Motivated by the potential to guide future resource allocation in plant breeding programs, a systematic approach to select molecular markers and, in sequence, design a training set population for genomic prediction will be explored. This study will combine probe filtering criteria and compare different training set algorithms, validating their performance in genomic prediction.
In order to identify genotypes that are well-suited to targeted environments, it is crucial to examine the variations in phenotypic performance across different environmental conditions. Therefore, this study aims to: (i) access the linear response of blueberry fruit quality traits to environmental variables across Florida environments and; (ii) understand GxE using explicit environmental covariables and trait stability.
The prediction and selection of unphenotyped individuals for target environments become unreliable with the demonstration of heterogeneity of genetic variance across environments. This study aims to compare the performance of the prediction of single and multi-environment GS models.
Genotyping large number of plants at the early stage of a breeding program is time consuming and expensive. From empirical studies, phenomic prediction with NIRS is promising. Therefore, this study will carryout comparative assessment of phenomic and genomic predictive ability.
Coffee is a perennial crop with a long juvenile phase, and subjected to significant temporal and spatial variations. This facts not only hinder the selection of promising materials, but also cause a majority of complaints among growers. In this study, we hypothesized that trait stability in coffee is genetically controlled, and therefore is predictable using molecular information. Generally, we found that (1) good predictive abilities could be found when data was collected in 3 years, an information that opens new avenues to reducing coffee breeding cycles, reduce costs, and ultimately lever-age genetic gains; (2) stability metrics could be predicted
The outermost surface of blueberry fruits is covered by a whitish bloom-like wax that serves as a virtual index of freshness and consumer appeal. Phenotyping for bloom in blueberry fruits is traditionally through visual scoring. This approach is subjective and could lead to inconsistency in ratings. To address this challenge, we developed a computer vision and machine learning workflow for automated bloom phenotyping from blueberry fruit images.
Our results showed high correlation coefficient between both approaches. We also found higher broad-sense and narrow-sense heritability, genomic predictive abilites and SNP-bloom associations for image-based approach compared to visual scoring.
Lipoxygenase enzymes, which contribute significantly to storage protein in faba seeds have been reported to cause the emission of volatile compounds associated with the generation of off-flavours. This study aimed at using bioinformatic tools to identify seed-borne LOX genes in faba bean lines. Generally, we found a full-length gene (i.e. contain both N-terminus and C-terminus region) and it is tentatively named VfLOX 3. We developed a molecular tool that can be used in the future for breeding
faba bean with null-allelic seed-borne LOX genes or low seed LOX gene activities.
Biofortification of a natural snack like popcorn is important for improved nutrition and health of children and adults. This study aimed at introgressing opaque-2 gene, a high lysine/tryptophan gene into the genetic background of popcorn. Generally, we found significant increase in protein and tryptophan contents and quality index for F2 crosses of QPM and popcorn. However, the protein quality decreased significantly upon popping.
Collecting NIR spectra data from blueberry fruit
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