Cubillos et al. BMC Genomics 2012, 13:117 http://www.biomedcentral.com/1471-2164/13/117 hidden factors [24,25]: greenhouse localisation, tempera- ture variation, sampling time and other undetected experi- mental perturbations can affect expression levels among samples, altering the subsequent analysis. A recent asso- ciation mapping study in A. thaliana already demon- strated the greater resolution obtained when considering confounding sources between genotypes (i.e. population structure) compared with standard approaches [26]. Hence, it is interesting to try and control these variables in order to improve the eQTL mapping. For this purpose, we utilised the probabilistic approach VBQTL together with R/eqtl to account for hidden confounding factors [24]. We tested this approach in the CviCol dataset for 0, 5, 10, 20 and 30 hidden factors, finding the lowest number of eQTLs for no-hidden factors and the highest for 10 factors (Additional file 3: Figure S5). We chose one known factor (time of harvest) and ten unknown factors to model the expression trait, since the lowest number of distant asso- ciations were explained away, in contrast to 20 and 30 fac- tors. Contrarily, using the same number of factors (or less) in the BurCol dataset, we found that many of the distant associations were lost (Figure 4). Using 10 factors we mapped 6,270 eQTLs at 5% FDR (42.6% of them are local) in the BurCol population, while only 29.2% of the distant associations were retained by both the standard and the VBQTL methods. Many of the distant associations exhi- biting high significance values were present in both data- sets; however, several distant associations with marginally significant LODs were lost (data not shown). The majority of the local associations detected by the standard approach were also mapped by VBQTL (80%). Even more, we observed a 60% increase in the number of local eQTLs detected by VBQTL. Nevertheless, the large number of Page 6 of 12 genetic associations explained away in the BurCol set hin- dered the subsequent data analysis, since many distant eQTLs affecting transcript levels should be considered (for example, it could be a major regulator causing a hotspot) and not treated as confounding sources [24,27]. We focused the utilisation of this approach on the CviCol population. Linkage analysis detected a total of 8,156 eQTLs (5% FDR; 7,817 at 1% FDR; Additional file 7: Table S5), which is almost twice as many eQTLs as with the standard method, demonstrating the sensitivity of the approach. Interestingly, the fraction of local and distant eQTLs remained almost unchanged. At the less conservative threshold (5% FDR) we mapped 44.6% of the eQTLs in the vicinity of the transcript, slightly less than from the analysis of the raw expression values. Moreover, the contribution to the total number of local and distant eQTLs was increased by 1,461 and 1,025 eQTLs respectively. Many of the local associations mapped by the standard approach were also mapped by VBQTL (92.6%) and, contrarily to the BurCol data set, 56.2% of the distant associations were also retained (Fig- ure 4). Approximately one third of the peaks that had remained only marginally significant (= significant at a lower threshold of 10% FDR) in the standard analysis, were now detected as significant at 5% FDR after VBQTL analysis, likely due to the negative effect of con- founding sources on power in the original approach. Evidence for directional allelic effect in Col-0 versus Cvi-0 accessions The independence of eQTLs is crucial for the identifica- tion of selection on transcript abundance in a group of genes with related functions [5,20]. In order to detect Figure 4 Bar plot showing overlap and specific eQTL detections when comparing the VBQTL and standard approach. Linkage mapping methods were compared and blue regions denote the percentage of common eQTLs mapped at a 5% FDR using both approaches. eQTLs solely mapped in one or the other strategy are depicted at the bar-extremes (in green and brown). Ten factors were included in the VBQTL analysis. a. CviCol set, b. BurCol set.