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Computational derivation of a molecular framework for hair follicle biology from disease genes

Knowledge about genetic drivers of disease increases the efficiency of interpreting patient DNA sequence and helps to identify and prioritize biological points of intervention. Discoveries of genes with single mutations exerting substantial phenotypic impact reliably provide new biological insight,...

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Autores principales: Severin, Rachel K., Li, Xinwei, Qian, Kun, Mueller, Andreas C., Petukhova, Lynn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701154/
https://www.ncbi.nlm.nih.gov/pubmed/29176608
http://dx.doi.org/10.1038/s41598-017-16050-9
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author Severin, Rachel K.
Li, Xinwei
Qian, Kun
Mueller, Andreas C.
Petukhova, Lynn
author_facet Severin, Rachel K.
Li, Xinwei
Qian, Kun
Mueller, Andreas C.
Petukhova, Lynn
author_sort Severin, Rachel K.
collection PubMed
description Knowledge about genetic drivers of disease increases the efficiency of interpreting patient DNA sequence and helps to identify and prioritize biological points of intervention. Discoveries of genes with single mutations exerting substantial phenotypic impact reliably provide new biological insight, although such approaches tend to generate knowledge that is disjointed from the complexity of biological systems governed by elaborate networks. Here we sought to facilitate diagnostic sequencing for hair disorders and assess the underlying biology by compiling an archive of 684 genes discovered in studies of monogenic disorders and identifying molecular annotations enriched by them. To demonstrate utility for this dataset, we performed two data driven analyses. First, we extracted and analyzed data implicating enriched signaling pathways and identified previously unrecognized contributions from Hippo signaling. Second, we performed hierarchical clustering on the entire dataset to investigate the underlying causal structure of hair disorders. We identified 35 gene clusters representing genetically derived biological modules that provide a foundation for the development of a new disease taxonomy grounded in biology, rather than clinical presentations alone. This Resource will be useful for diagnostic sequencing in patients with diseases affecting the hair follicle, improved characterization of hair follicle biology, and methods development in precision medicine.
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spelling pubmed-57011542017-11-30 Computational derivation of a molecular framework for hair follicle biology from disease genes Severin, Rachel K. Li, Xinwei Qian, Kun Mueller, Andreas C. Petukhova, Lynn Sci Rep Article Knowledge about genetic drivers of disease increases the efficiency of interpreting patient DNA sequence and helps to identify and prioritize biological points of intervention. Discoveries of genes with single mutations exerting substantial phenotypic impact reliably provide new biological insight, although such approaches tend to generate knowledge that is disjointed from the complexity of biological systems governed by elaborate networks. Here we sought to facilitate diagnostic sequencing for hair disorders and assess the underlying biology by compiling an archive of 684 genes discovered in studies of monogenic disorders and identifying molecular annotations enriched by them. To demonstrate utility for this dataset, we performed two data driven analyses. First, we extracted and analyzed data implicating enriched signaling pathways and identified previously unrecognized contributions from Hippo signaling. Second, we performed hierarchical clustering on the entire dataset to investigate the underlying causal structure of hair disorders. We identified 35 gene clusters representing genetically derived biological modules that provide a foundation for the development of a new disease taxonomy grounded in biology, rather than clinical presentations alone. This Resource will be useful for diagnostic sequencing in patients with diseases affecting the hair follicle, improved characterization of hair follicle biology, and methods development in precision medicine. Nature Publishing Group UK 2017-11-24 /pmc/articles/PMC5701154/ /pubmed/29176608 http://dx.doi.org/10.1038/s41598-017-16050-9 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Severin, Rachel K.
Li, Xinwei
Qian, Kun
Mueller, Andreas C.
Petukhova, Lynn
Computational derivation of a molecular framework for hair follicle biology from disease genes
title Computational derivation of a molecular framework for hair follicle biology from disease genes
title_full Computational derivation of a molecular framework for hair follicle biology from disease genes
title_fullStr Computational derivation of a molecular framework for hair follicle biology from disease genes
title_full_unstemmed Computational derivation of a molecular framework for hair follicle biology from disease genes
title_short Computational derivation of a molecular framework for hair follicle biology from disease genes
title_sort computational derivation of a molecular framework for hair follicle biology from disease genes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5701154/
https://www.ncbi.nlm.nih.gov/pubmed/29176608
http://dx.doi.org/10.1038/s41598-017-16050-9
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