Cubillos et al. BMC Genomics 2012, 13:117 http://www.biomedcentral.com/1471-2164/13/117 ‘CviCol’) and Bur-0 × Col-0 (hereafter ‘BurCol’) RIL sets, and one on developing seed transcripts in Bay-0 × Shah- dara (hereafter ‘BaySha’) RIL set. We will henceforth focus on and compare the results obtained for rosette samples, while the BaySha data is briefly presented as Additional file 1. Transcript abundance can be treated as a highly heri- table phenotype affected by natural genetic diversity [1]. In order to understand the genetics underlying gene expression variation among A. thaliana accessions, we utilised the CATMA array technology [22], which has the advantage of not being sensitive to punctual poly- morphisms (SNPs) between accessions’ transcripts thanks to the length of the probes used (Gene-specific Sequence Tags: GSTs). We measured expression levels for almost 35,000 traits (= GSTs) in three week-old rosettes of 314 RILs obtained from a connected-cross set between three parental accessions (Col-0 as the com- mon male parent, Cvi-0, Bur-0 [21]), including 158 RILs from CviCol and 156 RILs from BurCol. Initially, in order to characterise expression divergence between the accessions selected in this study, the number of differen- tially expressed genes between pairs of accessions was estimated (Cvi versus Col and Bur versus Col). A total of 1,709 differentially-expressed genes between Col-0 and Cvi-0 and 1,083 between Col-0 and Bur-0 (P < 5 × 10-2; Additional file 2: Table S1) were detected. This cut-off value represents, at least, 1.3 fold expression dif- ferences between the two allelic variants. From this dif- ferentially expressed set, none of the parents showed a significant excess of up- or down-regulated genes (49.0% and 54.3% of the probes revealed up-regulation in Cvi and in Bur, respectively, relative to Col). About half of the genes (48.3%) showing significant expression con- trast between Col-0 and Bur-0 were also found when comparing the other pair (Additional file 3: Figure S1). Interestingly, 92.3% of this overlap was similarly up- or down-regulated in Col-0 relative to the other two acces- sions, suggesting that many of the polymorphisms con- tributing to these differential expression patterns may arise from Col-0 or be shared by Cvi-0 and Bur-0. Local and distant eQTL distributions are highly divergent between crosses In order to detect linkage between genomic regions and transcript accumulation variation, we performed eQTL mapping implementing the novel R/eqtl package (see Methods). We identified 4,290 significant eQTLs at a FDR of 5% (3,906 at 1% FDR) from the CviCol dataset (Figure 1a; Additional file 4: Table S2a) and respectively 6,534 and 5,354 eQTLs from the BurCol dataset (Figure 1b; Additional file 4: Table S2b). This corresponds to a 1:1.5 ratio between sets, at most, a contrast essentially relying on eQTLs of low significance. Among the set of Page 3 of 12 eQTLs in each cross at a FDR of 5%, 2,528 and 4,020 were up-regulated in Col in the CviCol and BurCol populations, respectively. From the set of differentially expressed genes between the parental accessions, 43.1% and 56.9% of them showed a significant linkage to at least one genomic region in the CviCol and BurCol crosses respectively. When comparing the distribution of the phenotypic variance explained per eQTL in both crosses, a highly dissimilar pattern was observed (Kol- mogorov-Smirnov test, P < 2.2 × 10-16). In the CviCol set we found a higher average explained variance (13.3% vs. 10.2% in BurCol) with proportionally more eQTLs having larger effects (Additional file 3: Figure S2). Most of the traits were associated with a unique genomic region (89.9% CviCol, 86.3% BurCol), and only a minor- ity exhibited a significant association with two to five eQTLs (Additional file 3: Figure S3). When only consid- ering genes located in the nuclear genome, 13.2% (Cvi- Col) and 19% (BurCol) of these had at least one significant eQTL at a FDR of 5%. Altogether, these results demonstrate the complex nature of transcript variation between accessions and suggest a distinct eQTL landscape for each cross. Linkage analysis allows the examination of local and distant eQTLs influencing transcript abundance [1,14]. To detect local associations, we set a 1Mb cut-off dis- tance between the eQTL peak and the CATMA GST physical position. Any eQTL reaching a significance peak within 1Mb from the location of the probe it con- trols was considered as potentially acting in cis and hence classified as a local eQTL. In both populations we observed that the distributions of local and distant eQTLs were not uniform throughout the LOD scale (Figure 1c-d). Distant eQTLs were enriched at lower LODs, compared to local eQTLs, which were over- represented at high significance values. Hence, 50% (25.5%) of all eQTLs detected at 5% FDR mapped locally in the CviCol (BurCol) population, while this number would rise to 69.6% (53.6%) if using a much more con- servative FDR of 0.1% (P < 1 × 10-4 in CviCol and P < 5 × 10-5 in BurCol). Moreover, compared to distant eQTLs, local eQTLs explained a significantly higher fraction of the phenotypic variance per trait (Kolmo- gorov-Smirnov test, P < 2.2 × 10-16; Additional file 4: Table S2). These results can lead to contrasting conclu- sions as we vary threshold values (Figure 1c-d). The higher phenotypic variance explained by local eQTLs underlies the fact that most of the phenotypic variation in expression levels is due to local associations, and trans-acting factors would have essentially minor effects on single transcripts, but overall a widespread effect over many genes. In order to classify cross-specific and overlapping eQTLs, we estimated the number of local eQTLs shared