Optimising desired gain indices to maximise selection response.

Introduction: In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where "desired gain selection indices" come in handy, which can yield optimal gains in each trait based on the breeder's prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit. Methods: Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits. Results: We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction. Discussion: Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option.

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.

Sr65: a widely effective gene for stem rust resistance in wheat.

KEY MESSAGE: Sr65 in chromosome 1A of Indian wheat landrace Hango-2 is a potentially useful all-stage resistance gene that currently protects wheat from stem rust in Australia, India, Africa and Europe. Stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), threatened global wheat production with the appearance of widely virulent races that included TTKSK and TTRTF. Indian landrace Hango-2 showed resistance to Pgt races in India and Australia. Screening of a Hango-2/Avocet 'S' (AvS) recombinant inbred line population identified two stem rust resistance genes, a novel gene (temporarily named as SrH2) from Hango-2 and Sr26 from AvS. A mapping population segregating for SrH2 alone was developed from two recombinant lines. SrH2 was mapped on the short arm of chromosome 1A, where it was flanked by KASP markers KASP_7944 (proximal) and KASP_12147 (distal). SrH2 was delimited to an interval of 1.8-2.3 Mb on chromosome arm 1AS. The failure to detect candidate genes through MutRenSeq and comparative genomic analysis with the pan-genome dataset indicated the necessity to generate a Hango-2 specific assembly for detecting the gene sequence linked with SrH2 resistance. MutRenSeq however enabled identification of SrH2-linked KASP marker sunCS_265. Markers KASP_12147 and sunCS_265 showed 92% and 85% polymorphism among an Australian cereal cultivar diversity panel and can be used for marker-assisted selection of SrH2 in breeding programs. The effectiveness of SrH2 against Pgt races from Europe, Africa, India, and Australia makes it a valuable resource for breeding stem rust-resistant wheat cultivars. Since no wheat-derived gene was previously located in chromosome arm 1AS, SrH2 represents a new locus and named as SR65.

Wheat stripe rust resistance locus YR63 is a hot spot for evolution of defence genes - a pangenome discovery.

BACKGROUND: Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), poses a threat to global wheat production. Deployment of widely effective resistance genes underpins management of this ongoing threat. This study focused on the mapping of stripe rust resistance gene YR63 from a Portuguese hexaploid wheat landrace AUS27955 of the Watkins Collection. RESULTS: YR63 exhibits resistance to a broad spectrum of Pst races from Australia, Africa, Asia, Europe, Middle East and South America. It was mapped to the short arm of chromosome 7B, between two single nucleotide polymorphic (SNP) markers sunCS_YR63 and sunCS_67, positioned at 0.8 and 3.7 Mb, respectively, in the Chinese Spring genome assembly v2.1. We characterised YR63 locus using an integrated approach engaging targeted genotyping-by-sequencing (tGBS), mutagenesis, resistance gene enrichment and sequencing (MutRenSeq), RNA sequencing (RNASeq) and comparative genomic analysis with tetraploid (Zavitan and Svevo) and hexaploid (Chinese Spring) wheat genome references and 10+ hexaploid wheat genomes. YR63 is positioned at a hot spot enriched with multiple nucleotide-binding and leucine rich repeat (NLR) and kinase domain encoding genes, known widely for defence against pests and diseases in plants and animals. Detection of YR63 within these gene clusters is not possible through short-read sequencing due to high homology between members. However, using the sequence of a NLR member we were successful in detecting a closely linked SNP marker for YR63 and validated on a panel of Australian bread wheat, durum and triticale cultivars. CONCLUSIONS: This study highlights YR63 as a valuable source for resistance against Pst in Australia and elsewhere. The closely linked SNP marker will facilitate rapid introgression of YR63 into elite cultivars through marker-assisted selection. The bottleneck of this study reinforces the necessity for a long-read sequencing such as PacBio or Oxford Nanopore based techniques for accurate detection of the underlying resistance gene when it is part of a large gene cluster.

Physical Mapping of QTLs for Root Traits in a Population of Recombinant Inbred Lines of Hexaploid Wheat.

