Improved resistance to preharvest sprouting in modern bread wheat (Triticum aestivum. L.) can be achieved via the introgression of grain dormancy and would reduce both the incidence and severity of damage due to unfavourable weather at harvest. The dormancy phenotype is strongly influenced by environmental factors making selection difficult and time consuming and this trait an obvious candidate for marker assisted selection. A highly significant Quantitative Trait Locus (QTL) associated with grain dormancy and located on chromosome 4A was identified in three bread wheat genotypes, two white- and one red-grained, of diverse origin. Flanking SSR markers on either side of the putative dormancy gene were identified and validated in an additional population involving one of the dormant genotypes. Genotypes containing the 4A QTL varied in dormancy phenotype from dormant to intermediate dormant. Based on a comparison between dormant red- and white-grained genotypes, together with a white-grained mutant derived from the red-grained genotype, it is concluded that the 4A QTL is a critical component of dormancy; associated with at least an intermediate dormancy on its own and a dormant phenotype when combined with the R gene in the red-grained genotype and as yet unidentified gene(s) in the white-grained genotypes. These additional genes appeared to be different in AUS1408 and SW95-50213.

International audienceThe exponential development of molecular markers enables a more effective study of the genetic architecture of traits of economic importance, like test weight in wheat (Triticum aestivum L.), for which a high value is desired by most end-users. The association mapping (AM) method now allows more precise exploration of the entire genome. AM requires populations with substantial genetic variability of the traits of interest. The breeding lines at the end of a selection cycle, characterized for numerous traits, represent a potentially useful population for AM studies. Using three elite line populations, selected by several breeders and genotyped with about 2,500 Diversity Arrays Technology markers, several associations were identified between these markers and test weight, grain yield and heading date. To minimize spurious associations, we compared the general linear model and mixed linear model (MLM), which adjust for population structure and kinship differently. The MLM model with the kinship matrix was the most efficient. Finally, elite lines from several breeding programs had sufficient genetic variability to allow for the mapping of several chromosomal regions involved in the variation of three important traits

Five genomic prediction models were applied to three wheat agronomic traits—grain yield, heading date and grain test weight—in three breeding populations, each comprising about 350 doubled haploid or recombinant inbred lines evaluated in three locations during a 3-year period. The prediction accuracy, measured as the correlation between genomic estimated breeding value and observed trait, was in the range of previously published values for yield (r = 0.2–0.5), a trait with relatively low heritability. Accuracies for heading date and test weight, with relatively high heritabilities, were about 0.70. There was no improvement of prediction accuracy when two or three breeding populations were merged into one for a larger training set (e.g., for yield r ranged between 0.11 and 0.40 in the respective populations and between 0.18 and 0.35 in the merged populations). Cross-population prediction, when one population was used as the training population set and another population was used as the validation set, resulted in no prediction accuracy. This lack of cross-population prediction accuracy cannot be explained by a lower level of relatedness between populations, as measured by a shared SNP similarity, since it was only slightly lower between than within populations. Simulation studies confirm that cross-prediction accuracy decreases as the proportion of shared QTLs decreases, which can be expected from a higher level of QTL × environment interactions.Electronic supplementary materialThe online version of this article (doi:10.1007/s11032-014-0143-y) contains supplementary material, which is available to authorized users.

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