In genetic association studies, much effort has focused on moving beyond the initial single nucleotide polymorphism (SNP)-by-SNP analysis. One approach is to re-analyze a chromosomal region where an association has been detected, jointly analyzing the SNP thought to best represent that association with each additional SNP in the region. Such joint analyses may help identify additional, statistically independent association signals. However, it is possible for a single genetic effect to produce joint SNP results that would typically be interpreted as two distinct effects (e.g. both SNPs are significant in the joint model). We present a general approach that can (1) identify conditions under which a single variant could produce a given joint SNP result, and (2) use these conditions to identify variants from a list of known SNPs (e.g. 1000 Genomes) as candidates that could produce the observed signal. We apply this method to our previously reported joint result for smoking involving rs16969968 and rs588765 in CHRNA5. We demonstrate that it is theoretically possible for a joint SNP result suggestive of two independent signals to be produced by a single causal variant. Furthermore, this variant need not be highly correlated with the two tested SNPs nor must it have a large odds ratio. Our method aids in interpretation of joint SNP results by identifying new candidate variants for biological causation that would be missed by traditional approaches. Also, it can connect association findings that may seem disparate due to lack of high correlations among the associated SNPs.
genetic association; gametic disequilibrium; multi SNP analysis; candidate gene; smoking; nicotine dependence
Neuronal nicotinic acetylcholine receptor (nAChR) genes (CHRNA5/CHRNA3/CHRNB4) have been reproducibly associated with nicotine dependence, smoking behaviors, and lung cancer risk. Of the few reports that have focused on early smoking behaviors, association results have been mixed. This meta-analysis examines early smoking phenotypes and SNPs in the gene cluster to determine: (1) whether the most robust association signal in this region (rs16969968) for other smoking behaviors is also associated with early behaviors, and/or (2) if additional statistically independent signals are important in early smoking. We focused on two phenotypes: age of tobacco initiation (AOI) and age of first regular tobacco use (AOS). This study included 56,034 subjects (41 groups) spanning nine countries and evaluated five SNPs including rs1948, rs16969968, rs578776, rs588765, and rs684513. Each dataset was analyzed using a centrally generated script. Meta-analyses were conducted from summary statistics. AOS yielded significant associations with SNPs rs578776 (beta = 0.02, P = 0.004), rs1948 (beta = 0.023, P = 0.018), and rs684513 (beta = 0.032, P = 0.017), indicating protective effects. There were no significant associations for the AOI phenotype. Importantly, rs16969968, the most replicated signal in this region for nicotine dependence, cigarettes per day, and cotinine levels, was not associated with AOI (P = 0.59) or AOS (P = 0.92). These results provide important insight into the complexity of smoking behavior phenotypes, and suggest that association signals in the CHRNA5/A3/B4 gene cluster affecting early smoking behaviors may be different from those affecting the mature nicotine dependence phenotype.
CHRNA5; CHRNA3; CHRNB4; meta-analysis; nicotine; smoke
The genetic contribution to liability for opioid dependence is well-established; identification of the responsible genes has proved challenging.
To examine association of 1430 candidate gene single-nucleotide polymorphisms (SNPs) with heroin dependence, reporting here only the 71 SNPs in the chromosome 11 gene cluster (NCAM1, TTC12, ANKK1, DRD2) that include the strongest observed associations.
Case-control genetic association study that included two control groups (lacking an established optimal control group).
Semi-structured psychiatric interviews
Australian cases (N=1459) ascertained from opioid replacement therapy (ORT) clinics, neighborhood controls (N=531) ascertained from economically disadvantaged areas near opioid replacement therapy clinics, and unrelated Australian Twin Registry (ATR) controls (N=1495) not dependent on alcohol or illicit drugs selected from a twin and family sample.
Main Outcome Measure
Lifetime heroin dependence
Comparison of cases with Australian Twin Registry controls found minimal evidence of association for all chromosome 11 cluster SNPs (p≥.01); a similar comparison to neighborhood controls revealed greater differences (p≥1.8 × 10−4). Comparing cases (N=1459) with the subgroup of neighborhood controls not dependent on illicit drugs (N=340), three SNPs were significantly associated (correcting for multiple testing): ANKK1 SNP rs877138 [most strongly associated; odds ratio 1.59; 95%CI (1.32–1.92); p=9.7 × 10−7], ANKK1 SNP rs4938013 and TTC12 SNP rs7130431. A similar pattern of association was observed when comparing illicit drug-dependent (N=191) and non-dependent (N=340) neighborhood controls, suggesting that liability likely extends to non-opioid illicit drug dependence. Aggregate heroin dependence risk associated with two SNPs, rs877138 and rs4492854 (located in NCAM1), varied more than 4-fold (p= 2.74 × 10−9 for the risk-associated linear trend).
