GrainGenes: centralized small grain resources and digital platform for geneticists and breeders

Mapping quantitative trait loci for seminal root angle in a selected durum wheat population

Seminal root angle (SRA) is an important root architectural trait associated with drought adaptation in cereal crops. To date, all attempts to dissect the genetic architecture of SRA in durum wheat (Triticum durum Desf.) have used large association panels or structured mapping populations. Identifying changes in allele frequency generated by selection provides an alternative genetic mapping approach that can increase the power and precision of QTL detection. This study aimed to map quantitative trait loci (QTL) for SRA by genotyping durum lines created through divergent selection using a combination of marker-assisted selection (MAS) for the major SRA QTL (qSRA-6A) and phenotypic selection for SRA over multiple generations. The created 11 lines (BC1F2:5) were genotyped with genome-wide single-nucleotide polymorphism (SNP) markers to map QTL by identifying markers that displayed segregation distortion significantly different from the Mendelian expectation. QTL regions were further assessed in an independent validation population to confirm their associations with SRA. The experiment revealed 14 genomic regions under selection, 12 of which have not previously been reported for SRA. Five regions, including qSRA-6A, were confirmed in the validation population. The genomic regions identified in this study indicate that the genetic control of SRA is more complex than previously anticipated. Our study demonstrates that selection mapping is a powerful approach to complement genome-wide association studies for QTL detection. Moreover, the verification of qSRA-6A in an elite genetic background highlights the potential for MAS, although it is necessary to combine additional QTL to develop new cultivars with extreme SRA phenotypes.

Genome-wide association of an organic naked barley diversity panel identified quantitative trait loci for disease resistance

Foliar fungal diseases are a major limitation in organic naked barley (Hordeum vulgare L.) production. The lack of conventional fungicides in organic systems increases reliance on genetic resistance. We evaluated the severity of barley stripe rust (Puccinia striiformis f. sp. hordei Westend), leaf rust (Puccina hordei sp. hordei), spot blotch (Cochliobolus sativus, anamorph Bipolaris sorokiniana (S. Ito & Kurib.) Drechsler ex Dastur), and scald (Rhynchosporium commune Zaffarano, McDonald and Linde sp. nov) on a naked barley diversity panel of 350 genotypes grown in 13 environments to identify quantitative trait loci associated with disease resistance. Genome-wide association analyses across and within environments found 10 marker trait associations for barley stripe rust, four marker trait associations for leaf rust, one marker trait association for scald, and five marker trait associations for spot blotch. Structure analysis identified six Ward groups based on genotypic diversity. Resistance to susceptible allele ratios were high for stripe rust and spot blotch, moderate for leaf rust, and low for scald. Combined phenotypic analysis values for each disease overlayed by a principal component analysis found distinct resistance and susceptibility patterns for barley stripe rust and scald. Most significant marker trait associations were previously identified in the literature, providing confirmation and potential new sources of disease resistance for genetic improvement of naked barley germplasm.

Diverse epistatic effects in barley-powdery mildew interactions localize to host chromosome hotspots

Barley Mildew locus a (Mla) encodes a multi-allelic series of nucleotide-binding leucine-rich repeat (NLR) receptors that specify recognition to diverse cereal diseases. We exploited time-course transcriptome dynamics of barley and derived immune mutants infected with the powdery mildew fungus, Blumeria hordei (Bh), to infer gene effects governed by Mla6 and two other loci significant to disease development, Blufensin1 (Bln1), and Required for Mla6 resistance3 (rar3 = Sgt1 ΔKL308-309 ). Interactions of Mla6 and Bln1 resulted in diverse epistatic effects on the Bh-induced barley transcriptome, differential immunity to Pseudomonas syringae expressing the effector protease AvrPphB, and reaction to Bh. From a total of 468 barley NLRs, 115 were grouped under different gene effect models; genes classified under these models localized to host chromosome hotspots. The corresponding Bh infection transcriptome was classified into nine co-expressed modules, linking differential expression with pathogen structures, signifying that disease is regulated by an inter-organismal network that diversifies the response.

Integrated meta-QTL and in silico transcriptome assessment pinpoint major genomic regions responsible for spike length in wheat (Triticum aestivum L.)

Spike length (SL) is one of the major contributors to wheat yield. Uncovering major genetic regions affecting SL is an integral part of elucidating the genetic basis of wheat yield traits and goes further pivotal for marker-assisted selection breeding. A genome-wide meta-quantitative trait locus (MQTL) analysis of wheat SL resulted in the refinement of 48 MQTLs using 227 initial QTLs retrieved from previous studies published over the past decades. The average confidence interval (CI) of these MQTLs amounted to a 5.16-fold reduction compared to the mean CI of the initial QTLs. As many as 2240 putative candidate genes (CGs) were identified from the MQTL intervals using transcriptomics data in silico of wheat, of which 58 CGs were identified based on wheat-rice homology analysis. For the key CG affecting SL, a functional kompetitive allele-specific PCR (KASP) marker, TaPP2C-3B-KASP, was developed to distinguish TaPP2C-3B-Hap I and TaPP2C-3B-Hap II based on the single nucleotide polymorphism at the 272 bp (A/G). The frequency of the elite allelic variation TaPP2C-3B-Hap II with high SL remained relatively stable at about 49.62% from the 1960s to 1990s. Integration of MQTL analysis and in silico transcriptome data led to a significant increase in the reliability of CGs for the genetic regulation of wheat SL, and the haplotype analysis for key CGs TaPP2C-3B of SL provided insights into the biological function of the TaPP2C-3B gene.

