A new approach fits multivariate genomic prediction models efficiently.

ABSTRACT:

Background

Fast, memory-efficient, and reliable algorithms for estimating genomic estimated breeding values (GEBV) for multiple traits and environments are needed to make timely decisions in breeding. Multivariate genomic prediction exploits genetic correlations between traits and environments to increase accuracy of GEBV compared to univariate methods. These genetic correlations are estimated simultaneously with GEBV, because they are specific to year, environment, and management. However, estimating genetic parameters is computationally demanding with restricted maximum likelihood (REML) and Bayesian samplers, and canonical transformations or orthogonalizations cannot be used for unbalanced experimental designs.

Methods

We propose a multivariate randomized Gauss-Seidel alg

SUBMITTER: Xavier A 

PROVIDER: S-EPMC9204867 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature