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Báo cáo sinh học: "Estimation in a multiplicative mixed model involving a genetic relationship matrix"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học quốc tế đề tài: Estimation in a multiplicative mixed model involving a genetic relationship matrix | Genetics Selection Evolution BioMed Central Open Access Estimation in a multiplicative mixed model involving a genetic relationship matrix Alison M Kelly 1 Brian R Cullis2 Arthur R Gilmour3 John A Eccleston4 and Robin Thompson5 Address 1QDPI F Biometry Toowoomba Queensland Australia 2NSWDPI Biometrics Wagga Wagga Agricultural Institute Wagga Wagga NSW Australia 3NSWDPI Biometrics Orange Agricultural Institute Orange NSW Australia 4School of Physical Sciences University of Queensland Brisbane Queensland Australia and 5Rothamsted Research Harpenden Hertfordshire AL5 2JQ England UK Email Alison M Kelly - alison.kelly@dpi.qld.gov.au Brian R Cullis - brian.cullis@dpi.nsw.gov.au Arthur R Gilmour - arthur.gilmour@dpi.nsw.gov.au John A Eccleston - jae@maths.uq.edu.au Robin Thompson - robin.thompson@bbsrc.ac.uk Corresponding author Published 9 April 2009 Received 18 March 2009 Genetics Selection Evolution 2009 41 33 doi 10.1186 1297-9686-41-33 Accepted 9 April 2009 This article is available from http www.gsejournal.Org content 41 1 33 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. 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