Genome-wide association study and genomic prediction of resistance to stripe rust in current Central and Northern European winter wheat germplasm

Stripe or yellow rust, caused by the fungus Puccinia striiformis Westend f. sp. tritici, is one of the most destructive wheat diseases. Sustainable management of wheat stripe rust can be achieved through the deployment of rust resistant cultivars. To detect effective resistance loci for use in breeding programs, an association mapping panel of 230 winter wheat cultivars and breeding lines from Northern and Central Europe was employed. Genotyping with the Illumina® iSelect® 25 K Infinium® single nucleotide polymorphism (SNP) genotyping array yielded 8812 polymorphic markers. Structure analysis revealed two subpopulations with 92 Austrian breeding lines and cultivars, which were separated from the other 138 genotypes from Germany, Norway, Sweden, Denmark, Poland, and Switzerland. Genome-wide association study for adult plant stripe rust resistance identified 12 SNP markers on six wheat chromosomes which showed consistent effects over several testing environments. Among these, two marker loci on chromosomes 2BS (RAC875_c1226_652) and 6AL (Tdurum_contig29607_413) were highly predictive in three independent validation populations of 1065, 1001, and 175 breeding lines. Lines with the resistant haplotype at both loci were nearly free of stipe rust symptoms. By using mixed linear models with those markers as fixed effects, we could increase predictive ability in the three populations by 0.13–0.46 compared to a standard genomic best linear unbiased prediction approach. The obtained results facilitate an efficient selection for stripe rust resistance against the current pathogen population in the Northern and Central European winter wheat gene pool.publishedVersio

Ecological principles to guide the development of crop variety mixtures

Crop variety mixtures can provide many benefits, including pathogen suppression and increased yield and yield stability. However, these benefits do not necessarily occur in all mixtures, and the benefits of diversity may be compromised by disadvantages due to increased crop heterogeneity. In-field development of mixtures by assembling many combinations of crop genotypes without prior expectation about which genotypes need to be combined to produce well-performing mixtures results in prohibitively large designs. Therefore, effective tools are required to narrow down the number of promising variety mixtures, and to then identify in experiments which of these deliver the highest benefits. Here, we first review current knowledge about the mechanisms underlying effects in ecological diversity experiments and in current agricultural applications. We then discuss some of the principal difficulties arising in the application of this knowledge to develop good variety mixtures. We also discuss non-conventional approaches to solve some of these issues. In particular, we highlight the potential and limitations of trait-based methods to determine good variety mixing partners, and argue that nontraditional traits and trait-derived metrics may be needed for the trait-based approach to deliver its full potential. Specifically, we argue that good mixing partners can be identified using modern genetic and genomic approaches. Alternatively, good mixtures may be obtained by combining varieties that respond differently to environmental variation; such varieties could easily be identified in standard variety testing trials. Preliminary analyses show that niche differences underlying the different environmental responses can indicate functional complementarity and promote mixture yield and yield stability