This article is part of the supplement: Proceedings of the 13th European workshop on QTL mapping and marker assisted selection
Sensitivity of genomic selection to using different prior distributions
1 Animal Breeding and Genomics Centre, ASG Wageningen UR, PO Box 65, 8200 AB Lelystad, The Netherlands
2 Biosciences Research Division, Department of Primary Industries Victoria, 1 Park Drive, Bundoora 3083, Australia
3 Melbourne School of Land and Environment, The University of Melbourne, Parkville 3010, Australia
4 The Cooperative Research Centre for Beef Genetic Technologies, University of New England, Armidale, NSW 2351, Australia
Citation and License
BMC Proceedings 2010, 4(Suppl 1):S5 doi:10.1186/1753-6561-4-S1-S5Published: 31 March 2010
Genomic selection describes a selection strategy based on genomic estimated breeding values (GEBV) predicted from dense genetic markers such as single nucleotide polymorphism (SNP) data. Different Bayesian models have been suggested to derive the prediction equation, with the main difference centred around the specification of the prior distributions.
The simulated dataset of the 13th QTL-MAS workshop was analysed using four Bayesian approaches to predict GEBV for animals without phenotypic information. Different prior distributions were assumed to assess their affect on the accuracy of the predicted GEBV.
All methods produced GEBV that were highly correlated with the true breeding values. The models appear relatively insensitive to the choice of prior distributions for QTL-MAS data set and this is consistent with uniformity of performance of different methods found in real data.