A mixture model approach to multiple testing for the genetic analysis of gene expression

With the availability of very dense genome-wide maps of markers, multiple testing has become a major difficulty for genetic studies. In this context, the false-discovery rate (FDR) and related criteria are widely used. Here, we propose a finite mixture model to estimate the local FDR (lFDR), the FDR...

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Detalles Bibliográficos
Autores principales: Dalmasso, Cyril, Pickrell, Joseph, Tuefferd, Marianne, Génin, Emmanuelle, Bourgain, Catherine, Broët, Philippe
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Proceedings
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367480/
https://www.ncbi.nlm.nih.gov/pubmed/18466485
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author Dalmasso, Cyril
Pickrell, Joseph
Tuefferd, Marianne
Génin, Emmanuelle
Bourgain, Catherine
Broët, Philippe
author_facet Dalmasso, Cyril
Pickrell, Joseph
Tuefferd, Marianne
Génin, Emmanuelle
Bourgain, Catherine
Broët, Philippe
author_sort Dalmasso, Cyril
collection PubMed
description With the availability of very dense genome-wide maps of markers, multiple testing has become a major difficulty for genetic studies. In this context, the false-discovery rate (FDR) and related criteria are widely used. Here, we propose a finite mixture model to estimate the local FDR (lFDR), the FDR, and the false non-discovery rate (FNR) in variance-component linkage analysis. Our parametric approach allows empirical estimation of an appropriate null distribution. The contribution of our model to estimation of FDR and related criteria is illustrated on the microarray expression profiles data set provided by the Genetic Analysis Workshop 15 Problem 1.
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spelling pubmed-23674802008-05-06 A mixture model approach to multiple testing for the genetic analysis of gene expression Dalmasso, Cyril Pickrell, Joseph Tuefferd, Marianne Génin, Emmanuelle Bourgain, Catherine Broët, Philippe BMC Proc Proceedings With the availability of very dense genome-wide maps of markers, multiple testing has become a major difficulty for genetic studies. In this context, the false-discovery rate (FDR) and related criteria are widely used. Here, we propose a finite mixture model to estimate the local FDR (lFDR), the FDR, and the false non-discovery rate (FNR) in variance-component linkage analysis. Our parametric approach allows empirical estimation of an appropriate null distribution. The contribution of our model to estimation of FDR and related criteria is illustrated on the microarray expression profiles data set provided by the Genetic Analysis Workshop 15 Problem 1. BioMed Central 2007-12-18 /pmc/articles/PMC2367480/ /pubmed/18466485 Text en Copyright © 2007 Dalmasso 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
Dalmasso, Cyril
Pickrell, Joseph
Tuefferd, Marianne
Génin, Emmanuelle
Bourgain, Catherine
Broët, Philippe
A mixture model approach to multiple testing for the genetic analysis of gene expression
title A mixture model approach to multiple testing for the genetic analysis of gene expression
title_full A mixture model approach to multiple testing for the genetic analysis of gene expression
title_fullStr A mixture model approach to multiple testing for the genetic analysis of gene expression
title_full_unstemmed A mixture model approach to multiple testing for the genetic analysis of gene expression
title_short A mixture model approach to multiple testing for the genetic analysis of gene expression
title_sort mixture model approach to multiple testing for the genetic analysis of gene expression
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367480/
https://www.ncbi.nlm.nih.gov/pubmed/18466485
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