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
Imputation, the process of inferring genotypes for untyped variants, is used to identify and refine genetic association findings. Inaccuracies in imputed data can distort the observed association between variants and a disease. Many statistics are used to assess accuracy; some compare imputed to genotyped data and others are calculated without reference to true genotypes. Prior work has shown that the Imputation Quality Score (IQS), which is based on Cohen’s kappa statistic and compares imputed genotype probabilities to true genotypes, appropriately adjusts for chance agreement; however, it is not commonly used. To identify differences in accuracy assessment, we compared IQS with concordance rate, squared correlation, and accuracy measures built into imputation programs. Genotypes from the 1000 Genomes reference populations (AFR N = 246 and EUR N = 379) were masked to match the typed single nucleotide polymorphism (SNP) coverage of several SNP arrays and were imputed with BEAGLE 3.3.2 and IMPUTE2 in regions associated with smoking behaviors. Additional masking and imputation was conducted for sequenced subjects from the Collaborative Genetic Study of Nicotine Dependence and the Genetic Study of Nicotine Dependence in African Americans (N = 1,481 African Americans and N = 1,480 European Americans). Our results offer further evidence that concordance rate inflates accuracy estimates, particularly for rare and low frequency variants. For common variants, squared correlation, BEAGLE R2, IMPUTE2 INFO, and IQS produce similar assessments of imputation accuracy. However, for rare and low frequency variants, compared to IQS, the other statistics tend to be more liberal in their assessment of accuracy. IQS is important to consider when evaluating imputation accuracy, particularly for rare and low frequency variants.
The American College of Medical Genetics and Genomics (ACMG) recommends that clinical sequencing laboratories return secondary findings in 56 genes associated with medically actionable conditions. Our goal was to apply a systematic, stringent approach consistent with clinical standards to estimate the prevalence of pathogenic variants associated with such conditions using a diverse sequencing reference sample. Candidate variants in the 56 ACMG genes were selected from Phase 1 of the 1000 Genomes dataset, which contains sequencing information on 1,092 unrelated individuals from across the world. These variants were filtered using the Human Gene Mutation Database (HGMD) Professional version and defined parameters, appraised through literature review, and examined by a clinical laboratory specialist and expert physician. Over 70,000 genetic variants were extracted from the 56 genes, and filtering identified 237 variants annotated as disease causing by HGMD Professional. Literature review and expert evaluation determined that 7 of these variants were pathogenic or likely pathogenic. Furthermore, 5 additional truncating variants not listed as disease causing in HGMD Professional were identified as likely pathogenic. These 12 secondary findings are associated with diseases that could inform medical follow-up, including cancer predisposition syndromes, cardiac conditions, and familial hypercholesterolemia. The majority of the identified medically actionable findings were in individuals from the European (5/379) and Americas (4/181) ancestry groups, with fewer findings in Asian (2/286) and African (1/246) ancestry groups. Our results suggest that medically relevant secondary findings can be identified in approximately 1% (12/1092) of individuals in a diverse reference sample. As clinical sequencing laboratories continue to implement the ACMG recommendations, our results highlight that at least a small number of potentially important secondary findings can be selected for return. Our results also confirm that understudied populations will not reap proportionate benefits of genomic medicine, highlighting the need for continued research efforts on genetic diseases in these populations.
Rationale: The CHRNA5-CHRNA3-CHRNB4 locus is associated
with self-reported smoking behavior and also harbors the strongest genetic
associations with chronic obstructive pulmonary disease (COPD) and lung cancer.
Because the associations with lung disease remain after adjustment for self-reported
smoking behaviors, it has been asserted that CHRNA5-CHRNA3-CHRNB4
variants increase COPD and lung cancer susceptibility independently of their effects
Objectives: To compare the genetic associations of exhaled carbon
monoxide (CO), a biomarker of current cigarette exposure, with self-reported smoking
Methods: A total of 1,521 European American and 247 African American
current smokers recruited into smoking cessation studies were assessed for CO at
intake before smoking cessation. DNA samples were genotyped using the Illumina
Omni2.5 microarray. Genetic associations with CO and smoking behaviors (cigarettes
smoked per day, Fagerstrom test for nicotine dependence) were studied.
Measurements and Main Results: Variants in the
CHRNA5-CHRNA3-CHRNB4 locus, including rs16969968, a nonsynonymous
variant in CHRNA5, are genomewide association
study–significantly associated with CO (β = 2.66; 95% confidence
interval [CI], 1.74–3.58; P = 1.65 ×
10−8), and this association remains strong after adjusting for
smoking behavior (β = 2.18; 95% CI, 1.32–3.04; P
= 7.47 × 10−7). The correlation between CO and cigarettes
per day is statistically significantly lower (z = 3.43;
P = 6.07 × 10−4) in African Americans
(r = 0.14; 95% CI, 0.02–0.26; P
= 0.003) than in European-Americans (r = 0.36; 95% CI,
0.31–0.40; P = 0.0001).
