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CGMs require multiple seasonal environmental records, unlike WGP, which makes CGM-WGP less accurate when applied to historical reference populations that lack crucial environmental inputs.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

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.

Genome-wide association studies dissect the G × E interaction for agronomic traits in a worldwide collection of safflowers (Carthamus tinctorius L.).

Genome-wide association studies were conducted using a globally diverse safflower (Carthamus tinctorius L.) Genebank collection for grain yield (YP), days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and crude protein content (PR) in four environments (sites) that differed in water availability. Phenotypic variation was observed for all traits. YP exhibited low overall genetic correlations (rGoverall) across sites, while SW and OL had high rGoverall and high pairwise genetic correlations (rGij) across all pairwise sites. In total, 92 marker-trait associations (MTAs) were identified using three methods, single locus genome-wide association studies (GWAS) using a mixed linear model (MLM), the Bayesian multi-locus method (BayesR), and meta-GWAS. MTAs with large effects across all sites were detected for OL, SW, and PR, and MTAs specific for the different water stress sites were identified for all traits. Five MTAs were associated with multiple traits; 4 of 5 MTAs were variously associated with the three traits of SW, OL, and PR. This study provided insights into the phenotypic variability and genetic architecture of important safflower agronomic traits under different environments. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-022-01295-8.

Meta-analysis of genome-wide association studies reveal common loci controlling agronomic and quality traits in a wide range of normal and heat stressed environments.

KEY MESSAGE: Several stable QTL were detected using metaGWAS analysis for different agronomic and quality traits under 26 normal and heat stressed environments. Heat stress, exacerbated by global warming, has a negative influence on wheat production worldwide and climate resilient cultivars can help mitigate these impacts. Selection decisions should therefore depend on multi-environment experiments representing a range of temperatures at critical stages of development. Here, we applied a meta-genome wide association analysis (metaGWAS) approach to detect stable QTL with significant effects across multiple environments. The metaGWAS was applied to 11 traits scored in 26 trials that were sown at optimal or late times of sowing (TOS1 and TOS2, respectively) at five locations. A total of 2571 unique wheat genotypes (13,959 genotypes across all environments) were included and the analysis conducted on TOS1, TOS2 and both times of sowing combined (TOS1&2). The germplasm was genotyped using a 90 k Infinium chip and imputed to exome sequence level, resulting in 341,195 single nucleotide polymorphisms (SNPs). The average accuracy across all imputed SNPs was high (92.4%). The three metaGWAS analyses revealed 107 QTL for the 11 traits, of which 16 were detected in all three analyses and 23 were detected in TOS1&2 only. The remaining QTL were detected in either TOS1 or TOS2 with or without TOS1&2, reflecting the complex interactions between the environments and the detected QTL. Eight QTL were associated with grain yield and seven with multiple traits. The identified QTL provide an important resource for gene enrichment and fine mapping to further understand the mechanisms of gene × environment interaction under both heat stressed and unstressed conditions.

Genomic selection can accelerate the biofortification of spring wheat.

KEY MESSAGE: Genomic selection enabled accurate prediction for the concentration of 13 nutritional element traits in wheat. Wheat biofortification is one of the most sustainable strategies to alleviate mineral deficiency in human diets. Here, we investigated the potential of genomic selection using BayesR and Bayesian ridge regression (BRR) models to predict grain yield (YLD) and the concentration of 13 nutritional elements in grains (B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P and Zn) using a population of 1470 spring wheat lines. The lines were grown in replicated field trials with two times of sowing (TOS) at 3 locations (Narrabri-NSW, all lines; Merredin-WA and Horsham-VIC, 200 core lines). Narrow-sense heritability across environments (locations/TOS) ranged from 0.09 to 0.45. Co, K, Na and Ca showed low to negative genetic correlations with other traits including YLD, while the remaining traits were negatively correlated with YLD. When all environments were included in the reference population, medium to high prediction accuracy was observed for the different traits across environments. BayesR had higher average prediction accuracy for mineral concentrations (r = 0.55) compared to BRR (r = 0.48) across all traits and environments but both methods had comparable accuracies for YLD. We also investigated the utility of one or two locations (reference locations) to predict the remaining location(s), as well as the ability of one TOS to predict the other. Under these scenarios, BayesR and BRR showed comparable performance but with lower prediction accuracy compared to the scenario of predicting reference environments for new lines. Our study demonstrates the potential of genomic selection for enriching wheat grain with nutritional elements in biofortification breeding.

