Biomedical Stream
Professor Naomi Wray

Professor Naomi Wray

Uni. of Queensland, AUS

Naomi Wray is an NHMRC Principal Research Fellow and Fellow of the Australian Academy of Science. She holds joint Professorial positions between The Institute for Molecular Bioscience and The Queensland Brain Institute at The University of Queensland, Her research intersects quantitative genetics, statistical methodology and disorders of the brain. She plays a leading role in the International Psychiatric Genomics Consortia but also co-leads the Ice Bucket Challenge MNDRIA Sporadic ALS Australia Systems Genomics Consortium (SALSA)

View Prof. Wray’s publications on PubMed

Keynote Presentation: Big ‘omics data in ALS: Current results and future prospects

Technological advances of the last decade mean that the genome, epigenome, transcriptome, metabolome, proteome and microbiome are now more easily measurable. Each technology provides big data and some technologies have matured so that cost per data point is very small. For example, chip technology allows measurement of 500,000 DNA polymorphisms at an all-in-cost of $70/sample. These data, when compared between cases and controls identify disease associated genes. For example, new ALS risk genes such as C9orf72, MOBP, C21orf72, SCFD1I, GPX3/TNIP1 have been identified, which help to build a clearer picture of ALS biology. Whereas the genome is the same in every cell and throughout life (except in very rare circumstances), the other ‘omics measures can reflect biological responses to the environment and the disease process. Differences in ‘omics measures between cases and controls can generate hypotheses about disease process, and differences amongst cases may help understanding of disease progression and between individual-heterogeneity. One limitation of this approach is that causes and consequences of disease are confounded. Another approach is to integrate genomic data from ALS cases/controls with ‘omics data generated on healthy people. Integration is via DNA polymorphisms. For example, if a DNA polymorphism is associated with ALS, we can interrogate reference data sets to ask if the variant controls variation between people in DNA methylation or gene expression, and if so, is this control of gene expression tissue-specific. In this way, bioinformatics analyses can quickly and cheaply generate hypotheses for testing in a laboratory. The rapid changes in technology mean that this is a fast-moving field; the future potential for knowledge advancement is even greater. For example, reference data sets that identify DNA polymorphism that impact variation in specific cell types are still accumulating.

Drawing on examples of our in-house genome, epigenome, transcriptome and microbiome data in ALS and other disorders, we will conclude that these technologies hold great promise for furthering our understanding of ALS. Given the great clinical heterogeneity between those diagnosed with ALS and given the millions of measures created by the technologies, large sample cohorts are needed to generate valid and replicated results. Hence, contributing to research through providing both clinical measures and biological samples is a very important legacy for those with ALS today. It is with the vision of what current and future ‘omics technologies can deliver that we established the SALSA Systems Genomics Consortium, funded by the Ice Bucket Challenge and MNDRIA. We have established co-ordinated data and biological sample collection for the major ALS research clinics in Australia. With a staged roll-out since March 2106, we have recruited over 500 people with ALS, over 250 controls and have biobanked over 1000 biological samples.

Glossary: genome (i.e., DNA), epigenome (chemical changes of the genome, e.g., methylation of DNA bases,  which influence gene expression; the biological impact of environmental exposures is through the epigenome; e.g., from DNA methylation it is easy to determine if a person is a current, former or never smoker), transcriptome (i.e. gene expression), metabolome (i.e. levels of metabolic markers), proteome (i.e. levels of protein), microbiome (microbial communities, e.g., of gut).