Mapping of Leaf Rust Resistance Loci in Two Kenyan Wheats and Development of Linked Markers.

Leaf rust caused by the pathogen Puccinia triticina (Pt) is a destructive fungal disease of wheat that occurs in almost all wheat-growing areas across the globe. Genetic resistance has proven to be the best solution to mitigate the disease. Wheat breeders are continuously seeking new diversified and durable sources of resistance to use in developing new varieties. We developed recombinant inbred line (RIL) populations from two leaf rust-resistant genotypes (Kenya Kudu and AUS12568) introduced from Kenya to identify and characterize resistance to Pt and to develop markers linked closely to the resistance that was found. Our studies detected four QTL conferring adult plant resistance (APR) to leaf rust. Two of these loci are associated with known genes, Lr46 and Lr68, residing on chromosomes 1B and 7B, respectively. The remaining two, QLrKK_2B and QLrAus12568_5A, contributed by Kenya Kudu and AUS12568 respectively, are putatively new loci for Pt resistance. Both QLrKK_2B and QLrAus12568_5A were found to interact additively with Lr46 in significantly reducing the disease severity at adult plant growth stages in the field. We further developed a suite of six closely linked markers within the QLrAus12568_5A locus and four within the QLrKK_2B region. Among these, markers sunKASP_522 and sunKASP_524, flanking QLrAus12568_5A, and sunKASP_536, distal to QLrKK_2B, were identified as the most closely linked and reliable for marker-assisted selection. The markers were validated on a selection of 64 Australian wheat varieties and found to be polymorphic and robust, allowing for clear allelic discrimination. The identified new loci and linked molecular markers will enable rapid adoption by breeders in developing wheat varieties carrying diversified and durable resistance to leaf rust.

Statistical sampling of missing environmental variables improves biophysical genomic prediction in wheat.

KEY MESSAGE: The integration of genomic prediction with crop growth models enabled the estimation of missing environmental variables which improved the prediction accuracy of grain yield. Since the invention of whole-genome prediction (WGP) more than two decades ago, breeding programmes have established extensive reference populations that are cultivated under diverse environmental conditions. The introduction of the CGM-WGP model, which integrates crop growth models (CGM) with WGP, has expanded the applications of WGP to the prediction of unphenotyped traits in untested environments, including future climates. However, CGMs require multiple seasonal environmental records, unlike WGP, which makes CGM-WGP less accurate when applied to historical reference populations that lack crucial environmental inputs. Here, we investigated the ability of CGM-WGP to approximate missing environmental variables to improve prediction accuracy. Two environmental variables in a wheat CGM, initial soil water content (InitlSoilWCont) and initial nitrate profile, were sampled from different normal distributions separately or jointly in each iteration within the CGM-WGP algorithm. Our results showed that sampling InitlSoilWCont alone gave the best results and improved the prediction accuracy of grain number by 0.07, yield by 0.06 and protein content by 0.03. When using the sampled InitlSoilWCont values as an input for the traditional CGM, the average narrow-sense heritability of the genotype-specific parameters (GSPs) improved by 0.05, with GNSlope, PreAnthRes, and VernSen showing the greatest improvements. Moreover, the root mean square of errors for grain number and yield was reduced by about 7% for CGM and 31% for CGM-WGP when using the sampled InitlSoilWCont values. Our results demonstrate the advantage of sampling missing environmental variables in CGM-WGP to improve prediction accuracy and increase the size of the reference population by enabling the utilisation of historical data that are missing environmental records.

Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology.

Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM-WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it with CGM and WGP. The CGM-WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.

Using genomic prediction with crop growth models enables the prediction of associated traits in wheat.

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM-WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM-WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM-WGP model for heading and physiological maturity dates. However, the CGM-WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM-WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM-WGP modelling and suggest future efforts should focus on calibrating CGM-WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect.

Discovery of the New Leaf Rust Resistance Gene Lr82 in Wheat: Molecular Mapping and Marker Development.

