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  • 1
    Online Resource
    Online Resource
    Evaluation of Genomic Prediction Algorithms for Reducing Selection and Breeding Cycles in Shea Tree (Vitellaria Paradoxa)
    African Journals Online (AJOL) ; 2022
    In:  Uganda Journal of Agricultural Sciences Vol. 21, No. 1 ( 2022-09-30), p. 1-12
    Details
    In: Uganda Journal of Agricultural Sciences, African Journals Online (AJOL), Vol. 21, No. 1 ( 2022-09-30), p. 1-12
    Abstract: 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 〈 i 〉 Vitellaria paradoxa 〈 /i 〉 . 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 〈 i 〉 Vitellaria paradoxa 〈 /i 〉 . 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.
    Type of Medium: Online Resource
    ISSN: 2410-6909 , 1026-0919
    URL: Article
    DOI: 10.4314/ujas.v21i1.1
    Language: Unknown
    Publisher: African Journals Online (AJOL)
    Publication Date: 2022
    detail.hit.zdb_id: 2869354-1
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  • 2
    Online Resource
    Online Resource
    Data_Sheet_1.zip
    Frontiers Media SA ; 2021
    In:  Frontiers in Plant Science Vol. 12 ( 2021-7-5)
    Details
    In: Frontiers in Plant Science, Frontiers Media SA, Vol. 12 ( 2021-7-5)
    Abstract: Cassava production and productivity in Eastern, Central, and Southern Africa are ravaged by cassava brown streak disease (CBSD), causing yield losses of up to 100% when susceptible varieties are grown. Efforts to develop CBSD-resistant clones are underway. However, the methods for screening CBSD resistance currently vary between breeders and pathologists, with the limited empirical data to support their choices. In this study, we used the empirical CBSD foliar and root necrosis data from two breeding populations, termed cycle zero (C 0 ) and cycle one (C 1 ), to assess and compare the effectiveness of the CBSD screening methods of breeders vs. pathologists. On the one hand, the estimates of broad-sense heritability ( H 2 ) for the CBSD root necrosis assessment of breeder ranged from 0.15 to 0.87, while for the assessment method of pathologists, H 2 varied from 0.00 to 0.71 in C 0 clones. On the other hand, the marker-based heritability estimates ( h 2 ) for C 0 ranged from 0.00 to 0.70 for the assessment method of breeders and from 0.00 to 0.63 for the assessment method of pathologists. For cycle one (C 1 ) population, where both foliar and root necrosis data were analyzed for clones assessed at clonal evaluation trials (CETs) and advanced yield trials (AYTs), H 2 varied from 0.10 to 0.59 for the assessment method of breeders, while the H 2 values ranged from 0.09 to 0.35 for the CBSD computation method of pathologists. In general, higher correlations were recorded for foliar severity from the assessment method of breeders ( r = 0.4, p ≤ 0.01 for CBSD3s and r = 0.37, p ≤ 0.01 for CBSD6s) in C 1 clones evaluated at both clonal and advanced breeding stages than from the approach of pathologists. Ranking of top 10 C 1 clones by their indexed best linear unbiased predictors (BLUPs) for CBSD foliar and root necrosis showed four overlapping clones between clonal and advanced selection stages for the method of breeders; meanwhile, only a clone featured in both clonal and advanced selection stages from the CBSD assessment method of pathologists. Overall, the CBSD assessment method of breeders was more effective than the assessment method of pathologists, and thus, it justifies its continued use in CBSD resistance breeding.
