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.

Use of continuous genotypes for genomic prediction in sugarcane.

Genomic selection in sugarcane faces challenges due to limited genomic tools and high genomic complexity, particularly because of its high and variable ploidy. The classification of genotypes for single nucleotide polymorphisms (SNPs) becomes difficult due to the wide range of possible allele dosages. Previous genomic studies in sugarcane used pseudo-diploid genotyping, grouping all heterozygotes into a single class. In this study, we investigate the use of continuous genotypes as a proxy for allele-dosage in genomic prediction models. The hypothesis is that continuous genotypes could better reflect allele dosage at SNPs linked to mutations affecting target traits, resulting in phenotypic variation. The dataset included genotypes of 1318 clones at 58K SNP markers, with about 26K markers filtered using standard quality controls. Predictions for tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fiber content (Fiber) were made using parametric, non-parametric, and Bayesian methods. Continuous genotypes increased accuracy by 5%-7% for CCS and Fiber. The pseudo-diploid parametrization performed better for TCH. Reproducing kernel Hilbert spaces model with Gaussian kernel and AK4 (arc-cosine kernel with hidden layer 4) kernel outperformed other methods for TCH and CCS, suggesting that non-additive effects might influence these traits. The prevalence of low-dosage markers in the study may have limited the benefits of approximating allele-dosage information with continuous genotypes in genomic prediction models. Continuous genotypes simplify genomic prediction in polyploid crops, allowing additional markers to be used without adhering to pseudo-diploid inheritance. The approach can particularly benefit high ploidy species or emerging crops with unknown ploidy.

Accurate prediction of quantitative traits with failed SNP calls in canola and maize.

In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed allele calls. To overcome this problem, failed calls are often imputed, based on the assumption that failed SNP calls are purely technical. However, this ignores the biological causes for failed calls-for example: deletions-and there is increasing evidence that gene presence-absence and other kinds of genome structural variants can play a role in phenotypic expression. Because deletions are frequently not in linkage disequilibrium with their flanking SNPs, permutation of missing SNP calls can potentially obscure valuable marker-trait associations. In this study, we analyze published datasets for canola and maize using four parametric and two machine learning models and demonstrate that failed allele calls in genomic prediction are highly predictive for important agronomic traits. We present two statistical pipelines, based on population structure and linkage disequilibrium, that enable the filtering of failed SNP calls that are likely caused by biological reasons. For the population and trait examined, prediction accuracy based on these filtered failed allele calls was competitive to standard SNP-based prediction, underlying the potential value of missing data in genomic prediction approaches. The combination of SNPs with all failed allele calls or the filtered allele calls did not outperform predictions with only SNP-based prediction due to redundancy in genomic relationship estimates.

Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies.

Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealing for clonally propagated crops such as sugarcane. We conducted a comparative analysis of mate-allocation strategies, exploring utilising non-additive and heterozygosity effects to maximise clonal performance with schemes that solely consider additive effects to optimise breeding value. Using phenotypic and genotypic data from a population of 2,909 clones evaluated in final assessment trials of Australian sugarcane breeding programs, we focused on three important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and Fibre. By simulating families from all possible crosses (1,225) with 50 progenies each, we predicted the breeding and clonal values of progeny using two models: GBLUP (considering additive effects only) and extended-GBLUP (incorporating additive, non-additive, and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among selected parents. Compared to breeding value-based approaches, mate-allocation strategies based on clonal performance yielded substantial improvements, with predicted progeny values increasing by 57% for TCH, 12% for CCS, and 16% for fibre. Our simulation study highlights the effectiveness of mate-allocation approaches that exploit non-additive and heterozygosity effects, resulting in superior clonal performance. However, there was a notable decline in additive gain, particularly for TCH, likely due to significant epistatic effects. When selecting crosses based on clonal performance for TCH, the inbreeding coefficient of progeny was significantly lower compared to random mating, underscoring the advantages of leveraging non-additive and heterozygosity effects in mitigating inbreeding depression. Thus, mate-allocation strategies are recommended in clonally propagated crops to enhance clonal performance and reduce the negative impacts of inbreeding.

Nuclear exchange generates population diversity in the wheat leaf rust pathogen Puccinia triticina.

