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

A.C. Bouwman; L.L.G. Janss; H.C.M. Heuven

doi:10.1186/1753-6561-5-S3-S4

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

A.C. Bouwman, L.L.G. Janss, H.C.M. Heuven

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Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Background We analyzed simulated data from the 14th 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.

Original language	English
Number of pages	6
Journal	BMC Proceedings
Volume	5
Issue number	Suppl. 3
DOIs	https://doi.org/10.1186/1753-6561-5-S3-S4
Publication status	Published - 2011

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10.1186/1753-6561-5-S3-S4

A Bayesian approach to detect QTL
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title = "A Bayesian approach to detect QTL affecting a simulated binary and quantitative trait",

abstract = "Background We analyzed simulated data from the 14th 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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N2 - Background We analyzed simulated data from the 14th 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.

AB - Background We analyzed simulated data from the 14th 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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JF - BMC Proceedings

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

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