System Genetics of Alcoholism: Network-based Approaches for Genetic Association

描述(由申请者提供)：这项指导性研究科学家职业发展奖将为Rohan Palmer博士在酒精依赖和相关表型(AD)遗传学方面的计划研究提供重点培训。鉴于酒精中毒的基因鉴定研究(特别是全基因组关联研究)受到这样一个事实的挑战，即酒精中毒是一种受多个相互作用的基因影响的多因素障碍，每个基因的影响都很小，因此，对于得出新的分析策略和统计方法来说，公共卫生方面的相关性是巨大的。这项拟议的研究与国家酒精滥用和酒精中毒研究所的计划是一致的，该计划“通过利用新的和新兴的技术，从以前为酒精依赖的遗传学研究招募的研究人群的样本中，识别与酒精依赖易感性相关的基因”。显然，需要一种基于网络的方法来描述构成酒精依赖基础的特定神经或分子通路的遗传效应的联合分布。通过基因类型和表型数据库(DBGaP)获得的遗传样本和表型数据，以及从反社会药物依赖中心收集的数据，将被用来提供一个具有成本效益的机会，以确定改变AD易感性的基因网络，从而提高我们对AD的理解，同时为酒精遗传学领域引入一种新的方法。拟议中的研究有助于帕尔默博士实现他的职业生涯和培训目标：(1)精通统计遗传学和基于系统的关联方法，(2)熟练使用生物统计学工具来识别基因和蛋白质相互作用网络，(3)通过开发新的数据挖掘技术，识别进化稳健的生物系统中的变异，(4)制定一个研究计划，旨在确定受酒精和其他药物/药物影响的生物系统中可能被滥用的遗传变异的组合，以及(5)建立合作，以开发高质量的研究手稿和赠款。拟议研究的具体目标是：(1)利用遗传关联、生物信息学数据库、系统生物学以及基因和蛋白质相互作用建模工具的先验知识，得出描述基因之间关系的基因网络图(即影响图)；(2)识别目标1影响图中的基因子网络(这些网络将在考虑几个协变量(如严重抑郁症和选定的环境因素)之前和之后确定)；以及(3)使用具有类似表型和遗传数据的独立样本复制目标2的发现。培训和研究目标将通过教学课程、持续的密切指导、有指导的研究、研讨会和合作项目相结合来实现。这一应用将导致支持计划中的未来R01的研究结果，以及促进我们对基因-生物学-表型关系的理解的创新研究方法。

DESCRIPTION (provided by applicant): This Mentored Research Scientist Career Development Award will afford Dr. Rohan Palmer focused training for his programmatic line of research in the genetics of alcohol dependence and related phenotypes (AD). Given that gene identification studies (in particular genomewide association studies) on alcoholism are challenged by the fact that alcoholism is a multifactorial disorder influenced by multiple interacting genes, each with small effect, the public health relevance for deriving new analytical strategies and statistical methods is substantial. The proposed research aligns with the National Institute for Alcohol Abuse and Alcoholism's plan to "Identify genes associated with vulnerability for alcohol dependence by employing new and emerging technologies, on samples from study populations previously recruited for genetic research on alcohol dependence". It is evident that a network-based approach is necessary to describe the joint distribution of genetic effects that comprise specific neural or molecular pathways that underlie alcohol dependence. Genetic samples and phenotypic data available through the Database for Genotype and Phenotypes (dbGAP), as well as collected data from the Center for Antisocial Drug Dependence will be used to provide a cost-effective opportunity to identify gene networks that alter susceptibility to AD, thereby improving our understanding of AD while introducing a novel approach to the field of alcohol genetics. The proposed research assists Dr. Palmer in achieving his career and training goals to: (1) develop proficiency in statistical genetics and systems-based association methods, (2) become adept at using biostatistics tools to identify gene and protein interaction networks, (3) identify variation in evolutionarily robust biological systems by developing novel data mining techniques, (4) develop a program of research aimed at identifying combinations of genetic variation in biological systems affected by alcohol and other drug/pharmaceuticals with the potential for abuse, and (5) establish collaborations to develop high-quality research manuscripts and grants. The specific aims of the proposed research are to: (1) Derive gene network graphs that describe the relationships between genes (i.e., influence graphs) using prior knowledge of genetic association, bioinformatics databases, systems biology, and gene and protein interaction modeling tools; (2) identify sub networks of genes within the influence graphs from Aim 1 (these networks will be identified before and after accounting for several covariates (e.g., major depression, and select environmental factors); and (3) replicate findings from Aim 2 using independent samples with similar phenotypic and genetic data. Training and research goals will be achieved through a combination of didactic coursework, ongoing close mentorship, mentored research, workshops, and collaborative projects. This application will lead to research findings to support planned future R01s, as well as innovative research methods that advance our understanding of gene-biology-phenotype relationships.