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Abstract Details

Activity Number: 495
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #306759
Title: Identifying Gene Regulatory Networks Using Multiple-Tissue EQTL Data
Author(s): Jichun Xie*+ and Tony Cai and Hongzhe Li and Weidong Liu
Companies: University of Pennsylvania School of Medicine and University of Pennsylvania and University of Pennsylvania and Shanghai Jiao Tong University
Address: 423 Guardian Drive, Philadelphia, PA, 19104-6021, United States
Keywords: conditional dependency ; eQTL data ; multiple tissue

Precision matrices reveal conditional dependency structures between multivariate normal random variables. Some heterogenous data share similar dependency structures among different groups. One of the examples is the multiple-tissue gene expression data. Evidence has shown that the gene regulatory networks share similarities across different tissues and yet have tissue-specific interaction mechanism. To improve the estimation efficiency, we de velop a joint estimation method to infer precision matrices. Theoretical and Numerical studies show that our method has improved estimation efficiency by the scale $\sqrt{log(K)/K}$, where $K$ is the number of the tissues. Our method also has better support recovery results compared with seperate estimation methods as well as other competing joint estimation methods. We further apply our method to the mouse gene expression data and obtain the gene regulatory networks for multiple mouse tissues.

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