Bread wheat is one of the most important and broadly studied crops. However, due to the complexity of its genome and incomplete genome collection of wild populations, the bread wheat genome landscape and domestication history remain elusive. By investigating the whole-genome resequencing data of 93 accessions from worldwide populations of bread wheat and its diploid and tetraploid progenitors, together with 90 published exome-capture data, we find that the B subgenome has more variations than A and D subgenomes, including SNPs and deletions. Population genetics analyses support a monophyletic origin of domesticated wheat from wild emmer in northern Levant, with substantial introgressed genomic fragments from southern Levant. Southern Levant contributes more than 676 Mb in AB subgenomes and enriched in the pericentromeric regions. The AB subgenome introgression happens at the early stage of wheat speciation and partially contributes to their greater genetic diversity. Furthermore, we detect massive alien introgressions that originated from distant species through natural and artificial hybridizations, resulting in the reintroduction of ~ 709 Mb and ~ 1577 Mb sequences into bread wheat landraces and varieties, respectively. A large fraction of these intra- and inter-introgression fragments are associated with quantitative trait loci of important traits, and selection events are also identified. We reveal the significance of multiple introgressions from distant wild populations and alien species in shaping the genetic components of bread wheat, and provide important resources and new perspectives for future wheat breeding.

Summary: The incorporation of resistance genes into wheat commercial varieties is the ideal strategy to combat stripe or yellow rust (YR). In a search for novel resistance genes, we performed a large‐scale genomic association analysis with high‐density 660K single nucleotide polymorphism (SNP) arrays to determine the genetic components of YR resistance in 411 spring wheat lines. Following quality control, 371 972 SNPs were screened, covering over 50% of the high‐confidence annotated gene space. Nineteen stable genomic regions harbouring 292 significant SNPs were associated with adult‐plant YR resistance across nine environments. Of these, 14 SNPs were localized in the proximity of known loci widely used in breeding. Obvious candidate SNP variants were identified in certain confidence intervals, such as the cloned gene Yr18 and the major locus on chromosome 2BL, despite a large extent of linkage disequilibrium. The number of causal SNP variants was refined using an independent validation panel and consideration of the estimated functional importance of each nucleotide polymorphism. Interestingly, four natural polymorphisms causing amino acid changes in the gene TraesCS2B01G513100 that encodes a serine/threonine protein kinase (STPK) were significantly involved in YR responses. Gene expression and mutation analysis confirmed that STPK played an important role in YR resistance. PCR markers were developed to identify the favourable TraesCS2B01G513100 haplotype for marker‐assisted breeding. These results demonstrate that high‐resolution SNP‐based GWAS enables the rapid identification of putative resistance genes and can be used to improve the efficiency of marker‐assisted selection in wheat disease resistance breeding.

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is a devastating foliar disease that affects common wheat and barley throughout the world. The reasonable deployment of adult plant resistance (APR) wheat varieties is one of the best methods for controlling this disease. Wheat landraces are valuable resources for identifying the genes/QTLs responsible for disease resistance. Humai 15 is a Chinese spring wheat landrace and it has exhibited adequate levels of APR to the prevalent Pst races in field environments for many years. In this study, a population of 177 recombinant inbred lines (RILs) was derived from Humai 15 × Mingxian 169. After screening based on a 90K chip array using 45 RILs and Kompetitive Allelic Specific PCR marker genotyping for the population of RILs, a major effect QTL in Humai 15 was located on the centromere of chromosome 2B, where it accounted for up to 47.2% of the phenotypic variation. Two other minor QTL genes from Humai 15 were located on chromosome arms 3BS and 4BL. The Yr18 gene was identified on chromosome arm 7DS in Mingxian 169.

High throughput phenotyping (HTP) for wheat (Triticum aestivum L.) stay green (SG) is expected in field breeding as SG is a beneficial phenotype for wheat high yield and environment adaptability. The RGB and multispectral imaging based on the unmanned aerial vehicle (UAV) are widely popular multi-purpose HTP platforms for crops in the field. The purpose of this study was to compare the potential of UAV RGB and multispectral images (MSI) in SG phenotyping of diversified wheat germplasm. The multi-temporal images of 450 samples (406 wheat genotypes) were obtained and the color indices (CIs) from RGB and MSI and spectral indices (SIs) from MSI were extracted, respectively. The four indices (CIs in RGB, CIs in MSI, SIs in MSI, and CIs + SIs in MSI) were used to detect four SG stages, respectively, by machine learning classifiers. Then, all indices’ dynamics were analyzed and the indices that varied monotonously and significantly were chosen to calculate wheat temporal stay green rates (SGR) to quantify the SG in diverse genotypes. The correlations between indices’ SGR and wheat yield were assessed and the dynamics of some indices’ SGR with different yield correlations were tracked in three visual observed SG grades samples. In SG stage detection, classifiers best average accuracy reached 93.20–98.60% and 93.80–98.80% in train and test set, respectively, and the SIs containing red edge or near-infrared band were more effective than the CIs calculated only by visible bands. Indices’ temporal SGR could quantify SG changes on a population level, but showed some differences in the correlation with yield and in tracking visual SG grades samples. In SIs, the SGR of Normalized Difference Red-edge Index (NDRE), Red-edge Chlorophyll Index (CIRE), and Normalized Difference Vegetation Index (NDVI) in MSI showed high correlations with yield and could track visual SG grades at an earlier stage of grain filling. In CIs, the SGR of Normalized Green Red Difference Index (NGRDI), the Green Leaf Index (GLI) in RGB and MSI showed low correlations with yield and could only track visual SG grades at late grain filling stage and that of Norm Red (NormR) in RGB images failed to track visual SG grades. This study preliminarily confirms the MSI is more available and reliable than RGB in phenotyping for wheat SG. The index-based SGR in this study could act as HTP reference solutions for SG in diversified wheat genotypes.