Multivariate character process models for the analysis of two or more correlated function-valued traits

A - Papers appearing in refereed journals

Jaffrezic, F., Thompson, R. and Pletcher, S. D. 2004. Multivariate character process models for the analysis of two or more correlated function-valued traits. Genetics. 168 (1), pp. 477-487.

AuthorsJaffrezic, F., Thompson, R. and Pletcher, S. D.
Abstract

Various methods, including random regression, structured antedependence models, and character process models, have been proposed for the genetic analysis of longitudinal data and other function-valued traits. For univariate problems, the character process models have been shown to perform well in comparison to alternative methods. The aim of this article is to present an extension of these models to the simultaneous analysis of two or more correlated function-valued traits. Analytical forms for stationary and nonstationary cross-covariance functions are studied. Comparisons with the other approaches are presented in a simulation study and in an example of a bivariate analysis of genetic covariance in age-specific fecundity and mortality in Drosophila. As in the univariate case, bivariate character process models with an exponential correlation were found to be quite close to first-order structured antedependence models. The simulation study showed that the choice of the most appropriate methodology is highly dependent on the covariance structure of the data. The bivariate character process approach proved to be able to deal with quite complex nonstationary and nonsymmetric cross-correlation structures and was found to be the most appropriate for the real data example of the fruit fly Drosophila melanogaster.

Year of Publication2004
JournalGenetics
Journal citation168 (1), pp. 477-487
Digital Object Identifier (DOI)doi:10.1534/genetics.103.019554
Open accessPublished as bronze (free) open access
Funder project or code513
Application of non-linear mathematics and stochastic modelling to biological systems
Publisher's version
Output statusPublished
Publication dates
Online01 Sep 2004
Publication process dates
Accepted17 May 2004
Copyright licensePublisher copyright
PublisherGenetics Society of America (GSA)
ISSN0016-6731

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