Abstract

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.

Dorian J Garrick, Jack CM Dekkers, Bruce L Golden, Rohan L Fernando

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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