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Uncovering the burden of hidden cillopathies in the 100,000 genomes project: a reverse phenotyping approach

Uncovering the burden of hidden cillopathies in the 100,000 genomes project: a reverse phenotyping approach
Uncovering the burden of hidden cillopathies in the 100,000 genomes project: a reverse phenotyping approach
Background: the 100,000 Genomes Project (100K) recruited NHS 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 focussed analysis.

Methods: we developed a reverse phenotyping strategy to identify 100K participants with pathogenic variants in nine prioritised disease genes (BBS1, BBS10, ALMS1, OFD1, DYNC2H1, WDR34, NPHP1, TMEM67, CEP290), representative of the full phenotypic spectrum of multi‐systemic primary ciliopathies. We mapped genotype data “backwards” onto available clinical data to assess potential matches against phenotypes. Participants with novel molecular diagnoses and key clinical features compatible with the identified disease gene were reported to recruiting clinicians.

Results: we identified 62 reportable molecular diagnoses with variants in these nine ciliopathy genes. Forty‐four have been reported by 100K, five were previously unreported and 13 are new diagnoses. We identified 11 participants with un‐reportable, 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.

Conclusion: reverse 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 is therefore essential for accurate variant interpretation and improved patient benefit.
0022-2593
Best, Sunayna
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Yu, Jing
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Lord, Jenny
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Roche, Matthew
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Watson, Christopher M.
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Bevers, Roel P J
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Stuckey, Alex
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Madhusudhan, Savita
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Jewell, Rosalyn
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Sisodiya, Sanjay M.
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Lin, Siying
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Turner, Stephen
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Robinson, Hannah
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Joseph, Leslie
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Baple, Emma
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Toomes, Carmel
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Inglehearn, Chris F.
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Wheway, Gabrielle
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Johnson, Colin A.
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Genomics England Research Consortium
Best, Sunayna
69abeca2-f2cb-41fb-a3c4-8a46b4962710
Yu, Jing
5fd9d3a3-5552-4a34-be88-a6a14f95eaf4
Lord, Jenny
e1909780-36cd-4705-b21e-4580038d4ec6
Roche, Matthew
ae67c475-b6a7-4bbd-8cd4-d7f046ff6f20
Watson, Christopher M.
83b46b74-e6a1-4ee9-8078-c69693de008a
Bevers, Roel P J
3ebbe106-524a-4cfd-99ff-32dd27dc1a86
Stuckey, Alex
1d13040c-8966-4c3c-afca-cfe62e2b9207
Madhusudhan, Savita
d4ba51be-8292-4ce6-9953-503c1a6e5e3c
Jewell, Rosalyn
a1e0e019-b0b1-4eb0-9563-3553865cd8b0
Sisodiya, Sanjay M.
442600da-eda0-410b-97bb-cde8326d702d
Lin, Siying
a5f9720b-f9ef-46e5-90fc-c91d20438d3c
Turner, Stephen
a51d875a-66bb-4a18-b5b0-18ce3dc7d15c
Robinson, Hannah
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Joseph, Leslie
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Baple, Emma
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Toomes, Carmel
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Inglehearn, Chris F.
83e4579c-b071-40c5-b220-5ca9d591e86b
Wheway, Gabrielle
2e547e5d-b921-4243-a071-2208fd4cc090
Johnson, Colin A.
a34c9932-8edc-406f-b45f-16b984a379c0

Best, Sunayna, Yu, Jing and Lord, Jenny , Genomics England Research Consortium (2022) Uncovering the burden of hidden cillopathies in the 100,000 genomes project: a reverse phenotyping approach. Journal of Medical Genetics. (doi:10.1136/jmedgenet-2022-108476). (In Press)

Record type: Article

Abstract

Background: the 100,000 Genomes Project (100K) recruited NHS 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 focussed analysis.

Methods: we developed a reverse phenotyping strategy to identify 100K participants with pathogenic variants in nine prioritised disease genes (BBS1, BBS10, ALMS1, OFD1, DYNC2H1, WDR34, NPHP1, TMEM67, CEP290), representative of the full phenotypic spectrum of multi‐systemic primary ciliopathies. We mapped genotype data “backwards” onto available clinical data to assess potential matches against phenotypes. Participants with novel molecular diagnoses and key clinical features compatible with the identified disease gene were reported to recruiting clinicians.

Results: we identified 62 reportable molecular diagnoses with variants in these nine ciliopathy genes. Forty‐four have been reported by 100K, five were previously unreported and 13 are new diagnoses. We identified 11 participants with un‐reportable, 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.

Conclusion: reverse 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 is therefore essential for accurate variant interpretation and improved patient benefit.

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Accepted/In Press date: 7 June 2022

Identifiers

Local EPrints ID: 468233
URI: http://eprints.soton.ac.uk/id/eprint/468233
ISSN: 0022-2593
PURE UUID: ac70b0b0-6943-4ea2-aeae-f5edaf627ca9
ORCID for Jenny Lord: ORCID iD orcid.org/0000-0002-0539-9343
ORCID for Gabrielle Wheway: ORCID iD orcid.org/0000-0002-0494-0783

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Date deposited: 08 Aug 2022 16:38
Last modified: 13 Aug 2022 02:01

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Contributors

Author: Sunayna Best
Author: Jing Yu
Author: Jenny Lord ORCID iD
Author: Matthew Roche
Author: Christopher M. Watson
Author: Roel P J Bevers
Author: Alex Stuckey
Author: Savita Madhusudhan
Author: Rosalyn Jewell
Author: Sanjay M. Sisodiya
Author: Siying Lin
Author: Stephen Turner
Author: Hannah Robinson
Author: Leslie Joseph
Author: Emma Baple
Author: Carmel Toomes
Author: Chris F. Inglehearn
Author: Colin A. Johnson
Corporate Author: Genomics England Research Consortium

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