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CGMs require multiple seasonal environmental records, unlike WGP, which makes CGM-WGP less accurate when applied to historical reference populations that lack crucial environmental inputs.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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

An adult plant stripe rust resistance gene maps on chromosome 7A of Australian wheat cultivar Axe.

KEY MESSAGE: An adult plant stripe rust resistance gene Yr75 was located on the long arm of chromosome 7A. Fine mapping of the region identified markers closely linked with Yr75. Australian wheat cultivar Axe produced resistant to moderately resistant stripe rust responses under field conditions and was exhibiting seedling responses varying from 33C to 3+ under greenhouse conditions. Experiments covering tests at different growth stages (2nd, 3rd and 4th leaf stages) demonstrated the clear expression of resistance at the 4th leaf stage under controlled-environment greenhouse conditions. A recombinant inbred line (RIL) population was developed from the Axe/Nyabing-3 (Nyb) cross. Genetic analysis of Axe/Nyb RIL population in the greenhouse at the 4th leaf stage showed monogenic inheritance of stripe rust resistance. Selective genotyping using the iSelect 90 K Infinium SNP genotyping array was performed, and the resistance locus was mapped to the long arm of chromosome 7A and named Yr75. The Axe/Nyb RIL population was genotyped using a targeted genotype-by-sequencing assay, and the resistance-linked SNPs were converted into kompetitive allele-specific PCR (KASP) markers. These markers were tested on the entire Axe/Nyb RIL population, and markers sunKASP_430 and sunKASP_427 showed close association with Yr75 in the Axe/Nyb RIL population. A high-resolution mapping family of 1032 F2 plants from the Axe/Nyb cross was developed and genotyped with sunKASP_430 and sunKASP_427, and these markers flanked Yr75 at 0.3 cM and 0.4 cM, respectively. These markers cover 1.24 Mb of the physical map of Chinese Spring, and this information will be useful for map-based cloning of Yr75.

Lessons from a GWAS study of a wheat pre-breeding program: pyramiding resistance alleles to Fusarium crown rot.

Much has been published on QTL detection for complex traits using bi-parental and multi-parental crosses (linkage analysis) or diversity panels (GWAS studies). While successful for detection, transferability of results to real applications has proven more difficult. Here, we combined a QTL detection approach using a pre-breeding populations which utilized intensive phenotypic selection for the target trait across multiple plant generations, combined with rapid generation turnover (i.e. "speed breeding") to allow cycling of multiple plant generations each year. The reasoning is that QTL mapping information would complement the selection process by identifying the genome regions under selection within the relevant germplasm. Questions to answer were the location of the genomic regions determining response to selection and the origin of the favourable alleles within the pedigree. We used data from a pre-breeding program that aimed at pyramiding different resistance sources to Fusarium crown rot into elite (but susceptible) wheat backgrounds. The population resulted from a complex backcrossing scheme involving multiple resistance donors and multiple elite backgrounds, akin to a MAGIC population (985 genotypes in total, with founders, and two major offspring layers within the pedigree). A significant increase in the resistance level was observed (i.e. a positive response to selection) after the selection process, and 17 regions significantly associated with that response were identified using a GWAS approach. Those regions included known QTL as well as potentially novel regions contributing resistance to Fusarium crown rot. In addition, we were able to trace back the sources of the favourable alleles for each QTL. We demonstrate that QTL detection using breeding populations under selection for the target trait can identify QTL controlling the target trait and that the frequency of the favourable alleles was increased as a response to selection, thereby validating the QTL detected. This is a valuable opportunistic approach that can provide QTL information that is more easily transferred to breeding applications.

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.

Development of an Australian Bread Wheat Nested Association Mapping Population, a New Genetic Diversity Resource for Breeding under Dry and Hot Climates.

Genetic diversity, knowledge of the genetic architecture of the traits of interest and efficient means of transferring the desired genetic diversity into the relevant genetic background are prerequisites for plant breeding. Exotic germplasm is a rich source of genetic diversity; however, they harbor undesirable traits that limit their suitability for modern agriculture. Nested association mapping (NAM) populations are valuable genetic resources that enable incorporation of genetic diversity, dissection of complex traits and providing germplasm to breeding programs. We developed the OzNAM by crossing and backcrossing 73 diverse exotic parents to two Australian elite varieties Gladius and Scout. The NAM parents were genotyped using the iSelect wheat 90K Infinium SNP array, and the progeny were genotyped using a custom targeted genotyping-by-sequencing assay based on molecular inversion probes designed to target 12,179 SNPs chosen from the iSelect wheat 90K Infinium SNP array of the parents. In total, 3535 BC1F4:6 RILs from 125 families with 21 to 76 lines per family were genotyped and we found 4964 polymorphic and multi-allelic haplotype markers that spanned the whole genome. A subset of 530 lines from 28 families were evaluated in multi-environment trials over three years. To demonstrate the utility of the population in QTL mapping, we chose to map QTL for maturity and plant height using the RTM-GWAS approach and we identified novel and known QTL for maturity and plant height.

Genome-wide association reveals a complex architecture for rust resistance in 2300 worldwide bread wheat accessions screened under various Australian conditions.