Transcriptomic analysis revealed potential regulatory biomarkers and repurposable drugs for breast cancer treatment

Breast cancer (BC) is the most widespread cancer worldwide. Over 2 million new cases of BC were identified in 2020 alone. Despite previous studies, the lack of specific biomarkers and signaling pathways implicated in BC impedes the development of potential therapeutic strategies. We employed several RNAseq datasets to extract differentially expressed genes (DEGs) based on the intersection of all datasets, followed by protein-protein interaction network construction. Using the shared DEGs, we also identified significant gene ontology (GO) and KEGG pathways to understand the signaling pathways involved in BC development. A molecular docking simulation was performed to explore potential interactions between proteins and drugs. The intersection of the four datasets resulted in 146 DEGs common, including AURKB, PLK1, TTK, UBE2C, CDCA8, KIF15, and CDC45 that are significant hub-proteins associated with breastcancer development. These genes are crucial in complement activation, mitotic cytokinesis, aging, and cancer development. We identified key microRNAs (i.e., hsa-miR-16-5p, hsa-miR-1-3p, hsa-miR-147a, hsa-miR-195-5p, and hsa-miR-155-5p) that are associated with aggressive tumor behavior and poor clinical outcomes in BC. Notable transcription factors (TFs) were FOXC1, GATA2, FOXL1, ZNF24 and NR2F6. These biomarkers are involved in regulating cancer cell proliferation, invasion, and migration. Finally, molecular docking suggested Hesperidin, 2-amino-isoxazolopyridines, and NMS-P715 as potential lead compounds against BC progression. We believe that these findings will provide important insight into the BC progression as well as potential biomarkers and drug candidates for therapeutic development.

Updated guidelines for gene nomenclature in wheat

KEY MESSAGE

Here, we provide an updated set of guidelines for naming genes in wheat that has been endorsed by the wheat research community. The last decade has seen a proliferation in genomic resources for wheat, including reference- and pan-genome assemblies with gene annotations, which provide new opportunities to detect, characterise, and describe genes that influence traits of interest. The expansion of genetic information has supported growth of the wheat research community and catalysed strong interest in the genes that control agronomically important traits, such as yield, pathogen resistance, grain quality, and abiotic stress tolerance. To accommodate these developments, we present an updated set of guidelines for gene nomenclature in wheat. These guidelines can be used to describe loci identified based on morphological or phenotypic features or to name genes based on sequence information, such as similarity to genes characterised in other species or the biochemical properties of the encoded protein. The updated guidelines provide a flexible system that is not overly prescriptive but provides structure and a common framework for naming genes in wheat, which may be extended to related cereal species. We propose these guidelines be used henceforth by the wheat research community to facilitate integration of data from independent studies and allow broader and more efficient use of text and data mining approaches, which will ultimately help further accelerate wheat research and breeding.

Genetic mapping of the powdery mildew resistance gene Pm7 on oat chromosome 5D

Three independent experiments with different genetic backgrounds mapped the resistance gene Pm7 in the oat genome to the distal part of the long arm of chromosome 5D. Resistance of oat to Blumeria graminis DC. f. sp. avenae is an important breeding goal in Central and Western Europe. In this study, the position of the effective and widely used resistance gene Pm7 in the oat genome was determined based on three independent experiments with different genetic backgrounds: genome-wide association mapping in a diverse set of inbred oat lines and binary phenotype mapping in two bi-parental populations. Powdery mildew resistance was assessed in the field as well as by detached leaf tests in the laboratory. Genotyping-by-sequencing was conducted to establish comprehensive genetic fingerprints for subsequent genetic mapping experiments. All three mapping approaches located the gene to the distal part of the long arm of chromosome 5D in the hexaploid oat genome sequences of OT3098 and 'Sang.' Markers from this region were homologous to a region of chromosome 2Ce of the C-genome species, Avena eriantha, the donor of Pm7, which appears to be the ancestral source of a translocated region on the hexaploid chromosome 5D.

Investigating Pangenome Graphs Using Wheat Panache

Pangenome graphs quickly become the central data structure representing the diversity of variation we see across related genomes. Pangenome graphs have been published for some species, including plants of agronomic interest. However, visualizing these graphs is not easy as the graphs are large, and variants within these graphs are complex. Tools are needed to visualize graph data structures. Here, we present a workflow to search and visualize a wheat pangenome graph using Wheat Panache. The approach presented assists researchers interested in wheat genomics.

Comparison of linear and semi-parametric models incorporating genomic, pedigree, and associated loci information for the prediction of resistance to stripe rust in an Austrian winter wheat breeding program

We used a historical dataset on stripe rust resistance across 11 years in an Austrian winter wheat breeding program to evaluate genomic and pedigree-based linear and semi-parametric prediction methods. Stripe rust (yellow rust) is an economically important foliar disease of wheat (Triticum aestivum L.) caused by the fungus Puccinia striiformis f. sp. tritici. Resistance to stripe rust is controlled by both qualitative (R-genes) and quantitative (small- to medium-effect quantitative trait loci, QTL) mechanisms. Genomic and pedigree-based prediction methods can accelerate selection for quantitative traits such as stripe rust resistance. Here we tested linear and semi-parametric models incorporating genomic, pedigree, and QTL information for cross-validated, forward, and pairwise prediction of adult plant resistance to stripe rust across 11 years (2008-2018) in an Austrian winter wheat breeding program. Semi-parametric genomic modeling had the greatest predictive ability and genetic variance overall, but differences between models were small. Including QTL as covariates improved predictive ability in some years where highly significant QTL had been detected via genome-wide association analysis. Predictive ability was moderate within years (cross-validated) but poor in cross-year frameworks.

