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.
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.
We first investigated the distribution of the size of the eQTL lists associated with the best SBR model visited, for the transcripts that were below the 5% FDR using Jeffreys' scale of evidence (see Materials and Methods). Consistently across all tissues, ∼16% of all probe sets were under genetic control by one eQTL, although for a small proportion of probe sets (∼3%) multiple control points were detected (Table 1). As expected, adopting more conservative FDR levels the proportion of probe sets with multiple eQTLs decreases significantly (Table S2). The SBR model identified a similar number (or more) of eQTLs compared with the SSM approach, whereas it yielded substantially more eQTLs than QTL Reaper (∼2 fold increase) (Table S3). All methods identified a larger proportion of cis than trans-eQTLs to a varying degree, with enrichment for cis-eQTLs that were commonly detected by all methods (from 72% to 78% across tissues). The SBR outperformed both QTL Reaper and SSM approaches in detecting complex genetic regulation by two or more eQTLs. While QTL Reaper and SSM found no polygenic control in any tissue at 5% FDR, the SBR model revealed that ∼12% of the probe sets that were found to be under genetic control across tissues mapped to two or more distinct eQTLs, delineating a set of 140 polygenic expression traits (Table S4). Similarly, the full two-stage SSM that accounts for epistatic interaction between the primary and secondary locus [28] found no significant polygenic regulation (data not shown). This indicates that while the number of observations in this dataset is relatively small, when compared with either QTL Reaper or SSM approaches, the multi-locus approach implemented by SBR model offers a significant gain in power for eQTL detection, with increased sensitivity to identify polygenic effects at low FDR levels. A detailed comparison between the single tissue analyses using the different approaches is reported in Text S1. Thresholding the Jeffreys' scale of evidence to control the FDR at 5% level, the SBMR model identified 531 transcripts (∼27% of the total) under common genetic regulation in all tissues. We showed evidence of polygenic control by two or more distinct eQTLs for a significant proportion of probe sets (13%) (Table 1), and this fraction remains substantial, albeit decreasing, when more conservative FDR thresholds are considered (Table S2). This reflects the high sensitivity of the SBMR approach to identify potential pleiotropic loci even when their individual effect within each tissue is marginally weak. Although we specified priors on the model size to penalize highly polygenic models, the evidence provided by the data supports common genetic regulation for SBMR models with a high number of eQTLs (Figure S3). A key aspect of the SBMR approach is that it exploits additional information provided by the covariance structure between tissues to find a set of parsimonious models that jointly predict gene expression levels in all tissues. For illustration, Figure 1A–E shows contrasting case examples for Cd36 and Ascl3 genes, where the SBMR confirmed shared genetic effects due to a single cis-eQTL for Cd36 (marker Cd36) and indentifies a new cis- and trans-eQTL genetic model for Ascl3 (markers D1Rat55 and D7Mit8, respectively). The Hotelling' s T2-test found common genetic regulation for Cd36 gene, while it indentified only the cis-eQTL for Ascl3 but failed to detect the secondary trans-acting locus at the 5% FDR level (Figure S4). For comparison with the single tissue analyses using SBR, the cis-effect for Ascl3 is seen in fat, kidney and heart, while the trans-signal is detectable only in fat and heart (Figure S5). Similarly, both QTL Reaper and SSM failed to detect common genetic regulation in cis or trans across tissues for the Ascl3 gene (Figure S4). The proposed SBMR model directly provides information on potential systemic effects of the eQTL (s). To assess the extent by which the detected common eQTLs explain the correlation in gene expression across tissues, we calculated the raw empirical correlation matrix and the posterior mean of the residual correlation matrix given the putative eQTL markers (see Text S1). For both Cd36 and Ascl3 genes, Figure 1 shows that the SBMR approach pinpoints genetic regulators that explain the majority of the correlation structure between tissues as the off-diagonal residual correlations are considerably smaller (Figure 1, panels B, C and F, G, H). The probe set (1386901_at) representing Cd36 gene is derived from sequence in the 3′ untranslated region, that is constitutively deleted from the SHR genome [31], indicating a systemic effect of this cis-eQTL as shown by the posterior mean of the residual correlation matrix (Figure 1C). For the Ascl3 gene, the cis-eQTL alone (marker D1Rat55) is not sufficient to explain the pattern of correlation leading to substantial residual correlation (Figure 1G), whereas inclusion of the trans-locus (D7Mit8) significantly decreased the residual correlation in gene expression across tissues (Figure 1H). This suggests that both the cis- and trans-eQTLs might have a tissue-consistent pleiotropic effect on Ascl3 expression, similarly to Cd36. In addition, within the SBMR model we investigated the effect size of the eQTLs within each tissue by simulating their posterior density in a post processing analysis (see Materials and Methods). For each gene, we report the distribution of the eQTL effect size across tissues showing marked effects for the cis-eQTLs for both Cd36 and Ascl3 genes (Figure 1D and Figure 1I, respectively). In the latter case, despite the smaller effect size for the trans-acting eQTL (Figure 1L), its influence on expression in fat and heart tissues is visible. Taken together, these findings provide evidence of common genetic regulation by one or two loci in the case of Cd36 and Ascl3 genes, respectively. Using post-processing results we were able to uncover the tissue-specific contribution of each eQTL to the pleiotropic model. These illustrative examples show how the SBMR approach makes use of the information shared across tissues by joint modelling of mRNA levels to identify common genetic control points of gene expression across tissues. We investigated whether the common eQTLs mapped within each tissue by the SBR model were detected in the SBMR analysis. Ninety-three transcripts showed genetic regulation by the same eQTL that was independently detected in all tissues by SBR (FDR <5%) (Table S5). In contrast, at similar FDR levels, the SBMR approach identified 531 probe sets under genetic regulation in all tissues, yielding >5 fold increase in the number of common eQTLs when compared with the SBR (Table 1). When contrasting the SBMR approach with QTL Reaper and the SSM, which detected 50 and 59 shared eQTLs, respectively, we found ∼10 times more shared eQTLs at 5% FDR. The SBMR model was also more powerful in detecting shared trans-acting regulation when compared to SBR (or both QTL Reaper and SSM methods). While the SBR approach identified only five transcripts with common regulation by the same trans-eQTL in all tissues, SBMR yielded 42 models (2%) with one trans-acting eQTL, 147 models (7%) with trans-acting eQTLs that are observed in combination with a cis-eQTL and 95 models (∼5%) with multiple trans-eQTLs for the same transcript. This suggests that exploiting the dependence between gene expression levels among tissues greatly enhance identification of common trans-regulators that can be missed when eQTLs are mapped separately within individual tissues. Both SBMR and Hotelling' s T2-test approaches outperformed the single tissue analyses and identified a common set of 373 transcripts with tissue consistent pleiotropic eQTLs, where 277 were cis-eQTL genes (Table S6). This set of eQTLs can be considered as common regulatory control points of gene expression across all tissues that have been replicated using two independent statistical approaches. We compared the performance of the SBMR approach with that of the Hotelling' s T2-test and showed that our method found significantly more polygenic regulation, accounting for ∼13% of all transcripts, as compared with 3% found by the Hotelling' s T2-test. These analyses suggest that while both approaches agree in finding common cis-regulation, the SBMR model had increased power to discover complex genetic regulation of gene expression across tissues when compared with a traditional approach based on analyzing each marker separately (see Text S1 for detailed comparisons). While this increased power could be expected in principle from the use of a multivariate method, we shows that the SBMR algorithm succeeds in exploring effectively the vast space of possible multi-locus models, which is a very challenging task. In addition, we carried out a simulation study to investigate the power of our approach as compared with the Hotelling' s -test and a recently proposed generalised Lasso-type algorithm and associated software, the GFlasso [24], which also considers multi-locus models on the full set of markers and is specifically designed to borrow information across correlated phenotypes. In all simulated cases (see Text S1 for details), the SBMR outperformed both the Hotelling' s -test and the GFlasso algorithm, in particular in the detection of polygenic control by strong and weak eQTL