Breeding for leaf rust resistance has been successful worldwide and is underpinned by the discovery and characterisation of genetically diverse sources of resistance. An English scientist, Arthur Watkins, collected pre-Green Revolution wheat genotypes from 33 locations worldwide in the early part of the 20th Century and this collection is now referred to as the 'Watkins Collection'. A common wheat genotype, Aus27352 from Yugoslavia, showed resistance to currently predominating Australian pathotypes of the wheat leaf rust pathogen. We crossed Aus27352 with a leaf rust susceptible wheat selection Avocet S and a recombinant inbred line (RIL) F6 population of 200 lines was generated. Initial screening at F3 generation showed monogenic segregation for seedling response to leaf rust in Aus27352. These results were confirmed by screening the Aus27352/Avocet S RIL population. The underlying locus was temporarily named LrAW2. Bulked segregant analysis using the 90K Infinium SNP array located LrAW2 in the long arm of chromosome 2B. Tests with molecular markers linked to two leaf rust resistance genes, Lr50 and Lr58, previously located in chromosome 2B, indicated the uniqueness of LrAW2 and it was formally designated Lr82. Kompetitive allele-specific polymerase chain reaction assays were developed for Lr82-linked SNPs. KASP_22131 mapped 0.8 cM proximal to Lr82 and KASP_11333 was placed 1.2 cM distal to this locus. KASP_22131 showed 91% polymorphism among a set of 89 Australian wheat cultivars. We recommend the use of KASP_22131 for marker assisted pyramiding of Lr82 in breeding programs following polymorphism check on parents.

Lr80: A new and widely effective source of leaf rust resistance of wheat for enhancing diversity of resistance among modern cultivars.

KEY MESSAGE: A new leaf rust resistance gene Lr80 was identified and closely linked markers were developed for its successful pyramiding with other marker-tagged genes to achieve durable control of leaf rust. Common wheat landrace Hango-2, collected in 2006 from the Himalayan area of Hango, District Kinnaur, in Himachal Pradesh, exhibited a very low infection type (IT;) at the seedling stage to all Indian Puccinia triticina (Pt) pathotypes, except the pathotype 5R9-7 which produced IT 3+. Genetic analysis based on Agra Local/Hango-2-derived F3 families indicated monogenic control of leaf rust resistance, and the underlying locus was temporarily named LrH2. Bulked segregant analysis using 303 simple sequence repeat (SSR) markers located LrH2 in the short arm of chromosome 2D. An additional set of 10 chromosome 2DS-specific markers showed polymorphism between the parents and these were mapped on the entire Agra Local/Hango-2 F3 population. LrH2 was flanked by markers cau96 (distally) and barc124 (proximally). The 90 K Infinium SNP array was used to identify SNP markers linked with LrH2. Markers KASP_17425 and KASP_17148 showed association with LrH2. Comparison of seedling leaf rust response data and marker locations across different maps demonstrated the uniqueness of LrH2 and it was formally named Lr80. The Lr80-linked markers KASP_17425, KASP_17148 and barc124 amplified alleles/products different to Hango-2 in 82 Australian cultivars indicating their robustness for marker-assisted selection of this gene in wheat breeding programs.

Novel Design of Imputation-Enabled SNP Arrays for Breeding and Research Applications Supporting Multi-Species Hybridization.

Array-based single nucleotide polymorphism (SNP) genotyping platforms have low genotype error and missing data rates compared to genotyping-by-sequencing technologies. However, design decisions used to create array-based SNP genotyping assays for both research and breeding applications are critical to their success. We describe a novel approach applicable to any animal or plant species for the design of cost-effective imputation-enabled SNP genotyping arrays with broad utility and demonstrate its application through the development of the Illumina Infinium Wheat Barley 40K SNP array Version 1.0. We show that the approach delivers high quality and high resolution data for wheat and barley, including when samples are jointly hybridised. The new array aims to maximally capture haplotypic diversity in globally diverse wheat and barley germplasm while minimizing ascertainment bias. Comprising mostly biallelic markers that were designed to be species-specific and single-copy, the array permits highly accurate imputation in diverse germplasm to improve the statistical power of genome-wide association studies (GWAS) and genomic selection. The SNP content captures tetraploid wheat (A- and B-genome) and Aegilops tauschii Coss. (D-genome) diversity and delineates synthetic and tetraploid wheat from other wheat, as well as tetraploid species and subgroups. The content includes SNP tagging key trait loci in wheat and barley, as well as direct connections to other genotyping platforms and legacy datasets. The utility of the array is enhanced through the web-based tool, Pretzel (https://plantinformatics.io/) which enables the content of the array to be visualized and interrogated interactively in the context of numerous genetic and genomic resources to be connected more seamlessly to research and breeding. The array is available for use by the international wheat and barley community.

