Plant Genome最新文献

Root system architecture (RSA), shoot architecture, and shoot-to-root biomass allocation are critical for optimizing crop water and nutrient capture and ultimately grain yield. Nevertheless, only a few studies adequately dissected the genetic basis of RSA and its relationship to shoot development. Herein, we dissected at a high level of details the RSA-shoot QTLome in a panel of 194 elite durum wheat (Triticum turgidum ssp. durum Desf.) varieties from worldwide adopting high-throughput phenotyping platform (HTPP) and genome-wide association study (GWAS). Plants were grown in controlled conditions up to the seventh leaf appearance (late tillering) in the GROWSCREEN-Rhizo, a rhizobox platform integrated with automated monochrome camera for root imaging, which allowed us to phenotype the panel for 35 shoot and root architectural traits, including seminal, nodal, and lateral root traits, width and depth, leaf area, leaf, and tiller number on a time-course base. GWAS identified 180 quantitative trait loci (QTLs) (-log p-value ≥ 4) grouped in 39 QTL clusters. Among those, 10, 11, and 10 QTL clusters were found for seminal, nodal, and lateral root systems. Deep rooting, a key trait for adaptation to water limiting conditions, was controlled by three major QTLs on chromosomes 2A, 6A, and 7A. Haplotype distribution revealed contrasting selection patterns between the ICARDA rainfed and CIMMYT irrigated breeding programs, respectively. These results provide valuable insights toward a better understanding of the RSA QTLome and a more effective deployment of beneficial root haplotypes to enhance durum wheat yield in different environmental conditions.

The implementation of genomics-assisted breeding methodologies is helping to drive the genetic gain required to meet the grand challenge of producing more food using fewer resources in the face of a changing climate. Despite the documented usefulness of genomics-assisted breeding toward this end, its full infusion into most small- and medium-sized breeding programs is still incomplete. One major reason for limited routine application of genomic selection among most such programs is the lack of a single integrated software tool capable of assisting breeders throughout the entire genomic prediction pipeline. To help address this need, we have implemented a streamlined genomic prediction and selection pipeline designed for plant breeding programs using open-source tools. The steps implemented in the pipeline include processing genotypic data (e.g., filtering and imputing genotypic data), merging genotypic and phenotypic data, collecting enviromics covariates, estimating environmental kinship, optimizing training sets, cross-validating genomic prediction models, and implementing genomic prediction for single or multiple traits across single or multiple environments. Herein, we describe an R Shiny web application named "GS4PB" (Genomic Selection For Plant Breeding) that implements the above steps in the pipeline and discuss the rationale for each of the tools in the pipeline. We used this GS4PB application to conduct an experiment comparing phenotypic and genomic selection, and showed genomic selection worked as well as phenotypic selection for advancement of breeding lines. This publicly available analysis tool will help to lower entry barriers into advanced techniques of genomic prediction, enabling breeders to take advantage of these technologies to help drive genetic gain.

Perennial crops are positioned at a critical juncture, facing intensifying environmental challenges that threaten productivity. Despite the high value of these crops, breeding gains in perennials are notably slow due to prolonged breeding cycles, often exceeding several decades, and thereby limiting their capacity to adapt to increasing climatic stressors. In contrast, annual crops have begun to leverage predictive breeding methods to incorporate multi-omics data, paving the way for a new era of accelerated genetic improvement. Multi-omics approaches integrate diverse datasets, ranging from genomic to proteomic layers, and likely more comprehensively capturing system features of regulatory networks that link the genome and phenotype. In this review, we assess the current landscape of predictive breeding in perennials by examining single-omic approaches alongside emerging omics resources, and we compare these trends with established multi-omics-based prediction frameworks in annual crops that have yielded enhanced predictive ability and novel biological insights. Building on these comparisons, we outline key considerations for implementing multi-omics-based genetic improvement frameworks in perennials, emphasizing the need for an end-to-end, reproducible, and scalable system that integrates multidimensional datasets and models both additive and nonadditive genetic effects across genotype-by-environment-by-management interactions. We also address significant challenges, including high data dimensionality, complex genotype-by-environment interactions, and limited training population sizes, and propose cross-institutional collaborations to pool resources, as well as the use of breeding program simulation tools to optimize multi-omics integration into practical breeding strategies. Despite current limitations, multi-omics-based predictive breeding holds great promise as a powerful tool for rapid genetic improvement in perennial crops.

We present the first reference genome of the highly heterozygous autotetraploid Allium porrum (leek). Combining long-read sequencing with single-nucleotide polymorphism (SNP)-array screening of two experimental F1 populations, we generated a genetic map with 11,429 SNP markers across eight linkage groups and a chromosome-scale assembly of A. porrum (leek) totaling 15.2 Gbp in size. The high quality of the reference genome is substantiated by 97.2% BUSCO completeness and a mapping rate of 96% for full-length transcripts. The linkage map exposes the recombination landscape of leek and confirms that crossovers are predominantly proximal, located to the centromeres, contrasting with distal recombination landscapes observed in other Allium species. Comparative genomics reveals structural rearrangements between A. porrum and its relatives (Allium fistulosum, Allium sativum, and Allium cepa), suggesting a closer genomic relationship to A. sativum. Our annotated high-quality reference genome delivers crucial insights into the leek genome structure, recombination landscape, and evolutionary relationships within the Allium genus, with implications for species compatibility in breeding programs, facilitating marker-assisted selection and genetic improvement in leek.

