Key message We combined quantitative and population genetic methods to identify loci under selection for adult plant resistance to stripe rust in an Austrian winter wheat breeding population from 2008 to 2018. Abstract Resistance to stripe rust, a foliar disease caused by the fungus P. striiformis f. sp. tritici, in wheat (Triticum aestivum L.) is both qualitatively and quantitatively controlled. Resistance genes confer complete, race-specific resistance but are easily overcome by evolving pathogen populations, while quantitative resistance is controlled by many small- to medium-effect loci that provide incomplete yet more durable protection. Data on resistance loci can be applied in marker-assisted selection and genomic prediction frameworks. We employed genome-wide association to detect loci associated with stripe rust and selection testing to identify regions of the genome that underwent selection for stripe rust resistance in an Austrian winter wheat breeding program from 2008 to 2018. Genome-wide association mapping identified 150 resistance loci, 62 of which showed significant evidence of selection over time. The breeding population also demonstrated selection for resistance at the genome-wide level.

Key message Association mapping and phenotypic analysis of a diversity panel of 238 bread wheat accessions highlights differences in resistance against common vs. dwarf bunt and identifies genotypes valuable for bi-parental crosses. Abstract Common bunt caused by Tilletia caries and T. laevis was successfully controlled by seed dressings with systemic fungicides for decades, but has become a renewed threat to wheat yield and quality in organic agriculture where such treatments are forbidden. As the most efficient way to address this problem is the use of resistant cultivars, this study aims to broaden the spectrum of resistance sources available for breeders by identifying resistance loci against common bunt in bread wheat accessions of the USDA National Small Grains Collection. We conducted three years of artificially inoculated field trials to assess common bunt infection levels in a diversity panel comprising 238 wheat accessions for which data on resistance against the closely related pathogen Tilletia controversa causing dwarf bunt was already available. Resistance levels against common bunt were higher compared to dwarf bunt with 99 accessions showing $$\le$$ ≤ 1% incidence. Genome-wide association mapping identified six markers significantly associated with common bunt incidence in regions already known to confer resistance on chromosomes 1A and 1B and novel loci on 2B and 7A. Our results show that resistance against common and dwarf bunt is not necessarily controlled by the same loci but we identified twenty accessions with high resistance against both diseases. These represent valuable new resources for research and breeding programs since several bunt races have already been reported to overcome known resistance genes.

Pre‐harvest sprouting (PHS), germination of seeds before harvest, is a major problem in global wheat (Triticum aestivum L.) production, and leads to reduced bread‐making quality in affected grain. Breeding for PHS resistance can prevent losses under adverse conditions. Selecting resistant lines in years lacking pre‐harvest rain, requires challenging of plants in the field or in the laboratory or using genetic markers. Despite the availability of a wheat reference and pan‐genome, linking markers, genes, allelic, and structural variation, a complete understanding of the mechanisms underlying various sources of PHS resistance is still lacking. Therefore, we challenged a population of European wheat varieties and breeding lines with PHS conditions and phenotyped them for PHS traits, grain quality, phenological and agronomic traits to conduct genome‐wide association mapping. Furthermore, we compared these marker‐trait associations to previously reported PHS loci and evaluated their usefulness for breeding. We found markers associated with PHS on all chromosomes, with strong evidence for novel quantitative trait locus/loci (QTL) on chromosome 1A and 5B. The QTL on chromosome 1A lacks pleiotropic effect, for the QTL on 5B we detected pleiotropic effects on phenology and grain quality. Multiple peaks on chromosome 4A co‐located with the major resistance locus Phs‐A1, for which two causal genes, TaPM19 and TaMKK3, have been proposed. Mapping markers and genes to the pan‐genome and chromosomal alignments provide evidence for structural variation around this major PHS‐resistance locus. Although PHS is controlled by many loci distributed across the wheat genome, Phs‐A1 on chromosome 4A seems to be the most effective and widely deployed source of resistance, in European wheat varieties.

Key message We used a historical dataset on stripe rust resistance across 11 years in an Austrian winter wheat breeding program to evaluate genomic and pedigree-based linear and semi-parametric prediction methods. Abstract Stripe rust (yellow rust) is an economically important foliar disease of wheat (Triticum aestivum L.) caused by the fungus Puccinia striiformis f. sp. tritici. Resistance to stripe rust is controlled by both qualitative (R-genes) and quantitative (small- to medium-effect quantitative trait loci, QTL) mechanisms. Genomic and pedigree-based prediction methods can accelerate selection for quantitative traits such as stripe rust resistance. Here we tested linear and semi-parametric models incorporating genomic, pedigree, and QTL information for cross-validated, forward, and pairwise prediction of adult plant resistance to stripe rust across 11 years (2008–2018) in an Austrian winter wheat breeding program. Semi-parametric genomic modeling had the greatest predictive ability and genetic variance overall, but differences between models were small. Including QTL as covariates improved predictive ability in some years where highly significant QTL had been detected via genome-wide association analysis. Predictive ability was moderate within years (cross-validated) but poor in cross-year frameworks.

Estimating the breeding value of individuals using genome-wide marker data (genomic prediction) is currently one of the most important drivers of breeding progress in major crops. Recently, remote sensing technologies and aerial hyperspectral imaging of plant canopies combined with a variety of statistical and machine learning models allow prediction of the breeding value of individuals in the absence of genetic marker data. Hyperspectral measurements in the form of near-infrared spectroscopy have been used since the 80s to predict compositional parameters of harvest products, and some recent studies have also used this tool to predict the important parameter grain yield, suggesting that phenomic prediction can outperform genomic prediction. The genome of an individual can be considered static, however gene expression is tissue specific and differs under environmental influences, leading to a tissue and environment specific phenome. Here, we compare genomic prediction and phenomic prediction from hyperspectral measurements of wheat grains for the prediction of a variety of traits including grain yield. We show that phenomic predictions seem equivalent to genomic predictions for some traits in terms of predictive ability. However, phenomic predictions appear heavily biased towards the information present in the predictor. Future studies on this topic should investigate whether population parameters are conserved in phenomic predictions as they are in genomic predictions. Furthermore, we suggest a method to circumvent this issue, which reveals that unbiased phenomic prediction abilities are considerably lower than previously reported.