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

基本信息

批准号：
10406825
负责人：
GARY A PELTZ
金额：
$ 1.39万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
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/10406825
关键词：
Affect Alleles Biochemical Pathway Brain Chromosome Mapping Cocaine Communities Computing Methodologies Consumption Custom Data Data Analyses Data Set Development Drug Addiction Drug abuse Engineering Fentanyl Gene Expression Genes Genetic Genetic Transcription Genetic Variation Genome Genomic Segment Haplotypes Inbred Strain Inbred Strains Mice Individual Measures Methamphetamine Methods Morbidity - disease rate Morphine Mus Nicotine Pathway interactions Pattern Phenotype Play Population Populations at Risk Predisposition Prevention Property Public Health Research Personnel Role Science Testing Time Tissues Validation Work addiction base computerized tools cost drug of abuse experimental study genetic analysis genetic variant interest large datasets metabolomics multiple omics new therapeutic target novel response societal costs therapeutic development trait

项目摘要

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 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). 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.

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并可以为治疗开发提供潜在的新靶标，然而，这是生物医学领域的一个重大挑战。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）使用传统方法，通常需要多年的艰苦工作才能发现和发现。表征影响给定表型反应的遗传变异几年前，我们开发了一种更有效的方法。将基因映射到性状的有效方法，称为基于单倍型的计算遗传图谱（HBCGM）。在 HBCGM 实验中，测量近交系小鼠的感兴趣特性，并测量遗传因素；通过识别与遗传变异模式相关的基因组区域进行计算预测随着菌株之间性状值的分布，HBCGM 分析的完成速度比 HBCGM 快得多。然而，传统的遗传分析方法用于遗传因素的实验验证。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重要的因素待发现和实验表征的高通量版本的 HBCGM（HT-）。 HBCGM）将用于分析 8,225 个公开可用的数据集，这些数据集测量了面板中的 213,000 个回复我们采用了一种新方法，通过利用许多数据集中存在检查类似响应的新颖计算工具。还将开发促进遗传、转录和代谢组数据的综合分析。包括用于计算识别的专门代谢网络（针对大脑和其他 3 个组织）与基因表达或遗传差异相关的代谢组变化刺激其他研究人员。为了进行基因发现，该项目的所有结果和方法将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）数据集测量：（i）近交系组对四种 DOA（可卡因、甲基苯丙胺、芬太尼和尼古丁）；以及（ii）相应的 DOA 诱导的转录和对这些数据的综合分析将确定影响大脑代谢组变化的基因/途径。然后，我们应用高效方法将特定等位基因变化工程化到 DOA 中。近交系的基因组，并且工程系用于通过实验测试已鉴定的效果遗传因素对 DOA 反应的影响。