Abstract
Inherited retinal dystrophies (IRDs) are a genetically diverse group of vision loss disorders with over 360 implicated genes. However, 30-50% of cases remain unresolved after panel-based clinical testing and may benefit from exome or genome sequencing for a genetic diagnosis. To manage the extensive and analytically demanding datasets generated by genome sequencing, we developed ReDGAP (Retinal Degeneration Genome Analysis Pipeline), a phenotype-guided, semi-automated genome analysis pipeline that integrates clinical phenotyping with flexible variant scoring to prioritize variants of interest (https://github.com/vincentlab-la/ReDGAP). The pipeline supports the joint analysis of multiple variant classes, using an evidence-weighted scoring system informed by in silico predictors. Validation in eleven previously solved IRD cases achieved a 100% re-identification rate. Application to five unsolved cases yielded diagnoses in four (80%), including intronic variants in CRB1 and HGSNAT, a tandem duplication in OAT, and a 5′UTR deletion affecting a retina-specific promoter of RPGRIP1. Functional validation confirmed transcript-level disruptions in three variants, while computational analysis demonstrated regulatory impact in the fourth. Integrating phenotypic data with broad variant analysis offers a tailored model for improving IRD diagnostics, enabling timely molecular diagnoses and informing eligibility for emerging gene-targeted therapies. This positions ReDGAP as a tailored, clinically relevant model for investigating rare diseases within the evolving landscape of precision health.
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Data availability
Data supporting the findings of this study are available within the paper and its Supplementary Information.
Code availability
The underlying code for this pipeline is available on Github and can be accessed via the link https://github.com/vincentlab-la/ReDGAP.
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Acknowledgements
We thank the patients and their families for their participation in this study, and Bhooma Thiruvahindrapuram for technical support. LA and MM were awarded a scholarship from the Vision Science Research Program (Department of Ophthalmology and Vision Sciences, University of Toronto). This study was funded by the Foundation Fighting Blindness, USA [CDCMM-0224-0873-HSC-(AV)], Department of Ophthalmology Research Funds, Hospital for Sick Children Toronto), The Henry Brent Chair in Innovative Pediatric Ophthalmology Research and the Azrieli Precision Child Health Platform for ARD. The funders played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript.
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Conceptualization: L.A., E.T., J.M.L., K.A., C.R.M., E.H., and A.V. Patient phenotyping: G.N., E.H., and A.V. Dry lab strategy: L.A., E.T., J.M.L., A.D., C.R.M., and A.V. Wet lab strategy: L.A., E.T., J.M.L., K.A., T.P., G.C., and A.V. Dry and wet lab experiments: L.A., M.M., C.A., G.A.-S., R.O., K.G.-S., and G.C. Investigation and analysis: L.A., E.T., J.M.L., K.A., M.M., C.E., G.A.-S., R.O., A.R.D., T.P., G.C., and A.V. Manuscript Draft writing: L.A., E.T., K.A, M.M., and A.V. Manuscript Review and Editing: All authors. Funding acquisition: E.H. and A.V.
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Ahmed, L., Tavares, E., Li, J.M. et al. A novel phenotype-guided genome analysis pipeline for variant discovery. npj Genom. Med. (2026). https://doi.org/10.1038/s41525-026-00557-0
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DOI: https://doi.org/10.1038/s41525-026-00557-0