Root architecture is key in determining how effective plants are at intercepting and absorbing nutrients and water. Previously, the wheat (Triticum aestivum) cultivars Spica and Maringa were shown to have contrasting root morphologies. These cultivars were crossed to generate an F6:1 population of recombinant inbred lines (RILs) which was genotyped using a 90 K single nucleotide polymorphisms (SNP) chip. A total of 227 recombinant inbred lines (RILs) were grown in soil for 21 days in replicated trials under controlled conditions. At harvest, the plants were scored for seven root traits and two shoot traits. An average of 7.5 quantitative trait loci (QTL) were associated with each trait and, for each of these, physical locations of the flanking markers were identified using the Chinese Spring reference genome. We also compiled a list of genes from wheat and other monocotyledons that have previously been associated with root growth and morphology to determine their physical locations on the Chinese Spring reference genome. This allowed us to determine whether the QTL discovered in our study encompassed genes previously associated with root morphology in wheat or other monocotyledons. Furthermore, it allowed us to establish if the QTL were co-located with the QTL identified from previously published studies. The parental lines together with the genetic markers generated here will enable specific root traits to be introgressed into elite wheat lines. Moreover, the comprehensive list of genes associated with root development, and their physical locations, will be a useful resource for researchers investigating the genetics of root morphology in cereals.

Mining the Australian Grains Gene Bank for Rust Resistance in Barley.

Global barley production is threatened by plant pathogens, especially the rusts. In this study we used a targeted genotype-by-sequencing (GBS) assisted GWAS approach to identify rust resistance alleles in a collection of 287 genetically distinct diverse barley landraces and historical cultivars available in the Australian Grains Genebank (AGG) and originally sourced from Eastern Europe. The accessions were challenged with seven US-derived cereal rust pathogen races including Puccinia hordei (Ph-leaf rust) race 17VA12C, P. coronata var. hordei (Pch-crown rust) race 91NE9305 and five pathogenically diverse races of P. striiformis f. sp. hordei (Psh-stripe rust) (PSH-33, PSH-48, PSH-54, PSH-72 and PSH-100) and phenotyped quantitatively at the seedling stage. Novel resistance factors were identified on chromosomes 1H, 2H, 4H and 5H in response to Pch, whereas a race-specific QTL on 7HS was identified that was effective only to Psh isolates PSH-72 and PSH-100. A major effect QTL on chromosome 5HL conferred resistance to all Psh races including PSH-72, which is virulent on all 12 stripe rust differential tester lines. The same major effect QTL was also identified in response to leaf rust (17VA12C) suggesting this locus contains several pathogen specific rust resistance genes or the same gene is responsible for both leaf rust and stripe rust resistance. Twelve accessions were highly resistant to both leaf and stripe rust diseases and also carried the 5HL QTL. We subsequently surveyed the physical region at the 5HL locus for across the barley pan genome variation in the presence of known resistance gene candidates and identified a rich source of high confidence protein kinase and antifungal genes in the QTL region.

Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes.

Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400-1,900) were measured across 8 years (2012-2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5-0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69-0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle.

Haplotype-Based Genome-Wide Association Analysis Using Exome Capture Assay and Digital Phenotyping Identifies Genetic Loci Underlying Salt Tolerance Mechanisms in Wheat.

Soil salinity can impose substantial stress on plant growth and cause significant yield losses. Crop varieties tolerant to salinity stress are needed to sustain yields in saline soils. This requires effective genotyping and phenotyping of germplasm pools to identify novel genes and QTL conferring salt tolerance that can be utilised in crop breeding schemes. We investigated a globally diverse collection of 580 wheat accessions for their growth response to salinity using automated digital phenotyping performed under controlled environmental conditions. The results show that digitally collected plant traits, including digital shoot growth rate and digital senescence rate, can be used as proxy traits for selecting salinity-tolerant accessions. A haplotype-based genome-wide association study was conducted using 58,502 linkage disequilibrium-based haplotype blocks derived from 883,300 genome-wide SNPs and identified 95 QTL for salinity tolerance component traits, of which 54 were novel and 41 overlapped with previously reported QTL. Gene ontology analysis identified a suite of candidate genes for salinity tolerance, some of which are already known to play a role in stress tolerance in other plant species. This study identified wheat accessions that utilise different tolerance mechanisms and which can be used in future studies to investigate the genetic and genic basis of salinity tolerance. Our results suggest salinity tolerance has not arisen from or been bred into accessions from specific regions or groups. Rather, they suggest salinity tolerance is widespread, with small-effect genetic variants contributing to different levels of tolerance in diverse, locally adapted germplasm.

