Research in Arabidopsis and other model species has uncovered mechanisms regulating important biological processes in plants. However, as research in these model species does not always translate directly into crop species such as wheat, understanding gene function in crop species themselves is critical for crop improvement. With the advent of functional genomics resources in wheat and other crops, discoveries from model species can rapidly be tested and functional genetic studies can now be performed for agronomically-important traits directly in the crops themselves (Borrill, 2020).

The most common forms of domesticated wheat are tetraploid durum wheat (Triticum turgidum ssp. durum L.) and hexaploid bread wheat (Triticum aestivum L.). Polyploid wheat is derived from hybridisation events between different ancestral progenitor species reviewed in Matsuoka (2011), and thus each gene typically exists as two (tetraploid durum wheat) or three (hexaploid bread wheat) copies. These closely related copies, known as homoeologous genes, are on average >95% similar across their coding regions (Figure 1) and usually have a highly conserved gene structure. Tetraploid and hexaploid wheat have large genomes, 12 and 16 Gbp respectively, which consist mostly (>85%) of repetitive elements. The combination of these factors has, for a long time, hampered the development of genomics tools in wheat and other crops with large genomes, such as sugarcane (Garsmeur et al., 2018). Recent advances in sequencing technologies and bioinformatics tools has helped overcome these difficulties, and there are now a wide range of resources available for genomic analysis in wheat. The speed of wheat research has also been limited by its relatively long generation time, which ranges from four to six months depending on the requirement of cold periods (vernalisation) to induce flowering. Again, recent advances in the use of controlled growth conditions have radically changed these timeframes (Watson et al., 2018). Wheat has now become a tractable system for translational, comparative and functional genomics (Borrill et al., 2019).

Figure 1

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Here we describe some of the recent developments in wheat genomics, focussing on published and publicly available resources and tools, and lay out a roadmap for their use (Figure 2). We present available wheat genome assemblies and annotations and discuss a series of approaches to functionally characterise genes. We also outline strategies for growing, crossing and genotyping wheat using the latest available tools and techniques. Finally, we present a case study that encapsulates the above steps and highlights potential pitfalls. We focus mainly on the Ensembl Plants database, as it integrates many of the publicly available data on wheat. However, other databases such as URGI (https://wheat-urgi.versailles.inra.fr/; Alaux et al., 2018), the Wheat Information System (WheatIS; http://www.wheatis.org/), and GrainGenes (https://wheat.pw.usda.gov/GG3/; Blake et al., 2019) also host and integrate similar, but complementary, genetic, genomic and phenomic data for wheat. We expect this review will be a helpful guide for plant scientists who already work on wheat or who are considering expanding their research into crops with large genomes such as wheat.

Figure 2

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A high-quality genome reference sequence is an essential resource for functional genetics and genomics in any species. Several hexaploid wheat genome assemblies have been released over the past eight years (Brenchley et al., 2012; Mayer et al., 2014; Chapman et al., 2015; Clavijo et al., 2017; Zimin et al., 2017). The most comprehensive assembly, called RefSeqv1.0, is a chromosome-level genome assembly annotated with high and low confidence gene models (The International Wheat Genome Sequencing Consortium (IWGSC) et al., 2018). Two tetraploid wheat genomes have also been sequenced, assembled, and annotated to the same standard as RefSeqv1.0 — the wild tetraploid progenitor of wheat, wild emmer (Avni et al., 2017), and a modern durum wheat variety (Maccaferri et al., 2019). Diploid ancestral progenitor species have also been assembled to varying levels of completeness (Luo et al., 2017; Zhao et al., 2017; Ling et al., 2018; Miki et al., 2019). We summarise the annotated assemblies for polyploid wheat in Table 1; in this review we will focus mainly on the RefSeqv1.0 assembly.

RefSeqv1.0 is the most widely used assembly and annotation of hexaploid wheat (available on Ensembl Plants https://plants.ensembl.org/wheat). The information from previous assemblies and annotations (Chromosome Survey Sequence (CSS) and TGACv1) are also available in the Ensembl Plants archive (https://oct2017-plants.ensembl.org) or as tracks in the Ensembl Plants genome browser interface. Ensembl Plants enables access to additional information such as SNP variation, gene trees, homoeolog assignments, and TILLING (Targeting Induced Local Lesions in Genomes) mutant information. Through this interface users can also combine knowledge from the bread, durum and wild emmer wheat genomes.

