Successful seedling establishment depends on the optimum depth of seed placement especially in drought-prone conditions, providing an opportunity to exploit subsoil water and increase winter survival in winter wheat. Coleoptile length is a key determinant for the appropriate depth at which seed can be sown. Thus, understanding the genetic basis of coleoptile length is necessary and important for wheat breeding. We conducted a genome-wide association study (GWAS) using a diverse panel of 298 winter wheat genotypes to dissect the genetic architecture of coleoptile length. We identified nine genomic regions associated with the coleoptile length on seven different chromosomes. Of the nine genomic regions, five have been previously reported in various studies, including one mapped to previously known Rht-B1 region. Three novel quantitative trait loci (QTLs), QCL.sdsu-2AS, QCL.sdsu-4BL, and QCL.sdsu-5BL were identified in our study. QCL.sdsu-5BL has a large substitution effect which is comparable to Rht-B1's effect and could be used to compensate for the negative effect of Rht-B1 on coleoptile length. In total, the nine QTLs explained 59% of the total phenotypic variation. Cultivars 'Agate' and 'MT06103' have the longest coleoptile length and interestingly, have favorable alleles at nine and eight coleoptile loci, respectively. These lines could be a valuable germplasm for longer coleoptile breeding. Gene annotations in the candidate regions revealed several putative proteins of specific interest including cytochrome P450-like, expansins, and phytochrome A. The QTLs for coleoptile length linked to single-nucleotide polymorphism (SNP) markers reported in this study could be employed in marker-assisted breeding for longer coleoptile in wheat. Thus, our study provides valuable insights into the genetic and molecular regulation of the coleoptile length in winter wheat.

Optimizing wheat height to maximize yield has been an important aspect which is evident from a successful example of green revolution. Dwarfing genes (Rht) are known for yield gains due to lodging resistance and partitioning of assimilates into ear. The available and commercially exploited sources of dwarfism in Indian spring wheat are Rht1 and Rht2 genes inspite of availability of over 20 dwarfing genes. Rht8 a Gibberellic acid sensitive dwarfing gene is another reduced height gene commercially exploited in some Mediterranean countries. Two F2 populations segregating for Rht1 and Rht8 genes with each comprising 398 and 379 plants were developed by crossing European winter wheat cultivars Beauchamp and Capitole with Indian spring wheat cultivar PBW 621. Different genotypic combinations for Rht1 and Rht8 genes were selected from these populations through linked molecular markers and selected F3:4 lines were evaluated for various agronomic traits in a replicated trial. Reduction in plant height with Rht8 and Rht1 averaged 2.86% and 13.3% respectively as compared to the group of lines lacking dwarfing gene. Reduction was spread along all the internodes of wheat culm and reduction was lower as progress towards the lower internode. Grain number per spike and highest yield was observed in lines carrying only Rht1 gene. Reduction in plant biomass was observed with either of the dwarfing gene. Longest coleoptile length and seedling shoot length averaged 4.4 ± 0.09 cm and 19.5 ± 0.48, respectively was observed in lines lacking any of the dwarfing gene. Negligible reduction of 6.75% and 2.84% in coleoptile length and seedling shoot length, respectively was observed in lines carrying only Rht8 gene whereas F3:4 lines with Rht1 gene showed 21.64% and 23.35% reduction in coleoptile length and seedling shoot length, respectively. Additive effect of genes was observed as double dwarfs showed 43.31% and 43.34% reduction in coleoptile length and seedling shoot length.

