Trait variation and genetic diversity in a banana genomic selection training population

Stability of fruit ripening traits of banana (Musa species) across postharvest environments

Banana (Musa species) is the fourth most important export crop worldwide after cereals, oil crops and sugar. In spite of this socio-economic significance, the crop suffers massive postharvest losses caused by mechanical fruit damage, limited infrastructure for fruit ripening, postharvest diseases and physiological disorders. Although use of optimum postharvest environments such as packaging and storage temperatures can reduce fruit loss and improve ripening quality; information regarding the interaction between varieties and postharvest environments and stability of fruit ripening traits across postharvest environments is limited. The objectives of this study were to determine the magnitude of interaction of varieties with postharvest environments on fruit ripening traits and to identify stable banana variety for ripening across postharvest environments. Seven commonly grown banana varieties (Dwarf Cavendish, William I, Grand Naine, Poyu, Giant Cavendish, Butazu and Local variety) were laid out in a completely randomized design with five replications in ten varied postharvest environments. The result indicated that pulp and peel ratio had negative high principal component one (PCA1) score whereas the PCA1 score for postharvest period, peel weight and fruit weight were positive and high. Cluster analysis grouped Dwarf Cavendish and Grand Naine; Poyu and Butazu varieties together for postharvest traits whereas the local variety was clustered separately. This study has demonstrated that hybridization of local with the introduced varieties can be done to improve postharvest traits. AMMI depicted significant variation for genotype, postharvest environments and their interactions for all traits. The magnitude of environmental effect was higher than the genotype and interaction effects. AMMI and GGE biplot analyses identified Gran Naine, Poyu and William I as consistent for ripening traits across postharvest environments.

Genomic selection for crop improvement in fruits and vegetables: a systematic scoping review

To ensure the nutritional needs of an expanding global population, it is crucial to optimize the growing capabilities and breeding values of fruit and vegetable crops. While genomic selection, initially implemented in animal breeding, holds tremendous potential, its utilization in fruit and vegetable crops remains underexplored. In this systematic review, we reviewed 63 articles covering genomic selection and its applications across 25 different types of fruit and vegetable crops over the last decade. The traits examined were directly related to the edible parts of the crops and carried significant economic importance. Comparative analysis with WHO/FAO data identified potential economic drivers underlying the study focus of some crops and highlighted crops with potential for further genomic selection research and application. Factors affecting genomic selection accuracy in fruit and vegetable studies are discussed and suggestions made to assist in their implementation into plant breeding schemes. Genetic gain in fruits and vegetables can be improved by utilizing genomic selection to improve selection intensity, accuracy, and integration of genetic variation. However, the reduction of breeding cycle times may not be beneficial in crops with shorter life cycles such as leafy greens as compared to fruit trees. There is an urgent need to integrate genomic selection methods into ongoing breeding programs and assess the actual genomic estimated breeding values of progeny resulting from these breeding programs against the prediction models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01497-2.

Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop

Phenotypic selection of complex traits such as seed yield and protein in the preliminary yield trial (PYT) is often constrained by limited seed availability, resulting in trials with few environments and minimal to no replications. Multi-trait multi-environment enabled genomic prediction (MTME-GP) offers a valuable alternative to predict missing phenotypes of selection candidates for multiple traits and diverse environments. In this study, we assessed the efficiency of MTME-GP for improving seed protein and seed yield in field pea, the top two breeding targets but highly antagonistic traits in pulse crop. We utilized a set of 300 selection candidates in the PYT that virtually represented all possible families of the North Dakota State University field pea breeding program. Selection candidates were evaluated in three diverse, contrasting environments, as indicated by a range of heritability. Using whole- and split-environment cross validation schemes, MTME-GP had higher predictive ability than a standard additive G-BLUP model. Integrating a range of overlapping genotypes in between environments showed improvement on the predictive ability of the MTME-GP model but tends to plateau at 50%-80% training set size. Regardless of the cross-validation scheme, accuracy was among the lowest in stressed environments, presumably due to low heritability for seed protein and yield. This study provided insights into the potential of MTME-GP in a public pulse crop breeding program. The MTME-GP framework can be further improved with more testing environments and integration of additional orthogonal information in the early stages of the breeding pipeline.

