Plant Genome最新文献

Genomic prediction has been demonstrated to be an efficient approach for the selection of candidates based on marker information in many crops. However, efforts to understand the efficiency of genomic selection over phenotype-based selection in understudied crops such as dolichos bean (Lablab purpureus L. Sweet) are limited. Our objectives were to (i) explore the effective marker density for achieving high prediction accuracy and (ii) assess the effectiveness and efficiency of genomic selection over phenotype-based selection on seed yield at early segregating generations in dolichos bean. In this study, the training population, which consisted of F_5:6 recombinant inbreds, had a shared common parent with the breeding population, which consisted of F₂ generation breeding population. The populations were genotyped with newly synthesized genomic simple sequence repeat-based markers. The effective marker density for genomic prediction was assessed by using a varying number of markers in predictions using 11 different models. Furthermore, the effectiveness of genomic selection was assessed by comparing the genetic gains in progenies between genotypes selected based on predicted seed yield and phenotypically selected genotypes. Our results indicate that low-density markers that are evenly distributed throughout the genome are sufficient for the integration of genomic selection in dolichos breeding programs. The genomic selection was proved to be two times more effective than phenotypic selection in early-generation selection in dolichos beans. The results have a significant impact on adopting genomic selection in regular breeding programs of Dolichos beans at a low cost.

Multiplex gene editing allows for the simultaneous targeting and mutagenesis of multiple loci in a genome. This tool is particularly valuable for plant genetic improvement, as plant genomes often require mutations at multiple loci to confer useful and/or novel traits. However, the regulation of gene editing can vary depending on the number of loci targeted. In this study, we developed triple-mutant soybean (Glycine max (L.) Merrill) lines using different crop improvement strategies, including conventional backcross breeding of standing variant alleles and clustered regularly interspaced short palindromic repeats-based multiplex editing to introduce new alleles. The mutations were targeted to genes encoding seed antinutritional components, as previously described in a triple null soybean carrying knockout alleles for a Kunitz trypsin inhibitor, a soybean agglutinin, and the allergen P34 protein. The products developed from these respective genetic improvement pipelines were tested for differences between the triple-mutant lines and their parental lines. Analyses included genomics, seed proteomics, trypsin inhibition, seed protein digestibility, and harvestable yield of the different lines. We observed that both multiplex gene editing and conventional breeding approaches produced essentially equivalent products in comparison to their parental lines. We conclude that the multiplex gene editing strategy is not inherently riskier than conventional breeding for developing complex mutant lines of this type.

Achieving significant genetic gains in grain yield (GY) in wheat (Triticum aestivum L.) requires optimization of the key biomass partitioning traits such as spike partitioning index (SPI) and fruiting efficiency (FE). However, traditional manual phenotyping of these traits is labor-intensive and destructive, making it unsuitable for evaluating large germplasm panels. This study developed genomic prediction models to estimate these traits using diverse statistical methods while enhancing predictive ability (PA) by integrating environmental covariates (ECs) and secondary traits. A panel of 341 soft wheat elite lines was evaluated for biomass partitioning and yield-related traits from 2022 to 2024 in Citra, FL. Genomic best linear unbiased predictor (GBLUP) and Bayesian methods performed similarly or better than machine learning models for SPI, harvest index (HI), and GY. On the other hand, random forest models performed better in predicting effective tillers m^-2 (ET), 1000-grain weight (TGW), and grain numbers per m² (GN). Multi-kernel models incorporating ECs and secondary traits, such as plant height (PH) and aboveground biomass, substantially improved PA compared to genomics-only approaches. For 1000-grain weight, PA increased from 18% to 78%, with similar enhancements varying across other traits. Validations performed on separate breeding trial confirmed the reliability of the multi-kernel models, even though they showed a slightly lower PA compared to within-panel validations. These findings highlight the potential of integrating diverse data types or omics to enhance the prediction of biomass partitioning traits, speeding up genetic advancements, and the development of high-yield wheat varieties to address future food security challenges.

