Cubillos et al. BMC Genomics 2012, 13:117 http://www.biomedcentral.com/1471-2164/13/117 Page 5 of 12 Figure 2 Correlation between relative expression levels for potentially shared local eQTLs between the CviCol and BurCol sets. The relative allelic expression level at the eQTL was plotted for transcripts sharing local eQTLs with additive effects in the same direction in both populations. The linear regression and correlation is indicated with a dashed line. < 2 × 10-13 in CviCol, P < 1.3 × 10-8 in BurCol). Furthermore, we compared the top 11 bins (from a total of 116, corresponding to the top 10%) in both sets and detected 4 overlapping intervals with high eQTL density between crosses (Additional file 5: Table S3a). Interest- ingly, the 4 intervals were not among the top poly- morphic or gene-dense and therefore the overlapping cannot be solely attributed to these factors. Distant eQTL hotspots are cross-specific We considered the set of distant eQTLs mapped at 5% FDR to test for the presence of hotspots along the gen- ome. Similarly to the local eQTL analysis, we divided the Figure 3 Distribution of distant eQTLs across the genome and detection of hotspots. The number of distant eQTLs (y-axis) is plotted against the physical position of the 1Mb-window where they peak (x-axis). In each cross, intervals with an excess of eQTLs relative to the threshold estimated by permutation (dashed lines) were classified as hotspots. This figure refers to Additional file 5: Table S3b. genome into 1Mb windows and identified regions contain- ing a higher number of eQTLs than expected by chance, according to a permutation test (see Methods). In the Cvi- Col population, a total of 17 bins contained more than the 34 distant eQTL peaks expected by chance, and up to 170 peaks were found within the strongest hotspot (Figure 3; Additional file 5: Table S3b). These regions included 44.2% of all the distant eQTLs detected. Moreover, on two occasions, two or more adjacent bins were above the threshold, likely highlighting the wide distribution of eQTLs around a major hotspot due to a lack of precision when mapping small-effect loci. For example, the hotspot localised on chromosome 1 included four bins and 13.6% of all the distant-eQTLs, the strongest one in the genome. Within this interval, 170 eQTLs are located at the most dense bin (#6) with an average LOD of 3.6. A different depiction was obtained in the BurCol population: we detected 24 bins containing more eQTLs than expected by chance (> 60 eQTLs; Figure 3). In this population the hot- spots encompassed the majority (74.7%) of all the distant- eQTLs. Moreover, within the three major hotspots con- taining several significant adjacent bins on chromosome 1, 2 and 3 (bins #6-9, #29-31 and #52-53 respectively), we found a significant enrichment for distally-regulated genes of 4 GO categories in the branch ‘biological process’. These categories included response to chitin (P < 2 × 10-5) on chromosome 1 and cell-adhesion (P < 1.3 × 10-5) on chromosome 2 (Additional file 6: Table S4). We found little overlap between hotspots in the two populations (Spearman correlation test P = 0.21). From the 41 hotspots detected in total, only three were colli- near between sets, suggesting little conservation in the major determinants of distant eQTLs across the gen- ome. None of the hotspots co-localised with any of the major-effect developmental QTLs known to segregate in A. thaliana (i.e. ERECTA, FRIGIDA, CRY2, ...), except in BurCol where a marginally significant hotspot was found on chromosome 5 (bin #93), which contains FLC. Although 62 traits with a distant eQTL mapping to this region were observed, only 2 have been shown to inter- act with FLC [23], suggesting potentially a minor contri- bution from this locus. The gene-poor centromeres rich in transposons and pseudogenes were not detected in any of the hotspot intervals. The lack of a significant count of eQTLs extended for at least 1Mb from the centromere until the nearest hotspot, except in the Bur- Col set where we detected distant eQTLs-rich regions immediately downstream and upstream the centromeres on chromosome 1 and 2, respectively (Figure 3). Controlling for confounding factors increases eQTL detection in the CviCol dataset Variation in transcript abundance can also be influenced by non-genetic factors, such as experimental noise and