Our results provide further evidence of association for chromosome 11 gene cluster SNPs with substance dependence, including extension of liability to illicit drug dependence. Our findings highlight the necessity of considering drug exposure history when selecting control groups for genetic investigations of illicit drug dependence.
Recent studies have shown an association between cigarettes per day (CPD) and a nonsynonymous single-nucleotide polymorphism in CHRNA5, rs16969968.
To determine whether the association between rs16969968 and smoking is modified by age at onset of regular smoking.
Available genetic studies containing measures of CPD and the genotype of rs16969968 or its proxy.
Uniform statistical analysis scripts were run locally. Starting with 94 050 ever-smokers from 43 studies, we extracted the heavy smokers (CPD >20) and light smokers (CPD ≤10) with age-at-onset information, reducing the sample size to 33 348. Each study was stratified into early-onset smokers (age at onset ≤16 years) and late-onset smokers (age at onset >16 years), and a logistic regression of heavy vs light smoking with the rs16969968 genotype was computed for each stratum. Meta-analysis was performed within each age-at-onset stratum.
Individuals with 1 risk allele at rs16969968 who were early-onset smokers were significantly more likely to be heavy smokers in adulthood (odds ratio [OR]=1.45; 95% CI, 1.36–1.55; n=13 843) than were carriers of the risk allele who were late-onset smokers (OR = 1.27; 95% CI, 1.21–1.33, n = 19 505) (P = .01).
These results highlight an increased genetic vulnerability to smoking in early-onset smokers.
The associations between nicotine dependence and specific variants in the nicotinic receptor CHRNA5-A3-B4 subunit genes are irrefutable with replications in many studies. The relationship between the newly identified genetic risk variants for nicotine dependence and comorbid psychiatric disorders is unclear. We examined whether these genetic variants were associated with comorbid disorders and whether comorbid psychiatric disorders modified the genetic risk of nicotine dependence.
In a case control study of nicotine dependence with 2032 subjects of European descent, we used logistic regression models to examine the pleiotropy and risk moderation. Comorbid disorders examined were alcohol dependence, cannabis dependence, major depressive disorder, panic attack, social phobia, posttraumatic stress disorder (PTSD), attention deficit hyperactivity disorder (ADHD), conduct disorder, and antisocial personality disorder (ASPD).
Nicotine dependence was associated with every examined comorbid psychiatric disorders, with odds ratio varying from 1.75 to 3.33. No evidence supported the associations between the genetic variants and the comorbid disorders (pleiotropy). No evidence suggested that the risks for nicotine dependence associated with the genetic variants vary with comorbid psychiatric disorders in general, but the power was limited in detecting interactions.
The genetic risks of nicotine dependence associated with the CHRNA5-A3-B4 subunit genes are specific, and not shared among commonly comorbid psychiatric disorders. The risks for nicotine dependence associated with these genetic variants are not modified by comorbid psychiatric disorders such as major depressive disorder or alcohol dependence. However, the power is an important limitation in studying the interplay of comorbidity and genetic variants.
nicotine dependence; nicotinic receptor genes; case control study; comorbidity; pleiotropy; genetic epidemiology
Recent meta-analyses of European ancestry subjects show strong evidence for association between smoking quantity and multiple genetic variants on chromosome 15q25. This meta-analysis extends the examination of association between distinct genes in the CHRNA5-CHRNA3-CHRNB4 region and smoking quantity to Asian and African American populations to confirm and refine specific reported associations.
Association results for a dichotomized cigarettes smoked per day (CPD) phenotype in 27 datasets (European ancestry (N=14,786), Asian (N=6,889), and African American (N=10,912) for a total of 32,587 smokers) were meta-analyzed by population and results were compared across all three populations.
We demonstrate association between smoking quantity and markers in the chromosome 15q25 region across all three populations, and narrow the region of association. Of the variants tested, only rs16969968 is associated with smoking (p < 0.01) in each of these three populations (OR=1.33, 95%C.I.=1.25–1.42, p=1.1×10−17 in meta-analysis across all population samples). Additional variants displayed a consistent signal in both European ancestry and Asian datasets, but not in African Americans.
