Nicola Armstrong – Associate Professor School of Elec Eng, Comp and Math Sci, Curtin University

Sifting through the pieces to pinpoint the origins of disease 

Associate Professor Nicola Armstrong analyses genetic data to help medical researchers understand the genetic basis of diseases like cancer and degenerative disorders like dementia.

Nicola brings meaning to data, mostly coming from medical research.  With a background in theoretical and applied statistics, she undertook her PhD at the University of California in Berkeley during the late 1990s when new genetics technologies were revolutionising our understanding of biology and the human genome.  Being immersed in this rapidly developing medical research environment, she could see real-world applications of statistics, integrating computational and statistical methods into experimental and clinical research that was generating increasing volumes of novel data using new genetics technologies.

Now at Curtib University, Nicola develops and applies statistical methods for the analysis and integration of high-throughput genomic and epigenomic data to understand how the development of complex diseases and ageing processes vary between individuals.

What drew her to science?

“I’ve always been good with numbers, and enjoyed playing with them,” remembers Nicola.  “And I had a really good maths teacher in high school, who made the subject really enjoyable and interesting.  When I went to university I actually wanted to be an actuary – looking at the statistics of risk and uncertainty that underpins insurance – but I couldn’t study it locally at the time.  So I studied maths and statistics, which was the next best thing.”

“Now, my research is enabling other people to work on really amazing medical problems.  I love that statistical bioinformatics lets me pull together information and experts from lot of other scientific disciplines to try and make a difference to people’s health outcomes.”

Research with supercomputers

Nicola explains how genetics research has changed since the 1990s: “We might have done controlled experiments with 200 mice, and amplified particular sections of their DNA to study very specific regions of their genome.  It was slow, and the data produced was limited and very specific.  But now with next-generation sequencing technology, we can sequence the entire genome of a mouse overnight – we can correlate the onset of a disease with any genetic differences we see genome-wide.”

“Now the issue is these next-generation or massively parallel sequencing technologies give you millions of very small fragments of DNA overnight that still need to be pieced back together and mapped to the complete reference genome, whether mouse or human.  That ‘assembly’ or processing of the genetic data requires a supercomputer, especially if you’re looking at 500 whole genome sequences from a large study.”

Nicola is also now looking at epigenetic data on a large scale – evidence of which particular genes are being turned on and off under different conditions to cause changes in certain cells.  It’s yet another aspect to cell regulation and function that can now be measured at a genome-wide scale, with the resulting data then integrated with all of the genetic, diagnostic and clinical information that makes up a modern medical study.

Real world solutions

Nicola’s current research at Pawsey is working to identify individuals at high risk of developing dementia, particularly late-onset Alzheimer’s disease.  The cascade of events that lead to dementia currently begin many years before the disease is clinically diagnosed, and may only be tentatively identified through magnetic resonance imaging of the brain.  Even if structural changes in the brain are seen, the underlying causes of dementia are still not comprehensively understood.

As part of an international genetics of brain imaging project involving over 25,000 individuals across the US, Europe and Australia, Nicola is exploring the genetic and epigenetic variations that correlate with structural brain alterations identified through non-invasive brain scanning in people prior to the onset of clinical symptoms.

“The genetic data should let us identify people that might be at risk later,” notes Nicola.  “But the epigenetic data, looking at how the DNA is modified and which genes are expressed as a result of both an individual’s genetic makeup at birth and the effects of environmental factors, that information is potentially what will underpin future treatment options.  Exploring the genes expressed in the development of dementia is an essential step for identifying therapeutic drug targets or lifestyle interventions.”

Nicola Armstrong