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Gene-specific metrics to facilitate identification of disease genes for molecular diagnosis in patient genomes: a systematic review

Gene-specific metrics to facilitate identification of disease genes for molecular diagnosis in patient genomes: a systematic review
Gene-specific metrics to facilitate identification of disease genes for molecular diagnosis in patient genomes: a systematic review
The evolution of next-generation sequencing technologies has facilitated the detection of causal genetic variants in diseases previously undiagnosed at a molecular level. However, in genome sequencing studies, the identification of disease genes among a candidate gene list is often difficult because of the large number of apparently damaging (but usually neutral) variants. A number of variant prioritization tools have been developed to help detect disease-causal sites. However, the results may be misleading as many variants scored as damaging by these tools are often tolerated, and there are inconsistencies in prediction results among the different variant-level prediction tools. Recently, studies have indicated that understanding gene properties might improve detection of genes liable to have associated disease variation and that this information improves molecular diagnostics. The purpose of this systematic review is to evaluate how understanding gene-specific properties might improve filtering strategies in clinical sequence data to prioritize potential disease variants. Improved understanding of the ‘disease genome’, which includes coding, noncoding and regulatory variation, might help resolve difficult cases. This review provides a comprehensive assessment of existing gene-level approaches, the relationships between measures of gene-pathogenicity and how use of these prediction tools can be developed for molecular diagnostics.
2041-2649
Alyousfi, Dareen,
d3304c17-f4a4-4928-a721-cf8886302c0e
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Collins, Andrew
7daa83eb-0b21-43b2-af1a-e38fb36e2a64
Alyousfi, Dareen,
d3304c17-f4a4-4928-a721-cf8886302c0e
Baralle, Diana
faac16e5-7928-4801-9811-8b3a9ea4bb91
Collins, Andrew
7daa83eb-0b21-43b2-af1a-e38fb36e2a64

Alyousfi, Dareen,, Baralle, Diana and Collins, Andrew (2018) Gene-specific metrics to facilitate identification of disease genes for molecular diagnosis in patient genomes: a systematic review. Briefings in Functional Genomics. (doi:10.1093/bfgp/ely033).

Record type: Article

Abstract

The evolution of next-generation sequencing technologies has facilitated the detection of causal genetic variants in diseases previously undiagnosed at a molecular level. However, in genome sequencing studies, the identification of disease genes among a candidate gene list is often difficult because of the large number of apparently damaging (but usually neutral) variants. A number of variant prioritization tools have been developed to help detect disease-causal sites. However, the results may be misleading as many variants scored as damaging by these tools are often tolerated, and there are inconsistencies in prediction results among the different variant-level prediction tools. Recently, studies have indicated that understanding gene properties might improve detection of genes liable to have associated disease variation and that this information improves molecular diagnostics. The purpose of this systematic review is to evaluate how understanding gene-specific properties might improve filtering strategies in clinical sequence data to prioritize potential disease variants. Improved understanding of the ‘disease genome’, which includes coding, noncoding and regulatory variation, might help resolve difficult cases. This review provides a comprehensive assessment of existing gene-level approaches, the relationships between measures of gene-pathogenicity and how use of these prediction tools can be developed for molecular diagnostics.

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Accepted/In Press date: 20 September 2018
e-pub ahead of print date: 12 October 2018

Identifiers

Local EPrints ID: 425378
URI: https://eprints.soton.ac.uk/id/eprint/425378
ISSN: 2041-2649
PURE UUID: 03812335-3743-4bbf-b4f8-940539f3d8a6
ORCID for Andrew Collins: ORCID iD orcid.org/0000-0001-7108-0771

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Date deposited: 16 Oct 2018 16:30
Last modified: 14 Mar 2019 01:54

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