Ponsuksili et al. BMC Genomics 2010, 11:572 http://www.biomedcentral.com/1471-2164/11/572 Together with the growing knowledge of genome sequences and gene annotation, this gives insight into the architecture of regulatory networks. In order to relate eQTL analysis to the genetic background of any complex trait, either a positional link to QTL for the complex trait of interest (pQTL) and/or a functional link to the trait expression is required. Here we report on the detection of eQTL for some 11,000 transcripts found in porcine skeletal muscle by microarray analysis. We demonstrate that the global microarray eQTL analy- sis can serve for narrowing down the candidate genes for quantitative traits related to meat quality when it is integrated with the analysis of the correlation of expres- sion levels with the traits of interest. The genetic architecture of transcript level variation in the porcine F2 resource population was highly variable and complex. eQTL were detected for 53% of the 11,457 probe-sets, with many of them exhibiting more than one eQTL summing up to more than 9000 eQTL in total (Table 1). The expression levels of the majority of the transcripts showed quantitative variation. So the tran- script levels are probably quantitatively controlled. The link between the linkage maps and porcine assembly sequence enable discriminating cis or trans-eQTL. All 23,934 probe-sets sequence represented on the porcine microarray and the microsatellites sequences were BLASTed against the Ensembl porcine BAC sequence (Sscrofa9, April 2009). Our microsatellite order showed high accuracy compared to the Ensembl porcine BAC sequence. Correlations between transcript abundance and meat quality, combined with genetic positional information of eQTL allowed us to prioritize a small number of candi- date genes. We considered transcripts, which exhibited expression levels correlated with meat quality traits, and which had eQTL on the same chromosome as the tran- scripts itself. Our previous studies showed that cis regulation is a stable characteristic of individual transcripts. Conse- quently, a global microarray eQTL analysis of a limited number of samples can be used for exploring functional and regulatory gene networks and scanning for cis- eQTL. In particular, the assignment of eQTL to chro- mosomes is reliable; though some cis-eQTL change their position, they were consistently assigned to the same chromosome when comparing analyses based on 74 microarrays or 276 real time RT-PCRs [7]. Based on this observation, we decided to highlight only eQTL which were located on the gene itself or the same chro- mosome. Moreover, this relaxed measure was chosen in order to avoid the exclusion of any true cis-acting eQTL that are not precisely mapped, because of the limited resolution of the genetic map. The observation that cis- eQTL were more consistently detected than trans-eQTL Page 6 of 14 was also made by comparing the results from different studies of eQTL with different numbers of animals and different tissue types [14-17]. Expression QTL mapping, with its potential to cate- gorize cis and trans-effects, provides the mean to discri- minate between “effect” and “cause” with respect to trait-associated differential expression. Though a relaxed window of cis-eQTLs was used, a low proportion of 10% of putative cis-eQTL was found in this study, compared to other previous studies [18,19]. Cis-regulated genes are of interest, because the underlying genes are expected to harbour genetic variants that influence their own expression level, which may also influence the phy- siological traits of interest, if transcript abundance is correlated with the target phenotype [17]. This provides a function-related evidence of the candidacy of a parti- cular gene. The usefulness of eQTL for identification of quantita- tive trait genes was demonstrated [3,5,20,21]. The causal link between sequence variation, gene expression, and phenotype arises because the polymorphism might be responsible for both a cis-eQTL and the QTL contribut- ing to a quantitative phenotype, so called pQTL. QTL for traits related to meat quality were previously mapped in the population used here [12,22]. The matching of pQTL, eQTL and the localization of the corresponding genes with trait-correlated expression provides posi- tional and functional evidence for the potential role of the respective genes and strongly promote them as can- didate genes. Mapping of eQTL enables displaying regu- latory networks and localizing genomic variation affecting the mRNA expression of a gene either within the genes itself (cis) or distant from the gene (trans). The key advantage of eQTL mapping is that it connects variation at the level of RNA expression to variation at the level of DNA. Only the latter provides versatile tools for breeding whereas the first reveals information on the biology of a trait and directs to new candidate genes. Moreover, integration of (1) information on QTL for a trait of interest in breeding (pheneQTL = pQTL) with analysis of (2) trait correlated expression and with (3) mapping of expression QTL (eQTL) for the correspond- ing trait-dependent-regulated genes facilitates the identi- fication of genes and pathways with cumulative evidence of their involvement in the biology of the traits of inter- est and enable to built priority lists of candidate genes [23]. However, there are also some issues that limit of the use of the genetical genomics approach, in particular the resolution of the genetic maps that is depending on the number of markers and animals used and the struc- ture of the population used and artifacts caused by the limited sensitivity and specificity of microarray experi- ments [24].