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Estimation in a multiplicative mixed model involving a genetic relationship matrix.

Kelly, A.M. and Cullis, B.R. and Gilmour, A.R. and Eccleston, J.A. and Thompson, R. (2009) Estimation in a multiplicative mixed model involving a genetic relationship matrix. Genetics Selection Evolution, 41 (1). p. 33.

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Article Link(s): http://dx.doi.org/10.1186/1297-9686-41-33

Publisher URL: http://www.biomedcentral.com/

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.

Item Type:Article
Corporate Creators:Department of Employment, Economic Development and Innovation (DEEDI), Agri-Science, Crop and Food Science, QPIF
Business groups:Agri-Science, Crop and Food Science
Additional Information:© 2009 Kelly et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords:Numerator relationship matrix; wheat genotypes; breeding values; X environment; trials; information; covariances; relatives; algorithm; variety.
Subjects:Plant culture
Science > Statistics > Simulation modelling
Deposited On:20 Aug 2009 05:18
Last Modified:25 Oct 2011 04:38

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