Statistical Methods--Genes Associated with Diabetes

描述(申请人提供)：这项研究的目标是识别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，数以百万计的人受到不同类型糖尿病的影响。识别疾病相关基因是阐明疾病病因学的关键。最近，微阵列已被用于在基因组规模上研究糖尿病。此外，几个用于糖尿病研究的现有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。具体目的是：(I)识别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)确定与糖尿病发展相关的具有不同共表达模式的基因对。我们还将确定与大量其他基因形成差异共表达模式的基因，(Iii)确定差异基因表达和差异基因-基因共表达模式之间的协调。我们将在评估和比较现有方法的基础上，开发新的统计方法。这项研究将首先通过模拟研究验证统计方法，然后将其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现这些统计方法。这些计算机程序将被测试、记录并免费分发给科学界。这项研究的目的是识别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，这些数据集已被收集用于在基因组规模上研究糖尿病。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。

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.