Nested association mapping-based GWAS for grain yield and related traits in wheat grown under diverse Australian environments

Utilising a nested association mapping (NAM) population-based GWAS, 98 stable marker-trait associations with 127 alleles unique to the exotic parents were detected for grain yield and related traits in wheat. Grain yield, thousand-grain weight, screenings and hectolitre weight are important wheat yield traits. An understanding of their genetic basis is crucial for improving grain yield in breeding programmes. Nested association mapping (NAM) populations are useful resources for the dissection of the genetic basis of complex traits such as grain yield and related traits in wheat. Coupled with phenotypic data collected from multiple environments, NAM populations have the power to detect quantitative trait loci and their multiple alleles, providing germplasm that can be incorporated into breeding programmes. In this study, we evaluated a large-scale wheat NAM population with two recurrent parents in unbalanced trials in nine diverse Australian field environments over three years. By applying a single-stage factor analytical linear mixed model (FALMM) to the NAM multi-environment trials (MET) data and conducting a genome-wide association study (GWAS), we detected 98 stable marker-trait associations (MTAs) with their multiple alleles. 74 MTAs had 127 alleles that were derived from the exotic parents and were absent in either of the two recurrent parents. The exotic alleles had favourable effects on 46 MTAs of the 74 MTAs, for grain yield, thousand-grain weight, screenings and hectolitre weight. Two NAM RILs with consistently high yield in multiple environments were also identified, highlighting the potential of the NAM population in supporting plant breeding through provision of germplasm that can be readily incorporated into breeding programmes. The identified beneficial exotic alleles introgressed into the NAM population provide potential target alleles for the genetic improvement of wheat and further studies aimed at pinpointing the underlying genes.

Application of crop wild relatives in modern breeding: An overview of resources, experimental and computational methodologies

Global agricultural industries are under pressure to meet the future food demand; however, the existing crop genetic diversity might not be sufficient to meet this expectation. Advances in genome sequencing technologies and availability of reference genomes for over 300 plant species reveals the hidden genetic diversity in crop wild relatives (CWRs), which could have significant impacts in crop improvement. There are many ex-situ and in-situ resources around the world holding rare and valuable wild species, of which many carry agronomically important traits and it is crucial for users to be aware of their availability. Here we aim to explore the available ex-/in- situ resources such as genebanks, botanical gardens, national parks, conservation hotspots and inventories holding CWR accessions. In addition we highlight the advances in availability and use of CWR genomic resources, such as their contribution in pangenome construction and introducing novel genes into crops. We also discuss the potential and challenges of modern breeding experimental approaches (e.g. de novo domestication, genome editing and speed breeding) used in CWRs and the use of computational (e.g. machine learning) approaches that could speed up utilization of CWR species in breeding programs towards crop adaptability and yield improvement.

Breeding oat for resistance to the crown rust pathogen Puccinia coronata f. sp. avenae: achievements and prospects

Crown rust, caused by Puccinia coronata f. sp. avenae (Pca), is a significant impediment to global oat production. Some 98 alleles at 92 loci conferring resistance to Pca in Avena have been designated; however, allelic relationships and chromosomal locations of many of these are unknown. Long-term monitoring of Pca in Australia, North America and elsewhere has shown that it is highly variable even in the absence of sexual recombination, likely due to large pathogen populations that cycle between wild oat communities and oat crops. Efforts to develop cultivars with genetic resistance to Pca began in the 1950s. Based almost solely on all all-stage resistance, this has had temporary benefits but very limited success. The inability to eradicate wild oats, and their common occurrence in many oat growing regions, means that future strategies to control Pca must be based on the assumption of a large and variable prevailing pathogen population with high evolutionary potential, even if cultivars with durable resistance are deployed and grown widely. The presence of minor gene, additive APR to Pca in hexaploid oat germplasm opens the possibility of pyramiding several such genes to give high levels of resistance. The recent availability of reference genomes for diploid and hexaploid oat will undoubtedly accelerate efforts to discover, characterise and develop high throughput diagnostic markers to introgress and pyramid resistance to Pca in high yielding adapted oat germplasm.

Utility of Triti-Map for bulk-segregated mapping of causal genes and regulatory elements in Triticeae

Triticeae species, including wheat, barley, and rye, are critical for global food security. Mapping agronomically important genes is crucial for elucidating molecular mechanisms and improving crops. However, Triticeae includes many wild relatives with desirable agronomic traits, and frequent introgressions occurred during Triticeae evolution and domestication. Thus, Triticeae genomes are generally large and complex, making the localization of genes or functional elements that control agronomic traits challenging. Here, we developed Triti-Map, which contains a suite of user-friendly computational packages specifically designed and optimized to overcome the obstacles of gene mapping in Triticeae, as well as a web interface integrating multi-omics data from Triticeae for the efficient mining of genes or functional elements that control particular traits. The Triti-Map pipeline accepts both DNA and RNA bulk-segregated sequencing data as well as traditional QTL data as inputs for locating genes and elucidating their functions. We illustrate the usage of Triti-Map with a combination of bulk-segregated ChIP-seq data to detect a wheat disease-resistance gene with its promoter sequence that is absent from the reference genome and clarify its evolutionary process. We hope that Triti-Map will facilitate gene isolation and accelerate Triticeae breeding.

GrainGenes: a data-rich repository for small grains genetics and genomics

As one of the US Department of Agriculture-Agricultural Research Service flagship databases, GrainGenes (https://wheat.pw.usda.gov) serves the data and community needs of globally distributed small grains researchers for the genetic improvement of the Triticeae family and Avena species that include wheat, barley, rye and oat. GrainGenes accomplishes its mission by continually enriching its cross-linked data content following the findable, accessible, interoperable and reusable principles, enhancing and maintaining an intuitive web interface, creating tools to enable easy data access and establishing data connections within and between GrainGenes and other biological databases to facilitate knowledge discovery. GrainGenes operates within the biological database community, collaborates with curators and genome sequencing groups and contributes to the AgBioData Consortium and the International Wheat Initiative through the Wheat Information System (WheatIS). Interactive and linked content is paramount for successful biological databases and GrainGenes now has 2917 manually curated gene records, including 289 genes and 254 alleles from the Wheat Gene Catalogue (WGC). There are >4.8 million gene models in 51 genome browser assemblies, 6273 quantitative trait loci and >1.4 million genetic loci on 4756 genetic and physical maps contained within 443 mapping sets, complete with standardized metadata. Most notably, 50 new genome browsers that include outputs from the Wheat and Barley PanGenome projects have been created. We provide an example of an expression quantitative trait loci track on the International Wheat Genome Sequencing Consortium Chinese Spring wheat browser to demonstrate how genome browser tracks can be adapted for different data types. To help users benefit more from its data, GrainGenes created four tutorials available on YouTube. GrainGenes is executing its vision of service by continuously responding to the needs of the global small grains community by creating a centralized, long-term, interconnected data repository. Database URL:https://wheat.pw.usda.gov.

