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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.

项目总结/文摘