Fine mapping of root lesion nematode (Pratylenchus thornei) resistance loci on chromosomes 6D and 2B of wheat.

KEY MESSAGE: Resistance QTL to root lesion nematode (Pratylenchus thornei) in wheat (Triticum aestivum), QRlnt.sk-6D and QRlnt.sk-2B, were mapped to intervals of 3.5 cM/1.77 Mbp on chromosome 6D and 1.4 cM/2.19 Mbp on chromosome 2B, respectively. Candidate resistance genes were identified in the QTL regions and molecular markers developed for marker-assisted breeding. Two previously known resistance QTL for root lesion nematode (Pratylenchus thornei) in bread wheat (Triticum aestivum), QRlnt.sk-6D and QRlnt.sk-2B, were fine-mapped using a Sokoll (moderately resistant) by Krichauff (susceptible) doubled haploid (DH) population and six newly developed recombinant inbred line populations. Bulked segregation analysis with the 90K wheat SNP array identified linked SNPs which were subsequently converted to KASP assays for mapping in the DH and RIL populations. On chromosome 6D, 60 KASP and five SSR markers spanned a total genetic distance of 23.7 cM. QRlnt.sk-6D was delimited to a 3.5 cM interval, representing 1.77 Mbp in the bread wheat cv. Chinese Spring reference genome sequence and 2.29 Mbp in the Aegilops tauschii genome sequence. These intervals contained 42 and 43 gene models in the respective annotated genome sequences. On chromosome 2B, 41 KASP and 5 SSR markers produced a map spanning 19.9 cM. QRlnt.sk-2B was delimited to 1.4 cM, corresponding 3.14 Mbp in the durum wheat cv. Svevo reference sequence and 2.19 Mbp in Chinese Spring. The interval in Chinese Spring contained 56 high-confidence gene models. Intervals for both QTL contained genes with similarity to those previously reported to be involved in disease resistance, namely genes for phenylpropanoid biosynthetic pathway-related enzymes, NBS-LRR proteins and protein kinases. The potential roles of these candidate genes in P. thornei resistance are discussed. The KASP markers reported in this study could potentially be used for marker-assisted breeding of P. thornei-resistant wheat cultivars.

Increase in coleoptile length and establishment by Lcol-A1, a genetic locus with major effect in wheat.

BACKGROUND: Good establishment is important for rapid leaf area development in wheat crops. Poor establishment results in fewer, later-emerging plants, reduced leaf area and tiller number. In addition, poorly established crops suffer from increased soil moisture loss through evaporation and greater competition from weeds while fewer spikes are produced which can reduce grain yield. By protecting the emerging first leaf, the coleoptile is critical for achieving good establishment, and its length and interaction with soil physical properties determine the ability of a cultivar to emerge from depth. RESULTS: Here we characterise a locus on chromosome 1AS, that increases coleoptile length in wheat, which we designate as Lcol-A1. We identified Lcol-A1 by bulked-segregant analysis and used a Halberd-derived population to fine map the gene to a 2 cM region, equivalent to 7 Mb on the IWGSC genome reference sequence of Chinese Spring (RefSeqv1.0). By sowing recently released cultivars and near-isogenic lines in the field at both conventional and deep sowing depths, we confirmed that Locl-A1 was associated with increased emergence from depth in the presence and absence of conventional dwarfing genes. Flanking markers IWB58229 and IWA710 were developed to assist breeders to select for long coleoptile wheats. CONCLUSIONS: Increased coleoptile length is sought in many global wheat production areas to improve crop emergence. The identification of the gene Lcol-A1, together with tools to allow wheat breeders to track the gene, will enable improvements to be made for this important trait.

