Defining how molecular switches program cell identity during development. Aims: This project aims to investigate how molecular switches known as transcription factors, work together to turn genes on or off to program cell identity during development.
Significance: This project expects to generate new knowledge in the area of genetics and developmental biology using collaborative, cutting edge technologies.
Outcomes: Expected outcomes of this project include the identification of new genes impor ....Defining how molecular switches program cell identity during development. Aims: This project aims to investigate how molecular switches known as transcription factors, work together to turn genes on or off to program cell identity during development.
Significance: This project expects to generate new knowledge in the area of genetics and developmental biology using collaborative, cutting edge technologies.
Outcomes: Expected outcomes of this project include the identification of new genes important for programming the identity of cells that comprise our blood vessels, lymphatic vessels and circulating blood cells.
Benefits: Data generated will underpin the development of approaches to program/reprogram stem cells to produce mature cells for transplantation or tissue engineering purposes ex vivo.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