Genome Analysis: Data Accuracy Haplotyping and Mapping

基因组分析：数据准确性单倍型分析和作图

• 批准号: 7906465
• 负责人: MATTHEW STEPHENS
• 金额: $ 42.81万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-05 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7906465
• 关键词: Arts; Biological; Biological databases; Cations; Code; Complement; Complex; Computer software; Data; Data Analyses; Development; Diagnostic tests; Disease; Exons; Family; Funding; Genes; Genetic; Genome; Genomics; Genotype; Individual; Introns; Link; Maps; Medical; Methods; Modeling; Modification; Molecular; Parents; Pathway interactions; Phenotype; Population; Research Design; Sampling; Statistical Methods; Structure; Testing; United States National Institutes of Health; Variant; Work; base; case control; design; disorder prevention; effective therapy; falls; genetic pedigree; genetic variant; genome wide association study; genome-wide; grandparent; graphical user interface; improved; insight; interest; offspring; tool; user friendly software

DESCRIPTION (provided by applicant): This project will develop a comprehensive array of new statistical methods for analyzing genome-wide association studies, and will apply these methods, and other appropriate methods, to perform in-depth analyses of NIH-funded association studies that attempt to unravel the genetic basis of common complex diseases. The overall objective is for the work to produce, and enable others to produce, discoveries and insights that aid the development of medical diagnostic tests, more effective therapies, and, ultimately, prevention of disease. Our focus will be primarily on developing new Bayesian statistical methods, which complement and improve on existing analysis approaches. The specific aims include the refinement of existing Bayesian statistical approaches to assessing correlation between genotype and quantitative phenotype to improve their robustness to deviations from underlying modeling assumptions; extension of these methods to allow analysis of binary (case/control) phenotypes, and family-based designs; and modification of these approaches to incorporate relevant biological prior information (e.g.~information on molecular pathways). The result will be a suite of tools, implemented in user-friendly software, for performing both single-marker and multi-marker analyses for many of the most commonly-used association study designs, including both quantitative and binary (case/control) phenotypes for population samples and parent-offspring trios.

描述（由申请人提供）：该项目将开发一系列新的统计方法，用于分析全基因组关联研究，并将应用这些方法和其他适当的方法，以对NIH资助的关联研究进行深入分析，以试图揭示常见复杂疾病的遗传基础。 总体目的是使工作能够生产并使其他人能够产生，发现和见解，以帮助开发医学诊断测试，更有效的疗法以及最终预防疾病。我们的重点将主要是开发新的贝叶斯统计方法，这些方法补充并改进了现有的分析方法。具体目的包括改进现有的贝叶斯统计方法，以评估基因型和定量表型之间的相关性，以提高其对基本建模假设的偏差的稳健性；这些方法的扩展，以分析二进制（情况/控制）表型和基于家庭的设计；并修改这些方法以结合相关的生物学先验信息（例如〜关于分子途径的信息）。结果将是一套用户友好软件实现的工具，用于为许多最常用的关联研究设计进行单标记和多标记分析，包括针对人群样本和父母的定量和二进制（情况/对照）表型，以及父母的样本和父母。