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/.