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New insights into the genetic control of gene expression using a Bayesian multi-tissue approach

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Item Type:Article
Title:New insights into the genetic control of gene expression using a Bayesian multi-tissue approach
Creators Name:Petretto, E. and Bottolo, L. and Langley, S.R. and Heinig, M. and McDermott-Roe, C. and Sarwar, R. and Pravenec, M. and Huebner, N. and Aitman, T.J. and Cook, S.A. and Richardson, S.
Abstract:The majority of expression quantitative trait locus (eQTL) studies have been carried out in single tissues or cell types, using methods that ignore information shared across tissues. Although global analysis of RNA expression in multiple tissues is now feasible, few integrated statistical frameworks for joint analysis of gene expression across tissues combined with simultaneous analysis of multiple genetic variants have been developed to date. Here, we propose Sparse Bayesian Regression models for mapping eQTLs within individual tissues and simultaneously across tissues. Testing these on a set of 2,000 genes in four tissues, we demonstrate that our methods are more powerful than traditional approaches in revealing the true complexity of the eQTL landscape at the systems-level. Highlighting the power of our method, we identified a two-eQTL model (cis/trans) for the Hopx gene that was experimentally validated and was not detected by conventional approaches. We showed common genetic regulation of gene expression across four tissues for approximately 27% of transcripts, providing >5 fold increase in eQTLs detection when compared with single tissue analyses at 5% FDR level. These findings provide a new opportunity to uncover complex genetic regulatory mechanisms controlling global gene expression while the generality of our modelling approach makes it adaptable to other model systems and humans, with broad application to analysis of multiple intermediate and whole-body phenotypes.
Keywords:Adipose Tissue, Adrenal Glands, Algorithms, Bayes Theorem, Gene Expression Profiling, Gene Expression Regulation, Gene Regulatory Networks, Heart Ventricles, Kidney, Genetic Models, Quantitative Trait Loci, Regression Analysis, Reproducibility of Results, Sensitivity and Specificity, Animals
Source:PLoS Computational Biology
ISSN:1553-734X
Publisher:Public Library of Science (U.S.A.)
Volume:6
Number:4
Page Range:e1000737
Date:8 April 2010
Official Publication:https://doi.org/10.1371/journal.pcbi.1000737
PubMed:View item in PubMed

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