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    Relatedness and genotype x environment interaction affect prediction accuracies in genomic selection: a study in cassava

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    U13ArtLyRelatednessInthomDev.pdf (1.310Mb)
    Date
    2013-07
    Author
    Ly, D.
    Hamblin, M.
    Rabbi, Ismail Y
    Gedil, M.
    Bakare, M.A.
    Gauch Jr. H.
    Okechukwu, R.U.
    Dixon, A.
    Kulakow, P.A.
    Jannink, Jean-Luc
    Type
    Journal Article
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    Abstract/Description
    Before implementation of genomic selection, evaluation of the potential accuracy of prediction can be obtained by cross-validation. In this procedure, a population with both phenotypes and genotypes is split into training and validation sets. The prediction model is fitted using the training set, and its accuracy is calculated on the validation set. The degree of genetic relatedness between the training and validation sets may influence the expected accuracy as may the genotype × environment (G×E) interaction in those sets. We developed a method to assess these effects and tested it in cassava (Manihot esculenta Crantz). We used historical phenotypic data available from the International Institute of Tropical Agriculture Genetic Gain trial and performed genotyping by sequencing for these clones. We tested cross-validation sampling schemes preventing the training and validation sets from sharing (i) genetically close clones or (ii) similar evaluation locations. For 19 traits, plot-basis heritabilities ranged from 0.04 to 0.66. The correlation between predicted and observed phenotypes ranged from 0.15 to 0.47. Across traits, predicting for less related clones decreased accuracy from 0 to 0.07, a small but consistent effect. For 17 traits, predicting for different locations decreased accuracy between 0.01 and 0.18. Genomic selection has potential to accelerate gains in cassava and the existing training population should give a reasonable estimate of future prediction accuracies.
    https://dx.doi.org/10.2135/cropsci2012.11.0653
    Multi standard citation
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/1270
    Digital Object Identifier (DOI)
    https://dx.doi.org/10.2135/cropsci2012.11.0653
    IITA Subjects
    Cassava
    Agrovoc Terms
    Phenotypes; Genotypes; Cassava
    Regions
    Africa; West Africa
    Countries
    Nigeria
    Journals
    Crop Science
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    • Journal and Journal Articles4835
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