Skip to main content

Estimation in a multiplicative mixed model involving a genetic relationship matrix

Journal Article


Download full-text (Open Access)

Abstract


  • Genetic models partitioning additive and non-additive genetic effects for populations tested in

    replicated multi-environment trials (METs) in a plant breeding program have recently been

    presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment

    interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high

    correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.

Authors


  •   Kelly, Alison M. (external author)
  •   Cullis, Brian R.
  •   Gilmour, Arthur R. (external author)
  •   Eccleston, John A. (external author)
  •   Thompson, Robin (external author)

Publication Date


  • 2009

Citation


  • Kelly, A. M., Cullis, B. R., Gilmour, A. R., Eccleston, J. A. & Thompson, R. (2009). Estimation in a multiplicative mixed model involving a genetic relationship matrix. Genetics Selection Evolution, 41 (1), 1-9.

Scopus Eid


  • 2-s2.0-77949789041

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=9424&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2089

Has Global Citation Frequency


Number Of Pages


  • 8

Start Page


  • 1

End Page


  • 9

Volume


  • 41

Issue


  • 1

Place Of Publication


  • United Kingdom

Abstract


  • Genetic models partitioning additive and non-additive genetic effects for populations tested in

    replicated multi-environment trials (METs) in a plant breeding program have recently been

    presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment

    interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high

    correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments.

Authors


  •   Kelly, Alison M. (external author)
  •   Cullis, Brian R.
  •   Gilmour, Arthur R. (external author)
  •   Eccleston, John A. (external author)
  •   Thompson, Robin (external author)

Publication Date


  • 2009

Citation


  • Kelly, A. M., Cullis, B. R., Gilmour, A. R., Eccleston, J. A. & Thompson, R. (2009). Estimation in a multiplicative mixed model involving a genetic relationship matrix. Genetics Selection Evolution, 41 (1), 1-9.

Scopus Eid


  • 2-s2.0-77949789041

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=9424&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2089

Has Global Citation Frequency


Number Of Pages


  • 8

Start Page


  • 1

End Page


  • 9

Volume


  • 41

Issue


  • 1

Place Of Publication


  • United Kingdom