Advanced bioinformatics tools help sift through millions of genomic mutations to discover the origins of dementia and related neurodegenerative diseases as part of a network of national and international experts.
The Challenge: Understanding neurodegenerative diseases
Dementia, the pathological decrease in the ability to think and remember, is one of the leading causes of deaths in Australia (6% of all deaths in 2010) with numbers projected to triple over the next decades. One specific form of dementia, frontotemporal dementia (FTD) is responsible for approximately 20% of cases of presenile dementia where symptoms manifest relatively early in life, often between the age of 55 and 65 years. FTD has a strong genetic component and its clinical and molecular features overlap with another neurodegenerative disease: amyotrophic lateral sclerosis (ALS). Despite recent breakthrough discoveries through whole genome sequencing (WGS) and genome wide association studies (GWAS) the molecular and genetic origins of the majority of FTD and ALS cases are still poorly understood which makes prediction, application of appropriate preventive measures and personalised treatment difficult.
The Response: Using BigData machine learning to identify potential disease mutations
So far CSIRO researchers, Dr Natalie Twine and Dr Arash Bayat, have identified more than 80,000 potentially disease contributing mutations of familial FTD and ALS in a twin study conducted by Prof. Ian Blair and Dr. Kelly Williams at Macquarie University. By linking information from medical literature and large scale genomics projects, CSIRO helps to prioritize variants for further testing. This approach can be strengthened by tracking putative disease variants in large family units. CSIRO hence applied VariantSpark to the genomic sequences of 800 allegedly unrelated ALS patients to uncover hidden familial relationships thereby increasing the statistical power to classify disease variants. Currently, CSIRO is developing new approaches to improve the accuracy of this method. CSIRO have also applied their novel VariantSpark machine learning method to identify potentially disease causing interactions between gene variants, across 4281 ALS patients and augment findings from traditional linear association testing. Based on the variants identified in large datasets like this, our collaborators will develop molecular systems able to verify the neurodegenerative diseases association with individual genomic variants.