RESEARCH EDUCATION PROGRAM IN ASPECTS OF STATISTICAL GENETICS AND ADDICTION

统计遗传学和成瘾方面的研究教育计划

基本信息

批准号：
8530203
负责人：
Pamela Ann Madden
金额：
$ 45.64万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-01 至 2015-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8530203
关键词：
Address Advisory Committees Algorithms Area Basic Science Bioinformatics Biological Biometry Clinical Clinical Research Clinical Sciences Communities Computer Simulation Computer software Computing Methodologies Consultations Data Data Set Development Drug Addiction Education Educational Curriculum Educational process of instructing Environment Evaluation Faculty Fellowship Funding Genetic Genetic Predisposition to Disease Genetic Research Genomics Genotype Goals Health Human Genetics Illicit Drugs Individual Institutes Institution International Mathematics Mentors Methodology Modeling National Institute of Drug Abuse Participant Play Postdoctoral Fellow Postdoctoral Individual National Research Service Award Procedures Program Development Program Evaluation Psychology Public Health Qualifying Recruitment Activity Research Research Personnel Research Training Resources Respondent Risk Role Sampling Scheme Science Scientist Series Statistical Methods Statistical Models Testing Training Training and Education United States National Institutes of Health Universities Visit Washington Work addiction base career development cognitive neuroscience computer science computerized tools database of Genotypes and Phenotypes disease classification experience flexibility gene discovery genetic analysis genetic epidemiology genome wide association study infrastructure development innovation interest member metagenome method development multidisciplinary novel post-doctoral training programs tool tool development

项目摘要

DESCRIPTION (provided by applicant): This application seeks five years of support to develop, evaluate and fine-tune a new research education program (WU-STAGEM: Washington University Statistical Training in Addiction Genetics Methodology), to train postdoctoral level scientists (9 over the 5-year funding period). Trainees ("program participants") will include both US and foreign nationals, drawn from backgrounds such as biostatistics, mathematics, quantitative psychology and statistical genetics. We will provide trainees with both (i) the expertise to develop, test and apply new statistical and computational models to address genetics-based research problems in addiction, and (ii) sufficient training to allow participants to work collaboratively in multidisciplinary teams alongside basic and clinical researchers engaged in addiction research. Program postdoctoral scientists will train under the guidance of mentoring teams composed of (a) clinical researchers in addiction, (b) basic scientists and, most critically, (c) researchers with pertinent statistical or computational modeling expertise, with the support of individually tailored formal coursework. They will work with addiction geneticists at the host institution and elsewhere (e.g., through the NIDA Genetics Consortium) to identify and conduct research in areas where unique and urgently needed contributions to the field of addiction genetics can be made, producing products such as novel computer software, novel algorithms or approaches to statistical genetic analysis, to advance the field of addiction genetics. These priority areas will include (varying according to trainee backgrounds and interests and expert input): 1) incorporation of bioinformatics data in the statistical analysis and interpretation of GWAS data, 2) challenges in the combination of GWAS data across data-sets characterized by differing sampling schemes and somewhat different phenotypic assessments, and 3) modeling of genotype x environment interaction effects, and incorporation of consideration of GxE effects in efforts at gene-discovery. We will take advantage of existing data sets such as those at the NIDA Center for Genetic studies; zork.wustl.edu/nida/ and at dbGap, as well as more extensive data-sets associated with local investigators, in this training effort. Through infrastructure development and continuing evaluation efforts, building upon outstanding existing institutional strengths, we will seek to develop a self-sustaining program, drawing upon the wide-ranging expertise at Washington University that can supply highly trained experts in statistical and computational modeling to the field of addiction genetics. PUBLIC HEALTH RELEVANCE: Licit and illicit drug dependence represents a considerable personal and public health burden. The important role of individual genetic vulnerability in contributing to addiction risk is well established. However, there is a critical shortage of investigators with expertise in statistical genetics and computational genomics working in addiction research, a need that this application seeks to address through its postdoctoral research education program.

描述（由申请人提供）：本申请寻求五年的支持来开发、评估和微调新的研究教育计划（WU-STAGEM：华盛顿大学成瘾遗传学方法统计培训），以培训博士后级别的科学家（超过 9 名）资助期限为5年）。学员（“项目参与者”）将包括美国公民和外国人，他们具有生物统计学、数学、定量心理学和统计遗传学等背景。我们将为学员提供（i）开发、测试和应用新的统计和计算模型的专业知识，以解决成瘾中基于遗传学的研究问题，以及（ii）足够的培训，使参与者能够在多学科团队中与基础和从事成瘾研究的临床研究人员。项目博士后科学家将在指导团队的指导下进行培训，该指导团队由（a）成瘾临床研究人员，（b）基础科学家，最重要的是，（c）具有相关统计或计算建模专业知识的研究人员组成，并在单独定制的正式培训的支持下进行培训。课程作业。他们将与主办机构和其他地方的成瘾遗传学家合作（例如，通过 NIDA 遗传学联盟），以确定可以对成瘾遗传学领域做出独特且迫切需要的贡献的领域并进行研究，生产新型计算机等产品软件、新颖的算法或统计遗传分析方法，以推进成瘾遗传学领域的发展。这些优先领域将包括（根据学员背景、兴趣和专家意见而有所不同）：1）将生物信息学数据纳入 GWAS 数据的统计分析和解释中，2）跨数据集组合 GWAS 数据的挑战，这些数据集具有不同的特征抽样方案和有些不同的表型评估，以及 3) 基因型 x 环境相互作用效应的建模，以及在基因发现工作中考虑 GxE 效应。我们将利用现有的数据集，例如 NIDA 遗传研究中心的数据集； zork.wustl.edu/nida/ 和 dbGap，以及本次培训工作中与当地调查人员相关的更广泛的数据集。通过基础设施开发和持续评估工作，以现有的杰出机构优势为基础，我们将寻求开发一个自我维持的计划，利用华盛顿大学广泛的专业知识，为该领域提供训练有素的统计和计算建模专家成瘾遗传学。公共卫生相关性：合法和非法药物依赖对个人和公共卫生造成相当大的负担。个体遗传脆弱性在导致成瘾风险中的重要作用已得到充分证实。然而，在成瘾研究中具有统计遗传学和计算基因组学专业知识的研究人员严重短缺，该应用程序试图通过其博士后研究教育计划来解决这一需求。