Genetic variation in stripe rust resistance exists in Australian wheat breeding populations and is environmentally influenced. Stacking multiple resistance haplotypes or using whole-genome approaches will improve resistance stability and environmental specificity. Wheat stripe rust (Puccinia striiformis) is a fungal disease responsible for substantial yield losses globally. To maintain crop productivity in future climates, the identification of genetics offering durable resistance across diverse growing conditions is crucial. To stay one-step ahead of the pathogen, Australian wheat breeders are actively selecting for adult-plant resistance (APR), which is considered more durable than seedling resistance. However, deploying resistance that is stable or effective across environments and years is challenging as expression of underling APR loci often interacts with environmental conditions. To explore the underlying genetics and interactions with the environment for stripe rust resistance, we employ haplotype-based mapping using the local GEBV approach in elite wheat breeding populations. Our multi-environment trial analyses comprising 35,986 inbred lines evaluated across 10 environments revealed significant genotype-by-environment interactions for stripe rust. A total of 32 haploblocks associated with stripe rust resistance were identified, where 23 were unique to a specific environment and nine were associated with stable resistance across environments. Population structure analysis revealed commercial or advanced breeding lines carried desirable resistance haplotypes, highlighting the opportunity to continue to harness and optimise resistance haplotypes already present within elite backgrounds. Further, we demonstrate that in silico stacking of multiple resistance haplotypes through a whole-genome approach has the potential to substantially improve resistance levels. This represents the largest study to date exploring commercial wheat breeding populations for stripe rust resistance and highlights the breeding opportunities to improve stability of resistance across and within target environments.

Spot blotch (SB), a prevalent foliar disease of barley, is caused by the hemibiotrophic fungal pathogen Bipolaris sorokiniana. Predominately occurring in humid growing regions worldwide, SB can result in yield losses of up to 30

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.

We revealed the neglected genetic relationships of resistance for six major wheat diseases and established a haploblock-based catalogue with novel forms of resistance by multi-trait haplotype characterisation. Genetic potential to improve multiple disease resistance was highlighted through haplotype stacking simulations. Wheat production is threatened by numerous fungal diseases, but the potential to breed for multiple disease resistance (MDR) mechanisms is yet to be explored. Here, significant global genetic correlations and underlying local genomic regions were identified in the Vavilov wheat diversity panel for six major fungal diseases, including biotrophic leaf rust (LR), yellow rust (YR), stem rust (SR), hemibiotrophic crown rot (CR), and necrotrophic tan spot (TS) and Septoria nodorum blotch (SNB). By adopting haplotype-based local genomic estimated breeding values, derived from an integrated set of 34,899 SNP and DArT markers, we established a novel haplotype catalogue for resistance to the six diseases in over 20 field experiments across Australia and Ethiopia. Haploblocks with high variances of haplotype effects in all environments were identified for three rusts, and pleiotropic haploblocks were identified for at least two diseases, with four haploblocks affecting all six diseases. Through simulation, we demonstrated that stacking optimal haplotypes for one disease could improve resistance substantially, but indirectly affected resistance for other five diseases, which varied depending on the genetic correlation with the non-target disease trait. On the other hand, our simulation results combining beneficial haplotypes for all diseases increased resistance to LR, YR, SR, CR, TS, and SNB, by up to 48.1

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.

Many thousands and, in some cases, millions of individuals from the major crop and livestock species have been genotyped and phenotyped for the purpose of genomic selection. 'Ultimate genotypes', in which the marker allele haplotypes with the most favorable effects on a target trait or traits in the population are combined together in silico, can be constructed from these datasets. Ultimate genotypes display up to six times the performance of the current best individuals in the population, as demonstrated for net profit in dairy cattle (incorporating a range of economic traits), yield in wheat and 100-seed weight in chickpea. However, current breeding strategies that aim to assemble ultimate genotypes through conventional crossing take many generations. As a hypothetical thought piece, here, we contemplate three future pathways for rapidly achieving ultimate genotypes: accelerated recombination with gene editing, direct editing of whole-genome haplotype sequences and synthetic biology.

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.

Wheat stripe rust (Puccinia striiformis) is a fungal disease responsible for substantial yield losses globally. To maintain crop productivity in future climates, the identification of genetics offering durable resistance across diverse growing conditions is crucial. To stay one-step ahead of the pathogen, Australian wheat breeders are actively selecting for adult plant resistance (APR), which is considered more durable than seedling resistance. However, deploying resistance that is stable or effective across environments and years is challenging as expression of underling APR loci often interact with environmental conditions. To explore the underlying genetics and interactions with the environment for stripe rust resistance, we employ haplotype-based mapping using the local GEBV approach in elite wheat breeding populations. Our multi-environment trial (MET) analyses comprising 35,986 inbred lines evaluated across 10 environments revealed significant genotype by environment (GxE) interactions for stripe rust. A total of 32 haploblocks associated with stripe rust resistance were identified, where 24 were unique to a specific environment and 7 were associated with stable resistance across environments. Population structure analysis revealed commercial or advanced breeding lines carried desirable resistance haplotypes, highlighting the opportunity to continue to harness and optimise resistance haplotypes already present within elite backgrounds. Further, we demonstrate that in-silico stacking of multiple resistance haplotypes through a whole-genome approach has the potential to substantially improve resistance levels. This represents the largest study to date exploring commercial wheat breeding populations for stripe rust resistance and highlights the breeding opportunities to improve stability of resistance across and within target environments.

