Beyond pineal melatonin: sensing the seasons without the eye. The project will identify the causal connection between seasonal breeding in animals and a recently recognised brain biochemical pathway by applying experimental treatments mimicking seasonal environmental changes in a mutant and wild-type nematode worm model. Through experimentation we will identify useful biological targets that might be manipulated to enhance control of seasonal breeding in managed animals. With better control of r ....Beyond pineal melatonin: sensing the seasons without the eye. The project will identify the causal connection between seasonal breeding in animals and a recently recognised brain biochemical pathway by applying experimental treatments mimicking seasonal environmental changes in a mutant and wild-type nematode worm model. Through experimentation we will identify useful biological targets that might be manipulated to enhance control of seasonal breeding in managed animals. With better control of reproductive output in animals, farmers and managers can increase and/or decrease reproductive output as needed in managed species including livestock and vertebrate pests. This will enhance the use of precious land resources and minimize ecological damage from overbreeding.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