Variance component estimation with longitudinal data: a simulation study with alternative methods

Abstract

A pedigree structure distributed in three different places was generated. For each offspring, phenotypic information was generated for five different ages (12, 30, 48, 66 and 84 months). The data file was simulated allowing some information to be lost (10, 20, 30 and 40%) by a random process and by selecting the ones with lower phenotypic values, representing the selection effect. Three alternative analysis were used, the repeatability model, random regression model and multiple-trait model. Random regression showed to be more adequate to continually describe the covariance structure of growth over time than single-trait and repeatability models, when the assumption of a correlation between successive measurements in the same individual was different from one another. Without selection, random regression and multiple-trait models were very similar.