Gene-Gene/Gene-Environment Interactions Associated with Nicotine Dependence

描述(由申请人提供)：这项关于导师研究科学家奖的建议旨在为候选人提供必要的培训和增强的研究经验，以过渡到独立的药物依赖遗传统计学家/遗传流行病学家。拟议在药物依赖流行病学、精神病学遗传学和基于核心的统计相互作用分析领域的培训将补充候选人以前的培训和现有技能，并为候选人提供坚实的基础 成为一名独立调查员。这项培训将由一个拥有丰富经验和互补技能的多学科指导团队提供建议。首席导师Jim Anthony博士和共同导师Naomi Breslau博士将为候选人提供药物依赖流行病学方面的培训建议，并通过顾问提供病理生理学、病因学、临床诊断和治疗学方面的指导学习经验。共同导师Karen Friderici博士和合作者Laura Bierut博士将监督候选人在精神病学遗传学方面的培训。共同导师Daniel Schaid将为候选人在基于核的统计交互分析方面的培训提供建议。长期目标是确定导致烟草/尼古丁依赖和相关表型(NDRP)的基因-基因/基因-环境(G-G/G-E)相互作用，然后评估它们在NDRP预测和预防以及个性化治疗中的作用。本申请中提出的研究旨在通过使用创新的统计遗传学方法来改进NDRP相关G-G/G-E相互作用的检测。在拟议的研究中，将开发基于非参数核的统计遗传学方法，并将其应用于NDRP相关基因(例如，nAChRs亚单位基因)和环境决定因素(例如，儿童期虐待)，以(A)检测高阶交互作用，(B)发现低边际效应遗传和环境决定因素之间的交互作用，以及(C)确定导致NDRP和酒精依赖及相关表型的共同和独特的交互作用。来自成瘾研究：遗传学和环境的现有病例对照全基因组关联研究数据集将用于拟议的G-G/G-E交互作用分析。新的G-G/G-E相互作用的发现可能会为NDRP的病理生理和病因学过程提供新的见解，并对更好的NDRP预测和预防/治疗策略具有重要意义。从拟议研究中获得的培训和研究经验将作为独立研究计划的基础，包括将在K01奖励间隔的第二年期间提交的新的R01建议，以加强和改进与NDRP相关的G-G/G-E相互作用研究的现有方法。

DESCRIPTION (provided by applicant): This proposal for a Mentored Research Scientist Award is designed to provide the candidate with the necessary training and enhanced research experience to transition into an independent drug dependence genetic statistician/genetic epidemiologist. The proposed training, in the areas of drug dependence epidemiology, psychiatric genetics, and kernel-based statistical interaction analysis will complement the candidate's previous training and existing skills, and provide a solid foundation for the candidate to become an independent investigator. This training will be advised by a multi-discipline mentoring team with extensive experience and complementary skills. The Primary Mentor, Dr. Jim Anthony, and Co-Mentor, Dr. Naomi Breslau, will advise the candidate's training in drug dependence epidemiology, with guided learning experiences via consultants on pathophysiology, etiology, clinical diagnosis, and therapeutics. Co-Mentor, Dr. Karen Friderici, and Collaborator, Dr. Laura Bierut, will supervise the candidate's training in psychiatric genetics Co-Mentor, Daniel Schaid, will advise the candidate's training in kernel-based statistical interaction analysis. The long term goal is to identify gene-gene/gene-environment (G-G/G-E) interactions that account for tobacco/nicotine dependence and related phenotypes (NDRP), and then to evaluate their role in NDRP prediction and prevention, and in personalized treatment. The research proposed in this application is to improve the detection of NDRP-related G-G/G-E interactions by employing innovative statistical genetic approaches. In the proposed research, non-parametric kernel-based statistical genetic approaches will be developed and applied to NDRP-associated genes (e.g., nAChRs sub-unit genes) and environmental determinants (e.g., childhood abuse) for (a) detecting high-order interactions, (b) discovering interactions among low-marginal-effect genetic and environmental determinants, and (c) identifying common and unique interactions accounting for NDRP and alcohol dependence and related phenotypes. An existing case-control genome-wide association study dataset from the Study of Addiction: Genetics and Environment will be used for the proposed G-G/G-E interaction analysis. The novel G-G/G-E interaction findings will likely provide new insights into the underlying NDRP pathophysiological and etiological processes, as well as have significant implications for better NDRP prediction and prevention/treatment strategies. The training and research experience gained from the proposed study will serve as the groundwork for an independent research program, including a new R01 proposal to be submitted during the second year of the K01 award interval, in order to strengthen and refine current methods for NDRP-related G-G/G-E interaction research.