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Bioinformatics Advance Access originally published online on September 28, 2009
Bioinformatics 2009 25(23):3121-3127; doi:10.1093/bioinformatics/btp559
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Integration of heterogeneous expression data sets extends the role of the retinol pathway in diabetes and insulin resistance

Peter J. Park 1,2,3, Sek Won Kong 1,4, Toma Tebaldi 5,6, Weil R. Lai 3, Simon Kasif 1,7,8 and Isaac S. Kohane 1,2,3,*

1 Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, 2 Brigham and Women's Hospital, 3 Center of Biomedical Informatics, Harvard Medical School, 4 Department of Cardiology, Children's Hospital, Boston, MA 02115, USA, 5 Centre for Integrative Biology, 6 Department of Information Engineering and Computer Science, University of Trento, Italy, 7 Center for Advanced Genomic Technology, Boston University and 8 Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA

* To whom correspondence should be addressed.


  Abstract

Motivation: Type 2 diabetes is a chronic metabolic disease that involves both environmental and genetic factors. To understand the genetics of type 2 diabetes and insulin resistance, the DIabetes Genome Anatomy Project (DGAP) was launched to profile gene expression in a variety of related animal models and human subjects. We asked whether these heterogeneous models can be integrated to provide consistent and robust biological insights into the biology of insulin resistance.

Results: We perform integrative analysis of the 16 DGAP data sets that span multiple tissues, conditions, array types, laboratories, species, genetic backgrounds and study designs. For each data set, we identify differentially expressed genes compared with control. Then, for the combined data, we rank genes according to the frequency with which they were found to be statistically significant across data sets. This analysis reveals RetSat as a widely shared component of mechanisms involved in insulin resistance and sensitivity and adds to the growing importance of the retinol pathway in diabetes, adipogenesis and insulin resistance. Top candidates obtained from our analysis have been confirmed in recent laboratory studies.

Contact: Isaac_kohane{at}harvard.edu

Associate Editor: Joaquin Dopazo

Received on June 14, 2009; revised on September 7, 2009; accepted on September 22, 2009

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