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Title: New Insights into the Genetic Control of Gene Expression using a Bayesian Multi-tissue Approach}
Article:
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.
Introduction:
A number of integrated transcriptional profiling and linkage mapping studies have been published to date[1]–[8], however 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]. In addition, intersecting eQTL lists with similar False Discovery Rates( FDR) is likely to be a conservative approach and is potentially affected by variability between tissues[11]. A number of studies have investigated whether the cis- and/or trans-acting genetic control of gene expression is conserved across tissues, i. e. whether there is evidence of tissue-consistent 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). 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. This is likely a consequence of their strong genetic effects, levels of expression, high heritability or a combination of these factors, but could also result from a lack of power to detect smaller effects, typically trans-eQTLs. Cis-acting genetic variation can have important pathophysiological consequences at the level of the whole organism[5],[8],[13], likely reflecting modifications of key regulatory functions across tissues and cell-types. However, studies in plants have shown that cis-eQTLs can also exhibit strong tissue-specific dependency, and polymorphisms in cis-regulatory regions may affect gene transcription exclusively in a few crucial cell types[14],[15]. Identification of trans-eQTLs within and across tissues or cell lines is statistically challenging, and it is plausible that the relative paucity of shared trans-regulatory effects discovered to date is mainly due to their small genetic effect and higher FDR[5],[11],[16]. A review of the eQTL literature data reveals that many observations of trans-regulated gene expression are often contradictory[10], and detection of trans-eQTL hotspots can be affected by the permutation strategy used to assess their statistical significance[17]. Tissue-specific transcriptional regulation has been reported for differentially expressed genes and for regulatory genetic hotspots[18] or region-specific regulatory networks and genes regulated by trans-acting elements[19]. However, using inbred mouse lines, Bystrykh et al.[9] observed a substantial proportion of trans-eQTLs with identical genomic location in brain and stem cell tissues in the same animals. These discrepant observations highlight the uncertainty concerning cross-tissue conservation of cis and trans-acting genetic regulation. Although global analysis of mRNA expression in multiple tissues in now feasible[20],[21], few methods have addressed the problem of jointly performing an analysis of gene expression across tissues combined with multivariate analysis of a large number of genetic control points. Statistical methods for multiple QTL analyses have followed current developments of sparse regression methods designed to address the large p, small n paradigm, i. e., set-ups where the number of potential covariates( here, the genetic markers) is( much) larger than the number of available samples. In this context, two families of methods can broadly be distinguished: regularised multivariate regression approaches such as the Lasso[22], where the residual sum of squares is penalised and regression coefficients are shrunk towards zero, or methods using a variable selection formulation, typically implemented in a Bayesian framework. Regularised regressions are focussed on delivering overall good predictive ability rather than interpretability of the effect of a few key regressors, whereas variable selection methods are constructed to explore a large model space, seeking a set of well supported models, each including only a small number of interpretable regressors. In the eQTL context, regularisation methods have been proposed for single[23] and multiple phenotypes[24]. However, interpretability of the genetic effects is important as well as an adequate characterisation of uncertainty, and the Bayesian variable selection( BVS) approach that we and others[25]–[27] have adopted offers additional insights. In this paper we have implemented a new Bayesian variable selection method for multivariate mapping of single or multiple outcomes, and show an application to uncover simultaneous cis and trans-regulation of gene expression at the level of individual tissues as well as across tissues. We show that by implementing a computationally challenging multi-locus strategy, our model can identify substantially more cis- and trans-effects than commonly used single marker eQTL methods for the same FDR level and that it permits efficient identification of genetic regulation across multiple tissue types. These biological findings are complemented by a simulation study where our method is compared to classical and a recently proposed multi-locus penalised regression method and shown to have increased power.