The mosaic oat genome gives insights into a uniquely healthy cereal crop

Cultivated oat (Avena sativa L.) is an allohexaploid (AACCDD, 2n = 6x = 42) thought to have been domesticated more than 3,000 years ago while growing as a weed in wheat, emmer and barley fields in Anatolia^1,2. Oat has a low carbon footprint, substantial health benefits and the potential to replace animal-based food products. However, the lack of a fully annotated reference genome has hampered efforts to deconvolute its complex evolutionary history and functional gene dynamics. Here we present a high-quality reference genome of A. sativa and close relatives of its diploid (Avena longiglumis, AA, 2n = 14) and tetraploid (Avena insularis, CCDD, 2n = 4x = 28) progenitors. We reveal the mosaic structure of the oat genome, trace large-scale genomic reorganizations in the polyploidization history of oat and illustrate a breeding barrier associated with the genome architecture of oat. We showcase detailed analyses of gene families implicated in human health and nutrition, which adds to the evidence supporting oat safety in gluten-free diets, and we perform mapping-by-sequencing of an agronomic trait related to water-use efficiency. This resource for the Avena genus will help to leverage knowledge from other cereal genomes, improve understanding of basic oat biology and accelerate genomics-assisted breeding and reanalysis of quantitative trait studies.

Assembly of the hexaploid oat genome and its diploid and tetraploid relatives clarifies the evolutionary history of oat and allows mapping of genes for agronomic traits.

Comparative sequencing and SNP marker validation for oat stem rust resistance gene Pg6 in a diverse collection of Avena accessions

Comparative sequence analysis was used to design a SNP marker that aided in the identification of new sources of oat stem rust resistance. New races of Puccinia graminis f. sp. avenae (Pga) threaten global oat production. An A. strigosa accession known to carry the broadly effective oat stem rust resistance gene, Pg6, was crossed with two susceptible A. strigosa accessions to generate 198 F_2:3 families and 190 F_5:6 RILs. The RIL population was used to determine that Pg6 was a single dominant gene located between 475 and 491 Mbp on diploid chromosome AA2 of the A. atlantica genome. This region was further refined by identifying SNPs associated with Pg6 resistance in a panel of previously sequenced A-genome accessions. Twenty-four markers were developed from SNPs that showed perfect association between the Pg6 phenotype and 11 sequenced Avena diploid accessions. These markers were validated in the RILs and F_2:3 families, and the markers most closely linked with resistance were tested in a diverse panel of 253 accessions consisting of oat stem rust differentials, all available diploid Avena spp. accessions, and 41 A. vaviloviana accessions from the National Small Grains Collection. One SNP marker located at 483, 439, 497 bp on AA2, designated as AA2_483439497, was perfectly associated with the Pg6 phenotype in Avena strigosa diploids and was within several Kb of a resistance gene analog, RPP13. The marker results and seedling testing against Pga races DBD, KBD, TJS, and TQL enabled the postulation of Pg6 and potential new sources of resistance in the Avena panel. These results will be used to infer Pg6 presence in other germplasm collections and breeding programs and can assist with introgression, gene pyramiding, and cloning of Pg6.

GrainGenes: Tools and Content to Assist Breeders Improving Oat Quality

GrainGenes is the USDA-ARS database and Web resource for wheat, barley, oat, rye, and their relatives. As a community Web hub and database for small grains, GrainGenes strives to provide resources for researchers, students, and plant breeders to improve traits such as quality, yield, and disease resistance. Quantitative trait loci (QTL), genes, and genetic maps for quality attributes in GrainGenes represent the historical approach to mapping genes for groat percentage, test weight, protein, fat, and β-glucan content in oat (Avena spp.). Genetic maps are viewable in CMap, the comparative mapping tool that enables researchers to take advantage of highly populated consensus maps to increase the marker density around their genes-of-interest. GrainGenes hosts over 50 genome browsers and is launching an effort for community curation, including the manually curated tracks with beta-glucan QTL and significant markers found via GWAS and cloned cellulose synthase-like AsClF6 alleles.

SorghumBase: a web-based portal for sorghum genetic information and community advancement

SorghumBase provides a community portal that integrates genetic, genomic, and breeding resources for sorghum germplasm improvement. Public research and development in agriculture rely on proper data and resource sharing within stakeholder communities. For plant breeders, agronomists, molecular biologists, geneticists, and bioinformaticians, centralizing desirable data into a user-friendly hub for crop systems is essential for successful collaborations and breakthroughs in germplasm development. Here, we present the SorghumBase web portal ( https://www.sorghumbase.org ), a resource for the sorghum research community. SorghumBase hosts a wide range of sorghum genomic information in a modular framework, built with open-source software, to provide a sustainable platform. This initial release of SorghumBase includes: (1) five sorghum reference genome assemblies in a pan-genome browser; (2) genetic variant information for natural diversity panels and ethyl methanesulfonate (EMS)-induced mutant populations; (3) search interface and integrated views of various data types; (4) links supporting interconnectivity with other repositories including genebank, QTL, and gene expression databases; and (5) a content management system to support access to community news and training materials. SorghumBase offers sorghum investigators improved data collation and access that will facilitate the growth of a robust research community to support genomics-assisted breeding.

