The cost of sequencing the entire human genome, or exome the regions of the genome that are translated into proteins that affect cell behavior has decreased significantly, to the point where the cost of looking at the majority of a patient's genomic data may be less expensive than undertaking one or two targeted genetic tests. While efficient, the acquisition of this much genetic data in some cases as many as 1.5 to 2 million variants creates other challenges.
In a paper that appears today in the advance online edition of Genetics in Medicine, researchers from the University of North Carolina at Chapel Hill unveil an analysis framework aimed at helping clinicians spot "medically actionable findings" from genetic tests in an efficient manner.
"The challenge for medical geneticists is what do we do with the 'incidentalome' the large amount of genetic data that these tests generate which may be important but which was incidental that is, had nothing to do with why the patient underwent DNA analysis in the first place," said Jonathan Berg, MD, PhD, assistant professor of clinical genetics and a member of UNC Lineberger Comprehensive Cancer Center.
"Our team is faced with this issue in a clinical trial we are conducting called the NC GENES study. So we put together a framework that classifies genetic variations into three different 'bins': those that are linked to a treatable or preventable condition (the medically actionable); those that have a known link to conditions for which we don't have treatment options; and those for which there is no known direct association between a genetic variation and a disorder," he said.
The team then created an informatics approach to carry out a structured analysis on these three 'bins'.
"While there are still some challenges, we believe that this approach facilitates the analysis and streamlines the ability of the molecular analyst to go through a lot of data very quick
|Contact: Ellen de Graffenreid|
University of North Carolina Health Care