Vrn-A1 (VERNALIZATION A1) and Fr-A2 (FROST RESISTANCE A2) have been associated with variation in winter survival of wheat (Triticum aestivum L.). The beneficial alleles of Vrn-A1 and Fr-A2 are largely fixed in Canadian winter wheat germplasm, rendering the associated molecular markers ineffective for marker-assisted selection (MAS) in elite populations. The objectives were to (i) identify quantitative trait loci (QTLs) for winter survival in eastern Canada and determine their usefulness for MAS and (ii) explore the underlying genetic mechanisms of superior winter survival in the region. A subpopulation (n = 321) of the Canadian Winter Wheat Diversity Panel, consisting of genotypes that were fixed for the beneficial alleles of vrn-A1 and Fr-A2, was previously evaluated for winter survival in three eastern Canadian environments (Elora 2016-2017, CÉROM 2017-2018, and Elora 2017-2018). Genome-wide association mapping identified three significant QTLs for winter survival, a previously identified QTL on chromosome 5A, and two novel QTLs on chromosomes 5D and 7B. These QTLs were of low-to-moderate marker utility (0.1473-0.4796) and conferred a 0.7%-1.8% increase in mean winter survival. In silico analyses revealed that an array of biotic and abiotic stress responses are implicated in winter survival in eastern Canada, which challenges the notion that lethal temperature is the primary cause of winterkill in some regions. As significant winterkill events are sporadic in the region, it may be beneficial to identify individual components of winter survival that can be examined in artificial environments.

Genomic selection is an extension of marker-assisted selection by leveraging thousands of molecular markers distributed across the genome to capture the maximum possible proportion of the genetic variance underlying complex traits. In this study, genomic prediction models were developed by integrating phenological, physiological, and high-throughput phenotyping traits to predict grain yield in bread wheat (Triticum aestivum L.) under three environmental conditions: irrigation, drought stress, and terminal heat stress. Model performance was evaluated using both five-fold cross-validation and leave-one-environment-out (LOEO) schemes. Under five-fold cross-validation, the model incorporating vegetation indices derived from spectral datasets from the grain-filling phase achieved the highest accuracy. In LOEO validation, the model that included days to heading performed best under irrigation, whereas under drought stress, the model utilizing vegetation indices from the vegetative stage showed the highest accuracy. Under terminal heat stress, three models performed best: one incorporating genotype by environment interaction, one using vegetation indices during the vegetative stage, and one integrating spectral reflectance data from both the vegetative and grain-filling phases. Although incorporating multiple covariates can improve prediction accuracy or reduce the normalized root mean square error, using an extended model with all available covariates is not recommended due to the marginal predictive accuracy gains, increases in phenotyping, costs and complexity of data collection analysis. Overall, our findings show the importance of tailored phenomic inputs to specific environmental contexts to optimize genomic prediction of wheat yield.

Global wheat (Triticum aestivum L.) production faces significant challenges due to the destructive nature of leaf (Puccinia triticina; leaf rust [Lr]), stem (Puccinia graminis; stem rust [Sr]), and stripe (Puccinia striiformis; stripe rust [Yr]) rust diseases. Despite ongoing efforts to develop resistant varieties, these diseases remain a persistent challenge due to their highly evolving nature. Overcoming these challenges requires the identification and deployment of genetically diverse resistance genes in future cultivars. This study explored durable resistance against rust diseases by integrating data from five global populations. The populations exhibit diverse origins and were phenotypically evaluated in 16, 13, and 19 global field experiments, with total phenotypic observations of 12,694, 10,725, and 16,281 for Lr, Sr, and Yr, respectively. Field experiments showed moderate heritability of 0.43, 0.62, and 0.41 for Lr, Sr, and Yr, respectively. Genetic correlations were moderate among experiments for the same disease (0.34-0.59), but low among the three diseases (<0.21). The meta-genome-wide association studies (metaGWAS) analysis identified 19 quantitative trait loci (QTLs) associated with the resistance to Lr, 17 with the resistance to Sr, and five with the resistance to Yr. Six QTLs controlling resistance to more than one rust disease were also identified. Additionally, the study unveiled 13 potentially new QTLs (five for Lr and Yr each and three for Yr), contributing valuable insights into the genetic basis of wheat rust resistance. The integration of diverse populations and environments through metaGWAS enhanced the detection of stable QTL. This research provides breeders with additional resistance loci to combat rust pathogens.

Breeding for stalk lodging resistance is of paramount importance to maintain and improve maize (Zea mays L.) yield and quality and meet increasing food demand. The integration of environmental, phenotypic, and genotypic information offers the opportunity to develop genomic prediction strategies that can improve the genetic gain for complex traits such as stalk lodging. However, implementation of genomic predictions for stalk lodging resistance has been sparse primarily due to the lack of reliable and reproducible phenotyping strategies. In this study, we measured 10 traits related to stalk lodging resistance obtained from a novel phenotyping platform on approximately 31,000 individual stalks. These traits were combined with environmental information and whole-genome resequence data to investigate the predictive ability of different single and multi-environment genomic prediction models. In total, 555 maize inbred lines from the Wisconsin diversity panel were evaluated in four environments. The multi-environment models more than doubled the prediction accuracy compared to the single-environment model for most traits, particularly when predicting lines in a sparse testing design. Predictive correlations for stalk bending strength and stalk flexural stiffness, a nondestructive method for assessment of stalk lodging resistance, were moderately high and ranged between 0.32-0.89 and 0.26-0.88, respectively. In contrast, rind thickness was the most difficult trait to predict. Our results show that the use of multi-environmental data could improve genomic prediction accuracy for stalk lodging resistance and its intermediate phenotypes. This study will serve as a first step toward genetic improvement and the development of maize varieties resistant to stalk lodging.