Introducing Gaussian covariance graph models in genome-wide prediction

TL;DR

This paper introduces Gaussian covariance graph models for genome-wide prediction, allowing for correlated marker effects and improved prediction accuracy by incorporating biological information and complex covariance structures.

Contribution

It adapts Gaussian covariance graph models to genome-wide prediction, enabling modeling of correlated marker effects and integration of biological information.

Findings

Improved correlation between phenotypes and predicted breeding values.

Enhanced accuracy of predicted breeding values.

Models accommodate complex covariance structures and biological data.

Abstract

Several statistical models used in genome-wide prediction assume independence of marker allele substitution effects, but it is known that these effects might be correlated. In statistics, graphical models have been identified as a useful tool for covariance estimation in high dimensional problems and it is an area that has recently experienced a great expansion. In Gaussian covariance graph models (GCovGM), the joint distribution of a set of random variables is assumed to be Gaussian and the pattern of zeros of the covariance matrix is encoded in terms of an undirected graph G. 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 and real datasets, 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 multiallelic loci case is straightforward.