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Genomic prediction in a multiploid crop: genotype by environment interaction and allele dosage effects on predictive ability in banana

By M. Nyine, B. Uwimana, N. Blavet, E. Hřibová, H. Vanrespaille, M. Batte, V. Akech, A. Brown, J.H. Lorenzen, R.L. Swennen and Jaroslav Doležel

Abstract

Open Access Journal; Published online: 2 March 2018Improving the efficiency of selection in conventional crossbreeding is a major priority in banana (Musa 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.Bill & Melinda Gates FoundationPeer Revie

Topics: bananas, musa, genotypes, genomic prediction, genotype by environment interaction, allele dosage
Year: 2018
DOI identifier: 10.3835/plantgenome2017.10.0090
OAI identifier: oai:cgspace.cgiar.org:10568/92577
Provided by: CGSpace
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