Genome-wide atlas of rust resistance loci in wheat.

Rust diseases, including leaf rust, stripe/yellow rust, and stem rust, significantly impact wheat (Triticum aestivum L.) yields, causing substantial economic losses every year. Breeding and deployment of cultivars with genetic resistance is the most effective and sustainable approach to control these diseases. The genetic toolkit for wheat breeders to select for rust resistance has rapidly expanded with a multitude of genetic loci identified using the latest advances in genomics, mapping and cloning strategies. The goal of this review was to establish a wheat genome atlas that provides a comprehensive summary of reported loci associated with rust resistance. Our atlas provides a summary of mapped quantitative trait loci (QTL) and characterised genes for the three rusts from 170 publications over the past two decades. A total of 920 QTL or resistance genes were positioned across the 21 chromosomes of wheat based on the latest wheat reference genome (IWGSC RefSeq v2.1). Interestingly, 26 genomic regions contained multiple rust loci suggesting they could have pleiotropic effects on two or more rust diseases. We discuss a range of strategies to exploit this wealth of genetic information to efficiently utilise sources of resistance, including genomic information to stack desirable and multiple QTL to develop wheat cultivars with enhanced resistance to rust disease.

Characterizing stay-green in barley across diverse environments: unveiling novel haplotypes.

KEY MESSAGE: There is variation in stay-green within barley breeding germplasm, influenced by multiple haplotypes and environmental conditions. The positive genetic correlation between stay-green and yield across multiple environments highlights the potential as a future breeding target. Barley is considered one of the most naturally resilient crops making it an excellent candidate to dissect the genetics of drought adaptive component traits. Stay-green, is thought to contribute to drought adaptation, in which the photosynthetic machinery is maintained for a longer period post-anthesis increasing the photosynthetic duration of the plant. In other cereal crops, including wheat, stay-green has been linked to increased yield under water-limited conditions. Utilizing a panel of diverse barley breeding lines from a commercial breeding program we aimed to characterize stay-green in four environments across two years. Spatiotemporal modeling was used to accurately model senescence patterns from flowering to maturity characterizing the variation for stay-green in barley for the first time. Environmental effects were identified, and multi-environment trait analysis was performed for stay-green characteristics during grain filling. A consistently positive genetic correlation was found between yield and stay-green. Twenty-two chromosomal regions with large effect haplotypes were identified across and within environment types, with ten being identified in multiple environments. In silico stacking of multiple desirable haplotypes showed an opportunity to improve the stay-green phenotype through targeted breeding. This study is the first of its kind to model barley stay-green in a large breeding panel and has detected novel, stable and environment specific haplotypes. This provides a platform for breeders to develop Australian barley with custom senescence profiles for improved drought adaptation.

Mining the Vavilov wheat diversity panel for new sources of adult plant resistance to stripe rust.

KEY MESSAGE: Multi-year evaluation of the Vavilov wheat diversity panel identified new sources of adult plant resistance to stripe rust. Genome-wide association studies revealed the key genomic regions influencing resistance, including seven novel loci. Wheat stripe rust (YR) caused by Puccinia striiformis f. sp. tritici (Pst) poses a significant threat to global food security. Resistance genes commonly found in many wheat varieties have been rendered ineffective due to the rapid evolution of the pathogen. To identify novel sources of adult plant resistance (APR), 292 accessions from the N.I. Vavilov Institute of Plant Genetic Resources, Saint Petersburg, Russia, were screened for known APR genes (i.e. Yr18, Yr29, Yr46, Yr33, Yr39 and Yr59) using linked polymerase chain reaction (PCR) molecular markers. Accessions were evaluated against Pst (pathotype 134 E16 A + Yr17 + Yr27) at seedling and adult plant stages across multiple years (2014, 2015 and 2016) in Australia. Phenotypic analyses identified 132 lines that potentially carry novel sources of APR to YR. Genome-wide association studies (GWAS) identified 68 significant marker-trait associations (P < 0.001) for YR resistance, representing 47 independent quantitative trait loci (QTL) regions. Fourteen genomic regions overlapped with previously reported Yr genes, including Yr29, Yr56, Yr5, Yr43, Yr57, Yr30, Yr46, Yr47, Yr35, Yr36, Yrxy1, Yr59, Yr52 and YrYL. In total, seven QTL (positioned on chromosomes 1D, 2A, 3A, 3D, 5D, 7B and 7D) did not collocate with previously reported genes or QTL, indicating the presence of promising novel resistance factors. Overall, the Vavilov diversity panel provides a rich source of new alleles which could be used to broaden the genetic bases of YR resistance in modern wheat varieties.

