Einkorn genomics sheds light on history of the oldest domesticated wheat.

Einkorn (Triticum monococcum) was the first domesticated wheat species, and was central to the birth of agriculture and the Neolithic Revolution in the Fertile Crescent around 10,000 years ago1,2. Here we generate and analyse 5.2-Gb genome assemblies for wild and domesticated einkorn, including completely assembled centromeres. Einkorn centromeres are highly dynamic, showing evidence of ancient and recent centromere shifts caused by structural rearrangements. Whole-genome sequencing analysis of a diversity panel uncovered the population structure and evolutionary history of einkorn, revealing complex patterns of hybridizations and introgressions after the dispersal of domesticated einkorn from the Fertile Crescent. We also show that around 1% of the modern bread wheat (Triticum aestivum) A subgenome originates from einkorn. These resources and findings highlight the history of einkorn evolution and provide a basis to accelerate the genomics-assisted improvement of einkorn and bread wheat.

Wheat genomics: genomes, pangenomes, and beyond.

There is an urgent need to improve wheat for upcoming challenges, including biotic and abiotic stresses. Sustainable wheat improvement requires the introduction of new genes and alleles in high-yielding wheat cultivars. Using new approaches, tools, and technologies to identify and introduce new genes in wheat cultivars is critical. High-quality genomes, transcriptomes, and pangenomes provide essential resources and tools to examine wheat closely to identify and manipulate new and targeted genes and alleles. Wheat genomics has improved excellently in the past 5 years, generating multiple genomes, pangenomes, and transcriptomes. Leveraging these resources allows us to accelerate our crop improvement pipelines. This review summarizes the progress made in wheat genomics and trait discovery in the past 5 years.

Genomics-driven breeding for local adaptation of durum wheat is enhanced by farmers' traditional knowledge.

In the smallholder, low-input farming systems widespread in sub-Saharan Africa, farmers select and propagate crop varieties based on their traditional knowledge and experience. A data-driven integration of their knowledge into breeding pipelines may support the sustainable intensification of local farming. Here, we combine genomics with participatory research to tap into traditional knowledge in smallholder farming systems, using durum wheat (Triticum durum Desf.) in Ethiopia as a case study. We developed and genotyped a large multiparental population, called the Ethiopian NAM (EtNAM), that recombines an elite international breeding line with Ethiopian traditional varieties maintained by local farmers. A total of 1,200 EtNAM lines were evaluated for agronomic performance and farmers' appreciation in three locations in Ethiopia, finding that women and men farmers could skillfully identify the worth of wheat genotypes and their potential for local adaptation. We then trained a genomic selection (GS) model using farmer appreciation scores and found that its prediction accuracy over grain yield (GY) was higher than that of a benchmark GS model trained on GY. Finally, we used forward genetics approaches to identify marker-trait associations for agronomic traits and farmer appreciation scores. We produced genetic maps for individual EtNAM families and used them to support the characterization of genomic loci of breeding relevance with pleiotropic effects on phenology, yield, and farmer preference. Our data show that farmers' traditional knowledge can be integrated in genomics-driven breeding to support the selection of best allelic combinations for local adaptation.

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics.

MOTIVATION: Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plant breeding programs. While methods to predict yield from genotype or phenotype data have been proposed, improved performance and integrated models are needed. RESULTS: We propose a machine learning model that leverages both genotype and phenotype measurements by fusing genetic variants with multiple data sources collected by unmanned aerial systems. We use a deep multiple instance learning framework with an attention mechanism that sheds light on the importance given to each input during prediction, enhancing interpretability. Our model reaches 0.754 ± 0.024 Pearson correlation coefficient when predicting yield in similar environmental conditions; a 34.8% improvement over the genotype-only linear baseline (0.559 ± 0.050). We further predict yield on new lines in an unseen environment using only genotypes, obtaining a prediction accuracy of 0.386 ± 0.010, a 13.5% improvement over the linear baseline. Our multi-modal deep learning architecture efficiently accounts for plant health and environment, distilling the genetic contribution and providing excellent predictions. Yield prediction algorithms leveraging phenotypic observations during training therefore promise to improve breeding programs, ultimately speeding up delivery of improved varieties. AVAILABILITY AND IMPLEMENTATION: Available at https://github.com/BorgwardtLab/PheGeMIL (code) and https://doi.org/doi:10.5061/dryad.kprr4xh5p (data).