Like most of the previous hexaploid assemblies, RefSeqv1.0 is derived from the wheat landrace ‘Chinese Spring’. A combination of multiple Illumina and mate pair libraries were sequenced and assembled into scaffolds. Using a method of chromosome conformation capture called Hi-C, these scaffolds were further connected into pseudomolecules representing the 21 nuclear chromosomes of wheat, plus one additional ‘pseudo-chromosome’ (ChrU) containing all unassigned sequences (The International Wheat Genome Sequencing Consortium (IWGSC) et al., 2018).

The gene models for the RefSeqv1.0 assembly were annotated using two prediction pipelines, which were then consolidated into a single set of gene models (RefSeqv1.0 models). A subset of these (~2,000 gene models) were later re-annotated manually, resulting in the RefSeqv1.1 gene model set (Figure 3). Over half of high confidence protein coding genes are present as exactly three homoeologous copies (1:1:1 triads), while several other combinations exist (e.g. 2:1:1 whereby there are two paralogs on the A genome, and a single homoeolog each on the B and D genomes, e.g. Gene Y in Figure 1).

Figure 3

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The RefSeqv1.0 assembly and the RefSeqv1.1 gene models, as well as the durum and wild emmer assemblies and gene models, have been integrated into the publicly available Ensembl Plants genome browser (https://plants.ensembl.org) (Bolser et al., 2015; Howe et al., 2020). Existing variation data, both natural and induced, has been mapped to the RefSeqv1.0 hexaploid assembly and deposited in Ensembl Plants databases for visualisation via the genome browser. Integrating resources into a common reference facilitates their use and in the following sections we will discuss how to best access and utilise these resources.

Although DNA sequence homology does not equate to functional homology, it represents a good starting point for translational and/or comparative genomics. Correctly identifying orthologous genes in another plant species can be a difficult task however, especially between distantly related species like Arabidopsis and wheat. These two species are separated by ~200 million years of evolution and as a result both nucleotide and protein similarities are relatively low compared to more closely related species, for example, wheat and rice (Oryza sativa L.).

Conveniently, all the data and tools necessary for identifying putative gene orthologs from different plant species are available through the Ensembl Plants website (https://plants.ensembl.org) (Bolser et al., 2015; Howe et al., 2020). The Plant Compara pipeline has been integrated into Ensembl Plants to create ‘gene trees’ that identify and clearly display the likely orthologs of any given gene for all of the species available on its website (Vilella et al., 2009; Herrero et al., 2016). This includes the RefSeqv1.1, Arabidopsis TAIR10 and rice IGRSP-1.0 gene models, amongst others. This represents a quick and reliable way to identify putative wheat orthologs of a given gene (Figure 2). Tutorials for using Ensembl Plants interactively or programmatically can be found on their website or at www.wheat-training.com.

When performing a search for putative wheat orthologs via the Ensembl Plants pipeline, we would expect to find three orthologs in hexaploid wheat for most gene queries. These orthologs would normally be located on homoeologous chromosome groups, for example chromosomes 1A, 1B and 1D (Figure 1). A well-documented exception to this rule is the long arm of chromosome 4A (4AL), which has undergone translocation events with chromosome arms 5AL and 7BS (Devos et al., 1995; Ma et al., 2013). Therefore, orthologs within these translocated regions will be physically located on different chromosome groups, for example three homoeologous genes could be on chromosome arms 4AL, 5BL and 5DL. Furthermore, gene structure of wheat orthologs is often conserved with respect to rice and other closely related monocot species; this comparison can usually be done within Ensembl Plants. If this is not possible, wheat RNA-Seq data can be used to determine the gene structure. As an alternative to the Ensembl Plants Gene Trees, one can perform reciprocal protein BLAST searches to identify putative wheat orthologs. We exemplify the above-mentioned approaches, along with potential pitfalls, in more detail in the ‘Case Study’ section.

