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Ivone de Bem Oliveira

Researcher at University of Florida

Publications -  16
Citations -  251

Ivone de Bem Oliveira is an academic researcher from University of Florida. The author has contributed to research in topics: Population & Selection (genetic algorithm). The author has an hindex of 6, co-authored 14 publications receiving 148 citations. Previous affiliations of Ivone de Bem Oliveira include Universidade Federal de Goiás.

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Insights Into the Genetic Basis of Blueberry Fruit-Related Traits Using Diploid and Polyploid Models in a GWAS Context

TL;DR: The results showed that the importance of tetraploids models varied by trait and that the use of diploid models has hindered the detection of SNP-trait associations and, consequently, the genetic architecture of some commercially important traits in autotetraploid species.
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Genomic Prediction of Autotetraploids; Influence of Relationship Matrices, Allele Dosage, and Continuous Genotyping Calls in Phenotype Prediction.

TL;DR: Comparing the use of read depth as continuous parameterization with ploidy parameterizations in the context of genomic selection (GS) and the genotypic and phenotypic data used in this study are made available for comparative analysis of dosage calling and genomic selection prediction models in the contexts of autopolyploids.
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Breeding Alfalfa (Medicago sativa L.) Adapted to Subtropical Agroecosystems

TL;DR: The diversity present in subtropical alfalfa germplasm is examined and genetic parameters for forage production are reported, finding several families produced higher DMY than all checks, and they can be utilized to develop high yielding and adapted al falfa cultivars for subtropICAL agroecosystems.
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Optimizing whole-genomic prediction for autotetraploid blueberry breeding

TL;DR: It is suggested that marker density, sequencing depth, and training population size can be substantially reduced with no significant impact on model accuracy, allowing for a faster and more accurate release of varieties with a substantial reduction of resources for the application of genomic prediction in blueberry.