A Comparative Survey of Functional Evidence Use in Hearing and Vision Loss Genetics
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Background
Advances in sequencing and standardized guidelines for variant classification have improved genetic diagnostics, but a large proportion of variants, particularly in genetically heterogeneous disorders such as hearing and vision loss, remain of uncertain significance. Although emerging multiplexed assays of variant effects (MAVEs) offer a solution to resolve these uncertainties, their integration into clinical practice in these fields remains constrained by limited familiarity and variable implementation among genetics professionals.
Methods
We conducted a survey of hearing and ocular genetics professionals ( n = 82) to explore their practices, experiences, and challenges in variant classification. The survey captured both quantitative and qualitative insights, allowing for an assessment of how experts engage with functional evidence and identifying patterns, perspectives, and areas for potential improvement in clinical workflows.
Results
Experts across hearing and vision loss genetics frequently encountered variants of uncertain significance (VUSs) and reported that functional data, while useful, often failed to resolve classification uncertainty due to both limited availability and variable quality of existing studies. Highest confidence was expressed in transcript assays and patient-derived cell models, as well as in computational predictors, while limited confidence or familiarity was reported for non-mammalian models and high-throughput experimental assays. Across both domains, respondents emphasized the need for improved data accessibility, standardized guidelines in the assessment of functional data for different genes or disease areas and expanded training to enhance the integration and clinical utility of functional evidence in variant classification.
Conclusions
Our survey identifies both common and domain-specific challenges in applying functional evidence for variant classification in hearing and ocular genetics, emphasizing the need for frameworks that account for biological complexity, methodological limitations, and barriers to data accessibility.
