Search

Aegilops tauschii, the diploid wild progenitor of the D subgenome of bread wheat, is a reservoir of genetic diversity for improving bread wheat performance and environmental resilience. Here we sequenced 242 Ae. tauschii accessions and compared them to the wheat D subgenome to characterize genomic diversity. We found that a rare lineage of Ae. tauschii geographically restricted to present-day Georgia contributed to the wheat D subgenome in the independent hybridizations that gave rise to modern bread wheat. Through k-mer-based association mapping, we identified discrete genomic regions with candidate genes for disease and pest resistance and demonstrated their functional transfer into wheat by transgenesis and wide crossing, including the generation of a library of hexaploids incorporating diverse Ae. tauschii genomes. Exploiting the genomic diversity of the Ae. tauschii ancestral diploid genome permits rapid trait discovery and functional genetic validation in a hexaploid background amenable to breeding.

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The Wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the Wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10-14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 92% imputation accuracy with 0.01x sequence coverage, which was slightly lower than the accuracy obtained using the 0.5x sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (p-value < 2 x 10-14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2x GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequlibrium and proportion of identity-by-descent regions among accessions in the PHG database. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

Abstract Background Single nucleotide polymorphisms (SNPs) are ideally suited for the construction of high-resolution genetic maps, studying population evolutionary history and performing genome-wide association mapping experiments. Here, we used a genome-wide set of 1536 SNPs to study linkage disequilibrium (LD) and population structure in a panel of 478 spring and winter wheat cultivars (Triticum aestivum) from 17 populations across the United States and Mexico. Results Most of the wheat oligo pool assay (OPA) SNPs that were polymorphic within the complete set of 478 cultivars were also polymorphic in all subpopulations. Higher levels of genetic differentiation were observed among wheat lines within populations than among populations. A total of nine genetically distinct clusters were identified, suggesting that some of the pre-defined populations shared significant proportion of genetic ancestry. Estimates of population structure (FST) at individual loci showed a high level of heterogeneity across the genome. In addition, seven genomic regions with elevated FST were detected between the spring and winter wheat populations. Some of these regions overlapped with previously mapped flowering time QTL. Across all populations, the highest extent of significant LD was observed in the wheat D-genome, followed by lower LD in the A- and B-genomes. The differences in the extent of LD among populations and genomes were mostly driven by differences in long-range LD ( > 10 cM). Conclusions Genome- and population-specific patterns of genetic differentiation and LD were discovered in the populations of wheat cultivars from different geographic regions. Our study demonstrated that the estimates of population structure between spring and winter wheat lines can identify genomic regions harboring candidate genes involved in the regulation of growth habit. Variation in LD suggests that breeding and selection had a different impact on each wheat genome both within and among populations. The higher extent of LD in the wheat D-genome versus the A- and B-genomes likely reflects the episodes of recent introgression and population bottleneck accompanying the origin of hexaploid wheat. The assessment of LD and population structure in this assembled panel of diverse lines provides critical information for the development of genetic resources for genome-wide association mapping of agronomically important traits in wheat.

In a semi‐arid climate, heat and drought stress can significantly reduce photosynthesis during grain filling hence, the remobilization of stored carbon reserves from stems becomes important to wheat (Triticum aestivum L.) yield. A 2‐year field study was conducted to better understand the contributions of different internodes to grain yield. We measured remobilization of stored carbon reserves in eight wheat genotypes under irrigated and dryland conditions. For each genotype, plant samples consisting of ten culms were collected between anthesis and physiological maturity. There was no difference in single stem weight loss between two water levels, but the stem loss contribution to yield was higher in dryland wheat (54.4%) than that in irrigated wheat (38.5%). Among the genotypes, the stem loss contribution to yield ranged 38.9%–58.1% in the relatively wet season of 2017 and 36.5%–62.5% in the dry season of 2018. The amount of stem loss showed a strong relationship with grain yield, especially under the dryland condition. At each sampling date, length as well as the dry weight of peduncle (first internode from the top) was greater than the other internodes; however, more stem loss (in weight and percentage weight) occurred in the third internode, followed by second and fourth internodes. Results suggest that the stem loss contribution to yield is greater under dryland than irrigated condition and occurs mainly in the middle portion of the culm. [ABSTRACT FROM AUTHOR]

Fusarium head blight (FHB; caused by Fusarium graminearum Schwabe) is one of the most destructive wheat (Triticum aestivum L.) diseases worldwide. Sources of FHB resistance are limited. The objectives of this study were to screen selected spring wheat accessions in the USDA National Small Grains Collection for FHB reactions using FHB index, visual scabby kernel (VSK), and deoxynivalenol (DON) content. A total of 1035 spring wheat accessions were initially screened in unreplicated field evaluation nurseries in 1998 and 1999. Accessions with low FHB were selected as putative resistant materials and were tested in replicated trials from 1999 to 2002. After three or more years of evaluation, 73 accessions with resistance were identified, including 10 accessions previously reported as resistant to FHB. Selections from Europe had the highest percentage of resistance to VSK, followed by selections from South America and Asia. We concluded that there is diversity for FHB resistance in wheat. Fusarium head blight resistance identified from Europe appeared to be unique in that these accessions normally displayed a moderate level of disease in the field, but a higher level of resistance based on VSK and DON. The discovery of diverse resistant sources will provide diversity so that higher levels of resistance could be developed. The novelty and types of FHB resistance in these selections should be further characterized using molecular markers and different inoculation techniques.

Wheat streak mosaic virus (WSMV) (Family: Potyviridae; Genus: Tritimovirus), disseminated naturally by the wheat curl mite (Aceria tosichella Keifer), is an important disease of wheat (Triticum aestivam L. em Thell.) worldwide. Breeding for resistant cultivars remains the best strategy to control the disease. Nine winter wheat genotypes with differential reaction to WSMV were crossed in a complete diallel mating design to determine the combining ability of WSMV resistance. Parents, [F.sub.1], and reciprocal crosses were inoculated at the seedling (2-3 leaves) stage with a WSMV-SD isolate and evaluated for reaction under greenhouse conditions. Disease reaction was assessed twice (at 1-wk intervals) by a 1-to-5 scale (1 = no visible symptoms to light green streaks, 5 = severe yellow streaks and necrosis). Data were analyzed according to Griffing's Method 3 and Model 1, where one set of [F.sub.1] and reciprocal [F.sub.1] are included. Highly significant genotype effects (P < 0.01) were observed for WSMV resistance. General combining ability (GCA) and specific combining ability (SCA) effects for WSMV resistance were highly significant (P < 0.01), indicating that both additive and nonadditive genetic effects are involved in the inheritance of WSMV resistance. The reciprocal effects were not significant (P > 0.05). The ratio of combining ability variance components [(2[σ.sup.2.sub.GCA])/(2[σ.sup.2.sub.GCA] + [σ.sup.2.sub.GCA])] was small (0.1), indicating that nonadditive (i.e., dominance and epistasis) gene effects were more important than additive gene effects in controlling WSMV resistance in these crosses; therefore, progeny performance cannot be adequately predicted from GCA effects alone., WSMV, DISSEMINATED NATURALLY by the wheat curl mite, is an important disease in Canada, USA, and Europe, especially in areas where winter wheat is regularly grown (Slykhuis, 1955; Shawn and [...]