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.

Relocation of Sr48 to Chromosome 2D Using an Alternative Mapping Population and Development of a Closely Linked Marker Using Diverse Molecular Technologies.

The Ug99-effective stem rust resistance gene Sr48 was mapped to chromosome 2A based on its repulsion linkage with Yr1 in an Arina/Forno recombinant inbred line (RIL) population. Attempts to identify markers closely linked to Sr48 using available genomic resources were futile. This study used an Arina/Cezanne F5:7 RIL population to identify markers closely linked with Sr48. Using the Arina/Cezanne DArTseq map, Sr48 was mapped on the short arm of chromosome 2D and it co-segregated with 12 markers. These DArTseq marker sequences were used for BlastN search to identify corresponding wheat chromosome survey sequence (CSS) contigs, and PCR-based markers were developed. Two simple sequence repeat (SSR) markers, sun590 and sun592, and two Kompetitive Allele-Specific PCR (KASP) markers were derived from the contig 2DS_5324961 that mapped distal to Sr48. Molecular cytogenetic analysis using sequential fluorescent in situ hybridization (FISH) and genomic in situ hybridization (GISH) identified a terminal translocation of chromosome 2A in chromosome 2DL of Forno. This translocation would have led to the formation of a quadrivalent involving chromosomes 2A and 2D in the Arina/Forno population, which would have exhibited pseudo-linkage between Sr48 and Yr1 in chromosome 2AL. Polymorphism of the closet marker sunKASP_239 among a set of 178 wheat genotypes suggested that this marker can be used for marker-assisted selection of Sr48.

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.

A novel locus conferring resistance to Puccinia hordei maps to the genomic region corresponding to Rph14 on barley chromosome 2HS.

Barley leaf rust (BLR), caused by Puccinia hordei, is best controlled through genetic resistance. An efficient resistance breeding program prioritizes the need to identify, characterize, and map new sources of resistance as well as understanding the effectiveness, structure, and function of resistance genes. In this study, three mapping populations were developed by crossing Israelian barley lines "AGG-396," "AGG-397," and "AGG-403" (carrying unknown leaf rust resistance) with a susceptible variety "Gus" to characterize and map resistance. Genetic analysis of phenotypic data from rust testing F3s with a P. hordei pathotype 5457 P+ revealed monogenic inheritance in all three populations. Targeted genotyping-by-sequencing of the three populations detected marker trait associations in the same genomic region on the short arm of chromosome 2H between 39 and 57 Mb (AGG-396/Gus), 44 and 64 Mb (AGG-397/Gus), and 31 and 58 Mb (AGG-403/Gus), suggesting that the resistance in all three lines is likely conferred by the same locus (tentatively designated RphAGG396). Two Kompetitive allele-specific PCR (KASP) markers, HvGBSv2-902 and HvGBSv2-932, defined a genetic distance of 3.8 cM proximal and 7.1 cM distal to RphAGG396, respectively. To increase the marker density at the RphAGG396 locus, 75 CAPS markers were designed between two flanking markers. Integration of marker data resulted in the identification of two critical recombinants and mapping RphAGG396 between markers- Mloc-28 (40.75 Mb) and Mloc-41 (41.92 Mb) narrowing the physical window to 1.17 Mb based on the Morex v2.0 reference genome assembly. To enhance map resolution, 600 F2s were genotyped with markers- Mloc-28 and Mloc-41 and nine recombinants were identified, placing the gene at a genetic distance of 0.5 and 0.2 cM between the two markers, respectively. Two annotated NLR (nucleotide-binding domain leucine-rich repeat) genes (r2.2HG0093020 and r2.2HG0093030) were identified as the best candidates for RphAGG396. A closely linked marker was developed for RphAGG396 that can be used for marker-assisted selection.

Haplotype variants of the stripe rust resistance gene Yr28 in Aegilops tauschii.

