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

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.

journal_name

Brief Funct Genomics

authors

Alyousfi D,Baralle D,Collins A

doi

10.1093/bfgp/ely033

subject

Has Abstract

pub_date

2019-02-14 00:00:00

pages

23-29

issue

1

eissn

2041-2649

issn

2041-2657

pii

5126507

journal_volume

18

pub_type

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