"When they fall outside genes, what do they do?
"If we can answer this, then we can start to look for new ways to diagnose disease, predict its severity, or develop new approaches to treating it."
The new findings pave the way for future research that could lead to personalised medicine; improving prevention, diagnosis and targeting of drugs for a wide array of common chronic diseases.
New genetic drivers identified for type 1 diabetes is an autoimmune condition where the body's immune system mistakenly destroys cells in the pancreas that make insulin, meaning the body cannot take up glucose from the blood and turn it into energy.
As a substitute, the body burns its fats, leading to a build-up of dangerous chemicals in the blood, which can be life-threatening if not treated.
People with this condition – an estimated 10,000 to 20,000 in New Zealand - depend on daily insulin injections.
The exact cause is not yet known, but if you have a parent or sibling with type 1 diabetes you are more likely to develop the disease, and recent studies have linked certain mutations on the DNA to an increased risk.
These mutations are called SNPs – single nucleotide polymorphisms – regions that commonly vary between individuals and that have been linked to a disease.
Most SNPs, including these ones, fall outside genes in what was once labelled 'junk DNA' – viewed as little more than space-fillers between genes.
The Liggins Institute researchers wondered if the SNPs contributed to type 1 diabetes by interfering with the functioning of genes on other, far-flung segments of the DNA that they come into contact with through the way that DNA is tightly coiled into the cell nucleus.
DNA, the long molecules containing our entire genetic blueprint, are about 2m long, but packed into cell nuclei only 100th of a millimetre across.
Sure enough, using a new way of mapping genetic data in 3D devised by O'Sullivan's team, they identified 246 genes that physically come in contact with the type 1 diabetes-linked SNPs and belong to networks that are involved in the immune system.
These included not only genes already known to raise the risk of type 1 diabetes, but also ones never before linked to the disease but known to play a part in immune function, insulin expression and pancreatic beta cell function.
"We have shown for the first time 'new' genes that play a role in increasing the risk of type 1 diabetes," study lead author and Liggins Institute PhD student Denis Nyaga said.
"We also demonstrated that other organs, such as the liver, could be involved in the development of this disease."
The team is now keen to employ DNA editing techniques to dig deeper into these genetic networks behind the condition.
"Then we can move a step closer towards a much more precise way of helping people with type 1 diabetes," Nyaga said.
A second study focused on "multimorbidity" – or people's suffering of two or more chronic illnesses.
The challenges of living with multiple conditions – reduced quality of life, juggling medications, higher mortality – become more severe with age.
Yet our healthcare system and research is mostly focused on single diseases.
"The rise in multimorbidity requires an urgent shift from the single-disease approach to one in which the patient is seen and treated holistically," Liggins Institute PhD student Tayaza Fadason said.
In order to do that, he said, we need to understand the genetics that drive these conditions.
"But while we know some of the conditions have a genetic component, there is little information on the genetics of multimorbidity."
So Fadason and his team set out to hunt for the missing genetic links between the diseases.
They applied the new 3D genome mapping technique, which relied heavily on computational analysis, to 21,000 SNPs that have been implicated in 1350 human traits, including noncommunicable, or "lifestyle" chronic diseases and psychological disorders.
"We discovered clusters or groups within about 650 characteristics and disorders that share common genes, which are regulated by SNPs in the dark matter of the genome," he said.
"Some clusters contain traits that are closely related, such as hypertension and pulse pressure, and some contain conditions which are known to occur together in people, for example ovarian cancer, a lung disease, Alzheimer's disease and other mood disorders.
"Interestingly, other clusters contain conditions whose associations are controversial or unexpected, such as autism spectrum disorders and iron biomarker levels."
In the largest cluster, Crohn's disease, inflammatory bowel disease, body height, muscle strength, insulin sensitivity, colorectal cancer, throat cancer and cholesterol levels were all linked with changes in the activity of genes involved in metabolism of omega-3 fatty acids.
"The shared genes in each cluster could explain why the conditions tend to occur together in patients, and drugs targeting those genes could potentially be repurposed for other conditions in that cluster," Fadason said.
"So, instead of having to take maybe 10 different drugs, a patient may only need to take a couple of drugs that work on the underlying, common genetic drivers."
