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    Genomic prediction in a multiploid crop: genotype by environment interaction and allele dosage effects on predictive ability in banana

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    U18ArtNyineGenomicInthomNodev.pdf (1.401Mb)
    Date
    2018
    Author
    Nyine, M.
    Uwimana, B.
    Blavet, N.
    Hřibová, E.
    Vanrespaille, H.
    Batte, M.
    Akech, V.
    Brown, A.
    Lorenzen, J.H.
    Swennen, R.L.
    Doležel, J.
    Type
    Journal Article
    Target Audience
    Scientists
    Metadata
    Show full item record
    Abstract/Description
    Improving the efficiency of selection in conventional crossbreeding is a major priority in banana (Musa spp.) breeding. Routine application of classical marker assisted selection (MAS) is lagging in banana due to limitations in MAS tools. Genomic selection (GS) based on genomic prediction models can address some limitations of classical MAS, but the use of GS in banana has not been reported to date. The aim of this study was to evaluate the predictive ability of six genomic prediction models for 15 traits in a multi-ploidy training population. The population consisted of 307 banana genotypes phenotyped under low and high input field management conditions for two crop cycles. The single nucleotide polymorphism (SNP) markers used to fit the models were obtained from genotyping by sequencing (GBS) data. Models that account for additive genetic effects provided better predictions with 12 out of 15 traits. The performance of BayesB model was superior to other models particularly on fruit filling and fruit bunch traits. Models that included averaged environment data were more robust in trait prediction even with a reduced number of markers. Accounting for allele dosage in SNP markers (AD-SNP) reduced predictive ability relative to traditional bi-allelic SNP (BA-SNP), but the prediction trend remained the same across traits. The high predictive values (0.47– 0.75) of fruit filling and fruit bunch traits show the potential of genomic prediction to increase selection efficiency in banana breeding.
    http://dx.doi.org/10.3835/plantgenome2017.10.0090
    Multi standard citation
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/3199
    Non-IITA Authors ORCID
    Moses Nyinehttps://orcid.org/0000-0002-8409-7588
    Brigitte Uwimanahttps://orcid.org/0000-0001-7460-9001
    Michael Battehttps://orcid.org/0000-0002-6793-2967
    Allen Brownhttps://orcid.org/0000-0002-4468-5932
    Jim Lorenzenhttps://orcid.org/0000-0001-7930-5752
    Rony Swennenhttps://orcid.org/0000-0002-5258-9043
    Jaroslav Dolezelhttps://orcid.org/0000-0002-6263-0492
    Digital Object Identifier (DOI)
    http://dx.doi.org/10.3835/plantgenome2017.10.0090
    Research Themes
    BIOTECH & PLANT BREEDING
    IITA Subjects
    Banana; Genetic Improvement; Plant Breeding; Plant Genetic Resources; Plant Production
    Agrovoc Terms
    Bananas; Musa; Genotypes; Genomic Prediction; Genotype By Environment Interaction; Allele Dosage
    Regions
    Africa; East Africa
    Countries
    Uganda
    Journals
    Plant Genome
    Collections
    • Journal and Journal Articles4463
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