Plant Reactome and PubChem: The Plant Pathway and (Bio)Chemical Entity Knowledgebases

Plant Reactome (https://plantreactome.gramene.org) and PubChem ( https://pubchem.ncbi.nlm.nih.gov ) are two reference data portals and resources for curated plant pathways, small molecules, metabolites, gene products, and macromolecular interactions. Plant Reactome knowledgebase, a conceptual plant pathway network, is built by biocuration and integrating (bio)chemical entities, gene products, and macromolecular interactions. It provides manually curated pathways for the reference species Oryza sativa (rice) and gene orthology-based projections that extend pathway knowledge to 106 plant species. Currently, it hosts 320 reference pathways for plant metabolism, hormone signaling, transport, genetic regulation, plant organ development and differentiation, and biotic and abiotic stress responses. In addition to the pathway browsing and search functions, the Plant Reactome provides the analysis tools for pathway comparison between reference and projected species, pathway enrichment in gene expression data, and overlay of gene-gene interaction data on pathways. PubChem, a popular reference database of (bio)chemical entities, provides information on small molecules and other types of chemical entities, such as siRNAs, miRNAs, lipids, carbohydrates, and chemically modified nucleotides. The data in PubChem is collected from hundreds of data sources, including Plant Reactome. This chapter provides a brief overview of the Plant Reactome and the PubChem knowledgebases, their association to other public resources providing accessory information, and how users can readily access the contents.

CerealsDB: A Whistle-Stop Tour of an Open Access SNP Resource

The CerealsDB website, created by members of the Functional Genomics Group at the University of Bristol, provides access to a database containing SNP and genotyping data for hexaploid wheat and, to a lesser extent, its progenitors and several of its relatives. The site is principally aimed at plant breeders and research scientists who wish to obtain information regarding SNP markers; for example, obtain primers used for their identification or the sequences upon which they are based. The database underpinning the website contains circa one million putative varietal SNPs of which several hundreds of thousands have been experimentally validated on a range of common genotyping platforms. For each SNP marker, the site also hosts the allelic scores for thousands of elite wheat varieties, landrace cultivars, and wheat relatives. Tools are available to help negotiate and visualize the datasets. The website has been designed to be simple and straightforward to use and is completely open access.

In-Depth Sequence Analysis of Bread Wheat VRN1 Genes

The VERNALIZATION1 (VRN1) gene encodes a MADS-box transcription factor and plays an important role in the cold-induced transition from the vegetative to reproductive stage. Allelic variability of VRN1 homoeologs has been associated with large differences in flowering time. The aim of this study was to investigate the genetic variability of VRN1 homoeologs (VRN-A1, VRN-B1 and VRN-D1). We performed an in-depth sequence analysis of VRN1 homoeologs in a panel of 105 winter and spring varieties of hexaploid wheat. We describe the novel allele Vrn-B1f with an 836 bp insertion within intron 1 and show its specific expression pattern associated with reduced heading time. We further provide the complete sequence of the Vrn-A1b allele, revealing a 177 bp insertion in intron 1, which is transcribed into an alternative splice variant. Copy number variation (CNV) analysis of VRN1 homoeologs showed that VRN-B1 and VRN-D1 are present in only one copy. The copy number of recessive vrn-A1 ranged from one to four, while that of dominant Vrn-A1 was one or two. Different numbers of Vrn-A1a copies in the spring cultivars Branisovicka IX/49 and Bastion did not significantly affect heading time. We also report on the deletion of secondary structures (G-quadruplex) in promoter sequences of cultivars with more vrn-A1 copies.

The Genetic Architecture of Milling Quality in Spring Oat Lines of the Collaborative Oat Research Enterprise

Most oat grains destined for human consumption must possess the ability to pass through an industrial de-hulling process with minimal breakage and waste. Uniform grain size and a high groat to hull ratio are desirable traits related to milling performance. The purpose of this study was to characterize the genetic architecture of traits related to milling quality by identifying quantitative trait loci (QTL) contributing to variation among a diverse collection of elite and foundational spring oat lines important to North American oat breeding programs. A total of 501 lines from the Collaborative Oat Research Enterprise (CORE) panel were evaluated for genome-wide association with 6 key milling traits. Traits were evaluated in 13 location years. Associations for 36,315 markers were evaluated for trait means across and within location years, as well as trait variance across location years, which was used to assess trait stability. Fifty-seven QTL influencing one or more of the milling quality related traits were identified, with fourteen QTL mapped influencing mean and variance across location years. The most prominent QTL was Qkernel.CORE.4D on chromosome 4D at approximately 212 cM, which influenced the mean levels of all traits. QTL were identified that influenced trait variance but not mean, trait mean only and both.

Multiple Variant Calling Pipelines in Wheat Whole Exome Sequencing

The highly challenging hexaploid wheat (Triticum aestivum) genome is becoming ever more accessible due to the continued development of multiple reference genomes, a factor which aids in the plight to better understand variation in important traits. Although the process of variant calling is relatively straightforward, selection of the best combination of the computational tools for read alignment and variant calling stages of the analysis and efficient filtering of the false variant calls are not always easy tasks. Previous studies have analyzed the impact of methods on the quality metrics in diploid organisms. Given that variant identification in wheat largely relies on accurate mining of exome data, there is a critical need to better understand how different methods affect the analysis of whole exome sequencing (WES) data in polyploid species. This study aims to address this by performing whole exome sequencing of 48 wheat cultivars and assessing the performance of various variant calling pipelines at their suggested settings. The results show that all the pipelines require filtering to eliminate false-positive calls. The high consensus among the reference SNPs called by the best-performing pipelines suggests that filtering provides accurate and reproducible results. This study also provides detailed comparisons for high sensitivity and precision at individual and population levels for the raw and filtered SNP calls.

Genomic signatures of selection for resistance to stripe rust in Austrian winter wheat

We combined quantitative and population genetic methods to identify loci under selection for adult plant resistance to stripe rust in an Austrian winter wheat breeding population from 2008 to 2018. Resistance to stripe rust, a foliar disease caused by the fungus P. striiformis f. sp. tritici, in wheat (Triticum aestivum L.) is both qualitatively and quantitatively controlled. Resistance genes confer complete, race-specific resistance but are easily overcome by evolving pathogen populations, while quantitative resistance is controlled by many small- to medium-effect loci that provide incomplete yet more durable protection. Data on resistance loci can be applied in marker-assisted selection and genomic prediction frameworks. We employed genome-wide association to detect loci associated with stripe rust and selection testing to identify regions of the genome that underwent selection for stripe rust resistance in an Austrian winter wheat breeding program from 2008 to 2018. Genome-wide association mapping identified 150 resistance loci, 62 of which showed significant evidence of selection over time. The breeding population also demonstrated selection for resistance at the genome-wide level.

