The University of South Australia – Australia’s University of Enterprise – is seeking applications for an exceptional research degree project based at our Australian Centre for Precision Health (ACPreH), investigating the development of novel methods that can integrate multi-omics data to better understand the role of the genome and transcriptome in the phenotypic variance of complex disease.

The genomic era provides an unprecedented opportunity to shed light on the underlying genetic basis of complex diseases. However, there is a substantial proportion of phenotypic variance that is not explained by the variants in the genome. In fact, genetic risk prediction based on whole-genome information is not accurate enough to be applied in clinical care.

One plausible reason is that the genome is only part of the mechanism underlying the phenotypes of a complex disease, and multi-omics effects may also contribute to the combined effects of genomics and transcriptomics (gene expression) on phenotypes. Currently, the relationship between genome and transcriptome at the whole-genome level, and how this relationship is associated with complex disease phenotypes, is largely unknown. Moreover, the genomic and transcriptomic effects on phenotypes can be modulated by the environment, which is often difficult to jointly model using existing methods.

The method to be explored in this project involves fitting multi-omics kernel matrices simultaneously to partition their contributions to the phenotypic variance, as well as an advanced non-additive model, to estimate genome-by-environment and transcriptome-by-environment interactions.

Applying the proposed methods to a wide range of real datasets in this project will enable you to expand knowledge and understanding of aetiologies – a critical need in our fight against complex disease.

This project is open to application from both domestic and international applicants currently residing in Australia or New Zealand.

To apply please visit 

https://unisa.edu.au/research/degrees/research-projects#integrative-analyses-of-omics-data-for-comple