Exploitation of disease resistance genes in soybean (Glycine max (L.) Merr.), as an effective method for management of Phytophthora sojae (Kauf. & Gerd.), is on the verge of an impasse. Few of the known resistance genes are commercially exploited, and even fewer have been precisely identified. Therefore, little is known about the identities or relationships between those genes, a hindrance preventing optimal introgression of new sources of resistance into elite soybean lines. In this study, we have applied state-of-the-art nucleotide-binding and leucine-rich repeat gene capture (RenSeq) using a set of approximately 80,000 unique baits on near-isogenic lines, whole-genome resequencing, and bulked segregant analysis to uncover a resistance gene that has remained elusive for 40 years. This work highlights the reassessment of the Rps3b locus from Chr13 to Chr7 and the description of two alleles, from Turkish and Chinese landraces, of a sole candidate gene. We have identified Rps3b in four, fully resequenced, genetic backgrounds, including the original PI from 1985, in which the resistance gene was originally described. Specificity of the resistant alleles was achieved through phenotypic characterization with field isolates carrying virulent and avirulent forms of the corresponding effector, Avr3b. Surprisingly, these alleles showed extremely high synteny and sequence identity with Rps11 consistent with allelism, and conferred a resistance phenotype indistinguishable from that of the recently cloned Rps11. These results offer new sources of resistance for breeders that are effective against the current P. sojae pathotypes in the field.

For important food crops such as the common bean (Phaseolus vulgaris, L.), global demand continues to outpace the rate of genetic gain for quantitative traits. In this study, we leveraged the multi-environment trial (MET) dataset from the cooperative dry bean nursery (CDBN) to investigate the use of ensemble models for genomic prediction. This set spans 70 locations and 30 years, and accounts for over 150 phenotypes and hundreds of genotypes sequenced for 1.2 million single nucleotide polymorphism markers. We tested three models (linear regression, ridge regression, and neural networks). Each of the three models was implemented using three different approaches: (1) combining all data into one model (singular model), (2) all available single locations were used to train individual submodels comprising one ensemble model (ensemble model), and (3) optimized sets of single locations were used to train individual submodels comprising one ensemble model (optimized ensemble model). The optimized ensemble approach worked best for low-variance locations because the model variance was reduced by averaging across submodels in the ensemble. For models with low prediction accuracy, the ensemble approach can increase accuracy. In certain locations, prediction accuracy was able to overcome narrow-sense heritability, indicating that genomic selection is more efficient than phenotypic selection in these locations. This study indicates that breeding program collaboration can be a way to bypass the bottleneck of low data volume, as pooled data from the CDBN MET produced prediction accuracies of 0.70 for days to flowering, 0.54 for days to maturity, 0.95 for seed weight, and 0.67 for seed yield in individual locations.

The rubber tree (Hevea brasiliensis) is an important species in global natural rubber production. However, the mechanisms regulating the height of rubber trees remain poorly understood. In previous work, the dwarf mutant MU73397 was obtained through ethyl methanesulfonate mutagenesis. Compared to the wild-type CATAS73397, MU73397 exhibited significantly reduced plant height and stem diameter, slower xylem development, and decreased cellulose and lignin content. Phytohormone analysis revealed that gibberellin levels were reduced in both the apex and stem of MU73397, while jasmonic acid was increased in the apex and auxin was reduced in the stem. These differences in hormone levels may contribute to the dwarf phenotype. Transcriptome analysis identified nine key genes related to cell wall biosynthesis and hormone signaling, namely, FLA11 (Fasciclin-like arabinogalactan protein 11), TUBB1 (Tubulin Beta 1), TUBB6 (Tubulin Beta 6), CESA7 (cellulose synthase A 7), TUBA4 (Tubulin Alpha 4), LAC17 (Laccase 7), CTL2 (Chitinase-like protein 2), IRX9 (Irregular xylem 9), and KOR (korrigan). Overexpression of HbFLA11 in transgenic poplar resulted in significant increases in plant height and stem diameter. Gibberellin signaling genes and cell wall biosynthesis genes were significantly upregulated in the transgenic lines. These results suggest that HbFLA11 is involved in gibberellin signaling and cell wall biosynthesis, thereby regulating plant growth. This study provides valuable genetic resources and research foundations for targeted trait breeding in rubber tree.

