Statistical Methods for Identifying Genes Associated with Diabetes Development

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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.