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PubMedDate not supplied

Coadaptation in plant populations.

One of the most important questions of plant breeding is whether alleles at different loci act independently or whether the population genotype is structured so that favored combinations of alleles occur more frequently than expected under randomness. Studies employing allozyme loci as markers have demonstrated that the distribution of alleles in both natural and experimental populations of inbreeding plants is closely correlated with environment on both micro- and macrogeographic scales. Multilocus analyses have also revealed the occurrence within local populations of striking gametic phase disequilibrium (linkage disequilibrium). These observations demonstrate that selection acts to organize the population into sets of highly interacting coadapted gene complexes that promote high fitness to the local environment.

Adaptation, Biological↗
PubMed2026

New Insights into Genomic Variations and Mutational Events Associated with Plant-Pathogen Interactions.

Plant diseases threaten global food security, causing up to 40% crop yield losses and more than $220 billion in annual economic damage. This review synthesizes recent advances in understanding the genomic variations and mutational events underlying plant-pathogen interactions and durable plant disease resistance. Key insights into evolutionary dynamics, genetic variability, and coadaptive strategies reveal the complexity of host-pathogen relationships and the implications for developing durable disease resistance. Integrative approaches combining genome-wide association studies and functional genomics have uncovered the polygenic and epistatic architecture of quantitative resistance. Advances in pan-genomics and high-throughput sequencing have revealed extensive genetic variability in cultivated/elite germplasm and wild relatives. Emerging technologies, including gene editing, multi-omics, and machine learning, enable predictive modeling of resistance traits and support evolution that informs plant breeding strategies. Collectively, these advances provide a robust framework for developing durable resistance and sustainable crop protection in the face of global agricultural challenges.

Host-Pathogen Interactions↗
PubMed2026

Site-Specific Measurement of Meiotic Crossing-Over Rate with Droplet Digital PCR.

Understanding the frequency and distribution of meiotic crossovers (COs) is critical for both fundamental studies on meiosis and for practical applications in plant breeding, where controlling recombination can accelerate crop improvement. Determining CO rates at specific genomic loci has traditionally relied on labor-intensive methods that require the production and genotyping of large progenies. Here, we present a high-throughput protocol for site-specific quantification of meiotic COs in maize using droplet digital PCR (ddPCR). The method is based on genotyping individual pollen nuclei from hybrid plants to detect recombinant and nonrecombinant alleles at defined chromosomal intervals. By distributing several thousands of pollen nuclei into nanoliter-sized droplets and performing PCR with allele-specific fluorescent probes, this method allows precise quantification of CO frequency with high sensitivity. The protocol provides detailed guidance for nuclei isolation, probe master mix preparation, droplet generation, and data interpretation. This method can be easily adapted for use in other plants.

Journal Article↗
PubMed1979

Different manifestations of the pathogenity of some strains of Fusarium oxysporum f. sp. pisi.

Different cultivation and morphology characteristics were found in 10 monospore isolates of Fusarium oxysporum f. sp. pisi, obtained from yellowing and wilting plants of pea (Pisum sativum L.). The isolates of the fungus were obtained from distant geographical regions of Czechoslovakia and from various cultivars and hybrids of pea. After inoculation of roots, followed by constant conditions of incubation of the Meteor and Jupiter cultivars having their origin at the Plant-breeding Station at Luzany u Prestic, the isolates caused various symptoms of disease, each isolate showed a different degree of pathogenity. The variability of the pathogenity of the isolates depended on the host. Its manifestation, in turn, depended on the dynamics of growth and development of the pathogen as well as the host. The following symptoms could be observed during the pathogenesis: the dying of cotyledons after the contact of the main root with the inoculum, the dying of young plants (the plants usually forming two stems), wilting of young plants, yellowing of bottom leaves and wilting beginning from the bottom leaves, stunted growth, and plant deformation. The symptoms of disease are related to the changes in vascular system.

Czechoslovakia↗
PubMed2026

Integrating plant phenotypic and genotypic data in the AGENT project: a BrAPI service implementation.

MOTIVATION: The AGENT project established a network of actively cooperating European genebanks, integrating genomic and phenotypic data from accessions of wheat and barley. Due to specific storage demands for phenotypic and genotypic data, the project used separate database instances and backend technologies to manage integrated phenotypic and genotypic data. RESULTS: We discuss the challenges encountered when integrating dispersed data to serve through a single interface such as the Plant Breeding Application Programming Interface, BrAPI. We examine how the consistent mappability of genebank data to the BrAPI model can enable the implementation of effective services. The advantages of BrAPI in transparently linking distributed data entities through embedded, unique identifiers are highlighted. We present a technical solution involving a BrAPI proxy, which combines and merges separate BrAPI endpoints. Finally, we demonstrate the AGENT BrAPI implementation with an illustrative example that validates a suggested SNP for a trait from the literature by linking phenotypic, genotypic and passport data. AVAILABILITY AND IMPLEMENTATION: The BrAPI proxy implementation and documentation is available at the Python Package Index (https://pypi.org/project/brapi-proxy) and archived in Zenodo (doi: 10.5281/zenodo.19436445). SUPPLEMENTARY INFORMATION: A Jupyter Notebook file for the validation example using a marker-trait relationship found in the literature.

