A Bayesian approach to detect QTL affecting a simulated binary and quantitative trait

BACKGROUND: We analyzed simulated data from the 14(th) QTL-MAS workshop using a Bayesian approach implemented in the program iBay. The data contained individuals genotypes for 10,031 SNPs and phenotyped for a quantitative and a binary trait. RESULTS: For the quantitative trait we mapped 8 out of 30...

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Detalles Bibliográficos
Autores principales: Bouwman, Aniek C, Janss, Luc LG, Heuven, Henri CM
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Proceedings
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3103203/
https://www.ncbi.nlm.nih.gov/pubmed/21624174
http://dx.doi.org/10.1186/1753-6561-5-S3-S4
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author Bouwman, Aniek C
Janss, Luc LG
Heuven, Henri CM
author_facet Bouwman, Aniek C
Janss, Luc LG
Heuven, Henri CM
author_sort Bouwman, Aniek C
collection PubMed
description BACKGROUND: We analyzed simulated data from the 14(th) QTL-MAS workshop using a Bayesian approach implemented in the program iBay. The data contained individuals genotypes for 10,031 SNPs and phenotyped for a quantitative and a binary trait. RESULTS: For the quantitative trait we mapped 8 out of 30 additive QTL, 1 out of 3 imprinted QTL and both epistatic pairs of QTL successfully. For the binary trait we mapped 11 out of 22 additive QTL successfully. Four out of 22 pleiotropic QTL were detected as such. CONCLUSIONS: The Bayesian variable selection method showed to be a successful method for genome-wide association. This method was reasonably fast using dense marker maps.
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spelling pubmed-31032032011-05-28 A Bayesian approach to detect QTL affecting a simulated binary and quantitative trait Bouwman, Aniek C Janss, Luc LG Heuven, Henri CM BMC Proc Proceedings BACKGROUND: We analyzed simulated data from the 14(th) QTL-MAS workshop using a Bayesian approach implemented in the program iBay. The data contained individuals genotypes for 10,031 SNPs and phenotyped for a quantitative and a binary trait. RESULTS: For the quantitative trait we mapped 8 out of 30 additive QTL, 1 out of 3 imprinted QTL and both epistatic pairs of QTL successfully. For the binary trait we mapped 11 out of 22 additive QTL successfully. Four out of 22 pleiotropic QTL were detected as such. CONCLUSIONS: The Bayesian variable selection method showed to be a successful method for genome-wide association. This method was reasonably fast using dense marker maps. BioMed Central 2011-05-27 /pmc/articles/PMC3103203/ /pubmed/21624174 http://dx.doi.org/10.1186/1753-6561-5-S3-S4 Text en Copyright ©2011 Bouwman 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
Bouwman, Aniek C
Janss, Luc LG
Heuven, Henri CM
A Bayesian approach to detect QTL affecting a simulated binary and quantitative trait
title A Bayesian approach to detect QTL affecting a simulated binary and quantitative trait
title_full A Bayesian approach to detect QTL affecting a simulated binary and quantitative trait
title_fullStr A Bayesian approach to detect QTL affecting a simulated binary and quantitative trait
title_full_unstemmed A Bayesian approach to detect QTL affecting a simulated binary and quantitative trait
title_short A Bayesian approach to detect QTL affecting a simulated binary and quantitative trait
title_sort bayesian approach to detect qtl affecting a simulated binary and quantitative trait
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3103203/
https://www.ncbi.nlm.nih.gov/pubmed/21624174
http://dx.doi.org/10.1186/1753-6561-5-S3-S4
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