Determining if, when, where, and to what level a gene is expressed often constitutes one of the first steps towards its functional characterisation. Gene expression information can also be used to prioritise candidate genes underlying a quantitative trait locus (QTL) or to predict those members of a large gene family most relevant to a trait of interest. Numerous RNA-Seq datasets for wheat and many other crops have been generated and published. Although the raw data are often publicly available (e.g. via the NCBI sequence read archive, https://www.ncbi.nlm.nih.gov/sra), they are not sufficiently curated for rapid access and their use in direct comparisons is complicated due to the diversity of tissues, treatments, and origins of the samples. Expression browsers aim to centralise these public datasets and analyse them together, ideally allowing retrieval of expression information for a list of genes under different conditions. For wheat, four expression browsers are currently available: expVIP (http://www.wheat-expression.com; Borrill et al., 2016), wheat eFP browser (http://bar.utoronto.ca/efp_wheat/cgi-bin/efpWeb.cgi; Ramírez-González et al., 2018), EBI Gene Expression Atlas (https://www.ebi.ac.uk/gxa/experiments?species=triticum+aestivum), and WheatExp (https://wheat.pw.usda.gov/WheatExp; Pearce et al., 2015). Here we will focus on the first two given that they include a larger and more diverse set of samples and use the RefSeqv1.0 and v1.1 gene models described in Table 1.

Currently, expVIP includes expression data from 36 studies (1,016 RNA-Seq samples) across a diverse range of wheat tissues, developmental stages, cultivars, and environmental conditions including various abiotic and biotic stress treatments. It can display expression data for up to 250 genes at once, which can be particularly useful when working with a gene family, genes within a QTL interval, or genes involved in the same regulatory process. The expression values for each gene's homoeologs, based on the same homoeolog assignments as in Ensembl Plants, can also be displayed. The ‘homoeolog expression patterns’ of triads (genes that are present as exactly three homoeologous copies) can also be displayed as ternary plots and compared across tissues (Ramírez-González et al., 2018).

To allow comparisons across studies, the 1,016 RNA-Seq samples in expVIP were classified according to four high-level categories based on variety, tissue, developmental stage, and stress. These high-level categories are themselves divided into more detailed subcategories. These categories can be used to customise visualisation displays and allow users to select data relevant to their experimental comparisons. Data can be displayed both as transcripts per million (TPM) or as raw counts and can be directly downloaded to carry out differential gene expression analyses. Although the default gene model reference is RefSeqv1.1, users can also choose the CSS, TGACv1 and RefSeqv1.0 transcriptome references for legacy reasons. Tutorials describing expVIP are available on https://github.com/Uauy-Lab/expvip-web/wiki and www.wheat-training.com. Recently, expVIP was implemented for berry fruit species (Thole et al., 2019).

An additional resource is the electronic Fluorescent Pictograph (eFP) browser, which provides a simple visual assessment of expression data using pictures coloured according to a gene’s relative expression level. The eFP expression browser is available for several crops (e.g. potato, soybean, barley) and most recently wheat (http://bar.utoronto.ca/efp_wheat/cgi-bin/efpWeb.cgi). The wheat interface includes 209 RNA-Seq samples (also in expVIP) representing 22 tissue types from grain, root, leaf, and spike samples across multiple time points from a single hexaploid spring wheat cultivar (‘Azhurnaya’).

The available RNA-Seq data provides the opportunity to identify networks of co-expressed genes. Ramírez-González et al. (2018) constructed tissue and stress-specific co-expression networks in wheat to determine whether genes from the same triad showed variable spatiotemporal expression. In addition, a GENIE3 network was developed to predict transcription factor targets across the multiple RNA-Seq samples (Huynh-Thu et al., 2010; Ramírez-González et al., 2018). Together, these networks provide a powerful set of tools for hypothesis generation using wheat-specific datasets. We have recently validated the GENIE3 network using independent RNA-Seq data from tetraploid wheat (Harrington et al., 2019). Both co-expression and GENIE3 networks are incorporated into KnetMiner (https://knetminer.org/Triticum_aestivum/).

KnetMiner is a web-application for searching and visualising genome-scale knowledge networks of, for example, Arabidopsis, wheat, and human diseases (Hassani-Pak et al., 2016). It aims to provide research leads for scientists who are investigating the molecular basis of complex traits. KnetMiner accepts keywords in combination with a gene list and/or genomic regions as input and searches the underlying knowledge network to identify links between these user-provided genes and keywords. A network-based visualisation, named Network View, allows users to examine complex relationships between gene networks and traits. The networks contain nodes that represent different entities such as genes, single nucleotide polymorphisms (SNPs), publications, and traits (e.g. heat or drought tolerance) that are linked via different relation types (e.g. co-expression, GENIE3-targets, protein-protein-interaction, published-in). Together, KnetMiner and the integrated gene networks provide a powerful resource for gene discovery and hypothesis generation in wheat (see Case Study below).