A new leaf rust resistance gene Lr79 mapped in chromosome 3BL from the durum wheat landrace Aus26582.

KEY MESSAGE: A new leaf rust resistance gene Lr79 has been mapped in the long arm of chromosome 3B and a linked marker was identified for marker-assisted selection. Aus26582, a durum wheat landrace from the A. E. Watkins Collection, showed seedling resistance against durum-specific and common wheat-specific Puccinia triticina (Pt) pathotypes. Genetic analysis using a recombinant inbred line (RIL) population developed from a cross between Aus26582 and the susceptible parent Bansi with Australian Pt pathotype showed digenic inheritance and the underlying loci were temporarily named LrAW2 and LrAW3. LrAW2 was located in chromosome 6BS and this study focused on characterisation of LrAW3 using RILs lacking LrAW2. LrAW3 was incorporated into the DArTseq map of Aus26582/Bansi and was located in chromosome 3BL. Markers linked with LrAW3 were developed from the chromosome survey sequence contig 3B_10474240 in which closely-linked DArTseq markers 1128708 and 3948563 were located. Although bulk segregant analysis (BSA) with the 90 K Infinium array identified 51 SNPs associated with LrAW3, only one SNP-derived KASP marker mapped close to the locus. Deletion bin mapping of LrAW3-linked markers located LrAW3 between bins 3BL11-0.85-0.90 and 3BL7-0.63. Since no other all stage leaf rust resistance gene is located in chromosome 3BL, LrAW3 represented a new locus and was designated Lr79. Marker sun786 mapped 1.8 cM distal to Lr79 and Aus26582 was null for this locus. However, the marker can be reliably scored as it also amplifies a monomorphic fragment that serves as an internal control to differentiate the null status of Aus26582 from reaction failure. This marker was validated among a set of durum and common wheat cultivars and was shown to be useful for marker-assisted selection of Lr79 at both ploidy levels.

Exome sequence genotype imputation in globally diverse hexaploid wheat accessions.

KEY MESSAGE: Imputing genotypes from the 90K SNP chip to exome sequence in wheat was moderately accurate. We investigated the factors that affect imputation and propose several strategies to improve accuracy. Imputing genetic marker genotypes from low to high density has been proposed as a cost-effective strategy to increase the power of downstream analyses (e.g. genome-wide association studies and genomic prediction) for a given budget. However, imputation is often imperfect and its accuracy depends on several factors. Here, we investigate the effects of reference population selection algorithms, marker density and imputation algorithms (Beagle4 and FImpute) on the accuracy of imputation from low SNP density (9K array) to the Infinium 90K single-nucleotide polymorphism (SNP) array for a collection of 837 hexaploid wheat Watkins landrace accessions. Based on these results, we then used the best performing reference selection and imputation algorithms to investigate imputation from 90K to exome sequence for a collection of 246 globally diverse wheat accessions. Accession-to-nearest-entry and genomic relationship-based methods were the best performing selection algorithms, and FImpute resulted in higher accuracy and was more efficient than Beagle4. The accuracy of imputing exome capture SNPs was comparable to imputing from 9 to 90K at approximately 0.71. This relatively low imputation accuracy is in part due to inconsistency between 90K and exome sequence formats. We also found the accuracy of imputation could be substantially improved to 0.82 when choosing an equivalent number of exome SNP, instead of 90K SNPs on the existing array, as the lower density set. We present a number of recommendations to increase the accuracy of exome imputation.

