Genomics to rust proof the humble oat. This project aims to reduce the impact of the damaging and currently intractable fungal pathogen crown rust (OCR) in Australian oat production. The expected project outcomes are: new sources of enduring high value resistance to OCR, tools to accelerate the use of these resistances, and locally adapted OCR resistant oat germplasm for use in developing profitable oat varieties. The project will use new approaches to tap very recently released genomic resource ....Genomics to rust proof the humble oat. This project aims to reduce the impact of the damaging and currently intractable fungal pathogen crown rust (OCR) in Australian oat production. The expected project outcomes are: new sources of enduring high value resistance to OCR, tools to accelerate the use of these resistances, and locally adapted OCR resistant oat germplasm for use in developing profitable oat varieties. The project will use new approaches to tap very recently released genomic resources and unique oat/ OCR resources assembled over many years. It will lead to responsible stewardship of broadly effective OCR resistance in grazing/milling/hay oats, increasing grower profitability, reducing reliance on fungicides, and underpinning planned growth in our export oat market. Read moreRead less
Whole-genome multivariate reaction norm model for complex traits. This project aims to develop a multivariate whole-genome genotype-covariate correlation and interaction model that can be applied to a wide range of existing genome-wide association study (GWAS) datasets. Genotype-covariate correlation and interaction (GCCI) are fundamental in biology but there is no standard approach to disentangle interaction from correlation in the whole-genome analyses. This project will address the key featur ....Whole-genome multivariate reaction norm model for complex traits. This project aims to develop a multivariate whole-genome genotype-covariate correlation and interaction model that can be applied to a wide range of existing genome-wide association study (GWAS) datasets. Genotype-covariate correlation and interaction (GCCI) are fundamental in biology but there is no standard approach to disentangle interaction from correlation in the whole-genome analyses. This project will address the key feature in biology, which relates to dissecting the complex mechanism of association and interaction. The proposed statistical model implemented in a context of a novel design based on multiple GWAS data sets is a paradigm shifting-tool with applications to multiple industries.Read moreRead less