Ref ID: 7925
Ref Type: Journal
Authors: Apiolaza, L. A. and Garrick, D. J.
Pub Date: 2001
Journal Name: Forest Science
Volume: 47
Issue: 2
Start Page: 129
End Page: 140
ISBN/ISSN:
Keywords: breeding/value/control/parameter/heritability/longitudinal data/multivariate analysis/pine/selection/variance/wood
Abstract: Longitudinal data arise when trees are repeatedly assessed over time. The degree of genetic control of tree performance typically changes over time, creating relationships between breeding values at different ages. Longitudinal data allow modeling changes of heritability and genetic correlations with age. This paper presents a tree model, i.e. a model that explicitly includes a term for additive genetic effects of individual trees, for the analysis of longitudinal data from a multivariate perspective. The additive genetic covariance matrix for several ages can be expressed in terms of a correlation matrix pre- and post-multiplied by a diagonal matrix of genetic standard deviations. Several models to represent this correlation matrix (unstructured, banded correlations, autoregressive, full-fit and reduced-fit random regression, repeatability and uncorrelated) are presented and the relationship between them explained. Kirkpatrick's alternative approach for the analysis of longitudinal data using covariance functions is described, and its similarities with the other models discussed in this paper are detailed. The use of Akaike's information criterion for model selection considering likelihood and number of parameters is detailed. All models are illustrated through the analysis of weighted basic wood density (in kg/m3) at 4 ages (5, 10, 15 and 20 years) from Radiata Pine increment cores.
Notes:
Reprint: Not in File
Program: SPF Genetic Improvement
Project: A2
Deliverable:
Confidentiality:
Availability:
Report: Annual Report 1999/2000; Annual Report 2000/1
Type:
Address: Luis.Apiolaza@utas.edu.au