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

Petretto, Enrico, Bottolo, Leonardo, Langley, Sarah R. ORCID: https://orcid.org/0000-0003-4419-476X, Heinig, Matthias, McDermott-Roe, Chris, Sarwar, Rizwan, Pravenec, Michal, Hubner, Norbert, Aitman, Timothy J., Cook, Stuart A. and Richardson, Sylvia 2010. New insights into the genetic control of gene expression using a Bayesian multi-tissue approach. PLoS Computational Biology 10.1371/journal.pcbi.1000737

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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 ∼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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Biosciences
Publisher:	Public Library of Science
ISSN:	1553-7358
Last Modified:	05 Sep 2023 13:15
URI:	https://orca.cardiff.ac.uk/id/eprint/162137

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