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Genome-wide association mapping and genomic prediction of anther extrusion in CIMMYT hybrid wheat breeding program via modeling pedigree, genomic relationship, and interaction with the environment

Creator: Adhikari, A.
Creator: Basnet, B.R.
Creator: Crossa, J.
Creator: Dreisigacker, S.
Creator: Camarillo-Castillo, F.
Creator: Bhati, P.K.
Creator: Jarquin, D.
Creator: Yann Manes
Creator: Ibrahim, A.M.H.
Year: 2020
Language: English
Publisher: Frontiers Media
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Type: Article
Place of Publication: Switzerland
Volume: 11
DOI: 10.3389/fgene.2020.586687
Keywords: GWAS
Keywords: Anther Extrusion
Keywords: Floral Traits
Keywords: Hybrid Wheat
Keywords: Genome-Wide Prediction
Description: Anther extrusion (AE) is the most important male floral trait for hybrid wheat seed production. AE is a complex quantitative trait that is difficult to phenotype reliably in field experiments not only due to high genotype-by-environment effects but also due to the short expression window in the field condition. In this study, we conducted a genome-wide association scan (GWAS) and explored the possibility of applying genomic prediction (GP) for AE in the CIMMYT hybrid wheat breeding program. An elite set of male lines (n = 603) were phenotype for anther count (AC) and anther visual score (VS) across three field experiments in 2017–2019 and genotyped with the 20K Infinitum is elect SNP array. GWAS produced five marker trait associations with small effects. For GP, the main effects of lines (L), environment (E), genomic (G) and pedigree relationships (A), and their interaction effects with environments were used to develop seven statistical models of incremental complexity. The base model used only L and E, whereas the most complex model included L, E, G, A, and G × E and A × E. These models were evaluated in three cross-validation scenarios (CV0, CV1, and CV2). In cross-validation CV0, data from two environments were used to predict an untested environment; in random cross-validation CV1, the test set was never evaluated in any environment; and in CV2, the genotypes in the test set were evaluated in only a subset of environments. The prediction accuracies ranged from −0.03 to 0.74 for AC and −0.01 to 0.54 for VS across different models and CV schemes. For both traits, the highest prediction accuracies with low variance were observed in CV2, and inclusion of the interaction effects increased prediction accuracy for AC only. In CV0, the prediction accuracy was 0.73 and 0.45 for AC and VS, respectively, indicating the high reliability of across environment prediction. Genomic prediction appears to be a very reliable tool for AE in hybrid wheat breeding. Moreover, high prediction accuracy in CV0 demonstrates the possibility of implementing genomic selection across breeding cycles in related germplasm, aiding the rapid breeding cycle.
Agrovoc: ANTHERS
Agrovoc: WHEAT
Agrovoc: HYBRIDS
Agrovoc: GENOMES
Related Datasets:
Related Datasets:
ISSN: 1664-8021
Journal: Frontiers in Genetics
Article number: 586687

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This item appears in the following Collection(s)

  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP
  • Wheat
    Wheat - breeding, phytopathology, physiology, quality, biotech

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