Genetic characterization of adult-plant resistance to tan spot (syn, yellow spot) in wheat.

KEY MESSAGE: QTL mapping identified key genomic regions associated with adult-plant resistance to tan spot, which are effective even in the presence of the sensitivity gene Tsn1, thus serving as a new genetic solution to develop disease-resistant wheat cultivars. Improving resistance to tan spot (Pyrenophora tritici-repentis; Ptr) in wheat by eliminating race-specific susceptibility genes is a common breeding approach worldwide. The potential to exploit variation in quantitative forms of resistance, such as adult-plant resistance (APR), offers an alternative approach that could lead to broad-spectrum protection. We previously identified wheat landraces in the Vavilov diversity panel that exhibited high levels of APR despite carrying the sensitivity gene Tsn1. In this study, we characterised the genetic control of APR by developing a recombinant inbred line population fixed for Tsn1, but segregating for the APR trait. Linkage mapping using DArTseq markers and disease response phenotypes identified a QTL associated with APR to Ptr race 1 (producing Ptr ToxA- and Ptr ToxC) on chromosome 2B (Qts.313-2B), which was consistently detected in multiple adult-plant experiments. Additional loci were also detected on chromosomes 2A, 3D, 5A, 5D, 6A, 6B and 7A at the seedling stage, and on chromosomes 1A and 5B at the adult stage. We demonstrate that Qts.313-2B can be combined with other adult-plant QTL (i.e. Qts.313-1A and Qts.313-5B) to strengthen resistance levels. The APR QTL reported in this study provide a new genetic solution to tan spot in Australia and could be deployed in wheat cultivars, even in the presence of Tsn1, to decrease production losses and reduce the application of fungicides.

Physiological Changes in Barley mlo-11 Powdery Mildew Resistance Conditioned by Tandem Repeat Copy Number.

Wild barley accessions have evolved broad-spectrum defence against barley powdery mildew through recessive mlo mutations. However, the mlo defence response is associated with deleterious phenotypes with a cost to yield and fertility, with implications for natural fitness and agricultural productivity. This research elucidates the mechanism behind a novel mlo allele, designated mlo-11(cnv2), which has a milder phenotype compared to standard mlo-11. Bisulphite sequencing and histone ChIP-seq analyses using near-isogenic lines showed pronounced repression of the Mlo promoter in standard mlo-11 compared to mlo-11(cnv2), with repression governed by 24 nt heterochromatic small interfering RNAs. The mlo-11(cnv2) allele appears to largely reduce the physiological effects of mlo while still endorsing a high level of powdery mildew resistance. RNA sequencing showed that this is achieved through only partly restricted expression of Mlo, allowing adequate temporal induction of defence genes during infection and expression close to wild-type Mlo levels in the absence of infection. The two mlo-11 alleles showed copy number proportionate oxidase and peroxidase expression levels during infection, but lower amino acid and aromatic compound biosynthesis compared to the null allele mlo-5. Examination of highly expressed genes revealed a common WRKY W-box binding motif (consensus ACCCGGGACTAAAGG) and a transcription factor more highly expressed in mlo-11 resistance. In conclusion, mlo-11(cnv2) appears to significantly mitigate the trade-off between mlo defence and normal gene expression.