Conclusions: Exhaled CO, a biomarker that is simple to measure, captures
aspects of cigarette smoke exposure in current smokers beyond the number of
cigarettes smoked per day. Behavioral measures of smoking are therefore insufficient
indices of cigarette smoke exposure, suggesting that genetic associations with COPD
or lung cancer that persist after adjusting for self-reported smoking behavior may
still reflect genetic effects on smoking exposure.
smoking; nicotine; chronic obstructive pulmonary disease; lung cancer; nicotinic receptor
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
Studies have shown association between common variants in the α6–β3 nicotinic receptor subunit gene cluster and nicotine dependence in European Ancestry populations. We investigate whether this generalizes to African Americans, whether the association is specific to nicotine dependence, and whether this region contains additional genetic contributors to nicotine dependence.
We examined consistency of association across studies and race between the α6β3 nicotinic receptor subunit locus and nicotine, alcohol, marijuana, and cocaine dependence in three independent studies.
United States of America
European Americans and African Americans from three case control studies of substance dependence.
Subjects were evaluated using the Semi-Structured Assessment for the Genetics of Alcoholism. Nicotine dependence was determined using the Fagerström Test for Nicotine Dependence.
rs13273442 was significantly associated to nicotine dependence across all three studies in both ancestry groups (OR=0.75, p=5.8 × 10−4 European Americans; OR=0.80, p=0.05 African Americans). No other substance dependence was consistently associated to this variant in either group. Another SNP in the region, rs4952, remains modestly associated with nicotine dependence in the combined data after conditioning on rs13273442.
The common variant rs13273442 in the CHRNB3-CHNRA6 region is significantly associated to nicotine dependence in European Americans and African Americans across studies recruited for nicotine, alcohol, and cocaine dependence. Although these data are modestly powered for other substances, our results provide no evidence that correlates of rs13273442 represent a general substance dependence liability. Additional variants likely account for some of the association of this region to nicotine dependence.
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
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
Fifty percent of variability in HIV-1 susceptibility is attributable to host genetics. Thus identifying genetic associations is essential to understanding pathogenesis of HIV-1 and important for targeting drug development. To date, however, CCR5 remains the only gene conclusively associated with HIV acquisition. To identify novel host genetic determinants of HIV-1 acquisition, we conducted a genome-wide association study among a high-risk sample of 3,136 injection drug users (IDUs) from the Urban Health Study (UHS). In addition to being IDUs, HIV- controls were frequency-matched to cases on environmental exposures to enhance detection of genetic effects. We tested independent replication in the Women’s Interagency HIV Study (N=2,533). We also examined publicly available gene expression data to link SNPs associated with HIV acquisition to known mechanisms affecting HIV replication/infectivity. Analysis of the UHS nominated eight genetic regions for replication testing. SNP rs4878712 in FRMPD1 met multiple testing correction for independent replication (P=1.38x10-4), although the UHS-WIHS meta-analysis p-value did not reach genome-wide significance (P=4.47x10-7 vs. P<5.0x10-8) Gene expression analyses provided promising biological support for the protective G allele at rs4878712 lowering risk of HIV: (1) the G allele was associated with reduced expression of FBXO10 (r=-0.49, P=6.9x10-5); (2) FBXO10 is a component of the Skp1-Cul1-F-box protein E3 ubiquitin ligase complex that targets Bcl-2 protein for degradation; (3) lower FBXO10 expression was associated with higher BCL2 expression (r=-0.49, P=8x10-5); (4) higher basal levels of Bcl-2 are known to reduce HIV replication and infectivity in human and animal in vitro studies. These results suggest new potential biological pathways by which host genetics affect susceptibility to HIV upon exposure for follow-up in subsequent studies.
Using single-nucleotide polymorphism (SNP) genotypes and selected gene expression phenotypes from 14 CEPH (Centre d'Etude du Polymorphisme Humain) pedigrees provided for Genetic Analysis Workshop 15 (GAW15), we analyzed quantitative traits with artificial neural networks (ANNs). Our goals were to identify individual linkage signals and examine gene × gene interactions. First, we used classical multipoint methods to identify phenotypes having nominal linkage evidence at two or more loci. ANNs were then applied to sib-pair identity-by-descent (IBD) allele sharing across the genome as input variables and squared trait sums and differences for the sib pairs as output variables. The weights of the trained networks were analyzed to assess the linkage evidence at each locus as well as potential interactions between them.
