Abstract
As the applications and use of NGS has expanded, new analysis and interpretation tools have been needed. For example, FitzPatrick and his collaborators built VEP-G2P, which is an extension to Ensembl Variant Effect Predictor (VEP), another popular publicly available program that is used to predict the possible effects of a particular variant. VEP-G2P was built specifically to help diagnose patients with genetically heterogeneous clinical presentation, like those FitzPatrick sees in the clinic. Those are particularly hard cases. “The main problem is that each of us has several thousand variants,” he explains. “The challenge is filtering out the irrelevant ones.”
This project was powered by data from the Deciphering Developmental Disorders Study (DDD). That project recruited more than 13,000 patients with previously undiagnosed severe developmental disorders (DD) from the U.K. and the Republic of Ireland. Those patients, and their parents, were all sequenced. Next, a database of all known loci causing DDs was created, continually updated, and used in studies of DD. FitzPatrick and colleagues used the basic architecture and processes employed in building that database to create VEP-2GP and associated tools. Basically, they filter out variants that are found in healthy patients and then predict which of the remaining variants are likely to be pathogenic, that means going from a few thousand to 2-4 target variants. The program shows high sensitivity and precision compared to other public tools in a recent report in Nature Communications.
David FitzPatrick, Ph.D., University of Edinburgh