Identification and high-resolution mapping of a novel tiller number gene (tin6) by combining forward genetics screen and MutMap approach in bread wheat.

Wheat (Triticum aestivum) is one of the most important food crops worldwide, providing up to 20% of the caloric intake per day. Developing high-yielding wheat cultivars with tolerance against abiotic and biotic stresses is important to keep up with the increasing human population. Tiller number is one of the major yield-related traits, directly affecting the number of grains produced per plant; however, only a small number of QTL and underlining genes have been identified for this important factor. Identification of novel genetic variation underlying contrasting traits and their precise genetic mapping in wheat is considered difficult due to the complexity and size of the genome; however, advancements in genomic resources have made efficient gene localization more possible. In this study, we report the characterization of a novel tillering number gene using a mutant identified in the forward genetic screen of an ethyl methane sulfonate (EMS)-treated population of cv. "Jagger." By crossing the low tillering mutant with the Jagger wild-type plant, we generated an F2 population and used the MutMap approach to identify a novel physical interval on 11 Mb on chromosome 2DS. Using an F2 population of 442 gametes and polymorphic SNP markers, we were able to delineate the tin6 locus to a 2.1 Mb region containing 22 candidate genes.

Genetic characterization and curation of diploid A-genome wheat species.

A-genome diploid wheats represent the earliest domesticated and cultivated wheat species in the Fertile Crescent and include the donor of the wheat A sub-genome. The A-genome species encompass the cultivated einkorn (Triticum monococcum L. subsp. monococcum), wild einkorn (T. monococcum L. subsp. aegilopoides (Link) Thell.), and Triticum urartu. We evaluated the collection of 930 accessions in the Wheat Genetics Resource Center (WGRC) using genotyping by sequencing and identified 13,860 curated single-nucleotide polymorphisms. Genomic analysis detected misclassified and genetically identical (>99%) accessions, with most of the identical accessions originating from the same or nearby locations. About 56% (n = 520) of the WGRC A-genome species collections were genetically identical, supporting the need for genomic characterization for effective curation and maintenance of these collections. Population structure analysis confirmed the morphology-based classifications of the accessions and reflected the species geographic distributions. We also showed that T. urartu is the closest A-genome diploid to the A-subgenome in common wheat (Triticum aestivum L.) through phylogenetic analysis. Population analysis within the wild einkorn group showed three genetically distinct clusters, which corresponded with wild einkorn races α, ß, and γ described previously. The T. monococcum genome-wide FST scan identified candidate genomic regions harboring a domestication selection signature at the Non-brittle rachis 1 (Btr1) locus on the short arm of chromosome 3Am at ∼70 Mb. We established an A-genome core set (79 accessions) based on allelic diversity, geographical distribution, and available phenotypic data. The individual species core set maintained at least 79% of allelic variants in the A-genome collection and constituted a valuable genetic resource to improve wheat and domesticated einkorn in breeding programs.

Physical mapping of the wheat genes in low-recombination regions: radiation hybrid mapping of the C-locus.

KEY MESSAGE: This work reports the physical mapping of an important gene affecting spike compactness located in a low-recombination region of hexaploid wheat. This work paves the way for the eventual isolation and characterization of the factor involved but also opens up possibilities to use this approach to precisely map other wheat genes located on proximal parts of wheat chromosomes that show highly reduced recombination. Mapping wheat genes, in the centromeric and pericentromeric regions (~ 2/3rd of a given chromosome), poses a formidable challenge due to highly suppressed recombination. Using an example of compact spike locus (C-locus), this study provides an approach to precisely map wheat genes in the pericentromeric and centromeric regions that house ~ 30% of wheat genes. In club-wheat, spike compactness is controlled by the dominant C-locus, but previous efforts have failed to localize it, on a particular arm of chromosome 2D. We integrated radiation hybrid (RH) and high-resolution genetic mapping to locate C-locus on the short arm of chromosome 2D. Flanking markers of the C-locus span a physical distance of 11.0 Mb (231.0-242 Mb interval) and contain only 11 high-confidence annotated genes. This work demonstrates the value of this integrated strategy in mapping dominant genes in the low-recombination regions of the wheat genome. A comparison of the mapping resolutions of the RH and genetic maps using common anchored markers indicated that the RH map provides ~ 9 times better resolution that the genetic map even with much smaller population size. This study provides a broadly applicable approach to fine map wheat genes in regions of suppressed recombination.

