QIAGEN Supports New ACMG Guidelines with Integration into Data Interpretation Tools

The American College of Medical Genetics and Genomics (ACMG), in collaboration with the Association for Molecular Pathology (AMP), recently published new guidelines for the interpretation of sequence variants. These guidelines are significant in their inclusion of an evidence-based gene variant classification system and accompanying standard terminology designed to assist genetic testing laboratories and clinical geneticists tasked with assessing the pathogenicity of  individual variants.

In a statement, Heidi Rehm, Ph.D., Chief Laboratory Director at Partners Laboratory for Molecular Medicine said, “Navigating the complexity of genetic evidence and how to weigh the strength of that evidence is challenging for laboratories and this guidance will help provide a consistent framework for that process.”

QIAGEN is on the forefront of helping research scientists and clinicians interpret DNA sequence data and evaluate the disease-causing potential of genetic variation. We have been actively working with ACMG to compute classifications based on these new guidelines given how content-dependent, complex and time consuming it is for labs to manually score variants.

QIAGEN’s Ingenuity Variant Analysis, for example, implements the new ACMG guidelines computationally and leverages QIAGEN’s manually-curated clinical evidence from the literature to automatically provide ACMG classifications. Classifications are presented to users in a gene-relevant disease context for every variant in their datasets and there is visibility into which of the approximately 30 ACMG criteria have been applied to the computed classification.  Here is an example of how a BRCA missense variant appears in Ingenuity Variant Analysis when a user clicks on a classification finding deemed “Pathogenic:”

BRCA missense in IVA

The ACMG guidelines have also been incorporated into our soon-to-be-released platform for the interpretation of next-generation sequencing data in clinical settings. In this example, we are showing a BRCA2 variant (p.R3052W) in the disease context (HBOC = hereditary breast and/or ovarian cancer syndrome) that many clinical labs utilize to report these BRCA1/2 hereditary cancer variants.

Computational Classification explanation

QIAGEN is also supporting other community initiatives aimed at increasing the clinical utility of genomic information, including the recently announced Allele Frequency Community (AFC). The formation of the AFC addresses a key challenge in interpreting sequencing data for research and clinical applications: the lack of an extensive, high quality, ethnically-diverse collection of human genomes as a reference set. Scientists need diverse, large-scale data on allele frequencies to accurately identify potential disease-causing DNA changes in a population. Information on allele frequency also tells clinicians how common certain changes are within the population, helping to distinguish rare, disease-causing DNA changes from more common variations.

QIAGEN applauds the new guidelines and remains committed to working with organizations such as ACMG and our customers to develop technologies that help researchers and clinicians who are working to make genomic data more informative and actionable.