Publications by authors named "Jeremy D Edwards"

In this study, we model and predict rice yields by integrating molecular marker variation, varietal productivity, and climate, focusing on the Southern U.S. rice-growing region.

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Due to global climate change resulting in extreme temperature fluctuations, it becomes increasingly necessary to explore the natural genetic variation in model crops such as rice to facilitate the breeding of climate-resilient cultivars. To uncover genomic regions in rice involved in managing cold stress tolerance responses and to identify associated cold tolerance genes, two inbred line populations developed from crosses between cold-tolerant and cold-sensitive parents were used for quantitative trait locus (QTL) mapping of two traits: degree of membrane damage after 1 week of cold exposure quantified as percent electrolyte leakage (EL) and percent low-temperature seedling survivability (LTSS) after 1 week of recovery growth. This revealed four EL QTL and 12 LTSS QTL, all overlapping with larger QTL regions previously uncovered by genome-wide association study (GWAS) mapping approaches.

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The pigmented flavonoids, anthocyanins and proanthocyanidins, have health promoting properties. Previous work determined that the genes Pb and Rc turn on and off the biosynthesis of anthocyanins (purple) and proanthocyanidins (red), respectively. Not yet known is how the concentrations of these pigmented flavonoids are regulated in grain pericarps.

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Tillering and plant biomass are key determinants of rice crop productivity. Tillering at the vegetative stage is associated with weed competition, nutrient uptake, and methane emissions. However, little information is available on quantitative trait loci (QTLs) associated with tiller number (qTN), root biomass (qRB), and shoot biomass (qSB) at the active tillering stage which occurs approximately 6 weeks after planting.

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Article Synopsis
  • Crop wild relatives are important for breeding due to their genetic diversity, but they face threats in their habitats and are not well represented or characterized in genebanks.
  • This study focuses on the wild progenitor of Asian rice, exploring a collection of 240 accessions characterized through advanced genetic analysis and phenotyping for various plant traits.
  • The research utilized a Bayesian model to identify distinct phenotype-based groups, linking specific traits to plant morphology and reproductive habits, ultimately identifying key accessions for domestic breeding efforts.
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Background: Sheath blight (ShB) disease caused by Rhizoctonia solani Kühn, is one of the most economically damaging rice (Oryza sativa L.) diseases worldwide. There are no known major resistance genes, leaving only partial resistance from small-effect QTL to deploy for cultivar improvement.

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  • - Modern plant breeding combines advanced technologies like next-generation sequencing and phenomics to select the best parent plants, aiming to produce superior cultivars that farmers can easily adopt.
  • - A robust breeding database is essential for tracking various breeding materials, recording experimental and phenotypic data, storing genotypic information, and supporting analytical algorithms for breeding decisions.
  • - The Breedbase system, an open-source web application initially developed for cassava, has evolved to support multiple crops, offering a comprehensive platform for managing breeding data and enhancing decision-making processes in a digital environment.
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  • Global concerns about arsenic in rice trigger efforts to breed varieties that limit arsenic accumulation to ensure consumer safety, as well as tackle plant toxicity issues like straighthead disorder (StHD).
  • Genetic variation in resistance to StHD suggests that some rice plants may have developed natural mechanisms to reduce arsenic toxicity, possibly leading to co-located genetic markers for both reduced arsenic and StHD susceptibility.
  • Using advanced machine-learning methods and a comprehensive genome-wide analysis, researchers identified numerous quantitative trait loci (QTL) related to both arsenic content and StHD, providing valuable insights for future breeding strategies and genetic research in rice.
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  • Root system architecture (RSA) significantly influences how plants absorb resources and their overall productivity, making it a priority for breeders to enhance RSA through genetic tools.
  • This study identified quantitative trait loci (QTLs) related to RSA and other agronomic traits in a rice population, using both traditional linkage analysis and a machine learning method (Bayesian network).
  • Results indicated that multi-QTL models improved genomic prediction abilities for RSA traits, leading to better selections based on genetic data and a modified rank sum index, which demonstrated varying ranking accuracy for different RSA characteristics.
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  • The study focuses on understanding the genetic factors behind plant vigor, particularly in rice, and highlights the complexity of mapping this trait due to many genes with small effects and their interactions.
  • Researchers performed a long-read genomic assembly of a tropical japonica rice variety, Carolina Gold, to identify significant structural mutations and understand how these changes affect crop performance.
  • The findings indicate a history of tandem duplications and transposable element activity that contributed to genomic size variations, with structural mutations affecting gene exons being selected against in rice breeding programs over the last century.
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Arsenic (As) accumulation in rice grain is a significant public health concern. Inorganic As (iAs) is of particular concern because it has increased toxicity as compared to organic As. Irrigation management practices, such as alternate wetting and drying (AWD), as well as genotypic differences between cultivars, have been shown to influence As accumulation in rice grain.

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  • Rice grain quality is a complex trait that affects its market value, influenced by genetic and environmental factors; traditional measurement methods include chemical, physical, and visual analyses.
  • A study evaluated hyperspectral imaging technology to assess rice grain quality and categorize samples by genetic type and growing conditions, utilizing data from the USDA mini-core collection across various locations.
  • The findings suggest that visible and near-infrared spectroscopy can effectively identify variations in rice grain quality, particularly the chalky grain trait, and support the mapping of key genetic regions related to these traits, indicating hyperspectral imaging is a promising tool for non-destructive phenotyping.
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Background: Knowledge of the genetic diversity and spatial structure of Taiwan weedy red rice (WRR) populations, which adapted in a transplanting system, will facilitate the design of effective methods to control this weed by tracing its origins and dispersal patterns in a given region.