Genetic diversity and population structure in banana (Musa spp.) breeding germplasm

Bananas (Musa spp.) are one of the most highly consumed fruits globally, grown in the tropical and sub-tropical regions. We evaluated 856 Musa accessions from the breeding programs of the International Institute of Tropical Agriculture of Nigeria, Tanzania, and Uganda; the National Agricultural Research Organization of Uganda; the Brazilian Agricultural Research Corporation (Embrapa); and the National Research Centre for Banana of India. Accessions from the in vitro gene bank at the International Transit Centre in Belgium were included to provide a baseline of available global diversity. A total of 16,903 informative single nucleotide polymorphism markers were used to estimate and characterize the genetic diversity and population structure and identify overlaps and unique material among the breeding programs. Analysis of molecular variance displayed low genetic variation among accessions and diploids and a higher variation among tetraploids (p < 0.001). Structure analysis revealed two major clusters corresponding to genomic composition. The results indicate that there is potential for the banana breeding programs to increase the diversity in their breeding materials and should exploit this potential for parental improvement and to enhance genetic gains in future breeding efforts.

Striking variation in chromosome structure within Musa acuminata subspecies, diploid cultivars, and F1 diploid hybrids

The majority of cultivated bananas originated from inter- and intra(sub)specific crosses between two wild diploid species, Musa acuminata and Musa balbisiana. Hybridization and polyploidization events during the evolution of bananas led to the formation of clonally propagated cultivars characterized by a high level of genome heterozygosity and reduced fertility. The combination of low fertility in edible clones and differences in the chromosome structure among M. acuminata subspecies greatly hampers the breeding of improved banana cultivars. Using comparative oligo-painting, we investigated large chromosomal rearrangements in a set of wild M. acuminata subspecies and cultivars that originated from natural and human-made crosses. Additionally, we analyzed the chromosome structure of F1 progeny that resulted from crosses between Mchare bananas and the wild M. acuminata 'Calcutta 4' genotype. Analysis of chromosome structure within M. acuminata revealed the presence of a large number of chromosomal rearrangements showing a correlation with banana speciation. Chromosome painting of F1 hybrids was complemented by Illumina resequencing to identify the contribution of parental subgenomes to the diploid hybrid clones. The balanced presence of both parental genomes was revealed in all F1 hybrids, with the exception of one clone, which contained only Mchare-specific SNPs and thus most probably originated from an unreduced diploid gamete of Mchare.

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.

Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach

Maize (Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize.

A novel SNP panel developed for targeted genotyping-by-sequencing (GBS) reveals genetic diversity and population structure of Musa spp. germplasm collection

The Philippines is situated in the geographic region regarded as the center of diversity of banana and its wild relatives (Musa spp.). It holds the most extensive collection of B-genome germplasm in the world along with A-genome groups and several natural hybrids with A- and B-genome combinations. Management of this germplasm resource has relied immensely on identification using local names and morphological characters, and the extent of genetic diversity of the collection has not been achieved with molecular markers. A high-throughput and reliable genotyping method for banana and its relatives will facilitate germplasm management and support breeding initiatives toward a marker-based approach. Here, we developed a 1 K SNP genotyping panel based on filtering of high-quality genome-wide SNPs from the Musa Germplasm Information System and used it to assess the genetic diversity and population structure of 183 accessions from a Musa spp. germplasm collection containing Philippine and foreign accessions. Targeted GBS using SeqSNP™ technology generated 70,376,284 next-generation sequencing (NGS) reads with an average effective target SNP coverage of 340 × . Bioinformatics pipeline revealed 971 polymorphic SNPs containing 76.9% homozygous calls, 23.1% heterozygous calls and 4% with missing data. A final set of 952 SNPs detected 2,092 alleles. Pairwise genetic distance varied from 0.0021 to 0.3325 with most pairs of accessions distinguished with 250 to 300 loci. The SNP panel was able to detect seven (k = 7) genetically differentiated groups and its composition through Principal Component Analysis (PCA) with k-means clustering algorithm and Discriminant Analysis of Principal Components (DAPC). Accession-specific SNPs were also identified. The 1 K SNP panel effectively distinguishes between genomic groups and provides relatively good resolution of genome-wide nucleotide diversity of Musa spp. This panel is recommended for low-density genotyping for application in marker-assisted breeding and germplasm management, and could be further enhanced to increase marker density for other applications like genetic association and genomic selection in bananas.