The observed consistent association of rs16969968 with heavy smoking across multiple populations, combined with its known biological significance, suggests rs16969968 is most likely a functional variant that alters risk for heavy smoking. We interpret additional association results that differ across populations as providing evidence for additional functional variants, but we are unable to further localize the source of this association. Using the cross-population study paradigm provides valuable insights to narrow regions of interest and inform future biological experiments.
smoking; genetics; meta-analysis; cross-population
To facilitate an increase in the amount of data on minority subjects collected for genetic databases, the authors attempted to clarify barriers to African-American participation in genetic studies. They randomly sampled 78,072 subjects from the community (Missouri Family Registry, 2002–2007). Of these, 28,658 participated in a telephone screening interview, 3,179 were eligible to participate in the genetic study, and 1,919 participated in the genetic study. Response rates were examined in relation to the proportion of subjects in the area who were African-American according to US Census 2000 zip code demographic data. Compared with zip codes with fewer than 5% African Americans (average = 2% African-American), zip codes with at least 60% African Americans (average = 87% African-American) had higher proportions of subjects with an incorrect address or telephone number but lower proportions of subjects who did not answer the telephone and subjects who refused the telephone interview (P < 0.0001). Based on reported race from the telephone screening, 71% of eligible African Americans and 57% of eligible European Americans participated in the genetic study (P < 0.0001). The results of this study suggest that increasing the number of African Americans in genetic databases may be achieved by increasing efforts to locate and contact them.
African Americans; consumer participation; data collection; genetic association studies; genetics; minority groups
Folate metabolism is an important target for drug therapy. Drug-induced inhibition of folate metabolism often causes an elevation of plasma total homocysteine (tHcy). Plasma tHcy levels are influenced by several non-genetic (e.g., folate intake, age, smoking) as well as genetic factors. Over the last decade, several countries have implemented a nation-wide folate fortification program of all grain products. This investigation sought to determine the impact of folate fortification on the relative contribution of environmental and genetic factors to the variability of plasma tHcy.
Two cohorts were compared in this study, one from the U.S. (with folate fortification, n=281), and one from Austria (without folate fortification, n=139). Several environmental factors as well as previously identified gene variants important for tHcy levels (MTHFR C677T, MTHFR A1298C, MTRR A66G) were examined for their ability to predict plasma tHcy in a multiple linear regression model.
Non-genetic, environmental factors had a comparable influence on plasma tHcy between the two cohorts (R2 ~ 0.19). However, after adjusting for other covariates, the tested gene variants had a substantially smaller impact among patients from the folate fortified cohort (R2= 0.021) compared to the non-folate fortified cohort (R2= 0.095). The MTHFR C677T polymorphism was the single most important genetic factor. Male gender, smoking and folate levels were important predictors for non-folate fortified patients; age for folate fortified.
Population-wide folate fortification had a significant effect on the variability of plasma tHcy and reduced the influence of genetic factors, most importantly the MTHFR 677TT genotype, and may be an important confounder for a personalized drug therapy.
Homocysteine; folate fortification; folic acid
Results from genome-wide association studies of complex traits account for only a modest proportion of the trait variance predicted to be due to genetics. We hypothesize that joint analysis of polymorphisms may account for more variance. We evaluated this hypothesis on a case–control smoking phenotype by examining pairs of nicotinic receptor single-nucleotide polymorphisms (SNPs) using the Restricted Partition Method (RPM) on data from the Collaborative Genetic Study of Nicotine Dependence (COGEND). We found evidence of joint effects that increase explained variance. Four signals identified in COGEND were testable in independent American Cancer Society (ACS) data, and three of the four signals replicated. Our results highlight two important lessons: joint effects that increase the explained variance are not limited to loci displaying substantial main effects, and joint effects need not display a significant interaction term in a logistic regression model. These results suggest that the joint analyses of variants may indeed account for part of the genetic variance left unexplained by single SNP analyses. Methodologies that limit analyses of joint effects to variants that demonstrate association in single SNP analyses, or require a significant interaction term, will likely miss important joint effects.
Using single-nucleotide polymorphism (SNP) genotypes from the 1000 Genomes Project pilot3 data provided for Genetic Analysis Workshop 17 (GAW17), we applied Bayesian network structure learning (BNSL) to identify potential causal SNPs associated with the Affected phenotype. We focus on the setting in which target genes that harbor causal variants have already been chosen for resequencing; the goal was to detect true causal SNPs from among the measured variants in these genes. Examining all available SNPs in the known causal genes, BNSL produced a Bayesian network from which subsets of SNPs connected to the Affected outcome were identified and measured for statistical significance using the hypergeometric distribution. The exploratory phase of analysis for pooled replicates sometimes identified a set of involved SNPs that contained more true causal SNPs than expected by chance in the Asian population. Analyses of single replicates gave inconsistent results. No nominally significant results were found in analyses of African or European populations. Overall, the method was not able to identify sets of involved SNPs that included a higher proportion of true causal SNPs than expected by chance alone. We conclude that this method, as currently applied, is not effective for identifying causal SNPs that follow the simulation model for the GAW17 data set, which includes many rare causal SNPs.