Results: Taiwan WRR is genetically most similar to Taiwan indica cultivars and landraces according to genetic distance. The inbreeding coefficient of the Taiwan WRR population is greater than 0.

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Salt stress is a major constraint to rice acreage and production worldwide. The purpose of this study was to evaluate the natural genetic variation available in the United States Department of Agriculture (USDA) rice mini-core collection (URMC) for early vigor traits under salt stress and identify quantitative trait loci (QTLs) for seedling-stage salt tolerance via a genome-wide association study (GWAS). Using a hydroponic system, the seedlings of 162 accessions were subjected to electrical conductivity (EC) 6.

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The Rice Diversity Panel 1 (RDP1) was developed for genome-wide association (GWA) studies to explore five rice ( L.) subpopulations (, , , , and ). The RDP1 was evaluated for over 30 traits, including agronomic, panicle architecture, seed, and disease traits and genotyped with 700,000 single nucleotide polymorphisms (SNPs).

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Rice ( L.) end-use cooking quality is vital for producers and billions of consumers worldwide. Grain quality is a complex trait with interacting genetic and environmental factors.

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Motivation: Modern genomic breeding methods rely heavily on very large amounts of phenotyping and genotyping data, presenting new challenges in effective data management and integration. Recently, the size and complexity of datasets have increased significantly, with the result that data are often stored on multiple systems. As analyses of interest increasingly require aggregation of datasets from diverse sources, data exchange between disparate systems becomes a challenge.

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Plant resistance genes typically encode proteins with nucleotide binding site-leucine rich repeat (NLR) domains. Here we show that Ptr is an atypical resistance gene encoding a protein with four Armadillo repeats. Ptr is required for broad-spectrum blast resistance mediated by the NLR R gene Pi-ta and by the associated R gene Pi-ta2.

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Cold temperature is an important abiotic stress which negatively affects morphological development and seed production in rice (Oryza sativa L.). At the seedling stage, cold stress causes poor germination, seedling injury and poor stand establishment; and at the reproductive stage cold decreases seed yield.

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Article Synopsis
  • Genomic selection (GS) aims to enhance the accuracy of estimating breeding values for traits through advanced genetic methods, but it faces challenges in data management and analysis due to its complexity.* -
  • The tool solGS has been developed to help breeders predict genomic estimated breeding values (GEBVs) using a user-friendly web interface, enabling the selection of training populations and estimation of important genetic metrics.* -
  • With solGS, breeders can easily store data and estimate GEBVs online, making it adaptable to various breeding programs and improving their overall efficiency.*
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  • The Sol Genomics Network (SGN) is a web portal that offers genomic and phenotypic data for members of the Solanaceae family, like tomatoes and potatoes, and includes whole genome data for various species.
  • Users can upload and edit their own data using intuitive web interfaces, and the site provides tools like BLAST and GBrowse for effective genome browsing and analysis.
  • Recently, SGN introduced a new tool to enhance Virus-Induced Gene Silencing (VIGS) constructs and is expanding its resources to develop web-based breeding tools for more species.
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  • Decades of breeding tomatoes have integrated genes from wild relatives into cultivated varieties, allowing researchers to pinpoint desirable traits like disease resistance through genome analysis.
  • The study sequenced genomes of two tomato inbreds, revealing specific introgressions related to resistance against begomovirus and differences in their breeding histories.
  • The findings provide methods to identify and utilize wild genetic variations for improving tomatoes and can be applied to other crops for similar advancements.
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  • High-quality manual annotation methods need to adapt to the rapid increase of genomic data, and curating based on gene families and networks can enhance both efficiency and quality.
  • The Sol Genomics Network (SGN) serves as a comparative genomics platform, offering detailed genetic, genomic, and phenotypic information about the Solanaceae family and a community-driven curation system.
  • The article highlights a manual curation system specifically for gene families that aids in visualizing gene interactions and capturing large data sets, using WRKY and SAUR multigene families as examples of their significance in plant responses to abiotic stresses.
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  • Tomato Yellow Leaf Curl Virus Disease, caused by the Tomato yellow leaf curl virus (TYLCV), significantly impacts global tomato production due to various related begomovirus species.
  • This study focused on identifying and fine-mapping the Ty-1 and Ty-3 resistance genes from the wild tomato species, Solanum chilense, through the analysis of 12,000 plants and generating recombinant lines.
  • The genes were found to code for a novel RNA-dependent RNA polymerase (RDR) of the RDRγ type, which is distinct from the well-understood RDRα type, suggesting a new class of resistance gene that may play a role in the plant's defense against TYLCV.
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  • Developed a low-cost microarray platform for genetic mapping in rice, focusing on known polymorphic features rather than whole-genome analysis.
  • Created a genotyping microarray with 880 single feature polymorphism (SFP) elements, validating them using genomic DNA from two rice cultivars.
  • Successfully classified diverse rice subpopulations and mapped a gene for resistance to rice blast disease, highlighting its potential for widespread genetic studies.
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