by A team of researchers from Children’s Hospital of Philadelphia (CHOP), the Perelman School of Medicine at the University of Pennsylvania, and the National Cancer Institute (NCI) of the National Institutes of Health have developed a new tool called Automated Germline Variant Pathogenicity (AutoGVP) to help scientists annotate variant data from large-scale studies with clinically-focused classifications for the risk of childhood cancer and other diseases. This innovative tool is freely available to the research community and was recently published in the journal Bioinformatics.
Whole genome and exome sequencing have become increasingly popular tools in clinical research for identifying inherited (germline) genetic variants that could lead to various diseases. However, guidelines from the American College of Medical Genetics-Association for Molecular Pathology (ACMG-AMP) are frequently updated to help clinicians determine if germline variants are responsible for a patient’s disease. Automated tools may not always keep up with these updates.
“Our goal was to create a publicly available tool that could evolve with these guidelines while still utilizing many of the critical databases and approaches the research community has come to know,” said senior study author Sharon J. Diskin, Ph.D., a member of the faculty of the Department of Biomedical and Health Informatics at CHOP and Associate Professor of Pediatrics at Penn Medicine.
AutoGVP integrates germline variant pathogenicity annotations from the ClinVar database and sequence variant classifications from a modified version of the tool InterVar. This new tool returns pathogenicity classifications based on evolving ACMG-AMP guidelines through integration of ClinVar and InterVar. It also addresses the InterVar tool’s potential to overinterpret pathogenicity from loss-of-function variants that reduce the activity of a particular gene.
The need for AutoGVP was highlighted in a study published last year by Diskin and colleagues in the Journal of the National Cancer Institute. The study analyzed the germline DNA sequencing of 786 neuroblastoma patients and identified 116 pathogenic or likely pathogenic variants. Patients carrying these germline variants had a worse survival probability, and the study identified BARD1 as an important neuroblastoma predisposition gene with both common and rare germline pathogenic or likely pathogenic variations.