Peer smoking provides a socially reinforcing context of friends’ encouragement and approval that contributes to smoking behavior. Twin studies show correlations and interactions between peer substance use and genetic liability for substance use. However, none examined specific genes. Here we test the hypothesis that the nicotinic receptor genes CHRNA5 (rs16969968), CHRNA3 (rs578776), CHRNB3 (rs13277254), and CHRND (rs12466358) modify the risk for nicotine dependence (ND) associated with peer smoking.
Cases of current nicotine dependence (FTND ≥ 4) and smoking-exposed (smoked 100+ cigarettes lifetime), but non-dependent controls (lifetime FTND = 0) came from the Collaborative Genetic Study of Nicotine Dependence (n=2,038). Peer smoking was retrospectively assessed for grades 9–12.
Peer smoking and the four SNPs were associated with ND. A statistically significant interaction was found between peer smoking and rs16969968 (p = 0.0077). Overall risk of ND was highest for the rs16969968 AA genotype. However, variance in ND attributable to peer smoking was substantially lower among those with the AA genotype at rs16969968 than the lower risk genotypes: AA = 2.5%, GA/AG = 11.2%, GG = 14.2%; p ≤ 0.004.
Peer smoking had a substantially lower effect on ND among those with the high risk AA genotype at the functional SNP rs16969968 (CHRNA5) than among those with lower risk genotypes. Such results highlight the possibility that given drug exposure those with specific genetic risks may be less affected by social contexts and intervention strategies focused on social factors could have less influence on those at highest genetic risk.
nicotine dependence; peer smoking; gene-environmental interaction; nicotinic receptor genes; case control study
Several independent studies show that the chromosome 15q25.1 region, which contains the CHRNA5-CHRNA3-CHRNB4 gene cluster, harbors variants strongly associated with nicotine dependence, other smoking behaviors, lung cancer, and chronic obstructive pulmonary disease.
We investigated whether variants in other cholinergic nicotinic receptor subunit (CHRN) genes affect risk for nicotine dependence in a new sample of African-Americans (N = 710). We also analyzed this African-American sample together with a European-American sample (N=2062, 1608 of which have been previously studied), allowing for differing effects in the two populations. Cases are current nicotine-dependent smokers and controls are non-dependent smokers.
Variants in or near CHRND-CHRNG, CHRNA7, and CHRNA10 show modest association with nicotine dependence risk in the African-American sample. In addition, CHRNA4, CHRNB3-CHRNA6, and CHRNB1 show association in at least one population. CHRNG and CHRNA4 harbor SNPs that have opposite directions of effect in the two populations. In each of the population samples, these loci substantially increase the trait variation explained, although no loci meet Bonferroni-corrected significance in the African-American sample alone. The trait variation explained by three key associated SNPs in CHRNA5-CHRNA3-CHRNB4 is 1.9% in European-Americans and also 1.9% in African-Americans; this increases to 4.5% in EAs and 7.3% in AAs when we add six variants representing associations at other CHRN genes.
Multiple nicotinic receptor subunit genes outside of chromosome 15q25 are likely to be important in the biological processes and development of nicotine dependence, and some of these risks may be shared across diverse populations.
genetic association; smoking; cholinergic nicotinic receptors; nicotinic acetylcholine receptors
Nicotine dependence is moderately heritable, but identified genetic associations explain only modest portions of this heritability. We analyzed 3,369 SNPs from 349 candidate genes, and investigated whether incorporation of SNP-by-environment interaction into association analyses might bolster gene discovery efforts and prediction of nicotine dependence. Specifically, we incorporated the interaction between allele count and age-at-onset of regular smoking (AOS) into association analyses of nicotine dependence. Subjects were from the Collaborative Genetic Study of Nicotine Dependence, and included 797 cases ascertained for Fagerström nicotine dependence, and 811 non-nicotine dependent smokers as controls, all of European descent. Compared with main-effect models, SNP x AOS interaction models resulted in higher numbers of nominally significant tests, increased predictive utility at individual SNPs, and higher predictive utility in a multi-locus model. Some SNPs previously documented in main-effect analyses exhibited improved fits in the joint-analysis, including rs16969968 from CHRNA5 and rs2314379 from MAP3K4. CHRNA5 exhibited larger effects in later-onset smokers, in contrast with a previous report that suggested the opposite interaction (Weiss et al, PLOS Genetics, 4: e1000125, 2008). However, a number of SNPs that did not emerge in main-effect analyses were among the strongest findings in the interaction analyses. These include SNPs located in GRIN2B (p=1.5 × 10−5), which encodes a subunit of the NMDA receptor channel, a key molecule in mediating age-dependent synaptic plasticity. Incorporation of logically chosen interaction parameters, such as AOS, into genetic models of substance-use disorders may increase the degree of explained phenotypic variation, and constitutes a promising avenue for gene-discovery.
