Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species

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AuteurNKOULOU, LUTHER
AuteurCROS, DAVID
AuteurADJE, CHARLOTTE ABIKE
AuteurFASSINOU HOTEGNI, VODJO NICODEME
AuteurBELL, JOSEPH
AuteurACHIGAN DAKO, ENOCH GBENATO
AuteurNGALLE, HERMINE
Date d'ajout2026-06-02T16:06:57Z
Date de disponibilite2026-06-02T16:06:57Z
Date de publication2022
ResumeGenomic selection (GS) in plant breeding is explored as a promising tool to solve the problems related to the biotic and abiotic threats. Polyploid plants like bananas (Musa spp.) face the problem of drought and black sigatoka disease (BSD) that restrict their production. The conventional plant breeding is experiencing difficulties, particularly phenotyping costs and long generation interval. To overcome these difficulties, GS in plant breeding is explored as an alternative with a great potential for reducing costs and time in selection process. So far, GS does not have the same success in polyploid plants as with diploid plants because of the complexity of their genome. In this review, we present the main constraints to the application of GS in polyploid plants and the prospects for overcoming these constraints. Particular emphasis is placed on breeding for BSD and drought—two major threats to banana production—used in this review as a model of polyploid plant. It emerges that the difficulty in obtaining markers of good quality in polyploids is the first challenge of GS on polyploid plants, because the main tools used were developed for diploid species. In addition to that, there is a big challenge of mastering genetic interactions such as dominance and epistasis effects as well as the genotype by environment interaction, which are very common in polyploid plants. To get around these challenges, we have presented bioinformatics tools, as well as artificial intelligence approaches, including machine learning. Furthermore, a scheme for applying GS to banana for BSD and drought has been proposed. This review is of paramount impact for breeding programs that seek to reduce the selection cycle of polyploids despite the complexity of their genome.
DOI10.3389/fpls.2022.953133
Autre identifiantBECDB-16537
URIhttps://dspace.uac.bj/handle/123456789/13882
Languefr
Fait partie deFrontiers in Plant Science
SujetMusa spp
Sujetpolyploid crops
Sujetgenomic selection
Sujetblack sigatoka disease
Sujetdrought
Sujetplant breeding
TitrePerspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species
TypeArticle

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