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

Background: In the late 1920s, A. E. Watkins collected about 7000 landrace cultivars (LCs) of bread wheat (Triticum aestivum L.) from 32 different countries around the world. Among which 826 LCs remain viable and could be a valuable source of superior/favorable alleles to enhance disease resistance in wheat. In the present study, a core set of 121 LCs, which captures the majority of the genetic diversity of Watkins collection, was evaluated for identifying novel sources of resistance against tan spot, Stagonospora nodorum blotch (SNB), and Fusarium Head Blight (FHB). Results: A diverse response was observed in 121 LCs for all three diseases. The majority of LCs were moderately susceptible to susceptible to tan spot Ptr race 1 (84%) and FHB (96%) whereas a large number of LCs were resistant or moderately resistant against tan spot Ptr race 5 (95%) and SNB (54%). Thirteen LCs were identified in this study could be a valuable source for multiple resistance to tan spot Ptr races 1 and 5, and SNB, and another five LCs could be a potential source for FHB resistance. GWAS analysis was carried out using disease phenotyping score and 8807 SNPs data of 118 LCs, which identified 30 significant marker-trait associations (MTAs) with-log10 (p-value) > 3.0. Ten, five, and five genomic regions were found to be associated with resistance to tan spot Ptr race 1, race 5, and SNB, respectively in this study. In addition to Tsn1, several novel genomic regions Q.Ts1.sdsu-4BS and Q.Ts1.sdsu-5BS (tan spot Ptr race 1) and Q.Ts5.sdsu-1BL, Q.Ts5.sdsu-2DL, Q.Ts5.sdsu-3AL, and Q.Ts5.sdsu-6BL (tan spot Ptr race 5) were also identified. Our results indicate that these putative genomic regions contain several genes that play an important role in plant defense mechanisms. Conclusion: Our results suggest the existence of valuable resistant alleles against leaf spot diseases in Watkins LCs. The single-nucleotide polymorphism (SNP) markers linked to the quantitative trait loci (QTLs) for tan spot and SNB resistance along with LCs harboring multiple disease resistance could be useful for future wheat breeding.

Leaf rust caused by Puccinia triticina Eriks is one of the most problematic diseases of wheat throughout the world. The gene Lr42 confers effective resistance against leaf rust at both seedling and adult plant stages. Previous studies had reported Lr42 to be both recessive and dominant in hexaploid wheat; however, in diploid Aegilops tauschii (TA2450), we found Lr42 to be dominant by studying segregation in two independent F2 and their F2:3 populations. We further fine-mapped Lr42 in hexaploid wheat using a KS93U50/Morocco F5 recombinant inbred line (RIL) population to a 3.7 cM genetic interval flanked by markers TC387992 and WMC432. The 3.7 cM Lr42 region physically corresponds to a 3.16 Mb genomic region on chromosome 1DS based on the Chinese Spring reference genome (RefSeq v.1.1) and a 3.5 Mb genomic interval on chromosome 1 in the Ae. tauschii reference genome. This region includes nine nucleotide-binding domain leucine-rich repeat (NLR) genes in wheat and seven in Ae. tauschii, respectively, and these are the likely candidates for Lr42. Furthermore, we developed two kompetitive allele-specific polymorphism (KASP) markers (SNP113325 and TC387992) flanking Lr42 to facilitate marker-assisted selection for rust resistance in wheat breeding programs.

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Stagonospora nodorum blotch (SNB) is an economically important wheat disease caused by the necrotrophic fungus Parastagonospora nodorum. SNB resistance in wheat is controlled by several quantitative trait loci (QTLs). Thus, identifying novel resistance/susceptibility QTLs is crucial for continuous improvement of the SNB resistance. Here, the hard winter wheat association mapping panel (HWWAMP) comprising accessions from breeding programs in the Great Plains region of the US, was evaluated for SNB resistance and necrotrophic effectors (NEs) sensitivity at the seedling stage. A genome-wide association study (GWAS) was performed to identify single‐nucleotide polymorphism (SNP) markers associated with SNB resistance and effectors sensitivity. We found seven significant associations for SNB resistance/susceptibility distributed over chromosomes 1B, 2AL, 2DS, 4AL, 5BL, 6BS, and 7AL. Two new QTLs for SNB resistance/susceptibility at the seedling stage were identified on chromosomes 6BS and 7AL, whereas five QTLs previously reported in diverse germplasms were validated. Allele stacking analysis at seven QTLs explained the additive and complex nature of SNB resistance. We identified accessions (‘Pioneer-2180’ and ‘Shocker’) with favorable alleles at five of the seven identified loci, exhibiting a high level of resistance against SNB. Further, GWAS for sensitivity to NEs uncovered significant associations for SnToxA and SnTox3, co-locating with previously identified host sensitivity genes (Tsn1 and Snn3). Candidate region analysis for SNB resistance revealed 35 genes of putative interest with plant defense response-related functions. The QTLs identified and validated in this study could be easily employed in breeding programs using the associated markers to enhance the SNB resistance in hard winter wheat.

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