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.

The banana genome hub: a community database for genomics in the Musaceae

The Banana Genome Hub provides centralized access for genome assemblies, annotations, and the extensive related omics resources available for bananas and banana relatives. A series of tools and unique interfaces are implemented to harness the potential of genomics in bananas, leveraging the power of comparative analysis, while recognizing the differences between datasets. Besides effective genomic tools like BLAST and the JBrowse genome browser, additional interfaces enable advanced gene search and gene family analyses including multiple alignments and phylogenies. A synteny viewer enables the comparison of genome structures between chromosome-scale assemblies. Interfaces for differential expression analyses, metabolic pathways and GO enrichment were also added. A catalogue of variants spanning the banana diversity is made available for exploration, filtering, and export to a wide variety of software. Furthermore, we implemented new ways to graphically explore gene presence-absence in pangenomes as well as genome ancestry mosaics for cultivated bananas. Besides, to guide the community in future sequencing efforts, we provide recommendations for nomenclature of locus tags and a curated list of public genomic resources (assemblies, resequencing, high density genotyping) and upcoming resources-planned, ongoing or not yet public. The Banana Genome Hub aims at supporting the banana scientific community for basic, translational, and applied research and can be accessed at https://banana-genome-hub.southgreen.fr.

The Small Secreted Protein FoSsp1 Elicits Plant Defenses and Negatively Regulates Pathogenesis in Fusarium oxysporum f. sp. cubense (Foc4)

Fusarium wilt of banana (Musa spp.), a typical vascular wilt disease caused by the soil-borne fungus, Fusarium oxysporum f. sp. cubense race 4 (Foc4), seriously threatens banana production worldwide. Pathogens, including vascular wilt fungi, secrete small cysteine-rich proteins during colonization. Some of these proteins are required for pathogenicity. In this study, 106 small secretory proteins that contain a classic N-terminal signal peptide were identified using bioinformatic methods in Foc4. Among them, 11 proteins were selected to show transient expressions in tobacco. Interestingly, transient expression of FoSsp1 in tobacco, an uncharacterized protein (of 145 aa), induced necrotic cell death reactive oxygen burst, and callous deposition. Furthermore, the expression of FoSSP1 in Foc4 wild type (WT) was up-regulated during the stage of banana roots colonization. A split-marker approach was used to knock out FoSSP1 in the Foc4 WT strain. Compared with the WT, the deletion mutant Fossp1 was normal in growth rate but increased in conidiation and virulence. RT-qPCR analysis showed that the expression of four conidiation regulator genes in the Fossp1 deletion mutant was significantly decreased compared to the WT strain. In addition, the expression of four pathogenesis-related genes of bananas infected with Fossp1 deletion mutant was down-regulated in comparison with that of the WT. In summary, these results suggested that FoSSP1 is a putative elicitor that negatively regulates conidiation and pathogenicity in Foc4.