Adaptive Traits to Improve Durum Wheat Yield in Drought and Crown Rot Environments.

Durum wheat (Triticum turgidum L. ssp. durum) production can experience significant yield losses due to crown rot (CR) disease. Losses are usually exacerbated when disease infection coincides with terminal drought. Durum wheat is very susceptible to CR, and resistant germplasm is not currently available in elite breeding pools. We hypothesize that deploying physiological traits for drought adaptation, such as optimal root system architecture to reduce water stress, might minimize losses due to CR infection. This study evaluated a subset of lines from a nested association mapping population for stay-green traits, CR incidence and yield in field experiments as well as root traits under controlled conditions. Weekly measurements of normalized difference vegetative index (NDVI) in the field were used to model canopy senescence and to determine stay-green traits for each genotype. Genome-wide association studies using DArTseq molecular markers identified quantitative trait loci (QTLs) on chromosome 6B (qCR-6B) associated with CR tolerance and stay-green. We explored the value of qCR-6B and a major QTL for root angle QTL qSRA-6A using yield datasets from six rainfed environments, including two environments with high CR disease pressure. In the absence of CR, the favorable allele for qSRA-6A provided an average yield advantage of 0.57 t·ha-1, whereas in the presence of CR, the combination of favorable alleles for both qSRA-6A and qCR-6B resulted in a yield advantage of 0.90 t·ha-1. Results of this study highlight the value of combining above- and belowground physiological traits to enhance yield potential. We anticipate that these insights will assist breeders to design improved durum varieties that mitigate production losses due to water deficit and CR.

Discovering new alleles for yellow spot resistance in the Vavilov wheat collection.

KEY MESSAGE: GWAS detected 11 yellow spot resistance QTL in the Vavilov wheat collection. Promising adult-plant resistance loci could provide a sustainable genetic solution to yellow spot in modern wheat varieties. Yellow spot, caused by the fungal pathogen Pyrenophora tritici-repentis (Ptr), is the most economically damaging foliar disease of wheat in Australia. Genetic resistance is considered to be the most sustainable means for disease management, yet the genomic regions underpinning resistance to Ptr, particularly adult-plant resistance (APR), remain vastly unknown. In this study, we report results of a genome-wide association study using 295 accessions from the Vavilov wheat collection which were extensively tested for response to Ptr infections in glasshouse and field trials at both seedling an adult growth stages. Combining phenotypic datasets from multiple experiments in Australia and Russia with 25,286 genome-wide, high-quality DArTseq markers, we detected a total of 11 QTL, of which 5 were associated with seedling resistance, 3 with all-stage resistance, and 3 with APR. Interestingly, the novel APR QTL were effective even in the presence of host sensitivity gene Tsn1. These genomic regions could offer broad-spectrum yellow spot protection, not just to ToxA but also other pathogenicity or virulence factors. Vavilov wheat accessions carrying APR QTL combinations displayed enhanced levels of resistance highlighting the potential for QTL stacking through breeding. We propose that the APR genetic factors discovered in our study could be used to improve resistance levels in modern wheat varieties and contribute to the sustainable control of yellow spot.

Novel sources of resistance to Septoria nodorum blotch in the Vavilov wheat collection identified by genome-wide association studies.

