A new winter wheat genetic resource harbors untapped diversity from synthetic hexaploid wheat.

KEY MESSAGE: The NIAB_WW_SHW_NAM population, a large nested association mapping panel, is a useful resource for mapping QTL from synthetic hexaploid wheat that can improve modern elite wheat cultivars. The allelic richness harbored in progenitors of hexaploid bread wheat (Triticum aestivum L.) is a useful resource for addressing the genetic diversity bottleneck in modern cultivars. Synthetic hexaploid wheat (SHW) is created through resynthesis of the hybridisation events between the tetraploid (Triticum turgidum subsp. durum Desf.) and diploid (Aegilops tauschii Coss.) bread wheat progenitors. We developed a large and diverse winter wheat nested association mapping (NAM) population (termed the NIAB_WW_SHW_NAM) consisting of 3241 genotypes derived from 54 nested back-cross 1 (BC1) populations, each formed via back-crossing a different primary SHW into the UK winter wheat cultivar 'Robigus'. The primary SHW lines were created using 15 T. durum donors and 47 Ae. tauschii accessions that spanned the lineages and geographical range of the species. Primary SHW parents were typically earlier flowering, taller and showed better resistance to yellow rust infection (Yr) than 'Robigus'. The NIAB_WW_SHW_NAM population was genotyped using a single nucleotide polymorphism (SNP) array and 27 quantitative trait loci (QTLs) were detected for flowering time, plant height and Yr resistance. Across multiple field trials, a QTL for Yr resistance was found on chromosome 4D that corresponded to the Yr28 resistance gene previously reported in other SHW lines. These results demonstrate the value of the NIAB_WW_SHW_NAM population for genetic mapping and provide the first evidence of Yr28 working in current UK environments and genetic backgrounds. These examples, coupled with the evidence of commercial wheat breeders selecting promising genotypes, highlight the potential value of the NIAB_WW_SHW_NAM to variety improvement.

Phenomic and genomic prediction of yield on multiple locations in winter wheat.

Genomic selection has recently become an established part of breeding strategies in cereals. However, a limitation of linear genomic prediction models for complex traits such as yield is that these are unable to accommodate Genotype by Environment effects, which are commonly observed over trials on multiple locations. In this study, we investigated how this environmental variation can be captured by the collection of a large number of phenomic markers using high-throughput field phenotyping and whether it can increase GS prediction accuracy. For this purpose, 44 winter wheat (Triticum aestivum L.) elite populations, comprising 2,994 lines, were grown on two sites over 2 years, to approximate the size of trials in a practical breeding programme. At various growth stages, remote sensing data from multi- and hyperspectral cameras, as well as traditional ground-based visual crop assessment scores, were collected with approximately 100 different data variables collected per plot. The predictive power for grain yield was tested for the various data types, with or without genome-wide marker data sets. Models using phenomic traits alone had a greater predictive value (R2 = 0.39-0.47) than genomic data (approximately R2 = 0.1). The average improvement in predictive power by combining trait and marker data was 6%-12% over the best phenomic-only model, and performed best when data from one full location was used to predict the yield on an entire second location. The results suggest that genetic gain in breeding programmes can be increased by utilisation of large numbers of phenotypic variables using remote sensing in field trials, although at what stage of the breeding cycle phenomic selection could be most profitably applied remains to be answered.