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Disease insights through cross-species phenotype comparisons

New sequencing technologies have ushered in a new era for diagnosis and discovery of new causative mutations for rare diseases. However, the sheer numbers of candidate variants that require interpretation in an exome or genomic analysis are still a challenging prospect. A powerful approach is the co...

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Autores principales: Haendel, Melissa A., Vasilevsky, Nicole, Brush, Matthew, Hochheiser, Harry S., Jacobsen, Julius, Oellrich, Anika, Mungall, Christopher J., Washington, Nicole, Köhler, Sebastian, Lewis, Suzanna E., Robinson, Peter N., Smedley, Damian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4602072/
https://www.ncbi.nlm.nih.gov/pubmed/26092691
http://dx.doi.org/10.1007/s00335-015-9577-8
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author Haendel, Melissa A.
Vasilevsky, Nicole
Brush, Matthew
Hochheiser, Harry S.
Jacobsen, Julius
Oellrich, Anika
Mungall, Christopher J.
Washington, Nicole
Köhler, Sebastian
Lewis, Suzanna E.
Robinson, Peter N.
Smedley, Damian
author_facet Haendel, Melissa A.
Vasilevsky, Nicole
Brush, Matthew
Hochheiser, Harry S.
Jacobsen, Julius
Oellrich, Anika
Mungall, Christopher J.
Washington, Nicole
Köhler, Sebastian
Lewis, Suzanna E.
Robinson, Peter N.
Smedley, Damian
author_sort Haendel, Melissa A.
collection PubMed
description New sequencing technologies have ushered in a new era for diagnosis and discovery of new causative mutations for rare diseases. However, the sheer numbers of candidate variants that require interpretation in an exome or genomic analysis are still a challenging prospect. A powerful approach is the comparison of the patient’s set of phenotypes (phenotypic profile) to known phenotypic profiles caused by mutations in orthologous genes associated with these variants. The most abundant source of relevant data for this task is available through the efforts of the Mouse Genome Informatics group and the International Mouse Phenotyping Consortium. In this review, we highlight the challenges in comparing human clinical phenotypes with mouse phenotypes and some of the solutions that have been developed by members of the Monarch Initiative. These tools allow the identification of mouse models for known disease-gene associations that may otherwise have been overlooked as well as candidate genes may be prioritized for novel associations. The culmination of these efforts is the Exomiser software package that allows clinical researchers to analyse patient exomes in the context of variant frequency and predicted pathogenicity as well the phenotypic similarity of the patient to any given candidate orthologous gene.
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spelling pubmed-46020722015-10-16 Disease insights through cross-species phenotype comparisons Haendel, Melissa A. Vasilevsky, Nicole Brush, Matthew Hochheiser, Harry S. Jacobsen, Julius Oellrich, Anika Mungall, Christopher J. Washington, Nicole Köhler, Sebastian Lewis, Suzanna E. Robinson, Peter N. Smedley, Damian Mamm Genome Article New sequencing technologies have ushered in a new era for diagnosis and discovery of new causative mutations for rare diseases. However, the sheer numbers of candidate variants that require interpretation in an exome or genomic analysis are still a challenging prospect. A powerful approach is the comparison of the patient’s set of phenotypes (phenotypic profile) to known phenotypic profiles caused by mutations in orthologous genes associated with these variants. The most abundant source of relevant data for this task is available through the efforts of the Mouse Genome Informatics group and the International Mouse Phenotyping Consortium. In this review, we highlight the challenges in comparing human clinical phenotypes with mouse phenotypes and some of the solutions that have been developed by members of the Monarch Initiative. These tools allow the identification of mouse models for known disease-gene associations that may otherwise have been overlooked as well as candidate genes may be prioritized for novel associations. The culmination of these efforts is the Exomiser software package that allows clinical researchers to analyse patient exomes in the context of variant frequency and predicted pathogenicity as well the phenotypic similarity of the patient to any given candidate orthologous gene. Springer US 2015-06-20 2015 /pmc/articles/PMC4602072/ /pubmed/26092691 http://dx.doi.org/10.1007/s00335-015-9577-8 Text en © The Author(s) 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Haendel, Melissa A.
Vasilevsky, Nicole
Brush, Matthew
Hochheiser, Harry S.
Jacobsen, Julius
Oellrich, Anika
Mungall, Christopher J.
Washington, Nicole
Köhler, Sebastian
Lewis, Suzanna E.
Robinson, Peter N.
Smedley, Damian
Disease insights through cross-species phenotype comparisons
title Disease insights through cross-species phenotype comparisons
title_full Disease insights through cross-species phenotype comparisons
title_fullStr Disease insights through cross-species phenotype comparisons
title_full_unstemmed Disease insights through cross-species phenotype comparisons
title_short Disease insights through cross-species phenotype comparisons
title_sort disease insights through cross-species phenotype comparisons
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4602072/
https://www.ncbi.nlm.nih.gov/pubmed/26092691
http://dx.doi.org/10.1007/s00335-015-9577-8
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