Phenotype↗
PubMed1978

Nutritional evaluation of low-erucic-acid rapeseed oils.

Detailed morphometric studies performed in heart tissue from Swiss mice and Wistar rats show that, in comparison with other edible oils, long-term feeding of the new rapeseed oils, poor in erucic acid, do not significantly affect the incidence of myocardial background lesions, in contrast to high-erucic-acid rapeseed oil. The strong predisposition of the Sprague-Dawley rat, however, to develop myocardial necrosis is re-emphasized. The factors underlying this particularity need further clarification. The data presented and the available evidence from experiments involving pigs, monkeys and poultry show that a reduction of the content of erucic acid in rapeseed lipids, as has been achieved by selective plant breeding, considerably improves the nutritional status of the cruciferous oils.

Animals↗
PubMed2026

Artificial intelligence-driven advancements in agricultural biotechnology.

The need for faster and more informative data processing for better decision-making is driving the adoption of artificial intelligence (AI) in the agricultural sector. Thanks to recent advancements in computer science and the increase in computational powers of modern computers, AI is not only augmenting traditional solutions, but also helping in developing novel solutions to existing challenging matters. AI-driven models have an exceptional ability to identify patterns and combine a diverse collection of data together and make inference. The increasing pressure on farmlands posed by the growing global population and climate change is lessening growth, yield, and productivity ultimately posing risk to food security worldwide. Incorporation of AI in agriculture has the potential to drive farming efficiency to new heights. This comprehensive review critically evaluates the evolution of AI in agricultural biotechnology from a theoretical concept to a global phenomenon. A comprehensive literature search was performed using major scientific databases, including PubMed, Web of Science, Embase, Scopus, Lens and the Cochrane Library. In this review, we empirically demonstrate the fields advancement toward more capable AI systems and discuss the current applications of AI across crop improvement and precision agriculture such as crop improvement and genetic engineering, genomic selection and plant breeding, pest and disease detection, precision agriculture and smart farming, soil health and nutrient management, climate resilient crop development, livestock biotechnology, challenges and ethical considerations in AI based agricultural biotechnology. Furthermore, this review addresses the exponential growth of commercial intellectual property in the field and contrast it with academic publication outputs. Finally, we critically assess the ethical challenges impeding equitable adoption of AI including data sovereignty and digital divide, while projecting future frontiers involving quantum computing. This review will help build sustainable agricultural systems capable of adapting to climate change, contribute to the development of climate-resilient and high-yielding crops, and address global food security challenges.

Agriculture↗
PubMed1979

Determination of oxalate in urine using oxalate oxidase: comparison with oxalate decarboxylase.

The oxalate content of urine is determined by means of oxalate oxidase and simple pH measurement. The enzyme specifically decarboxylates oxalate, producing two moles CO2 per mole oxalate. The CO2 diffuses into an alkaline buffer solution (Hallson, P. C. & Rose, G. A. (1974), Clin. Chim. Acta 55, 29--39) in the closed reaction vessel, and reduces the pH value, which is measured with an electrode. Only 125 microliter native urine is required to measure oxalate concentrations in the range of 80 mumol/l to 1.6 mmol/l (corresponding to 7 to 144 mg anhydrous oxalic acid per liter). The limit of detection is 10 nmol oxalate, and the accuracy is 101% with a coefficient of variation of 6%. The method described is insensitive to various interfering factors, such as reducing and oxidizing substances, cloudy or colored samples. It is therefore also suitable for oxalate determination in food technology and plant breeding.

Carboxy-Lyases↗
PubMedDate not supplied

Do mosquitoes breed in maiza plant axils?

One thousand five hundred and seven tasselled maize plants in Lusaka and 96 in a rural village where mosquitoes were plentiful, have been surveyed. About 28% of plants contained water mainly around developing cobs. No mosquito larvae were found. It is concluded that maize slashing as part of the programme for malaria control is unjustified.

Breeding↗
PubMed2026

Chromosome-scale genome assembly and genomic prediction of essential oil compounds in Atractylodes lancea for genomics-assisted breeding.

