Hidden complexity in microRNA function. This project aims to determine the extent to which microRNAs function through “non-canonical” mechanisms within cell nuclei, how their roles are expanded by naturally occurring sequence variation and how their activity is controlled by little known families of genes that sequester and inhibit their availability. The knowledge generated is significant as microRNAs regulate the expression of virtually all genes and biological processes, yet these mechanisms ....Hidden complexity in microRNA function. This project aims to determine the extent to which microRNAs function through “non-canonical” mechanisms within cell nuclei, how their roles are expanded by naturally occurring sequence variation and how their activity is controlled by little known families of genes that sequester and inhibit their availability. The knowledge generated is significant as microRNAs regulate the expression of virtually all genes and biological processes, yet these mechanisms of function remain poorly characterised and seldom considered. The expected outcome of better understanding mechanisms through which microRNAs work should provide significant benefit to safe and effective development of microRNAs for future agricultural or therapeutic application.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