KEY MESSAGE: The fungus Parastagonospora nodorum causes Septoria nodorum blotch (SNB) of wheat. A genetically diverse wheat panel was used to dissect the complexity of SNB and identify novel sources of resistance. The fungus Parastagonospora nodorum is the causal agent of Septoria nodorum blotch (SNB) of wheat. The pathosystem is mediated by multiple fungal necrotrophic effector-host sensitivity gene interactions that include SnToxA-Tsn1, SnTox1-Snn1, and SnTox3-Snn3. A P. nodorum strain lacking SnToxA, SnTox1, and SnTox3 (toxa13) retained wild-type-like ability to infect some modern wheat cultivars, suggesting evidence of other effector-mediated susceptibility gene interactions or the lack of host resistance genes. To identify genomic regions harbouring such loci, we examined a panel of 295 historic wheat accessions from the N. I. Vavilov Institute of Plant Genetic Resources in Russia, which is comprised of genetically diverse landraces and breeding lines registered from 1920 to 1990. The wheat panel was subjected to effector bioassays, infection with P. nodorum wild type (SN15) and toxa13. In general, SN15 was more virulent than toxa13. Insensitivity to all three effectors contributed significantly to resistance against SN15, but not toxa13. Genome-wide association studies using phenotypes from SN15 infection detected quantitative trait loci (QTL) on chromosomes 1BS (Snn1), 2DS, 5AS, 5BS (Snn3), 3AL, 4AL, 4BS, and 7AS. For toxa13 infection, a QTL was detected on 5AS (similar to SN15), plus two additional QTL on 2DL and 7DL. Analysis of resistance phenotypes indicated that plant breeders may have inadvertently selected for effector insensitivity from 1940 onwards. We identify accessions that can be used to develop bi-parental mapping populations to characterise resistance-associated alleles for subsequent introgression into modern bread wheat to minimise the impact of SNB.

Evolutionary computing to assemble standing genetic diversity and achieve long-term genetic gain.

Loss of genetic diversity in elite crop breeding pools can severely limit long-term genetic gains and limit ability to make gains in new traits, like heat tolerance, that are becoming important as the climate changes. Here, we investigate and propose potential breeding program applications of optimal haplotype stacking (OHS), a selection method that retains useful diversity in the population. OHS selects sets of candidates containing, between them, haplotype segments with very high segment breeding values for the target trait. We compared the performance of OHS, a similar method called optimal population value (OPV), truncation selection on genomic estimated breeding values (GEBVs), and optimal contribution selection (OCS) in stochastic simulations of recurrent selection on founder wheat genotypes. After 100 generations of intercrossing and selection, OCS and truncation selection had exhausted the genetic diversity, while considerable diversity remained in the OHS population. Gain under OHS in these simulations ultimately exceeded that from truncation selection or OCS. OHS achieved faster gains when the population size was small, with many progeny per cross. A promising hybrid strategy, involving a single cycle of OHS in the first generation followed by recurrent truncation selection, substantially improved long-term gain compared with truncation selection and performed similarly to OCS. The results of this study provide initial insights into where OHS could be incorporated into breeding programs.

Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches.

Sugarcane smut and Pachymetra root rots are two serious diseases of sugarcane, with susceptible infected crops losing over 30% of yield. A heritable component to both diseases has been demonstrated, suggesting selection could improve disease resistance. Genomic selection could accelerate gains even further, enabling early selection of resistant seedlings for breeding and clonal propagation. In this study we evaluated four types of algorithms for genomic predictions of clonal performance for disease resistance. These algorithms were: Genomic best linear unbiased prediction (GBLUP), including extensions to model dominance and epistasis, Bayesian methods including BayesC and BayesR, Machine learning methods including random forest, multilayer perceptron (MLP), modified convolutional neural network (CNN) and attention networks designed to capture epistasis across the genome-wide markers. Simple hybrid methods, that first used BayesR/GWAS to identify a subset of 1000 markers with moderate to large marginal additive effects, then used attention networks to derive predictions from these effects and their interactions, were also developed and evaluated. The hypothesis for this approach was that using a subset of markers more likely to have an effect would enable better estimation of interaction effects than when there were an extremely large number of possible interactions, especially with our limited data set size. To evaluate the methods, we applied both random five-fold cross-validation and a structured PCA based cross-validation that separated 4702 sugarcane clones (that had disease phenotypes and genotyped for 26k genome wide SNP markers) by genomic relationship. The Bayesian methods (BayesR and BayesC) gave the highest accuracy of prediction, followed closely by hybrid methods with attention networks. The hybrid methods with attention networks gave the lowest variation in accuracy of prediction across validation folds (and lowest MSE), which may be a criteria worth considering in practical breeding programs. This suggests that hybrid methods incorporating the attention mechanism could be useful for genomic prediction of clonal performance, particularly where non-additive effects may be important.