Accelerated Domestication of New Crops: Yield is Key.

Sustainable agriculture in the future will depend on crops that are tolerant to biotic and abiotic stresses, require minimal input of water and nutrients and can be cultivated with a minimal carbon footprint. Wild plants that fulfill these requirements abound in nature but are typically low yielding. Thus, replacing current high-yielding crops with less productive but resilient species will require the intractable trade-off of increasing land area under cultivation to produce the same yield. Cultivating more land reduces natural resources, reduces biodiversity and increases our carbon footprint. Sustainable intensification can be achieved by increasing the yield of underutilized or wild plant species that are already resilient, but achieving this goal by conventional breeding programs may be a long-term prospect. De novo domestication of orphan or crop wild relatives using mutagenesis is an alternative and fast approach to achieve resilient crops with high yields. With new precise molecular techniques, it should be possible to reach economically sustainable yields in a much shorter period of time than ever before in the history of agriculture.

Genetic architecture and QTL selection response for Kernza perennial grain domestication traits.

KEY MESSAGE: Analysis of multi-year breeding program data revealed that the genetic architecture of an intermediate wheatgrass population was highly polygenic for both domestication and agronomic traits, supporting the use of genomic selection for new crop domestication. Perennial grains have the potential to provide food for humans and decrease the negative impacts of annual agriculture. Intermediate wheatgrass (IWG, Thinopyrum intermedium, Kernza®) is a promising perennial grain candidate that The Land Institute has been breeding since 2003. We evaluated four consecutive breeding cycles of IWG from 2016 to 2020 with each cycle containing approximately 1100 unique genets. Using genotyping-by-sequencing markers, quantitative trait loci (QTL) were mapped for 34 different traits using genome-wide association analysis. Combining data across cycles and years, we found 93 marker-trait associations for 16 different traits, with each association explaining 0.8-5.2% of the observed phenotypic variance. Across the four cycles, only three QTL showed an FST differentiation > 0.15 with two corresponding to a decrease in floret shattering. Additionally, one marker associated with brittle rachis was 216 bp from an ortholog of the btr2 gene. Power analysis and quantitative genetic theory were used to estimate the effective number of QTL, which ranged from a minimum of 33 up to 558 QTL for individual traits. This study suggests that key agronomic and domestication traits are under polygenic control and that molecular methods like genomic selection are needed to accelerate domestication and improvement of this new crop.

Genetic networks underlying salinity tolerance in wheat uncovered with genome-wide analyses and selective sweeps.

