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.
Highlights
A number of integrated transcriptional profiling and linkage mapping studies have been published to date [1,2,3,4,5,6,7,8], most of these studies were restricted to analysis in single tissues or cell types
Integrated analysis of genome-wide genetic polymorphisms and gene expression profiles from different tissues or cell types has been highly successful in identifying genes modulating complex phenotypes in animal models and humans
An important limitation of the current approaches consists in their sole application to individual tissues, ignoring information shared across different tissues
Summary
Background A number of integrated transcriptional profiling and linkage mapping studies have been published to date [1,2,3,4,5,6,7,8], most of these studies were restricted to analysis in single tissues or cell types. Even when expression profiles are available from multiple tissues, expression QTL (eQTL) mapping is usually carried out at the level of the individual tissue and the lists of significant eQTLs are subsequently compared across experiments [3,8,9] One limitation of this approach is that different false positive (and/or false negative) rates across studies inflate the discrepancies between the lists of eQTLs [10]. EQTL studies in the rat [3,11], mouse [12] and in humans [8] have shown that detection of eQTLs with a systemic effect (i.e., detected across multiple tissues) is strongly biased towards cis-eQTLs. By a slight abuse of terminology, here, we refer for simplicity to tissue-consistent eQTL as ‘‘pleiotropic eQTL’’, i.e. when an eQTL for the same probe set expression is detected across multiple tissues (not necessarily exerting multiple cellular functions). Studies in plants have shown that ciseQTLs can exhibit strong tissue-specific dependency, and polymorphisms in cis-regulatory regions may affect gene transcription exclusively in a few crucial cell types [14,15]
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