Bipolar disorder

Both genetic and environmental factors are involved in the development of bipolar disorder, a severe mood disorder characterised by oscillations from normal mood to periods of elevated mood (mania) or low mood (depression). Dr Jan Fullerton and Professor Peter Schofield are investigating the genetic contributors to bipolar disorder using Australian families with multiple individuals who have been diagnosed with the disorder. Together with PhD student Erica McAuley, the group has recently identified a bipolar susceptibility locus located on chromosome 15 in a pooled analysis of 35 families. Further, more detailed analysis of this region using a newly acquired campus Illumina beadstation facility for the analysis of SNP markers has identified a single gene, which confers an increased susceptibility to bipolar disorder. They are now aiming to understand how these alterations translate into an increased genetic susceptibility by characterising the biological pathways involved.

Because of the complex pattern of genetic transmission, they expect that multiple genes will contribute to susceptibility to bipolar disorder. It is possible that combinations of genes will be stronger risk factors for developing bipolar disorder than individual genes, so they are examining gene-gene interactions (genetic epistasis) throughout the genome to identify genes which, in concert, may increase susceptibility. This analysis has led to the identification of multiple such interacting regions, and they are now seeking to identify the specific genes involved in these interactions.

Recent advances in technology have enabled sequencing at the level of the entire genome to become a reality. We have access to number of rare families with highly heritable forms of bipolar disorder, for which we will apply this powerful technology to identify specific genetic factors which increase disease risk. We will assess loss-of-function variation within genes, at both the level of single base mutations and variation in gene copy number, which track with illness in these families to identify new genes which contribute to illness.

Because of the complex pattern of genetic transmission, it is expected that multiple genes will contribute to susceptibility to bipolar disorder. It is possible that combinations of genes will be stronger risk factors for developing bipolar disorder than individual genes, so we are examining gene-gene interactions (genetic epistasis) throughout the genome to identify genes which, in concert, may increase susceptibility. This analysis has led to the identification of multiple such interacting regions, and the group is now seeking to identify the specific genes involved in these interactions.

In addition to genes identified in our own laboratory, we have also been involved in assessing the risk attributed by genes identified by other groups and in sharing data and samples in large international collaborative studies. We are contributors to the Psychiatric Genomics Consortium (PGC) and Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortia, which aim to identify risk genes which contribute to disease, and examine their effect on brain structure, function and disease.

The offspring of individuals with bipolar disorder are at increased risk of mental illness, but our tools to predict which of these genetically at-risk young people will eventually develop disorder are very imprecise. Longitudinal studies that ascertain at-risk participants and monitor them prospectively are an effective approach for identifying early clinical and biological markers of future illness. In collaboration with the Black Dog Institute plus groups from four independent US-based sites, including: Johns Hopkins University; University of Michigan; Washington University in St. Louis; Indiana University; we are following a cohort of young kids and siblings of bipolar disorder patients with annual clinical, neurocognitive and lifestyle assessments; plus bi-annual brain imaging of the Australian participants. We are assessing the genetic load of multiple risk variants across the genome in these at-risk individuals to determine if we can use genetic information to help predict which individuals will ultimately transition to illness, and whether genetic load will influence early structural brain changes which are seen prior to onset of symptoms which lead to a clinical diagnosis.

We are also examining whether epigenetic changes – which occur on-top-of the DNA sequence in response to environmental influences – are involved in transition from health to illness. Early identification of those most likely to develop illness will provide a firm basis on which to develop preventive and early intervention strategies to reduce the impact of this devastating disorder.