Pathway-based analysis of addiction susceptibility genes

描述（由申请人提供）：已知酒精和物质依赖的易感性受遗传因素影响。然而，很少有特定的遗传变异，改变成瘾的易感性已被确定。这部分是因为成瘾是一种多基因性状，受到许多遗传变异的影响，每一种都有很小的边际效应。表型特征受特定基因产物影响的相互作用的生化和生理途径网络控制。虽然单个遗传变异导致对复杂疾病的易感性的微小变化，但可能驱动表型表达的是生化途径内基因的组合效应。 本申请中提出的研究旨在应用新的基于途径的方法来分析来自酒精中毒全基因组关联研究的现有数据。将分析由Laura Bierut博士及其同事收集的成瘾研究：遗传学与环境（SAGE）的病例对照酒精依赖数据。将应用的方法考虑到基因及其产物之间的已知关系，并评估代表生物途径的基因组的影响，而不是评估单个基因的影响。一旦确定了重要的途径，将对这些途径中的基因进行全面分析，以表征遗传效应，包括遗传变异之间的相互作用。这些分析将利用随机森林方法和具有基因-基因相互作用效应的LASSO逻辑回归。 本研究的长期目标是通过应用最佳统计技术，改善物质成瘾和物质使用障碍亚型的相互作用遗传风险因素的检测。虽然从全基因组扫描中研究SNP之间所有可能的相互作用可能不切实际，但研究相关途径内遗传相互作用的集中方法预计将更强大，并产生可解释的结果。 使用新的统计方法分析现有数据，解释作为常见神经或分子通路一部分的基因之间的关系，具有很大的潜力来识别导致成瘾易感性个体差异的遗传变异。这些遗传风险因素的发现具有重要意义，包括增加我们对成瘾发展途径和风险预测的理解。也许更重要的是，这些知识有望帮助识别需要不同干预措施的成瘾亚型，从而提高成功率。 公共卫生相关性：虽然在了解酒精中毒和其他物质使用障碍的遗传方面取得了进展，但很少确定具体的遗传风险因素。最近，已经提出了用于分析遗传数据的新方法，该方法考虑了有助于共同生物途径的基因之间的已知关系。本申请中提出的研究将应用这种方法来分析来自酒精中毒遗传研究的数据。这项研究的结果预计将有助于确定有助于物质依赖易感性个体差异的基因组，从而改善基于个性化治疗方法的成瘾诊断和管理。

DESCRIPTION (provided by applicant): Susceptibility to alcohol and substance dependence is known to be influenced by genetic factors. However, few specific genetic variations that alter susceptibility to addiction have been identified. This is partly because addiction is a polygenic trait, influenced by many genetic variations, each with a small marginal effect. Phenotypic characteristics are controlled by networks of interacting biochemical and physiological pathways influenced by the products of specific genes. While single genetic variants result in small changes in susceptibility to complex diseases, it is the combined effects of genes within a biochemical pathway that likely drive phenotype expression. The research proposed in this application aims to apply novel pathway-based methods to analyze existing data from a genome wide association study of alcoholism. Case-control alcohol dependence data from the Study of Addiction: Genetics and Environment (SAGE), collected by Dr. Laura Bierut and colleagues, will be analyzed. The methods that will be applied take into account known relationships between genes and their products and assess the effect of gene-sets that represent biological pathways, rather than assessing individual gene effects. Once significant pathways are identified, comprehensive analyses of genes within these pathways will be performed, to characterize the genetic effects, including interactions between genetic variations. These analyses will utilize random forest methods and LASSO logistic regression with gene-gene interaction effects. The long term goals of this research are to improve the detection of interacting genetic risk factors for substance addiction and subtypes of substance use disorders, by applying optimal statistical techniques. While studying all possible interactions between SNPs from a genome-wide scan may not be practical, a focused approach of investigating genetic interactions within a relevant pathway is expected to be more powerful and yield interpretable results. Analysis of existing data using new statistical methods that account for the relationships between genes that act as part of common neural or molecular pathways has great potential to identify genetic variations that contribute to individual differences in addiction susceptibility. Discovery of such genetic risk factors has important implications including increasing our understanding of the pathways of development of addiction and risk prediction. Perhaps more importantly, this knowledge is expected to help identify subtypes of addiction that require different interventions leading to personalized treatment with increased success rates. PUBLIC HEALTH RELEVANCE: Although progress has been made in terms of understanding the heritable aspects of alcoholism and other substance use disorders, few specific genetic risk factors have been identified. Recently, new methods for analyzing genetic data have been proposed that take into account known relationships between genes that contribute to common biological pathways. The research proposed in this application will apply such approaches to analyze data from a genetic study of alcoholism. Results of this study are expected to help identify sets of genes that contribute to individual differences in susceptibility to substance dependence, leading to improved diagnosis and management of addiction based on an individualized treatment approach.