Summary: The economically valuable Brassica species include the six related members of U’s Triangle. Despite the agronomic and economic importance of these Brassicas, the impacts of evolution and relatively recent domestication events on the genetic landscape of seed development have not been comprehensively examined in these species. Here we present a 3D transcriptome atlas for the six species of U’s Triangle, producing a unique resource that captures gene expression data for the major subcompartments of the seed, from the unfertilized ovule to the mature embryo and seed coat. This comprehensive dataset for seed development in tetraploid and ancestral diploid Brassicas provides new insights into evolutionary divergence and expression bias at the gene and subgenome levels during the domestication of these valued crop species. Comparisons of gene expression associated with regulatory networks and metabolic pathways operating in the embryo and seed coat during seed development reveal differences in storage reserve accumulation and fatty acid metabolism among the six Brassica species. This study illustrates the genetic underpinnings of seed traits and the selective pressures placed on seed production, providing an immense resource for continued investigation of Brassica polyploid biology, genomics and evolution.

Durum wheat is an economically important crop for Canadian farmers. Fusarium head blight (FHB) is one of the most destructive diseases that threatens durum production in Canada. FHB reduces yield and end-use quality and most commonly contaminates the grain with the fungal mycotoxin deoxynivalenol, also known as DON. Serious outbreaks of FHB can occur in durum wheat in Canada, and combining genetic resistance with fungicide application is a cost effective approach to control this disease. However, there is limited variation for genetic resistance to FHB in elite Canadian durum cultivars. To explore and identify useful genetic FHB resistance variation for the improvement of Canadian durum wheat, we assembled an association mapping (AM) panel of diverse durum germplasms and performed genome wide association analysis (GWAS). Thirty-one quantitative trait loci (QTL) across all 14 chromosomes were significantly associated with FHB resistance. On 3BS, a stable QTL with a larger effect for resistance was located close to the centromere of 3BS. Three haplotypes ofFhb1QTL were identified, with an emmer wheat haplotype contributing to disease susceptibility. The large number of QTL identified here can provide a rich resource to improve FHB resistance in commercially grown durum wheat. Among the 31 QTL most were associated with plant height and/or flower time. QTL 1A.1, 1A.2, 3B.2, 5A.1, 6A.1, 7A.3 were associated with FHB resistance and not associated or only weakly associated with flowering time nor plant height. These QTL have features that would make them good targets for FHB resistance breeding.

Fusarium head blight (FHB) resistance is quantitatively inherited, controlled by multiple minor effect genes, and highly affected by the interaction of genotype and environment. This makes genomic selection (GS) that uses genome-wide molecular marker data to predict the genetic breeding value as a promising approach to select superior lines with better resistance. However, various factors can affect accuracies of GS and better understanding how these factors affect GS accuracies could ensure the success of applying GS to improve FHB resistance in wheat. In this study, we performed a comprehensive evaluation of factors that affect GS accuracies with a multi-parental population designed for FHB resistance. We found larger sample sizes could get better accuracies. Training population designed by CDmean based optimization algorithms significantly increased accuracies than random sampling approach, while mean of predictor error variance (PEVmean) had the poorest performance. Different genomic selection models performed similarly for accuracies. Including prior known large effect quantitative trait loci (QTL) as fixed effect into the GS model considerably improved the predictability. Multi-traits models had almost no effects, while the multi-environment model outperformed the single environment model for prediction across different environments. By comparing within and across family prediction, better accuracies were obtained with the training population more closely related to the testing population. However, achieving good accuracies for GS prediction across populations is still a challenging issue for GS application.

Fusarium head blight (FHB) is a devastating fungal disease of small-grain cereals that results in severe yield and quality losses. FHB resistance is controlled by resistance components including incidence, field severity, visual rating index, Fusarium damaged kernels (FDKs), and the accumulation of the mycotoxin deoxynivalenol (DON). Resistance conferred by each of these components is partial and must be combined to achieve resistance sufficient to protect wheat from yield losses. In this study, two biparental mapping populations were analyzed in Canadian FHB nurseries and quantitative trait loci (QTL) mapped for the traits listed above. Nine genomic loci, on 2AS, 2BS, 3BS, 4AS, 4AL, 4BS, 5AS, 5AL, and 5BL, were enriched for the majority of the QTL controlling FHB resistance. The previously validated FHB resistance QTL on 3BS and 5AS affected resistance to severity, FDK, and DON in these populations. The remaining seven genomic loci colocalize with flowering time and/or plant height QTL. The QTL on 4B was a major contributor to all field resistance traits and plant height in the field. QTL on 4AL showed contrasting effects for FHB resistance between Eastern and Western Canada, indicating a local adapted resistance to FHB. In addition, we also found that the 2AS QTL contributed a major effect for DON, and the 2BS for FDK, while the 5AL conferred mainly effect for both FDK/DON. Results presented here provide insight into the genetic architecture underlying these resistant components and insight into how FHB resistance in wheat is controlled by a complex network of interactions between genes controlling flowering time, plant height, local adaption, and FHB resistance components.