KEY MESSAGE: A genetic framework underpinning salinity tolerance at reproductive stage was revealed by genome-wide SNP markers and major adaptability genes in synthetic-derived wheats, and trait-associated loci were used to predict phenotypes. Using wild relatives of crops to identify genes related to improved productivity and resilience to climate extremes is a prioritized area of crop genetic improvement. High salinity is a widespread crop production constraint, and development of salt-tolerant cultivars is a sustainable solution. We evaluated a panel of 294 wheat accessions comprising synthetic-derived wheat lines (SYN-DERs) and modern bread wheat advanced lines under control and high salinity conditions at two locations. The GWAS analysis revealed a quantitative genetic framework of more than 200 loci with minor effect underlying salinity tolerance at reproductive stage. The significant trait-associated SNPs were used to predict phenotypes using a GBLUP model, and the prediction accuracy (r2) ranged between 0.57 and 0.74. The r2 values for flag leaf weight, days to flowering, biomass, and number of spikes per plant were all above 0.70, validating the phenotypic effects of the loci discovered in this study. Furthermore, the germplasm sets were compared to identify selection sweeps associated with salt tolerance loci in SYN-DERs. Six loci associated with salinity tolerance were found to be differentially selected in the SYN-DERs (12.4 Mb on chromosome (chr)1B, 7.1 Mb on chr2A, 11.2 Mb on chr2D, 200 Mb on chr3D, 600 Mb on chr6B, and 700.9 Mb on chr7B). A total of 228 reported markers and genes, including 17 well-characterized genes, were uncovered using GWAS and EigenGWAS. A linkage disequilibrium (LD) block on chr5A, including the Vrn-A1 gene at 575 Mb and its homeologs on chr5D, were strongly associated with multiple yield-related traits and flowering time under salinity stress conditions. The diversity panel was screened with more than 68 kompetitive allele-specific PCR (KASP) markers of functional genes in wheat, and the pleiotropic effects of superior alleles of Rht-1, TaGASR-A1, and TaCwi-A1 were revealed under salinity stress. To effectively utilize the extensive genetic information obtained from the GWAS analysis, a genetic interaction network was constructed to reveal correlations among the investigated traits. The genetic network data combined with GWAS, selective sweeps, and the functional gene survey provided a quantitative genetic framework for identifying differentially retained loci associated with salinity tolerance in wheat.

Genomic selection for spot blotch in bread wheat breeding panels, full-sibs and half-sibs and index-based selection for spot blotch, heading and plant height.

KEY MESSAGE: Genomic selection is a promising tool to select for spot blotch resistance and index-based selection can simultaneously select for spot blotch resistance, heading and plant height. A major biotic stress challenging bread wheat production in regions characterized by humid and warm weather is spot blotch caused by the fungus Bipolaris sorokiniana. Since genomic selection (GS) is a promising selection tool, we evaluated its potential for spot blotch in seven breeding panels comprising 6736 advanced lines from the International Maize and Wheat Improvement Center. Our results indicated moderately high mean genomic prediction accuracies of 0.53 and 0.40 within and across breeding panels, respectively which were on average 177.6% and 60.4% higher than the mean accuracies from fixed effects models using selected spot blotch loci. Genomic prediction was also evaluated in full-sibs and half-sibs panels and sibs were predicted with the highest mean accuracy (0.63) from a composite training population with random full-sibs and half-sibs. The mean accuracies when full-sibs were predicted from other full-sibs within families and when full-sibs panels were predicted from other half-sibs panels were 0.47 and 0.44, respectively. Comparison of GS with phenotypic selection (PS) of the top 10% of resistant lines suggested that GS could be an ideal tool to discard susceptible lines, as greater than 90% of the susceptible lines discarded by PS were also discarded by GS. We have also reported the evaluation of selection indices to simultaneously select non-late and non-tall genotypes with low spot blotch phenotypic values and genomic-estimated breeding values. Overall, this study demonstrates the potential of integrating GS and index-based selection for improving spot blotch resistance in bread wheat.

De Novo Genome Assembly of the Japanese Wheat Cultivar Norin 61 Highlights Functional Variation in Flowering Time and Fusarium-Resistant Genes in East Asian Genotypes.

Bread wheat is a major crop that has long been the focus of basic and breeding research. Assembly of its genome has been difficult because of its large size and allohexaploid nature (AABBDD genome). Following the first reported assembly of the genome of the experimental strain Chinese Spring (CS), the 10+ Wheat Genomes Project was launched to produce multiple assemblies of worldwide modern cultivars. The only Asian cultivar in the project is Norin 61, a representative Japanese cultivar adapted to grow across a broad latitudinal range, mostly characterized by a wet climate and a short growing season. Here, we characterize the key aspects of its chromosome-scale genome assembly spanning 15 Gb with a raw scaffold N50 of 22 Mb. Analysis of the repetitive elements identified chromosomal regions unique to Norin 61 that encompass a tandem array of the pathogenesis-related 13 family. We report novel copy-number variations in the B homeolog of the florigen gene FT1/VRN3, pseudogenization of its D homeolog and the association of its A homeologous alleles with the spring/winter growth habit. Furthermore, the Norin 61 genome carries typical East Asian functional variants different from CS, ranging from a single nucleotide to multi-Mb scale. Examples of such variation are the Fhb1 locus, which confers Fusarium head-blight resistance, Ppd-D1a, which confers early flowering, Glu-D1f for Asian noodle quality and Rht-D1b, which introduced semi-dwarfism during the green revolution. The adoption of Norin 61 as a reference assembly for functional and evolutionary studies will enable comprehensive characterization of the underexploited Asian bread wheat diversity.

