P633: Benchmarking variant classification quality: Concordance of an ACMG-based framework with >400,000 ClinVar submissions from over a decade of clinical testing

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Authors: Flavia M. Facio, Sarah R. Poll, Dana Mitchell, Kirsten D. Kelly, Laure Frésard, Krystal L. Brown, Keith Nykamp, Britt A. Johnson
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Abstract

Accurate genomic variant classification is foundational to clinical interpretation and patient care. Although the American College of Medical Genetics and Genomics and Association of Molecular Pathology (ACMG/AMP) guidelines offer a standardized scaffold for evaluating evidence and assigning pathogenicity, their application requires nuanced judgment, continuous quality oversight, and standardization protocols for implementation. Variant classification at our laboratory is a semi-automated process informed by an ACMG/AMP-based framework, utilizing information from public resources and internal databases, machine-learning algorithms, and in-depth evaluation by a large team of skilled professionals.

Fields of Study & Subjects

Genomics and Rare Diseases Clinical Laboratory Practices and Quality Control Artificial Intelligence in Healthcare and Education

Publication details

Journal / source
Genetics in Medicine Open
Volume
4
Pages
104123–104123
Publication date
1 January 2026
Language
EN