In clonally reproducing dikaryotic rust fungi, non-sexual processes such as somatic nuclear exchange are postulated to play a role in diversity but have been difficult to detect due to the lack of genome resolution between the two haploid nuclei. We examined three nuclear-phased genome assemblies of Puccinia triticina, which causes wheat leaf rust disease. We found that the most recently emerged Australian lineage was derived by nuclear exchange between two pre-existing lineages, which originated in Europe and North America. Haplotype-specific phylogenetic analysis reveals that repeated somatic exchange events have shuffled haploid nuclei between long-term clonal lineages, leading to a global P. triticina population representing different combinations of a limited number of haploid genomes. Thus, nuclear exchange seems to be the predominant mechanism generating diversity and the emergence of new strains in this otherwise clonal pathogen. Such genomics-accelerated surveillance of pathogen evolution paves the way for more accurate global disease monitoring.

Partitioning the forms of genotype-by-environment interaction in the reaction norm analysis of stability.

KEY MESSAGE: The reaction norm analysis of stability can be enhanced by partitioning the contribution of different types of G × E to the variation in slope. The slope of regression in a reaction norm model, where the performance of a genotype is regressed over an environmental covariable, is often used as a measure of stability of genotype performance. This method could be developed further by partitioning variation in the slope of regression into the two sources of genotype-by-environment interaction (G × E) which cause it: scale-type G × E (heterogeneity of variance) and rank-type G × E (heterogeneity of correlation). Because the two types of G × E have very different properties, separating their effect would enable a clearer understanding of stability. The aim of this paper was to demonstrate two methods which seek to achieve this in reaction norm models. Reaction norm models were fit to yield data from a multi-environment trial in Barley (Hordeum vulgare), with the adjusted mean yield from each environment used as the environmental covariable. Stability estimated from factor-analytic models, which can disentangle the two types of G × E and estimate stability based on rank-type G × E, was used for comparison. Adjusting the reaction norm slope to account for scale-type G × E using a genetic regression more than tripled the correlation with factor-analytic estimates of stability (0.24-0.26 to 0.80-0.85), indicating that it removed variation in the reaction norm slope that originated from scale-type G × E. A standardisation procedure had a more modest increase (055-0.59) but could be useful when curvilinear reaction norms are required. Analyses which use reaction norms to explore the stability of genotypes could gain additional insight into the mechanisms of stability by applying the methods outlined in this study.

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

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

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.

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.

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

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

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.

A toolkit to rapidly modify root systems through single plant selection.

BACKGROUND: The incorporation of root traits into elite germplasm is typically a slow process. Thus, innovative approaches are required to accelerate research and pre-breeding programs targeting root traits to improve yield stability in different environments and soil types. Marker-assisted selection (MAS) can help to speed up the process by selecting key genes or quantitative trait loci (QTL) associated with root traits. However, this approach is limited due to the complex genetic control of root traits and the limited number of well-characterised large effect QTL. Coupling MAS with phenotyping could increase the reliability of selection. Here we present a useful framework to rapidly modify root traits in elite germplasm. In this wheat exemplar, a single plant selection (SPS) approach combined three main elements: phenotypic selection (in this case for seminal root angle); MAS using KASP markers (targeting a root biomass QTL); and speed breeding to accelerate each cycle. RESULTS: To develop a SPS approach that integrates non-destructive screening for seminal root angle and root biomass, two initial experiments were conducted. Firstly, we demonstrated that transplanting wheat seedlings from clear pots (for seminal root angle assessment) into sand pots (for root biomass assessment) did not impact the ability to differentiate genotypes with high and low root biomass. Secondly, we demonstrated that visual scores for root biomass were correlated with root dry weight (r = 0.72), indicating that single plants could be evaluated for root biomass in a non-destructive manner. To highlight the potential of the approach, we applied SPS in a backcrossing program which integrated MAS and speed breeding for the purpose of rapidly modifying the root system of elite bread wheat line Borlaug100. Bi-directional selection for root angle in segregating generations successfully shifted the mean root angle by 30° in the subsequent generation (P ≤ 0.05). Within 18 months, BC2F4:F5 introgression lines were developed that displayed a full range of root configurations, while retaining similar above-ground traits to the recurrent parent. Notably, the seminal root angle displayed by introgression lines varied more than 30° compared to the recurrent parent, resulting in lines with both narrow and wide root angles, and high and low root biomass phenotypes. CONCLUSION: The SPS approach enables researchers and plant breeders to rapidly manipulate root traits of future crop varieties, which could help improve productivity in the face of increasing environmental fluctuations. The newly developed elite wheat lines with modified root traits provide valuable materials to study the value of different root systems to support yield in different environments and soil types.

