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Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting

Accurate interpretation of gene testing is a key component in customizing patient therapy. Where confirming evidence for a gene variant is lacking, computational prediction may be employed. A standardized framework, however, does not yet exist for quantitative evaluation of disease association for u...

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Detalles Bibliográficos
Autores principales: Crockett, David K, Ridge, Perry G, Wilson, Andrew R, Lyon, Elaine, Williams, Marc S, Narus, Scott P, Facelli, Julio C, Mitchell, Joyce A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3506914/
https://www.ncbi.nlm.nih.gov/pubmed/22640420
http://dx.doi.org/10.1186/gm347
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author Crockett, David K
Ridge, Perry G
Wilson, Andrew R
Lyon, Elaine
Williams, Marc S
Narus, Scott P
Facelli, Julio C
Mitchell, Joyce A
author_facet Crockett, David K
Ridge, Perry G
Wilson, Andrew R
Lyon, Elaine
Williams, Marc S
Narus, Scott P
Facelli, Julio C
Mitchell, Joyce A
author_sort Crockett, David K
collection PubMed
description Accurate interpretation of gene testing is a key component in customizing patient therapy. Where confirming evidence for a gene variant is lacking, computational prediction may be employed. A standardized framework, however, does not yet exist for quantitative evaluation of disease association for uncertain or novel gene variants in an objective manner. Here, complementary predictors for missense gene variants were incorporated into a weighted Consensus framework that includes calculated reference intervals from known disease outcomes. Data visualization for clinical reporting is also discussed.
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spelling pubmed-35069142012-11-28 Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting Crockett, David K Ridge, Perry G Wilson, Andrew R Lyon, Elaine Williams, Marc S Narus, Scott P Facelli, Julio C Mitchell, Joyce A Genome Med Method Accurate interpretation of gene testing is a key component in customizing patient therapy. Where confirming evidence for a gene variant is lacking, computational prediction may be employed. A standardized framework, however, does not yet exist for quantitative evaluation of disease association for uncertain or novel gene variants in an objective manner. Here, complementary predictors for missense gene variants were incorporated into a weighted Consensus framework that includes calculated reference intervals from known disease outcomes. Data visualization for clinical reporting is also discussed. BioMed Central 2012-05-28 /pmc/articles/PMC3506914/ /pubmed/22640420 http://dx.doi.org/10.1186/gm347 Text en Copyright ©2012 Crockett et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Crockett, David K
Ridge, Perry G
Wilson, Andrew R
Lyon, Elaine
Williams, Marc S
Narus, Scott P
Facelli, Julio C
Mitchell, Joyce A
Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting
title Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting
title_full Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting
title_fullStr Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting
title_full_unstemmed Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting
title_short Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting
title_sort consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3506914/
https://www.ncbi.nlm.nih.gov/pubmed/22640420
http://dx.doi.org/10.1186/gm347
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