Discussion:
We have shown that our Sparse Bayesian Regression models coupled with an efficient computational algorithm( Evolutionary Stochastic Search, ESS hereafter) provide significant advantages over other methods in eQTL mapping within and across multiple tissues. A key feature of the proposed approach is its ability to uncover polygenic regulation of gene expression, with greater power to identify secondary trans-eQTLs than traditional methods. Notably, while the standard univariate approaches tested found no significant polygenic control in any tissue, the SBR model revealed a set of 140 probe sets that mapped to two or more distinct eQTLs in at least one tissue( Table S4). To illustrate the power of our method for capturing complex genetic regulation of gene expression, we report a new example of co-existing cis- and trans-acting eQTLs for the Hopx gene in the heart, which was not detected by conventional approaches and that we validated experimentally( Figure 3). We extended the SBR model to accommodate multiple phenotypic responses such as expression profiles in multiple tissues, and showed increased power to discover pleiotropic genetic regulation of gene expression, that was unappreciated by single tissue analyses or other multivariate approaches. We showed that the SBMR model yielded >5 fold increase in the number of common eQTLs when compared with the SBR model. We identified a set of 277 cis-eQTLs using SBMR, which was replicated by the Hotelling's T2-test analysis, highlighting the increased power provided by multivariate approaches when compared with intersection of lists of eQTLs mapped within individual tissues. An additional major advantage of the SBMR approach is its ability to assess systemic genetic effects, as illustrated for the Cd36[31] and the Ascl3 genes( Figure 1). In the latter case, we confirmed systemic cis-regulation, previously reported in kidney, liver, skeletal muscle, fat[33], and suggest a role for an additional trans-locus that explains a substantial amount of the correlation in gene expression between tissues. Detection of systemic genetic effects may shed light on tissues that are biologically active for specific disease processes at the organism level which otherwise would not be appreciated. For detection of common polygenic and trans-acting regulation of gene expression, SBMR outperformed the multivariate Hotelling's T2-test. The extra power gained by SMBR in the real and simulated data sets relative to that of the Hotelling's T2-test is attributable to its full multivariate modelling of both predictors( markers) and responses( expression profiles in multiple tissues), whereas the Hotelling's T2-test is multivariate only on the responses. In addition, our simulations show that SBMR is more competitive than the multivariate Lasso-based algorithm GFlasso[24], which is specifically designed to borrow information across correlated phenotypes( Figure 2). Overall, this highlights the advantage of performing a powerful multivariate analysis of genetic and genomic data to uncover complex regulatory mechanisms at the systems-level. Computationally, our ESS algorithm implemented for SBR and SBMR is more efficient than other Bayesian variable selection methods since we sample just the vectors of selection indicators( see Materials and Methods), while the remaining parameters( i. e., size of the genetic effects and correlation structure) are analytically integrated out, allowing a fast mixing of the MCMC. When these latter parameters are of interest, they can be simulated in a post processing analysis. Moreover, using multiple chains run in parallel with search moves inspired from genetic algorithms, we significantly improve the exploration of good combinations of markers that predict the variation of gene expression. The software to perform SBR and SBMR analyses is freely available from the authors as a Matlab program, and we have demonstrated that it can scale efficiently to search over tens of thousands of predictors( L. B., S. R., unpublished data). Our approach is quite flexible and the underlying linear regression model as well as the model search could be extended to handle more complex scenarios, including human data and other genetic study designs. This versatility is currently being implemented in our software, enabling data from different sources to be analysed, for example with applications to gene expression and epigenetic profiles, or to deal with binary outcomes and quantitative predictors in a similar manner, as well as extending the search space to include epistatic interactions within the predictor subsets. One important additional benefit of our Bayesian variable selection approach is that, besides providing a best visited model with a list of eQTLs, it also addresses the inherent uncertainty in finding best predictor subsets. Looking marginally at the role of each marker, we can average over a set of well supported models to assess the overall marginal contribution of each eQTL to explain gene expression variability. Moreover, we can use the same set of models to perform further post-processing analysis, for example to focus on eQTLs with noticeable biological effects in all tissues( see Text S1 for illustrative examples). In conclusion, we have shown that the SBR and SBMR approaches have distinctive features and perform significantly better than the existing eQTL mapping methods tested. The proposed modelling approaches provide a general and powerful framework for investigating complex genetic regulatory mechanisms controlling gene expression at the systems-level.
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 . However , an important limitation of the current approaches consists in their sole application to individual tissues , thus ignoring information shared across different tissues . To uncover complex genetic regulatory mechanisms controlling gene expression at the whole organism's level , it is essential to develop appropriate analytical methods for the analysis of genome-wide genetic polymorphisms and gene expression profiles simultaneously in multiple tissues . This paper presents a novel , fully integrated Bayesian approach for mapping the genetic components of gene expression within and across multiple tissues . In addition to increased power and enhanced mapping resolution when compared with traditional approaches , our model directly provides information on potential systemic effects on transcriptional profiles and co-existing local ( cis ) and distant ( trans ) genetic control of gene expression . We also discuss the possibility to extend our approach for the analysis of different phenotypes , and other study designs , thus providing an integrated computational tool to explore the genetic control underlying transcriptional regulation at the systems-level , beyond the single tissue resolution .