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

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.

Author summary

New high-throughput sequencing technologies can produce massive amounts of information and are used by laboratories to explain the often complex genetic etiology of hereditary diseases. To use these sequencing technologies effectively, software tools have been developed that can aid researchers interpreting genetic data by semi-automatically classifying the biologic (and, thus, potentially medical) impact of the detected variants (i.e. alterations of the patient’s genome compared to the human reference genome). Variant classification itself is a very complex process: various information has to be taken into account and carefully judged, allowing room for interpretation. Over the last decade, the quality of genetic analysis evaluation and the consistency between different laboratories have improved substantially. This is in particular a result of increasing standardization, which extends to gene- or disease-specific considerations. Hereby, a largely uniform variant interpretation is possible even for common but heterogeneous disorders.

To design a reliable tool that can accommodate these precise specifications, we chose hearing loss as a model disease because it is the most common sensory disorder, often hereditary and has a high impact on patients every-day lifes. The currently available genetic variant classification tools are either not designed specifically for the interpretation of variants detected in subjects with hearing loss or they do not allow researchers to use them for batch classification of all variants detected, e.g. in a study group. To address this drawback, we developed GenOtoScope, an open-source tool that automates the pathogenicity classification of variants potentially associated with congenital hearing loss. By adjusting specific parameters, the algorithm could also be easily adapted to other medical conditions. GenOtoScope can be applied for the automatic classification of all variants detected in a set of individuals.