar, C., Kalra, D., Hale, W., Muzny, D., Reid, J.G., Wang, J., Dan, X., Guo, X., Li, G., Li, Y., Ye, C., Zheng, X., Altshuler, D.M., Flicek, P., Clarke, L., ZhengBradley, X., Bentley, D.R., Cox, A., Humphray, S., Kahn, S., Sudbrak, R., Albrecht, M.W., Lienhard, M., Larson, D., Craig, D.W., Izatt, T., Kurdoglu, A.A., Sherry, S.T., Xiao, C., Haussler, D., Abecasis, G.R., McVean, G.A., Durbin, R.M., Balasubramaniam, S., Keane, T.M., McCarthy, S., Stalker, J., Chakravarti, A., Knoppers, B.M., Abecasis, G.R., Barnes, K.C., Beiswanger, C., Burchard, E.G., Bustamante, C.D., Cai, H., Cao, H., Durbin, R.M., Gerry, N.P., Gharani, N., Gibbs, R.A., Gignoux, C.R., Gravel, S., Henn, B., Jones, D., Jorde, L., Kaye, J.S., Keinan, A., Kent, A., Kerasidou, A., Li, Y., Mathias, R., McVean, G.A., Moreno-Estrada, A., Ossorio, P.N., Parker, M., Resch, A.M., Rotimi, C.N., Royal, C.D., Sandoval, K., Su, Y., Sudbrak, R., Tian, Z., Tishkoff, S., Toji, L.H., Tyler-Smith, C., Via, M., Wang, Y., Yang, H., Yang, L., Zhu, J., Bodmer, W., Bedoya, G., Ruiz-Linares, A., Cai, Z., Gao, Y., Chu, J., Peltonen, L., Garcia-Montero, A., Orfao, A., Dutil, J., Martinez-Cruzado, J.C., Oleksyk, T.K., Barnes, K.C., Mathias, R.A., Hennis, A., Watson, H., McKenzie, C., Qadri, F., LaRocque, R., Sabeti, P.C., Zhu, J., Deng, X., Sabeti, P.C., Asogun, D., Folarin, O., Happi, C., Omoniwa, O., Stremlau, M., Tariyal, R., Jallow, M., Sisay Joof, F., Corrah, T., Rockett, K., Kwiatkowski, D., Kooner, J., Tịnh Hiê'n, T., Dunstan, S.J., Thuy Hang, N., Fonnie, R., Garry, R., Kanneh, L., Moses, L., Sabeti, P.C., Schieffelin, J., Grant, D.S., Gallo, C., Poletti, G., Saleheen, D., Rasheed, A., Brooks, L.D., Felsenfeld, A.L., McEwen, J.E., Vaydylevich, Y., Green, E.D., Duncanson, A., Dunn, M., Schloss, J.A., Wang, J., Yang, H., Auton, A., Brooks, L.D., Durbin, R.M., Garrison, E.P., Min Kang, H., Korbel, J.O., Marchini, J.L., McCarthy, S., McVean, G.A., Abecasis, G.R., 2015. A global reference for human genetic variation. Nature 526, 68–74. http://dx.doi.org/10.1038/nature15393. Anon, 2014. CYP2A6 allele nomenclature, Hum. Cytochrome P450 Allele Nomencl. Database. http://www.cypalleles.ki.se/cyp2a6.htm. Arinami, T., Ishiguro, H., Onaivi, E.S., 2000. Polymorphisms in genes involved in neurotransmission in relation to smoking. Eur. J. Pharmacol. 410, 215–226. Barrett, J.C., Fry, B., Maller, J., Daly, M.J., 2005. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265. http://dx.doi.org/10.1093/ bioinformatics/bth457. Bergen, A.W., Michel, M., Nishita, D., Krasnow, R., Javitz, H.S., Conneely, K.N., LessovSchlaggar, C.N., Hops, H., Zhu, A.Z.X., Baurley, J.W., McClure, J.B., Hall, S.M., Baker, T.B., Conti, D.V., Benowitz, N.L., Lerman, C., Tyndale, R.F., Swan, G.E., 2015. Transdisciplinary research in cancer of the lung research team, drug metabolizing