Ordas et al. BMC Genomics 2010, 11:174 http://www.biomedcentral.com/1471-2164/11/174 from the date of planting to the date of anthesis, plant height, grain humidity at harvest (%), and yield at 140 g kg-1 moisture content. In addition to the previous traits, shank and cob damage ratings (9 = without injury; 1 = wholly damaged) were estimated for each plot. Individual analyses of variance and adjusted means were calculated for all traits according to a a-lattice design using the Mixed Procedure of SAS [64]. Com- bined analysis of variance over years was computed using the adjusted means. Variance components were estimated by restricted maximum likelihood (REML). Computations were performed with SAS [64]. Phenoty- pic (rp) and genetic (rg) correlations between traits were estimated with a multivariate REML procedure following Holland [65] and using the SAS programs developed by the author http://www4.ncsu.edu/~jholland/correlation/ correlation.html. QTL analysis DNA of ten plants picked at random from each RIL was extracted according to Liu and Whittier [66] with modi- fications. SSR amplifications were performed as described by Butron et al. [67]. SSR products were sepa- rated after amplification by electrophoresis using 1 TBE on a 6% non-denaturing acrylamide gel (approximately 250 V for 3 h) [68]. Two hundred twenty six SSR pri- mers pairs were used to genotype the RILs. From these, eighty four SSR that were polymorphic and give clear bands patterns were used for linkage mapping and QTL analysis, resulting in a uniform distribution of markers along the genome. The linkage map was built using MAPMAKER 3.0b [69]. Loci were assigned to linkage groups which were anchored to chromosomes using default parameters (minimum LOD of 3.00, maximum distance of 30 cM and maximum unlinked LOD of 2.00). Multipoint linkage analysis was performed for each linkage group by the “order” command using an informativeness criteria of 100 individual and a distance between markers of 2.00 cM. Charts of chromosomes and QTLs were obtained by using MapChart [70]. Com- posite interval mapping [71] was conducted with PLABQTL [72] with cofactor selection performed fol- lowing PLABQTL’s recommendations and using an “F- to-enter” and an “F-to-delete” value of 7. A LOD thresh- old of 2.4 was determined by permutation tests that ensures an experiment wise error rate of p < 0.20. A simultaneous fit with the detected QTLs was performed for each environment and a QTL ANOVA was carried out with PLABQTL [73]. The mean squares of the ANOVA were used to obtain an estimate of the propor- tion of the genetic variante explained by the detected QTL which is adjusted by QTL × environment interac- tion [73]. Page 8 of 10 Fivefold cross validation (CV/G) was performed, fol- lowing the procedures described by Utz et al. [74], to estimate the additive effects and the proportion of geno- typic variance explained by the QTLs. The whole data set was randomly split into k = 5 data subsets. Four of these subsets were combined to form the estimation set (ES) and the remaining subset formed the test set (TS) in which predictions derived from ES were tested for their validity by correlating predicted and observed data. We used 1000 replicated CV/G runs. For a particular QTL and its confidence interval estimated using the whole data set, the frequency of QTL detection across the CV/ G runs was calculated by counting the number of CV/G runs in which a QTL was located within that confidence interval. The frequency of QTL detection gives us an esti- mation of the precision of QTL localization [73]. Acknowledgements This research was supported by the National Plan for Research and Development of Spain (Project Cod. AGL2006-13140). B Ordas and Rogelio Santiago acknowledge a contract from the Xunta the Galicia (Isidro Parga Pondal program) and from the Spanish Ministry for Science and Innovation (Juan de la Cierva program), respectively. Authors’ contributions BO carried out field experiments, performed data analysis and drafted the manuscript. RAM conceived the study, participated in its design and in its coordination. RS assisted BO in field experiments. AB conceived the study, participated in its design, carried out molecular analysis and assisted BO in data analysis. All authors read and approved the final manuscript. Received: 6 October 2009 Accepted: 15 March 2010 Published: 15 March 2010 References 1. Malvar RA, Butron A, Revilla P, Ordas A: Resistance to the pink stem borer, Sesamia nonagrioides, in maize. 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