effects (i. e. , one cis-QTL and multiple trans-QTLs) or several weak effects (i. e. , trans-QTLs), Figure 2 and Text S1. To validate eQTL linkages detected by microarray using Bayesian model approaches, we measured mRNA abundance in the BXH/HXB RI strains by quantitative RT-PCR (qRT-PCR) for cis- and trans-acting eQTLs, including complex polygenic effects. We confirmed eQTL findings for strong cis-acting linkages, such as EndoG and Card9 (FDR <5%) (Figure S6), as well as for weaker trans-acting linkages that were observed for two transcription factors, Stat4 and Irf7 (FDR <5%) (Figure S7). Although traditional eQTL mapping approaches identified the trans-linkage for Stat4, they failed to detect the trans-eQTL for Irf7 at the FDR cut-off of 5%. By contrast, our Bayesian approach identified these trans-eQTLs with high confidence (FDR <5%, for both genes), indicating that the data provide convincing evidence for these eQTLs (i. e. , large Bayes Factor [32]) despite a model formulation that gives a priori a larger weight to the null model (i. e. , no genetic control) (see Materials and Methods). As a further example to highlight the power of our method when compared to other approaches, we also validated polygenic regulation for the Hopx gene, where the best model indicated two coexisting eQTLs at markers D14Rat362 (cis) and D2Rat136 (trans), respectively (Figure 3A–B). Notably, both QTL Reaper and the SSM identified the cis-eQTL at marker D14Rat362 but failed to detect significant trans-regulation for Hopx expression (Figure 3C–D). This may reflect the strong cis-acting regulation of Hopx (D14Rat362, fold change = 4. 4, P = 7×10−14, by RT-PCR), which masks the weaker but still detectable trans-eQTL linkage on chromosome 2 (D2Rat136, fold change = 1. 6, P = 0. 02, by RT-PCR) (Figure 3). However, even when we accounted for the effect of the cis-eQTL by using composite interval mapping, the trans-eQTL at D2Rat136 was not significantly detected by standard methods (genome-wide significance, PGW = 0. 316). These results indicate that our Sparse Bayesian model is more powerful for identifying polygenic control relative to the other methods, and that both cis and trans regulation can simultaneously contribute to variation in gene expression levels, emphasizing the complex nature of gene expression regulation in this system.
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.
Here we used data previously described by Petretto et al. 2006 [11] who measured gene expression levels in four tissues in a panel of 29 rat Recombinant Inbred (RI) strains derived from a cross between the Spontaneously Hypertensive Rat (SHR) and the Brown Norway (BN) strains [3]. We used a panel of 770 non-redundant genetic markers; missing values (accounting for ∼3% of all genotypes) were imputed by interpolating the genotype values between flanking markers [34]. We investigated whether substantial genotype imputation (at least 10% of genotypes of each marker) have an effect on the identified eQTLs and found that imputed genotypes accounted for a small fraction (<10%) of the total number of eQTLs mapped within single tissues or across tissues by SBR and SBMR, respectively. Gene expression measurements were standardized across tissues to reduce potential batch effects, by computing expression summary values using the Robust Multichip Average (RMA) algorithm [35] and pooling together Affymetrix GeneChip data for all tissues. We assume that mRNA levels of each gene measured within each tissue are dependent within each strain and thus can be treated as multiple response of the same feature. To show the benefit of the proposed statistical method, in this pilot study we analyzed a subset of 2,000 probe sets from the original set of 15,923 that are common in the four tissues. In particular we chose a set of 2,000 probe sets that have the largest variation across tissues, measured as, where is the standard deviation of the probe set in the tissue. We investigated if the proposed selection criteria introduce some bias in the between-tissue correlation pattern for each pairs of probe sets: when compared with the whole set of probe sets, the correlation structure shows no evidence of alteration with a slight increment of positive correlation among the selected probe sets (Figure S1). Cis- and trans-eQTLs were mapped using standard regression-based approach (Haley-Knott regression) as implemented in the QTL Reaper program (http: //sourceforge. net/projects/qtlreaper/) [29] and using a modified version of the two-stage Sequential Search Method (SSM) for multiple eQTLs [28], without including an additional gene×gene interaction term (Text S1 2. 