Alcohol-related disorders result in significant personal costs and burdens to society (Anthony and Echeagaray-Wagner, 2000), fueling the need for studies of initiation of alcohol use and of susceptibility factors leading to later dependence. Alcohol dependence and/or abuse are common, with a lifetime prevalence of 13% (Grant et al., 2004) and are the third leading preventable cause of death in the United States (McGinnis & Foege, 1993). Experimentation with alcohol often begins early, in adolescence and young adulthood. The 2012 data from the Monitoring the Future report (Johnston et al., 2013) show that 64% of 12th graders reported using alcohol, 45% reported having been drunk, and binge drinking occurred at a rate of 24% in 2012. Binge drinking, defined as the consumption within 2 hours of 4 drinks for females or 5 for males (National Institute on Alcohol Abuse and Alcoholism [NIAAA], www.niaaa.nih.gov), is an alcohol consumption behavior that is of major concern from a public health perspective. Binge drinking accounts for over half of all alcohol-related deaths and three-quarters of the estimated economic costs of excessive alcohol use (CDC-MMWR 2012, 2013). Discovering risk factors for this behavior in particular could have an important public health impact.
Binge drinking and other measures of heavy use have been of particular interest for studies of genetic risk. In a nonclinically ascertained adult study population, Kendler and colleagues (2010) showed that the highest association of any single alcohol behavior measure with alcohol dependence was frequency of being drunk. Furthermore, this phenotypic association had a strong genetic basis, consistent across both men and women. As a result, we have focused this genetic study on the frequency of binge drinking in our cohort.
Genetic studies of alcohol behaviors will benefit from a focus on young adulthood. While initiation and patterns of alcohol use in early adolescence have been shown to have substantial social and environmental components, alcohol use has increasingly strong genetic influence from the ages of 15 to 23 (Kendler et al., 2008), with a corresponding drop in familial environmental influences. This large twin study showed that heritable effects of alcohol use rose from about 0% at age 13 years to the adult level of approximately 40 to 45% by about age 19 to 21 years. Importantly, this is the age of the subjects in our study (mean = 21.4 years). Similarly, the shared family environmental effects dropped from about 50% to about 10% over the same age range. Individual-specific environmental factors remained at a relatively steady 45%.
Although genetic etiology for alcohol use and dependence has been well supported (Heath et al., 1997; Madden and Heath, 2002; Madden et al., 2000; Reich et al., 1998; Schuckit, 2009; Wolff, 1972), much work remains to elucidate particular genetic risk mutations and understand genetic mechanisms. Alcohol genetic studies have focused primarily on alcohol dehydrogenases and related genes and also on gamma-aminobutyric acid (GABA) genes. In this study, we have instead investigated neuronal nicotinic receptor (CHRN) subunit genes, which encode nicotinic acetylcholine receptors (Mineur and Picciotto, 2008). These receptors are implicated biologically in alcohol consumption. Alcohol causes an increase in the neurotransmitter acetylcholine (Ericson et al., 2003). Acetylcholine activates nicotinic receptors, which in turn modulate the dopaminergic activation that is associated with drug reward response (Hendrickson et al., 2010; Soderpalm et al., 2009). In addition to many associations with nicotine dependence and smoking behaviors, CHRN subunit genes, particularly CHRNA4 and CHRNB2, have also been implicated in alcohol and other substance use, and CHRN gene variants may contribute to both conditions (Hoft et al., 2009; Schlaepfer et al., 2008a,b; Tuesta et al., 2011; Wang et al., 2009).
The parent study for this project is a longitudinal study of cigarette and other substance use in adolescents and young adults (Social and Emotional Contexts of Adolescent Smoking Patterns [SECASP] study; Dierker and Mermelstein, 2010; Selya et al., 2013). The relations between CHRN genes and smoking heaviness in this cohort have been reported previously (Cannon et al., 2013). Given the evidence for CHRN associations with smoking heaviness in youth (Cannon et al., 2013; Ducci et al., 2011; Rodriguez et al., 2011), the CHRN associations with alcohol (Hendrickson et al., 2010; Hoft et al., 2009; Tuesta et al., 2011), the comorbidity between cigarette and alcohol use in adolescents (Kendler et al., 2008), and tests of independent associations between CHRN genes and alcohol and smoking phenotypes are warranted.