Loci identified by classical linkage analysis could also be identified by our ANN analysis. However some ANN results were noisy, and our attempts to use cross-validated training to avoid overtraining and thereby improve results were only partially successful. Potential interactions between loci with high-ranked weight measures were also evaluated, with the resulting patterns suggesting existence of both synergistic and antagonistic effects between loci.
Our results suggest that ANNs can serve as a useful method to analyze quantitative traits and are a potential tool for detecting gene × gene interactions. However, for the approach implemented here, optimizing the ANNs and obtaining stable results remains challenging.
The devastating consequences of tobacco smoking for individuals and societies motivate studies to identify and understand the biological pathways that drive smoking behaviors, so that more effective preventions and treatments can be developed. Cigarette smokers respond to nicotine in different ways, with a small number of smokers remaining lifelong low-level smokers who never exhibit any symptoms of dependence, and a larger group becoming nicotine dependent. Whether or not a smoker transitions to nicotine dependence has clear genetic contributions, and variants in the genes encoding the α5-α3-β4 nicotinic receptor subunits most strongly contribute to differences in the risk for developing nicotine dependence among smokers. More recent work reveals a differential response to pharmacologic treatment for smoking cessation based on these same genetic variants in the α5-α3-β4 nicotinic receptor gene cluster. We anticipate a continuing acceleration of the translation of genetic discoveries into more successful treatment for smoking cessation. Given that over 400,000 people in the United States and over 5 million people world-wide die each year from smoking related illnesses, an improved understanding of the mechanisms underlying smoking behavior and smoking cessation must be a high public health priority so we can best intervene at both the public health level and the individual level.
Nicotine dependence; Smoking cessation; Genetics; Nicotinic receptor genes; Nicotine metabolizing genes
A great promise of publicly sharing genome-wide association data is the potential to create composite sets of controls. However, studies often use different genotyping arrays, and imputation to a common set of SNPs has shown substantial bias: a problem which has no broadly applicable solution. Based on the idea that using differing genotyped SNP sets as inputs creates differential imputation errors and thus bias in the composite set of controls, we examined the degree to which each of the following occurs: (1) imputation based on the union of genotyped SNPs (i.e., SNPs available on one or more arrays) results in bias, as evidenced by spurious associations (type 1 error) between imputed genotypes and arbitrarily assigned case/control status; (2) imputation based on the intersection of geno-typed SNPs (i.e., SNPs available on all arrays) does not evidence such bias; and (3) imputation quality varies by the size of the intersection of genotyped SNP sets. Imputations were conducted in European Americans and African Americans with reference to HapMap phase II and III data. Imputation based on the union of genotyped SNPs across the Illumina 1M and 550v3 arrays showed spurious associations for 0.2 % of SNPs: ~2,000 false positives per million SNPs imputed. Biases remained problematic for very similar arrays (550v1 vs. 550v3) and were substantial for dissimilar arrays (Illumina 1M vs. Affymetrix 6.0). In all instances, imputing based on the intersection of genotyped SNPs (as few as 30 % of the total SNPs genotyped) eliminated such bias while still achieving good imputation quality.
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.
Debate is ongoing about what role, if any, variation in the serotonin transporter linked polymorphic region (5-HTTLPR) plays in depression. Some studies report an interaction between 5-HTTLPR variation and stressful life events affecting the risk for depression, others report a main effect of 5-HTTLPR variation on depression, while others find no evidence for either a main or interaction effect. Meta-analyses of multiple studies have also reached differing conclusions.
To improve understanding of the combined roles of 5-HTTLPR variation and stress in the development of depression, we are conducting a meta-analysis of multiple independent datasets. This coordinated approach utilizes new analyses performed with centrally-developed, standardized scripts. This publication documents the protocol for this collaborative, consortium-based meta-analysis of 5-HTTLPR variation, stress, and depression.
Study eligibility criteria: Our goal is to invite all datasets, published or unpublished, with 5-HTTLPR genotype and assessments of stress and depression for at least 300 subjects. This inclusive approach is to minimize potential impact from publication bias.
Data sources: This project currently includes investigators from 35 independent groups, providing data on at least N = 33,761 participants.
The analytic plan was determined prior to starting data analysis. Analyses of individual study datasets will be performed by the investigators who collected the data using centrally-developed standardized analysis scripts to ensure a consistent analytical approach across sites. The consortium as a group will review and interpret the meta-analysis results.
Variation in 5-HTTLPR is hypothesized to moderate the response to stress on depression. To test specific hypotheses about the role of 5-HTTLPR variation on depression, we will perform coordinated meta-analyses of de novo results obtained from all available data, using variables and analyses determined a priori. Primary analyses, based on the original 2003 report by Caspi and colleagues of a GxE interaction will be supplemented by secondary analyses to help interpret and clarify issues ranging from the mechanism of effect to heterogeneity among the contributing studies. Publication of this protocol serves to protect this project from biased reporting and to improve the ability of readers to interpret the results of this specific meta-analysis upon its completion.
