Prof Brett Lidbury and colleagues at the John Curtain School of Medical Research, Australian National University in Canberra are conducting an ongoing programme, funded by charities in Australia, which aims to find biomarkers for ME/CFS using a range of sources – bioinformatics, genetics and pathological testing. Prof Lidbury has a background in virus–host interactions, particularly the
Ross River virus which is endogenous to Australia and can cause long term post-viral syndromes such as ME/CFS. Today, much of his work is on ME/CFS, and his team benefits from access to a large well-defined group of Canadian Consensus Criteria-defined patients recruited by the
CFS Discovery Clinic in Victoria, for whom full clinical histories, and pathological and physiological data are available. In fact, it recently published a report on the co-occurrence of postural orthostatic tachycardia syndrome and ME/CFS using data from this cohort (
read more).
ME Research UK has
provided funding to the group to acquire additional genetic data using DNA pooling. They will apply two dimensional DNA sequencing to attempt to identify ME/CFS-associated genetic changes across the entire genome in a clinically well-defined group of 100 patients and up to 40 controls. Two DNA pooling techniques will be used – a pooling/bootstrap genome-wide method which has been developed and used for the detection of genetic markers of Alzheimer’s Disease by Prof Lidbury’s collaborators on this project, Dr Mastronardi and Prof Arcos-Burgos of the Genomics and Predictive Medicine Group at the John Curtain School (
read more), and a 2D DNA pooling method for rare variant detection. The team recently completed a pilot study which successfully applied machine learning methods to pathology and clinical data alone, revealing patterns associated both with the presence and severity of ME/CFS. So, when the genetic data is available from the DNA pooling studies, it will be possible to apply similar machine learning techniques and statistical analyses to an integrated data set combining genetic, clinical and pathological information.
