ArticlePloS one2017
Trait variation and genetic diversity in a banana genomic selection training population.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 63 citations in OpenAlex.
- Unveiling the potential of banana (Plant signaling & behavior · 2026Review
- Integrating genomic additive relationship matrices improves the efficiency in diploid banana breeding.Horticulture research · 2026Article
- Banana breeding by genome design.Journal of integrative plant biology · 2025Review
- Combining ability of banana triploid hybrid progenitors and genomic prediction of cross performance for agro-morphological traits.Genetics · 2025Article
- Genome-wide association for agro-morphological traits in a triploid banana population with large chromosome rearrangements.Horticulture research · 2025Article
- Stability of fruit ripening traits of banana (Heliyon · 2024Article
- Genetic diversity and population structure in banana (Musa spp.) breeding germplasm.The plant genome · 2024Article
- Genomic selection for crop improvement in fruits and vegetables: a systematic scoping review.Molecular breeding : new strategies in plant improvement · 2024Article
- Multi-trait multi-environment genomic prediction of preliminary yield trial in pulse crop.The plant genome · 2024Article
- Striking variation in chromosome structure withinFrontiers in plant science · 2024Article
- Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach.Frontiers in plant science · 2023Article
- Draft Sequencing Crested Wheatgrass Chromosomes Identified Evolutionary Structural Changes and Genes and Facilitated the Development of SSR Markers.International journal of molecular sciences · 2022Article
- Genomic prediction models for traits differing in heritability for soybean, rice, and maize.BMC plant biology · 2022Article
- The Small Secreted Protein FoSsp1 Elicits Plant Defenses and Negatively Regulates Pathogenesis inFrontiers in plant science · 2022Article
- The banana genome hub: a community database for genomics in the Musaceae.Horticulture research · 2022Article
- Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species.Frontiers in plant science · 2022Review
- The East African highland cooking bananas 'Matooke' preferences of farmers and traders: Implications for variety development.International journal of food science & technology · 2021Article
- A Comparison of the Adoption of Genomic Selection Across Different Breeding Institutions.Frontiers in plant science · 2021Article
- Chromosome Painting in Cultivated Bananas and Their Wild Relatives (International journal of molecular sciences · 2020Article
- Metabolite database for root, tuber, and banana crops to facilitate modern breeding in understudied crops.The Plant journal : for cell and molecular biology · 2020Article
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Authors and funding
9 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Banana (Musa spp.) is an important crop in the African Great Lakes region in terms of income and food security, with the highest per capita consumption worldwide. Pests, diseases and climate change hamper sustainable production of bananas. New breeding tools with increased crossbreeding efficiency are being investigated to breed for resistant, high yielding hybrids of East African Highland banana (EAHB). These include genomic selection (GS), which will benefit breeding through increased genetic gain per unit time. Understanding trait variation and the correlation among economically important traits is an essential first step in the development and selection of suitable GS models for banana. In this study, we tested the hypothesis that trait variations in bananas are not affected by cross combination, cycle, field management and their interaction with genotype. A training population created using EAHB breeding material and its progeny was phenotyped in two contrasting conditions. A high level of correlation among vegetative and yield related traits was observed. Therefore, genomic selection models could be developed for traits that are easily measured. It is likely that the predictive ability of traits that are difficult to phenotype will be similar to less difficult traits they are highly correlated with. Genotype response to cycle and field management practices varied greatly with respect to traits. Yield related traits accounted for 31-35% of principal component variation under low and high input field management conditions. Resistance to Black Sigatoka was stable across cycles but varied under different field management depending on the genotype. The best cross combination was 1201K-1xSH3217 based on selection response (R) of hybrids. Genotyping using simple sequence repeat (SSR) markers revealed that the training population was genetically diverse, reflecting a complex pedigree background, which was mostly influenced by the male parents.
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