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

识别影响药物滥用反应的遗传因素的计算方法

• 批准号: 10075085
• 负责人: GARY A PELTZ
• 金额: $ 12.69万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10075085
• 关键词: Acute; Affect; Aftercare; Alleles; Biochemical Pathway; Biological; Brain; Brain region; Candidate Disease Gene; Chromosome Mapping; Cocaine; Communities; Computer Analysis; Computing Methodologies; Consumption; Custom; Data; Data Analyses; Data Set; Databases; Development; Dose; Drug Addiction; Drug abuse; Engineering; Exhibits; Fentanyl; Gene Expression; Genes; Genetic; Genetic Transcription; Genetic Variation; Genome; Genome engineering; Genomic Segment; Haplotypes; Inbred Strain; Inbred Strains Mice; Individual; Maps; Measures; Methamphetamine; Methods; Morbidity - disease rate; Morphine; Mus; Nicotine; Nicotine Withdrawal; Pathway interactions; Pattern; Phenotype; Play; Population; Populations at Risk; Predisposition; Prevention; Procedures; Property; Public Health; Research Personnel; Role; Science; Self Administration; Testing; Time; Tissues; Validation; Work; addiction; base; computerized tools; conditioned fear; conditioned place preference; cost; drug of abuse; experimental study; genetic analysis; genetic variant; human tissue; improved; interest; large datasets; metabolomics; multiple omics; new therapeutic target; nicotine exposure; novel; phenome; response; societal costs; therapeutic development; trait; transcriptome sequencing

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 because this will aid in identifying at risk populations . 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). and could provide potential novel targets for therapeutic development 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 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 responses in panels of inbred mouse strains. We deploy a novel method that 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.

项目概要/摘要 由于药物滥用的发病率和社会成本增加，影响药物滥用的遗传因素的鉴定 对滥用药物 (DOA) 的反应特别令人感兴趣 因为这将有助于识别高危人群 。然而，生物医学领域面临的重大挑战 科学正在确定群体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，通常需要多年的艰苦工作才能发现和 表征影响给定表型反应的遗传变异。几年前，我们开发了一个 将基因映射到性状的更有效方法，称为基于单倍型的计算遗传映射 （HBCGM）。 并可以为治疗开发提供潜在的新靶标 在 HBCGM 实验中，在近交系小鼠品系中测量了感兴趣的特性；和遗传 通过识别遗传变异模式所在的基因组区域来计算预测因素 与菌株之间性状值的分布相关。 HBCGM 分析已完成更多 比传统的遗传分析方法更快。然而，用于实验验证的方法 遗传因素有局限性并且耗时。 该项目将进一步开发计算方法，使遗传因素能够影响许多 有待发现和实验表征的重要生物医学特征。高通量版本 HBCGM (HT-HBCGM) 将用于分析 8,225 个公开可用的数据集，其中测量了 213,000 增加基因发现 通过利用检查类似响应的许多数据集中存在的冗余来增强功能。小说 促进遗传、转录和代谢组数据综合分析的计算工具将 也得以开发。这包括 近交系小鼠品系组中的反应。我们部署了一种新颖的方法 专门的代谢网络（针对大脑和其他 3 个组织） 通过计算识别与基因表达或遗传差异相关的代谢组变化。到 激励其他研究人员做出基因发现，该项目的所有结果和方法都将被 充分提供给科学界。这些计算工具将用于分析定制的 “多组学”（遗传、转录和代谢组学）数据集，用于测量：（i）近交的十五种反应 四种 DOA（可卡因、甲基苯丙胺、芬太尼和尼古丁）的菌株组； (ii) 相应的 DOA- 诱导大脑转录和代谢组的变化。对该数据的综合分析将确定 影响 DOA 反应的基因/途径。 然后我们应用高效的工程方法 近交系基因组中的特定等位基因变化，并且工程系用于实验 测试已确定的遗传因素对 DOA 反应的影响。