Atractylodes lancea rhizomes are used as crude drugs. Essential oil compounds, including atractylodin, hinesol, β-eudesmol, and atractylon, are key determinants of crude drug quality. Conventional breeding of A. lancea is difficult because of its perennial growth. In this study, a chromosome-scale reference genome of A. lancea (4.79 Gb) was generated, and genome-wide association studies (GWAS) and genomic predictions of essential oil compounds were conducted to explore the potential for genome-assisted breeding. Genotyping of 480 lines using double-digest restriction-site-associated DNA-sequencing yielded 29,136 high-quality SNPs. All the compounds showed high genomic heritability (h2 = 0.758-0.915), indicating strong genetic control. Despite the high genomic heritability, GWAS detected only one weak association with atractylon and no significant loci for the three compounds. However, genomic prediction achieved moderate to high accuracy across multiple models, particularly the ridge regression, genomic best linear unbiased prediction, and Bayesian approaches. The prediction accuracy, measured as the Pearson correlation coefficient between the observed and predicted values, exceeded 0.6 for all four essential oil compounds. These results demonstrate the efficacy of genomic selection for improving essential oil compound levels in A. lancea and provide a foundation for genome-assisted breeding of medicinal plants with long breeding cycles.

Atractylodes lancea↗
PubMed2025

Haplotype stacking to improve stability of stripe rust resistance in wheat.

Genotype-by-environment interaction analysis and haplotype-level characterisation provide novel insights into the stability of stripe rust resistance. Breeding selection strategies are proposed to achieve rapid and stable genetic gains across environments. This study investigated stripe/yellow rust (YR) responses in the Vavilov wheat diversity panel evaluated across 11 field experiments conducted in Australia and Ethiopia during 2014-2021. Genotype-by-environment interaction (GEI) was analysed using a factor analytic (FA) model. Genotype-level selection was performed with overall performance (OP) and root-mean-square deviation (RMSD), which reflected average performance and stability of YR resistance across environments, respectively. Genomic estimated breeding values (GEBV) for these traits were calculated and compared with those from a multi-trait GBLUP model with average performance represented by the mean GEBV across environments and stability by the standard deviation of GEBV across environments. The FA-based and multi-trait GBLUP GEBV had high correlations. Haplotypes with large effects on OP and RMSD were identified using the local GEBV method. Favourable haplotypes were then used for stacking in breeding simulations, using the Vavilov collection as a base. Compared to truncation selection, optimal haplotype selection (OHS) using an artificial intelligence (AI)-based algorithm achieved longer-term genetic gains for both OP and RMSD (after many generations) by initially selecting founder parents that maximised favourable haplotypes. Simulations using YR responses from diverse environments that mimicked fluctuating environmental conditions across seasons were conducted to evaluate strategies for selection of YR resistance that is stable across years. Strategies which gave most weight to OP, but some weight to RMSD were optimal in these conditions, and substantially reduced variation of performance across years. This study provides useful information for breeding cultivars with both high YR resistance and high stability of resistance across environments.

Triticum↗
PubMed2026

Selection of GhTT2-A07 promoter enhances fiber quality in improved cotton varieties.

Modern cultivated cotton fibers are predominantly white with enhanced quality compared to their wild ancestors. However, the molecular mechanisms and evolutionary drivers linking fiber color to quality remain least focused. In this study, we identified FQC1 (Fiber Quality and Color 1), a major quantitative trait locus (QTL) on chromosome A07 that concurrently regulates both fiber quality and pigmentation. Through map-based cloning, we revealed that Gossypium hirsutum TRANSPARENT TESTA2-A07 (GhTT2-A07), an R2R3-MYB transcription factor, resides within this locus. GhTT2-A07 modulates fiber development by directly activating genes in the general phenylpropanoid pathway, thereby promoting the metabolic flux toward downstream secondary metabolites. Variations in the GhTT2-A07 promoter led to its reduced expression in modern white cotton cultivars. This down-regulation suppresses the accumulation of S/G/H-type lignin monomers and proanthocyanidins, resulting in altered secondary cell wall composition and ultimately enhancing the quality of mature white fibers. Population genetic analyses further indicate that the white-fiber allele GhTT2-A07W has been fixed in modern breeding genotypes, underscoring the impact of artificial selection during cotton domestication. Overall, our study elucidates the biochemical and molecular mechanisms underlying fiber quality and pigmentation in cotton, clarifies the selection criteria for high-quality white fibers in modern cultivars, and provides a theoretical basis for future targeted genetic improvement of cotton fibers.

Alleles↗
PubMed2026

From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement.

