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EvoTol: a protein-sequence based evolutionary intolerance framework for disease-gene prioritization

EvoTol: a protein-sequence based evolutionary intolerance framework for disease-gene prioritization
EvoTol: a protein-sequence based evolutionary intolerance framework for disease-gene prioritization

Methods to interpret personal genome sequences are increasingly required. Here, we report a novel framework (EvoTol) to identify disease-causing genes using patient sequence data from within protein coding-regions. EvoTol quantifies a gene's intolerance to mutation using evolutionary conservation of protein sequences and can incorporate tissue-specific gene expression data. We apply this framework to the analysis of whole-exome sequence data in epilepsy and congenital heart disease, and demonstrate EvoTol's ability to identify known disease-causing genes is unmatched by competing methods. Application of EvoTol to the human interactome revealed networks enriched for genes intolerant to protein sequence variation, informing novel polygenic contributions to human disease.

Amino Acid Sequence/genetics, Computational Biology/methods, Evolution, Molecular, Exome/genetics, Genetic Predisposition to Disease/genetics, Heart Defects, Congenital/genetics, Humans, Mutation, Phylogeny, Polymorphism, Single Nucleotide, Protein Interaction Maps/genetics, Proteins/classification, Reproducibility of Results, Sequence Analysis, DNA/methods
0305-1048
e33
Rackham, Owen J. L.
8122eb1f-6e9f-4da5-90e1-ce108ccbbcbf
Shihab, Hashem A.
3bd2f11f-6332-4517-bed5-8a8631e772da
Johnson, Michael R.
33a0d8cb-491b-4b3f-b193-540a331ac705
Petretto, Enrico
a8a7d254-ea06-4ab3-ba7e-b653349a29f4
Rackham, Owen J. L.
8122eb1f-6e9f-4da5-90e1-ce108ccbbcbf
Shihab, Hashem A.
3bd2f11f-6332-4517-bed5-8a8631e772da
Johnson, Michael R.
33a0d8cb-491b-4b3f-b193-540a331ac705
Petretto, Enrico
a8a7d254-ea06-4ab3-ba7e-b653349a29f4

Rackham, Owen J. L., Shihab, Hashem A., Johnson, Michael R. and Petretto, Enrico (2015) EvoTol: a protein-sequence based evolutionary intolerance framework for disease-gene prioritization. Nucleic Acids Research, 43 (5), e33. (doi:10.1093/nar/gku1322).

Record type: Article

Abstract

Methods to interpret personal genome sequences are increasingly required. Here, we report a novel framework (EvoTol) to identify disease-causing genes using patient sequence data from within protein coding-regions. EvoTol quantifies a gene's intolerance to mutation using evolutionary conservation of protein sequences and can incorporate tissue-specific gene expression data. We apply this framework to the analysis of whole-exome sequence data in epilepsy and congenital heart disease, and demonstrate EvoTol's ability to identify known disease-causing genes is unmatched by competing methods. Application of EvoTol to the human interactome revealed networks enriched for genes intolerant to protein sequence variation, informing novel polygenic contributions to human disease.

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More information

Accepted/In Press date: 5 December 2014
e-pub ahead of print date: 29 December 2014
Published date: 11 March 2015
Additional Information: © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
Keywords: Amino Acid Sequence/genetics, Computational Biology/methods, Evolution, Molecular, Exome/genetics, Genetic Predisposition to Disease/genetics, Heart Defects, Congenital/genetics, Humans, Mutation, Phylogeny, Polymorphism, Single Nucleotide, Protein Interaction Maps/genetics, Proteins/classification, Reproducibility of Results, Sequence Analysis, DNA/methods

Identifiers

Local EPrints ID: 446725
URI: http://eprints.soton.ac.uk/id/eprint/446725
ISSN: 0305-1048
PURE UUID: b457ed76-7b79-4fc5-bbaa-23d798c1ca5b
ORCID for Owen J. L. Rackham: ORCID iD orcid.org/0000-0002-4390-0872
ORCID for Michael R. Johnson: ORCID iD orcid.org/0000-0002-5566-6147

Catalogue record

Date deposited: 19 Feb 2021 17:31
Last modified: 01 Jul 2022 02:05

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Contributors

Author: Hashem A. Shihab
Author: Michael R. Johnson ORCID iD
Author: Enrico Petretto

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