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Gene selection tool (GST): A R-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data
Whole-exome sequencing (WES) can identify causative mutations in hereditary diseases. However, WES data might have a large candidate variant list, including false positives. Moreover, in families, it is more difficult to select disease-associated variants because many variants are shared among membe...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5619773/ https://www.ncbi.nlm.nih.gov/pubmed/28957403 http://dx.doi.org/10.1371/journal.pone.0185514 |
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author | Lee, Sugi Jung, Minah Jung, Jaeeun Park, Kunhyang Ryu, Jea-Woon Kim, Jeongkil Kim, Dae-soo |
author_facet | Lee, Sugi Jung, Minah Jung, Jaeeun Park, Kunhyang Ryu, Jea-Woon Kim, Jeongkil Kim, Dae-soo |
author_sort | Lee, Sugi |
collection | PubMed |
description | Whole-exome sequencing (WES) can identify causative mutations in hereditary diseases. However, WES data might have a large candidate variant list, including false positives. Moreover, in families, it is more difficult to select disease-associated variants because many variants are shared among members. To reduce false positives and extract accurate candidates, we used a multilocus variant instead of a single-locus variant (SNV). We set up a specific window to analyze the multilocus variant and devised a sliding-window approach to observe all variants. We developed the gene selection tool (GST) based on proportion tests for linkage analysis using WES data. This tool is R program coded and has high sensitivity. We tested our code to find the gene for hereditary spastic paraplegia using SNVs from a specific family and identified the gene known to cause the disease in a significant gene list. The list identified other genes that might be associated with the disease. |
format | Online Article Text |
id | pubmed-5619773 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-56197732017-10-17 Gene selection tool (GST): A R-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data Lee, Sugi Jung, Minah Jung, Jaeeun Park, Kunhyang Ryu, Jea-Woon Kim, Jeongkil Kim, Dae-soo PLoS One Research Article Whole-exome sequencing (WES) can identify causative mutations in hereditary diseases. However, WES data might have a large candidate variant list, including false positives. Moreover, in families, it is more difficult to select disease-associated variants because many variants are shared among members. To reduce false positives and extract accurate candidates, we used a multilocus variant instead of a single-locus variant (SNV). We set up a specific window to analyze the multilocus variant and devised a sliding-window approach to observe all variants. We developed the gene selection tool (GST) based on proportion tests for linkage analysis using WES data. This tool is R program coded and has high sensitivity. We tested our code to find the gene for hereditary spastic paraplegia using SNVs from a specific family and identified the gene known to cause the disease in a significant gene list. The list identified other genes that might be associated with the disease. Public Library of Science 2017-09-28 /pmc/articles/PMC5619773/ /pubmed/28957403 http://dx.doi.org/10.1371/journal.pone.0185514 Text en © 2017 Lee et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Lee, Sugi Jung, Minah Jung, Jaeeun Park, Kunhyang Ryu, Jea-Woon Kim, Jeongkil Kim, Dae-soo Gene selection tool (GST): A R-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data |
title | Gene selection tool (GST): A R-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data |
title_full | Gene selection tool (GST): A R-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data |
title_fullStr | Gene selection tool (GST): A R-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data |
title_full_unstemmed | Gene selection tool (GST): A R-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data |
title_short | Gene selection tool (GST): A R-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data |
title_sort | gene selection tool (gst): a r-based tool for genetic disorders based on the sliding-window proportion test using whole-exome sequencing data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5619773/ https://www.ncbi.nlm.nih.gov/pubmed/28957403 http://dx.doi.org/10.1371/journal.pone.0185514 |
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