This is the most comprehensive survey of the nicotinic receptor subunit genes for involvement in nicotine dependence to date. We have further explored the relationship between nicotine dependence and this gene family using 226 SNPs genotyped in the NICSNP sample of nicotine dependent cases and non-dependent smoking controls. Four distinct findings, two in the CHRNA5-CHRNA3-CHRNB4
cluster, one in CHRNB3-CHRNA6
, and one in CHRND-CHRNG
are significant after multiple test correction across the CHRN
gene family. Additional genes CHRNA4
harbor nominally significant SNPs. Those CHRN
genes that were not nominally associated in our initial reports (Bierut et al., 2007
; Saccone et al., 2007a
) still lack significant association with nicotine dependence with the denser SNP coverage.
This study improved our coverage of an important gene family using a sample previously genotyped for a joint GWAS and large-scale candidate gene study (Bierut et al., 2007
; Saccone et al., 2007a
). Given the current popularity and importance of the GWAS design, the question of how best to interpret new analyses of GWAS datasets or samples in the context of the original large-scale GWAS is a challenge facing not only this study but the field as a whole. In the present case, although the NICSNP sample was used for a large-scale GWAS and candidate gene study, the CHRN
gene family was in fact given the highest priority in our original design and would have been targeted even if our resources had been limited to only a few hundred SNPs. Therefore we feel it is appropriate and useful to report significant results based on multiple test correction for the 226 SNPs (111 r2
bins) tested here. However, these findings are not significant after formal multiple test correction for all the genotyping performed on this sample to date. In general, clearly citing any previous, overlapping GWAS or large-scale candidate gene studies is important to allow fully informed interpretation of results.
Our results continue to support an important role for the nonsynonymous CHRNA5
SNP rs16969968 in determining vulnerability to nicotine dependence, as first reported in (Saccone et al., 2007a
), and separate in vitro
evidence indicates that rs16969968 may indeed be the functional variant explaining the association across the other correlated members of this LD bin (Bierut et al., In press
). Due to the extensive LD encompassing not only the CHRNA5-CHRNA3-CHRNB4
cluster but also the neighboring genes LOC123688
(Figures , and S2), further work to definitively identify a causal variant from among all SNPs correlated with rs16969968 will be important.
A second variant in the CHRNA5-CHRNA3-CHRNB4
cluster, rs578776, constitutes a distinct, significantly associated locus that also warrants further study. This SNP was previously noted to have a false discovery rate of 0.09 (Saccone et al., 2007a
); with our denser coverage it remains the most significant representative of this second locus. Joint analysis of the uncorrelated SNPs rs578776 and rs16969968 indicates that these two variants each exert independent influence on nicotine dependence vulnerability. Though |D’| between these SNPs is 1, the low r2
between them means that the disease association at one does not statistically explain the disease association at the other. These two variants demonstrate an interesting evolutionary history, with the risk allele for rs16969968 occurring on the background of the higher risk variant for rs578776 (). Although not all genotype combinations occur because of this history, the three genotypes at one locus on a fixed background for the other demonstrate a clear pattern of altered risk (, first row and first column). It is unclear from our genetic data whether the functional variants underlying this susceptibility reside within the same CHRN
gene or reflect variation in two different genes.
Our third significant single-SNP finding is at the 5′ end of CHRNB3-CHRNA6
, tagged by rs13277254, and here again the functional source of this signal is unclear. Interestingly, a study in non-small cell cancer tumors found higher expression of CHRNA6
in non-smokers compared to smokers (Lam et al., 2007
), suggesting potential mechanisms of action related to exposure to smoking and nicotine dependence.
