Genomic Epidemiology and Analysis


Advance our understanding of the biological mechanisms underlying complex disorders by integrating and analysing multiple levels of genomics information with clinical data.

Program Co-leads

Program Summary

Genomic epidemiology is the foundational study of the role of genomic factors in determining health and disease in families and populations. Genomic epidemiology encompasses complicated statistical and bioinformatic analyses and has produced important new knowledge on the role of genomic variation in the development and mechanisms of numerous human health-related diseases and traits for which little is currently known about their aetiology.

The Genomic Epidemiology and Analysis Research Program will centre on the identification of genomic risk factors and the detection of common genomic links between disorders, which provides the first crucial step to uncovering the underlying biological pathways that may be new targets for development of novel treatment strategies. Furthermore, the research in this program seeks to understand the genomic mechanisms of complex traits through advanced statistical analysis.

The program will incorporate and integrate different layers of genomics information, such as DNA sequence, structural variation and regulatory information (i.e., gene expression, microRNAs, DNA methylation, chromosome conformation capture, and histone modifications) with clinical information to identify and discover genetic factors associated with complex disorders. In addition, systems integration of different ‘omics data; including transcriptomics, epigenomics and genetics data will be performed to uncover genes and pathways associated with neurological, psychiatric, ageing and other complex human disorders.

Powerful and comprehensive data-driven approaches including methods such as regression modelling, Bayesian methods, advanced clustering methods, genetic imputations, gene alignments, predictions, support vectors and other machine learning techniques, bootstrapping and permutations will be undertaken utilising QUT’s high performance computing facilities using open source software including R and Bioconductor.