Gaussian covariance graph models accounting for correlated marker effects in genome ‐wide prediction

In this study, methods adapting the theory of GCovGM to genome‐wide prediction were developed (Bayes GCov, Bayes GCov‐KR and Bayes GCov‐H). In simulated data sets, improvements in correlation between phenotypes and predicted breeding values and accuracies of predicted breeding values were found. Our models account for correlation of marker effects and permit to accommodate general structures as opposed to models proposed in previous studies, which consider spatial correlation only. In addition, they allow incorporation of biological information in the prediction process through its use when constructing graph G, and their extension to the multi‐allelic loci case is straightforward.

Source: Journal of Animal Breeding and Genetics - August 1, 2017 Category: Genetics & Stem Cells Authors: C.A. Mart ínez, K. Khare, S. Rahman, M.A. Elzo Tags: ORIGINAL ARTICLE Source Type: research