Computational Methods for Identification of Genetic Factors Affecting the Response to Drug Abuse

项目摘要/摘要 由于药物滥用的发病率和社会成本的增加，确定影响药物滥用的遗传因素 应对药物滥用(DOA)特别令人感兴趣 并可能为治疗开发提供潜在的新靶点。然而，生物医学领域的一个主要挑战 科学正在确定一个种群内的遗传差异如何影响特性(即表型、性状) 一个人的。使用传统方法，往往需要多年的艰苦工作才能发现和 描述影响给定表型反应的遗传变异的特征。几年前，我们开发了一种 将基因定位到性状的更有效方法，称为基于单倍型的计算遗传作图 (HBCGM)。 因为这将有助于识别高危人群 在HBCGM实验中，在近亲交配的小鼠品系中测量了感兴趣的特性；以及基因 通过识别遗传变异模式所在的基因组区域来计算预测因子 与品系间性状值的分布有关。HBCGM的分析完成得更多 比传统的遗传分析方法更快。然而，用于实验验证的方法 遗传因素是有局限性的，而且很耗时。 该项目将进一步开发计算方法，使遗传因素能够影响许多 重要的生物医学特征有待发现和实验表征。高吞吐量版本的 HBCGM(HT-HBCGM)将用于分析8,225个公开可用的数据集，这些数据集的度量为213,000 近交系小鼠品系的反应。我们部署了一种新的方法， 增加基因发现 通过利用检查相似反应的许多数据集中存在的冗余来增强能力。小说 有助于对遗传、转录和代谢数据进行综合分析的计算工具将 也被开发出来。这包括 专门的代谢网络(针对大脑和其他3个组织) 通过计算确定与基因表达或遗传差异相关的代谢变化。至 刺激其他研究人员进行基因发现，这个项目的所有结果和方法都将 完全向科学界提供。这些计算工具将用于分析定制的 “多组”(遗传、转录和代谢)数据集，测量：(I)近交系的15个反应 四种DOA(可卡因、甲基苯丙胺、芬太尼和尼古丁)的菌株板；和(Ii)相应的DOA- 诱导脑内转录和代谢的改变。对此数据的综合分析将确定 影响对DOA反应的基因/途径。 然后，我们将一种高效的方法应用于工程 近交系的基因组发生了特定的等位基因变化，这些工程化的品系被用于实验 测试已确定的遗传因素对DOA反应的影响。

Project Summary/Abstract Due to the increased morbidity and societal cost of drug abuse, identification of genetic factors affecting the response to drugs of abuse (DOA) are of particular interest and could provide potential novel targets for therapeutic development . However, a major challenge in biomedical science is determining how genetic differences within a population affect the properties (i.e. phenotypes, traits) of an individual. Using conventional methods, it often requires years of painstaking work to discover and characterize a genetic variant that affects a given phenotypic response. Several years ago, we developed a more efficient method for mapping genes to traits, called haplotype-based computational genetic mapping (HBCGM). because this will aid in identifying at risk populations In an HBCGM experiment, a property of interest is measured in inbred mouse strains; and genetic factors are computationally predicted by identifying the genomic regions where the pattern of genetic variation correlates with the distribution of trait values among the strains. HBCGM analyses are completed much more quickly than conventional genetic analysis methods. However, the methods used for experimental validation of genetic factors have limitations and are time consuming. This project will further develop computational methods that will enable genetic factors affecting many important biomedical traits to be discovered and experimentally characterized. A high-throughput version of HBCGM (HT-HBCGM) will be used to analyze 8,225 publicly available datasets, which measure 213,000 responses in panels of inbred mouse strains. We deploy a novel method that increases genetic discovery power by exploiting the redundancy present in the many datasets that examine similar responses. Novel computational tools that facilitate the integrated analysis of genetic, transcriptional and metabolomic data will also be developed. This includes specialized metabolic networks (for brain and 3 other tissues) for computationally identifying metabolomic changes that correlate with gene expression or genetic differences. To stimulate other investigators to make genetic discoveries, all results and methods from this project will be made fully available to the scientific community. These computational tools will be used to analyze customized `multi-omic' (genetic, transcriptional, and metabolomic) datasets that measure: (i) fifteen responses of inbred strain panels to four DOA (cocaine, methamphetamine, fentanyl, and nicotine); and (ii) corresponding DOA- induced transcriptional and metabolomic changes in brain. Integrated analysis of this data will identify genes/pathways affecting the response to DOA. We then apply a high efficiency method for engineering specific allelic changes into the genome of inbred strains, and the engineered lines are used to experimentally test the effect of an identified genetic factor on the response to a DOA.