Beyond the gene: epigenetic and cis-regulatory targets offer new breeding potential for the future.

For millennia, natural and artificial selection has combined favourable alleles for desirable traits in crop species. While modern plant breeding has achieved steady increases in crop yields over the last century, on the current trajectory we will simply not meet demand by 2045. Novel breeding strategies and sources of genetic variation will be required to sustainably fill predicted yield gaps and meet new consumer preferences. Here, we highlight that stepping up to meet this grand challenge will increasingly require thinking 'beyond the gene'. Significant progress has been made in understanding the contributions of both epigenetic variation and cis-regulatory variation to plant traits. This non-genic variation has great potential in future breeding, synthetic biology and biotechnology applications.

A chickpea genetic variation map based on the sequencing of 3,366 genomes.

Zero hunger and good health could be realized by 2030 through effective conservation, characterization and utilization of germplasm resources1. So far, few chickpea (Cicer arietinum) germplasm accessions have been characterized at the genome sequence level2. Here we present a detailed map of variation in 3,171 cultivated and 195 wild accessions to provide publicly available resources for chickpea genomics research and breeding. We constructed a chickpea pan-genome to describe genomic diversity across cultivated chickpea and its wild progenitor accessions. A divergence tree using genes present in around 80% of individuals in one species allowed us to estimate the divergence of Cicer over the last 21 million years. Our analysis found chromosomal segments and genes that show signatures of selection during domestication, migration and improvement. The chromosomal locations of deleterious mutations responsible for limited genetic diversity and decreased fitness were identified in elite germplasm. We identified superior haplotypes for improvement-related traits in landraces that can be introgressed into elite breeding lines through haplotype-based breeding, and found targets for purging deleterious alleles through genomics-assisted breeding and/or gene editing. Finally, we propose three crop breeding strategies based on genomic prediction to enhance crop productivity for 16 traits while avoiding the erosion of genetic diversity through optimal contribution selection (OCS)-based pre-breeding. The predicted performance for 100-seed weight, an important yield-related trait, increased by up to 23% and 12% with OCS- and haplotype-based genomic approaches, respectively.

A linkage disequilibrium-based approach to position unmapped SNPs in crop species.

BACKGROUND: High-density SNP arrays are now available for a wide range of crop species. Despite the development of many tools for generating genetic maps, the genome position of many SNPs from these arrays is unknown. Here we propose a linkage disequilibrium (LD)-based algorithm to allocate unassigned SNPs to chromosome regions from sparse genetic maps. This algorithm was tested on sugarcane, wheat, and barley data sets. We calculated the algorithm's efficiency by masking SNPs with known locations, then assigning their position to the map with the algorithm, and finally comparing the assigned and true positions. RESULTS: In the 20-fold cross-validation, the mean proportion of masked mapped SNPs that were placed by the algorithm to a chromosome was 89.53, 94.25, and 97.23% for sugarcane, wheat, and barley, respectively. Of the markers that were placed in the genome, 98.73, 96.45 and 98.53% of the SNPs were positioned on the correct chromosome. The mean correlations between known and new estimated SNP positions were 0.97, 0.98, and 0.97 for sugarcane, wheat, and barley. The LD-based algorithm was used to assign 5920 out of 21,251 unpositioned markers to the current Q208 sugarcane genetic map, representing the highest density genetic map for this species to date. CONCLUSIONS: Our LD-based approach can be used to accurately assign unpositioned SNPs to existing genetic maps, improving genome-wide association studies and genomic prediction in crop species with fragmented and incomplete genome assemblies. This approach will facilitate genomic-assisted breeding for many orphan crops that lack genetic and genomic resources.

Fast-forward breeding for a food-secure world.

Crop production systems need to expand their outputs sustainably to feed a burgeoning human population. Advances in genome sequencing technologies combined with efficient trait mapping procedures accelerate the availability of beneficial alleles for breeding and research. Enhanced interoperability between different omics and phenotyping platforms, leveraged by evolving machine learning tools, will help provide mechanistic explanations for complex plant traits. Targeted and rapid assembly of beneficial alleles using optimized breeding strategies and precise genome editing techniques could deliver ideal crops for the future. Realizing desired productivity gains in the field is imperative for securing an adequate future food supply for 10 billion people.