Mapping quantitative trait loci for seminal root angle in a selected durum wheat population.

Seminal root angle (SRA) is an important root architectural trait associated with drought adaptation in cereal crops. To date, all attempts to dissect the genetic architecture of SRA in durum wheat (Triticum durum Desf.) have used large association panels or structured mapping populations. Identifying changes in allele frequency generated by selection provides an alternative genetic mapping approach that can increase the power and precision of QTL detection. This study aimed to map quantitative trait loci (QTL) for SRA by genotyping durum lines created through divergent selection using a combination of marker-assisted selection (MAS) for the major SRA QTL (qSRA-6A) and phenotypic selection for SRA over multiple generations. The created 11 lines (BC1F2:5) were genotyped with genome-wide single-nucleotide polymorphism (SNP) markers to map QTL by identifying markers that displayed segregation distortion significantly different from the Mendelian expectation. QTL regions were further assessed in an independent validation population to confirm their associations with SRA. The experiment revealed 14 genomic regions under selection, 12 of which have not previously been reported for SRA. Five regions, including qSRA-6A, were confirmed in the validation population. The genomic regions identified in this study indicate that the genetic control of SRA is more complex than previously anticipated. Our study demonstrates that selection mapping is a powerful approach to complement genome-wide association studies for QTL detection. Moreover, the verification of qSRA-6A in an elite genetic background highlights the potential for MAS, although it is necessary to combine additional QTL to develop new cultivars with extreme SRA phenotypes.

Fusarium wilt constrains mungbean yield due to reduction in source availability.

Mungbean is an important source of plant protein for consumers and a high-value export crop for growers across Asia, Australia and Africa. However, many commercial cultivars are highly vulnerable to biotic stresses, which rapidly reduce yield within the season. Fusarium oxysporum is a soil-borne pathogen that is a growing concern for mungbean growers globally. This pathogen causes Fusarium wilt by infecting the root system of the plant resulting in devastating yield reductions. To understand the impact of Fusarium on mungbean development and productivity and to identify tolerant genotypes, a panel of 23 diverse accessions was studied. Field trials conducted in 2016 and 2021 in Warwick, Queensland, Australia under rainfed conditions investigated the variation in phenology, canopy and yield component traits under disease and disease-free conditions. Analyses revealed a high degree of genetic variation for all traits. By comparing the performance of these traits across these two environments, we identified key traits that underpin yield under disease and disease-free conditions. Aboveground biomass components at 50 % flowering were identified as significant drivers of yield development under disease-free conditions and when impacted by Fusarium resulted in up to 96 % yield reduction. Additionally, eight genotypes were identified to be tolerant to Fusarium. These genotypes were found to display differing phenological and morphological behaviours, thereby demonstrating the potential to breed tolerant lines with a range of diverse trait variations. The identification of tolerant genotypes that sustain yield under disease pressure may be exploited in crop improvement programs.

Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non-additive variation for key traits.

Sugarcane has a complex, highly polyploid genome with multi-species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral species. As such, genomic prediction in sugarcane presents an interesting case for machine learning (ML) methods, which are purportedly able to deal with high levels of complexity in prediction. Here, we investigated deep learning (DL) neural networks, including multilayer networks (MLP) and convolution neural networks (CNN), and an ensemble machine learning approach, random forest (RF), for genomic prediction in sugarcane. The data set used was 2912 sugarcane clones, scored for 26,086 genome wide single nucleotide polymorphism markers, with final assessment trial data for total cane harvested (TCH), commercial cane sugar (CCS), and fiber content (Fiber). The clones in the latest trial (2017) were used as a validation set. We compared prediction accuracy of these methods to genomic best linear unbiased prediction (GBLUP) extended to include dominance and epistatic effects. The prediction accuracies from GBLUP models were up to 0.37 for TCH, 0.43 for CCS, and 0.48 for Fiber, while the optimized ML models had prediction accuracies of 0.35 for TCH, 0.38 for CCS, and 0.48 for Fiber. Both RF and DL neural network models have comparable predictive ability with the additive GBLUP model but are less accurate than the extended GBLUP model.

Harnessing adult-plant resistance genes to deploy durable disease resistance in crops.

Adult-plant resistance (APR) is a type of genetic resistance in cereals that is effective during the later growth stages and can protect plants from a range of disease-causing pathogens. Our understanding of the functions of APR-associated genes stems from the well-studied wheat-rust pathosystem. Genes conferring APR can offer pathogen-specific resistance or multi-pathogen resistance, whereby resistance is activated following a molecular recognition event. The breeding community prefers APR to other types of resistance because it offers broad-spectrum protection that has proven to be more durable. In practice, however, deployment of new cultivars incorporating APR is challenging because there is a lack of well-characterised APRs in elite germplasm and multiple loci must be combined to achieve high levels of resistance. Genebanks provide an excellent source of genetic diversity that can be used to diversify resistance factors, but introgression of novel alleles into elite germplasm is a lengthy and challenging process. To overcome this bottleneck, new tools in breeding for resistance must be integrated to fast-track the discovery, introgression and pyramiding of APR genes. This review highlights recent advances in understanding the functions of APR genes in the well-studied wheat-rust pathosystem, the opportunities to adopt APR genes in other crops and the technology that can speed up the utilisation of new sources of APR in genebank accessions.

genomicSimulation: fast R functions for stochastic simulation of breeding programs.

Simulation tools are key to designing and optimizing breeding programs that are multiyear, high-effort endeavors. Tools that operate on real genotypes and integrate easily with other analysis software can guide users toward crossing decisions that best balance genetic gains and genetic diversity required to maintain gains in the future. Here, we present genomicSimulation, a fast and flexible tool for the stochastic simulation of crossing and selection based on real genotypes. It is fully written in C for high execution speeds, has minimal dependencies, and is available as an R package for the integration with R's broad range of analysis and visualization tools. Comparisons of a simulated recreation of a breeding program to a real data set demonstrate the simulated offspring from the tool correctly show key population features, such as genomic relationships and approximate linkage disequilibrium patterns. Both versions of genomicSimulation are freely available on GitHub: The R package version at https://github.com/vllrs/genomicSimulation/ and the C library version at https://github.com/vllrs/genomicSimulationC/.

Effect of Drought and Low P on Yield and Nutritional Content in Common Bean.

Common bean (Phaseolus vulgaris L.) production in the tropics typically occurs in rainfed systems on marginal lands where yields are low, primarily as a consequence of drought and low phosphorus (P) availability in soil. This study aimed to investigate the physiological and chemical responses of 12 bush bean genotypes for adaptation to individual and combined stress factors of drought and low P availability. Water stress and P deficiency, both individually and combined, decreased seed weight and aboveground biomass by ∼80%. Water deficit and P deficiency decreased photosynthesis and stomatal conductance during plant development. Maximum rates of carboxylation, electron transport, and triose phosphate utilization were superior for two common bean genotypes (SEF60 and NCB226) that are better adapted to combined stress conditions of water deficit and low P compared to the commercial check (DOR390). In response to water deficit treatment, carbon isotope fractionation in the leaf tissue decreased at all developmental stages. Within the soluble leaf fraction, combined water deficit and low P, led to significant changes in the concentration of key nutrients and amino acids, whereas no impact was detected in the seed. Our results suggest that common bean genotypes have a degree of resilience in yield development, expressed in traits such as pod harvest index, and conservation of nutritional content in the seed. Further exploration of the chemical and physiological traits identified here will enhance the resilience of common bean production systems in the tropics.

