Authors
R Arda İnan, Barış Kayaalp, Fatimah Safieh, M Ece Kars, David Stein, David N Cooper, Peter D Stenson, Özlen Konu, Jean-Laurent Casanova, Yuval Itan, A Nazlı Başak, Tayfun Özçelik
Published in
Genetics in medicine : official journal of the American College of Medical Genetics. Pages 102624. Jun 01, 2027. Epub Jun 01, 2027.
Abstract
Classification of DNA sequence data requires the implementation of the American College of Medical Genetics and Genomics (ACMG) standards and guidelines. Therefore, automated tools have been developed. However, these tools often lack robust and up-to-date methodologies. This study reports the development of a new tool and examines its performance for diagnostic and research purposes.
The automated ACMG-based variant classifier (AAVC), presented here, computationally analyzes sequence variants following the ACMG guidelines, the ClinGen specifications, and a novel framework by leveraging large public databases and in silico prediction tools.
AAVC demonstrated a high concordance (94.39%) with the FDA-recognized variant classifications, outperforming currently available tools. It classified 55% of the variants of uncertain significance in ClinVar into clinically significant categories. We identified, in the Turkish Variome, 215 novel pathogenic, likely pathogenic or VUS-high variants in the secondary finding genes and revealed that 1 in 10 individuals carried an actionable genotype.
AAVC constitutes a robust framework for accurate classification of human germline sequence diversity available at https://aavc.bilkent.edu.tr/, offering a highly accurate, rapid, and up-to-date platform for clinical laboratories and research groups to automatically interpret sequence variants.
PMID:
42454476
Bibliographic data and abstract were imported from PubMed on 15 Jul 2026.
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