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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2015
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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. |
format | Online Article Text |
id | pubmed-4602072 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
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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