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Introduction: Brain-derived neurotrophic factor (BDNF) is a growth factor in the central nervous system. There is evidence for the involvement of BDNF in addictions and mental disorders. We aimed to replicate the earlier reported association of a functional genetic variant of BDNF with smoking initiation (SI) using a large population-based sample and to test whether the association is independent of depression.Methods: Our sample was drawn from the Finnish population-based FINRISK surveys conducted in 1992, 1997, 2002, and 2007. We had nonmissing data on the genotype BDNF  Val66Met (G/A) variant (rs6265) and self-reported never (n = 10 619) versus ever (n = 16 028) smoking among 26 647 adults aged 25-74 years. The association between BDNF  Val66Met and SI was modeled using logistic regression adjusted for age and sex, and in secondary analyses also for depression. Depression was defined as self-reported depression diagnosed or treated by physician during the past year.Results: The sex- and age-adjusted analysis confirmed that the major (Val) allele increased the risk of being a lifetime ever smoker (per allele odds ratio [OR] = 1.07; 95% CI = 1.01 to 1.12; p = .01). When depression, which itself was significantly associated with SI (OR = 1.58; 95% CI = 1.37 to 1.82; p < .001), was added to the model, the association of the gene with SI remained significant (per allele OR = 1.06; 95% CI = 1.01 to 1.12; p = .01). Exclusion of depressed individuals did not change the results (OR = 1.06; 95% CI = 1.01 to 1.12; p = .02).Conclusions: In a Finnish population sample, we replicated the earlier reported association of BDNF Val66Met with SI. Our data further suggest that this association is independent of depression.Implications: Earlier finding about the association between the BDNF gene and smoking initiation is replicated and shown to be independent of depression within Finnish adult population. [ABSTRACT FROM AUTHOR]

Implications: This article outlines a framework for the consistent integration of biological data/samples into smoking cessation pharmacotherapy trials, aligned with the objectives of the recently unveiled Precision Medicine Initiative. Our goal is to encourage and provide support for treatment researchers to consider biosample collection and genotyping their existing samples as well as integrating genetic analyses into their study design in order to realize precision medicine in treatment of nicotine dependence. [ABSTRACT FROM AUTHOR]

Introduction: Human genetic research has succeeded in definitively identifying multiple genetic variants associated with risk for nicotine dependence and heavy smoking. To build on these advances, and to aid in reducing the prevalence of smoking and its consequent health harms, the next frontier is to identify genetic predictors of successful smoking cessation and also of the efficacy of smoking cessation treatments ("pharmacogenomics"). More broadly, additional biomarkers that can be quantified from biosamples also promise to aid "Precision Medicine" and the personalization of treatment, both pharmacological and behavioral.Aims and Methods: To motivate ongoing and future efforts, here we review several compelling genetic and biomarker findings related to smoking cessation and treatment.Results: These Key results involve genetic variants in the nicotinic receptor subunit gene CHRNA5, variants in the nicotine metabolism gene CYP2A6, and the nicotine metabolite ratio. We also summarize reports of epigenetic changes related to smoking behavior.Conclusions: The results to date demonstrate the value and utility of data generated from biosamples in clinical treatment trial settings. This article cross-references a companion paper in this issue that provides practical guidance on how to incorporate biosample collection into a planned clinical trial and discusses avenues for harmonizing data and fostering consortium-based, collaborative research on the pharmacogenomics of smoking cessation.Implications: Evidence is emerging that certain genotypes and biomarkers are associated with smoking cessation success and efficacy of smoking cessation treatments. We review key findings that open potential avenues for personalizing smoking cessation treatment according to an individual's genetic or metabolic profile. These results provide important incentive for smoking cessation researchers to collect biosamples and perform genotyping in research studies and clinical trials. [ABSTRACT FROM AUTHOR]