addiction; SNP; age-at-onset; interaction; environment; nicotine dependence
Several studies have found replicable associations between nicotine dependence and specific variants in the nicotinic receptor genes CHRNA5(rs16969968) and CHRNA3(rs3743078). How these newly identified genetic risks combine with known environmental risks is unknown. This study examined whether the level of parent monitoring during early adolescence modified the risk of nicotine dependence associated with these genetic variants.
In a cross-sectional case control study of US-based community sample of 2027 subjects, we use a systematic series of regression models to examine the effect of parent monitoring on risk associated with two distinct variants in the nicotinic receptor genes CHRNA5(rs16969968) and CHRNA3(rs3743078).
Low parent monitoring as well as the previously identified genetic variants were associated with an increased risk of nicotine dependence. An interaction was found between the SNP(rs16969968) and parent monitoring (p=0.034). The risk for nicotine dependence increased significantly with the risk genotype of SNP(rs16969968) when combined with lowest quartile parent monitoring. In contrast, there was no evidence of an interaction between SNP(rs3743078) and parent monitoring (p=0.80).
The genetic risk of nicotine dependent associated with rs16969968 was modified by level of parent monitoring, while the genetic risk associated with rs3743078 was not, suggesting that the increased risk due to some genes may be mitigated by environmental factors such as parent monitoring.
nicotine dependence; parent monitoring; phenotype; gene-environmental interaction; nicotinic receptor genes; case control study
Genetic association studies have demonstrated the importance of variants in the CHRNA5-CHRNA3-CHRNB4 cholinergic nicotinic receptor subunit gene cluster on chromosome 15q24-25.1 in risk for nicotine dependence, smoking, and lung cancer in populations of European descent. We have now carried out a detailed study of this region using dense genotyping in both European- and African-Americans.
We genotyped 75 known single-nucleotide-polymorphisms (SNPs) and one sequencing-discovered SNP in an African-American (AA) sample (N = 710) and European-American (EA) sample (N = 2062). Cases were nicotine-dependent and controls were non-dependent smokers.
The non-synonymous CHRNA5 SNP rs16969968 is the most significant SNP associated with nicotine dependence in the full sample of 2772 subjects (p = 4.49×10−8, OR 1.42 (1.25–1.61)) as well as in AAs only (p = 0.015, OR = 2.04 (1.15–3.62)) and EAs only (p = 4.14×10−7, OR = 1.40 (1.23–1.59)). Other SNPs that have been shown to affect mRNA levels of CHRNA5 in EAs are associated with nicotine dependence in AAs but not in EAs. The CHRNA3 SNP rs578776, which has low correlation with rs16969968, is associated with nicotine dependence in EAs but not in AAs. Less common SNPs (frequency ≤ 5%) also are associated with nicotine dependence.
In summary, multiple variants in this gene cluster contribute to nicotine dependence risk, and some are also associated with functional effects on CHRNA5. The non-synonymous SNP rs16969968, a known risk variant in European-descent populations, is also significantly associated with risk in African-Americans. Additional SNPs contribute in distinct ways to risk in these two populations.
genetic association; smoking; cholinergic nicotinic receptors; nicotinic acetylcholine receptors
Nicotine dependence risk and lung cancer risk are associated with variants in a region of chromosome 15 encompassing genes encoding the nicotinic receptor subunits CHRNA5, CHRNA3 and CHRNB4. To identify potential biological mechanisms that underlie this risk, we tested for cis-acting eQTLs for CHRNA5, CHRNA3 and CHRNB4 in human brain. Using gene expression and disease association studies, we provide evidence that both nicotine-dependence risk and lung cancer risk are influenced by functional variation in CHRNA5. We demonstrated that the risk allele of rs16969968 primarily occurs on the low mRNA expression allele of CHRNA5. The non-risk allele at rs16969968 occurs on both high and low expression alleles tagged by rs588765 within CHRNA5. When the non-risk allele occurs on the background of low mRNA expression of CHRNA5, the risk for nicotine dependence and lung cancer is significantly lower compared to those with the higher mRNA expression. Together, these variants identify three levels of risk associated with CHRNA5. We conclude that there are at least two distinct mechanisms conferring risk for nicotine dependence and lung cancer: altered receptor function caused by a D398N amino acid variant in CHRNA5 (rs16969968) and variability in CHRNA5 mRNA expression.
