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A weighted accumulation test for associating rare genetic variation with quantitative phenotypes

Currently there is a great deal of interest in developing methods for testing the role that rare variation plays in disease development. Here we propose a weighted association test that accumulates genetic variation across a signaling pathway. We evaluate our approach by analyzing simulated phenotyp...

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Detalles Bibliográficos
Autores principales: Xing, Chuanhua, Satten, Glen A, Allen, Andrew S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287898/
https://www.ncbi.nlm.nih.gov/pubmed/22373271
http://dx.doi.org/10.1186/1753-6561-5-S9-S6
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author Xing, Chuanhua
Satten, Glen A
Allen, Andrew S
author_facet Xing, Chuanhua
Satten, Glen A
Allen, Andrew S
author_sort Xing, Chuanhua
collection PubMed
description Currently there is a great deal of interest in developing methods for testing the role that rare variation plays in disease development. Here we propose a weighted association test that accumulates genetic variation across a signaling pathway. We evaluate our approach by analyzing simulated phenotype data from an exome sequencing study of 697 unrelated individuals from the Genetic Analysis Workshop 17 (GAW17) data set. Although our weighted approach identifies several interesting pathways associated with phenotype Q1, so does an alternative unweighted accumulation approach. Such a result is not unexpected because there is no systematic relationship between the allele frequency of a variant and its effect on phenotype in the GAW17 simulation model.
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spelling pubmed-32878982012-02-28 A weighted accumulation test for associating rare genetic variation with quantitative phenotypes Xing, Chuanhua Satten, Glen A Allen, Andrew S BMC Proc Proceedings Currently there is a great deal of interest in developing methods for testing the role that rare variation plays in disease development. Here we propose a weighted association test that accumulates genetic variation across a signaling pathway. We evaluate our approach by analyzing simulated phenotype data from an exome sequencing study of 697 unrelated individuals from the Genetic Analysis Workshop 17 (GAW17) data set. Although our weighted approach identifies several interesting pathways associated with phenotype Q1, so does an alternative unweighted accumulation approach. Such a result is not unexpected because there is no systematic relationship between the allele frequency of a variant and its effect on phenotype in the GAW17 simulation model. BioMed Central 2011-11-29 /pmc/articles/PMC3287898/ /pubmed/22373271 http://dx.doi.org/10.1186/1753-6561-5-S9-S6 Text en Copyright ©2011 Xing 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
Xing, Chuanhua
Satten, Glen A
Allen, Andrew S
A weighted accumulation test for associating rare genetic variation with quantitative phenotypes
title A weighted accumulation test for associating rare genetic variation with quantitative phenotypes
title_full A weighted accumulation test for associating rare genetic variation with quantitative phenotypes
title_fullStr A weighted accumulation test for associating rare genetic variation with quantitative phenotypes
title_full_unstemmed A weighted accumulation test for associating rare genetic variation with quantitative phenotypes
title_short A weighted accumulation test for associating rare genetic variation with quantitative phenotypes
title_sort weighted accumulation test for associating rare genetic variation with quantitative phenotypes
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287898/
https://www.ncbi.nlm.nih.gov/pubmed/22373271
http://dx.doi.org/10.1186/1753-6561-5-S9-S6
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