Evidence map›Paper›PMID 35764379›Full record

ArticleJournal of medical genetics2022

Uncovering the burden of hidden ciliopathies in the 100 000 Genomes Project: a reverse phenotyping approach.

Sunayna Best, Jing Yu, Jenny Lord, Matthew Roche, Christopher Mark Watson, Roel P J Bevers, Alex Stuckey, Savita Madhusudhan, Rosalyn Jewell, Sanjay M Sisodiya and 10 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of medical genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.7field-weighted citation impact, top 15% of its field
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

8 citing papers in PubMed, 10 citations in OpenAlex.

  1. Genetic testing in chronic kidney disease of uneXplained cause (CKDx): clinical insights and evolving diagnostic paradigms.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026
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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

20 authors at 11 institutions in 1 country.

Sunayna BestDivision of Molecular Medicine, Leeds Institute of Medical Research, University of Leeds, Leeds, UK.
Jing YuNuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
Jenny LordUniversity Hospital Southampton NHS Foundation Trust, Southampton, UK.
Matthew RocheWindsor House Group Practice, Mid Yorkshire Hospitals NHS Trust, Leeds, UK.
Christopher Mark WatsonDivision of Molecular Medicine, Leeds Institute of Medical Research, University of Leeds, Leeds, UK.ORCID 0000-0003-2371-1844
Roel P J BeversGenomics England, Queen Mary University of London, London, UK.
Alex StuckeyGenomics England, Queen Mary University of London, London, UK.
Savita MadhusudhanSt. Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, UK.
Rosalyn JewellYorkshire Regional Genetics Service, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Sanjay M SisodiyaUniversity College London (UCL) Queen Square Institute of Neurology, London, UK.
Siying LinDepartment of Ophthalmology, Torbay and South Devon NHS Foundation Trust, Torquay, UK.
Stephen TurnerDepartment of Ophthalmology, Torbay and South Devon NHS Foundation Trust, Torquay, UK.
Hannah RobinsonExeter Genomics Laboratory, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK.
Joseph S LeslieRILD Wellcome Wolfson Centre, University of Exeter Medical School, Exeter, UK.
Emma BapleRILD Wellcome Wolfson Centre, University of Exeter Medical School, Exeter, UK.
Genomics England Research Consortium
Carmel ToomesDivision of Molecular Medicine, Leeds Institute of Medical Research, University of Leeds, Leeds, UK.
Chris InglehearnDivision of Molecular Medicine, Leeds Institute of Medical Research, University of Leeds, Leeds, UK.
Gabrielle WhewayUniversity Hospital Southampton NHS Foundation Trust, Southampton, UK.ORCID 0000-0002-0494-0783
Colin A JohnsonDivision of Molecular Medicine, Leeds Institute of Medical Research, University of Leeds, Leeds, UK c.johnson@leeds.ac.uk.ORCID 0000-0002-2979-8234
University of Leeds · GBQueen Mary University of London · GBRoyal Devon & Exeter NHS Foundation Trust · GBUniversity Hospital Southampton NHS Foundation Trust · GBUniversity of Exeter · GBJohn Radcliffe Hospital · GBLeeds Teaching Hospitals NHS Trust · GBMid Yorkshire Hospitals NHS Trust · GBNational Hospital for Neurology and Neurosurgery · GBTorbay and South Devon NHS Foundation Trust · GBUniversity of Liverpool · GB

Funding

Cancer Research UKDepartment of Health RP-2016-07-011Medical Research Council MC_PC_14089Medical Research Council MR/M000532/1Medical Research Council MR/M009203/1Medical Research Council MR/T017503/1Wellcome TrustWellcome Trust 203914/Z/16/ZWellcome Trust 204378/Z/16/Z
6 · The paper itself

Abstract

backgroundThe 100 000 Genomes Project (100K) recruited National Health Service patients with eligible rare diseases and cancer between 2016 and 2018. PanelApp virtual gene panels were applied to whole genome sequencing data according to Human Phenotyping Ontology (HPO) terms entered by recruiting clinicians to guide focused analysis.

methodsWe developed a reverse phenotyping strategy to identify 100K participants with pathogenic variants in nine prioritised disease genes (

resultsWe identified 62 reportable molecular diagnoses with variants in these nine ciliopathy genes. Forty-four have been reported by 100K, 5 were previously unreported and 13 are new diagnoses. We identified 11 participants with unreportable, novel molecular diagnoses, who lacked key clinical features to justify reporting to recruiting clinicians. Two participants had likely pathogenic structural variants and one a deep intronic predicted splice variant. These variants would not be prioritised for review by standard 100K diagnostic pipelines.

conclusionReverse phenotyping improves the rate of successful molecular diagnosis for unsolved 100K participants with primary ciliopathies. Previous analyses likely missed these diagnoses because incomplete HPO term entry led to incorrect gene panel choice, meaning that pathogenic variants were not prioritised. Better phenotyping data are therefore essential for accurate variant interpretation and improved patient benefit.

Indexed as

Bardet-Biedl SyndromeCiliopathiesAntigens, NeoplasmCarrier ProteinsCell Cycle ProteinsCytoskeletal ProteinsGenome, HumanGenotypeHumansMicrotubule-Associated ProteinsPhenotypeState MedicineAntigens, NeoplasmBbs1 protein, humanCarrier ProteinsCell Cycle ProteinsCep290 protein, humanCytoskeletal ProteinsMicrotubule-Associated ProteinsWDR34 protein, humangenetics, medicalgenomics

Identifiers

PMID35764379
PMCPMC9691823
OpenAlexW4283705069

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.