Increasing amphibian immunity to combat disease causing mass extinction. This project aims to increase amphibian survival to combat the devastating chytrid fungus by identifying resistance genes and increasing their frequency in the host population. The project is interdisciplinary and uses targeted genetic manipulation techniques developed for agriculture to improve disease resistance in wildlife for the first time. Expected outcomes include 1) enhanced international collaborations in comparati ....Increasing amphibian immunity to combat disease causing mass extinction. This project aims to increase amphibian survival to combat the devastating chytrid fungus by identifying resistance genes and increasing their frequency in the host population. The project is interdisciplinary and uses targeted genetic manipulation techniques developed for agriculture to improve disease resistance in wildlife for the first time. Expected outcomes include 1) enhanced international collaborations in comparative immunology, 2) a comprehensive understanding of immunity to chytridiomycosis, and 3) disease resistant amphibians. The anticipated benefit is ability to apply the optimal method to improve conservation of wildlife threatened by emerging disease, such as marker assisted selective breeding or genetic engineering.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