on chromosome 2 harbors a fourth significant locus, with an additional interesting, though only nominally significant, uncorrelated locus in the neighboring gene CHRNG
. Efforts to replicate these findings in independent samples would be of great interest. The CHRND-CHRNG
cluster lies at the end of chromosome 2q in a region of linkage that has been persistently reported in the literature for nicotine dependence (Straub et al., 1999
) and other addiction phenotypes (Agrawal et al., 2008
; Gelernter et al., 2005
; Gelernter et al., 2006
). The γ subunit is known only to be fetally expressed and replaced by ε in late fetal development (Mishina et al., 1986
), suggesting an unexpected mechanism for influencing addiction risk if indeed CHRNG
Our CIDR genotyping included the CHRNA3
SNPs rs1317286 and rs6495308 recently reported by Berrettini et al. to be associated with cigarettes-per-day with p
-values of 0.0000026 and 0.000069 respectively (Berrettini et al., 2008
). With our comprehensive coverage we are thus able to make a direct comparison and confirm that their results are in line with our two distinct findings represented by rs16969968 and rs578776. In our sample, rs1317286 (primary p
= 0.00034, OR = 1.28) is in the same LD bin and highly correlated (r2
= 0.975) with the slightly more significant rs16969968 (p
= 0.00013, OR = 1.30), confirming that these findings coincide. We believe that rs16969968 is the best functional candidate among the correlated SNPs representing this locus, and further work to test this is underway. The second SNP rs6495308 is correlated (r2
= 0.76) with our separate significant finding at rs578776. This suggests that the signal at rs6495308 (p
= 0.0019, OR = 1.29) may be an attenuation of the stronger association at rs578776 (p
= 0.00011, OR = 1.34). The correlation between rs16969968 and rs6495308 is low (r2
= 0.15), again indicating that these represent two different signals.
cluster is of further interest because of recent reports of significant association with lung cancer (Amos et al., 2008
; Hung et al., 2008
; Thorgeirsson et al., 2008
), a disease for which cigarette smoking is known to be the major risk factor. The associations with lung cancer were either at the non-synonymous CHRNA5
SNP rs16969968 (p
-value 1 × 10−20
(Hung et al., 2008
)), or at SNPs highly correlated with it (p
= 1.5 × 10−8
at rs1051730 (Thorgeirsson et al., 2008
= 7× 10−18
at rs1051730 and p
= 3× 10−18
at rs8034191 (Amos et al., 2008
)). The lung cancer risk allele matches the risk allele for nicotine dependence.
Those reports differed in their interpretation of this association with lung cancer — that is, whether it is evidence of a direct effect on lung cancer vulnerability, or whether it can be explained entirely through the indirect effect of increased risk for smoking. What is clear is that this locus is a risk factor for nicotine dependence and smoking quantity. However, it is interesting to note that for lung cancer, the odds ratios for the effect of one copy of the risk allele were 1.30 (95% confidence interval 1.23-1.38) for rs16969968 (Hung et al., 2008
), 1.32 (1.24-1.41) for rs8034191 and 1.32 (1.23-1.39) for rs1051730 (Amos et al., 2008
), and 1.31 (1.19-1.44) for rs1051730 (Thorgeirsson et al., 2008
). These therefore match the odds ratio we see for nicotine dependence: 1.31 (1.14-1.5) for rs16969968. The lung cancer studies highlighted this single risk locus in this region, and appear to indicate weaker association between lung cancer and rs578776, which tags our second nicotine dependence locus in the CHRNA5-CHRNA3-CHRNB4
cluster. In our nicotine dependence study, rs16969968 (p
, OR 1.31 (1.14-1.5)) and rs578776 (p
, OR 1.34 (1.16-1.56)) have comparable evidence and effect size for association. In contrast, (Hung et al., 2008
) typed both SNPs in the discovery subset of their sample and in that subsample found lung cancer association p
-values of 5×10−9
(OR 1.32 (1.2-1.44)) for rs16969968 versus 2.5×10−4
(OR 1.2 (1.08-1.33)) for rs578776. Future work to clarify the direct versus indirect effects of these variants on lung cancer will be of great interest.