Detection and validation of genomic regions associated with resistance to rust diseases in a worldwide hexaploid wheat landrace collection using BayesR and mixed linear model approaches.

KEY MESSAGE: BayesR and MLM association mapping approaches in common wheat landraces were used to identify genomic regions conferring resistance to Yr, Lr, and Sr diseases. Deployment of rust resistant cultivars is the most economically effective and environmentally friendly strategy to control rust diseases in wheat. However, the highly evolving nature of wheat rust pathogens demands continued identification, characterization, and transfer of new resistance alleles into new varieties to achieve durable rust control. In this study, we undertook genome-wide association studies (GWAS) using a mixed linear model (MLM) and the Bayesian multilocus method (BayesR) to identify QTL contributing to leaf rust (Lr), stem rust (Sr), and stripe rust (Yr) resistance. Our study included 676 pre-Green Revolution common wheat landrace accessions collected in the 1920-1930s by A.E. Watkins. We show that both methods produce similar results, although BayesR had reduced background signals, enabling clearer definition of QTL positions. For the three rust diseases, we found 5 (Lr), 14 (Yr), and 11 (Sr) SNPs significant in both methods above stringent false-discovery rate thresholds. Validation of marker-trait associations with known rust QTL from the literature and additional genotypic and phenotypic characterisation of biparental populations showed that the landraces harbour both previously mapped and potentially new genes for resistance to rust diseases. Our results demonstrate that pre-Green Revolution landraces provide a rich source of genes to increase genetic diversity for rust resistance to facilitate the development of wheat varieties with more durable rust resistance.

Fast-forward genetics by radiation hybrids to saturate the locus regulating nuclear-cytoplasmic compatibility in Triticum.

The nuclear-encoded species cytoplasm specific (scs) genes control nuclear-cytoplasmic compatibility in wheat (genus Triticum). Alloplasmic cells, which have nucleus and cytoplasm derived from different species, produce vigorous and vital organisms only when the correct version of scs is present in their nucleus. In this study, bulks of in vivo radiation hybrids segregating for the scs phenotype have been genotyped by sequencing with over 1.9 million markers. The high marker saturation obtained for a critical region of chromosome 1D allowed identification of 3318 reads that mapped in close proximity of the scs. A novel in silico approach was deployed to extend these short reads to sequences of up to 70 Kb in length and identify candidate open reading frames (ORFs). Markers were developed to anchor the short contigs containing ORFs to a radiation hybrid map of 650 individuals with resolution of 288 Kb. The region containing the scs locus was narrowed to a single Bacterial Artificial Chromosome (BAC) contig of Aegilops tauschii. Its sequencing and assembly by nano-mapping allowed rapid identification of a rhomboid gene as the only ORF existing within the refined scs locus. Resequencing of this gene from multiple germplasm sources identified a single nucleotide mutation, which gives rise to a functional amino acid change. Gene expression characterization revealed that an active copy of this rhomboid exists on all homoeologous chromosomes of wheat, and depending on the specific cytoplasm each copy is preferentially expressed. Therefore, a new methodology was applied to unique genetic stocks to rapidly identify a strong candidate gene for the control of nuclear-cytoplasmic compatibility in wheat.

Mapping of a new stem rust resistance gene Sr49 in chromosome 5B of wheat.