The length of fruit branches significantly influences plant architecture in upland cotton (Gossypium hirsutum L.), which is crucial for optimizing fiber yield and quality. In this study, a comprehensive genome-wide association study was conducted based on whole-genome resequencing data that identified 249 significant SNPs associated with fruit branch length (FBL), forming 79 distinct quantitative trait loci (QTL) regions. Notably, stable QTL regions qFBL-A10-4 and qFBL-D03-17 were identified, harboring key candidate genes such as Ghir_A10G014390 and Ghir_D03G011390. Superior haplotypes of these genes significantly enhance FBL, fiber yield, and quality, offering valuable targets for cotton breeding programs focused on optimizing plant architecture and productivity.

Advancements in genomic and epigenetic research in both plants and animals have transformed breeding methods and biotechnological strategies for crop improvement, particularly in the face of extreme weather challenges. These breakthroughs in plant biology and agriculture have laid a strong foundation for ensuring food security, promoting environmental sustainability, enhancing nutritional health, and driving basic science advances, as exemplified by Mendel's discovery of genetic principles and McClintock's discovery of transposable elements. Plant epigenetics has held a transformative potential for developing high-yielding and resilient crops. In this review, I will examine various relevant epigenetic phenomena, including nucleolar dominance, paramutation, imprinting, somaclonal variation, and transgenerational epigenetic inheritance, to explore strategies for overcoming yield limitations in an increasingly volatile climate. This perspective aligns with the vision for plant breeding and sustainable agriculture championed by the late Professor Ronald L. Phillips.

Selecting parents and crosses is a critical step for a successful breeding program. The ability to design crosses with high means that will maintain genetic variation in the population is the goal for long-term applications. Herein, we describe a new computational package for mate allocation in a breeding program. SimpleMating is a flexible and open-source R package originally designed to predict and optimize breeding crosses in crops with different reproductive systems and breeding designs. Divided into modules, SimpleMating first estimates the cross performance (criterion), such as mid-parental value, cross total genetic value, and/or usefulness of a set of crosses. The second module implements an optimization algorithm to maximize a target criterion while minimizing next-generation inbreeding. The software is flexible, enabling users to specify the desired number of crosses, set maximum and minimum crosses per parent, and define the maximum allowable parent relationship for creating crosses. As an outcome, SimpleMating generates a mating plan from the target parental population using single or multi-trait criteria. For example, we implemented and tested SimpleMating in a simulated maize breeding program obtained through stochastic simulations. The crosses designed via SimpleMating showed a large genetic mean over time (up to 22% more genetic gain than conventional genomic selection programs, with lesser loss of genetic diversity over time), supporting the use of this tool, as well as the use of data-driven decisions in breeding programs.

Developments in genomics and phenomics have provided valuable tools for use in cultivar development. Genomic prediction (GP) has been used in commercial soybean [Glycine max L. (Merr.)] breeding programs to predict grain yield and seed composition traits. Phenomic prediction (PP) is a rapidly developing field that holds the potential to be used for the selection of genotypes early in the growing season. The objectives of this study were to compare the performance of GP and PP for predicting soybean seed yield, protein, and oil. We additionally conducted genome-wide association studies (GWAS) to identify significant single-nucleotide polymorphisms (SNPs) associated with the traits of interest. The GWAS panel of 292 diverse accessions was grown in six environments in replicated trials. Spectral data were collected at two time points during the growing season. A genomic best linear unbiased prediction (GBLUP) model was trained on 269 accessions, while three separate machine learning (ML) models were trained on vegetation indices (VIs) and canopy traits. We observed that PP had a higher correlation coefficient than GP for seed yield, while GP had higher correlation coefficients for seed protein and oil contents. VIs with high feature importance were used as covariates in a new GBLUP model, and a new random forest model was trained with the inclusion of selected SNPs. These models did not outperform the original GP and PP models. These results show the capability of using ML for in-season predictions for specific traits in soybean breeding and provide insights on PP and GP inclusions in breeding programs.