Analysis of Longitudinal Data from Progeny Tests: Some Multivariate Approaches
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 the changes of heritability and genetic correlation with age. This article 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 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 relationships among 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 article 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 weighed basic wood density (in kg/m3) at four ages (5,10,15, and 20 yr) from radiata pine increment cores. FOR. SCI. 47(2):129–140.
Keywords: BLUP; Multivariate analysis; covariance functions; covariance structures; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; natural resource management; natural resources; tree model
Document Type: Miscellaneous
Affiliations: 1: Quantitative Geneticist Cooperative Research Centre for Sustainable Production Forestry, School of Plant Science, University of Tasmania, GPO Box 252-55 Hobart, Tasmania, 7001, Australia, Phone: (+61) 3 6226 2213; Fax: (+61) 3 6226 2698 luis.apiol 2: A. L. Rae Chair in Animal Breeding and Genetics Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand, Phone: (+64) 6 350 5103 email@example.com
Publication date: 2001-05-01
- Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2015 Impact Factor: 1.702
Ranking: 16 of 66 in forestry
Average time from submission to first decision: 62.5 days*
June 1, 2016 to Feb. 28, 2017
Also published by SAF:
Journal of Forestry
Other SAF Publications
- Submit a Paper
- Membership Information
- Author Guidelines
- Ingenta Connect is not responsible for the content or availability of external websites