KEY MESSAGE: A new stem rust resistance gene Sr49 was mapped to chromosome 5BL of wheat. Usefulness of the closely linked markers sun209 and sun479 for marker-assisted selection of Sr49 was demonstrated. Landrace AUS28011 (Mahmoudi), collected from Ghardimaou, Tunisia, produced low stem rust response against Australian pathotypes of Puccinia graminis f. sp. tritici (Pgt) carrying virulence for several stem rust resistance genes deployed in modern wheat cultivars. Genetic analysis based on a Mahmoudi/Yitpi F3 population indicated the involvement of a single all-stage stem rust resistance gene and it was temporarily named SrM. Bulked segregant analysis using multiplex-ready SSR technology located SrM on the long arm of chromosome 5B. Since there is no other all-stage stem rust resistance gene located in chromosome 5BL, SrM was permanently designated Sr49. The Mahmoudi/Yitpi F3 population was enhanced to generate F6 recombinant inbred line (RIL) population for detailed mapping of Sr49 using publicly available genomic resources. Markers sun209 and sun479 flanked Sr49 at 1.5 and 0.9 cM distally and proximally, respectively. Markers sun209 and sun479 amplified PCR products different than the Sr49-linked alleles in 146 and 145 common wheat cultivars, respectively. Six and seven cultivars, respectively, carried the resistance-linked marker alleles sun209 148bp and sun479 200bp ; however, none of the cultivars carried both resistance-linked alleles. These results demonstrated the usefulness of these markers for marker-assisted selection of Sr49 in breeding programs.

A haplotype map of allohexaploid wheat reveals distinct patterns of selection on homoeologous genomes.

BACKGROUND: Bread wheat is an allopolyploid species with a large, highly repetitive genome. To investigate the impact of selection on variants distributed among homoeologous wheat genomes and to build a foundation for understanding genotype-phenotype relationships, we performed population-scale re-sequencing of a diverse panel of wheat lines. RESULTS: A sample of 62 diverse lines was re-sequenced using the whole exome capture and genotyping-by-sequencing approaches. We describe the allele frequency, functional significance, and chromosomal distribution of 1.57 million single nucleotide polymorphisms and 161,719 small indels. Our results suggest that duplicated homoeologous genes are under purifying selection. We find contrasting patterns of variation and inter-variant associations among wheat genomes; this, in addition to demographic factors, could be explained by differences in the effect of directional selection on duplicated homoeologs. Only a small fraction of the homoeologous regions harboring selected variants overlapped among the wheat genomes in any given wheat line. These selected regions are enriched for loci associated with agronomic traits detected in genome-wide association studies. CONCLUSIONS: Evidence suggests that directional selection in allopolyploids rarely acted on multiple parallel advantageous mutations across homoeologous regions, likely indicating that a fitness benefit could be obtained by a mutation at any one of the homoeologs. Additional advantageous variants in other homoelogs probably either contributed little benefit, or were unavailable in populations subjected to directional selection. We hypothesize that allopolyploidy may have increased the likelihood of beneficial allele recovery by broadening the set of possible selection targets.

Identification of a stem rust resistance locus effective against Ug99 on wheat chromosome 7AL using a RAD-Seq approach.

KEY MESSAGE: A locus of major effect for stem rust resistance, effective against Ug99 and possibly a target of a suppressor on chromosome arm 7DL in wheat cultivar Canthatch, was mapped to 7AL. Wheat stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), is responsible for major production losses around the world. The development of resistant cultivars is an effective and environmentally friendly way to manage the disease, but outbreaks can occur when new pathogen races overcome the existing host resistance genes. Ug99 (race TTKSK) and related Pgt races are virulent to the majority of existing cultivars, which presents a potential threat to global wheat production. The hexaploid wheat cultivar Canthatch has long been known to carry a suppressor of stem rust resistance on chromosome arm 7DL. Multiple "non-suppressor" mutants of Canthatch are reported to have gained resistance to Pgt races, including Ug99 (TTKSK) and related races TTKST and TTTSK. To genetically map the suppressor locus, a mapping population was developed from a cross between the susceptible cultivar Columbus, thought to possess the suppressor, and Columbus-NS766, a resistant, near-isogenic line believed to contain a mutant non-suppressor allele introgressed from Canthatch. Genetic mapping using a 9K SNP genotyping assay and restriction site-associated DNA sequencing (RAD-Seq) on bulked segregants led to the identification of markers linked to a locus of stem rust resistance. Surprisingly, genomic sequence information revealed the markers to be located on 7AL instead of 7DL, indicating that the resistance phenotype was due to a new resistance locus, rather than the inactivated suppressor. We suggest that the 7AL locus of resistance is most likely suppressed by the 7DL suppressor.