The Aegilops ventricosa 2NvS segment in bread wheat: cytology, genomics and breeding.

KEY MESSAGE: The first cytological characterization of the 2NvS segment in hexaploid wheat; complete de novo assembly and annotation of 2NvS segment; 2NvS frequency is increasing 2NvS and is associated with higher yield. The Aegilops ventricosa 2NvS translocation segment has been utilized in breeding disease-resistant wheat crops since the early 1990s. This segment is known to possess several important resistance genes against multiple wheat diseases including root knot nematode, stripe rust, leaf rust and stem rust. More recently, this segment has been associated with resistance to wheat blast, an emerging and devastating wheat disease in South America and Asia. To date, full characterization of the segment including its size, gene content and its association with grain yield is lacking. Here, we present a complete cytological and physical characterization of this agronomically important translocation in bread wheat. We de novo assembled the 2NvS segment in two wheat varieties, 'Jagger' and 'CDC Stanley,' and delineated the segment to be approximately 33 Mb. A total of 535 high-confidence genes were annotated within the 2NvS region, with > 10% belonging to the nucleotide-binding leucine-rich repeat (NLR) gene families. Identification of groups of NLR genes that are potentially N genome-specific and expressed in specific tissues can fast-track testing of candidate genes playing roles in various disease resistances. We also show the increasing frequency of 2NvS among spring and winter wheat breeding programs over two and a half decades, and the positive impact of 2NvS on wheat grain yield based on historical datasets. The significance of the 2NvS segment in wheat breeding due to resistance to multiple diseases and a positive impact on yield highlights the importance of understanding and characterizing the wheat pan-genome for better insights into molecular breeding for wheat improvement.

Sequenced-based paternity analysis to improve breeding and identify self-incompatibility loci in intermediate wheatgrass (Thinopyrum intermedium).

KEY MESSAGE: Paternity assignment and genome-wide association analyses for fertility were applied to a Thinopyrum intermedium breeding program. A lack of progeny between combinations of parents was associated with loci near self-incompatibility genes. In outcrossing species such as intermediate wheatgrass (IWG, Thinopyrum intermedium), polycrossing is often used to generate novel recombinants through each cycle of selection, but it cannot track pollen-parent pedigrees and it is unknown how self-incompatibility (SI) genes may limit the number of unique crosses obtained. This study investigated the potential of using next-generation sequencing to assign paternity and identify putative SI loci in IWG. Using a reference population of 380 individuals made from controlled crosses of 64 parents, paternity was assigned with 92% agreement using Cervus software. Using this approach, 80% of 4158 progeny (n = 3342) from a polycross of 89 parents were assigned paternity. Of the 89 pollen parents, 82 (92%) were represented with 1633 unique full-sib families representing 42% of all potential crosses. The number of progeny per successful pollen parent ranged from 1 to 123, with number of inflorescences per pollen parent significantly correlated to the number of progeny (r = 0.54, p < 0.001). Shannon's diversity index, assessing the total number and representation of families, was 7.33 compared to a theoretical maximum of 8.98. To test our hypothesis on the impact of SI genes, a genome-wide association study of the number of progeny observed from the 89 parents identified genetic effects related to non-random mating, including marker loci located near putative SI genes. Paternity testing of polycross progeny can impact future breeding gains by being incorporated in breeding programs to optimize polycross methodology, maintain genetic diversity, and reveal genetic architecture of mating patterns.

