Essentiality-specific pathogenicity prioritization gene score to improve filtering of disease sequence data
Essentiality-specific pathogenicity prioritization gene score to improve filtering of disease sequence data
The causal genetic variants underlying more than 50% of single gene (monogenic) disorders are yet to be discovered. Many patients with conditions likely to have a monogenic basis do not receive a confirmed molecular diagnosis which has potential impacts on clinical management. We have developed a gene-specific score, essentiality-specific pathogenicity prioritization (ESPP), to guide the recognition of genes likely to underlie monogenic disease variation to assist in filtering of genome sequence data. When a patient genome is sequenced, there are frequently several plausibly pathogenic variants identified in different genes. Recognition of the single gene most likely to include pathogenic variation can guide the identification of a causal variant. The ESPP score integrates gene-level scores which are broadly related to gene essentiality. Previous work towards the recognition of monogenic disease genes proposed a model with increasing gene essentiality from ‘non-essential’ to ‘essential’ genes (for which pathogenic variation may be incompatible with survival) with genes liable to contain disease variation positioned between these two extremes. We demonstrate that the ESPP score is useful for recognizing genes with high potential for pathogenic disease-related variation. Genes classed as essential have particularly high scores, as do genes recently recognized as strong candidates for developmental disorders. Through the integration of individual gene-specific scores, which have different properties and assumptions, we demonstrate the utility of an essentiality-based gene score to improve sequence genome filtering.
disease genome, gene essentiality, gene-level metrics, gene-specific score, monogenic disease, whole genome sequence
1782-1789
Alyousfi, Dareen Mohammed
d3304c17-f4a4-4928-a721-cf8886302c0e
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Collins, Andrew
7daa83eb-0b21-43b2-af1a-e38fb36e2a64
18 March 2020
Alyousfi, Dareen Mohammed
d3304c17-f4a4-4928-a721-cf8886302c0e
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Collins, Andrew
7daa83eb-0b21-43b2-af1a-e38fb36e2a64
Alyousfi, Dareen Mohammed, Baralle, Diana and Collins, Andrew
(2020)
Essentiality-specific pathogenicity prioritization gene score to improve filtering of disease sequence data.
Briefings in Bioinformatics, 22 (2), , [bbaa029].
(doi:10.1093/bib/bbaa029).
Abstract
The causal genetic variants underlying more than 50% of single gene (monogenic) disorders are yet to be discovered. Many patients with conditions likely to have a monogenic basis do not receive a confirmed molecular diagnosis which has potential impacts on clinical management. We have developed a gene-specific score, essentiality-specific pathogenicity prioritization (ESPP), to guide the recognition of genes likely to underlie monogenic disease variation to assist in filtering of genome sequence data. When a patient genome is sequenced, there are frequently several plausibly pathogenic variants identified in different genes. Recognition of the single gene most likely to include pathogenic variation can guide the identification of a causal variant. The ESPP score integrates gene-level scores which are broadly related to gene essentiality. Previous work towards the recognition of monogenic disease genes proposed a model with increasing gene essentiality from ‘non-essential’ to ‘essential’ genes (for which pathogenic variation may be incompatible with survival) with genes liable to contain disease variation positioned between these two extremes. We demonstrate that the ESPP score is useful for recognizing genes with high potential for pathogenic disease-related variation. Genes classed as essential have particularly high scores, as do genes recently recognized as strong candidates for developmental disorders. Through the integration of individual gene-specific scores, which have different properties and assumptions, we demonstrate the utility of an essentiality-based gene score to improve sequence genome filtering.
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Accepted/In Press date: 17 February 2020
e-pub ahead of print date: 18 March 2020
Published date: 18 March 2020
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© 2020 The Author(s) 2020. Published by Oxford University Press. All rights reserved.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
Keywords:
disease genome, gene essentiality, gene-level metrics, gene-specific score, monogenic disease, whole genome sequence
Identifiers
Local EPrints ID: 446053
URI: http://eprints.soton.ac.uk/id/eprint/446053
ISSN: 1467-5463
PURE UUID: d34586aa-cd60-4def-ab0e-4f45387c5de8
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Date deposited: 19 Jan 2021 17:33
Last modified: 17 Mar 2024 03:13
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Author:
Dareen Mohammed Alyousfi
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