Computational Efficient Statistical Tools for Analyzing Substance Dependence Sequencing Data

用于分析物质依赖性测序数据的高效计算统计工具

• 批准号: 9922519
• 负责人: Qing Lu
• 金额: $ 41.22万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9922519
• 关键词: Accounting; Address; Advanced Development; Attention; Behavioral Genetics; Bioinformatics; Catalogs; Clinical; Collaborations; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Developmental Process; Diagnostic; Disease model; Environment; Epidemiology; Etiology; Family; Future; Genes; Genetic; Goals; Human Genome; Joints; Knowledge; Large-Scale Sequencing; Lead; Measurement; Methodology; Methods; National Institute of Drug Abuse; Performance; Phenotype; Population; Population Analysis; Prevention; Process; Psychiatry; Research; Research Design; Research Personnel; Role; Scientist; Software Design; Statistical Methods; Substance Addiction; Technology; Testing; Twin Multiple Birth; Variant; analytical method; base; design; gene environment interaction; genetic variant; high dimensionality; improved; insight; multidimensional data; neglect; next generation sequencing; novel; population based; population stratification; rare variant; simulation; software development; success; tool

Project Summary With advancements in next-generation sequencing technologies, sequencing studies has become increasingly used in substance dependence (SD) research. These studies generate a massive amount of sequencing data and allow researchers to comprehensively investigate the role of a deep catalog of genetic variants in SD. Although the ongoing sequencing studies hold great promise for unraveling novel variants that contribute to SD, the high-dimensional data, low frequent variants, complex SD etiology, and heterogeneous SD phenotypes create tremendous analytic and computational challenges. Developing robust and powerful methods and computationally efficient software will address the challenges in SD sequencing data analysis and enhance our ability to identify new SD-related variants. The goals of this application are to develop new methods and software for designing and analyzing population-based and family-based sequencing data with single or multiple phenotypes, and to use them in collaborative research to investigate genetic variants and gene-gene/gene-environment (G-G/G-E) interactions associated with SD. Based on the preliminary simulation results, our central hypothesis is that the proposed methods are more computationally efficient than existing methods, and attain a more robust and powerful performance for various types of phenotypes. The planned specific aims are to: 1) develop a new non-parametric method for the design and analysis of sequencing data with one or multiple SD phenotypes; 2) develop a Joint-U method for high-dimensional G-G/G-E interaction analysis with SD sequencing data; 3) develop a family-similarity-U method for family-based SD sequencing data analysis, accounting for population stratification and rare variants enriched in families; and 4) facilitate the use of the new methods through software development and collaboration. The proposed research will be initiated by an early-stage new investigator (NIDA K01 awardee), who has assembled a team of scientists with expertise in statistical genetics, bioinformatics/software development, SD epidemiology, behavioral genetics, and clinical psychiatry. The successful completion of this project will address several important statistical and computational gaps in ongoing sequencing studies, and advance the methodology and software development for SD sequencing data analysis. The application of the new methods and software to large-scale SD sequencing datasets also holds promise for the discovery of new SD-associated variants and G-G/G-E interactions, which will ultimately lead to a better understanding of SD etiology, with resulting potential benefits for SD prevention and treatment.

项目摘要 随着下一代测序技术的进步，测序研究变得越来越多 用于物质依赖（SD）研究。这些研究产生了大量的测序数据 并允许研究人员全面研究SD遗传变异的深层目录的作用。 尽管正在进行的测序研究对揭示有助于促进的新型变种有很大的希望 SD，高维数据，低频变体，复杂的SD病因和异质SD 表型引起了巨大的分析和计算挑战。发展强大而有力 方法和计算高效的软件将解决SD测序数据分析中的挑战 并增强我们识别新型SD相关变体的能力。该应用的目标是开发新的 使用基于人群和家庭的测序数据设计和分析的方法和软件 单个或多种表型，并在协作研究中使用它们来研究遗传变异和 与SD相关的基因基因/基因环境（G-G/G-E）相互作用。基于初步模拟 结果，我们的中心假设是所提出的方法在计算上比现有方法更有效 方法，并为各种表型实现更强大，更强大的性能。计划 具体目的是：1）开发一种新的非参数方法来设计和分析测序数据 具有一个或多个SD表型； 2）开发一种用于高维g-g/g-E相互作用的联合-U方法 使用SD测序数据分析； 3）开发一种基于家庭的SD测序的family Simality-U方法 数据分析，占人口分层的解释以及富含家庭的稀有变体； 4）促进 通过软件开发和协作使用新方法。拟议的研究将是 由早期新调查员（NIDA K01获奖者）发起的，他与 统计遗传学，生物信息学/软件开发，SD流行病学，行为遗传学的专业知识， 和临床精神病学。该项目的成功完成将解决几个重要的统计和 正在进行的测序研究中的计算差距，并推进方法和软件开发 用于SD测序数据分析。将新方法和软件应用于大规模SD 测序数据集还有望发现新的SD相关变体和G-G/G-E 互动最终将导致对SD病因的更好理解，从而带来潜在的好处 用于SD预防和治疗。