QTL for resistance to root lesion nematode (Pratylenchus thornei) from a synthetic hexaploid wheat source.

KEY MESSAGE: A whole genome average interval mapping approach identified eight QTL associated with P. thornei resistance in a DH population from a cross between the synthetic-derived wheat Sokoll and cultivar Krichauff. Pratylenchus thornei are migratory nematodes that feed and reproduce within the wheat root cortex, causing cell death (lesions) resulting in severe yield reductions globally. Genotypic selection using molecular markers closely linked to Pratylenchus resistance genes will accelerate the development of new resistant cultivars by reducing the need for laborious and expensive resistance phenotyping. A doubled haploid wheat population (150 lines) from a cross between the synthetic-derived cultivar Sokoll (P. thornei resistant) and cultivar Krichauff (P. thornei moderately susceptible) was used to identify quantitative trait loci (QTL) associated with P. thornei resistance. The resistance identified in the glasshouse was validated in a field trial. A genetic map was constructed using Diversity Array Technology and the QTL regions identified were further targeted with simple sequence repeat (SSR) and single-nucleotide polymorphism (SNP) markers. Six significant and two suggestive P. thornei resistance QTL were detected using a whole genome average interval mapping approach. Three QTL were identified on chromosome 2B, two on chromosome 6D, and a single QTL on each of chromosomes 2A, 2D and 5D. The QTL on chromosomes 2BS and 6DS mapped to locations previously identified to be associated with Pratylenchus resistance. Together, the QTL on 2B (QRlnt.sk-2B.1-2B.3) and 6D (QRlnt.sk-6D.1 and 6D.2) explained 30 and 48 % of the genotypic variation, respectively. Flanking PCR-based markers based on SSRs and SNPs were developed for the major QTL on 2B and 6D and provide a cost-effective high-throughput tool for marker-assisted breeding of wheat with improved P. thornei resistance.

Comparative analysis of syntenic genes in grass genomes reveals accelerated rates of gene structure and coding sequence evolution in polyploid wheat.

Cycles of whole-genome duplication (WGD) and diploidization are hallmarks of eukaryotic genome evolution and speciation. Polyploid wheat (Triticum aestivum) has had a massive increase in genome size largely due to recent WGDs. How these processes may impact the dynamics of gene evolution was studied by comparing the patterns of gene structure changes, alternative splicing (AS), and codon substitution rates among wheat and model grass genomes. In orthologous gene sets, significantly more acquired and lost exonic sequences were detected in wheat than in model grasses. In wheat, 35% of these gene structure rearrangements resulted in frame-shift mutations and premature termination codons. An increased codon mutation rate in the wheat lineage compared with Brachypodium distachyon was found for 17% of orthologs. The discovery of premature termination codons in 38% of expressed genes was consistent with ongoing pseudogenization of the wheat genome. The rates of AS within the individual wheat subgenomes (21%-25%) were similar to diploid plants. However, we uncovered a high level of AS pattern divergence between the duplicated homeologous copies of genes. Our results are consistent with the accelerated accumulation of AS isoforms, nonsynonymous mutations, and gene structure rearrangements in the wheat lineage, likely due to genetic redundancy created by WGDs. Whereas these processes mostly contribute to the degeneration of a duplicated genome and its diploidization, they have the potential to facilitate the origin of new functional variations, which, upon selection in the evolutionary lineage, may play an important role in the origin of novel traits.

Transcriptome-scale homoeolog-specific transcript assemblies of bread wheat.