KEY MESSAGE: Stripe rust resistance gene YrAet672 from Aegilops tauschii accession CPI110672 encodes a nucleotide-binding and leucine-rich repeat domain containing protein similar to YrAS2388 and both these members were haplotypes of Yr28. New sources of host resistance are required to counter the continued emergence of new pathotypes of the wheat stripe rust pathogen Puccinia striiformis Westend. f. sp. tritici Erikss. (Pst). Here, we show that CPI110672, an Aegilops tauschii accession from Turkmenistan, carries a single Pst resistance gene, YrAet672, that is effective against multiple Pst pathotypes, including the four predominant Pst lineages present in Australia. The YRAet672 locus was fine mapped to the short arm of chromosome 4D, and a nucleotide-binding and leucine-rich repeat gene was identified at the locus. A transgene encoding the YrAet672 genomic sequence, but lacking a copy of a duplicated sequence present in the 3' UTR, was transformed into wheat cultivar Fielder and Avocet S. This transgene conferred a weak resistance response, suggesting that the duplicated 3' UTR region was essential for function. Subsequent analyses demonstrated that YrAet672 is the same as two other Pst resistance genes described in Ae. tauschii, namely YrAS2388 and Yr28. They were identified as haplotypes encoding identical protein sequences but are polymorphic in non-translated regions of the gene. Suppression of resistance conferred by YrAet672 and Yr28 in synthetic hexaploid wheat lines (AABBDD) involving Langdon (AABB) as the tetraploid parent was associated with a reduction in transcript accumulation.

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.

Haplotype variants of Sr46 in Aegilops tauschii, the diploid D genome progenitor of wheat.

KEY MESSAGE: Stem rust resistance genes, SrRL5271 and Sr672.1 as well as SrCPI110651, from Aegilops tauschii, the diploid D genome progenitor of wheat, are sequence variants of Sr46 differing by 1-2 nucleotides leading to non-synonymous amino acid substitutions. The Aegilops tauschii (wheat D-genome progenitor) accessions RL 5271 and CPI110672 were identified as resistant to multiple races (including the Ug99) of the wheat stem rust pathogen Puccinia graminis f. sp. tritici (Pgt). This study was conducted to identify the stem rust resistance (Sr) gene(s) in both accessions. Genetic analysis of the resistance in RL 5271 identified a single dominant allele (SrRL5271) controlling resistance, whereas resistance segregated at two loci (SR672.1 and SR672.2) for a cross of CPI110672. Bulked segregant analysis placed SrRL5271 and Sr672.1 in a region on chromosome arm 2DS that encodes Sr46. Molecular marker screening, mapping and genomic sequence analysis demonstrated SrRL5271 and Sr672.1 are alleles of Sr46. The amino acid sequence of SrRL5271 and Sr672.1 is identical but differs from Sr46 (hereafter referred to as Sr46_h1 by following the gene nomenclature in wheat) by a single amino acid (N763K) and is thus designated Sr46_h2. Screening of a panel of Ae. tauschii accessions identified an additional allelic variant that differed from Sr46_h2 by a different amino acid (A648V) and was designated Sr46_h3. By contrast, the protein encoded by the susceptible allele of Ae. tauschii accession AL8/78 differed from these resistance proteins by 54 amino acid substitutions (94% nucleotide sequence gene identity). Cloning and complementation tests of the three resistance haplotypes confirmed their resistance to Pgt race 98-1,2,3,5,6 and partial resistance to Pgt race TTRTF in bread wheat. The three Sr46 haplotypes, with no virulent races detected yet, represent a valuable source for improving stem resistance in wheat.

Multi-donor × elite-based populations reveal QTL for low-lodging wheat.

KEY MESSAGE: Low-lodging high-yielding wheat germplasm and SNP-tagged novel alleles for lodging were identified in a process that involved selecting donors through functional phenotyping for underlying traits with a designed phenotypic screen, and a crossing strategy involving multiple-donor × elite populations. Lodging is a barrier to achieving high yield in wheat. As part of a study investigating the potential to breed low-lodging high-yielding wheat, populations were developed crossing four low-lodging high-yielding donors selected based on lodging related traits, with three cultivars. Lodging was evaluated in single rows in an early generation and subsequently in plots in 2 years with contrasting lodging environment. A large number of lines lodged less than their recurrent parents, and some were also higher yielding. Heritability for lodging was high, but the genetic correlation between contrasting environments was intermediate-low. Lodging genotypic rankings in single rows did not correlate well with plots. Populations from the highest lodging background were genotyped (90 K iSelect BeadChip array). Fourteen markers on nine chromosomes were associated with lodging, differing under high- versus low-lodging conditions. Of the fourteen markers, ten were found to co-locate with previously identified QTL for lodging-related traits or at homoeologous locations for previously identified lodging-related QTL, while the remaining four markers (in chromosomes 2D, 4D, 7B and 7D) appear to map to novel QTL for lodging. Lines with more favourable markers lodged less, suggesting value in these markers as a selection tool. This study demonstrates that the combination of donor functional phenotyping, screen design and crossing strategy can help identify novel alleles in germplasm without requiring extensive bi-parental populations.

