Evidence map›Paper›PMID 41904215›Full record

ArticleNPJ genomic medicine2026

A novel phenotype-guided genome analysis pipeline for variant discovery.

Layla Ahmed, Erika Tavares, Janice Min Li, Kashif Ahmed, Maanik Mehta, Christabel Eileen, Genevieve Ah-Sen, Rahma Osman, Kit Green-Sanderson, Anna Dvaladze and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ genomic medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Layla AhmedUniversity of Toronto, Toronto, ON, Canada.
Erika TavaresGenetics and Genomic Biology, The Hospital for Sick Children, Toronto, ON, Canada.
Janice Min LiGenetics and Genomic Biology, The Hospital for Sick Children, Toronto, ON, Canada.
Kashif AhmedGenetics and Genomic Biology, The Hospital for Sick Children, Toronto, ON, Canada.
Maanik MehtaGenetics and Genomic Biology, The Hospital for Sick Children, Toronto, ON, Canada.
Christabel EileenUniversity of Toronto, Toronto, ON, Canada.
Genevieve Ah-SenGenetics and Genomic Biology, The Hospital for Sick Children, Toronto, ON, Canada.
Rahma OsmanGenetics and Genomic Biology, The Hospital for Sick Children, Toronto, ON, Canada.
Kit Green-SandersonUniversity of Toronto, Toronto, ON, Canada.
Anna DvaladzeUniversity of Toronto, Toronto, ON, Canada.
Graeme NimmoClinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, ON, Canada.
Ashish R DeshwarClinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, ON, Canada.
Tara PatonThe Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada.
Guillermo CasalloThe Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada.
Christian R MarshallThe Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada.
Elise HeonUniversity of Toronto, Toronto, ON, Canada.
Ajoy VincentUniversity of Toronto, Toronto, ON, Canada. ajoy.vincent@sickkids.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID41904215
PMCPMC13194906

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.