We utilized 2300 wheat accessions including worldwide landraces, cultivars and primary synthetic-derived germplasm with three Australian cultivars: Annuello, Yitpi and Correll, to investigate field-based resistance to leaf (Lr) rust, stem (Sr) rust and stripe (Yr) rust diseases across a range of Australian wheat agri-production zones. Generally, the resistance in the modern Australian cultivars, synthetic derivatives, South and North American materials outperformed other geographical subpopulations. Different environments for each trait showed significant correlations, with average r values of 0.53, 0.23 and 0.66 for Lr, Sr and Yr, respectively. Single-trait genome-wide association studies (GWAS) revealed several environment-specific and multi-environment quantitative trait loci (QTL). Multi-trait GWAS confirmed a cluster of Yr QTL on chromosome 3B within a 4.4-cM region. Linkage disequilibrium and comparative mapping showed that at least three Yr QTL exist within the 3B cluster including the durable rust resistance gene Yr30. An Sr/Lr QTL on chromosome 3D was found mainly in the synthetic-derived germplasm from Annuello background which is known to carry the Agropyron elongatum 3D translocation involving the Sr24/Lr24 resistance locus. Interestingly, estimating the SNP effects using a BayesR method showed that the correlation among the highest 1% of QTL effects across environments (excluding GWAS QTL) had significant correlations, with average r values of 0.26, 0.16 and 0.55 for Lr, Sr and Yr, respectively. These results indicate the importance of small effect QTL in achieving durable rust resistance which can be captured using genomic selection.

Genome wide association study reveals novel QTL for barley yellow dwarf virus resistance in wheat.

BACKGROUND: Barley yellow dwarf (BYD) is an important virus disease that causes significant reductions in wheat yield. For effective control of Barley yellow dwarf virus through breeding, the identification of genetic sources of resistance is key to success. In this study, 335 geographically diverse wheat accessions genotyped using an Illumina iSelect 90 K single nucleotide polymorphisms (SNPs) bead chip array were used to identify new sources of resistance to BYD in different environments. RESULTS: A genome-wide association study (GWAS) performed using all the generalised and mixed linkage models (GLM and MLM, respectively) identified a total of 36 significant marker-trait associations, four of which were consistently detected in the K model. These four novel quantitative trait loci (QTL) were identified on chromosomes 2A, 2B, 6A and 7A and associated with markers IWA3520, IWB24938, WB69770 and IWB57703, respectively. These four QTL showed an additive effect with the average visual symptom score of the lines containing resistance alleles of all four QTL being much lower than those with less favorable alleles. Several Chinese landraces, such as H-205 (Baimazha) and H-014 (Dahongmai) which have all four favorable alleles, showed consistently higher resistance in different field trials. None of them contained the previously described Bdv2, Bdv3 or Bdv4 genes for BYD resistance. CONCLUSIONS: This study identified multiple novel QTL for BYD resistance and some resistant wheat genotypes. These will be useful for breeders to generate combinations with and/or without Bdv2 to achieve higher levels and more stable BYD resistance.

Hybridisation-based target enrichment of phenology genes to dissect the genetic basis of yield and adaptation in barley.

Barley (Hordeum vulgare L.) is a major cereal grain widely used for livestock feed, brewing malts and human food. Grain yield is the most important breeding target for genetic improvement and largely depends on optimal timing of flowering. Little is known about the allelic diversity of genes that underlie flowering time in domesticated barley, the genetic changes that have occurred during breeding, and their impact on yield and adaptation. Here, we report a comprehensive genomic assessment of a worldwide collection of 895 barley accessions based on the targeted resequencing of phenology genes. A versatile target-capture method was used to detect genome-wide polymorphisms in a panel of 174 flowering time-related genes, chosen based on prior knowledge from barley, rice and Arabidopsis thaliana. Association studies identified novel polymorphisms that accounted for observed phenotypic variation in phenology and grain yield, and explained improvements in adaptation as a result of historical breeding of Australian barley cultivars. We found that 50% of genetic variants associated with grain yield, and 67% of the plant height variation was also associated with phenology. The precise identification of favourable alleles provides a genomic basis to improve barley yield traits and to enhance adaptation for specific production areas.

Extension of a haplotype-based genomic prediction model to manage multi-environment wheat data using environmental covariates.

KEY MESSAGE: A multi-environment genomic prediction model incorporating environmental covariates increased the prediction accuracy of wheat grain protein content. The advantage of the haplotype-based model was dependent upon the trait of interest. The inclusion of environment covariates (EC) in genomic prediction models has the potential to precisely model environmental effects and genotype-by-environment interactions. Together with EC, a haplotype-based genomic prediction approach, which is capable of accommodating the interaction between local epistasis and environment, may increase the prediction accuracy. The main objectives of our study were to evaluate the potential of EC to portray the relationship between environments and the relevance of local epistasis modelled by haplotype-based approaches in multi-environment prediction. The results showed that among five traits: grain yield (GY), plant height, protein content, screenings percentage (SP) and thousand kernel weight, protein content exhibited a 2.1% increase in prediction accuracy when EC was used to model the environmental relationship compared to treatment of the environment as a regular random effect without a variance-covariance structure. The approach used a Gaussian kernel to characterise the relationship among environments that displayed no advantage in contrast to the use of a genomic relationship matrix. The prediction accuracies of haplotype-based approaches for SP were consistently higher than the genotype-based model when the numbers of single-nucleotide polymorphisms (SNP) in a haplotype were from three to ten. In contrast, for GY, haplotype-based models outperformed genotype-based methods when two to four SNPs were used to construct the haplotype.