Abstract
Multivariate models are commonly used to estimate phenotypic, genetic and environmental variances, covariances, and correlations for multiple traits in plant and animal breeding programs. When traits are correlated, breeding value predictions from a multivariate model can be more accurate than univariate models. In this chapter we introduce multivariate models for two data sets: a maize inbred line multi-environment trial and pig data with pedigree information appropriate for an animal model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Literature Cited
Holland, J. B. (2006). Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Science, 46, 642–654.
Author information
Authors and Affiliations
Electronic Supplementary Material
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Isik, F., Holland, J., Maltecca, C. (2017). Multivariate Models. In: Genetic Data Analysis for Plant and Animal Breeding. Springer, Cham. https://doi.org/10.1007/978-3-319-55177-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-55177-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55175-3
Online ISBN: 978-3-319-55177-7
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)