The accuracy of genomic prediction for key traits in sugarcane remains low relative to other crops, even with growing reference sets. This is potentially due to high-level polyploidy, frequent aneuploidy and complex genetic architectures. Here we aim to assess the impact of genotype transformations typically used in polyploid crops on the accuracy of genomic prediction. We focus on the effect of using “diploid” genotype calls and aneuploidy. We simulated a sugarcane genome with multi-species ancestry and genome duplications, resulting in 96 chromosomes. We included a range of allele dosage to phenotype maps across ploidies including additive linear, dominant, non-linear sigmoid and log shaped maps. Genomic prediction accuracies were compared from six algorithms including genomic BLUP (GBLUP), extended GBLUP, BayesR, Multilayer perceptron, Convolutional neural network and Attention network. Accuracy of prediction for phenotypes controlled by a higher proportion of additive effects were more sensitive to diploid transformation and aneuploidy events. Conversely, accuracy of prediction for phenotypes involving directional dominance were less affected by diploid transformation but were still substantially affected by aneuploidy. For traits controlled by non-linear allele dosage to phenotype maps, the predictive accuracy was significantly affected by diploid transformation, but the influence of aneuploidy was not obvious. Extended GBLUP performed well and gave higher accuracies than other linear based models across most of scenarios, while the DL models were more competitive when the allele action across ploidies became more complex. Overall, our results demonstrate that genotypes that capture allele dosage should be used in genomic prediction for complex polyploids.

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.

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.

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.

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.

Durum wheat (Triticum durum Desf.) breeding programs face many challenges surrounding the development of stable varieties with high quality and yield. Therefore, researchers and breeders are focused on deciphering the genetic architecture of biotic and abiotic traits with the aim of pyramiding desirable traits. These efforts require access to diverse genetic resources, including wild relatives, germplasm collections and mapping populations. Advances in accelerated generation technologies have enabled the rapid development of mapping populations with significant genetic diversity. Here, we describe the development of a durum Nested Association Mapping (dNAM) population, which represents a valuable genetic resource for mapping the effects of different alleles on trait performance. We created this population to understand the quantitative nature of drought-adaptive traits in durum wheat. We developed 920 F6 lines in only 18 months using speed breeding technology, including the F4 generation in the field. Large variation in above- and below-ground traits was observed, which could be harnessed using genetic mapping and breeding approaches. We genotyped the population using 13,393 DArTseq markers. Quality control resulted in 6,785 high-quality polymorphic markers used for structure analysis, linkage disequilibrium decay, and marker-trait association analyses. To demonstrate the effectiveness of dNAM as a resource for elucidating the genetic control of quantitative traits, we took a genome-wide mapping approach using the FarmCPU method for plant height and days to flowering. These results highlight the power of using dNAM as a tool to dissect the genetics of durum wheat traits, supporting the development of varieties with improved adaptation and yield.

Abstract Aims Rhizoboxes allow non-invasive phenotyping of root systems and are often used as an alternative to evaluation in the field which typically requires excavation, a laborious endeavour. Semi-automated rhizobox methods can be used to screen large numbers of plants, but these platforms can be expensive due to the cost of customised components, assembly, and maintenance, which limits the accessibility for many root researchers. To widen access to the rhizobox method—for example for preliminary screening of germplasm for root system architecture traits—we present a method to build a simple, low-cost rhizobox method using widely available materials, which should allow any research group to conduct root experiments and phenotype root system architecture in their own laboratories and greenhouses. Methods The detailed construction of 80 wooden rhizoboxes is described (each 40 cm width x 90 cm height x 6 cm depth; total cost 1,786 AUD, or 22 AUD or [$15 USD] per rhizobox). Using a panel of 20 spring wheat lines, including parental lines and derived intro-selection lines selected for divergent seedling root traits (seminal root angle and root biomass), genotypic variation in root biomass distribution were examined in the upper (0–30 cm), middle (30–60 cm) and lower sections (60–90 cm) of the rhizobox. At the conclusion of the experiment, rhizobox covers were removed and the exposed roots were imaged prior to destructive root washing. Root morphological traits were extracted from the images using RhizoVision Explorer (Seethepalli and York 2020). Results There were significant genotypic differences in total root biomass in the upper and middle sections of the rhizobox, but differences were not detected in the deepest section. Compared with the recurrent elite parent Borlaug100, some of the intro-selection lines showed greater biomass (or less), depending on the status of the root biomass QTL on chromosome 5B. Genotypes also differed in shoot biomass and tiller number. The donor lines for high and low root biomass showed corresponding differences in shoot biomass. Additional root parameters such as total root length and branching frequency were obtained through image analysis and genotypic effects were detected at different depths. Conclusions The rhizobox set up is easy-to-build-and-implement for phenotyping the root distribution of wheat. This will support root research and breeding efforts to identify and utilise sources of genetic variation for target root traits that are needed to develop future wheat cultivars with improved resource use efficiency and yield stability.

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 QTLs, 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.

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/.

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 degrees 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.