A Genetic Association Research Statistical Framework

遗传关联研究统计框架

• 批准号: 7429644
• 负责人: ROSS LAZARUS
• 金额: $ 50.42万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-05-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7429644
• 关键词: Address; Adoption; Algorithms; Alleles; Asthma; Bioconductor; Bioinformatics; Cardiovascular Diseases; Clinical; Code; Collaborations; Communities; Complex; Computer software; Count; Data; Databases; Development; Diabetes Mellitus; Disease; Disease Association; Documentation; Education; Educational workshop; Engineering; Ensure; Environment; Environmental Exposure; Experimental Designs; Family; Frequencies; Funding; Genes; Genetic; Genetic Research; Genetic Variation; Genomics; Genotype; Goals; Hand; Harvest; Imagery; Laboratories; Language; Leadership; Learning; Linkage Disequilibrium; Metadata; Methods; Motivation; Output; Pharmaceutical Preparations; Pharmacogenetics; Phenotype; Phylogenetic Analysis; Population Genetics; Production; Productivity; Public Health; Publications; Range; Research; Research Infrastructure; Research Personnel; Research Project Grants; Resources; Role; Sample Size; Services; Solid; Source; Source Code; Standards of Weights and Measures; Statistical Methods; Testing; Training; United States National Institutes of Health; Variant; Visual; Work; base; case control; data structure; design; disease characteristic; experience; field study; file format; gene environment interaction; gene interaction; genetic analysis; genetic association; genetic epidemiology; innovation; insight; novel; open source; programs; research study; skills; tool; trait

DESCRIPTION (provided by applicant): Insight into genetic contributions to common, complex diseases of increasing public health importance such as asthma, diabetes and cardiovascular disease, is growing relatively slowly. Practical access barriers to newer bioinformatic resources and to appropriate statistical methods impede progress. Leveraging the existing R language and Bioconductor framework, an integrated suite of applications supporting activities common to complex disease association research will be created, directly addressing those barriers. Specific aims include software support for importing experimental data and genomic annotation; methods for statistical power calculations and for selecting maximally informative subsets of markers during experimental design; methods for visualizing and summarizing experimental results; established and recently developed methods supporting statistical inference on single markers, multiple markers and on the epistatic and gene by environment interactions characteristic of these diseases and needed for emerging fields of study such as pharmacogenetics. These deliverables will share a coherent user interface and common data structures, and will be accompanied by tutorials, example code and data, introductory and advanced documentation for users, and detailed source code and technical documentation for methods developers. The resulting packages will be widely distributed through the existing Bioconductor network and through the I(2)B(2) NCBC, as an immediately usable, open-source, interoperable and flexible genetic association experimental design and analysis software suite. Method developers will gain access to an integrated framework and a user base for building and distributing their work. Researchers will gain free and ready access to an increasingly rich range of bioinformatic resources and new statistical methods for genetic association analysis, because many existing practical challenges in their installation and use will have been overcome. Developed in the context of airways disease, the software will be immediately and directly applicable to genetic research in other common, complex disorders.

描述（由申请人提供）：对哮喘、糖尿病和心血管疾病等对公共卫生日益重要的常见、复杂疾病的遗传贡献的研究进展相对缓慢。获取新的生物信息学资源和适当的统计方法的实际障碍阻碍了进展。利用现有的R语言和Bioconductor框架，将创建一套集成的应用程序，支持复杂疾病关联研究的常见活动，直接解决这些障碍。具体目标包括为导入实验数据和基因组注释提供软件支持；在实验设计中，统计功率计算和选择信息量最大的标记子集的方法；实验结果的可视化和总结方法；已建立和最近开发的方法支持对这些疾病特征的单标记，多标记以及环境相互作用的上位性和基因进行统计推断，并且需要药物遗传学等新兴研究领域。这些可交付成果将共享一个一致的用户界面和通用的数据结构，并将伴随着教程、示例代码和数据、用户的介绍和高级文档，以及方法开发人员的详细源代码和技术文档。由此产生的软件包将通过现有的Bioconductor网络和I(2)B(2) NCBC广泛分发，作为立即可用的、开源的、可互操作的、灵活的遗传关联实验设计和分析软件套件。方法开发人员将获得访问集成框架和用户基础的权限，以构建和分发他们的工作。研究人员将获得免费和随时可用的日益丰富的生物信息学资源和用于遗传关联分析的新的统计方法，因为在它们的安装和使用方面的许多现有的实际挑战将被克服。该软件是在呼吸道疾病的背景下开发的，将立即直接适用于其他常见、复杂疾病的基因研究。

项目成果

A marginal mixture model for selecting differentially expressed genes across two types of tissue samples.

用于选择两种类型组织样本中差异表达基因的边际混合模型。

• DOI: 10.2202/1557-4679.1093
• 发表时间: 2008
• 期刊: The international journal of biostatistics
• 作者: Qiu,Weiliang;He,Wenqing;Wang,Xiaogang;Lazarus,Ross
• 通讯作者: Lazarus,Ross

Interpreting genetics of gene expression: integrative architecture in Bioconductor.

解释基因表达的遗传学：Bioconductor 中的整合架构。

• DOI: 10.1142/9789812836939_0036
• 发表时间: 2009
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Carey,VJ;Gentleman,R
• 通讯作者: Gentleman,R

Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes.

• DOI: 10.1186/1752-0509-5-89
• 发表时间: 2011-05-31
• 期刊: BMC systems biology
• 作者: Chu JH;Lazarus R;Carey VJ;Raby BA
• 通讯作者: Raby BA

Genetic adaptation of the antibacterial human innate immunity network.

• DOI: 10.1186/1471-2148-11-202
• 发表时间: 2011-07-11
• 期刊: BMC evolutionary biology
• 影响因子: 3.4
• 作者: Casals F;Sikora M;Laayouni H;Montanucci L;Muntasell A;Lazarus R;Calafell F;Awadalla P;Netea MG;Bertranpetit J
• 通讯作者: Bertranpetit J