The multiplicity problem in linkage analysis of gene expression data – the power of differentiating cis- and trans-acting regulators

In this report, we focused on the multiplicity issue in Problem 1 of Genetic Analysis Workshop 15. We investigated and compared the performance of the stratified false-discovery rate control method with the traditional aggregated approach, in an application to genome-wide linkage analyses of single-...

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Detalles Bibliográficos
Autores principales: Huang, Baisong, Rangrej, Jagadish, Paterson, Andrew D, Sun, Lei
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Proceedings
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367579/
https://www.ncbi.nlm.nih.gov/pubmed/18466486
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author Huang, Baisong
Rangrej, Jagadish
Paterson, Andrew D
Sun, Lei
author_facet Huang, Baisong
Rangrej, Jagadish
Paterson, Andrew D
Sun, Lei
author_sort Huang, Baisong
collection PubMed
description In this report, we focused on the multiplicity issue in Problem 1 of Genetic Analysis Workshop 15. We investigated and compared the performance of the stratified false-discovery rate control method with the traditional aggregated approach, in an application to genome-wide linkage analyses of single-nucleotide polymorphism-to-gene expression data. We showed the importance of utilizing the available map information and demonstrated the power gained by conducting false-discovery rate control separately for cis and trans regulators under three different frameworks: fixed rejection region, fixed false-discovery rate, and fixed number of rejections.
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spelling pubmed-23675792008-05-06 The multiplicity problem in linkage analysis of gene expression data – the power of differentiating cis- and trans-acting regulators Huang, Baisong Rangrej, Jagadish Paterson, Andrew D Sun, Lei BMC Proc Proceedings In this report, we focused on the multiplicity issue in Problem 1 of Genetic Analysis Workshop 15. We investigated and compared the performance of the stratified false-discovery rate control method with the traditional aggregated approach, in an application to genome-wide linkage analyses of single-nucleotide polymorphism-to-gene expression data. We showed the importance of utilizing the available map information and demonstrated the power gained by conducting false-discovery rate control separately for cis and trans regulators under three different frameworks: fixed rejection region, fixed false-discovery rate, and fixed number of rejections. BioMed Central 2007-12-18 /pmc/articles/PMC2367579/ /pubmed/18466486 Text en Copyright © 2007 Huang 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
Huang, Baisong
Rangrej, Jagadish
Paterson, Andrew D
Sun, Lei
The multiplicity problem in linkage analysis of gene expression data – the power of differentiating cis- and trans-acting regulators
title The multiplicity problem in linkage analysis of gene expression data – the power of differentiating cis- and trans-acting regulators
title_full The multiplicity problem in linkage analysis of gene expression data – the power of differentiating cis- and trans-acting regulators
title_fullStr The multiplicity problem in linkage analysis of gene expression data – the power of differentiating cis- and trans-acting regulators
title_full_unstemmed The multiplicity problem in linkage analysis of gene expression data – the power of differentiating cis- and trans-acting regulators
title_short The multiplicity problem in linkage analysis of gene expression data – the power of differentiating cis- and trans-acting regulators
title_sort multiplicity problem in linkage analysis of gene expression data – the power of differentiating cis- and trans-acting regulators
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367579/
https://www.ncbi.nlm.nih.gov/pubmed/18466486
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