Adult plant stem rust resistance in durum wheat Glossy Huguenot: mapping, marker development and validation.

KEY MESSAGE: Adult plant stem rust resistance locus, QSrGH.cs-2AL, was identified in durum wheat Glossy Huguenot and mendelised as Sr63. Markers closely linked with Sr63 were developed. An F3 population from a Glossy Huguenot (GH)/Bansi cross used in a previous Australian study was advanced to F6 for molecular mapping of adult plant stem rust resistance. Maturity differences among F6 lines confounded assessments of stem rust response. GH was crossed with a stem rust susceptible F6 recombinant inbred line (RIL), GHB14 (M14), with similar maturity and an F6:7 population was developed through single seed descent method. F7 and F8 RILs were tested along with the parents at different locations. The F6 individual plants and both parents were genotyped using the 90 K single nucleotide polymorphism (SNP) wheat array. Stem rust resistance QTL on the long arms of chromosomes 1B (QSrGH.cs-1BL) and 2A (QSrGH.cs-2AL) were detected. QSrGH.cs-1BL and QSrGH.cs-2AL were both contributed by GH and explained 22% and 18% adult plant stem rust response variation, respectively, among GH/M14 RIL population. RILs carrying combinations of these QTL reduced more than 14% stem rust severity compared to those that possessed QSrGH.cs-1BL and QSrGH.cs-2AL individually. QSrGH.cs1BL was demonstrated to be the same as Sr58/Lr46/Yr29/Pm39 through marker genotyping. Lines lacking QSrGH.cs-1BL were used to Mendelise QSrGH.cs-2AL. Based on genomic locations of previously catalogued stem rust resistance genes and the QSrGH.cs-2AL map, it appeared to represent a new APR locus and was permanently named Sr63. SNP markers associated with Sr63 were converted to kompetetive allele-specific PCR (KASP) assays and were validated on a set of durum cultivars.

Genome-wide superior alleles, haplotypes and candidate genes associated with tolerance on sodic-dispersive soils in wheat (Triticum aestivum L.).

KEY MESSAGE: The pleiotropic SNPs/haplotypes, overlapping genes (metal ion binding, photosynthesis), and homozygous/biallelic SNPs and transcription factors (HTH myb-type and BHLH) hold great potential for improving wheat yield potential on sodic-dispersive soils. Sodic-dispersive soils have multiple subsoil constraints including poor soil structure, alkaline pH and subsoil toxic elemental ion concentration, affecting growth and development in wheat. Tolerance is required at all developmental stages to enhance wheat yield potential on such soils. An in-depth investigation of genome-wide associations was conducted using a field phenotypic data of 206 diverse Focused Identification of Germplasm Strategy (FIGS) wheat lines for two consecutive years from different sodic and non-sodic plots and the exome targeted genotyping by sequencing (tGBS) assay. A total of 39 quantitative trait SNPs (QTSs), including 18 haplotypes were identified on chromosome 1A, 1B, 1D, 2A, 2B, 2D, 3A, 3B, 5A, 5D, 6B, 7A, 7B, 7D for yield and yield-components tolerance. Among these, three QTSs had common associations for multiple traits, indicating pleiotropism and four QTSs had close associations for multiple traits, within 32.38 Mb. The overlapping metal ion binding (Mn, Ca, Zn and Al) and photosynthesis genes and transcription factors (PHD-, Dof-, HTH myb-, BHLH-, PDZ_6-domain) identified are known to be highly regulated during germination, maximum stem elongation, anthesis, and grain development stages. The homozygous/biallelic SNPs having allele frequency above 30% were identified for yield and crop establishment/plants m-2. These SNPs correspond to HTH myb-type and BHLH transcription factors, brassinosteroid signalling pathway, kinase activity, ATP and chitin binding activity. These resources are valuable in haplotype-based breeding and genome editing to improve yield potential on sodic-dispersive soils.