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A clustering approach to identify rare variants associated with hypertension

With the development of the next-generation sequencing technology, the influence of rare variants on complex disease has gathered increasing attention. In this paper, we propose a clustering-based approach, the clustering sum test, to test the effects of rare variants association by using the simula...

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Autores principales: Sun, Rui, Deng, Qiao, Hu, Inchi, Zee, Benny Chung-Ying, Wang, Maggie Haitian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133508/
https://www.ncbi.nlm.nih.gov/pubmed/27980628
http://dx.doi.org/10.1186/s12919-016-0022-0
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author Sun, Rui
Deng, Qiao
Hu, Inchi
Zee, Benny Chung-Ying
Wang, Maggie Haitian
author_facet Sun, Rui
Deng, Qiao
Hu, Inchi
Zee, Benny Chung-Ying
Wang, Maggie Haitian
author_sort Sun, Rui
collection PubMed
description With the development of the next-generation sequencing technology, the influence of rare variants on complex disease has gathered increasing attention. In this paper, we propose a clustering-based approach, the clustering sum test, to test the effects of rare variants association by using the simulated data provided by the Genetic Analysis Workshop 19 with an unbalanced case-control ratio. The control individuals are (a) clustered into several subgroups, (b) statistics of the separate subcontrol groups as compared to the case group are calculated, and (c) a combined statistic value is obtained based on a distance score. Collapsing of rare variants is used together with the proposed method. In our results, comparing the same statistical test with and without clustering, the clustering strategy increases the number of true positives identified in the top 100 markers by 17.24 %. Compared to the sequence kernel association test, the proposed method is more robust in terms of replicated frequencies in the replicates data sets. The results suggest that the clustering approach could improve the power of nonparametric tests and that the clustering sum test has the potential to serve as a practical tool when dealing with rare variants with unbalanced case-control data in genome-wide case-control studies.
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spelling pubmed-51335082016-12-15 A clustering approach to identify rare variants associated with hypertension Sun, Rui Deng, Qiao Hu, Inchi Zee, Benny Chung-Ying Wang, Maggie Haitian BMC Proc Proceedings With the development of the next-generation sequencing technology, the influence of rare variants on complex disease has gathered increasing attention. In this paper, we propose a clustering-based approach, the clustering sum test, to test the effects of rare variants association by using the simulated data provided by the Genetic Analysis Workshop 19 with an unbalanced case-control ratio. The control individuals are (a) clustered into several subgroups, (b) statistics of the separate subcontrol groups as compared to the case group are calculated, and (c) a combined statistic value is obtained based on a distance score. Collapsing of rare variants is used together with the proposed method. In our results, comparing the same statistical test with and without clustering, the clustering strategy increases the number of true positives identified in the top 100 markers by 17.24 %. Compared to the sequence kernel association test, the proposed method is more robust in terms of replicated frequencies in the replicates data sets. The results suggest that the clustering approach could improve the power of nonparametric tests and that the clustering sum test has the potential to serve as a practical tool when dealing with rare variants with unbalanced case-control data in genome-wide case-control studies. BioMed Central 2016-10-18 /pmc/articles/PMC5133508/ /pubmed/27980628 http://dx.doi.org/10.1186/s12919-016-0022-0 Text en © The Author(s). 2016 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Proceedings
Sun, Rui
Deng, Qiao
Hu, Inchi
Zee, Benny Chung-Ying
Wang, Maggie Haitian
A clustering approach to identify rare variants associated with hypertension
title A clustering approach to identify rare variants associated with hypertension
title_full A clustering approach to identify rare variants associated with hypertension
title_fullStr A clustering approach to identify rare variants associated with hypertension
title_full_unstemmed A clustering approach to identify rare variants associated with hypertension
title_short A clustering approach to identify rare variants associated with hypertension
title_sort clustering approach to identify rare variants associated with hypertension
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133508/
https://www.ncbi.nlm.nih.gov/pubmed/27980628
http://dx.doi.org/10.1186/s12919-016-0022-0
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