Cubillos et al. BMC Genomics 2012, 13:117 http://www.biomedcentral.com/1471-2164/13/117 evidence for a directional allelic effect in the CviCol popu- lation, we used the larger set of local eQTLs identified by the VBQTL approach. We considered functional cate- gories with an overrepresentation of eQTLs and a signifi- cant bias in the number of genes with either Col or Cvi alleles upregulated. To test for categories with more eQTLs than expected by chance (~1 eQTL every 10 genes, see Methods), the gene functional classification assigned by the Gene Ontology Consortium was used [28]. In parti- cular, we focused on the branch ‘biological process’, since GO categories here contained could give insight into the divergent adaptation events occurred in each accession. We identified 3 significant overrepresented categories (hypergeometric test, P < 8 × 10-4, Table 1): “Response to stress”, “Response to Biotic and Abiotic stimulus” and “Transport”. From the 318 GO sets within these cate- gories, we only analysed those containing more than 20 genes (86 sets). Subsequently, we estimated the number of eQTLs within each set either up or down regulated in any accession and test for deviation from the genome-wide 1.6:1 Col:Cvi eQTL ratio using a hypergeometric test [20]. We found 6 sets showing a significant skew (P < 0.05; Table 2), all of them part of the response to stress or biotic and abiotic stimulus, including: defense, hypoxia and plant-type hypersensitive response. Interestingly, in all cases most of the members were upregulated in the Col accession (Table 2). These results demonstrate the pre- sence of a systematic directional allelic effect in sets of genes having related functions and validate the utilisation of expression traits to identify potential intraspecific diver- sification events. Discussion We have performed quantitative genetic analyses for genome-wide expression traits in a connected-cross set Page 7 of 12 between three accessions in the model organism A. thaliana. Utilising the R/eqtl package in both crosses, we observed a 52.3% difference in the number of signifi- cant eQTLs between sets, with the Bur-0 × Col-0 cross being the one with the highest count (Figure 1). Regard- less of this difference, the conservative figures of eQTLs obtained in both sets resemble the one previously described in RILs between accessions Ler-0 and Cvi-0 [10], but they significantly differ from a previous study involving Bay-0 and Shahdara accessions, where more than 36,000 eQTLs were detected [3]. In our study, the use of a common approach for both sets allowed us to perform straightforward comparisons between them, drawing conclusions not necessarily affected by differ- ences in statistical power [11]. We observed that 17.4% of all local eQTLs detected were shared between crosses, likely due to polymorphisms solely present in Col-0 or shared by Cvi-0 and Bur-0 (although independent SNPs or some allelic heterogeneity pattern could also lead to this observation). An argument to support this hypoth- esis is the high level of shared eQTLs with an additive effect in the same direction (88.4%) and of similar strength (Figure 2). However, the low level of overlap- ping eQTLs demonstrates the greater number of cross- specific eQTLs, possibly due to the great complexity of expression traits [1,11,29] and their high potential for evolution [17]. Our results are consistent with a recent survey in several Arabidopsis accessions that identified high allelic heterogeneity within local regulatory eQTLs [6,12], suggesting the existence of many private poly- morphisms between all three accessions extending the eQTL landscape. Although cis-acting regulation among local eQTLs remains to be confirmed, the co-localisation of the eQTL and the controlled gene provides a robust Table 1 Overrepresented GO categories and terms among CviCol local-eQTLs Expected Number of eQTLs Observed Number of eQTLs P-value Functional Category cell organization and biogenesis developmental processes DNA or RNA metabolism electron transport or energy pathways other biological processes other cellular processes other metabolic processes protein metabolism response to abiotic or biotic stimulus response to stress signal transduction transport unknown biological processes 165 296 50 38 274 1457 1322 524 276 305 160 240 1534 187 283 54 45 295 1534 1364 490 378 414 156 289 1112 0.034 0.76 0.29 0.098 0.074 0.004 0.041 0.94 7.70 × 10-11 (S*) 2.19 × 10-11 (S*) 0.61 1 0.0002 (S*) Number of eQTLs per categories among GO ‘Biological process’. Categories with a significant excess (after Bonferroni, P < 8 × 10-4) of eQTLs are indicated with ‘S*’. The number of expected eQTLs was estimated from a whole-genome scan considering the fraction of genes in each functional category