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- Supporting Information
This work presents a significant association between SNPs in CHRNA4 and the frequency of alcohol binging in 702 Hispanic and non-Hispanic White young adults who are participants in the SECASP longitudinal study of nicotine (Dierker and Mermelstein, 2010; Selya et al., 2013). We chose to focus on this measure for its impact on public health (CDC-MMWR 2012, 2013), its association with alcohol dependence, and its potential as a genetic trait (Kendler et al., 2010). Several correlated measures of alcohol quantity and frequency also showed associations with CHRNA4 (see detailed results in Tables S3 and S4), although the strongest findings occurred with binge frequency. We analyzed Hispanic and non-Hispanic Whites together because this ethnic distinction did not significantly impact the binge frequency phenotype. In addition, the SNPs that we chose for CHRNA4 tag the same underlying haplotype structure for the Hispanic and non-Hispanic Whites (see Fig. S2). Substantive results were similar when analyses were carried out stratified by ethnicity (Table S5).
Somewhat surprisingly, our study identified a genetic effect of CHRNA4 on alcohol binge frequency independent of smoking. While we demonstrated a significant phenotypic association between binge frequency and smoking status in our study cohort, adjustment for smoking did not substantively affect the genetic association with CHRNA4. This result suggests that the genetic association is independent of the well-documented association between smoking and alcohol use. This independence is also consistent with previous results in our cohort showing genetic associations between cigarette use and SNPs in CHRNB3A6, CHRNA5A3B4, and CHRNA2, but not CHRNA4 or CHRNB2 (Cannon et al., 2013). Conversely, the present study of this cohort focusing instead on alcohol found no association with SNPs in CHRNB3A6, CHRNA5A3B4, or CHRNA2, but a robust association with CHRNA4. We acknowledge a modest increase in the chance of observing a positive result with our CHRN SNPs by testing 2 phenotypes in our cohort (cigarette use and binge frequency).
Our SNP effect sizes were small, explaining 2 to 3% of the variance in frequency of binging. There was a direct relationship between strength of association and strength of LD with the strongest SNP, rs4522666, suggesting that these other results are secondary to the rs4522666 result. In Tables S2–S4, significant associations were again found between other measures of drinking frequency and CHRNA4, but evidence was not as strong as that found for frequency of binging. For all of the SNP associations with CHRNA4 in our cohort, the minor alleles were associated with greater frequency of binging, resulting in an increase of about one-third to one-half of a standard deviation from the major allele homozygotes to the minor allele homozygotes.
Previous studies of human subjects reveal an interesting mix of evidence for effects of CHRNA4 on alcohol and nicotine phenotypes. A study of Korean adult alcoholics revealed an association with CHRNA4, in addition to significant effects of several other candidate genes (Kim et al., 2004). Focusing on the subjective responses to nicotine and alcohol, Ehringer and colleagues (2007) showed a modest CHRNA4 association with alcohol (but not nicotine) in Caucasian adolescents recruited from substance use treatment centers, the criminal justice system, and community-based studies. Subsequent studies from this research group using this cohort have gone on to reveal modest associations between CHRNA4 and adolescent nicotine dependence (Kamens et al., 2013). Similar associations between CHRNA4 and nicotine dependence have been reported in studies of adults (Breitling et al., 2009; Feng et al., 2004; Li et al., 2005). Several studies have also demonstrated associations between CHRNA4 and subjective effects of nicotine (Picciotto et al., 1998; Tapper et al., 2004). Many of these associations involve high-frequency SNPs within the same LD bins measured in this study, including rs1044396 and rs2236196. The SNPs rs4522666 and rs2236196 are both located in the CHRNA4 3′ UTR, and functional studies have implicated rs2236196 in gene expression levels (Hutchison et al., 2007). The SNP rs4522666 has not been associated with smoking phenotypes, but in an exploratory analysis of risk-taking constructs it was associated with harm avoidance in young adults (Roe et al., 2009).