An important, unique feature of our sample is the definition of controls: smokers (> 100 cigarettes lifetime) who have never exhibited symptoms of dependence. Genetic associations from this study thus provide insight into the transition from smoking to nicotine dependence. This design also circumvents noise that may occur if the control group includes non-smoking (unexposed) but genetically vulnerable individuals. We expect our results to overlap with those from studies of alternative phenotypes such as cigarettes-per-day, but differences in genetic findings may reflect important differences in the phenotypes.
Recently, (Zeiger et al., 2008
) demonstrated that the CHRNB3-CHRNA6
cluster is associated with early subjective responses to tobacco in adolescents. One of their most significant results is tagged by rs4950 in CHRNB3
; this SNP is significantly associated with nicotine dependence in our sample (p = 0.00010) and is in the LD bin tagged by rs13277254 (r2
= 0.995). This concordance of genetic findings for these two phenotypes suggests that subjective effects may mediate the association between this locus and addiction, and raises the possibility that early interventions may be effective in disrupting this particular pathway leading to increased addiction risk.
Other groups have reported significant associations between measures of cigarette use or nicotine dependence and some CHRN
genes. Nicotine dependence was associated with two CHRNA4
SNPs (rs1044396 and rs1044397) in a family-based study of Chinese male smokers (Feng et al., 2004
). We tested both of these SNPs and did not replicate the univariate findings, perhaps because of ethnic differences across our samples.
A study of six CHRNA4
SNPs in European Americans (EA) and African Americans (AA) reported various nominally significant findings for specific phenotypes or samples, with rs2236196 highlighted in AA women after correction for multiple testing (Li et al., 2005
). Our current study provides some support for rs2236196 (p = 0.0048), although we see a stronger association with nicotine dependence at rs2273504 (p = 0.0023). Importantly, these two SNPs are uncorrelated (r2
= 0.07), indicating that we have modest evidence for potentially two distinct loci affecting nicotine dependence risk, one which is novel and the other which replicates the earlier finding in (Li et al., 2005
A follow-up to the above cited study in the same sample reported association between rs2302763 in CHRNB1
and smoking quantity in EA (Lou et al., 2006
). We did not test this specific SNP, but genotyped other highly correlated SNPs according to HapMap Centre d’Etudie du Polymorphisme Humain (CEPH) from Utah (CEU) data (The International HapMap Consortium, 2005
) (e.g. rs2302765, rs4796418, rs3855924). None were associated with nicotine dependence in our sample. We do observe a separate, nominally significant signal in CHRNB1
tagged by rs17732878; this LD bin is not correlated with rs2302763 (HapMap CEU r2
A study of 39 SNPs in 11 CHRN
genes in Israeli women reported main effects of rs2072660 (CHRNB2
) with history of daily smoking, and of rs1909884 (CHRNA7
), rs4861065 (CHRNA9
), and rs9298629 (CHRNB3
) with nicotine dependence (Greenbaum et al., 2006
). Our sample provides weak support for rs9298629 (p
= 0.019). Another study reported associations between subjective reactions to cigarettes and rs2072660 and rs2072658 in CHRNB2
, in Caucasian and Hispanic young adults (Ehringer et al., 2007
). We do not see association between these SNPs and nicotine dependence in our sample.
In summary, we have strong evidence that at least four distinct variants in CHRNA5-CHRNA3-CHRNB4, CHRNB3-CHRNA6 and CHRND-CHRNG influence nicotine dependence risk. These four significant nicotinic receptor findings, though important, together account for less than 10% of the phenotypic variance in our sample, indicating that additional risk factors are yet to be discovered and illustrating the challenge of complex disease genetics. Our ongoing work will continue the search for other genes and epistatic effects; we will also extend our analyses to diverse population samples having contrasting LD structure, as such studies can help narrow down among correlated SNPs and localize the most likely functional source of an association signal. Ultimately, determining the mechanism of action for these variants via functional studies can help improve prevention and cessation therapies.