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

International audienceGenomic 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

Within-family genomic selection in rubber tree (Hevea brasiliensis) increases genetic gain for rubber production

International audienceGenomic selection (GS) could make more efficient the two-stage phenotypic breeding scheme used for rubber production in Hevea brasiliensis. It was evaluated using two trials in Cote d'Ivoire comprising 189 and 143 clones of the cross PB 260 x RRIM 600, genotyped with 332 simple sequence repeat markers. The effect of statistical genomic prediction methods, training size, and marker data on GS accuracy was investigated when predicting unobserved clone production within and between sites. Simulations using these empirical data assessed the efficiency of replacing current first stage of phenotypic selection (evaluation of seedling phenotype) by genomic preselection, prior to clone trials. Genomic selection accuracy in between-site validations using all clones for training and all markers was 0.53. Marker density and training size strongly affected accuracy, but 300 markers were sufficient and using more than 175 training clones would have marginally improved accuracy. Using the 125-200 markers with the highest heterozygosity, between-site GS accuracy reached 0.56. Prediction methods did not affect GS accuracy. Simulations showed that genomic preselection on 3000 seedlings of the considered cross would have increased selection response for rubber production by 10.3%. Hevea breeding programs can be optimized by the use of within-family GS. Further studies considering other crosses and traits, consecutive breeding cycles, more contrasted environments, and cost-benefit ratio are required