Possible clues to the potential mechanisms underlying these various associations may lie in the animal and drug response studies. Focusing on alcohol, animal models have demonstrated that nicotinic receptor genes moderate the ethanol-induced release of dopamine (Larsson et al., 2005; Soderpalm et al., 2000), and ethanol may also interact directly with the function of nicotinic acetylcholine receptors (Liu et al., 1994; Wood et al., 1995). In particular, animal studies of nicotinic receptors containing the alpha4 subunit have shown a significant relationship with the reward response to alcohol (Liu et al., 2013) and to alcohol withdrawal (Butt et al., 2004). Further evidence is provided in recent animal studies of varenicline, an alpha4–beta2 nicotinic receptor partial agonist used in smoking cessation. This drug reduces not only nicotine dependence, but also alcohol consumption in rats (Steensland et al., 2007). Hendrickson and colleagues (2010) showed that this effect depends specifically on activation of alpha4 nicotinic receptors. In a mouse knockout line that did not express alpha4, varenicline did not reduce alcohol consumption. Conversely, in another mouse line that overexpressed alpha4, a very low dose of varenicline that was not efficacious in wild-type animals dramatically reduced alcohol consumption.
Human studies of the alpha4–beta2 partial agonist varenicline have again implicated alpha4 in alcohol consumption. Epidemiological data have shown that varenicline is associated with significant reduction in drinking as compared with nicotine replacement or no smoking cessation medication (McKee et al., 2013). Several controlled trials in humans have also demonstrated significant decreases in alcohol consumption in smokers (Fucito et al., 2011; McKee et al., 2009; Mitchell et al., 2012).
Reward response may provide an important common denominator in the CHRNA4 genetic findings for alcohol and nicotine. While associations in our cohort with variables reflecting frequency of use showed independence between alcohol and nicotine, it is possible that a future focus on phenotypes that reflect reward response may reveal associations across substances. Additional phenotypes may provide further insights into these potential mechanisms. Field and colleagues (2013) have reported a significant correlation between alcohol binging, smoking, and obesity in adolescents. Consistent with this observation, Landgren and colleagues (2009) report an interesting association with CHRNA4 and obesity, particularly within heavy alcohol users. This result may not be surprising given that the cholinergic dopaminergic reward effect seen with alcohol can also be seen with food (Larsson et al., 2005). Associations between CHRNA4 and attention deficit hyperactivity disorder (Todd et al., 2003) and cognitive attention (Greenwood et al., 2012) may suggest yet other mechanisms underlying the relationship between CHRNA4 and frequency of binging. The data in the SECASP longitudinal study will allow future genetic explorations of additional phenotypes related to reward response for both nicotine and alcohol. In addition, it will be possible to explore dependence of associations on correlated measures of temperament, such as impulsivity.
Because of the longitudinal nature of SECASP, it was not feasible to ascertain the very large sample sizes of many contemporary genetic association studies. It is possible therefore that we may have missed effects due to lack of statistical power. To minimize this limitation, we focused on a candidate gene system with prior evidence of involvement in substance use, restricting the number of tests performed. We also note that our sample detected the small effects of these SNPs. While we may have missed other SNP effects, such effects would have to have explained less than 2% of the variance in the phenotype and may therefore be of lower interest as contributing risk factors. In addition, our findings require replication in other samples and, in particular, in samples with subjects of other ethnicities/races.
These findings suggest that variants in CHRNA4 may contribute to risk of binge drinking, although confirmation of this association awaits replication in independent samples. Further study of this association could be important in the understanding and control of this significant alcohol behavior. Our longitudinal sample cohort will allow us to test for associations with other related phenotypes and to determine whether the strength of these associations weakens or strengthens over time.