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Journal of Animal Science

.: Home > Journal of Animal Science > 2009 > Volume 87 Number 12 > S. Forni*,**, D. Gianola*,**,***,2, G. J. M. Rosa{dagger} and G. de los Campos*

A dynamic linear model for genetic analysis of longitudinal traits

S. Forni*,**, D. Gianola*,**,***,2, G. J. M. Rosa{dagger} and G. de los Campos*
* Department of Animal Sciences, and and ** Department of Dairy Science, University of Wisconsin, Madison 53706; and *** Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, 1432 ├ůs, Norway
Abstract :

A Bayesian model for quantitative genetic analysis of longitudinaltraits is presented. It connects the model known as the Kalmanfilter (KF) with the standard mixed model of quantitative genetics.The KF model can be implemented easily in a Bayesian frameworkbecause, under standard prior assumptions, all fully conditionalposterior distributions have closed forms. An analysis of beefcattle growth data including comparisons with a standard multivariatemodel was performed to assess applicability of the KF to animalbreeding. Models were compared using the deviance informationcriterion and the Bayes factor. Models in which a KF acted onadditive genetic and maternal effects were favored by the devianceinformation criterion, although KF did not describe residual(co)variance adequately. The Bayes factor did not provide conclusiveevidence in favor of a specific model. Fitting KF to longitudinaltraits provides estimates of genetic value for a whole rangeof time points, assuming that there are genetic differencesthrough time between and within individuals. Different modelsembedding the KF in a mixed model were demonstrated to providea more parsimonious (co)variance structure than a standard multitraitspecification for the quantitative genetic analysis of longitudinaldata.

Keywords :
Bayes factor, beef cattle, deviance information criterion, growth, Kalman filter, longitudinal data

Date Deposited : 11 Jan 2011 15:11

Last Modified : 11 Jan 2011 15:11

Official URL:

Volume 87, Number 12, December 2009

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