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Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases
Copy number variations (CNVs) are genomic structural variations (deletions, duplications, or translocations) that represent the 4.8–9.5% of human genome variation in healthy individuals. In some cases, CNVs can also lead to disease, being the etiology of many known rare genetic/genomic disorders. De...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138686/ https://www.ncbi.nlm.nih.gov/pubmed/29946186 http://dx.doi.org/10.1038/s41431-018-0139-x |
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author | Bueno, Anibal Rodríguez-López, Rocío Reyes-Palomares, Armando Rojano, Elena Corpas, Manuel Nevado, Julián Lapunzina, Pablo Sánchez-Jiménez, Francisca Ranea, Juan A. G. |
author_facet | Bueno, Anibal Rodríguez-López, Rocío Reyes-Palomares, Armando Rojano, Elena Corpas, Manuel Nevado, Julián Lapunzina, Pablo Sánchez-Jiménez, Francisca Ranea, Juan A. G. |
author_sort | Bueno, Anibal |
collection | PubMed |
description | Copy number variations (CNVs) are genomic structural variations (deletions, duplications, or translocations) that represent the 4.8–9.5% of human genome variation in healthy individuals. In some cases, CNVs can also lead to disease, being the etiology of many known rare genetic/genomic disorders. Despite the last advances in genomic sequencing and diagnosis, the pathological effects of many rare genetic variations remain unresolved, largely due to the low number of patients available for these cases, making it difficult to identify consistent patterns of genotype–phenotype relationships. We aimed to improve the identification of statistically consistent genotype–phenotype relationships by integrating all the genetic and clinical data of thousands of patients with rare genomic disorders (obtained from the DECIPHER database) into a phenotype–patient–genotype tripartite network. Then we assessed how our network approach could help in the characterization and diagnosis of novel cases in clinical genetics. The systematic approach implemented in this work is able to better define the relationships between phenotypes and specific loci, by exploiting large-scale association networks of phenotypes and genotypes in thousands of rare disease patients. The application of the described methodology facilitated the diagnosis of novel clinical cases, ranking phenotypes by locus specificity and reporting putative new clinical features that may suggest additional clinical follow-ups. In this work, the proof of concept developed over a set of novel clinical cases demonstrates that this network-based methodology might help improve the precision of patient clinical records and the characterization of rare syndromes. |
format | Online Article Text |
id | pubmed-6138686 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-61386862018-09-17 Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases Bueno, Anibal Rodríguez-López, Rocío Reyes-Palomares, Armando Rojano, Elena Corpas, Manuel Nevado, Julián Lapunzina, Pablo Sánchez-Jiménez, Francisca Ranea, Juan A. G. Eur J Hum Genet Article Copy number variations (CNVs) are genomic structural variations (deletions, duplications, or translocations) that represent the 4.8–9.5% of human genome variation in healthy individuals. In some cases, CNVs can also lead to disease, being the etiology of many known rare genetic/genomic disorders. Despite the last advances in genomic sequencing and diagnosis, the pathological effects of many rare genetic variations remain unresolved, largely due to the low number of patients available for these cases, making it difficult to identify consistent patterns of genotype–phenotype relationships. We aimed to improve the identification of statistically consistent genotype–phenotype relationships by integrating all the genetic and clinical data of thousands of patients with rare genomic disorders (obtained from the DECIPHER database) into a phenotype–patient–genotype tripartite network. Then we assessed how our network approach could help in the characterization and diagnosis of novel cases in clinical genetics. The systematic approach implemented in this work is able to better define the relationships between phenotypes and specific loci, by exploiting large-scale association networks of phenotypes and genotypes in thousands of rare disease patients. The application of the described methodology facilitated the diagnosis of novel clinical cases, ranking phenotypes by locus specificity and reporting putative new clinical features that may suggest additional clinical follow-ups. In this work, the proof of concept developed over a set of novel clinical cases demonstrates that this network-based methodology might help improve the precision of patient clinical records and the characterization of rare syndromes. Springer International Publishing 2018-06-26 2018-10 /pmc/articles/PMC6138686/ /pubmed/29946186 http://dx.doi.org/10.1038/s41431-018-0139-x Text en © The Author(s) 2018 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 Bueno, Anibal Rodríguez-López, Rocío Reyes-Palomares, Armando Rojano, Elena Corpas, Manuel Nevado, Julián Lapunzina, Pablo Sánchez-Jiménez, Francisca Ranea, Juan A. G. Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases |
title | Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases |
title_full | Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases |
title_fullStr | Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases |
title_full_unstemmed | Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases |
title_short | Phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases |
title_sort | phenotype-loci associations in networks of patients with rare disorders: application to assist in the diagnosis of novel clinical cases |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138686/ https://www.ncbi.nlm.nih.gov/pubmed/29946186 http://dx.doi.org/10.1038/s41431-018-0139-x |
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