Molecular diversity and landscape genomics of the crop wild relative Triticum urartu across the Fertile Crescent.

Modern plant breeding can benefit from the allelic variation that exists in natural populations of crop wild relatives that evolved under natural selection in varying pedoclimatic conditions. In this study, next-generation sequencing was used to generate 1.3 million genome-wide single nucleotide polymorphisms (SNPs) on ex situ collections of Triticum urartu L., the wild donor of the Au subgenome of modern wheat. A set of 75 511 high-quality SNPs were retained to describe 298 T. urartu accessions collected throughout the Fertile Crescent. Triticum urartu showed a complex pattern of genetic diversity, with two main genetic groups distributed sequentially from west to east. The incorporation of geographical information on sampling points showed that genetic diversity was correlated to the geographical distance (R2  = 0.19) separating samples from Jordan and Lebanon, from Syria and southern Turkey, and from eastern Turkey, Iran and Iraq. The wild emmer genome was used to derive the physical positions of SNPs on the seven chromosomes of the Au subgenome, allowing us to describe a relatively slow decay of linkage disequilibrium in the collection. Outlier loci were described on the basis of the geographic distribution of the T. urartu accessions, identifying a hotspot of directional selection on chromosome 4A. Bioclimatic variation was derived from grid data and related to allelic variation using a genome-wide association approach, identifying several marker-environment associations (MEAs). Fifty-seven MEAs were associated with altitude and temperature measures while 358 were associated with rainfall measures. The most significant MEAs and outlier loci were used to identify genomic loci with adaptive potential (some already reported in wheat), including dormancy and frost resistance loci. We advocate the application of genomics and landscape genomics on ex situ collections of crop wild relatives to efficiently identify promising alleles and genetic materials for incorporation into modern crop breeding.

Local adaptation, genetic divergence, and experimental selection in a foundation grass across the US Great Plains' climate gradient.

Many prior studies have uncovered evidence for local adaptation using reciprocal transplant experiments. However, these studies are rarely conducted for a long enough time to observe succession and competitive dynamics in a community context, limiting inferences for long-lived species. Furthermore, the genetic basis of local adaptation and genetic associations with climate has rarely been identified. Here, we report on a long-term (6-year) experiment conducted under natural conditions focused on Andropogon gerardii, the dominant grass of the North American Great Plains tallgrass ecosystem. We focus on this foundation grass that comprises 80% of tallgrass prairie biomass and is widely used in 20,000 km2 of restoration. Specifically, we asked the following questions: (a) Whether ecotypes are locally adapted to regional climate in realistic ecological communities. (b) Does adaptive genetic variation underpin divergent phenotypes across the climate gradient? (c) Is there evidence of local adaptation if the plants are exposed to competition among ecotypes in mixed ecotype plots? Finally, (d) are local adaptation and genetic divergence related to climate? Reciprocal gardens were planted with 3 regional ecotypes (originating from dry, mesic, wet climate sources) of Andropogon gerardii across a precipitation gradient (500-1,200 mm/year) in the US Great Plains. We demonstrate local adaptation and differentiation of ecotypes in wet and dry environments. Surprisingly, the apparent generalist mesic ecotype performed comparably under all rainfall conditions. Ecotype performance was underpinned by differences in neutral diversity and candidate genes corroborating strong differences among ecotypes. Ecotype differentiation was related to climate, primarily rainfall. Without long-term studies, wrong conclusions would have been reached based on the first two years. Further, restoring prairies with climate-matched ecotypes is critical to future ecology, conservation, and sustainability under climate change.

Production of a complete set of wheat-barley group-7 chromosome recombinants with increased grain ß-glucan content.

