Conventional pedigree- and performance-based national evaluations typically involve hundreds of thousands if not millions of animals. But only a small proportion of individuals with performance records have typically been genotyped to date. Bayesian methods have been widely adopted for analysis of these genotyped individuals, but implementation typically involves two-step approaches to blend genomic predictions on genotyped individuals with information from conventional analyses for non genotyped animals. Here we present a Bayesian approach that extends commonly-used methods including BayesA, BayesB, BayesC, and BayesCπ, to a single step method using observations from all genotyped and non genotyped individuals. Unlike single-step GBLUP, our approach does not require direct inversion of any matrices and is well suited to parallel computing approaches.
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Genetic Improvement Programs: Selection using molecular information, , 053, 2014
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