Whole-genome prediction (WGP) is widely used in livestock breeding. Among various statistical methods for WGP, two independent strategies, i.e., joint prediction of multiple traits and the antedependence model, show their respective advantages. To take advantage of both the strategies, we propose a Bayesian multivariate antedependence-based method for joint prediction of multiple quantitative traits by modeling a linear relationship of effect vector between each pair of adjacent markers. Using simulation and the 16th QTL-MAS workshop dataset, we demonstrate that our proposed WGP method is more accurate than corresponding traditional counterparts (Bayes A and multi-trait Bayes A). Our method can be readily extended to deal with categorical traits and missing phenotypes, offering a feasible way to jointly predict multiple complex traits in plant and livestock breeding.
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Methods and Tools: Statistical methods - linear and nonlinear models (Posters), , 706, 2014
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