KEY MESSAGE: Wheat-barley group-7 recombinant chromosomes were selected using molecular cytogenetics and SNP markers; increased grain ß-glucan content was observed in wheat plants with two and four copies of HvCslF6. The soluble dietary fiber (1-3)(1-4) mixed linked ß-D-glucan from cereal grains is a valuable component of a healthy diet, which reduces risks of coronary disease and diabetes. Although wheat is an important cereal crop providing a substantial portion of daily calories and protein intake in the human diet, it has a low level of ß-glucan. Owing to the plasticity of the polyploid wheat genome, agronomically important traits absent in the wheat primary gene pool can be introgressed from distant relatives. Barley (Hordeum vulgare L.) has a high grain ß-glucan content. Earlier, we introgressed this trait into wheat in the form of whole arm compensating Robertsonian translocations (RobT) involving group-7 chromosomes of barley and all three sub-genomes of hexaploid wheat (Triticum aestivum L). In the presented research, we shortened the barley 7HL arms in these RobTs to small pericentromeric segments, using induced wheat-barley homoeologous recombination. The recombinants were selected using SNP markers and molecular cytogenetics. Plants, comprising barley cellulose synthase-like F6 gene (HvCslF6), responsible for ß-glucan synthesis, had a higher grain ß-glucan content than the wheat control. Three wheat-barley group-7 recombinant chromosomes involving the A, B and D sub-genomes laid the basis for a multiple-copy gene introgression to hexaploid wheat. It is hypothesized that further increases in the ß-glucan content in wheat grain can be obtained by increasing the number of HvCslF6 copies through combining several recombinant chromosomes in one line. The wheat lines with four copies of HvCslF6 exceeded the ß-glucan content of the lines with two copies.

Genome mapping of quantitative trait loci (QTL) controlling domestication traits of intermediate wheatgrass (Thinopyrum intermedium).

Allohexaploid (2n = 6x = 42) intermediate wheatgrass (Thinopyrum intermedium), abbreviated IWG, is an outcrossing perennial grass belonging to the tertiary gene pool of wheat. Perenniality would be valuable option for grain production, but attempts to introgress this complex trait from wheat-Thinopyrum hybrids have not been commercially successful. Efforts to breed IWG itself as a dual-purpose forage and grain crop have demonstrated useful progress and applications, but grain yields are significantly less than wheat. Therefore, genetic and physical maps have been developed to accelerate domestication of IWG. Herein, these maps were used to identify quantitative trait loci (QTLs) and candidate genes associated with IWG grain production traits in a family of 266 full-sib progenies derived from two heterozygous parents, M26 and M35. Transgressive segregation was observed for 17 traits related to seed size, shattering, threshing, inflorescence capacity, fertility, stem size, and flowering time. A total of 111 QTLs were detected in 36 different regions using 3826 genotype-by-sequence markers in 21 linkage groups. The most prominent QTL had a LOD score of 15 with synergistic effects of 29% and 22% over the family means for seed retention and percentage of naked seeds, respectively. Many QTLs aligned with one or more IWG gene models corresponding to 42 possible domestication orthogenes including the wheat Q and RHT genes. A cluster of seed-size and fertility QTLs showed possible alignment to a putative Z self-incompatibility gene, which could have detrimental grain-yield effects when genetic variability is low. These findings elucidate pathways and possible hurdles in the domestication of IWG.

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage.

Genomic selection (GS) models have been validated for many quantitative traits in wheat (Triticum aestivum L.) breeding. However, those models are mostly constrained within the same growing cycle and the extension of GS to the case of across cycles has been a challenge, mainly due to the low predictive accuracy resulting from two factors: reduced genetic relationships between different families and augmented environmental variances between cycles. Using the data collected from diverse field conditions at the International Wheat and Maize Improvement Center, we evaluated GS for grain yield in three elite yield trials across three wheat growing cycles. The objective of this project was to employ the secondary traits, canopy temperature, and green normalized difference vegetation index, which are closely associated with grain yield from high-throughput phenotyping platforms, to improve prediction accuracy for grain yield. The ability to predict grain yield was evaluated reciprocally across three cycles with or without secondary traits. Our results indicate that prediction accuracy increased by an average of 146% for grain yield across cycles with secondary traits. In addition, our results suggest that secondary traits phenotyped during wheat heading and early grain filling stages were optimal for enhancing the prediction accuracy for grain yield.

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat.

Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center's elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress-resilience within years.