Introduction
Hearing loss is a primary global health concern, affecting about 6.8% of the world’s population (Wilson, Tucci, Merson, & O’Donoghue, 2017). Genetic factors account for at least 50 to 60% of childhood hearing loss (Morton & Nance, 2006). To date, over 150 deafness-related genes were discovered (Azaiez et al., 2018). The advancement of next-generation sequencing technology and its continually decreasing cost have facilitated the genetic diagnosis of hearing loss (Abou Tayoun et al., 2016). One of the remaining significant challenges, however, is the accurate interpretation of a large number of identified variants.
In 2015, the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) published a joint guideline to standardize variant interpretation (Richards et al., 2015). The guideline offered a common framework for curators to resolve variant classification differences, and to improve interpretation consistency across laboratories (Amendola et al., 2016). To evolve the guideline over time, National Institutes of Health-funded Clinical Genome Resource (ClinGen) developed groups of disease experts to refine recommendations for different disease areas, including genetic hearing loss (Oza et al., 2018), MYH7 -associated inherited cardiomyopathies (Kelly et al., 2018), germline CDH1 (Lee et al., 2018), andPTEN -associated hereditary cancer (Mester et al., 2018).
Using bioinformatics tools to facilitate and standardize variant interpretation is one of top priorities, and has been shown to be useful for curators (Kopanos et al., 2019). InterVar is one such tool semi-automating 18 criteria in the ACMG/AMP guidelines (Li & Wang, 2017). However, generic tools may not fulfill the need because many interpreting standards are disease-specific and vary dramatically amongst different diseases (Kanavy et al., 2019). In 2018, two tools named CardioClassifier (Whiffin et al., 2018) and CardioVAI (Nicora et al., 2018) were explicitly developed for interpreting variants in cardiovascular diseases. In 2019, a semi-automated tool called “Variant Interpretation for Cancer” (VIC) was developed to accelerate the interpretation process in Cancer (He et al., 2019).
Here, we developed aV ariant I nterpretation P latform for geneticH earing L oss (VIP-HL). This new online tool utilizes the framework outlined by the ClinGen Hearing Loss Expert Panel (HL-EP) (Oza et al., 2018) to automatically annotate variants in 142 hearing loss related genes across 13 ACMG/AMP rules. VIP-HL is freely accessible for non-commercial users in a web server athttp://hearing.genetics.bgi.com/.