Researchers at the International Institute of Tropical Agriculture (IITA) in Ibadan, Nigeria, and research institutes in the Czech Republic and in Belgium were able to prove in a recent study that it is possible to speed up banana breeding using genomic prediction models that accurately select banana hybrids with desired traits.
The models use the plant’s genetic data (DNA landmarks) to estimate its usefulness in breeding and predict the physical traits such as height, yield, and disease resistance before it is taken to the field. This study, published in the scientific journal The Plant Genome, “Genomic prediction in a multiploid crop: genotype by environment interaction and allele dosage effects on predictive ability in banana”, provides the first empirical evidence on the use of genomic prediction in a banana population.
Why speed up banana breeding?
Bananas, an important staple and source of income for millions of people in 120 tropical and subtropical countries, are by nature, sterile crops. They reproduce through suckers, which limits the mingling and recombination (shuffling) of genes from the parents to children. Therefore, there is limited diversity in banana growing in a particular region, which makes the plants prone to the same pests, diseases, and environmental stress.
Efforts to develop improved high-yielding and disease-resistant varieties are thus frustrated by the sterile nature of the plant. This complicates the breeding process, and it can take up to 20 years to deliver improved varieties to farmers.
Moses Nyine, a scientist at IITA and a PhD student at the Czech Palacký University, is the lead researcher. He says the findings of the study present a significant breakthrough for banana breeding in the face of the myriad pests and diseases affecting banana in Africa. These include banana weevil, nematodes, Black Sigatoka, Banana Xanthomonas wilt and others.
IITA has been running a comprehensive banana improvement programme for more than 30 years that covers the whole breeding pipeline. It has developed numerous improved plantain varieties, called PITA, and improved highland cooking bananas together with NARO (National Agricultural Research Organization, Uganda) called NARITAs. These hybrids have been distributed to 15 countries in Africa, Latin America, and Asia.
Biotechnology and statistics to the rescue
For their study, the researchers collected data from the East Africa Highland Banana and their hybrids planted in two fields in Uganda for two crop cycles between 2013 and 2016. In total, 307 banana types of banana were observed under low and high input field management conditions. Researchers gathered data on 15 key traits of the crop and grouped them into five categories: plant stature, suckering behaviour, black leaf streak resistance, fruit bunch and fruit filling.
The DNA differences between the bananas were mapped out, using a technology called genotyping by sequencing (GBS). These data sets were then applied to test the ability of six genomic prediction models to determine the bananas with the best traits through cross validation. The BayesB model was found to be superior to other models, particularly in predicting fruit filling and fruit bunch traits.
The results demonstrate that genomic prediction is possible in banana breeding and the prediction accuracy can be improved by using models based on data from many different environments. The prediction accuracy within the training population based on genomic estimated breeding values (GEBV) was above 75 per cent even with models that had low predictive abilities.
The study was conducted as part of a collaboration of researchers from IITA, the Czech Institute of Experimental Botany, the Centre of Plant Structural and Functional Genomics, Palacký University, Olomouc, Czech Republic, and the Laboratory of Tropical Crop Improvement, Division of Crop Biotechnics, Katholieke Universiteit, Belgium.
Moses Nyine et al.: Genomic Prediction in a Multiploid Crop: Genotype by Environment Interaction and Allele Dosage Effects on Predictive Ability in Banana; doi:10.3835/plantgenome2017.10.0090