Genetic improvement of bananas for East Africa

Abstract

Banana (Musa spp.) is an important food and income security crop for millions of people in the global tropical and subtropical South. However, its improvement through crossbreeding is challenging because of the limited number of offspring for evaluation, unsynchronized data, and a long, expensive and tedious breeding process that leads to low rates of genetic gains. This thesis aimed to determine how much genetic diversity exists for Musa breeding and explore the potential of accelerating genetic gain through optimization of the Mchare breeding pipeline, considering three approaches; 1) evaluate the crop’s genetic diversity and population structure, 2) use the clonal model to determine the level of genetic variation and its decomposition into additive, dominance and epistasis and 3) integrating genomic information to improve estimates of genetic parameters for key traits in banana trials.

The genetic diversity was low, especially among the diploids and tetraploids. Musa accessions with the potential to be used for parental improvement were identified. The Additive variation was significant (p ≤ 0.001) and was the strongest genetic variance for all yield-related traits. Therefore, focusing on parental improvement and selection of the best parents is recommended for genetic gains in Mchare banana populations. The highest positive correlations were observed among the yield-related traits. The correlation between the plant crop and the first ratoon crop for all yield-related traits was significant, positive and of high magnitude (0.62 > r 0.50 for all yield-related traits. The hybrid, H-BLUP (best linear unbiased predictor) model had the highest prediction accuracies for yield-related traits, while the pedigree-based, P-BLUP, model had the highest prediction accuracies for agronomic traits. The implications of this research extend to the identification of available genetic diversity, and enhancement of the accuracy of estimating genetic parameters using marker-based models. The research aids the breeder in the amount of genetic gain that can be anticipated and also paves the way for further genomic exploration in diploid banana breeding programs.

Keywords

Additive variance; clonal model; G-BLUP; genetic diversity; H-BLUP; heritability; Musa; non-additive variance; P-BLUP; prediction accuracy

Published in

Acta Universitatis Agriculturae Sueciae
2024, number: 2024:94
Publisher: Swedish University of Agricultural Sciences

SLU Authors

• Akech, Violet

  • Department of Plant Breeding, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Genetics and Breeding

Publication identifier

• DOI: https://doi.org/10.54612/a.4617i0mre4
• ISBN: 978-91-8046-421-5
• eISBN: 978-91-8046-429-1

Permanent link to this page (URI)

https://res.slu.se/id/publ/132425