English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Variant prioritization of exome sequencing (ES) data for molecular diagnosis of sensorineural hearing loss (SNHL) with extreme etiologic heterogeneity poses a significant challenge. This study used an automated variant prioritization system (“EVIDENCE”) to analyze SNHL patient data and assess its diagnostic accuracy. We performed ES of 263 probands manifesting mild to moderate or higher degrees of SNHL. Candidate variants were classified according to the 2015 American College of Medical Genetics guidelines, and we compared the accuracy, call rates, and efficiency of variant prioritizations performed manually by humans or using EVIDENCE. In our in silico panel, 21 synthetic cases were successfully analyzed by EVIDENCE. In our cohort, the ES diagnostic yield for SNHL by manual analysis was 50.19% (132/263) and 50.95% (134/263) by EVIDENCE. EVIDENCE processed ES data 24-fold faster than humans, and the concordant call rate between humans and EVIDENCE was 97.72% (257/263). Additionally, EVIDENCE outperformed human accuracy, especially at discovering causative variants of rare syndromic deafness, whereas flexible interpretations that required predefined specific genotype–phenotype correlations were possible only by manual prioritization. The automated variant prioritization system remarkably facilitated the molecular diagnosis of hearing loss with high accuracy and efficiency, fostering the popularization of molecular genetic diagnosis of SNHL.

Powerful use of automated prioritization of candidate variants in genetic hearing loss with extreme etiologic heterogeneity