Cubillos et al. BMC Genomics 2012, 13:117 http://www.biomedcentral.com/1471-2164/13/117 the mapping of thousands of traits in parallel. It includes functions to systematically and automatically identify and select QTLs with supporting intervals from R/qtl simple genome scan results (interval mapping), defining QTL as covariates for a composite interval mapping, calculating additive effect, estimating heritabil- ity for single QTL and for QTL × QTL interactions, classifying eQTL as candidate for trans- (distant) and cis-acting (local) eQTL (according to the position of the controlled gene with respect to the eQTL support inter- val and/or an arbitrary physical window), projecting and plotting the QTL estimated location relative to the gene location and summarising eQTL data at the genome scale with various generic plots and tabular files. R/eqtl is a free and open-source multi-platform package devel- oped under the statistical language R, and is available under the GPLv3 license. R/qtl and its extension R/eqtl are hosted and can be downloaded from the Compre- hensive R Archive Network (CRAN) at http://cran.r-pro- ject.org. Interval Mapping (IM) and Composite Interval Map- ping (CIM) were performed on each cross for all of the 32,300 non-technical and non-repetitive probes on the CATMA array, as implemented in the R/eqtl package. The Cvi × Col and Bur × Col RIL populations have been previously genotyped for 90 and 87 markers, respectively [21]. For each probe, IM was first applied to identify a primary set of eQTLs and then this set was used as co-factor in CIM to detect less significant QTLs. Taking into account population size and, hence, the number of observed informative recombinants that con- ditions the ability to distinguish two linked QTL, a 15 cM exclusionary window on each side of all QTL peaks was applied to refrain from trying to detect too closely linked eQTLs or interpreting large peaks into multiple eQTLs. The analysis was completed with the estimation of the additive effect by averaging the phenotype value for each allele at the QTL marker (or pseudomarker) and estimating the relative contribution of each allele as Xxx minus Col (so that a negative allelic effect indicates that Col is up-regulated with respect to Xxx). We also estimated the proportion of the phenotypic variation explained by the segregation of each individual eQTL or significant eQTL × eQTL interaction (R2) by analysis of variance. QTLs were defined using a LOD significance threshold computed by permuting the phenotypes while maintaining the genotype across the RIL set [41] and a 1.5 LOD drop-support interval [42]. In order to obtain a genome-wide threshold we determined the 95-percentile permutation threshold among 500 randomly chosen traits and estimated the 95% upper-bound from the traits distribution. We called an eQTL significant if the LOD score was above the threshold (genome-wide False Discovery Rate, FDR = 0.05)[3,16]. To correct for Page 10 of 12 multiple-traits testing, we estimated the minimum P- value from each randomly chosen trait and estimated the corresponding q-value [43]. P-values for transcrip- tome-wide FDR 5% (P = 6 × 10-3 in CviCol and P = 9 × 10-3 in Burcol) and 1% (P = 1 × 10-3 in CviCol and Bur- Col) were also used as significance threshold. QTLstore All results are recorded in QTLstore, a comprehensive web service which allows to store, manage, explore and cross QTL experiment results from the single-trait scan to genome-wide expression QTL experiments. QTLstore is hosted at http://qtlstore.versailles.inra.fr/ and is com- posed of an extensive SQL database and a web interface, which are available under the GPLv2 license. The SQL database is convenient for all kinds of QTL experiments and analysis, by being able to store all the experiments primary data separately from all subsequent analyses results. It typically stores and interrogates QTL para- meters such as location, significance, effect and type, among others. Local and distant eQTL distribution Local and distant eQTLs were classified in 1Mb bins as in [1]. Briefly, we divided the genome into 116 physical bins (independent of the number of markers on each population) of 1Mb each (the chromosome ends were included into the previous 1Mb bin if the remaining region was smaller than 500 Kb). For distant eQTLs we performed a permutation test as previously described [3]. Bins containing a higher number than the maximum expected by chance (a = 0.05) were considered as trans- hotspots. For local eQTLs, we performed a regression analysis against SNP (for Cvi-0 and Bur-0, SNP data was downloaded from the 1001genomes project website http://1001genomes.org/) and gene densities as described in [10]. Significance was estimated using a one-way ANOVA. Significance for GO enrichment was assessed utilising a P < 2 × 10-5 after Bonferroni correction. VBQTL and test for directional allelic effect We applied the VBQTL (Variational Bayesian QTL Mapper) approach as previously described [24] on the dataset obtained in the Cvi-0 × Col-0 population, using one known factor to model hidden confounding sources. Residuals of the estimated effect were used as phenoty- pic values and eQTLs were detected again using R/eqtl and the statistical procedure implemented for the stan- dard approach (P < 1 × 10-2, FDR 5%). We tested for 5, 10, 20 and 30 factors and chose the model with the highest number of additional linkages (FDR 5%). Next, potential directional selection was tested as previously described with modifications [20]. Briefly, only local