Research

Estimation in a multiplicative mixed model involving a genetic relationship matrix

Alison M Kelly¹ , Brian R Cullis² , Arthur R Gilmour³ , John A Eccleston⁴ and Robin Thompson⁵

¹  QDPI&F, Biometry, Toowoomba, Queensland, Australia

²  NSWDPI, Biometrics, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, Australia

³  NSWDPI, Biometrics, Orange Agricultural Institute, Orange, NSW, Australia

⁴  School of Physical Sciences, University of Queensland, Brisbane, Queensland, Australia

⁵  Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, England, UK

author email corresponding author email

Genetics Selection Evolution 2009, 41:33doi:10.1186/1297-9686-41-33

Published:	9 April 2009

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.