Pathway-based analysis of addiction susceptibility genes

描述(由申请人提供)：已知对酒精和物质依赖的敏感性受遗传因素的影响。然而，很少有特定的基因变异可以改变成瘾的易感性。这在一定程度上是因为上瘾是一种多基因特征，受到许多遗传变异的影响，每一种变异都有很小的边际影响。表型特征受特定基因产物影响的相互作用的生化和生理通路网络控制。虽然单一的遗传变异会导致对复杂疾病的易感性发生微小变化，但可能是生物化学途径中的基因的综合作用才可能驱动表型表达。在这项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。由Laura Bierut博士和他的同事收集的来自成瘾：遗传学与环境研究(SAGE)的病例对照酒精依赖数据将被分析。将采用的方法考虑了基因及其产物之间的已知关系，并评估了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。一旦确定了重要途径，将对这些途径中的基因进行全面分析，以确定遗传效应，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-基因互作效应的套索Logistic回归。这项研究的长期目标是通过应用最优的统计技术，改进对物质成瘾和物质使用障碍亚型相互作用的遗传风险因素的检测。虽然从全基因组扫描中研究SNPs之间所有可能的相互作用可能并不现实，但集中研究相关途径中的遗传相互作用的方法预计会更强大，并产生可解释的结果。使用新的统计方法对现有数据进行分析，这些方法解释了作为共同神经或分子通路一部分的基因之间的关系，这对于识别导致成瘾易感性个体差异的遗传变异具有巨大的潜力。这种遗传危险因素的发现具有重要的意义，包括增加我们对成瘾发展途径的理解和风险预测。也许更重要的是，这一知识有望帮助识别需要不同干预措施的成瘾亚型，从而实现个性化治疗，提高成功率。 公共卫生相关性：尽管在了解酒精中毒和其他物质使用障碍的可遗传方面取得了进展，但确定的具体遗传风险因素很少。最近，已经提出了分析遗传数据的新方法，这些方法考虑了促成共同生物途径的基因之间的已知关系。在这项申请中提出的研究将应用这样的方法来分析酒精中毒的遗传学研究数据。这项研究的结果有望帮助确定导致药物依赖易感性个体差异的几组基因，从而改进基于个体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.