Statistical methods for genetic data

遗传数据的统计方法

• 批准号: 8103072
• 负责人: JOHN D STOREY
• 金额: $ 29.2万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-19 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8103072
• 关键词: Accounting; Algorithms; Bioinformatics; Biological; Biology; Cells; Computer software; DNA Microarray Chip; Data; Data Set; Dependence; Disease; Etiology; Experimental Designs; Gene Expression; Genes; Genetic; Genome; Genomics; Genotype; Goals; Grant; Health; Inherited; Knowledge; Measurement; Measures; Medicine; Methodology; Methods; Modeling; Molecular Biology; Molecular Genetics; Pattern; Play; Procedures; Programming Languages; Randomized; Research; Role; Source; Source Code; Statistical Methods; Techniques; Technology; Testing; Time; Update; Variant; Work; Writing; analytical tool; base; functional genomics; genome-wide; graphical user interface; human disease; interest; novel; programs; research study; web site

DESCRIPTION (provided by applicant): Studies carried out at the genome-wide level now play a central role in modern biology and medicine. There is a substantial need for new statistical methods that can be applied in these studies. The overall goal of the proposed research is to develop statistical methods and software useful in understanding genomic data. The particular focus is in functional genomics, where data from DNA microarrays and large-scale genotyping can be used to study how large numbers of genes work to accomplish various functional roles. New statistical methods for these high-dimensional data sets will be developed, where biological knowledge is taken into account whenever possible. Genetics plays a role in almost every human disease, whether the disease itself is inherited or the disease is associated with a substantial change in the activity of genes. This work is aimed at contributing to the understanding of the molecular biology and genetic basis of human disease by providing analytical tools for genomics studies. The particular focus of this competitive renewal is to develop a broad framework for modeling the inter-dependence of expression levels among genes as manifested in differential expression variation and their regulatory networks, by (i) borrowing strength across the genes' expression measurements through noel multivariate models, (ii) utilizing multiple data types such as large-scale genotyping and gene expression to build a framework for dissecting causation from correlation, and (iii) rethinking randomization and experimental design as it can be utilized in this high-dimensional genomics setting. From this work, the aim is to provide methodology that allows one to characterize gene expression variation in terms of common sources of variation among genes as well as specific causal relationships among pairs of genes. PUBLIC HEALTH RELEVANCE: The primary mechanism by which information in the genome is transferred into our cells is through gene expression. It has been shown that changes in gene expression are associated with many important human diseases. Technologies that measure the expression of thousands of gene simultaneously are now in widespread use. This grant will aid in the understanding of the role of gene expression in human diseases by providing quantitative methods for understanding how expression variation is functioning on a large-scale.

描述(由申请人提供)：在全基因组水平上进行的研究现在在现代生物学和医学中发挥着核心作用。对可应用于这些研究的新的统计方法有很大的需求。拟议研究的总体目标是开发有助于理解基因组数据的统计方法和软件。特别关注的是功能基因组学，来自DNA微阵列和大规模基因分型的数据可以用来研究大量基因是如何发挥作用来完成各种功能角色的。将为这些高维数据集开发新的统计方法，其中尽可能考虑到生物学知识。遗传学在几乎每一种人类疾病中都发挥着作用，无论疾病本身是遗传的，还是与基因活动的实质性变化有关。这项工作旨在通过为基因组学研究提供分析工具，帮助理解人类疾病的分子生物学和遗传学基础。 这次竞争性更新的特别重点是建立一个广泛的框架，用于模拟差异表达变异及其调控网络中表现出的基因之间表达水平的相互依赖，方法是(I)通过NOEL多变量模型借用基因表达测量的力量，(Ii)利用多种数据类型，如大规模基因分型和基因表达，建立一个从相关性中剖析因果关系的框架，以及(Iii)重新思考随机化和实验设计，因为它可以在这种高维基因组学环境中使用。通过这项工作，目的是提供一种方法，使人们能够根据基因之间的共同变异来源以及基因对之间的特定因果关系来表征基因表达的差异。与公共健康相关：基因组中的信息转移到细胞中的主要机制是通过基因表达。已有研究表明，基因表达的变化与许多重要的人类疾病有关。同时测量数千个基因表达的技术现在得到了广泛的应用。这笔赠款将通过提供定量方法来理解表达变异如何在大规模发挥作用，从而帮助理解基因表达在人类疾病中的作用。