WheatQTLdb: a QTL database for wheat

During the last three decades, QTL analysis in wheat has been conducted for a variety of individual traits, so that thousands of QTL along with the linked markers, their genetic positions and contribution to phenotypic variation (PV) for concerned traits are now known. However, no exhaustive database for wheat QTL is currently available at a single platform. Therefore, the present database was prepared which is an exhaustive information resource for wheat QTL data from the published literature till May, 2020. QTL data from both interval mapping and genome-wide association studies (GWAS) have been included for the following classes of traits: (i) morphological traits, (ii) N and P use efficiency, (iii) traits for biofortification (Fe, K, Se, and Zn contents), (iv) tolerance to abiotic stresses including drought, water logging, heat stress, pre-harvest sprouting and salinity, (v) resistance to biotic stresses including those due to bacterial, fungal, nematode and insects, (vi) quality traits, and (vii) a variety of physiological traits, (viii) developmental traits, and (ix) yield and its related traits. For the preparation of the database, literature was searched for data on QTL/marker-trait associations (MTAs), curated and then assembled in the form of WheatQTLdb. The available information on metaQTL, epistatic QTL and candidate genes, wherever available, is also included in the database. Information on QTL in this WheatQTLdb includes QTL names, traits, associated markers, parental genotypes, crosses/mapping populations, association mapping panels and other useful information. To our knowledge, WheatQTLdb prepared by us is the largest collection of QTL (11,552), epistatic QTL (107) and metaQTL (330) data for hexaploid wheat to be used by geneticists and plant breeders for further studies involving fine mapping, cloning, and marker-assisted selection (MAS) during wheat breeding.

KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL. KnetMiner is available at: https://knetminer.org.

Reduced free asparagine in wheat grain resulting from a natural deletion of TaASN-B2: investigating and exploiting diversity in the asparagine synthetase gene family to improve wheat quality

BACKGROUND

Understanding the determinants of free asparagine concentration in wheat grain is necessary to reduce levels of the processing contaminant acrylamide in baked and toasted wheat products. Although crop management strategies can help reduce asparagine concentrations, breeders have limited options to select for genetic variation underlying this trait. Asparagine synthetase enzymes catalyse a critical step in asparagine biosynthesis in plants and, in wheat, are encoded by five homeologous gene triads that exhibit distinct expression profiles. Within this family, TaASN2 genes are highly expressed during grain development but TaASN-B2 is absent in some varieties.

RESULTS

Natural genetic diversity in the asparagine synthetase gene family was assessed in different wheat varieties revealing instances of presence/absence variation and other polymorphisms, including some predicted to affect the function of the encoded protein. The presence and absence of TaASN-B2 was determined across a range of UK and global common wheat varieties and related species, showing that the deletion encompassing this gene was already present in some wild emmer wheat genotypes. Expression profiling confirmed that TaASN2 transcripts were only detectable in the grain, while TaASN3.1 genes were highly expressed during the early stages of grain development. TaASN-A2 was the most highly expressed TaASN2 homeologue in most assayed wheat varieties. TaASN-B2 and TaASN-D2 were expressed at similar, lower levels in varieties possessing TaASN-B2. Expression of TaASN-A2 and TaASN-D2 did not increase to compensate for the absence of TaASN-B2, so total TaASN2 expression was lower in varieties lacking TaASN-B2. Consequently, free asparagine concentrations in field-produced grain were, on average, lower in varieties lacking TaASN-B2, although the effect was lost when free asparagine accumulated to very high concentrations as a result of sulphur deficiency.

CONCLUSIONS

Selecting wheat genotypes lacking the TaASN-B2 gene may be a simple and rapid way for breeders to reduce free asparagine concentrations in commercial wheat grain.

Genome-Wide Association Mapping for Yield and Related Traits Under Drought Stressed and Non-stressed Environments in Wheat

Understanding the genetics of drought tolerance in hard red spring wheat (HRSW) in northern USA is a prerequisite for developing drought-tolerant cultivars for this region. An association mapping (AM) study for drought tolerance in spring wheat in northern USA was undertaken using 361 wheat genotypes and Infinium 90K single-nucleotide polymorphism (SNP) assay. The genotypes were evaluated in nine different locations of North Dakota (ND) for plant height (PH), days to heading (DH), yield (YLD), test weight (TW), and thousand kernel weight (TKW) under rain-fed conditions. Rainfall data and soil type of the locations were used to assess drought conditions. A mixed linear model (MLM), which accounts for population structure and kinship (PC+K), was used for marker–trait association. A total of 69 consistent QTL involved with drought tolerance-related traits were identified, with p ≤ 0.001. Chromosomes 1A, 3A, 3B, 4B, 4D, 5B, 6A, and 6B were identified to harbor major QTL for drought tolerance. Six potential novel QTL were identified on chromosomes 3D, 4A, 5B, 7A, and 7B. The novel QTL were identified for DH, PH, and TKW. The findings of this study can be used in marker-assisted selection (MAS) for drought-tolerance breeding in spring wheat.

Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics

Safeguarding crop yields in a changing climate requires bioinformatics advances in harnessing data from vast phenomics and genomics datasets to translate research findings into climate smart crops in the field. Climate change and an additional 3 billion mouths to feed by 2050 raise serious concerns over global food security. Crop breeding and land management strategies will need to evolve to maximize the utilization of finite resources in coming years. High-throughput phenotyping and genomics technologies are providing researchers with the information required to guide and inform the breeding of climate smart crops adapted to the environment. Bioinformatics has a fundamental role to play in integrating and exploiting this fast accumulating wealth of data, through association studies to detect genomic targets underlying key adaptive climate-resilient traits. These data provide tools for breeders to tailor crops to their environment and can be introduced using advanced selection or genome editing methods. To effectively translate research into the field, genomic and phenomic information will need to be integrated into comprehensive clade-specific databases and platforms alongside accessible tools that can be used by breeders to inform the selection of climate adaptive traits. Here we discuss the role of bioinformatics in extracting, analysing, integrating and managing genomic and phenomic data to improve climate resilience in crops, including current, emerging and potential approaches, applications and bottlenecks in the research and breeding pipeline.