BACKGROUND: Bread wheat is one of the world's most important food crops and considerable efforts have been made to develop genomic resources for this species. This includes an on-going project by the International Wheat Genome Sequencing Consortium to assemble its large and complex genome, which is hexaploid and contains three closely related 'homoeologous' copies for each chromosome. This multi-national effort avoids the complications polyploidy entails for correct assembly of the genome by sequencing flow-sorted chromosome arms one at a time. Here we report on an alternate approach, a direct homoeolog-specific assembly of the expressed portion of the genome, the transcriptome. RESULTS: After assessment of the ability of various assemblers to generate homoeolog-specific assemblies, we employed a two-stage assembly process to produce a high-quality assembly of the transcriptome of hexaploid wheat from Roche-454 and Illumina GAIIx paired-end sequence reads. The assembly process made use of a rapid partitioning of expressed sequences into homoeologous clusters, followed by a parallel high-fidelity assembly of each cluster on a 1150-processor compute cloud. We assessed assembly quality through comparison to known wheat gene sequences and found that in ca. 98.5% of cases the assembly was sufficiently accurate for homoeologous triplets to be cleanly separated into either two or three separate contigs. Comparison to publicly available transcript collections suggests that the assembly covers ~75-80% of the complete transcriptome. CONCLUSIONS: This work therefore describes the first homoeolog-specific sequence assembly of the wheat transcriptome and provides a reference transcriptome for future wheat research. Furthermore, our assembly methodology is transferable to other polyploid organisms.

Single nucleotide polymorphism discovery from wheat next-generation sequence data.

Single nucleotide polymorphisms (SNPs) are the most abundant type of molecular genetic marker and can be used for producing high-resolution genetic maps, marker-trait association studies and marker-assisted breeding. Large polyploid genomes such as wheat present a challenge for SNP discovery because of the potential presence of multiple homoeologs for each gene. AutoSNPdb has been successfully applied to identify SNPs from Sanger sequence data for several species, including barley, rice and Brassica, but the volume of data required to accurately call SNPs in the complex genome of wheat has prevented its application to this important crop. DNA sequencing technology has been revolutionized by the introduction of next-generation sequencing, and it is now possible to generate several million sequence reads in a timely and cost-effective manner. We have produced wheat transcriptome sequence data using 454 sequencing technology and applied this for SNP discovery using a modified autoSNPdb method, which integrates SNP and gene annotation information with a graphical viewer. A total of 4,694,141 sequence reads from three bread wheat varieties were assembled to identify a total of 38 928 candidate SNPs. Each SNP is within an assembly complete with annotation, enabling the selection of polymorphism within genes of interest.

Bread matters: a national initiative to profile the genetic diversity of Australian wheat.

The large and complex genome of wheat makes genetic and genomic analysis in this important species both expensive and resource intensive. The application of next-generation sequencing technologies is particularly resource intensive, with at least 17 Gbp of sequence data required to obtain minimal (1×) coverage of the genome. A similar volume of data would represent almost 40× coverage of the rice genome. Progress can be made through the establishment of consortia to produce shared genomic resources. Australian wheat genome researchers, working with Bioplatforms Australia, have collaborated in a national initiative to establish a genetic diversity dataset representing Australian wheat germplasm based on whole genome next-generation sequencing data. Here, we describe the establishment and validation of this resource which can provide a model for broader international initiatives for the analysis of large and complex genomes.

A multiparent advanced generation inter-cross population for genetic analysis in wheat.

We present the first results from a novel multiparent advanced generation inter-cross (MAGIC) population derived from four elite wheat cultivars. The large size of this MAGIC population (1579 progeny), its diverse genetic composition and high levels of recombination all contribute to its value as a genetic resource. Applications of this resource include interrogation of the wheat genome and the analysis of gene-trait association in agronomically important wheat phenotypes. Here, we report the utilization of a MAGIC population for the first time for linkage map construction. We have constructed a linkage map with 1162 DArT, single nucleotide polymorphism and simple sequence repeat markers distributed across all 21 chromosomes. We benchmark this map against a high-density DArT consensus map created by integrating more than 100 biparental populations. The linkage map forms the basis for further exploration of the genetic architecture within the population, including characterization of linkage disequilibrium, founder contribution and inclusion of an alien introgression into the genetic map. Finally, we demonstrate the application of the resource for quantitative trait loci mapping using the complex traits plant height and hectolitre weight as a proof of principle.