With the availability of the wheat genome, increasing interest has turned towards the wheat epigenome, that is heritable modifications to the genome that do not affect the DNA sequence itself, such as histone and DNA methylation. The global DNA methylome of polyploid wheat has been explored in multiple studies (Gardiner et al., 2015; Gardiner et al., 2018; Li et al., 2019). The methylome of the reference cultivar Chinese Spring was initially captured at the seedling stage (Gardiner et al., 2015), with more recent work focussing on the variation present in the seedling methylome of the 104 landraces from the Watkins core collection (Table 2; Gardiner et al., 2018). Researchers have also examined the changes in DNA methylation status as a result of biotic stress in wheat seedlings (Geng et al., 2019). The raw bisulfite sequencing data from these experiments is available through public archives, however it is not immediately accessible on genome browsers. More recently, new epigenomic data from Chinese Spring seedlings has been released which includes a wide variety of epigenetic marks such as DNA methylation, seven histone modifications, and chromatin accessibility (Li et al., 2019). This data has been made publicly available through a bespoke genome browser (http://bioinfo.sibs.ac.cn/cs_epigenome) and can be readily accessed by researchers to gain insight into the epigenomic landscape surrounding their genes of interest.

After identifying a gene of interest there are now several options and resources available for functional characterisation and validation in wheat (Figure 2). These include resources based both on natural and induced variation and can involve both transgenic and non-transgenic approaches. It is important to remember that due to the polyploid nature of wheat, there is often functional redundancy between homoeologs (Borrill et al., 2015). This means that it may be necessary to manipulate all homoeologs and paralogs simultaneously to measure a strong phenotypic effect (see the ‘Strategies for Use’ section below for more information).

To put the previous resources into context, we present a case study for obtaining wheat mutants and expression data using a gene of interest from Arabidopsis thaliana. The heat shock factor-like transcription factor TBF1, also known as HsfB1, is a critical regulator of the plant growth-to-defence transition (Pajerowska-Mukhtar et al., 2012), and the response to heat stress (Guo et al., 2016). We therefore hypothesise that its wheat orthologs may have a similar role in regulating defence and/or abiotic stress responses (Ikeda et al., 2011). The first step to test this hypothesis is to identify wheat TBF1 orthologs, which can be done using the Ensembl Plants Gene Tree (Bolser et al., 2015), which displays predicted orthologs for all species included in Ensembl Plants. TBF1 is one of five HSFB orthologs, named HSFB1, 2A, 2B, 4, and 5, respectively. Examination of the Ensembl Plants Gene Tree shows a single wheat triad that falls within the HSFB1 clade, located on the group five chromosomes (Figure 5A). It is important to note that most gene models were annotated in an automated manner and hence gene structures are likely to contain some errors, pending manual curation. We would thus recommend that researchers manually inspect the annotation of their genes of interest before proceeding further with their analyses.

Figure 5

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To support the predicted Arabidopsis-wheat orthologs obtained from Ensembl Plants, we recommend carrying out comparisons between wheat and rice to establish orthology between these cereal species. Both the wheat homoeologs and the rice gene model Os09g0456800 have the same gene structure, consisting of two exons with a conserved intron/exon boundary position. To further support the relationship of the rice gene to the wheat homoeologs, the predicted rice protein can be used as a query for BLASTp analysis of the wheat proteome in Ensembl Plants; the expected wheat orthologs are the top three hits for the A, B, and D genomes (Figure 5B).

