Nicotine addiction from tobacco smoking is responsible for over three million deaths annually, making it the leading cause of preventable mortality in the world (1
). In the USA in 2003, 21.6% of adults were smokers, where 24% of men and 19% of women were smokers (26
). Previous association studies have been limited to narrowly focussed candidate gene studies. Our candidate gene study was more extensive, genotyping 3713 SNPs for 348 candidates in 1050 nicotine-dependent cases and 879 non-dependent smokers, where our control group definition was particularly strict.
Our top FDR-controlled findings were dominated by nicotinic receptor genes. Our positive association findings for the α
5 and β
3 nicotinic receptor subunits are novel. To date, most human genetic and biological studies of the nicotinic receptors and nicotine dependence have focussed on the α
4 and β
2 subunits because they co-occur in high-affinity receptors and are widely expressed in the brain (27
). However, mouse studies have demonstrated that of the α
2 containing receptors that mediate dopamine release, a substantial proportion contain α
5 as well (28
). This is consistent with our evidence for an important role of α
5 in nicotine dependence susceptibility. Furthermore, in a brain α
2 receptor, an α
5 or β
3 subunit can take the fifth position in the pentamer, corresponding to β
1 of muscle. Although neither α
5 nor β
3 is thought to participate in forming binding sites, they are able to affect channel properties and influence agonist potency because they participate in the conformational changes associated with activation and desensitization (27
The most compelling biological evidence of a risk factor for nicotine dependence is from the non-synonymous SNP rs16969968
. This SNP causes a change in amino acid 398 from asparagine (encoded by the G allele) to aspartic acid (encoded by A, the risk allele), which results in a change in the charge of the amino acid in the second intracellular loop of the α
5 subunit (29
). The risk allele appeared to act in a recessive mode, in which individuals who were homozygous for the A allele are at a 2-fold risk to develop nicotine dependence. Although the α
5 subunit has not been studied extensively and there are no reports of known functional effects of this polymorphism, it is striking that a non-synonymous charge-altering polymorphism in the corresponding intracellular loop of the α
4 nAChR subunit has been shown to alter nAChR function in mice in response to nicotine exposure (30
). This variant is common in the populations of European descent (allele frequency of A allele ~42%), but uncommon in populations of Asian or African descent (<5%, data from International HapMap project, http://www.hapmap.org
Also among the top 39 FDR-controlled signals were the genes KCNJ6
(also known as GIRK2
) and GABRA4
. These were the only other genes besides nicotinic receptors with SNPs that had P
-values less than 0.001. KCNJ6
belongs to the inwardly rectifying potassium channel (GIRK) family of genes. GIRK provides a common link between numerous neurotransmitter receptors and the regulation of synaptic transmission (34
). GABA is the major inhibitory neurotransmitter in the mammalian central nervous system and is critical for the reinforcing effects of nicotine (3
). We found significant evidence that the risk due to genotype is much stronger in men than in women (), where the male odds ratio was 2.2 (95% CI 1.4–3.3).
Previously reported findings in other nicotinic receptors were not among our most significant findings. In prior studies of CHRNA4
, nominal association with nicotine dependence measures was reported for the SNPs rs2236196
in African-American families and rs2273504
in European-Americans, but only rs2236196
in African-Americans remained after multiple testing correction (9
). Also in CHRNA4
were associated with both Fagerström test for nicotine dependence (FTND) score and qualitative nicotine dependence in a family-based sample of Asian male smokers (8
). In our sample of European descent, we tested 11 SNPs for CHRNA4
including the above-mentioned SNPs except rs2273504
, which did not pass our stringent quality control standards. The lowest primary P
-value across all 11 SNPs was 0.026 for rs2236196
(study-wide rank = 132); this particular result may be considered a single test given the specific prior finding for this SNP, thus providing modest evidence for replication. The remaining four previously reported SNPs that we analyzed showed P
-values greater than 0.8. Contrasts in these results are possibly due in part to the different ethnicities of the respective samples.
A recent study of smoking initiation and severity of nicotine dependence in Israeli women (10
) analyzed 39 SNPs in 11 nicotinic receptor subunit genes. Their single SNP analyses also did not detect association with SNPs in α4, including rs2236196
, although finding nominal significance in the α7, α9, β2 and β3 subunits. Their study did not include the same SNPs in the β3 subunit and α5–α3–β4 cluster comprising our four strongest associations in nicotinic receptor genes; they did analyze our fifth ranking nicotinic receptor SNP, rs1051730
, and found a suggestive P
-value of 0.08 when comparing ‘high’ nicotine-dependent subjects with ‘low’ nicotine-dependent subjects in a much smaller sample than ours.