1). For the probe sets that mapped to unique positions in the genome, we determined which eQTLs were regulated in cis or in trans by defining cis-eQTLs as those with a linkage peak within 10 Mbp of the physical location of the probe set [11]. In order to avoid an inflated number of eQTLs, for each probe set we investigated the genetic control point (s) and, within a 5 cM window, we removed redundant eQTLs which may result from linkage of expression values to multiple adjacent markers, as previously described [3]. Hotelling' s -test [36], the multivariate extension of the t-test, was used to detect linkage between each marker and the level of gene expression in the four tissues simultaneously. In each independent two-sample test, we also checked the homogeneity of the covariance matrices between the two groups applying the Box' s statistics [36] with significant level equal to 0. 05. For all non-Bayesian methods, to account for multiple testing of the number of expression traits, we calculated the FDR using the q-value approach [37]. Here we are using a Bayesian variable selection (BVS) approach. BVS methods for mapping multiple quantitative loci have been implemented for single trait [25], [26] or extended to consider multiple traits [27]. Besides different choices of prior distribution for the regression coefficients, these methods differ mostly in their implementation of MCMC algorithms, in particular with respect to the update moves that are used and to whether regression coefficients are integrated out or sampled. Gibbs sampling combined with spike and slab priors for the regression coefficients [25], [26] or local adaptation [38] are relatively straightforward to implement but the chains will be highly auto-correlated by construction as when the covariates are non-orthogonal with a strong linear dependence between the regression coefficients. As a result in both cases there could be the tendency to mix slowly. In this vast multi-modal model space analytic integration of the parameters can speed up the convergence of the MCMC: fast mixing is possible because the variable selection indicator does not depend on the value of the effect coefficient [39]. Furthermore, performing a full scan Gibbs sampling of all the covariates at each sweep of the algorithm becomes quickly too computationally demanding when the number of markers is larger than a few hundreds. Our implementation of BVS differs from these works in several key aspects: i) a model formulation where regression coefficients are integrated out and not updated at each sweep of the algorithm, ii) moves on the model space that involve only the selection indicators and iii) a novel class of algorithms, so-called Evolutionary MCMC algorithms, designed to search efficiently multi-modal space by using parallel chains at different temperature [40], discussed in the context of variable selection by [41]. These aspects are important for being able to scale up to well mixing implementations involving thousands of markers. The code for running the SBR and SBMR model will be available upon request from the authors. Besides the difference in computational schemes associated to BVS, an important extension is the simultaneous analysis of multiple traits. Banerjee et al. [27] consider a broad class of multiple traits models, that in particular include a model (referred to as TMV in [27]) similar to the one considered in our paper, where the BVS search is focussed on finding a set of markers associated with all the traits (i. e. , expression levels measured in four tissues). However, the flexibility in the model specification in Banerjee et al. requires to increase considerably the number of predictors (number of traits × genetic markers), making the search in the model space rather difficult. On the other hand, the mixture over markers model (MOM) proposed in Kendziorski et al. [42] is aimed at borrowing information across a large set of traits (e. g. , transcripts) in order to better estimate the marginal probability that each marker is a genetic control point. The MOM method is designed for a large set of traits, larger than the number of markers, and hence not appropriate to our context where the number of predictors is substantial and larger than the number of traits. The analysis of multiple complex traits has been also considered in the different context of family-based data and variance component models by Liu et al. [38].
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.
computational biology/transcriptional regulation
genetics and genomics/gene expression
computational biology/genomics
computational biology
mathematics/statistics
genetics and genomics
computational biology/systems biology
New Insights into the Genetic Control of Gene Expression using a Bayesian Multi-tissue Approach