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A novel method to detect rare variants using both family and unrelated case-control data

To detect rare variants associated with a phenotype, we develop a novel statistical method that can use both family and unrelated case-control data. Unlike the currently existing methods, we first use family data to calculate weights to be given to rare variants, differentiating between concordantly...

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Detalles Bibliográficos
Autores principales: Feng, Tao, Elston, Robert C, Zhu, Xiaofeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287921/
https://www.ncbi.nlm.nih.gov/pubmed/22373319
http://dx.doi.org/10.1186/1753-6561-5-S9-S80
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author Feng, Tao
Elston, Robert C
Zhu, Xiaofeng
author_facet Feng, Tao
Elston, Robert C
Zhu, Xiaofeng
author_sort Feng, Tao
collection PubMed
description To detect rare variants associated with a phenotype, we develop a novel statistical method that can use both family and unrelated case-control data. Unlike the currently existing methods, we first use family data to calculate weights to be given to rare variants, differentiating between concordantly affected and discordant sib pairs. These weights are then used in an association test applied to the unrelated case-control data. We applied the proposed method to the simulated sequencing data in Genetic Analysis Workshop 17 and identified two genes associated with the disease.
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spelling pubmed-32879212012-02-28 A novel method to detect rare variants using both family and unrelated case-control data Feng, Tao Elston, Robert C Zhu, Xiaofeng BMC Proc Proceedings To detect rare variants associated with a phenotype, we develop a novel statistical method that can use both family and unrelated case-control data. Unlike the currently existing methods, we first use family data to calculate weights to be given to rare variants, differentiating between concordantly affected and discordant sib pairs. These weights are then used in an association test applied to the unrelated case-control data. We applied the proposed method to the simulated sequencing data in Genetic Analysis Workshop 17 and identified two genes associated with the disease. BioMed Central 2011-11-29 /pmc/articles/PMC3287921/ /pubmed/22373319 http://dx.doi.org/10.1186/1753-6561-5-S9-S80 Text en Copyright ©2011 Feng et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Feng, Tao
Elston, Robert C
Zhu, Xiaofeng
A novel method to detect rare variants using both family and unrelated case-control data
title A novel method to detect rare variants using both family and unrelated case-control data
title_full A novel method to detect rare variants using both family and unrelated case-control data
title_fullStr A novel method to detect rare variants using both family and unrelated case-control data
title_full_unstemmed A novel method to detect rare variants using both family and unrelated case-control data
title_short A novel method to detect rare variants using both family and unrelated case-control data
title_sort novel method to detect rare variants using both family and unrelated case-control data
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287921/
https://www.ncbi.nlm.nih.gov/pubmed/22373319
http://dx.doi.org/10.1186/1753-6561-5-S9-S80
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