Genome sequencing is currently tapped to diagnose diseases and to personalize cancer treatments. But can it also be used to predict disease?
Michael Snyder, PhD, professor and chair of genetics at Stanford, and colleagues have devised an algorithm that incorporates genetic sequences and electronic health information to figure out the likelihood that someone will have a certain genetic disease. They began with a condition called abdominal aortic aneurysm, or AAA, which affects more than 3 million people every year, can be caused by genetic factors and is tricky to detect.
A paper describing their research appears in Cell. Snyder and Philip Tsao, PhD, professor of medicine at Stanford are the senior authors.
Our press release explains:
The method seeks to identify any likely disease-causing culprits in an 'agnostic' manner, meaning that it combs through an onslaught of genetic information from patients with AAA, looking for commonalities.
This, Snyder said, is the key to unraveling any number of genetic diseases. It's not often the case that one, two or even a handful of genes take sole responsibility for a condition. Far more likely is that it's a whole bunch of them. The idea is that it takes a village to cause a disease, and by using this new method, those villagers can be identified.
The algorithm, dubbed Hierarchical Estimate From Agnostic Learning, or HEAL, employs a form of artificial intelligence called machine learning, and although Snyder and his collaborators have only used the framework to predict the likelihood of AAA, it's a "proof of principle," showing that this kind of approach could identify the molecular patterns that convey risk for just about any genetic disease.
From the release:
Even for diseases that have these big 'red flag' genomic markers, HEAL could offer a leg up, Snyder said.
'For example, in familiar cases like breast cancer, for which we know of specific 'culprit' genes, you have to remember that these genes -- BRCA1, BRCA2 and a couple others -- only explain about 30 percent of the genetics of the disease,' Snyder said. 'That means 70 percent is still unexplained. There are probably multiple genes and mutations involved, and that's where we think HEAL may kick in big time.'
Next, Snyder plans to use HEAL to help understand the genetic signatures of preterm birth and autism, two conditions whose genetic underpinnings remain murky.
Photo by Thor_Deichmann