enzyme and transporter gene variation, nicotine metabolism, prospective abstinence, and cigarette consumption. PLoS One 10, e0126113. http://dx.doi.org/10.1371/ journal.pone.0126113. Binnington, M.J., Zhu, A.Z.X., Renner, C.C., Lanier, A.P., Hatsukami, D.K., Benowitz, N.L., Tyndale, R.F., 2012. CYP2A6 and CYP2B6 genetic variation and its association with nicotine metabolism in South Western Alaska Native people. Pharmacogenet. Genomics 22, 429–440. http://dx.doi.org/10.1097/FPC.0b013e3283527c1c. Bloom, A.J., Hinrichs, A.L., Wang, J.C., von Weymarn, L.B., Kharasch, E.D., Bierut, L.J., Goate, A., Phil, D., Murphy, S.E., 2011. The contribution of common CYP2A6 alleles to variation in nicotine metabolism among European Americans. Pharmacogenet. Genomics 21, 403–416. http://dx.doi.org/10.1097/FPC.0b013e328346e8c0.The. CDC Epi Info, Epi Info™ 7, 2010. https://www.cdc.gov/epiinfo/ (accessed April 13, 2017). Dempsey, D., Tutka, P., Jacob, P., Allen, F., Schoedel, K., Tyndale, R.F., Benowitz, N.L., 2004. Nicotine metabolite ratio as an index of cytochrome P450 2A6 metabolic activity. Clin. Pharmacol. Ther. 76, 64–72. http://dx.doi.org/10.1016/j.clpt.2004.02. 011. Djordjevic, M.V., Stellman, S.D., Zang, E., 2000. Doses of nicotine and lung carcinogens delivered to cigarette smokers. J. Natl. Cancer Inst. 92, 106–111. http://dx.doi.org/ 10.1093/jnci/92.2.106. Fiatal, S., Tóth, R., Moravcsik-Kornyicki, Á., Kósa, Z., Sándor, J., McKee, M., Ádány, R., 2016. High prevalence of smoking in the roma population seems to have no genetic background. Nicotine Tob. Res. 18, 2260–2267. http://dx.doi.org/10.1093/ntr/ ntw161. Gutiérrez, J., Rivera-Dommarco, A., Shamah-Levy, J., Villalpando-Hernández, T., Franco, S., Cuevas-Nasu, L., Romero-Martínez, M., Hernández-Ávila, M., 2012. Encuesta Nacional de Salud y Nutrición 2012. Resultados nacionales, Cuernavaca, México. http://ensanut.insp.mx/informes/ENSANUT2012ResultadosNacionales.pdf. Hukkanen, J., Jacob III, P., Benowitz, N.L., 2005. Metabolism and disposition kinetics of nicotine. Pharmacol. Rev. 57, 79–115. http://dx.doi.org/10.1124/pr.57.1.3. López-Flores, L.A., Pérez-Rubio, G., Falfán-Valencia, R., 2017. Distribution of polymorphic variants of CYP2A6 and their involvement in nicotine addiction. EXCLI J. 16, 174–196. http://dx.doi.org/10.17179/excli2016-847. Loukola, A., Buchwald, J., Gupta, R., Palviainen, T., Hällfors, J., Tikkanen, E., Korhonen, T., Ollikainen, M., Sarin, A.-P., Ripatti, S., Lehtimäki, T., Raitakari, O., Salomaa, V., Rose, R.J., Tyndale, R.F., Kaprio, J., 2015. A genome-wide association study of a biomarker of nicotine metabolism. PLoS Genet. 11, e1005498. http://dx.doi.org/10. 1371/journal.pgen.1005498. Malaiyandi, V., Sellers, E.M., Tyndale, R.F., 2005. Implications of CYP2A6 genetic

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