Chutimanitsakun et al. BMC Genomics 2011, 12:4 http://www.biomedcentral.com/1471-2164/12/4 gene sequences. In this study, although only 11.0% of RAD sequences align to known barley genes, we report 78% of RAD markers show co-segregation with unigene- EST SNP markers from the Szűcs, et al. OWB map [3]. The observed association of RAD markers with known genic-SNPs indicates they are genetically linked, suggest- ing some physical proximity, though the distances may be on the order of megabases. Additionally, the relative paucity of RAD markers that align to barley genes or other plant genomes indicates that only a small fraction of RAD markers originate from within coding or other conserved sequences. RAD marker development efforts from other grass species for which there is a reference genome show similar distributions of markers across coding and intergenic space [29]. When a complete bar- ley genome sequence is available, the sequence identity and location of RAD loci will become clear. In the interim, the current availability of all barley RAD sequences is an advantage over DArTs, where only limited sequence data are publicly available. QTL mapping One of the principal applications of linkage maps to crop improvement has been QTL mapping in bi-paren- tal crosses [2]. A principal problem with many QTL mapping efforts is the limited size of the mapping popu- lation [30-33]. Recognizing that the small size of the OWB population (n = 93 and n = 82 when removing identical lines) will lead to biased estimates of QTL significance, effect, and interaction [34-37], we nonetheless proceeded with a QTL analysis of the eight traits, due to the high heritabilities (Table 2 and Table 3) and our inter- est in addressing two issues. The OWB population is a widely-used resource for genetic analysis and instruction: reporting the relationships of QTLs with the morphologi- cal and phenological characters segregating in the popula- tion will further develop this community resource. The RAD markers added to the map reported by Szűcs et al. [3] represent very high quality and novel data and we were interested in determining if their addition would fill gaps in the previous map and thus allow for higher resolution QTL detection. As shown in Table 2, a total of 26 QTLs were found using the higher density map, with a range of one to five QTL for each individual trait. Twenty-six QTLs were also detected with the RAD-only map with a range of two to five QTL for each trait (Table 3). Twenty- three QTLs were significant and detected in both maps. Of the three QTL that were significant in the full map, but not the RAD-only map, all showed a trend in the RAD-only map but did not reach the LOD threshold. Three QTL significant in the RAD-only map but not in the full map showed a trend in the full map but did not reach the LOD threshold. Therefore, RADs alone, or in Page 6 of 13 combination with other markers, are suitable for QTL mapping. This supports the quality of the RAD data, since a key issue for QTL detection is marker quality, given adequate genome coverage [37]. The following results highlight findings from the higher density skeleton map (Table 2), based on the assumption that by providing the most thorough cover- age it optimizes QTL estimates. However, the same large-effect QTL were detected with the RAD-only map (Table 3). As shown in Table 2, eleven of the twenty-six QTL were associated with four genes: ZEO-1, VRS-1, VRN-H1 and VRN-H2, and the largest effect QTL for all traits were associated with ZEO-1and/or VRS-1. The favorable alleles for height, spike length, grain number and grain yield came from the OWB recessive parent (normal height, long spike, and six-row) at ZEO-1. The OWB recessive parent also contributed favorable alleles for floret and grain number at VRS-1. At this locus, the OWB dominant parent (dwarf height, short spike, and two-row) contributed favorable alleles for spike number and hundred grain weight. Although VRS-1 and ZEO-1 were both coincident with yield component QTL, only ZEO-1 had a significant effect on grain yield. This is probably due to yield component compensation asso- ciated with VRS-1 and negative pleiotropic effects of the ZEO-1 dwarf allele. This extreme dwarfing allele will not be as immediately useful to agriculture as the Rth- B1 and Rht-D1 genes of wheat [38]. Interestingly, QTLs for final leaf number were coincident with VRN-H1 and VRN-H2. These two genes interact epistatically to deter- mine vernalization sensitivity [39]. The OWB dominant and recessive parents, respectively, have dominant (winter) and spring (recessive) alleles at VRN-H2 allele. Therefore, it is of interest that the OWB dominant allele at VRN-H2 is associated with higher final leaf number, even though there is no binding site in Vrn-H1 for the repressor encoded by VRN-H2 since both parents have the same recessive (spring) allele at VRN-H1 [40]. The higher final leaf number QTL allele coincident with VRN-H1 may be a consequence of regulation of other regions in VRN-H1 besides VRN-H2. There were epistatic QTL interactions for spike length, and grain number but these effects were very small in comparison to the main effects. The QTL we report for the OWB population can be aligned with QTL for other traits assessed in other germplasm via the GrainGenes QTL summary http://wheat.pw.usda.gov/ggpages/maps/OWB/. Conclusions In this study we showed that sequenced RAD markers were sufficient to generate a high quality linkage map comparable to current OWB SNP-based maps. The suc- cess of linkage map construction supports the reliability of the sequenced RAD markers based on the following