Recently, genetic association findings for nicotine dependence, smoking behavior, and smoking-related diseases converged to implicate the chromosome 15q25.1 region, which includes the CHRNA5-CHRNA3-CHRNB4 cholinergic nicotinic receptor subunit genes. In particular, association with the nonsynonymous CHRNA5 SNP rs16969968 and correlates has been replicated in several independent studies. Extensive genotyping of this region has suggested additional statistically distinct signals for nicotine dependence, tagged by rs578776 and rs588765. One goal of the Consortium for the Genetic Analysis of Smoking Phenotypes (CGASP) is to elucidate the associations among these markers and dichotomous smoking quantity (heavy versus light smoking), lung cancer, and chronic obstructive pulmonary disease (COPD). We performed a meta-analysis across 34 datasets of European-ancestry subjects, including 38,617 smokers who were assessed for cigarettes-per-day, 7,700 lung cancer cases and 5,914 lung-cancer-free controls (all smokers), and 2,614 COPD cases and 3,568 COPD-free controls (all smokers). We demonstrate statistically independent associations of rs16969968 and rs588765 with smoking (mutually adjusted p-values<10−35 and <10−8 respectively). Because the risk alleles at these loci are negatively correlated, their association with smoking is stronger in the joint model than when each SNP is analyzed alone. Rs578776 also demonstrates association with smoking after adjustment for rs16969968 (p<10−6). In models adjusting for cigarettes-per-day, we confirm the association between rs16969968 and lung cancer (p<10−20) and observe a nominally significant association with COPD (p = 0.01); the other loci are not significantly associated with either lung cancer or COPD after adjusting for rs16969968. This study provides strong evidence that multiple statistically distinct loci in this region affect smoking behavior. This study is also the first report of association between rs588765 (and correlates) and smoking that achieves genome-wide significance; these SNPs have previously been associated with mRNA levels of CHRNA5 in brain and lung tissue.
Nicotine binds to cholinergic nicotinic receptors, which are composed of a variety of subunits. Genetic studies for smoking behavior and smoking-related diseases have implicated a genomic region that encodes the alpha5, alpha3, and beta4 subunits. We examined genetic data across this region for over 38,000 smokers, a subset of which had been assessed for lung cancer or chronic obstructive pulmonary disease. We demonstrate strong evidence that there are at least two statistically independent loci in this region that affect risk for heavy smoking. One of these loci represents a change in the protein structure of the alpha5 subunit. This work is also the first to report strong evidence of association between smoking and a group of genetic variants that are of biological interest because of their links to expression of the alpha5 cholinergic nicotinic receptor subunit gene. These advances in understanding the genetic influences on smoking behavior are important because of the profound public health burdens caused by smoking and nicotine addiction.
Alcohol abuse and dependence are major causes of morbidity and mortality worldwide, and have a strong familial component. Several linkage and association studies have identified chromosomal regions and/or genes that affect alcohol consumption, notably in genes involved in the two-stage pathway of alcohol metabolism.
Here, we use multiple regression models to test for associations and interactions between two alcohol related phenotypes and SNPs in 17 genes involved in alcohol metabolism in the U.S. Caucasian subset of the Collaborative Genetic Study of Nicotine Dependence (COGEND) participants.
Several SNPs across six genes showed evidence for association with either maximum number of drinks consumed in a 24-hour period or DSM-IV symptom count. The strongest evidence for association was between rs1229984, a non-synonymous coding SNP in ADH1B, and DSM-IV symptom count (P = 0.0003). This SNP was also associated with maximum drinks (P = 0.0004). Each minor allele at this SNP predicts 45% fewer DSM-IV symptoms and 18% fewer max drinks. Another SNP in a splice site in ALDH1A1 (rs8187974) showed evidence for association with both phenotypes as well. Minor alleles at this SNP predict greater alcohol consumption. In addition, pairwise interactions were observed between SNPs in several genes (P = 0.00002).