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.
Smoking is highly intractable and the genetic influences on cessation are unclear. Identifying the genetic factors affecting smoking cessation could elucidate the nature of tobacco dependence, enhance risk assessment, and support treatment algorithm development. This study tests whether variants in the nicotinic receptor gene cluster (CHRNA5-CHRNA3-CHRNB4) predict age of smoking cessation and relapse to smoking after a quit attempt.
In a community-based, cross-sectional study (N=5,216) and a randomized comparative effectiveness smoking cessation trial (N=1,073), we used survival analyses and logistic regression to model relations between smoking cessation (self-reported quit age in a community study and point-prevalence abstinence at end-of-treatment in a clinical trial) and three common haplotypes in the CHRNA5-CHRNA3-CHRNB4 region defined by rs16969968 and rs680244.
The genetic variants in the CHRNA5-CHRNA3-CHRNB4 region that predict nicotine dependence also predict a later age of smoking cessation in a community-based sample (X2=8.46, df=2, p=0.015). In the smoking cessation trial, these variants predict abstinence at end-of-treatment in individuals receiving placebo medication, but not amongst individuals receiving active medication. Genetic variants interact with treatment in affecting cessation success (X2=8.97, df=2, p=0.011).
Smokers with the high risk genetic variants have a three-fold increased likelihood of responding to pharmacologic cessation treatments, compared to smokers with the low risk genetic variants. The high-risk variants increase the risk of cessation failure, and this increased risk can be ameliorated by cessation pharmacotherapy. By identifying a high-risk genetic group with heightened response to smoking cessation pharmacotherapy, this work may support the development of personalized cessation treatments.
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
Genotype imputation, used in genome-wide association studies to expand coverage of single nucleotide polymorphisms (SNPs), has performed poorly in African Americans compared to less admixed populations. Overall, imputation has typically relied on HapMap reference haplotype panels from Africans (YRI), European Americans (CEU), and Asians (CHB/JPT). The 1000 Genomes project offers a wider range of reference populations, such as African Americans (ASW), but their imputation performance has had limited evaluation. Using 595 African Americans genotyped on Illumina’s HumanHap550v3 BeadChip, we compared imputation results from four software programs (IMPUTE2, BEAGLE, MaCH, and MaCH-Admix) and three reference panels consisting of different combinations of 1000 Genomes populations (February 2012 release): (1) 3 specifically selected populations (YRI, CEU, and ASW); (2) 8 populations of diverse African (AFR) or European (AFR) descent; and (3) all 14 available populations (ALL). Based on chromosome 22, we calculated three performance metrics: (1) concordance (percentage of masked genotyped SNPs with imputed and true genotype agreement); (2) imputation quality score (IQS; concordance adjusted for chance agreement, which is particularly informative for low minor allele frequency [MAF] SNPs); and (3) average r2hat (estimated correlation between the imputed and true genotypes, for all imputed SNPs). Across the reference panels, IMPUTE2 and MaCH had the highest concordance (91%–93%), but IMPUTE2 had the highest IQS (81%–83%) and average r2hat (0.68 using YRI+ASW+CEU, 0.62 using AFR+EUR, and 0.55 using ALL). Imputation quality for most programs was reduced by the addition of more distantly related reference populations, due entirely to the introduction of low frequency SNPs (MAF≤2%) that are monomorphic in the more closely related panels. While imputation was optimized by using IMPUTE2 with reference to the ALL panel (average r2hat = 0.86 for SNPs with MAF>2%), use of the ALL panel for African American studies requires careful interpretation of the population specificity and imputation quality of low frequency SNPs.
A coding variant in ADH1B (rs1229984) that leads to the replacement of Arg48 with His48 is common in Asian populations and reduces their risk for alcoholism, but because of very low allele frequencies the effects in European or African populations have been difficult to detect. We genotyped and analyzed this variant in three large European and African-American case-control studies in which alcohol dependence was defined by DSM-IV criteria, and demonstrated a strong protective effect of the His48 variant (odds ratio of 0.34, 95% confidence interval 0.24, 0.48) for alcohol dependence, with genome-wide significance (6.6 × 10−10). The hypothesized mechanism of action involves an increased aversive reaction to alcohol; in keeping with this hypothesis, the same allele is strongly associated with a lower maximum number of drinks in a 24 hour period (lifetime), with p = 3×10−13. We also tested the effects of this allele on the development of alcoholism in adolescents and young adults and demonstrated a significant protective effect. This variant has the strongest effect on risk for alcohol dependence of any tested in European populations.
alcohol dependence; ADH1B; alcohol dehydrogenase; protective allele; genetics; association study
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