Article

Comparison of Multitrait and Single-Trait Multiple Parity Evaluations by Monte Carlo Simulation

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Abstract

Three methods of analysis: 1) multiparity analysis with the first three parities analyzed as correlated traits, 2) multiparity analysis with all lactations analyzed as a single trait, and 3) first parity only analysis were compared on simulated data. A selection index was used to compute multiparity evaluations from the separate parity evaluations of the multitrait analyses. Twenty simulated populations, each of 8,500 cows, were generated by an algorithm that approximated the multitrait model. Populations were simulated with both random and yield-based culling of cows after first and second parity. Populations simulated with yield-based culling were analyzed both with the first parity records of all cows included and with an arbitrary one-third of first parity records deleted. First parity records of all cows were included in the analyses of the randomly culled populations. Accuracy of evaluation, estimated by correlations between true effects and evaluations and prediction error variances, was highest by the multitrait analysis and lowest by the first parity only analysis. Evaluations obtained by the two multilactation methods were nearly identical with random culling. Regression of effect on evaluation was close to unity for the multitrait evaluation; was only .94 and .90 for the all lactation single trait evaluation with random and yield culling, respectively; and was .80 for the index of sire effects on the first parity only analysis. Single-trait multilactation method may be preferred, as it is nearly as accurate as the multitrait method and easier computationally.

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... The R 2 of the simulated values was more than five-fold for the ML estimates, as compared to the LS estimates, but both were < 0.1. For an estimate to be unbiased, the regression of the true value on the estimate should be unity [21]. Both regressions of the simulated values on the estimates were < 0.5, but the regression on the ML estimate was nearly double the regression of the LS estimate. ...
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