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GSA-SNP: a general approach for gene set analysis of polymorphisms

Genome-wide association (GWA) study aims to identify the genetic factors associated with the traits of interest. However, the power of GWA analysis has been seriously limited by the enormous number of markers tested. Recently, the gene set analysis (GSA) methods were introduced to GWA studies to add...

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Detalles Bibliográficos
Autores principales: Nam, Dougu, Kim, Jin, Kim, Seon-Young, Kim, Sangsoo
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896081/
https://www.ncbi.nlm.nih.gov/pubmed/20501604
http://dx.doi.org/10.1093/nar/gkq428
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author Nam, Dougu
Kim, Jin
Kim, Seon-Young
Kim, Sangsoo
author_facet Nam, Dougu
Kim, Jin
Kim, Seon-Young
Kim, Sangsoo
author_sort Nam, Dougu
collection PubMed
description Genome-wide association (GWA) study aims to identify the genetic factors associated with the traits of interest. However, the power of GWA analysis has been seriously limited by the enormous number of markers tested. Recently, the gene set analysis (GSA) methods were introduced to GWA studies to address the association of gene sets that share common biological functions. GSA considerably increased the power of association analysis and successfully identified coordinated association patterns of gene sets. There have been several approaches in this direction with some limitations. Here, we present a general approach for GSA in GWA analysis and a stand-alone software GSA-SNP that implements three widely used GSA methods. GSA-SNP provides a fast computation and an easy-to-use interface. The software and test datasets are freely available at http://gsa.muldas.org. We provide an exemplary analysis on adult heights in a Korean population.
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spelling pubmed-28960812010-07-02 GSA-SNP: a general approach for gene set analysis of polymorphisms Nam, Dougu Kim, Jin Kim, Seon-Young Kim, Sangsoo Nucleic Acids Res Stand-Alone Programs for High-Throughput Data Genome-wide association (GWA) study aims to identify the genetic factors associated with the traits of interest. However, the power of GWA analysis has been seriously limited by the enormous number of markers tested. Recently, the gene set analysis (GSA) methods were introduced to GWA studies to address the association of gene sets that share common biological functions. GSA considerably increased the power of association analysis and successfully identified coordinated association patterns of gene sets. There have been several approaches in this direction with some limitations. Here, we present a general approach for GSA in GWA analysis and a stand-alone software GSA-SNP that implements three widely used GSA methods. GSA-SNP provides a fast computation and an easy-to-use interface. The software and test datasets are freely available at http://gsa.muldas.org. We provide an exemplary analysis on adult heights in a Korean population. Oxford University Press 2010-07-01 2010-05-25 /pmc/articles/PMC2896081/ /pubmed/20501604 http://dx.doi.org/10.1093/nar/gkq428 Text en © The Author(s) 2010. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Stand-Alone Programs for High-Throughput Data
Nam, Dougu
Kim, Jin
Kim, Seon-Young
Kim, Sangsoo
GSA-SNP: a general approach for gene set analysis of polymorphisms
title GSA-SNP: a general approach for gene set analysis of polymorphisms
title_full GSA-SNP: a general approach for gene set analysis of polymorphisms
title_fullStr GSA-SNP: a general approach for gene set analysis of polymorphisms
title_full_unstemmed GSA-SNP: a general approach for gene set analysis of polymorphisms
title_short GSA-SNP: a general approach for gene set analysis of polymorphisms
title_sort gsa-snp: a general approach for gene set analysis of polymorphisms
topic Stand-Alone Programs for High-Throughput Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2896081/
https://www.ncbi.nlm.nih.gov/pubmed/20501604
http://dx.doi.org/10.1093/nar/gkq428
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