||5R21CA226300-02 Interpret this number
||Pennsylvania State University-Univ Park
||The Moderating Role of Smoking Exposure in the Relationship Between the Nicotinic Acetylcholine Receptor Gene Cluster and Nicotine Dependence
Progress in our ability to evaluate the interaction between genetic effects and environmental factors in
understanding risk for nicotine dependence has been significantly impeded by inadequate single study sample
sizes and meta analyses that are necessarily limited to the estimation of effect sizes for research questions that
have been previously addressed within single studies. Fortunately, recent methodological innovations now permit
the combination of data sets that differ in terms of study design, populations, and measures. This integrative data
analysis (IDA) allows us to pool the raw data from multiple studies, allowing us to test new hypotheses and
results in more powerful, more comprehensive, and more rigorous studies than we achieve through analysis of
single studies or by using traditional meta analytic techniques. The present research will combine both an
innovative approach (IDA) and innovative statistical methods (moderated nonlinear factor analysis; MNLFA and
time-varying effects models; TVEM), to evaluate the association between genetic variants in the
CHRNA5/CHRNA3/CHRNB4 and CHRNA6/CHRNB3 regions and nicotine dependence symptoms across levels
of smoking exposure. Taking advantage of existing resources, we will pool extant data from the Social Emotional
Contexts of Adolescent Smoking Project (SECASP), The National Longitudinal Study of Adolescent Health
(AddHealth), the National Youth Survey Family Study (NYSFS) and the Study of Addiction: Genetics and
Environment (SAGE) to evaluate changes in the association between SNPs in the acetylcholine receptor gene
clusters and an empirically harmonized nicotine dependence score across levels of smoking exposure and to
estimate the additional contribution of timing of smoking exposure in explaining the association between
individual genetic variants and nicotine dependence symptoms. The innovative methods of data integration with
MNLFA will allow us to begin to explore exposure varying effects in an extremely cost-effective manner in that we
will leverage previous investment in tracking, contacting, assessing, and collecting DNA from 4 large studies
assessing adolescents and adults. The empirically based nicotine dependence symptom score derived from the
cross-study MNLFA model will provide a much more sensitive measure of the nicotine dependence phenotype
than traditional methods of scoring within study, and one that is uniquely harmonized to account for between
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