Introgression of Thinopyrum elongatum DNA fragments carrying resistance to fusarium head blight into Triticum aestivum cultivar Chinese Spring is associated with alteration of gene expression

The tall wheatgrass species Thinopyrum elongatum carries on the long arm of chromosome 7E, a locus that contributes strongly to resistance to fusarium head blight (FHB), a devastating fungal disease affecting wheat crops in all temperate areas of the world. Introgression of Th. elongatum 7E chromatin into chromosome 7D of wheat was induced by the ph1b mutant of CS. Recombinants between chromosome 7E and wheat chromosome 7D, induced by the ph1b mutation, were monitored by a combination of molecular markers and phenotyping for FHB resistance. Progeny of up to five subsequent generations derived from two lineages, 64-8 and 32-5, were phenotyped for FHB symptoms and genotyped using published and novel 7D- and 7E-specific markers. Fragments from the distal end of 7EL, still carrying FHB resistance and estimated to be less than 114 and 66 Mbp, were identified as introgressed into wheat chromosome arm 7DL of progeny derived from 64-8 and 32-5, respectively. Gene expression analysis revealed variation in the expression levels of genes from the distal ends of 7EL and 7DL in the introgressed progeny. The 7EL introgressed material will facilitate the use of the 7EL FHB resistance locus in wheat breeding programs.

Genome-wide discovery of G-quadruplexes in barley

G-quadruplexes (G4s) are four-stranded nucleic acid structures with closely spaced guanine bases forming square planar G-quartets. Aberrant formation of G4 structures has been associated with genomic instability. However, most plant species are lacking comprehensive studies of G4 motifs. In this study, genome-wide identification of G4 motifs in barley was performed, followed by a comparison of genomic distribution and molecular functions to other monocot species, such as wheat, maize, and rice. Similar to the reports on human and some plants like wheat, G4 motifs peaked around the 5′ untranslated region (5′ UTR), the first coding domain sequence, and the first intron start sites on antisense strands. Our comparative analyses in human, Arabidopsis, maize, rice, and sorghum demonstrated that the peak points could be erroneously merged into a single peak when large window sizes are used. We also showed that the G4 distributions around genic regions are relatively similar in the species studied, except in the case of Arabidopsis. G4 containing genes in monocots showed conserved molecular functions for transcription initiation and hydrolase activity. Additionally, we provided examples of imperfect G4 motifs.

Building a successful international research community through data sharing: The case of the Wheat Information System (WheatIS)

The International Wheat Information System (WheatIS) Expert Working Group (EWG) was initiated in 2012 under the Wheat Initiative with a broad range of contributing organizations. The mission of the WheatIS EWG was to create an informational infrastructure, establish data standards, and build a single portal that allows search, retrieval, and display of globally distributed wheat data sets that are indexed in standard data formats at servers around the world. The web portal at WheatIS.org was released publicly in 2015, and by 2020, it expanded to 8 geographically-distributed nodes and around 20 organizations under its umbrella.  

In this paper, we present our experience, the challenges we faced, and the answer we brought for establishing an international research community to build an informational infrastructure. Our hope is that our experience with building wheatis.org will guide current and future research communities to facilitate institutional and international challenges to create global tools and resources to help their respective scientific communities.

LncMachine: a machine learning algorithm for long noncoding RNA annotation in plants

Following the elucidation of the critical roles they play in numerous important biological processes, long noncoding RNAs (lncRNAs) have gained vast attention in recent years. Manual annotation of lncRNAs is restricted by known gene annotations and is prone to false prediction due to the incompleteness of available data. However, with the advent of high-throughput sequencing technologies, a magnitude of high-quality data has become available for annotation, especially for plant species such as wheat. Here, we compared prediction accuracies of several machine learning algorithms using a 10-fold cross-validation. This study includes a comprehensive feature selection step to refine irrelevant and repeated features. We present a crop-specific, alignment-free coding potential prediction tool, LncMachine, that performs at higher prediction accuracies than the currently available popular tools (CPC2, CPAT, and CNIT) when used with the Random Forest algorithm. Further, LncMachine with Random Forest performed well on human and mouse data, with an average accuracy of 92.67%. LncMachine only requires either a FASTA file or a TAB separated CSV file containing features as input files. LncMachine can deploy several user-provided algorithms in real time and therefore be effortlessly applied to a wide range of studies.

QTL mapping of agronomic traits in wheat using the UK Avalon × Cadenza reference mapping population grown in Kazakhstan

Background

The success of wheat production is largely dependent on local breeding projects that focus on the development of high-yielding cultivars with the use of novel molecular tools. One strategy for improving wheat productivity involves the deployment of diverse germplasms with a high potential yield. An important factor for achieving success involves the dissection of quantitative trait loci (QTLs) for complex agronomic traits, such as grain yield components, in targeted environments for wheat growth.

Methods

In this study, we tested the United Kingdom (UK) spring set of the doubled haploid (DH) reference population derived from the cross between two British cultivars, Avalon (winter wheat) and Cadenza (spring wheat), in the Northern, Central, and Southern regions (Karabalyk, Karaganda, Kyzylorda) of Kazakhstan over three years (2013–2015). The DH population has previously been genotyped by UK scientists using 3647 polymorphic DNA markers. The list of tested traits includes the heading time, seed maturation time, plant height, spike length, productive tillering, number of kernels per spike, number of kernels per meter, thousand kernel weight, and yield per square meter. Windows QTL Cartographer was applied for QTL mapping using the composite interval mapping method.

