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Bioinformatics Advance Access published online on October 5, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm483
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Evaluation and Integration of 49 Genome-wide Experiments and the Prediction of Previously Unknown Obesity-related Genes

Sangeeta B. English 1 and Atul J. Butte 1,*

1Stanford Medical Informatics, Department of Medicine and Department of Pediatrics, Stanford University School of Medicine, and Lucile Packard Children's Hospital, Stanford, CA 94305 USA

*To whom correspondence should be addressed. Atul J. Butte, E-mail: abutte{at}stanford.edu


  Abstract

Motivation: Genome-wide experiments only rarely show resounding success in yielding genes associated with complex polygenic disorders. We evaluate 49 obesity-related genome-wide experiments with publicly-available findings, including microarray, genetics, proteomics and gene knock-down from human, mouse, rat and worm, in terms of their ability to rediscover a comprehensive set of genes previously found to be causally associated or having variants associated with obesity.

Results: Individual experiments show poor predictive ability for rediscovering known obesity-associated genes. We show that intersecting the results of experiments significantly improves the sensitivity, specificity and precision of the prediction of obesity-associated genes. We create an integrative model that statistically significantly outperforms all 49 individual genome-wide experiments. We find that genes known to be associated with obesity are significantly implicated in more obesity-related experiments and use this to provide a list of genes that we predict to have the highest likelihood of association for obesity. The approach described here can include any number and type of genome-wide experiments and might be useful for other complex polygenic disorders as well.

Contact: abutte{at}stanford.edu

Supplementary Information: Available Online and at http://buttelab.stanford.edu/doku.php?id=public:obesityintegration

Associate Editor: Dr. Trey Ideker

Received on July 13, 2007; revised on August 29, 2007; accepted on September 21, 2007

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