Multiple and Single Trait Analyses for Estimating Genetic Parameters in Simulated Populations Under Selection

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Abstract
Records were simulated for 2 traits for a 1-way model with random sire groups. Sire effects and residuals were generated from bivariate normal distributions with heritabilities of 0.30 for each trait. Genetic correlations simulated were 0.15, 0.45 and 0.75 while residual correlations were 0.15, 0.35 and 0.55. Genetic values for 50 sires and residuals corresponding to 100 daughters per sire were simulated. Initially, all daughters were assigned 2 records. If a daughter''s 1st record placed her in the bottom 0, 20, 40 or 60% of the population her 2nd record was not included in the corresponding analysis. An iterative restricted maximum likelihood procedure was used to estimate variance and covariance components by 2 multiple trait and 1 single trait algorith. One multiple trait method required an assumption of zero residual covariance, but the other did not. After 30 replications, results were compared by selection intensities and genetic and residual correlations. With no selection, all estimation methods produced estimates within 1 SE of the parameters. Under selection, the multiple trait algorithm requiring no assumptions about residual covariance was superior to the other 2 in producing accurate estimates, especially with heavy culling and with high underlying correlations of residuals or sires.