    Type of Medium: Online Resource
    ISSN: 1664-462X
    URL: Article
    URL: Article
    DOI: 10.3389/fpls.2021.648436
    DOI: 10.3389/fpls.2021.648436.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2687947-5
    detail.hit.zdb_id: 2613694-6
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  • 3
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    Online Resource
    Utility of Ugandan genomic selection cassava breeding populations for prediction of cassava viral disease resistance and yield in West African clones
    Frontiers Media SA ; 2022
    In:  Frontiers in Plant Science Vol. 13 ( 2022-11-23)
    Details
    In: Frontiers in Plant Science, Frontiers Media SA, Vol. 13 ( 2022-11-23)
    Abstract: Cassava ( Manihot esculenta Crantz) is a staple crop for ~800 million people in sub-Saharan Africa. Its production and productivity are being heavily affected by the two viral diseases: cassava brown streak disease (CBSD) and cassava mosaic disease (CMD), impacting greatly on edible root yield. CBSD is currently endemic to central, eastern and southern Africa, if not contained could spread to West Africa the largest cassava producer and consumer in the continent. Genomic selection (GS) has been implemented in Ugandan cassava breeding for accelerated development of virus resistant and high yielding clones. This study leveraged available GS training data in Uganda for pre-emptive CBSD breeding in W. Africa alongside CMD and fresh root yield (FRW). First, we tracked genetic gain through the current three cycles of GS in Uganda. The mean genomic estimated breeding values (GEBVs), indicated general progress from initial cycle zero (C0) to cycle one (C1) and cycle two (C2) for CBSD traits and yield except for CMD. Secondly, we used foliar data of both CBSD and CMD, as well as harvest root necrosis and yield data to perform cross-validation predictions. Cross-validation prediction accuracies of five GS models were tested for each of the three GS cycles and West African (WA) germplasm as a test set. In all cases, cross-validation prediction accuracies were low to moderate, ranging from -0.16 to 0.68 for CBSD traits, -0.27 to 0.57 for CMD and -0.22 to 0.41 for fresh root weight (FRW). Overall, the highest prediction accuracies were recorded in C0 for all traits tested across models and the best performing model in cross-validation was G-BLUP. Lastly, we tested the predictive ability of the Ugandan training sets to predict CBSD in W. African clones. In general, the Ugandan training sets had low prediction accuracies for all traits across models in West African germplasm, varying from -0.18 to 0.1. Based on the findings of this study, the cassava breeding program in Uganda has made progress through application of GS for most target traits, but the utility of the training population for pre-emptive breeding in WA is limiting. In this case, efforts should be devoted to sharing Ugandan germplasm that possess resistance with the W. African breeding programs for hybridization to fully enable deployment of genomic selection as a pre-emptive CBSD breeding strategy in W. Africa.
    Type of Medium: Online Resource
    ISSN: 1664-462X
    URL: Article
    DOI: 10.3389/fpls.2022.1018156
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2687947-5
    detail.hit.zdb_id: 2613694-6
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  • 4
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    Online Resource
    Prospects for Genomic Selection in Cassava Breeding
    Wiley ; 2017
    In:  The Plant Genome Vol. 10, No. 3 ( 2017-11)
    Details
    In: The Plant Genome, Wiley, Vol. 10, No. 3 ( 2017-11)
    Abstract: Cassava ( Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross‐validation within populations, cross‐population prediction and cross‐generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross‐validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross‐population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within‐generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid‐cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one‐third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden.
    Type of Medium: Online Resource
    ISSN: 1940-3372 , 1940-3372
    URL: Article
    DOI: 10.3835/plantgenome2017.03.0015
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 2440458-5
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  • 5
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    Online Resource
    Effectiveness of genomic selection for improving provitamin A carotenoid content and associated traits in cassava
    Oxford University Press (OUP) ; 2021
    In:  G3 Genes|Genomes|Genetics Vol. 11, No. 9 ( 2021-09-06)
    Details
    In: G3 Genes|Genomes|Genetics, Oxford University Press (OUP), Vol. 11, No. 9 ( 2021-09-06)
    Abstract: Global efforts are underway to develop cassava with enhanced levels of provitamin A carotenoids to sustainably meet increasing demands for food and nutrition where the crop is a major staple. Herein, we tested the effectiveness of genomic selection (GS) for rapid improvement of cassava for total carotenoids content and associated traits. We evaluated 632 clones from Uganda’s provitamin A cassava breeding pipeline and 648 West African introductions. At harvest, each clone was assessed for level of total carotenoids, dry matter content, and resistance to cassava brown streak disease (CBSD). All clones were genotyped with diversity array technology and imputed to a set of 23,431 single nucleotide polymorphic markers. We assessed predictive ability of four genomic prediction methods in scenarios of cross-validation, across population prediction, and inclusion of quantitative trait loci markers. Cross-validations produced the highest mean prediction ability for total carotenoids content (0.52) and the lowest for CBSD resistance (0.20), with G-BLUP outperforming other models tested. Across population, predictions showed low ability of Ugandan population to predict the performance of West African clones, with the highest predictive ability recorded for total carotenoids content (0.34) and the lowest for CBSD resistance (0.12) using G-BLUP. By incorporating chromosome 1 markers associated with carotenoids content as independent kernel in the G-BLUP model of a cross-validation scenario, prediction ability slightly improved from 0.52 to 0.58. These results reinforce ongoing efforts aimed at integrating GS into cassava breeding and demonstrate the utility of this tool for rapid genetic improvement.
    Type of Medium: Online Resource
    ISSN: 2160-1836
    URL: Article
    DOI: 10.1093/g3journal/jkab160
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2629978-1
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