Screening of Triploid Banana Population Under Natural and Controlled Black Sigatoka Disease for Genomic Selection.

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 Center 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.

Analysis of genetic diversity and agronomic variation in banana sub-populations for genomic selection under drought stress in southern Benin.

In the perspective of investigating genomic selection (GS) among Musa genotypes in West and Central Africa, banana accessions were phenotyped under natural drought stress in Benin and genotyped using genotyping by sequencing. Sixty-one (61) accessions grouped into three major genomic groups AAA, AAB and ABB and those without genomic affiliation information were used. Variation within the population was determined by phenotypic variables while population structure and clustering analysis were carried out to understand the genetic diversity at the molecular level. Among the genomic groups evaluated, the group AAB showed the best performance for fruit weight at maturity, (3.41 ± 1.99 kg) and for plant height (198.46 ± 12.66 cm). At the accession level, HD 117 S1 and NIA 27 showed the best plant height (263.16 ± 20.98 cm) and the best fruit weight at maturity (9.43 ± 0.0 kg) respectively. Phenotypic data did not reveal clear genetic diversity among accessions; however, the genetic diversity was conspicuous at the molecular level using 5000 markers. The affiliations of local accessions in genomic groups were determined for the first time based on the phenotypic and molecular data obtained in this study. The knowledge generated allows the possibility to apply GS in banana.

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

Genomic 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.