Having identified the wheat orthologs of Arabidopsis TBF1, we can examine and compare expression profiles using the expVIP browser (www.wheat-expression.com) (Borrill et al., 2016; Ramírez-González et al., 2018; Figure 5C). All three wheat homoeologs have similar expression profiles, with expression changes in the spike under disease and abiotic stress. This is consistent with the eFP browser data which shows high expression in the spikelet and awns of the non-stressed plants, as well as in more mature leaf tissues (Winter et al., 2007; Ramírez-González et al., 2018). The expression data suggests that the wheat TBF1 homoeologs are most strongly expressed in the spike and may have differential expression in response to biotic and abiotic stress. We can also explore the epigenetic environment of the three homoeologs using the bread wheat epigenomic map (http://bioinfo.sibs.ac.cn/cs_epigenome; Li et al., 2019). A large peak for the H3K9ac histone modification at the 5′ end of the homoeologs is indicative of active transcription from the promoter, corresponding with the observed gene expression. In contrast, the A-homoeolog TraesCS5A02G237900 is flanked by two genes which have low expression at the seedling stage, and correspondingly low levels of H3K9ac modifications in their promoters. It is worth noting that the epigenomic browser uses RefSeqv1.0 gene models, rather than the RefSeqv1.1 gene models used on Ensembl Plants.

Further investigation of these homoeologs can be performed using the KnetMiner knowledge network. For wheat TBF1 orthologs, this includes homology, co-expression data, and associated TILLING mutants, combined with other wheat-specific information such as GENIE3 networks, wheat related publications, gene-phenotype relations extracted from the literature, GWAS data and Arabidopsis protein-protein interactions. Here the wheat genes, referred to as HSFB1, are orthologous to the Arabidopsis gene TBF1 as demonstrated earlier, and the three wheat homoeologs fall into a module associated with responses to abiotic stresses (Figure 6). In addition, the HSFB1 B and D homoeologs are predicted in the GENIE3 network to target the LRK10 and PPD genes, which have known links to drought tolerance and sensitivity (Figure 6). The KnetMiner database also recapitulates the relationship between the wheat HSFB1 homoeologs and their rice and Arabidopsis orthologs, which regulate heat stress responses (Figure 6). Considered as a whole, these data support the hypothesis that the HSFB1 wheat genes are involved in the response to abiotic stress, perhaps specifically in drought response.

Figure 6

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After evaluating in silico expression levels, we can then characterise the phenotype of wheat TBF1 mutants using the exome-sequenced wheat TILLING mutant populations (Figure 2). We suggest to initially use the Kronos population, as it is based on a tetraploid line and thus contains only two copies of the gene (A and B homoeologs). This means that only two mutants need to be crossed to generate a full knockout. The hexaploid Cadenza TILLING population could also be used, but this would require an additional generation to combine mutant alleles across all three homoeologs (Figure 4).

All TILLING mutations identified against the more recent RefSeqv1.0 genome can be accessed directly from Ensembl Plants in the ‘Genetic Variation’ section. Available mutations in the gene of interest can be visualised as a table or positioned along the gene using the ‘Variant Image’ or ‘Variant Table’ option. We can thus rapidly identify mutations that are predicted to lead to a premature termination codon (PTC). However, if no appropriate PTC mutations are available, splice-site mutations predicted to lead to downstream frameshifts, or missense mutations in highly conserved amino acid residues with low SIFT (Sorting Intolerant from Tolerant; Ng and Henikoff, 2003) scores are good alternatives. SIFT scores predict the effect of a mutation on protein function and are based on the physical properties of the alternative amino acid as well as sequence homology.

For both the A and the B genome TBF1 homoeologs in Kronos, no PTC mutations are available. However, we identified missense mutations in highly conserved residues with SIFT scores of 0 suggesting that these mutations are likely to have a deleterious effect on protein function (Figure 5D). In addition to SIFT, we also recommend using the PSSM viewer (https://www.ncbi.nlm.nih.gov/Class/Structure/pssm/pssm_viewer.cgi) to help predict the effect of specific missense mutations on conserved protein domains.

TILLING lines from both populations can be ordered via the GRU (https://www.seedstor.ac.uk/shopping-cart-tilling.php) in the UK or from the Dubcovsky lab (https://dubcovskylab.ucdavis.edu/wheat-tilling) in the USA. To maximise the chance of having selected functionally important mutants, we recommend choosing two independent mutant lines for each homoeolog and carrying out crosses between each mutant in the A and B genomes (four crosses shown in Figure 5D). Detailed guides on growing wheat plants, genotyping TILLING mutants, and crossing mutants can be found on www.wheat-training.com.

