PUBLIC RELEASE DATE:

18-Dec-2014

RJ Taylor 647-228-4358 rj.taylor@utoronto.ca

Lindsay Jolivet Writer & Media Relations Specialist Canadian Institute for Advanced Research 416-971-4876; lindsay.jolivet@cifar.ca

In the decade since the genome was sequenced in 2003, scientists and doctors have struggled to answer an all-consuming question: Which DNA mutations cause disease?

A new computational technique developed at the University of Toronto may now be able to tell us.

A Canadian research team led by professor Brendan Frey has developed the first method for 'ranking' genetic mutations based on how living cells 'read' DNA, revealing how likely any given alteration is to cause disease. They used their method to discover unexpected genetic determinants of autism, hereditary cancers and spinal muscular atrophy, a leading genetic cause of infant mortality.

Their findings appear in today's issue of the leading journal Science.

Think of the human genome as a mysterious text, made up of three billion letters. "Over the past decade, a huge amount of effort has been invested into searching for mutations in the genome that cause disease, without a rational approach to understanding why they cause disease," says Frey, also a senior fellow at the Canadian Institute for Advanced Research. "This is because scientists didn't have the means to understand the text of the genome and how mutations in it can change the meaning of that text." Biologist Eric Lander of the Massachusetts Institute of Technology captured this puzzle in his famous quote: "Genome. Bought the book. Hard to read."

What was Frey's approach? We know that certain sections of the text, called exons, describe the proteins that are the building blocks of all living cells. What wasn't appreciated until recently is that other sections, called introns, contain instructions for how to cut and paste exons together, determining which proteins will be produced. This 'splicing' process is a crucial step in the cell's process of converting DNA into proteins, and its disruption is known to contribute to many diseases.

The rest is here:

Machine learning reveals unexpected genetic roots of cancers, autism and other disorders