We replicated the large effect of rs1229984 on alcohol behavior, and although not common (MAF = 4%), this polymorphism may be highly relevant from a public health perspective in European Americans. Another SNP, rs8187974, may also affect alcohol behavior but requires replication. Also, interactions between polymorphisms in genes involved in alcohol metabolism are likely determinants of the parameters that ultimately affect alcohol consumption.
Alcoholism; Alcohol Metabolism; Genetic Association
Tobacco smoking continues to be a leading cause of preventable death. Recent research has underscored the important role of specific cholinergic nicotinic receptor subunit (CHRN) genes in risk for nicotine dependence and smoking. To detect and characterize the influence of genetic variation on vulnerability to nicotine dependence, we analyzed 226 SNPs covering the complete family of 16 CHRN genes, which encode the nicotinic acetylcholine receptor (nAChR) subunits, in a sample of 1050 nicotine-dependent cases and 879 non-dependent controls of European descent. This expanded SNP coverage has extended and refined the findings of our previous large scale genome-wide association and candidate gene study. After correcting for the multiple tests across this gene family, we found significant association for two distinct loci in the CHRNA5-CHRNA3-CHRNB4 gene cluster, one locus in the CHRNB3-CHRNA6 gene cluster, and a fourth, novel locus in the CHRND-CHRNG gene cluster. The two distinct loci in CHRNA5-CHRNA3-CHRNB4 are represented by the non-synonymous SNP rs16969968 in CHRNA5 and by rs578776 in CHRNA3, respectively, and joint analyses show that the associations at these two SNPs are statistically independent. Nominally significant single-SNP association was detected in CHRNA4 and CHRNB1. In summary, this is the most comprehensive study of the CHRN genes for involvement with nicotine dependence to date. Our analysis reveals significant evidence for at least four distinct loci in the nicotinic receptor subunit genes that each influence the transition from smoking to nicotine dependence and may inform the development of improved smoking cessation treatments and prevention initiatives.
cholinergic nicotinic receptors; nicotinic acetylcholine receptors; smoking; genetic association
A previous association analysis identified polymorphisms in GABRA4 and GABRA2 to be associated with nicotine dependence, as assessed by a score of 4 or more on the Fagerström Test for Nicotine Dependence (FTND). In the present report, we extend the previous study by significantly expanding our genotyping efforts for these two genes.
In 1,049 cases (FTND of 4 or more) and 872 controls (smokers with FTND of 0), from the U.S. and Australia, we examine the association between 23 GABRA4 and 39 GABRA2 recently genotyped single nucleotide polymorphisms (SNPs) and nicotine dependence using logistic regression-based association analyses in PLINK.
Two and 18 additional SNPs in GABRA4 and GABRA2 respectively were associated with nicotine dependence. The SNPs identified in GABRA4 (p value = 0.002) were restricted to introns 1 and 2, exon 1 and the 5’ end of the gene, while those in GABRA2 localized to the 3’ end of the gene and spanned introns 9 through 3, and were in moderate to high linkage disequilibrium (as measured by r2) with each other and with previously studied polymorphisms.
Our findings consistently demonstrate the role of GABRA4 and GABRA2 in nicotine dependence. However, further research is needed to identify the biological influence of these intronic variations and to isolate functionally relevant polymorphisms neighboring them.
Association; nicotine dependence; GABRA2; NICSNP
Comorbidity of psychiatric and substance use disorders represents a significant complication in the clinical course of both disorders. Bipolar Disorder (BD) is a psychiatric disorder characterized by severe mood swings, ranging from mania to depression, and up to a 70% rate of comorbid Tobacco Use Disorder (TUD). We found epidemiological evidence consistent with a common underlying etiology for BD and TUD, as well as evidence of both genetic and environmental influences on BD and TUD. Therefore, we hypothesized a common underlying genetic etiology, interacting with nicotine exposure, influencing susceptibility to both BD and TUD.
Using meta-analysis, we compared TUD rates for BD patients and the general population. We identified candidate genes showing statistically significant, replicated, evidence of association with both BD and TUD. We assessed commonality among these candidate genes and hypothesized broader, multi-gene network influences on the comorbidity. Using Fisher Exact tests we tested our hypothesized genetic networks for association with the comorbidity, then compared the inferences drawn with those derived from the commonality assessment. Finally, we prioritized candidate SNPs for validation.
We estimate risk for TUD among BD patients at 2.4 times that of the general population. We found three candidate genes associated with both BD and TUD (COMT, SLC6A3, and SLC6A4) and commonality analysis suggests that these genes interact in predisposing psychiatric and substance use disorders. We identified a 69 gene network that influences neurotransmitter signaling and shows significant over-representation of genes associated with BD and TUD, as well as genes differentially expressed with exposure to tobacco smoke. Twenty four of these genes are known drug targets.
