Screening of triploid banana population under natural and controlled black sigatoka disease for genomic selection

Mbo Nkoulou, L. F., Fifen Nkouandou, Y., Bille Ngalle, H., Cros, D., Martin, G., Molo, T., Clement Eya’a, Charles Essome, Martine Zandjanakou-Tachin, Hervé Degbey, Joseph Bell, and Enoch G. Achigan-Dako (2024). Screening of triploid banana population under natural and controlled black sigatoka disease for genomic selection. Plant Dis., PDIS-04-23-0741-RE. doi: https://doi.org/10.1094/PDIS-04-23-0741-RE

Black sigatoka disease (BSD) is the most important foliar threat in banana production, and breeding efforts against it should take advantage of genomic selection (GS), which has become one of the most explored tools to increase genetic gain, save time, and reduce selection costs. To evaluate the potential of GS in banana for BSD, 210 triploid accessions were obtained from the African Banana and Plantain Research Centre to constitute a training population. The variability in the population was assessed at the phenotypic level using BSD- and agronomic-related traits and at the molecular level using single-nucleotide polymorphisms (SNPs). The analysis of variance showed a significant difference between accessions for almost all traits measured, although at the genomic group level, there was no significant difference for BSD-related traits. The index of non-spotted leaves among accessions ranged from 0.11 to 0.8. The

accessions screening in controlled conditions confirmed the susceptibility of all genomic groups to BSD. The principal components analysis with phenotypic data revealed no clear diversity partition of the population. However, the structure analysis and the hierarchical clustering analysis with SNPs grouped the population into four clusters and two subpopulations, respectively. The field and laboratory screening of the banana GS training population confirmed that all genomic groups are susceptible to BSD but did not reveal any genetic structure, whereas SNP markers exhibited clear genetic structure and provided useful information in the perspective of applying GS.

The phenotypic variation of growth, yield and BSD-related traits among genomic groups in the population

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