Seedlings are genotyped to confirm that the correct mutation is present and to select for homozygous individuals for crossing. To do this, we design genome-specific primers to use in a KASP assay as outlined above and on www.wheat-training.com. For most TILLING mutations genome-specific primers have been predesigned and are available in Ensembl Plants. If there are no suitable predesigned primers, online tools such as PolyMarker can be used (Ramirez-Gonzalez et al., 2015), or if needed, can be designed manually. After carrying out the initial cross, we grow the F₁ individuals under speed breeding conditions, and self-pollinate to obtain the F₂ seed. We then grow F₂ individuals and select via genetic markers individuals homozygous for one or both mutant alleles, as well as homozygous wild type control individuals (Figure 4). We can then carry out our first phenotypic evaluation on the F₂ plants using the homozygous wild type lines as controls without the need for backcrossing to Kronos. We can do this because the background mutations in the chosen lines will be segregating within both the mutant and the wild type lines, leading to an equivalent background mutation load between the sibling genotypes (Uauy et al., 2017). Backcrossing to Kronos can be started either with the single mutants while carrying out the initial cross and/or with the F₂ double mutant at a later stage. Backcrossing to remove background mutations is especially important when studying quantitative traits, such as yield components (Simmonds et al., 2016), and when plants are intended for field phenotyping.

In the last few years there has been a dramatic expansion in the resources available to carry out functional genomics in wheat, largely based upon improvements in the available reference sequences. Within a few years a step-change has been achieved from a highly fragmented assembly with incomplete gene models to a full pseudomolecule reference sequence alongside a detailed gene model annotation. This reference sequence allows the physical anchoring of genes in complete chromosomal order and provides improved gene models facilitating the design of transgenic constructs and primers. Most resources described in this review are integrated with the recent bread wheat reference genome sequence including the expVIP and eFP expression browsers, TILLING mutants, and Ensembl Plants sequence analyses and display tools. As a result, it is now easier to use these resources as they are unified by a common reference genome and gene models. Furthermore, a pan-genome of wheat is being produced which will provide high quality genome sequences for multiple varieties of wheat. These genomes will facilitate functional studies in a range of different genetic backgrounds and enhance the value of the populations containing natural variation captured from diverse wheat varieties.

Whilst many major advances have been made in the last five years to lay the groundwork for gene discovery and functional characterisation in polyploid wheat, looking to the future several key challenges remain.

1. Polyploidy is a common challenge amongst crop species. In wheat, we frequently assume that due to functional redundancy it will be necessary to knock out all three homoeologs of a gene to assess its phenotypic impact. Yet the extent of homoeolog functional redundancy is still unclear (Borrill et al., 2019). Transcriptomics and proteomics approaches will help generate hypotheses as to the extent of homoeolog redundancy in wheat and allow researchers to specifically target the most phenotypically relevant homoeolog for genetic manipulation.

2. Defining accessible (open) chromatin regions allows the identification of cis-regulatory sequences of potential functional significance. In animals and plants, genetic variants associated with quantitative traits are significantly enriched in these open chromatin sequences (Maurano et al., 2012; Rodgers-Melnick et al., 2016). In wheat, where over 98% of the genome is non-coding, it will be critical to identify open chromatin regions to more precisely define non-coding variation that may be of functional relevance. Work in tomato has elegantly shown how a wide range of phenotypic variation for quantitative traits can be engineered by genome editing of cis-regulatory regions of transcription factors (Rodríguez-Leal et al., 2017).

3. To more readily test these hypotheses, increased transformation efficiency and reduced costs will also reshape the future of wheat research, perhaps one day becoming as accessible for wheat researchers as floral dip transformation is for Arabidopsis. It is becoming clear from research in wheat and other species that genetic background can have a strong influence on gene function. Therefore, it is essential to develop new protocols to transform multiple wheat varieties to account for these effects and to ensure that the potential of gene editing approaches is fulfilled.

4. Genomic databases have been powerful in integrating data from multiple studies and international efforts are now bringing together phenotypic data alongside genotypic data (e.g. Blake et al., 2016 and Howe et al., 2020). Challenges remain to standardise phenotype collection protocols and ontologies, which will realise the full power of this information (e.g. Papoutsoglou et al., 2020). Expanding these databases to include environmental conditions will allow assessments of interactions between genotypes, phenotypes and the environment.

High quality genome sequences facilitate moving beyond gene-based analysis, revealing the effects of non-genic regions on phenotype. Whilst working in crops with complex genomes will remain challenging, the advance of genomic techniques has enabled the wheat community to leverage lessons learnt in model species. The approaches taken in wheat provide a framework to understand biologically important traits in other species with large genomes.

