Methods for detecting interacting risk factors for addictions

描述（由申请人提供）：对酒精和物质依赖的易感性受遗传因素的影响。然而，很少发现改变成瘾易感性的特定遗传变异。这在一定程度上是因为成瘾是一种多基因特征，受许多遗传变异的影响，每个变异都有很小的边际效应。然而，这些遗传变异的集体效应及其与其他遗传和环境因素的相互作用可能对酒精和其他物质使用障碍及相关表型的易感性非常重要。目前尚不清楚哪种研究设计和分析方法最适合用于检测导致复杂性状的相互作用的危险因素。不幸的是，用于分析遗传数据的常用统计方法可能不适合这项具有挑战性的任务。我们研究的长期目标是通过应用最佳统计技术，提高对导致物质/酒精使用障碍发展的相互作用的遗传和环境风险因素的检测。本申请中提出的研究旨在开发分析遗传数据的替代方法，评估所提出方法的性能，并重要地将这些方法应用于物质使用障碍的现有遗传数据。特别是，基于随机森林的方法和相关的基于重采样的数据挖掘方法将被考虑。发展领域将包括评估单倍型和基因水平效应的方法，新的排列算法，以及检测相互作用因素的能力的改进。我们将在用户友好的软件中实现这些方法，这些软件能够分析基因组全关联扫描产生的大量数据。模拟将用于评估新方法的性能，并将其与传统的遗传关联测试方法进行比较。在本研究项目中开发的最佳方法将应用于现有的物质依赖和其他成瘾相关表型的数据集。具体而言，将分析由Laura Bierut博士及其同事收集的NICSNP项目和成瘾：遗传与环境研究（SAGE）的病例对照数据。利用解释遗传相互作用的新统计方法对现有数据进行分析，很有可能查明导致对药物滥用和依赖易感性的个体差异的新的遗传变异。发现影响物质依赖和相关疾病的遗传和环境因素，或这些疾病的治疗结果，具有重要的意义，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，这些知识可能有助于识别需要不同干预措施的成瘾亚型，从而提高个性化治疗的成功率。

DESCRIPTION (provided by applicant): Susceptibility to alcohol and substance dependence is influenced by genetic factors. However, few specific genetic variations that alter susceptibility to addiction have been discovered. This is partly because addiction is a polygenic trait, influenced by many genetic variations, each with a small marginal effect. However, the collective effects of those genetic variations and their interactions with other genetic and environmental factors may be quite important in predisposition to alcohol and other substance use disorders and related phenotypes. It is still not clear which study designs and analysis methods are most suitable for detecting the interacting risk factors that contribute to complex traits. Unfortunately, the commonly used statistical approaches for analysis of genetic data may not be optimal for this challenging task. The long term goals of our research are to improve the detection of interacting genetic and environmental risk factors that contribute to the development of substance/alcohol use disorders, by applying optimal statistical techniques. The research proposed in this application aims to develop alternative methods for analyzing genetic data, assess the performance of the proposed methods, and importantly apply these methods to existing genetic data on substance use disorders. In particular, methods based on random forests and related resampling-based data-mining approaches will be considered. Areas of development will include methods for assessing haplotype and gene-level effects, novel permutation algorithms, and improvements in power to detect interacting factors. We will implement these methods in user-friendly software capable of analyzing the vast amounts of data produced by genome wide association scans. Simulations will be used to assess performance of the novel approaches and compare them to traditional genetic association testing methods. The optimal approaches developed in this research program will then be applied to existing datasets on substance dependence and other addiction-related phenotypes. Specifically, case-control data from the NICSNP project and the Study of Addiction: Genetics and Environment (SAGE), collected by Dr. Laura Bierut and colleagues, will be analyzed. Analysis of existing data using new statistical methods that account for genetic interactions has great potential to identify novel genetic variations that contribute to individual differences in susceptibility to substance abuse and dependence. Discovery of genetic and environmental factors that influence substance dependence and related disorders, or outcomes of treatment for these disorders, has important implications including increasing our understanding of the pathways of development of addiction and risk prediction. Perhaps more importantly, this knowledge may 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 substance and alcohol use disorders, few specific genetic risk factors have been identified. This is partly because the small changes in susceptibility to these disorders conferred by relevant genetic variations are individually very difficult to detect, and currently used statistical approaches for analysis of genetic data are not optimal for this challenging task. The research proposed in this application aims to develop alternative methods for analyzing genetic data, assess the performance of the proposed methods, and apply the methods to existing genetic data on substance use disorders to identify genetic risk factors for addiction and related traits.