Draft Sequencing Crested Wheatgrass Chromosomes Identified Evolutionary Structural Changes and Genes and Facilitated the Development of SSR Markers

Crested wheatgrass (Agropyron cristatum), a wild relative of wheat, is an attractive source of genes and alleles for their improvement. Its wider use is hampered by limited knowledge of its complex genome. In this work, individual chromosomes were purified by flow sorting, and DNA shotgun sequencing was performed. The annotation of chromosome-specific sequences characterized the DNA-repeat content and led to the identification of genic sequences. Among them, genic sequences homologous to genes conferring plant disease resistance and involved in plant tolerance to biotic and abiotic stress were identified. Genes belonging to the important groups for breeders involved in different functional categories were found. The analysis of the DNA-repeat content identified a new LTR element, Agrocen, which is enriched in centromeric regions. The colocalization of the element with the centromeric histone H3 variant CENH3 suggested its functional role in the grass centromere. Finally, 159 polymorphic simple-sequence-repeat (SSR) markers were identified, with 72 of them being chromosome- or chromosome-arm-specific, 16 mapping to more than one chromosome, and 71 mapping to all the Agropyron chromosomes. The markers were used to characterize orthologous relationships between A. cristatum and common wheat that will facilitate the introgression breeding of wheat using A. cristatum.

Genomic prediction models for traits differing in heritability for soybean, rice, and maize

BACKGROUND

Genomic selection is a powerful tool in plant breeding. By building a prediction model using a training set with markers and phenotypes, genomic estimated breeding values (GEBVs) can be used as predictions of breeding values in a target set with only genotype data. There is, however, limited information on how prediction accuracy of genomic prediction can be optimized. The objective of this study was to evaluate the performance of 11 genomic prediction models across species in terms of prediction accuracy for two traits with different heritabilities using several subsets of markers and training population proportions. Species studied were maize (Zea mays, L.), soybean (Glycine max, L.), and rice (Oryza sativa, L.), which vary in linkage disequilibrium (LD) decay rates and have contrasting genetic architectures.

RESULTS

Correlations between observed and predicted GEBVs were determined via cross validation for three training-to-testing proportions (90:10, 70:30, and 50:50). Maize, which has the shortest extent of LD, showed the highest prediction accuracy. Amongst all the models tested, Bayes B performed better than or equal to all other models for each trait in all the three crops. Traits with higher broad-sense and narrow-sense heritabilities were associated with higher prediction accuracy. When subsets of markers were selected based on LD, the accuracy was similar to that observed from the complete set of markers. However, prediction accuracies were significantly improved when using a subset of total markers that were significant at P ≤ 0.05 or P ≤ 0.10. As expected, exclusion of QTL-associated markers in the model reduced prediction accuracy. Prediction accuracy varied among different training population proportions.

CONCLUSIONS

We conclude that prediction accuracy for genomic selection can be improved by using the Bayes B model with a subset of significant markers and by selecting the training population based on narrow sense heritability.

The East African highland cooking bananas 'Matooke' preferences of farmers and traders: Implications for variety development

'Matooke' is a staple food made from Highland cooking bananas in the Great Lakes region of East Africa. Genetic improvement of these bananas for resistance to pests and diseases has been a priority breeding objective. However, there is insufficient information on fruit quality characteristics that different users prefer, resulting in sub-optimal adoption of new varieties. This study identified matooke characteristics preferred by farmers and traders, using survey data from 123 farmers, 14 focus group discussions and 40 traders. Gender differences were considered. The main characteristics that were found to drive variety preferences were agronomic (big bunch, big fruits) and quality (soft texture, good taste, good aroma, yellow food). There were minimal geographical and gender differences for trait preferences. Quality characteristics need to be defined in terms of physical-chemical underpinnings so that breeding programmes can apply accurate high-throughput systems, thereby improving adoption and impact of new banana varieties.

Chromosome Painting in Cultivated Bananas and Their Wild Relatives (Musa spp.) Reveals Differences in Chromosome Structure

Edible banana cultivars are diploid, triploid, or tetraploid hybrids, which originated by natural cross hybridization between subspecies of diploid Musa acuminata, or between M. acuminata and diploid Musa balbisiana. The participation of two other wild diploid species Musa schizocarpa and Musa textilis was also indicated by molecular studies. The fusion of gametes with structurally different chromosome sets may give rise to progenies with structural chromosome heterozygosity and reduced fertility due to aberrant chromosome pairing and unbalanced chromosome segregation. Only a few translocations have been classified on the genomic level so far, and a comprehensive molecular cytogenetic characterization of cultivars and species of the family Musaceae is still lacking. Fluorescence in situ hybridization (FISH) with chromosome-arm-specific oligo painting probes was used for comparative karyotype analysis in a set of wild Musa species and edible banana clones. The results revealed large differences in chromosome structure, discriminating individual accessions. These results permitted the identification of putative progenitors of cultivated clones and clarified the genomic constitution and evolution of aneuploid banana clones, which seem to be common among the polyploid banana accessions. New insights into the chromosome organization and structural chromosome changes will be a valuable asset in breeding programs, particularly in the selection of appropriate parents for cross hybridization.

