Ranking non-synonymous single nucleotide polymorphisms based on disease concepts

As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional varian...

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Published in:	Human genomics Vol. 8; no. 1; p. 11
Main Authors:	Shihab, Hashem A, Gough, Julian, Mort, Matthew, Cooper, David N, Day, Ian NM, Gaunt, Tom R
Format:	Journal Article
Language:	English
Published:	London BioMed Central 30.06.2014 Springer Nature B.V
Subjects:	Algorithms Amino Acid Substitution - genetics Bioinformatics Biomedical and Life Sciences Biomedicine Cancer Computational Biology Human Genetics Humans Internet Markov Chains Metabolic disorders Mutation Mutation - genetics Phenotype Polymorphism, Single Nucleotide - genetics Primary Research Proteins Proteomics Software Studies Training FATHMM PolyPhen SIFT nsSNPs Disease-causing SNV HMMs Bioinformatics Disease-specific
ISSN:	1479-7364, 1473-9542, 1479-7364
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Abstract	As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional variants for further study. A large proportion of existing prediction algorithms are ‘disease agnostic’ but are nevertheless quite capable of predicting when a mutation is likely to be deleterious. However, most clinical and research applications of these algorithms relate to specific diseases and would therefore benefit from an approach that discriminates between functional variants specifically related to that disease from those which are not. In a whole-exome/whole-genome sequencing context, such an approach could substantially reduce the number of false positive candidate mutations. Here, we test this postulate by incorporating a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm. When compared to traditional prediction algorithms, we observed an overall reduction in the number of false positives identified using a disease-specific approach to functional prediction across 17 distinct disease concepts/categories. Our results illustrate the potential benefits of making disease-specific predictions when prioritizing candidate variants in relation to specific diseases. A web-based implementation of our algorithm is available at http://fathmm.biocompute.org.uk .
AbstractList	As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional variants for further study. A large proportion of existing prediction algorithms are ‘disease agnostic’ but are nevertheless quite capable of predicting when a mutation is likely to be deleterious. However, most clinical and research applications of these algorithms relate to specific diseases and would therefore benefit from an approach that discriminates between functional variants specifically related to that disease from those which are not. In a whole-exome/whole-genome sequencing context, such an approach could substantially reduce the number of false positive candidate mutations. Here, we test this postulate by incorporating a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm. When compared to traditional prediction algorithms, we observed an overall reduction in the number of false positives identified using a disease-specific approach to functional prediction across 17 distinct disease concepts/categories. Our results illustrate the potential benefits of making disease-specific predictions when prioritizing candidate variants in relation to specific diseases. A web-based implementation of our algorithm is available at http://fathmm.biocompute.org.uk. As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional variants for further study. A large proportion of existing prediction algorithms are 'disease agnostic' but are nevertheless quite capable of predicting when a mutation is likely to be deleterious. However, most clinical and research applications of these algorithms relate to specific diseases and would therefore benefit from an approach that discriminates between functional variants specifically related to that disease from those which are not. In a whole-exome/whole-genome sequencing context, such an approach could substantially reduce the number of false positive candidate mutations. Here, we test this postulate by incorporating a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm. When compared to traditional prediction algorithms, we observed an overall reduction in the number of false positives identified using a disease-specific approach to functional prediction across 17 distinct disease concepts/categories. Our results illustrate the potential benefits of making disease-specific predictions when prioritizing candidate variants in relation to specific diseases. A web-based implementation of our algorithm is available at http://fathmm.biocompute.org.uk.As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional variants for further study. A large proportion of existing prediction algorithms are 'disease agnostic' but are nevertheless quite capable of predicting when a mutation is likely to be deleterious. However, most clinical and research applications of these algorithms relate to specific diseases and would therefore benefit from an approach that discriminates between functional variants specifically related to that disease from those which are not. In a whole-exome/whole-genome sequencing context, such an approach could substantially reduce the number of false positive candidate mutations. Here, we test this postulate by incorporating a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm. When compared to traditional prediction algorithms, we observed an overall reduction in the number of false positives identified using a disease-specific approach to functional prediction across 17 distinct disease concepts/categories. Our results illustrate the potential benefits of making disease-specific predictions when prioritizing candidate variants in relation to specific diseases. A web-based implementation of our algorithm is available at http://fathmm.biocompute.org.uk. As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional variants for further study. A large proportion of existing prediction algorithms are ‘disease agnostic’ but are nevertheless quite capable of predicting when a mutation is likely to be deleterious. However, most clinical and research applications of these algorithms relate to specific diseases and would therefore benefit from an approach that discriminates between functional variants specifically related to that disease from those which are not. In a whole-exome/whole-genome sequencing context, such an approach could substantially reduce the number of false positive candidate mutations. Here, we test this postulate by incorporating a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm. When compared to traditional prediction algorithms, we observed an overall reduction in the number of false positives identified using a disease-specific approach to functional prediction across 17 distinct disease concepts/categories. Our results illustrate the potential benefits of making disease-specific predictions when prioritizing candidate variants in relation to specific diseases. A web-based implementation of our algorithm is available at http://fathmm.biocompute.org.uk .
ArticleNumber	11
Author	Cooper, David N Mort, Matthew Shihab, Hashem A Gaunt, Tom R Day, Ian NM Gough, Julian
AuthorAffiliation	2 Department of Computer Science, University of Bristol, The Merchant Venturers Building, Bristol BS8 1UB, UK 1 Bristol Centre for Systems Biomedicine and MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK 3 Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK
AuthorAffiliation_xml	– name: 3 Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK – name: 1 Bristol Centre for Systems Biomedicine and MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK – name: 2 Department of Computer Science, University of Bristol, The Merchant Venturers Building, Bristol BS8 1UB, UK
Author_xml	– sequence: 1 givenname: Hashem A surname: Shihab fullname: Shihab, Hashem A organization: Bristol Centre for Systems Biomedicine and MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol – sequence: 2 givenname: Julian surname: Gough fullname: Gough, Julian organization: Department of Computer Science, University of Bristol – sequence: 3 givenname: Matthew surname: Mort fullname: Mort, Matthew organization: Institute of Medical Genetics, School of Medicine, Cardiff University – sequence: 4 givenname: David N surname: Cooper fullname: Cooper, David N organization: Institute of Medical Genetics, School of Medicine, Cardiff University – sequence: 5 givenname: Ian NM surname: Day fullname: Day, Ian NM organization: Bristol Centre for Systems Biomedicine and MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol – sequence: 6 givenname: Tom R surname: Gaunt fullname: Gaunt, Tom R email: Tom.Gaunt@bristol.ac.uk organization: Bristol Centre for Systems Biomedicine and MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol
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Copyright	Shihab et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated. Copyright BioMed Central 2014 Copyright © 2014 Shihab et al.; licensee BioMed Central Ltd. 2014 Shihab et al.; licensee BioMed Central Ltd.
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Snippet	As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers...
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SubjectTerms	Algorithms Amino Acid Substitution - genetics Bioinformatics Biomedical and Life Sciences Biomedicine Cancer Computational Biology Human Genetics Humans Internet Markov Chains Metabolic disorders Mutation Mutation - genetics Phenotype Polymorphism, Single Nucleotide - genetics Primary Research Proteins Proteomics Software Studies Training
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Title	Ranking non-synonymous single nucleotide polymorphisms based on disease concepts
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