Sugarcane (Saccharum spp.) underpins global sugar and bioenergy supply and is increasingly valued as a renewable biomass feedstock. Sustained improvement in commercial traits and resilience is constrained by long breeding cycles, clonal propagation, multi-stage testing, and a highly polyploid, heterozygous, and frequently aneuploid genome with substantial non-additive genetic variation. Genomic selection has demonstrated value for predicting elite-clone performance, yet its operational use remains limited at earlier decision points, including family selection, parent evaluation, and cross design. This review examines the biological, statistical, and genomic factors that shape these decisions, with emphasis on the Australian breeding context based on progeny assessment trials (PATs), clonal assessment trials (CATs), and final assessment trials (FATs). We evaluate challenges arising from family plot means, the use of different full-sib samples as nominal family replicates, spatial heterogeneity, competition, genotype-by-environment interaction, and the partitioning of additive and non-additive effects. We also assess the integration of pedigree and genomic relationship, genotype representation, allele-dosage estimation, aneuploidy, genomic prediction models, and training-population design. We then consider genomic prediction of cross performance and constrained mate allocation as approaches for improving expected family performance, accounting for cross-specific non-additive effects and managing relatedness. We propose a decision-centred framework that links family and clonal data across breeding stages, tracks the propagation of information and uncertainty, and supports parent recycling and cross allocation. We conclude with a practical research agenda for stage-integrated mixed-model and single-step analyses that connect early family evaluation with genomic prediction and cross-level decision support in sugarcane breeding.

Saccharum↗
PubMed2025

Molecular breeding of tomato: Advances and challenges.

The modern cultivated tomato (Solanum lycopersicum) was domesticated from Solanum pimpinellifolium native to the Andes Mountains of South America through a "two-step domestication" process. It was introduced to Europe in the 16th century and later widely cultivated worldwide. Since the late 19th century, breeders, guided by modern genetics, breeding science, and statistical theory, have improved tomatoes into an important fruit and vegetable crop that serves both fresh consumption and processing needs, satisfying diverse consumer demands. Over the past three decades, advancements in modern crop molecular breeding technologies, represented by molecular marker technology, genome sequencing, and genome editing, have significantly transformed tomato breeding paradigms. This article reviews the research progress in the field of tomato molecular breeding, encompassing genome sequencing of germplasm resources, the identification of functional genes for agronomic traits, and the development of key molecular breeding technologies. Based on these advancements, we also discuss the major challenges and perspectives in this field.

Solanum lycopersicum↗
PubMed2025

Genetic and genomic resources for turfgrasses: status, applications, and prospects.

Turfgrasses are integral to urban landscapes, providing social, economic, and ecological benefits. With increasing urbanization, there is a growing demand for turfgrasses that remain visually appealing while being resilient to environmental stresses. In this review, we highlight the current state of genetic and genomic resources for turfgrasses, focusing on advancements in the understanding of genes and pathways associated with traits such as disease resistance, stress tolerance, and environmental adaptability. Additionally, we discuss recent progress in implementing genome editing technologies for turfgrasses and their transformative potential for breeding and functional genomics. We examine progress, challenges, and future prospects in leveraging genomic tools for turfgrass improvement. By synthesizing knowledge from diverse studies, we provide a comprehensive overview of the techniques, discoveries, and innovations shaping the future of turfgrass breeding and management.

Poaceae↗
PubMed2024

Genomic selection for tolerance to aluminum toxicity in a synthetic population of upland rice.

Over half of the world's arable land is acidic, which constrains cereal production. In South America, different rice-growing regions (Cerrado in Brazil and Llanos in Colombia and Venezuela) are particularly affected due to high aluminum toxicity levels. For this reason, efforts have been made to breed for tolerance to aluminum toxicity using synthetic populations. The breeding program of CIAT-CIRAD is a good example of the use of recurrent selection to increase productivity for the Llanos in Colombia. In this study, we evaluated the performance of genomic prediction models to optimize the breeding scheme by hastening the development of an improved synthetic population and elite lines. We characterized 334 families at the S0:4 generation in two conditions. One condition was the control, managed with liming, while the other had high aluminum toxicity. Four traits were considered: days to flowering (FL), plant height (PH), grain yield (YLD), and zinc concentration in the polished grain (ZN). The population presented a high tolerance to aluminum toxicity, with more than 72% of the families showing a higher yield under aluminum conditions. The performance of the families under the aluminum toxicity condition was predicted using four different models: a single-environment model and three multi-environment models. The multi-environment models differed in the way they integrated genotype-by-environment interactions. The best predictive abilities were achieved using multi-environment models: 0.67 for FL, 0.60 for PH, 0.53 for YLD, and 0.65 for ZN. The gain of multi-environment over single-environment models ranged from 71% for YLD to 430% for FL. The selection of the best-performing families based on multi-trait indices, including the four traits mentioned above, facilitated the identification of suitable families for recombination. This information will be used to develop a new cycle of recurrent selection through genomic selection.

Oryza↗