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Author details

1. Nikolai M Adamski

   John Innes Centre, Norwich Research Park, Norwich, United Kingdom

   Contributed equally with

   Philippa Borrill, Jemima Brinton, Sophie A Harrington and Clémence Marchal

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1329-5138

2. Philippa Borrill

   School of Biosciences, University of Birmingham, Birmingham, United Kingdom

   Contributed equally with

   Nikolai M Adamski, Jemima Brinton, Sophie A Harrington and Clémence Marchal

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7623-8256

3. Jemima Brinton

   John Innes Centre, Norwich Research Park, Norwich, United Kingdom

   Contributed equally with

   Nikolai M Adamski, Philippa Borrill, Sophie A Harrington and Clémence Marchal

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7027-0450

4. Sophie A Harrington

   John Innes Centre, Norwich Research Park, Norwich, United Kingdom

   Contributed equally with

   Nikolai M Adamski, Philippa Borrill, Jemima Brinton and Clémence Marchal

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0754-0678

5. Clémence Marchal

   John Innes Centre, Norwich Research Park, Norwich, United Kingdom

   Contributed equally with

   Nikolai M Adamski, Philippa Borrill, Jemima Brinton and Sophie A Harrington

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1669-3270

6. Alison R Bentley

   John Bingham Laboratory, Cambridge, United Kingdom

   Competing interests

   No competing interests declared

7. William D Bovill

   Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food (CSIRO), Canberra, Australia

   Competing interests

   No competing interests declared

8. Luigi Cattivelli

   Council for Agricultural Research and Economics, Research Centre for Genomics and Bioinformatics, Fiorenzuola d'Arda, Italy

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6067-4600

9. James Cockram

   John Bingham Laboratory, Cambridge, United Kingdom

   Competing interests

   No competing interests declared

10. Bruno Contreras-Moreira

    European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom

    Competing interests

    No competing interests declared

11. Brett Ford

    Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food (CSIRO), Canberra, Australia

    Competing interests

    No competing interests declared

12. Sreya Ghosh

    John Innes Centre, Norwich Research Park, Norwich, United Kingdom

    Competing interests

    No competing interests declared

13. Wendy Harwood

    John Innes Centre, Norwich Research Park, Norwich, United Kingdom

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4323-9009

14. Keywan Hassani-Pak

    Rothamsted Research, Harpenden, United Kingdom

    Competing interests

    No competing interests declared

15. Sadiye Hayta

    John Innes Centre, Norwich Research Park, Norwich, United Kingdom

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2920-4720

16. Lee T Hickey

    Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, Australia

    Competing interests

    No competing interests declared

17. Kostya Kanyuka

    Rothamsted Research, Harpenden, United Kingdom

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6324-4123

18. Julie King

    Division of Plant and Crop Sciences, The University of Nottingham, Sutton Bonington Campus, Loughborough, United Kingdom

    Competing interests

    No competing interests declared

19. Marco Maccaferrri

    Department of Agricultural and Food Sciences (DISTAL), Alma Mater Studiorum - Università di Bologna (University of Bologna), Bologna, Italy

    Competing interests

    No competing interests declared

20. Guy Naamati

    European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom

    Competing interests

    No competing interests declared

21. Curtis J Pozniak

    Crop Development Centre, University of Saskatchewan, Saskatoon, Canada

    Competing interests

    No competing interests declared

22. Ricardo H Ramirez-Gonzalez

    John Innes Centre, Norwich Research Park, Norwich, United Kingdom

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5745-7085

23. Carolina Sansaloni

    International Maize and Wheat Improvement Center (CIMMYT), El Batán, Mexico

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2675-4524

24. Ben Trevaskis

    Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food (CSIRO), Canberra, Australia

    Competing interests

    No competing interests declared

25. Luzie U Wingen

    John Innes Centre, Norwich Research Park, Norwich, United Kingdom

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4682-4764

26. Brande BH Wulff

    John Innes Centre, Norwich Research Park, Norwich, United Kingdom

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4044-4346

27. Cristobal Uauy

    John Innes Centre, Norwich Research Park, Norwich, United Kingdom

    For correspondence

    cristobal.uauy@jic.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9814-1770

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