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
Descripción
Sumario: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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