Metabolite database for root, tuber, and banana crops to facilitate modern breeding in understudied crops

Roots, tubers, and bananas (RTB) are vital staples for food security in the world's poorest nations. A major constraint to current RTB breeding programmes is limited knowledge on the available diversity due to lack of efficient germplasm characterization and structure. In recent years large-scale efforts have begun to elucidate the genetic and phenotypic diversity of germplasm collections and populations and, yet, biochemical measurements have often been overlooked despite metabolite composition being directly associated with agronomic and consumer traits. Here we present a compound database and concentration range for metabolites detected in the major RTB crops: banana (Musa spp.), cassava (Manihot esculenta), potato (Solanum tuberosum), sweet potato (Ipomoea batatas), and yam (Dioscorea spp.), following metabolomics-based diversity screening of global collections held within the CGIAR institutes. The dataset including 711 chemical features provides a valuable resource regarding the comparative biochemical composition of each RTB crop and highlights the potential diversity available for incorporation into crop improvement programmes. Particularly, the tropical crops cassava, sweet potato and banana displayed more complex compositional metabolite profiles with representations of up to 22 chemical classes (unknowns excluded) than that of potato, for which only metabolites from 10 chemical classes were detected. Additionally, over 20% of biochemical signatures remained unidentified for every crop analyzed. Integration of metabolomics with the on-going genomic and phenotypic studies will enhance 'omics-wide associations of molecular signatures with agronomic and consumer traits via easily quantifiable biochemical markers to aid gene discovery and functional characterization.

Association genetics of bunch weight and its component traits in East African highland banana (Musa spp. AAA group)

The major quantitative trait loci associated with bunch weight and its component traits in the East African highland banana-breeding population are located on chromosome 3. Bunch weight increase is one of the major objectives of banana improvement programs, but little is known about the loci controlling bunch weight and its component traits. Here we report for the first time some genomic loci associated with bunch weight and its component traits in banana as revealed through a genome-wide association study. A banana-breeding population of 307 genotypes varying in ploidy was phenotyped in three locations under different environmental conditions, and data were collected on bunch weight, number of hands and fruits; fruit length and circumference; and diameter of both fruit and pulp for three crop cycles. The population was genotyped with genotyping by sequencing and 27,178 single nucleotide polymorphisms (SNPs) were generated. The association between SNPs and the best linear unbiased predictors of traits was performed with TASSEL v5 using a mixed linear model accounting for population structure and kinship. Using Bonferroni correction, false discovery rate, and long-range linkage disequilibrium (LD), 25 genomic loci were identified with significant SNPs and most were localized on chromosome 3. Most SNPs were located in genes encoding uncharacterized and hypothetical proteins, but some mapped to transcription factors and genes involved in cell cycle regulation. Inter-chromosomal LD of SNPs was present in the population, but none of the SNPs were significantly associated with the traits. The clustering of significant SNPs on chromosome 3 supported our hypothesis that fruit filling in this population was under control of a few quantitative trait loci with major effects.

Crossbreeding East African Highland Bananas: Lessons Learnt Relevant to the Botany of the Crop After 21 Years of Genetic Enhancement

East African highland bananas (EAHB) were regarded as sterile. Their screening for female fertility with "Calcutta 4" as male parent revealed that 37 EAHB were fertile. This was the foundation for the establishment of the EAHB crossbreeding programs by the International Institute of Tropical Agriculture (IITA) and the National Agricultural Research Organization (NARO) in Uganda in the mid-1990s. The aim of this study was to assess the progress and efficiency of the EAHB breeding program at IITA, Sendusu in Uganda. Data on pollinations, seeds generated and germinated, plus hybrids selected between 1995 and 2015 were analyzed. Pollination success and seed germination percentages for different cross combinations were calculated. The month of pollination did not result in significantly different (P = 0.501) pollination success. Musa acuminata subsp. malaccensis accession 250 had the highest pollination success (66.8%), followed by the cultivar "Rose" (66.6%) among the diploid males. Twenty-five EAHB out of 41 studied for female fertility produced up to 305 seeds per pollinated bunch, and were therefore deemed fertile. The percentage of seed germination varied among crosses: 26% for 2x × 4x, 23% for 2x × 2x, 11% for 3x × 2x, and 7% for 4x × 2x. Twenty-seven NARITA hybrids (mostly secondary triploids ensuing from the 4x × 2x) were selected for further evaluation in the East African region. One so far -"NARITA 7"- was officially released to farmers in Uganda. Although pollination of EAHB can be conducted throughout the year, the seed set and germination is low. Thus, further research on pollination conditions and optimization of embryo culture protocols should be done to boost seed set and embryo germination, respectively. More research in floral biology and seed germination as well as other breeding strategies are required to increase the efficiency of the EAHB breeding program.

Effects of In Vitro Polyploidization on Agronomic Characteristics and Fruit Carotenoid Content; Implications for Banana Genetic Improvement

Bananas (Musa spp.), native to South East Asia, have spread worldwide and are integrated into the diets of millions of people in tropical regions. Carotenoid content varies dramatically between different banana genotypes, providing an opportunity for vitamin A biofortification. Polyploidization is a useful tool for crop improvement with potential to generate new diversity, especially in a polyploid crop like bananas. Ten induced tetraploids generated from six diploid banana genotypes were evaluated for their agronomic attributes and fruit carotenoid content in comparison to their diploid progenitors. Tetraploids had distinct plant morphology, but generally displayed inferior vegetative and yield characteristics with 20% lower bunch weights than their original diploids. Similarly, a 50% decrease in fruit provitamin A carotenoids (α-carotene, 13-cis β-carotene, 9-cis β-carotene, trans-β-carotene) accompanied by a corresponding increase in lutein was recorded in induced tetraploids in comparison to their original diploids. Additionally, all lines were subjected to pollen viability tests to assess their fertility. Pollen viability tests indicated over 70% viability for induced tetraploids and diploid controls, suggesting their possible use in crosses. These findings provide a basis for the application of induced polyploidization in bananas to generate useful genetic material for integration in hybridization programmes aiming to produce vitamin A enriched triploids valuable to malnourished populations.

Genomic Prediction in a Multiploid Crop: Genotype by Environment Interaction and Allele Dosage Effects on Predictive Ability in Banana

Improving the efficiency of selection in conventional crossbreeding is a major priority in banana ( spp.) breeding. Routine application of classical marker assisted selection (MAS) is lagging in banana due to limitations in MAS tools. Genomic selection (GS) based on genomic prediction models can address some limitations of classical MAS, but the use of GS in banana has not been reported to date. The aim of this study was to evaluate the predictive ability of six genomic prediction models for 15 traits in a multi-ploidy training population. The population consisted of 307 banana genotypes phenotyped under low and high input field management conditions for two crop cycles. The single nucleotide polymorphism (SNP) markers used to fit the models were obtained from genotyping by sequencing (GBS) data. Models that account for additive genetic effects provided better predictions with 12 out of 15 traits. The performance of BayesB model was superior to other models particularly on fruit filling and fruit bunch traits. Models that included averaged environment data were more robust in trait prediction even with a reduced number of markers. Accounting for allele dosage in SNP markers (AD-SNP) reduced predictive ability relative to traditional bi-allelic SNP (BA-SNP), but the prediction trend remained the same across traits. The high predictive values (0.47- 0.75) of fruit filling and fruit bunch traits show the potential of genomic prediction to increase selection efficiency in banana breeding.