Speed vernalization to accelerate generation advance in winter cereal crops.

There are many challenges facing the development of high-yielding, nutritious crops for future environments. One limiting factor is generation time, which prolongs research and plant breeding timelines. Recent advances in speed breeding protocols have dramatically reduced generation time for many short-day and long-day species by optimizing light and temperature conditions during plant growth. However, winter crops with a vernalization requirement still require up to 6-10 weeks in low-temperature conditions before the transition to reproductive development. Here, we tested a suite of environmental conditions and protocols to investigate whether the vernalization process can be accelerated. We identified a vernalization method consisting of exposing seeds at the soil surface to an extended photoperiod of 22 h day:2 h night at 10°C with transfer to speed breeding conditions that dramatically reduces generation time in both winter wheat (Triticum aestivum) and winter barley (Hordeum vulgare). Implementation of the speed vernalization protocol followed by speed breeding allowed the completion of up to five generations per year for winter wheat or barley, whereas only two generations can be typically completed under standard vernalization and plant growth conditions. The speed vernalization protocol developed in this study has great potential to accelerate biological research and breeding outcomes for winter crops.

Genetic Characterization of Resistance to Pyrenophora teres f. teres in the International Barley Differential Canadian Lake Shore.

Genetic resistance to net form of net blotch in the international barley differential Canadian Lake Shore (CLS) was characterized and mapped. A doubled haploid (DH) population generated from a cross between CLS and susceptible cultivar Harrington was evaluated at the seedling stage using eight diverse Pyrenophora teres f. teres (Ptt) isolates and at the adult stage in the field using natural inoculum. To effectively map the CLS resistance, comparative marker frequency analysis (MFA) was performed using 8,762 polymorphic DArT-seq markers, where 'resistant' and 'susceptible' groups each comprised 40 DH lines displaying the most extreme phenotypes. Five DArTseq markers were consistently detected in eight disease assays, which was designated qPttCLS and deemed to harbor the locus underpinning CLS resistance. Four of these markers were present onto the barley DArTseq physical map and spans a region between 398203862 and 435526243 bp which were found to consist several genes involved in important plant functions such as disease response and signaling pathways. While MFA only detected the 3H region, genetic analyses based on segregation patterns were inconsistent, suggesting complex inheritance or variation in phenotypic expression of qPttCLS, particularly in the field. This study represents progress toward connecting Ptt pathotype surveys with the corresponding resistance genes in barley differentials. The markers associated with qPttCLS are useful for marker-assisted selection in breeding programs.

A Major Root Architecture QTL Responding to Water Limitation in Durum Wheat.

The optimal root system architecture (RSA) of a crop is context dependent and critical for efficient resource capture in the soil. Narrow root growth angle promoting deeper root growth is often associated with improved access to water and nutrients in deep soils during terminal drought. RSA, therefore is a drought-adaptive trait that could minimize yield losses in regions with limited rainfall. Here, GWAS for seminal root angle (SRA) identified seven marker-trait associations clustered on chromosome 6A, representing a major quantitative trait locus (qSRA-6A) which also displayed high levels of pairwise LD (r 2 = 0.67). Subsequent haplotype analysis revealed significant differences between major groups. Candidate gene analysis revealed loci related to gravitropism, polar growth and hormonal signaling. No differences were observed for root biomass between lines carrying hap1 and hap2 for qSRA-6A, highlighting the opportunity to perform marker-assisted selection for the qSRA-6A locus and directly select for wide or narrow RSA, without influencing root biomass. Our study revealed that the genetic predisposition for deep rooting was best expressed under water-limitation, yet the root system displayed plasticity producing root growth in response to water availability in upper soil layers. We discuss the potential to deploy root architectural traits in cultivars to enhance yield stability in environments that experience limited rainfall.