The National Horticultural Tree Genomics Program will advance the horticulture sectors’ ability to rapidly develop and implement horticultural tree cultivars that accentuate key productivity and profitability traits through the coordinated development of three complementary toolboxes: Genomics, Genotype Prediction, Phenotype Prediction.
Genomics
The National Tree Genomics Molecular Physiology project aims to improve understanding of the molecular mechanisms regulating productivity traits in avocado, mango and macadamia. Four research questions were proposed for the molecular physiology team to address. These are:
- What molecular signals are associated with determinate and indeterminate growth and flowering in tree crops?
- What molecular signals are associated with precocity in tree crops?
- What molecular signals alter tree architecture?
- What molecular changes in the plant lead to early fruit loss?
Field trials and the collection of samples from field trials have been completed for this project and the research team has:
- Investigated the effects of paclobutrazol on floral and vegetative growth in avocado
- Continued sample collection from field trials comparing varieties of macadamia that exhibit very early flowering and late flowering to identify molecular differences in floral initiation signals associated with precocity
- Identified and selected rootstocks associated with low, medium and high vigour in macadamia for molecular analysis and identified gene sequences of interest from HAES741 potentially associated with vigour control
- Commenced new project MG21004 on investigating the control of fruit drop in mango and designed field cameras to assist with the monitoring of remote field sites. Method optimization was undertaken for the extraction of RNA (for gene expression) and phytohormones, in particular gibberellins, for molecular analysis.
Genotype prediction
The research team has been focusing on delivering SNP markers associated with a shortened juvenile phase to the citrus breeding program, providing sequencing data to the almond breeding program for a population segregating for shortened juvenile phase, validating and characterising important genes that effect flowering and architecture in almond, mango and macadamia. The team has further improved the utility of viral induced regulation to test the function of tree crop genes with the intent to fast-forward the selection of desirable traits for horticultural innovation. Progress delivers on the set objectives to produce a functional genomic toolbox focused on the development of improving new cultivars that have predictable flower and/or bud dormancy traits that underpin crop production. Benefits include:
- reduced time that growers wait for a return on investment after planting new varieties,
- reduced breeding cycle in perennial tree crops, which accelerates the ability of breeders to develop new cultivars.
Phenotype prediction
The aim of this project is to develop resources and tools for implementation of appropriate DNA-based selection strategies in horticulture tree crop breeding programs. To support data conservation and access, a repository for data and script for analyses undertaken with these data were established on the UQ Research Data Management system at UQ and access provided to project partners and others. In mango, additional data on truck circumference, fruit blush colour score, and fruit firmness and combined with historical data.
An experiment was undertaken to establish that hue angle from a spectrometer could be used as an objective instrumental assessment of fruit blush colour score. DNA-based models for these traits were extended to identify a large effect DNA region for fruit blush colour, include non- additive effects (important for selection of clones) and weight loci effects by GWAS results. Data from breeding program progeny is being used to validate these prediction models for selection of parents and untested progeny. However, preliminary results suggest use of DNA-models to improve accuracy of rootstock effect from existing field trials may not be effective.
In almond, data on fruit quality characteristics across 14 years (1998 – 2019) was accumulated and the analysis has commenced to validate the use of DNA-based model to predict breeding values of parent. A collaboration with University of California demonstrated bloom time in almond could be estimated from images collected by a UAV and these data were used to develop a prediction model for this trait.
The QU-GENE platform was used to validate input phenotypic and molecular data from the macadamia breeding program. These models are now being extended to evaluate alternative genomic strategies to improve confidence in the implementation of these strategies. New research areas have been developed in low-cost genotyping to support implementation of DNA-based and accumulating historical data from multiple breeding programs to improve cultivar-environment matching and biosecurity resilience.
The outputs form this project are expected to assist improve the efficiency of breeding programs to deliver greater gains for the same input costs in a short time. Results may also be used to evaluate the performance of exotic germplasm in Australian current and future abiotic and biotic environments.
