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A Novel Support Vector Machine-Based Approach for Rare Variant Detection
Advances in next-generation sequencing technologies have enabled the identification of multiple rare single nucleotide polymorphisms involved in diseases or traits. Several strategies for identifying rare variants that contribute to disease susceptibility have recently been proposed. An important fe...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3737136/ https://www.ncbi.nlm.nih.gov/pubmed/23940698 http://dx.doi.org/10.1371/journal.pone.0071114 |
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author | Fang, Yao-Hwei Chiu, Yen-Feng |
author_facet | Fang, Yao-Hwei Chiu, Yen-Feng |
author_sort | Fang, Yao-Hwei |
collection | PubMed |
description | Advances in next-generation sequencing technologies have enabled the identification of multiple rare single nucleotide polymorphisms involved in diseases or traits. Several strategies for identifying rare variants that contribute to disease susceptibility have recently been proposed. An important feature of many of these statistical methods is the pooling or collapsing of multiple rare single nucleotide variants to achieve a reasonably high frequency and effect. However, if the pooled rare variants are associated with the trait in different directions, then the pooling may weaken the signal, thereby reducing its statistical power. In the present paper, we propose a backward support vector machine (BSVM)-based variant selection procedure to identify informative disease-associated rare variants. In the selection procedure, the rare variants are weighted and collapsed according to their positive or negative associations with the disease, which may be associated with common variants and rare variants with protective, deleterious, or neutral effects. This nonparametric variant selection procedure is able to account for confounding factors and can also be adopted in other regression frameworks. The results of a simulation study and a data example show that the proposed BSVM approach is more powerful than four other approaches under the considered scenarios, while maintaining valid type I errors. |
format | Online Article Text |
id | pubmed-3737136 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-37371362013-08-12 A Novel Support Vector Machine-Based Approach for Rare Variant Detection Fang, Yao-Hwei Chiu, Yen-Feng PLoS One Research Article Advances in next-generation sequencing technologies have enabled the identification of multiple rare single nucleotide polymorphisms involved in diseases or traits. Several strategies for identifying rare variants that contribute to disease susceptibility have recently been proposed. An important feature of many of these statistical methods is the pooling or collapsing of multiple rare single nucleotide variants to achieve a reasonably high frequency and effect. However, if the pooled rare variants are associated with the trait in different directions, then the pooling may weaken the signal, thereby reducing its statistical power. In the present paper, we propose a backward support vector machine (BSVM)-based variant selection procedure to identify informative disease-associated rare variants. In the selection procedure, the rare variants are weighted and collapsed according to their positive or negative associations with the disease, which may be associated with common variants and rare variants with protective, deleterious, or neutral effects. This nonparametric variant selection procedure is able to account for confounding factors and can also be adopted in other regression frameworks. The results of a simulation study and a data example show that the proposed BSVM approach is more powerful than four other approaches under the considered scenarios, while maintaining valid type I errors. Public Library of Science 2013-08-07 /pmc/articles/PMC3737136/ /pubmed/23940698 http://dx.doi.org/10.1371/journal.pone.0071114 Text en © 2013 Fang, Chiu http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Fang, Yao-Hwei Chiu, Yen-Feng A Novel Support Vector Machine-Based Approach for Rare Variant Detection |
title | A Novel Support Vector Machine-Based Approach for Rare Variant Detection |
title_full | A Novel Support Vector Machine-Based Approach for Rare Variant Detection |
title_fullStr | A Novel Support Vector Machine-Based Approach for Rare Variant Detection |
title_full_unstemmed | A Novel Support Vector Machine-Based Approach for Rare Variant Detection |
title_short | A Novel Support Vector Machine-Based Approach for Rare Variant Detection |
title_sort | novel support vector machine-based approach for rare variant detection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3737136/ https://www.ncbi.nlm.nih.gov/pubmed/23940698 http://dx.doi.org/10.1371/journal.pone.0071114 |
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