Results

In total, 83 out of 232 QTLs were identified as stable QTLs from at least two environments. A literature survey suggests that 40 QTLs had previously been reported elsewhere, indicating that this study identified 43 QTLs that are presumably novel marker-trait associations (MTA) for these environments. Hence, the phenotyping of the DH population in new environments led to the discovery of novel MTAs. The identified SNP markers associated with agronomic traits in the DH population could be successfully used in local Kazakh breeding projects for the improvement of wheat productivity.

Genome-wide association analysis of Russian wheat aphid (Diuraphis noxia) resistance in Dn4 derived wheat lines evaluated in South Africa

Russian wheat aphid (RWA; Diuraphis noxia Kurdjumov) resistance on the 1D chromosome of wheat has been the subject of intensive research. Conversely, the deployment of the Dn4 derived RWA resistant varieties diminished in recent years due to the overcoming of the resistance it imparts in the United States of America. However, this resistance has not been deployed in South Africa despite reports that Dn4 containing genotypes exhibited varying levels of resistance against the South African RWA biotypes. It is possible that there may be certain genetic differences within breeding lines or cultivars that influence the expression of resistance. The aim of this study was to identify single nucleotide polymorphism (SNP) markers associated with resistance to South African RWA biotypes. A panel of thirty-two wheat lines were phenotyped for RWA resistance using four South African RWA biotypes and a total of 181 samples were genotyped using the Illumina 9K SNP wheat chip. A genome wide association study using 7598 polymorphic SNPs showed that the population was clustered into two distinct subpopulations. Twenty-seven marker trait associations (MTA) were identified with an average linkage disequilibrium of 0.38 at 10 Mbp. Four of these markers were highly significant and three correlated with previously reported quantitative trait loci linked to RWA resistance in wheat. Twenty putative genes were annotated using the IWGSC RefSeq, three of which are linked to plant defence responses. This study identified novel chromosomal regions that contribute to RWA resistance and contributes to unravelling the complex genetics that control RWA resistance in wheat.

DEFECTIVE ENDOSPERM-D1 (Dee-D1) is crucial for endosperm development in hexaploid wheat

Hexaploid wheat (Triticum aestivum L.) is a natural allopolyploid and provides a usable model system to better understand the genetic mechanisms that underlie allopolyploid speciation through the hybrid genome doubling. Here we aimed to identify the contribution of chromosome 1D in the development and evolution of hexaploid wheat. We identified and mapped a novel DEFECTIVE ENDOSPERM–D1 (Dee-D1) locus on 1DL that is involved in the genetic control of endosperm development. The absence of Dee-D1 leads to non-viable grains in distant crosses and alters grain shape, which negatively affects grain number and thousand-grain weight. Dee-D1 can be classified as speciation locus with a positive effect on the function of genes which are involved in endosperm development in hybrid genomes. The presence of Dee-D1 is necessary for the normal development of endosperm, and thus play an important role in the evolution and improvement of grain yield in hexaploid wheat.

Natalia Tikhenko et al. investigate the genetic contribution of the wheat chromosome 1D to its development and evolution. They find a novel locus, DEFECTIVE ENDOSPERM-D1, on the long arm of 1D that is required for normal endosperm development as its absence leads to non-viable grains and altered grain shape.

JBrowse Connect: A server API to connect JBrowse instances and users

We describe JBrowse Connect, an optional expansion to the JBrowse genome browser, targeted at developers. JBrowse Connect allows live messaging, notifications for new annotation tracks, heavy-duty analyses initiated by the user from within the browser, and other dynamic features. We present example applications of JBrowse Connect that allow users 1) to specify and execute BLAST searches by either running on the same host as the webserver, with a self-contained BLAST module leveraging NCBI Blast+ commands, or via a managed Galaxy instance that can optionally run on a different host, and 2) to run the primer design service Primer3. JBrowse Connect allows users to track job progress and view results in the context of the browser. The software is available under a choice of open source licenses including LGPL and the Artistic License.

Genome-Wide Discovery of G-Quadruplexes in Wheat: Distribution and Putative Functional Roles

G-quadruplexes are nucleic acid secondary structures formed by a stack of square planar G-quartets. G-quadruplexes were implicated in many biological functions including telomere maintenance, replication, transcription, and translation, in many species including humans and plants. For wheat, however, though it is one of the world's most important staple food, no G-quadruplex studies have been reported to date. Here, we computationally identify putative G4 structures (G4s) in wheat genome for the first time and compare its distribution across the genome against five other genomes (human, maize, Arabidopsis, rice, and sorghum). We identified close to 1 million G4 motifs with a density of 76 G4s/Mb across the whole genome and 93 G4s/Mb over genic regions. Remarkably, G4s were enriched around three regions, two located on the antisense and one on the sense strand at the following positions: 1) the transcription start site (TSS) (antisense), 2) the first coding domain sequence (CDS) (antisense), and 3) the start codon (sense). Functional enrichment analysis revealed that the gene models containing G4 motifs within these peaks were associated with specific gene ontology (GO) terms, such as developmental process, localization, and cellular component organization or biogenesis. We investigated genes encoding MADS-box transcription factors and showed examples of G4 motifs within critical regulatory regions in the VRN-1 genes in wheat. Furthermore, comparison with other plants showed that monocots share a similar distribution of G4s, but Arabidopsis shows a unique G4 distribution. Our study shows for the first time the prevalence and possible functional roles of G4s in wheat.

A roadmap for gene functional characterisation in crops with large genomes: Lessons from polyploid wheat

Understanding the function of genes within staple crops will accelerate crop improvement by allowing targeted breeding approaches. Despite their importance, a lack of genomic information and resources has hindered the functional characterisation of genes in major crops. The recent release of high-quality reference sequences for these crops underpins a suite of genetic and genomic resources that support basic research and breeding. For wheat, these include gene model annotations, expression atlases and gene networks that provide information about putative function. Sequenced mutant populations, improved transformation protocols and structured natural populations provide rapid methods to study gene function directly. We highlight a case study exemplifying how to integrate these resources. This review provides a helpful guide for plant scientists, especially those expanding into crop research, to capitalise on the discoveries made in Arabidopsis and other plants. This will accelerate the improvement of crops of vital importance for food and nutrition security.