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. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21DK075004-02
Application #
7230066
Study Section
Special Emphasis Panel (ZRG1-GGG-J (10))
Program Officer
Blondel, Olivier
Project Start
2006-06-01
Project End
2010-05-31
Budget Start
2007-06-01
Budget End
2010-05-31
Support Year
2
Fiscal Year
2007
Total Cost
$143,723
Indirect Cost
Name
George Washington University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
043990498
City
Washington
State
DC
Country
United States
Zip Code
20052
Lai, Yinglei (2011) On the adaptive partition approach to the detection of multiple change-points. PLoS One 6:e19754
Markitsis, Anastasios; Lai, Yinglei (2010) A censored beta mixture model for the estimation of the proportion of non-differentially expressed genes. Bioinformatics 26:640-6
Lai, Yinglei (2010) Differential expression analysis of Digital Gene Expression data: RNA-tag filtering, comparison of t-type tests and their genome-wide co-expression based adjustments. Int J Bioinform Res Appl 6:353-65
Lai, Yinglei (2008) Genome-wide co-expression based prediction of differential expressions. Bioinformatics 24:666-73
Hua, Dong; Lai, Yinglei (2007) An ensemble approach to microarray data-based gene prioritization after missing value imputation. Bioinformatics 23:747-54
Lai, Yinglei; Adam, Bao-ling; Podolsky, Robert et al. (2007) A mixture model approach to the tests of concordance and discordance between two large-scale experiments with two-sample groups. Bioinformatics 23:1243-50
Lai, Yinglei (2007) Conservative adjustment of permutation p-values when the number of permutations is limited. Int J Bioinform Res Appl 3:536-46
Lai, Yinglei (2007) A moment-based method for estimating the proportion of true null hypotheses and its application to microarray gene expression data. Biostatistics 8:744-55
Lai, Yinglei (2006) On the identification of differentially expressed genes: improving the generalized F-statistics for Affymetrix microarray gene expression data. Comput Biol Chem 30:321-6