GenOtoScope: Towards automating ACMG classification of variants associated with congenital hearing loss

Damianos P Melidis; Christian Landgraf; Gunnar Schmidt; Anja Schöner-Heinisch; Sandra von Hardenberg; Anke Lesinski-Schiedat; Wolfgang Nejdl; Bernd Auber

doi:10.1371/journal.pcbi.1009785

GenOtoScope: Towards automating ACMG classification of variants associated with congenital hearing loss

PLoS Comput Biol. 2022 Sep 21;18(9):e1009785. doi: 10.1371/journal.pcbi.1009785. eCollection 2022 Sep.

Authors

Damianos P Melidis¹, Christian Landgraf², Gunnar Schmidt², Anja Schöner-Heinisch², Sandra von Hardenberg², Anke Lesinski-Schiedat^{3

4}, Wolfgang Nejdl^{1

5}, Bernd Auber²

Affiliations

¹ L3S Research Center, Leibniz University Hannover, Hannover, Germany.
² Department of Human Genetics, Hannover Medical School, Hannover, Germany.
³ Department of Otorhinolaryngology, Hannover Medical School, Hannover, Germany.
⁴ Hearing4all Cluster of Excellence, Hannover Medical School, Hannover, Germany.
⁵ Knowledge-based Systems Laboratory, Leibniz University Hannover, Hannover, Germany.

Abstract

Since next-generation sequencing (NGS) has become widely available, large gene panels containing up to several hundred genes can be sequenced cost-efficiently. However, the interpretation of the often large numbers of sequence variants detected when using NGS is laborious, prone to errors and is often difficult to compare across laboratories. To overcome this challenge, the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) have introduced standards and guidelines for the interpretation of sequencing variants. Additionally, disease-specific refinements have been developed that include accurate thresholds for many criteria, enabling highly automated processing. This is of particular interest for common but heterogeneous disorders such as hearing impairment. With more than 200 genes associated with hearing disorders, the manual inspection of possible causative variants is particularly difficult and time-consuming. To this end, we developed the open-source bioinformatics tool GenOtoScope, which automates the analysis of all ACMG/AMP criteria that can be assessed without further individual patient information or human curator investigation, including the refined loss of function criterion ("PVS1"). Two types of interfaces are provided: (i) a command line application to classify sequence variants in batches for a set of patients and (ii) a user-friendly website to classify single variants. We compared the performance of our tool with two other variant classification tools using two hearing loss data sets, which were manually annotated either by the ClinGen Hearing Loss Gene Curation Expert Panel or the diagnostics unit of our human genetics department. GenOtoScope achieved the best average accuracy and precision for both data sets. Compared to the second-best tool, GenOtoScope improved the accuracy metric by 25.75% and 4.57% and precision metric by 52.11% and 12.13% on the two data sets, respectively. The web interface is accessible via: http://genotoscope.mh-hannover.de:5000 and the command line interface via: https://github.com/damianosmel/GenOtoScope.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Genetic Testing
Genetic Variation / genetics
Genome, Human*
Hearing Loss* / genetics
Humans
Mutation
United States

Grants and funding

The authors would like to acknowledge the financial support through the project Understanding Cochlear Implant Outcome Variability using Big Data and Machine Learning Approaches, project id: ZN3429, funded by Volkswagen Foundation, through the Ministry for Science and Culture of Lower Saxony Germany (MWK: Ministerium fuer Wissenschaft und Kultur). SvH, ALS, WN and BA received funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.