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Abstract The introgression of chromosome segments from wild relatives is an established strategy to enrich crop germplasm with disease-resistance genes 1 . Here we use mutagenesis and transcriptome sequencing to clone the leaf rust resistance gene Lr9 , which was introduced into bread wheat from the wild grass species Aegilops umbellulata 2 . We established that Lr9 encodes an unusual tandem kinase fusion protein. Long-read sequencing of a wheat Lr9 introgression line and the putative Ae. umbellulata Lr9 donor enabled us to assemble the ~28.4-Mb Lr9 translocation and to identify the translocation breakpoint. We likewise cloned Lr58 , which was reportedly introgressed from Aegilops triuncialis 3 , but has an identical coding sequence compared to Lr9 . Cytogenetic and haplotype analyses corroborate that the two genes originate from the same translocation event. Our work sheds light on the emerging role of kinase fusion proteins in wheat disease resistance, expanding the repertoire of disease-resistance genes for breeding. 

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.

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.

Available at https://github.com/BorgwardtLab/PheGeMIL (code) and https://doi.org/doi:10.5061/dryad.kprr4xh5p (data).

 

The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines (Triticum aestivum, CDC Stanley x CDC Landmark), wheat-barley (T.aestivumxHordeum vulgare) and wheat-wheatgrass (Triticum durum x Thinopyrum intermedium) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.

 

The wheat stem sawfly (WSS,Cephus cinctus) is a major pest of wheat (Triticum aestivum) and can cause significant yield losses. WSS damage results from stem boring and/or cutting, leading to the lodging of wheat plants. Although solid‐stem wheat genotypes can effectively reduce larval survival, they may have lower yields than hollow‐stem genotypes and show inconsistent solidness expression. Because of limited resistance sources to WSS, evaluating diverse wheat germplasm for novel resistance genes is crucial. We evaluated 91 accessions across five wild wheat species (Triticum monococcum,T. urartu,T. turgidum,T. timopheevii, andAegilops tauschii) and common wheat cultivars (T. aestivum) for antixenosis (host selection) and antibiosis (host suitability) to WSS. Host selection was measured as the number of eggs after adult oviposition, and host suitability was determined by examining the presence or absence of larval infestation within the stem. The plants were grown in the greenhouse and brought to the field for WSS infestation. In addition, a phylogenetic analysis was performed to determine the relationship between the WSS traits and phylogenetic clustering.

Overall,Ae. tauschii,T. turgidumandT. urartuhad lower egg counts and larval infestation thanT. monococcum, andT. timopheevii.T. monococcum,T. timopheevii,T. turgidum, andT. urartuhad lower larval weights compared withT. aestivum.

This study shows that wild relatives of wheat could be a valuable source of alleles for enhancing resistance to WSS and identifies specific germplasm resources that may be useful for breeding. © 2024 The Authors.Pest Management Sciencepublished by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

 

Abstract 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, β, 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. 

Central to the diversity of wheat products was the origin of hexaploid bread wheat, which added the D-genome ofAegilops tauschiito tetraploid wheat giving rise to superior dough properties in leavened breads. The polyploidization, however, imposed a genetic bottleneck, with only limited diversity introduced in the wheat D-subgenome. To understand genetic variants for quality, we sequenced 273 accessions spanning the known diversity ofAe. tauschii. We discovered 45 haplotypes inGlu-D1, a major determinant of quality, relative to the two predominant haplotypes in wheat. The wheat allele2 + 12was found inAe. tauschiiLineage 2, the donor of the wheat D-subgenome. Conversely, the superior quality wheat allele5 + 10allele originated in Lineage 3, a recently characterized lineage ofAe. tauschii, showing a unique origin of this important allele. These two wheat alleles were also quite similar relative to the total observed molecular diversity inAe. tauschiiatGlu-D1.Ae. tauschiiis thus a reservoir for uniqueGlu-D1alleles and provides the genomic resource to begin utilizing new alleles for end-use quality improvement in wheat breeding programs.

 

The wheat wild relativeAegilops tauschiiwas previously used to transfer theLr42leaf rust resistance gene into bread wheat.Lr42confers resistance at both seedling and adult stages, and it is broadly effective against all leaf rust races tested to date.Lr42has been used extensively in the CIMMYT international wheat breeding program with resulting cultivars deployed in several countries. Here, using a bulked segregant RNA-Seq (BSR-Seq) mapping strategy, we identify three candidate genes forLr42. Overexpression of a nucleotide-binding site leucine-rich repeat (NLR) gene AET1Gv20040300 induces strong resistance to leaf rust in wheat and a mutation of the gene disrupted the resistance. TheLr42resistance allele is rare inAe. tauschiiand likely arose from ectopic recombination. Cloning ofLr42provides diagnostic markers and over 1000 CIMMYT wheat lines carryingLr42have been developed documenting its widespread use and impact in crop improvement.