Evaluation of the genetic architecture and the potential of genomics-assisted breeding of quality traits in two large panels of durum wheat.

KEY MESSAGE: New QTL for important quality traits in durum were identified, but for most QTL their effect varies depending on the investigated germplasm. Most of the global durum wheat (Triticum turgidum ssp. durum) production is used for human consumption via pasta and to a lower extent couscous and bulgur. Therefore, durum wheat varieties have to fulfill high demands regarding quality traits. In this study, we evaluated the quality traits protein content, sedimentation volume, falling number, vitreousity and thousand kernel weight in a Central European (CP) and a Southern and Western European panel (SP) with 183 and 159 durum lines, respectively, and investigated their genetic architecture by genome-wide association mapping. Except for protein content, we identified QTL explaining a large proportion of the genotypic variance for different traits. However, most of them were identified only in one panel. Nevertheless, for sedimentation volume a genomic region on chromosome 1B appeared important in both durum panels and a BLAST search against the emmer and bread wheat reference genomes points toward the candidate gene Glu-B3. This was further supported by the protein subunit banding pattern via SDS-PAGE gel electrophoresis. For vitreousity, genomic regions on chromosome 7A explained a larger proportion of the genotypic variance in both panels, whereas one QTL, possibly related to the Pinb-2 locus, also slightly influenced the protein content. Within each panel, high prediction abilities for genomic selection were obtained, which, however, dropped considerably when predicting across both panels. Nevertheless, the across-panel prediction ability was still larger than 0.4 for protein content and sedimentation volume, underlining the potential for genomics-aided durum breeding, if laboratory and logistical facilities are available.

Simultaneous improvement of grain yield and protein content in durum wheat by different phenotypic indices and genomic selection.

KEY MESSAGE: Simultaneous improvement of protein content and grain yield by index selection is possible but its efficiency largely depends on the weighting of the single traits. The genetic architecture of these indices is similar to that of the primary traits. Grain yield and protein content are of major importance in durum wheat breeding, but their negative correlation has hampered their simultaneous improvement. To account for this in wheat breeding, the grain protein deviation (GPD) and the protein yield were proposed as targets for selection. The aim of this work was to investigate the potential of different indices to simultaneously improve grain yield and protein content in durum wheat and to evaluate their genetic architecture towards genomics-assisted breeding. To this end, we investigated two different durum wheat panels comprising 159 and 189 genotypes, which were tested in multiple field locations across Europe and genotyped by a genotyping-by-sequencing approach. The phenotypic analyses revealed significant genetic variances for all traits and heritabilities of the phenotypic indices that were in a similar range as those of grain yield and protein content. The GPD showed a high and positive correlation with protein content, whereas protein yield was highly and positively correlated with grain yield. Thus, selecting for a high GPD would mainly increase the protein content whereas a selection based on protein yield would mainly improve grain yield, but a combination of both indices allows to balance this selection. The genome-wide association mapping revealed a complex genetic architecture for all traits with most QTL having small effects and being detected only in one germplasm set, thus limiting the potential of marker-assisted selection for trait improvement. By contrast, genome-wide prediction appeared promising but its performance strongly depends on the relatedness between training and prediction sets.

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat.

Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a cross-validation approach. For heading time, the high accuracy seen with marker-assisted selection severely dropped with genomic selection approaches RR-BLUP (ridge regression best linear unbiased prediction) and BayesCπ, whereas for plant height, accuracy was low with marker-assisted selection as well as RR-BLUP and BayesCπ. Differences in the linkage disequilibrium structure of the functional and single-nucleotide polymorphism markers relevant for the two traits were identified in a simulation study as a likely explanation for the different trends in accuracies of prediction. A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection.

Relatedness severely impacts accuracy of marker-assisted selection for disease resistance in hybrid wheat.

The accuracy of genomic selection depends on the relatedness between the members of the set in which marker effects are estimated based on evaluation data and the types for which performance is predicted. Here, we investigate the impact of relatedness on the performance of marker-assisted selection for fungal disease resistance in hybrid wheat. A large and diverse mapping population of 1739 elite European winter wheat inbred lines and hybrids was evaluated for powdery mildew, leaf rust and stripe rust resistance in multi-location field trials and fingerprinted with 9 k and 90 k SNP arrays. Comparison of the accuracies of prediction achieved with data sets from the two marker arrays revealed a crucial role for a sufficiently high marker density in genome-wide association mapping. Cross-validation studies using test sets with varying degrees of relationship to the corresponding estimation sets revealed that close relatedness leads to a substantial increase in the proportion of total genotypic variance explained by the identified QTL and consequently to an overoptimistic judgment of the precision of marker-assisted selection.

Association mapping for quality traits in soft winter wheat.

Improvement of end-use quality in bread wheat (Triticum aestivum L.) depends on a thorough understanding of the genetic basis of important quality traits. The main goal of our study was to investigate the genetic basis of 1,000-kernel weight, protein content, sedimentation volume, test weight, and starch concentration using an association mapping approach. We fingerprinted 207 diverse European elite soft winter wheat lines with 115 SSR markers and evaluated the genotypes in multi-environment trials. The principal coordinate analysis revealed absence of a clear population but presence of a family structure. Therefore, we used linear mixed models and marker-based kinship matrices to correct for family structure. In genome-wide scans, we detected main effect QTL for all five traits. In contrast, epistatic QTL were only observed for sedimentation volume and test weight explaining a small proportion of the genotypic variation. Consequently, our findings suggested that integrating epistasis in marker-assisted breeding will not lead to substantially increased selection gain for quality traits in soft winter wheat.