Our study was unable to corroborate reported association findings of Beuten et al.
) for the β2 subunit of the GABAB
(also known as GABABR2
). We genotyped 32 SNPs in GABBR2
including five SNPs reported by Beuten et al.
), three of which were the most significant in European-Americans by at least one test in that study. The primary P
-value in our study was greater than 0.07 for all 32 SNPs and greater than 0.3 for the five previously reported SNPs.
Similarly, we do not find evidence for nominal association in our primary test of the 31 SNPs we genotyped for the DDC
gene, which includes an SNP previously reported significant in European-Americans (35
). And of the 11 SNPs covering the gene BDNF
, three (rs6265
) were previously reported as associated in European-American males (21
); these three were not significant in our sample (primary P
= 0.86, 0.088 and 0.12, respectively), and the lowest primary P
-value among the remaining eight SNPs was 0.02, which does not survive correction for the six LD bins covering the gene. Note that our primary test uses a log-additive model, whereas previous reports sometimes found their strongest results under other models (e.g. recessive and dominant); however, for these previously reported associations, our tests for departure from the log-additive model did not find evidence for improvement under alternative modes of inheritance.
Our primary association analysis was a two-degree-of-freedom test of the significance of adding genotype and genotype by gender interaction terms to the base predictors sex and site. This approach helps to ensure that we detect associations that are significantly influenced by gender. The disadvantage is that the extra degree of freedom makes associations with insignificant gender interaction appears to be less significant overall.
Because our controls were highly selected and could even be considered ‘protected’ against susceptibility to nicotine dependence, interpretation of our results must consider the possibility that an association signal from our study may actually represent protective rather than risk effects. We used the allele more frequent in cases for reporting these data as a convention to facilitate comparison of the odds ratios among SNPs; this should not be viewed as a conclusion of how a particular variant influences the risk for nicotine dependence. The precise determination of the mechanism by which a variant alters risk can only come from functional studies.
We performed additional tests for association using only the individuals from the US sample to determine whether our primary conclusions still hold in this subset of 797 cases and 813 controls (the Australian sample alone is too small to test for association, with only 253 cases and 66 controls). We used the same logistic regression method as for the entire sample except for the omission of the term ‘site’. The Spearman rank-order correlation of the P-values between the two tests for association was 0.87. Supplementary Material, Table S2 shows the results of the US-only analysis for the 39 SNPs from our list of top associations (), with the original ordering and FDR filtering, side by side with results from the US sample. Supplementary Material, Table S3 describes the result of completely starting over and using only the US sample to order by P-value, filter by FDR <40% and compute LD bins. In this case, 30 of 39 (77%) SNPs in our original set of top signals () appeared in the list of top signals in the US-only analysis (Supplementary Material, Table S3), which includes the genes CHRNA5 and CHRNB3, the top genes from our initial analysis. Hence, although there were some changes in the order of the results, the primary conclusion of association with the nicotinic receptors CHRNB3 and CHRNA5 remains valid when the analysis is performed on the US subsample.
As a companion to the candidate gene study, a GWAS was carried out in parallel (23
). Approximately 2.4 million SNPs were genotyped across the human genome in a two-stage design that began with pooled genotyping in a portion of the sample and followed with individual genotyping of the entire sample for the top 40 000 signals. The 21st strongest signal from the GWAS was due to an SNP 3 kb upstream of the first 5′ promoter of CHRNB3
, the gene with the strongest signal from our candidate gene study. This signal came from the SNP rs13277254
(genotyped only for the GWAS and not for our candidate gene study) and had a P
-value of 6.52 × 10−5
. This convergence from two different study designs provides further support that the signals in this gene are not random effects.
In conclusion, we have identified several genetic variants as being associated with nicotine dependence in candidate genes, the majority of which are nicotinic receptor genes. One of the SNPs implicated has a number of biologically relevant consequences, making it a particularly plausible candidate for influencing smoking behavior. These variants should be considered potential sources of genetic risk. Additional research is required to establish replication and possibly its role in the pharmacogenetics of response to nicotine dosing as well as to treatments for nicotine dependence.