Dataset Information

Genetics of nicotine dependence and pharmacotherapy.

ABSTRACT: Nicotine dependence is substantially heritable. Several regions across the genome have been implicated in containing genes that confer liability to nicotine dependence and variation in individual genes has been associated with nicotine dependence. Smoking cessation measures are also heritable, and measured genetic variation is associated with nicotine dependence treatment efficacy. Despite significant strides in the understanding of the relative contribution of genetic and environmental factors to nicotine dependence and treatment, emergent challenges necessitate interdisciplinary coordinated effort for effective problem solving. These challenges include refinement of the nicotine dependence phenotype, better understanding of the dynamic interplay between genes and environment in nicotine dependence etiology, application and development of molecular and statistical methodology that can adequately address vast amounts of data, and continuous translational cross-talk.

SUBMITTER: Lessov-Schlaggar CN

PROVIDER: S-EPMC2238639 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genetics of nicotine dependence and pharmacotherapy.

Lessov-Schlaggar Christina N CN Pergadia Michele L ML Khroyan Taline V TV Swan Gary E GE

Biochemical pharmacology 20070819 1

Nicotine dependence is substantially heritable. Several regions across the genome have been implicated in containing genes that confer liability to nicotine dependence and variation in individual genes has been associated with nicotine dependence. Smoking cessation measures are also heritable, and measured genetic variation is associated with nicotine dependence treatment efficacy. Despite significant strides in the understanding of the relative contribution of genetic and environmental factors ...[more]

PMID: 17888884

Similar Datasets

Project description:BackgroundClassical genetic studies indicate that nicotine dependence is a substantially heritable complex disorder. Genetic vulnerabilities to nicotine dependence largely overlap with genetic vulnerabilities to dependence on other addictive substances. Successful abstinence from nicotine displays substantial heritable components as well. Some of the heritability for the ability to quit smoking appears to overlap with the genetics of nicotine dependence and some does not. We now report genome wide association studies of nicotine dependent individuals who were successful in abstaining from cigarette smoking, nicotine dependent individuals who were not successful in abstaining and ethnically-matched control subjects free from substantial lifetime use of any addictive substance.ResultsThese data, and their comparison with data that we have previously obtained from comparisons of four other substance dependent vs control samples support two main ideas: 1) Single nucleotide polymorphisms (SNPs) whose allele frequencies distinguish nicotine-dependent from control individuals identify a set of genes that overlaps significantly with the set of genes that contain markers whose allelic frequencies distinguish the four other substance dependent vs control groups (p < 0.018). 2) SNPs whose allelic frequencies distinguish successful vs unsuccessful abstainers cluster in small genomic regions in ways that are highly unlikely to be due to chance (Monte Carlo p < 0.00001).ConclusionThese clustered SNPs nominate candidate genes for successful abstinence from smoking that are implicated in interesting functions: cell adhesion, enzymes, transcriptional regulators, neurotransmitters and receptors and regulation of DNA, RNA and proteins. As these observations are replicated, they will provide an increasingly-strong basis for understanding mechanisms of successful abstinence, for identifying individuals more or less likely to succeed in smoking cessation efforts and for tailoring therapies so that genotypes can help match smokers with the treatments that are most likely to benefit them.

Project description:IntroductionThe nicotine metabolite ratio and nicotine equivalents are measures of metabolism rate and intake. Genome-wide prediction of these nicotine biomarkers in multiethnic samples will enable tobacco-related biomarker, behavioral, and exposure research in studies without measured biomarkers.Aims and methodsWe screened genetic variants genome-wide using marginal scans and applied statistical learning algorithms on top-ranked genetic variants, age, ethnicity and sex, and, in additional modeling, cigarettes per day (CPD), (in additional modeling) to build prediction models for the urinary nicotine metabolite ratio (uNMR) and creatinine-standardized total nicotine equivalents (TNE) in 2239 current cigarette smokers in five ethnic groups. We predicted these nicotine biomarkers using model ensembles and evaluated external validity using dependence measures in 1864 treatment-seeking smokers in two ethnic groups.ResultsThe genomic regions with the most selected and included variants for measured biomarkers were chr19q13.2 (uNMR, without and with CPD) and chr15q25.1 and chr10q25.3 (TNE, without and with CPD). We observed ensemble correlations between measured and predicted biomarker values for the uNMR and TNE without (with CPD) of 0.67 (0.68) and 0.65 (0.72) in the training sample. We observed inconsistency in penalized regression models of TNE (with CPD) with fewer variants at chr15q25.1 selected and included. In treatment-seeking smokers, predicted uNMR (without CPD) was significantly associated with CPD and predicted TNE (without CPD) with CPD, time-to-first-cigarette, and Fagerström total score.ConclusionsNicotine metabolites, genome-wide data, and statistical learning approaches developed novel robust predictive models for urinary nicotine biomarkers in multiple ethnic groups. Predicted biomarker associations helped define genetically influenced components of nicotine dependence.ImplicationsWe demonstrate development of robust models and multiethnic prediction of the uNMR and TNE using statistical and machine learning approaches. Variants included in trained models for nicotine biomarkers include top-ranked variants in multiethnic genome-wide studies of smoking behavior, nicotine metabolites, and related disease. Association of the two predicted nicotine biomarkers with Fagerström Test for Nicotine Dependence items supports models of nicotine biomarkers as predictors of physical dependence and nicotine exposure. Predicted nicotine biomarkers may facilitate tobacco-related disease and treatment research in samples with genomic data and limited nicotine metabolite or tobacco exposure data.

Dataset Information

Genetics of nicotine dependence and pharmacotherapy.

Publications

Genetics of nicotine dependence and pharmacotherapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets