Researchers use DNA prediction models to speed up banana breeding

This study, published in a paper, Genomic prediction in a multiploid crop: genotype by environment interaction and allele dosage effects on predictive ability in banana, in The Plant Genome provides the first empirical evidence on the use of genomic prediction in a banana population.
9 months ago

An international team of scientists have for the first time demonstrated 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 the plant is taken to the field. This study, published in a paper, Genomic prediction in a multiploid crop: genotype by environment interaction and allele dosage effects on predictive ability in banana, in The Plant Genome  provides the first empirical evidence on the use of genomic prediction in a banana population.

Why speed up banana breeding?

Banana, 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 them 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, making it lengthy and costly, taking up to 20 years to deliver improved varieties to farmers.

Moses Nyine from the International Institute of Tropical Agriculture (IITA) and a PhD student at 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, Banana Bunchy top virus, and more recently Fusarium Wilt (Panama disease/Tropical Race 4 (TR4) detected in Africa for the first time in Mozambique in 2013.

Photo: Moses Nyine of IITA explains about his research in a banana field in Uganda (credit: International Institute of Tropical Agriculture (IITA))

IITA has a comprehensive banana improvement program since 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

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. They collected data on 15 key traits of the crop and grouped them into five categories: plant stature, suckering behavior, 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 used 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 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% even with models that had low predictive abilities.

The study was conducted as part of a collaboration of researchers from IITA, the 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.

Source: International Institute of Tropical Agriculture (IITA)



No comments to display.

Related posts

Continental free trade area to boost e-commerce in Africa

High-level session at UNCTAD's Africa eCommerce Week shows that the continent has the potential to scale e-commerce enterprises - but its new free trade area will be key.

Bayer helps farmers keep sows healthy with new BCS SowDition smartphone application

BCS SowDition enables accurate and standardized body condition scoring of sows in four simple steps, contributing to better health and management.

Call for applications: 2019 Syngenta Crop Challenge in Analytics prize

Corn is one of the world’s most important crops. Each year, breeders create several new corn products, known as experimental hybrids. Corn breeders work to create corn hybrids that can maintain high yield across a wide range of environments.
Application Deadline in a month

Application of the IoT technologies in the poultry chain

The growth of poultry relies on the environment in which the bird feels comfortable, as well as on good-quality feed and water. In this use case, the performance of the poultry production chain is optimised through IoT driven technologies.

New Initiative to Mitigate Risk for Global Solar Scale-up

The World Bank and Agence Française de Développement (AFD) are developing a joint Global Solar Risk Mitigation Initiative (SRMI), an integrated approach to tackle policy, technical and financial issues associated with scaling up solar energy deployment, especially in some of the world’s poorest countries.

Challenges and opportunities of introducing Internet of Things and Artificial Intelligence applications into Supply Chain Management

The study examines the challenges and opportunities of introducing Artificial Intelligence (AI) and the Internet of Things (IoT) into the Supply Chain Management (SCM). This research focuses on the Logistic Management. The central research question is “What are the key challenges and opportunities of introducing AI and IoT applications into the Supply Chain Management?”

Internet of Robotic Things – Converging Sensing/Actuating, Hyperconnectivity, Artificial Intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing new developments in various application domains, such as the Internet of Mobile Things (IoMT), Autonomous Internet of Things (A-IoT), Autonomous System of Things (ASoT), Internet of Autonomous Things (IoAT), Internet of Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc. that are progressing/advancing by using IoT technology.

3D Food Printers Market Driven by Growing Demand for Customized Food

The 3D food printers market is expected to grow at a robust CAGR from 2017 to 2028. This market growth is expected to be driven by factors such as the growing demand for customised food and the development of the industrial sector in this region.

Vision-Based Apple Classification for Smart Manufacturing

In this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN).

Intel Drone Solutions Modernize and Increase Efficiency for US Bridge Inspections

Intel collaborated with two departments of transportation to improve bridge inspections, supplementing manual inspections of the Daniel Carter Beard Bridge connecting Ohio and Kentucky and the Stone Arch Bridge in Minnesota. Throughout the inspections, Intel’s advanced automated commercial drone hardware and software solutions increased efficiency and produced more reliable data in a fraction of the time and cost of traditional methods.