A gene pathogenicity tool ‘GenePy’ identifies missed biallelic diagnoses in the 100,000 Genomes Project
A gene pathogenicity tool ‘GenePy’ identifies missed biallelic diagnoses in the 100,000 Genomes Project
Purpose: The 100,000 Genomes Project diagnosed a quarter of affected participants, but 26% of diagnoses were not on the applied gene panel(s); with many being de novo variants. Assessing biallelic variants without a gene panel is more challenging. Methods: We sought to identify missed biallelic diagnoses using GenePy, which incorporates allele frequency, zygosity, and a user-defined deleterious metric, generating an aggregate GenePy score per gene, per participant. We calculated GenePy scores for 2862 recessive disease genes in 78,216 100,000 Genomes Project participants. For each gene, we ranked participant GenePy scores and scrutinized affected participants without a diagnosis, whose scores ranked among the top 5 for each gene. In cases which participant phenotypes overlapped with the disease gene of interest, we extracted rare variants and applied phase, ClinVar, and ACMG classification. Results: 3184 affected individuals without a molecular diagnosis had a top-5-ranked GenePy score and 682 of 3184 (21%) had phenotypes overlapping with a top-ranking gene. In 122 of 669 (18%) phenotype-matched cases (excluding 13 withdrawn participants), we identified a putative missed diagnosis (2.2% of all undiagnosed participants). A further 334 of 669 (50%) cases have a possible missed diagnosis but require functional validation. Conclusion: Applying GenePy at scale has identified 456 potential diagnoses, demonstrating the value of novel diagnostic strategies.
Diagnostic uplift, Next-generation sequencing, Novel methods, Rare disease, Recessive disease
Seaby, Eleanor G.
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Leggatt, Gary
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Cheng, Guo
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Thomas, N. Simon
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Ashton, James J.
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Stafford, Imogen
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Baralle, Diana
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Rehm, Heidi L.
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O’Donnell-Luria, Anne
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Ennis, Sarah
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Genomics England Research Consortium
1 April 2024
Seaby, Eleanor G.
f9011f96-bbc5-4364-970a-0f510489c539
Leggatt, Gary
546eb2be-3056-4e1b-bbef-66b6313280af
Cheng, Guo
fdfb3e03-f185-49b1-9c53-05b93bb6c8d0
Thomas, N. Simon
1a601957-288d-4f12-a9f7-4f4279b7f9b3
Ashton, James J.
1c0bfa29-794c-4fd5-93e0-6769e6037d72
Stafford, Imogen
50987dc1-3772-408f-9093-9124f3d6b2cd
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Rehm, Heidi L.
0e4087b7-519d-4b95-84ac-412d1c0173d1
O’Donnell-Luria, Anne
2c660818-57d6-4eb4-9e70-908bdbefadb3
Ennis, Sarah
7b57f188-9d91-4beb-b217-09856146f1e9
Seaby, Eleanor G., Leggatt, Gary, Cheng, Guo, Thomas, N. Simon, Ashton, James J., Stafford, Imogen, Baralle, Diana, Rehm, Heidi L., O’Donnell-Luria, Anne and Ennis, Sarah
,
Genomics England Research Consortium
(2024)
A gene pathogenicity tool ‘GenePy’ identifies missed biallelic diagnoses in the 100,000 Genomes Project.
Genetics in Medicine, 26 (4), [101073].
(doi:10.1016/j.gim.2024.101073).
Abstract
Purpose: The 100,000 Genomes Project diagnosed a quarter of affected participants, but 26% of diagnoses were not on the applied gene panel(s); with many being de novo variants. Assessing biallelic variants without a gene panel is more challenging. Methods: We sought to identify missed biallelic diagnoses using GenePy, which incorporates allele frequency, zygosity, and a user-defined deleterious metric, generating an aggregate GenePy score per gene, per participant. We calculated GenePy scores for 2862 recessive disease genes in 78,216 100,000 Genomes Project participants. For each gene, we ranked participant GenePy scores and scrutinized affected participants without a diagnosis, whose scores ranked among the top 5 for each gene. In cases which participant phenotypes overlapped with the disease gene of interest, we extracted rare variants and applied phase, ClinVar, and ACMG classification. Results: 3184 affected individuals without a molecular diagnosis had a top-5-ranked GenePy score and 682 of 3184 (21%) had phenotypes overlapping with a top-ranking gene. In 122 of 669 (18%) phenotype-matched cases (excluding 13 withdrawn participants), we identified a putative missed diagnosis (2.2% of all undiagnosed participants). A further 334 of 669 (50%) cases have a possible missed diagnosis but require functional validation. Conclusion: Applying GenePy at scale has identified 456 potential diagnoses, demonstrating the value of novel diagnostic strategies.
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Accepted/In Press date: 11 January 2024
e-pub ahead of print date: 18 January 2024
Published date: 1 April 2024
Additional Information:
Acknowledgements: this research was made possible through access to the data and findings generated by the 100,000 Genomes Project. The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health and Social Care). The 100,000 Genomes Project is funded by the National Institute for Health Research and NHS England. The Wellcome Trust, Cancer Research UK and the Medical Research Council have also funded research infrastructure. The 100,000 Genomes Project uses data provided by patients and collected by the National Health Service as part of their care and support. We would further like to extend our thanks to all the patients and their families for participation in the 100,000 Genomes Project.
Funding statement: EGS was supported by the Kerkut Charitable Trust, a Foulkes Fellowship from the Foulkes Foundation, and the University of Southampton’s Presidential Scholarship Award; HLR and AO’D-L and sequencing were supported by the National Human Genome Research Institute (NHGRI) grant U01HG011755 as part of the GREGoR consortium and HR by NHGRI R01HG009141. DB was generously supported by a National Institute of Health Research (NIHR) Research Professorship RP-2016-07-011. JJA is funded by an NIHR advanced fellowship (NIHR302478). This study was supported by the National Institute for Health Research (NIHR) Southampton Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.
Publisher Copyright:
© 2024 The Authors
Keywords:
Diagnostic uplift, Next-generation sequencing, Novel methods, Rare disease, Recessive disease
Identifiers
Local EPrints ID: 487423
URI: http://eprints.soton.ac.uk/id/eprint/487423
ISSN: 1098-3600
PURE UUID: dbf77ff0-1724-47d7-a894-ee6279d00798
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Date deposited: 20 Feb 2024 12:56
Last modified: 03 May 2024 16:39
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Contributors
Author:
Eleanor G. Seaby
Author:
Gary Leggatt
Author:
Guo Cheng
Author:
N. Simon Thomas
Author:
James J. Ashton
Author:
Imogen Stafford
Author:
Heidi L. Rehm
Author:
Anne O’Donnell-Luria
Corporate Author: Genomics England Research Consortium
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