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This article is part of the supplement: Proceedings of the 14th European workshop on QTL mapping and marker assisted selection (QTL-MAS)

Open Access Proceedings

Association analyses of the MAS-QTL data set using grammar, principal components and Bayesian network methodologies

Burak Karacaören1*, Tomi Silander2, José M Álvarez-Castro13, Chris S Haley14 and Dirk Jan de Koning1

Author Affiliations

1 The Roslin Institute and R(D)SVS, University of Edinburgh, EH25 9PS, Roslin, UK

2 Tomi Silander,A*STAR Institute of High Performance Computing Fusionopolis, 1 Fusionopolis Way, 16-16 Connexis, 138632, Singapore

3 Department of Genetics, University of Santiago de Compostela, ES-27002 Lugo, Galiza, Spain

4 MRC Human Genetics Unit, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK

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BMC Proceedings 2011, 5(Suppl 3):S8  doi:10.1186/1753-6561-5-S3-S8

Published: 27 May 2011

Additional files

Additional file 1:

Loadings of first 2 principal component of binary trait from top 109(AXX) markers using principal component stratification model. Although some markers cluster according to high linkage disequilibrium and by chromosome, this is not consistently true over the genome. Loadings of first 2 principal component of binary trait from top 109(AXX) markers using principal component stratification model. Although some markers cluster according to high linkage disequilibrium and by chromosome, this is not consistently true over the genome.

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Additional files 2:

Learned general Bayesian network for binary trait using top 109 markers obtained from principal component stratification methodology. Learned general Bayesian network for binary trait using top 109 markers obtained from principal component stratification methodology.

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Additional file 3:

Learned Bayesian Forest for binary trait using top 109 markers obtained from principal component stratification methodology. Learned Bayesian Forest for binary trait using top 109 markers obtained from principal component stratification methodology.

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Open Data