New Tool Enables Better Classification of Inherited Disease-Causing Variants

Whole genome and exome sequencing are increasingly available for clinical research, aiding in the detection of inherited genetic variants that may cause various diseases. The American College of Medical Genetics-Association for Molecular Pathology (ACMG-AMP) provides regularly updated guidelines to help clinicians assess whether germline variants are likely related to a patient’s disease. However, automated tools often struggle to stay current with these updates. A new tool has now been developed that allows researchers to annotate variant data from large-scale studies with clinically relevant classifications for risks of childhood cancer and other diseases, aligning older applications with the latest guidelines. This tool is freely available to the research community.

Developed by a team including scientists from Children’s Hospital of Philadelphia (CHOP, Philadelphia, PA, USA), the tool, named Automated Germline Variant Pathogenicity (AutoGVP), incorporates germline variant pathogenicity annotations from the ClinVar database and variant classifications from a modified version of InterVar. AutoGVP provides pathogenicity classifications that adhere to the evolving ACMG-AMP guidelines by integrating information from both ClinVar and InterVar. It also addresses the limitations of the InterVar tool, particularly its tendency to overestimate the pathogenicity of loss-of-function variants that reduce the activity of a specific gene.

The utility of AutoGVP was highlighted in a study conducted by the research team, which analyzed germline DNA sequencing from 786 neuroblastoma patients and found 116 pathogenic or likely pathogenic variants. The study revealed that these patients had a lower survival probability and identified BARD1 as a significant predisposition gene for neuroblastoma, featuring both common and rare pathogenic or likely pathogenic germline variations. AutoGVP was created to aid the large-scale annotation of germline variants and assign pathogenicity automatically. The team is currently applying AutoGVP to genetic data from pediatric brain tumor patients and larger neuroblastoma cohorts.

“With AutoGVP, we can streamline variant classification and swiftly incorporate new information as more and more biobanks release large sequencing data,” said Jung Kim, PhD, a staff scientist at the Division of Cancer Epidemiology and Genetics at the NCI. “Furthermore, AutoGVP reduces hands-on curating of variants and allows for reproducibility of the variant curation.”

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