Genomic Epidemiology and Analysis

Vision

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

Professor Dale Nyholt

Professor Dale Nyholt is a human geneticist expert in the statistical analysis of common complex traits. His expertise spans statistical genetic and genomics analysis, epidemiology,  bioinformatics and biostatistics. His laboratory studies the role of genetics in the development and mechanism of human traits with particular emphasis on migraine, with the specific goal of identifying genetic risk factors and detecting common genetic links with other disorders, including depression, endometriosis and epilepsy. The identification of such causal genetic factors and how they interact with environment exposures will provide a deeper understanding of the aetiology of migraine and out ability to prevent and treat attacks, and facilitate the discovery of novel pathways and thus identify new targets for drug therapy. His laboratory also developed novel bioinformatics programs used by researchers worldwide. View Professor Dale Nyholt's profile                

Program Leader: Associate Professor Divya Mehta

Associate Professor Divya Mehta is a geneticist and biostatistician with an international reputation in statistical genomics in mental health, with a focus on Posttraumatic stress disorder and depression. The aim of her research is to identify the biological mechanisms of mental health disorders and understand how environmental risk factors such as stress, diet, exercise and sleep affect our gene activity and function. By investigating short and long-term trajectories of health after exposure to trauma in a range of populations, her research has identified novel acute and chronic markers of stress and stress-related health outcomes. Her research integrates psychological, social, genomics and clinical data to identify risk and protective factors associated with the response to trauma, providing knowledge that will facilitate early detection, improved diagnosis and personalised treatment of stress-related disorders. View Associate Professor Divya Mehta’s profile

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.