Optimising growth rates by postnatal programming of brain pathways regulating metabolism. Australian agriculture relies on achieving optimal healthy animal growth. A strategy to accelerate growth rates is to exploit the vulnerability of the early life period to long-term programming. This project will investigate how neonatal overfeeding alters brain circuitry controlling weight to determine methods to manipulate weight gain in industry.
Evolution, selection and estimation of polygenic epistatic networks in quantitative traits. Traits observed in organisms, such as height, are the result of an individual's genes and how they relate to the environment. But genes do not act alone; they work together in complex interactions. This project aims to understand these interactions and their role in animal production and human disease.
Discovery Early Career Researcher Award - Grant ID: DE210101063
Funder
Australian Research Council
Funding Amount
$462,948.00
Summary
Bacterial cell invasion factors as vaccine targets. This project aims to determine the virulence factors responsible for cellular invasion and systemic spread of Mycoplasma bovis, and use genome editing technologies (CRISPR-Cas9) to create gene knock out mutants that cannot invade host cells and test their potential as vaccine candidates in animals. Mycoplasma bovis is an emerging cause of mastitis, the most important infectious disease in the dairy industry, and causes significant economic loss ....Bacterial cell invasion factors as vaccine targets. This project aims to determine the virulence factors responsible for cellular invasion and systemic spread of Mycoplasma bovis, and use genome editing technologies (CRISPR-Cas9) to create gene knock out mutants that cannot invade host cells and test their potential as vaccine candidates in animals. Mycoplasma bovis is an emerging cause of mastitis, the most important infectious disease in the dairy industry, and causes significant economic losses. The vaccine candidates developed in this project are expected to be used to control outbreaks of mastitis, and to improve biosecurity, production and animal welfare in the Australian and global dairy industries.Read moreRead less
The extent, causes and implications of pleiotropy among complex traits. The project seeks to understand how a DNA mutation can affect many characters or traits. Many traits are called complex because they are controlled by a very large number of genes, most of which have small effects. Complex traits include traits important in medicine (such as susceptibility to heart disease) and in agriculture (such as tenderness of meat). Because there are many genes affecting each trait, most genes have sma ....The extent, causes and implications of pleiotropy among complex traits. The project seeks to understand how a DNA mutation can affect many characters or traits. Many traits are called complex because they are controlled by a very large number of genes, most of which have small effects. Complex traits include traits important in medicine (such as susceptibility to heart disease) and in agriculture (such as tenderness of meat). Because there are many genes affecting each trait, most genes have small effects which makes them hard to identify. The fact that a mutation that has a small effect on a complex trait also has a larger effect on a less complex trait may help us to identify the mutation and use it in agriculture or medicine.Read moreRead less
Prediction of phenotype for multiple traits from multi-omic data. This project aims to develop better methods for predicting traits in an individual based on their genome sequence. This method will be tested in agricultural animals and plants and in humans. The prediction formula is derived from a training dataset that has information on the traits and genome sequence of a sample of individuals. The prediction formula can then be applied to predict the trait in individuals where the trait is un ....Prediction of phenotype for multiple traits from multi-omic data. This project aims to develop better methods for predicting traits in an individual based on their genome sequence. This method will be tested in agricultural animals and plants and in humans. The prediction formula is derived from a training dataset that has information on the traits and genome sequence of a sample of individuals. The prediction formula can then be applied to predict the trait in individuals where the trait is unknown. This is useful for selecting the best parents for breeding in agriculture and for predicting the future phenotype of animals, crops and people. The proposed method uses data on very many traits to identify sequence variants that have a function and to predict the traits affected by each variant.Read moreRead less