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.

High-resolution mapping of rachis nodes per rachis, a critical determinant of grain yield components in wheat.

KEY MESSAGE: Exploring large genomic data sets based on the latest reference genome assembly identifies the rice ortholog APO1 as a key candidate gene for number of rachis nodes per spike in wheat. Increasing grain yield in wheat is a key breeding objective worldwide. Several component traits contribute to grain yield with spike attributes being among the most important. In this study, we performed a genome-wide association analysis for 12 grain yield and component traits measured in field trials with contrasting agrochemical input levels in a panel of 220 hexaploid winter wheats. A highly significant, environmentally consistent QTL was detected for number of rachis nodes per rachis (NRN) on chromosome 7AL. The five most significant SNPs formed a strong linkage disequilibrium (LD) block and tagged a 2.23 Mb region. Using pairwise LD for exome SNPs located across this interval in a large worldwide hexaploid wheat collection, we reduced the genomic region for NRN to a 258 Kb interval containing four of the original SNP and six high-confidence genes. The ortholog of one (TraesCS7A01G481600) of these genes in rice was ABBERANT PANICLE ORGANIZATION1 (APO1), which is known to have significant effects on panicle attributes. The APO1 ortholog was the best candidate for NRN and was associated with a 115 bp promoter deletion and two amino acid (C47F and D384 N) changes. Using a large worldwide collection of tetraploid and hexaploid wheat, we found 12 haplotypes for the NRN QTL and evidence for positive enrichment of two haplotypes in modern germplasm. Comparison of five QTL haplotypes in Australian yield trials revealed their relative, context-dependent contribution to grain yield. Our study provides diagnostic SNPs and value propositions to support deployment of the NRN trait in wheat breeding.

Zinc finger nuclease-mediated precision genome editing of an endogenous gene in hexaploid bread wheat (Triticum aestivum) using a DNA repair template.

Sequence-specific nucleases have been used to engineer targeted genome modifications in various plants. While targeted gene knockouts resulting in loss of function have been reported with relatively high rates of success, targeted gene editing using an exogenously supplied DNA repair template and site-specific transgene integration has been more challenging. Here, we report the first application of zinc finger nuclease (ZFN)-mediated, nonhomologous end-joining (NHEJ)-directed editing of a native gene in allohexaploid bread wheat to introduce, via a supplied DNA repair template, a specific single amino acid change into the coding sequence of acetohydroxyacid synthase (AHAS) to confer resistance to imidazolinone herbicides. We recovered edited wheat plants having the targeted amino acid modification in one or more AHAS homoalleles via direct selection for resistance to imazamox, an AHAS-inhibiting imidazolinone herbicide. Using a cotransformation strategy based on chemical selection for an exogenous marker, we achieved a 1.2% recovery rate of edited plants having the desired amino acid change and a 2.9% recovery of plants with targeted mutations at the AHAS locus resulting in a loss-of-function gene knockout. The latter results demonstrate a broadly applicable approach to introduce targeted modifications into native genes for nonselectable traits. All ZFN-mediated changes were faithfully transmitted to the next generation.

Zebularine treatment is associated with deletion of FT-B1 leading to an increase in spikelet number in bread wheat.

