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: 10166816
• 负责人: Qing Lu
• 金额: $ 41.3万
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10166816
• 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; polysubstance use; 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调查深层次的遗传变异在SD中的作用。 尽管正在进行的测序研究为解开有助于 SD数据维度高，变异频率低，SD病因复杂，SD异质性强 表型带来了巨大的分析和计算挑战。发展强健有力 方法和计算效率高的软件将解决SD测序数据分析中的挑战 并增强我们识别与SD相关的新变种的能力。这个应用程序的目标是开发新的 用于设计和分析基于人群和基于家庭的测序数据的方法和软件 单一或多个表型，并在合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)交互作用。基于初步模拟 结果，我们的中心假设是，提出的方法比现有的方法在计算上更有效 方法，并获得了更健壮和强大的各种表型表现。计划中的 具体目标是：1)发展一种新的非参数方法，用于序列数据的设计和分析 具有一个或多个SD表型；2)建立高维G-G/G-E相互作用的联合-U方法 利用SD测序数据进行分析；3)提出了一种基于家族相似性的SD测序方法 数据分析，说明人口分层和家庭中丰富的稀有变异；以及4)促进 通过软件开发和协作使用新方法。拟议的研究将是 由一位早期的新研究员(NIDA K01奖获得者)发起，他组建了一个科学家团队，与 在统计遗传学、生物信息学/软件开发、SD流行病学、行为遗传学、 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，并推动方法和软件开发 用于SD测序数据分析。新方法和新软件在大规模标测中的应用 测序数据集也为发现新的SD相关变异和G-G/G-E提供了希望 互动，这最终将导致对SD病因的更好理解，从而产生潜在的好处 用于SD的预防和治疗。

项目成果

External validation of non-invasive prediction models for identifying ultrasonography-diagnosed fatty liver disease in a Chinese population.

用于识别中国人群中超声诊断的脂肪肝疾病的非侵入性预测模型的外部验证。

• DOI: 10.1097/md.0000000000007610
• 发表时间: 2017-07
• 期刊: Medicine
• 影响因子: 1.6
• 作者: Shen YN;Yu MX;Gao Q;Li YY;Huang JJ;Sun CM;Qiao N;Zhang HX;Wang H;Lu Q;Wang T
• 通讯作者: Wang T