Results from a landmark international study using state of the art technology has revealed new genetic mutations that cause epilepsy. The findings could help to advance treatments for the most severe forms of epilepsy.
The global study, led by the University of Melbourne and Austin Hospital (Aus), Duke University and the University of California, San Francisco (US), used advanced gene technology known as exome sequencing to identify new genes that cause severe childhood epilepsies.
Epilepsy is a brain condition that affects an estimated 50 million people worldwide.
As part of a larger project analysing 4,000 genomes from epilepsy patients around the world, researchers have discovered in one group of patients, two new genes and 25 epilepsy-causing mutations. The research suggests there will be common pathways to target epilepsies with drugs and other therapies.
It was published today in Nature.
Co study leader Professor Sam Berkovic, Director of the Epilepsy Research Centre, University of Melbourne and Austin Hospital said the study was a major conceptual advance in how we analyse epilepsies, helping researchers to better identify their genetic causes and improve treatment options.
"These findings will help to fast track discoveries of the genetic causes of some of the most devastating childhood epilepsies, many of which had been previously unknown," he said.
The study was part of a $25M worldwide project, funded by the National Institutes of Health, called Epilepsy 4000 (Epi4K).
Epi4K's mission is to use the latest genetic techniques to sequence and analyse DNA from 4000 epilepsy patients and their relatives. It facilitated the sharing of DNA sequences and patient information among the dozens of research institutions participating in the project.
Exomes essentially represent all of a person's genes. Their DNA sequences provide the instructions for constructing all the proteins made by the body.
The researchers compared exome sequences of 264 children with the sequences of their parents who do not have epilepsy. Differences in the sequences of these subject trios were analyzed using a number of statistical tools to identify potential disease causing mutations.