The number of rachis nodes (spikelets) on a wheat spike is a component of grain yield that correlates with flowering time. The genetic basis regulating flowering in cereals is well understood, but there are reports that flowering time can be modified at a high frequency by selective breeding, suggesting that it may be regulated by both epigenetic and genetic mechanisms. We investigated the role of DNA methylation in regulating spikelet number and flowering time by treating a semi-spring wheat with the demethylating agent, Zebularine. Three lines with a heritable increase in spikelet number were identified. The molecular basis for increased spikelet number was not determined in 2 lines, but the phenotype showed non-Mendelian inheritance, suggesting that it could have an epigenetic basis. In the remaining line, the increased spikelet phenotype behaved as a Mendelian recessive trait and late flowering was associated with a deletion encompassing the floral promoter, FT-B1. Deletion of FT-B1 delayed the transition to reproductive growth, extended the duration of spike development, and increased spikelet number under different temperature regimes and photoperiod. Transiently disrupting DNA methylation can generate novel flowering behaviour in wheat, but these changes may not be sufficiently stable for use in breeding programs.

Characterisation and mapping of adult plant stripe rust resistance in wheat accession Aus27284.

KEY MESSAGE: A new adult plant stripe rust resistance gene, Yr80, was identified in a common wheat landrace Aus27284. Linked markers were developed and validated for their utility in marker-assisted selection. Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is among the most important constraints to global wheat production. The identification and characterisation of new sources of host plant resistance enrich the gene pool and underpin deployment of resistance gene pyramids in new cultivars. Aus27284 exhibited resistance at the adult plant stage against predominant Pst pathotypes and was crossed with a susceptible genotype Avocet S. A recombinant inbred line (RIL) population comprising 121 lines was developed and tested in the field at three locations in 2016 and two in 2017 crop seasons. Monogenic segregation for adult plant stripe rust response was observed among the Aus27284/Avocet S RIL population and the underlying locus was temporarily designated YrAW11. Bulked-segregant analysis using the Infinium iSelect 90K SNP wheat array placed YrAW11 in chromosome 3B. Kompetitive allele specific PCR (KASP) primers were designed for the linked SNPs and YrAW11 was flanked by KASP_65624 and KASP_53292 (3 cM) proximally and KASP_53113 (4.9 cM) distally. A partial linkage map of the genomic region carrying YrAW11 comprised nine KASP and two SSR markers. The physical position of KASP markers in the pseudomolecule of chromosome 3B placed YrAW11 in the long arm and the location of markers gwm108 and gwm376 in the deletion bin 3BL2-0.22 supported this conclusion. As no other stripe rust resistance locus has been reported in chromosome 3BL, YrAW11 was formally designated Yr80. Marker KASP_ 53113 was polymorphic among 94% of 81 Australian wheat cultivars used for validation.

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.

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.

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.

Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars.

Domesticated crops experience strong human-mediated selection aimed at developing high-yielding varieties adapted to local conditions. To detect regions of the wheat genome subject to selection during improvement, we developed a high-throughput array to interrogate 9,000 gene-associated single-nucleotide polymorphisms (SNP) in a worldwide sample of 2,994 accessions of hexaploid wheat including landraces and modern cultivars. Using a SNP-based diversity map we characterized the impact of crop improvement on genomic and geographic patterns of genetic diversity. We found evidence of a small population bottleneck and extensive use of ancestral variation often traceable to founders of cultivars from diverse geographic regions. Analyzing genetic differentiation among populations and the extent of haplotype sharing, we identified allelic variants subjected to selection during improvement. Selective sweeps were found around genes involved in the regulation of flowering time and phenology. An introgression of a wild relative-derived gene conferring resistance to a fungal pathogen was detected by haplotype-based analysis. Comparing selective sweeps identified in different populations, we show that selection likely acts on distinct targets or multiple functionally equivalent alleles in different portions of the geographic range of wheat. The majority of the selected alleles were present at low frequency in local populations, suggesting either weak selection pressure or temporal variation in the targets of directional selection during breeding probably associated with changing agricultural practices or environmental conditions. The developed SNP chip and map of genetic variation provide a resource for advancing wheat breeding and supporting future population genomic and genome-wide association studies 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.

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.

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.