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Project Topic
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The availability of high-density low-cost marker genotyping platforms in wheat and other crops has enabled a paradigm shift in plant breeding, by making genomic prediction and selection feasible. Genomic selection (GS) enables the prediction of breeding values of progeny lines without costly phenotyping, saving time and money, increasing intensity of selection as well as accuracy of trait prediction. This technology has already revolutionized animal breeding, and in recent years, much research has been invested in developing GS methodology for plant breeding. Simulations and implementations of GS in livestock breeding show that genetic gains can be doubled and sometimes tripled, but this potential has yet to be realized in plant breeding practices. Much room exists to improve the prediction accuracies of disease resistance such as stripe rust and Fusarium Head Blight (FHB), which are targeted in this proposal. The current GS approach used by many national and international breeding programs is based on utilizing a large number of genome-wide markers for obtaining genomic estimated breeding values (GEBV) to make selection decisions. While being useful, prediction accuracies from this approach are fairly low for complex disease resistances like stripe rust and FHB. The core idea behind this project is to make use of the huge established knowledgebase of biologically relevant data, quantitative trait loci (QTL) and marker-trait relationships to improve prediction accuracy by including prior knowledge into the GS models. Traditional marker-assisted selection (MAS) is built on known genetic linkages of traits with markers, and usually only major effect genetic loci are exploited this way. This contrasts with the anonymous association of markers in GS where GEBVs of breeding lines are estimated. In this project, we seek to bridge this gap, by incorporating state-of-the art knowledge of host-pathogen relationships and validated QTL into models to improve the prediction accuracy of GS for disease resistance in wheat. To highlight the potential of this approach, we have chosen two of the most devastating plant diseases affecting European and North American wheat production - FHB and stripe rust. The WheatSustain project will develop new models and methods for incorporating multi-trait relation to disease resistance traits that will facilitate the prediction of unobserved new wheat lines. Genotyping of pathogen races will add new dimensions by allowing the study of race x host line genomic interactions. By making use of the huge established knowledge of disease resistance loci with known effects, insight into host-pathogen interactions, race specificity of resistance genes, and genetic correlations among traits, GS methodology for disease resistance in wheat can be lifted to new unprecedented levels by application of our concept of knowledge-based genomic prediction models. These models will be tested and validated on actual wheat breeding material from collaborating breeding programs, and continuously improved by close interaction with the respective breeders. WheatSustain will establish a close collaboration among world leading experts on genomic prediction modeling in plants and animals, bioinformatics, wheat genomics and leaders in the field of plant pathology and host-pathogen relationships for stripe rust and FHB resistance in wheat. An interdisciplinary research team is established involving cutting-edge research groups from Norway, Ireland, Germany, Austria, Mexico, USA and Canada. Plant breeders from public and private breeding programs will take active part in the research by providing germplasm with phenotypic and genotypic data, take part in disease evaluations and test out the developed breeding methodologies in their breeding programs. This dynamic research environment will also provide excellent research training for PhD students and postdocs, thereby advancing the education of future researchers and plant breeders.
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