Statistical Methods--Genes Associated with Diabetes

统计方法——与糖尿病相关的基因

• 批准号: 7073913
• 负责人: YINGLEI LAI
• 金额: $ 14.65万
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7073913
• 关键词: animal data; computer program /software; diabetes mellitus genetics; disease /disorder classification; disease /disorder etiology; gene expression; gene interaction; genetic registry /resource /referral center; genetic susceptibility; human data; insulin dependent diabetes mellitus; microarray technology; molecular biology information system; noninsulin dependent diabetes mellitus; statistics /biometry

DESCRIPTION (provided by applicant): The objectives of this study are to identify genes associated with diabetes development and to understand interactions among these genes during the progress of disease stages. Diabetes, such as type 1 diabetes or type 2 diabetes, is a complex disease. Each year, millions of people are affected by different types of diabetes. Identifying disease related genes is crucial to elucidate disease etiology. Recently, microarrays have been used to study diabetes at a genomic scale. Furthermore, several existing microarray gene expression data sets for diabetes studies contain disease development information. We will conduct statistical analyses of these existing microarray gene expression data sets. The specific aims are to: (i) Identify genes with differential expressions associated with diabetes development. We will also estimate the proportion of these genes in a microarray data set. (ii) Identify pairs of genes with differential co-expression patterns associated with diabetes development. We will also identify genes that form differential co-expression patterns with a significant number of other genes, (iii) Identify coordination between differential gene expressions and differential gene-gene co-expression patterns. We will develop novel statistical methods based on the evaluations and comparisons of existing methods. The statistical methods to be developed in this study will be first validated through simulation studies and then applied to microarray gene expression data sets for diabetes studies. We will develop R-package based computer programs to implement these statistical methods. These computer programs will be tested, documented, and freely distributed to the scientific community. The objectives of this study are to identify genes associated with diabetes development and to understand interactions among these genes during the progress of disease stages. Statistical methods will be developed to analyze existing microarray gene expression data sets, which have been collected to study diabetes at a genomic scale. R-package based computer programs will be developed, tested, documented, and freely distributed to the scientific community.

描述（由申请人提供）：本研究的目的是鉴定与糖尿病发展相关的基因，并了解这些基因在疾病阶段进展过程中的相互作用。糖尿病，如 1 型糖尿病或 2 型糖尿病，是一种复杂的疾病。每年，数百万人受到不同类型的糖尿病的影响。识别疾病相关基因对于阐明疾病病因至关重要。最近，微阵列已被用于在基因组规模上研究糖尿病。此外，一些现有的用于糖尿病研究的微阵列基因表达数据集包含疾病发展信息。我们将对这些现有的微阵列基因表达数据集进行统计分析。具体目标是： (i) 鉴定与糖尿病发展相关的差异表达基因。我们还将估计这些基因在微阵列数据集中的比例。 (ii) 鉴定具有与糖尿病发展相关的差异共表达模式的基因对。我们还将鉴定与大量其他基因形成差异共表达模式的基因，（iii）鉴定差异基因表达和差异基因-基因共表达模式之间的协调。我们将在对现有方法的评估和比较的基础上开发新的统计方法。本研究中开发的统计方法将首先通过模拟研究进行验证，然后应用于糖尿病研究的微阵列基因表达数据集。我们将开发基于 R 包的计算机程序来实施这些统计方法。这些计算机程序将经过测试、记录并免费分发给科学界。本研究的目的是确定与糖尿病发展相关的基因，并了解这些基因在疾病阶段进展过程中的相互作用。将开发统计方法来分析现有的微阵列基因表达数据集，这些数据集是为了在基因组规模上研究糖尿病而收集的。基于 R 包的计算机程序将被开发、测试、记录并免费分发给科学界。