This work highlights novel bioinformatics resources and demonstrates the effectiveness of using an integrated bioinformatics approach to improve our understanding of complex disease etiology. We illustrate the development and testing of hypotheses for a comorbidity predisposed by both genetic and environmental influences. Consistent with our hypothesis, the selected network models multiple interacting genetic influences on comorbid BD with TUD, as well as the environmental influence of nicotine. This network nominates candidate genes for validation and drug testing, and we offer a panel of SNPs prioritized for follow-up.
Motivation: A challenging problem after a genome-wide association study (GWAS) is to balance the statistical evidence of genotype–phenotype correlation with a priori evidence of biological relevance.
Results: We introduce a method for systematically prioritizing single nucleotide polymorphisms (SNPs) for further study after a GWAS. The method combines evidence across multiple domains including statistical evidence of genotype–phenotype correlation, known pathways in the pathologic development of disease, SNP/gene functional properties, comparative genomics, prior evidence of genetic linkage, and linkage disequilibrium. We apply this method to a GWAS of nicotine dependence, and use simulated data to test it on several commercial SNP microarrays.
Availability: A comprehensive database of biological prioritization scores for all known SNPs is available at http://zork.wustl.edu/gin. This can be used to prioritize nicotine dependence association studies through a straightforward mathematical formula—no special software is necessary.
Supplementary information: Supplementary data are available at Bioinformatics online.
A challenging problem after a genome-wide association study (GWAS) is to balance the statistical evidence of geno-type-phenotype correlation with a priori evidence of biological relevance.
We introduce a method for systematically prioritizing single nucleotide polymorphisms (SNPs) for further study after a GWAS. The method combines evidence across multiple domains, including statistical evidence of genotype-phenotype correlation, known pathways in the pathologic development of disease, SNP/gene functional properties, comparative genomics, prior evidence of genetic linkage, and linkage disequilibrium. We apply this method to a GWAS of nicotine dependence, and use simulated data to test it on several commercial SNP microarrays.
Individuals’ dependence on nicotine, primarily through cigarette smoking, is a major source of morbidity and mortality worldwide. Many smokers attempt but fail to quit smoking, motivating researchers to identify the origins of this dependence. Because of the known heritability of nicotine-dependence phenotypes, considerable interest has been focused on discovering the genetic factors underpinning the trait. This goal, however, is not easily attained: no single factor is likely to explain any great proportion of dependence because nicotine dependence is thought to be a complex trait (i.e., the result of many interacting factors). Genomewide association studies are powerful tools in the search for the genomic bases of complex traits, and in this context, novel candidate genes have been identified through single nucleotide polymorphism (SNP) association analyses. Beyond association, however, genetic data can be used to generate predictive models of nicotine dependence. As expected in the context of a complex trait, individual SNPs fail to accurately predict nicotine dependence, demanding the use of multivariate models. Standard approaches, such as logistic regression, are unable to consider large numbers of SNPs given existing sample sizes. However, using Bayesian networks, one can overcome these limitations to generate a multivariate predictive model, which has markedly enhanced predictive accuracy on fitted values relative to that of individual SNPs. This approach, combined with the data being generated by genomewide association studies, promises to shed new light on the common, complex trait nicotine dependence.
addiction; nicotine; genetics; prediction
Commercial SNP microarrays now provide comprehensive and affordable coverage of the human genome. However, some diseases have biologically relevant genomic regions that may require additional coverage. Addiction, for example, is thought to be influenced by complex interactions among many relevant genes and pathways. We have assembled a list of 486 biologically relevant genes nominated by a panel of experts on addiction. We then added 424 genes that showed evidence of association with addiction phenotypes through mouse QTL mappings and gene co-expression analysis. We demonstrate that there are a substantial number of SNPs in these genes that are not well represented by commercial SNP platforms. We address this problem by introducing a publicly available SNP database for addiction. The database is annotated using numeric prioritization scores indicating the extent of biological relevance. The scores incorporate a number of factors such as SNP/gene functional properties (including synonymy and promoter regions), data from mouse systems genetics and measures of human/mouse evolutionary conservation. We then used HapMap genotyping data to determine if a SNP is tagged by a commercial microarray through linkage disequilibrium. This combination of biological prioritization scores and LD tagging annotation will enable addiction researchers to supplement commercial SNP microarrays to ensure comprehensive coverage of biologically relevant regions.