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.

项目摘要/摘要 由于滥用药物的发病率和社会成本增加，鉴定了影响该遗传因素 对滥用药物（DOA）的反应特别感兴趣 因为这将有助于识别风险人群 。但是，生物医学的主要挑战 科学正在确定人群中的遗传差异如何影响特性（即表型，特征） 一个人。使用常规方法，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 将基因映射到特征的更有效的方法，称为基于单倍型的计算遗传学映射 （HBCGM）。 并可以为治疗发展提供潜在的新目标 在HBCGM实验中，在近交小鼠菌株中测量了感兴趣的特性。和遗传 通过识别遗传变异模式的基因组区域，可以在计算上预测因素 与菌株之间特质值的分布相关。 HBCGM分析完成了更多 比常规的遗传分析方法迅速。但是，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，该方法将使遗传因素影响许多 要发现和实验表征的重要生物医学特征。高通量版本的 HBCGM（HT-HBCGM）将用于分析8,225个公开可用的数据集，该数据集量度为213,000 增加遗传发现 通过利用许多数据集中存在的冗余来利用相似响应的功率。小说 促进遗传，转录和代谢组数据综合分析的计算工具将 也可以开发。这包括 近交小鼠菌株面板中的响应。我们部署了一种新颖的方法 专门的代谢网络（用于大脑和其他3个组织） 在计算上识别与基因表达或遗传差异相关的代谢组变化。到 刺激其他研究人员进行遗传发现，该项目的所有结果和方法将是 完全可以向科学界提供。这些计算工具将用于分析自定义 “多摩变”（遗传，转录和代谢组）数据集：（i）15个近交响的响应 应变面板到四个DOA（可卡因，甲基苯丙胺，芬太尼和尼古丁）； （ii）相应的doa- 诱导大脑的转录和代谢组变化。该数据的综合分析将识别 影响对DOA反应的基因/途径。 然后，我们应用高效方法进行工程 特定的等位基因变化对近交菌株的基因组，并且工程线用于实验 测试鉴定遗传因子对DOA反应的影响。