Browsing by Author "Odong, T. L."

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    Evaluation of Genomic Prediction Algorithms for Reducing Selection and Breeding Cycles in Shea Tree (Vitellaria Paradoxa)
    (Uganda Journal of Agricultural Sciences, 2021) Odoi, J. B.; Prasad, H.; Arfang, B.; Kitiyo, R.; Ozimati, A.; Gibson, P.; Edema, R.; Gwali, S.; Odong, T. L.
    The focus of this study was to determine the genomic prediction (GP) algorithms with the highest prediction accuracies for reducing the breeding and selection cycles in Vitellaria paradoxa. The efficiency of the GP algorithms were compared to evaluate five Shea tree growth traits in 708 genotypes with 30734 Single Nucleotide Polymorphic (SNPs) markers, which were reduced to 27063 after removing duplicates. Five hundred forty-nine (77.54%) Shea tree training population and 159 (22.46%) training population were genotyped for 30734 single nucleotide polymorphisms (SNPs) and phenotyped for five Shea tree growth traits. We built a model using phenotype and marker data from a training population by optimizing its genomic prediction accuracy for effectiveness of GS. The phenotype and marker data were used for cross validation of the prediction accuracies of the different models. Prediction accuracies varied among the genomic prediction algorithms based on the five phenotypic traits. We determined the best genomic algorithm that is more suitable for reduction of selection and breeding cycles in Vitellaria paradoxa. The GP algorithms were evaluated and we conclude that rrBLUP is the best for improving the prediction accuracy for reducing the breeding cycle in Shea tree.
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    Quality of core collections for effective utilization of genetic resources review, discussion and interpretation
    (Theoretical and Applied Genetics, 2013) Odong, T. L.; Jansen, J.; van Eeuwijk, F. A.; Hintum, T. J. L. van
    Definition of clear criteria for evaluation of the quality of core collections is a prerequisite for selecting high-quality cores. However, a critical examination of the different methods used in literature, for evaluating the quality of core collections, shows that there are no clear guidelines on the choices of quality evaluation criteria and as a result, inappropriate analyses are sometimes made leading to false conclusions being drawn regarding the quality of core collections and the methods to select such core collections. The choice of criteria for evaluating core collections appears to be based mainly on the fact that those criteria have been used in earlier publications rather than on the actual objectives of the core collection. In this study, we provide insight into different criteria used for evaluating core collections. We also discussed different types of core collections and related each type of core collection to their respective evaluation criteria. Two new criteria based on genetic distance are introduced. The consequences of the different evaluation criteria are illustrated using simulated and experimental data. We strongly recommend the use of the distance-based criteria since they not only allow the simultaneous evaluation of all variables describing the accessions, but they also provide intuitive and interpretable criteria, as compared with the univariate criteria generally used for the evaluation of core collections. Our findings will provide genebank curators and researchers with possibilities to make informed choices when creating, comparing and using core collections.
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    Statistical Techniques for Defining Reference Sets of Accessions and Microsatellite Markers
    (Crop science, 2011) Odong, T. L.; Hecrwaarden, J. van; Jansen, J.; van Hintum, T.J. L.; Eeuwijk, F. A. van
    Exploitation of the available genetic resources around the world requires information about the relationships and genetic diversify present among gene bank collections. These relations can be established by defining for each crop a small but informative set of accessions, together with a small set of reliable molecular markers, that can be used as reference material. In this study, various strategies to arrive at small but informative reference sets are discussed. For selection of accessions, we proposed genetic distance optimization (GDOpt) method, which selects a subset of accessions that optimally represent the accessions not included in the core collection. The performance of GDOpt was compared with Core Hunter, an advanced stochastic local search algorithm for selecting core subsets. For the selection of molecular markers, we evaluated (i) the backward elimination (BE) method and (ii) methods based on principal component analysis (PCA). We examined the performance of the proposed methodologies using five real datasets. Relative to average distance between an accession and the nearest selected accession (repressiveness), GDOpt outperformed Core Hunter. However, Core Hunter outperformed GDOpt with respect to allelic richness. The BE performed much better than other methods in selecting subsets of markers. Methods based on PCA showed that, for practical purposes, the inclusion of the first few (two or three) principal components (PCs) was often sufficient. To obtain robust and high qualify reference sets of accessions and markers we advise a combination of GDOpt (for accessions) and BE or methods based on PCA using a few PCs (for subsets of markers).