Estimation of genetic parameters and response to selection for a continuous trait subject to culling before testing


T. Árnason, Agricultural University of Iceland, IS-311 Borgarnes, Iceland. Tel:+46 224 60109; E-mail:


The consequences of assuming a zero environmental covariance between a binary trait ‘test-status’ and a continuous trait on the estimates of genetic parameters by restricted maximum likelihood and Gibbs sampling and on response from genetic selection when the true environmental covariance deviates from zero were studied. Data were simulated for two traits (one that culling was based on and a continuous trait) using the following true parameters, on the underlying scale: h2 = 0.4; rA = 0.5; rE = 0.5, 0.0 or −0.5. The selection on the continuous trait was applied to five subsequent generations where 25 sires and 500 dams produced 1500 offspring per generation. Mass selection was applied in the analysis of the effect on estimation of genetic parameters. Estimated breeding values were used in the study of the effect of genetic selection on response and accuracy. The culling frequency was either 0.5 or 0.8 within each generation. Each of 10 replicates included 7500 records on ‘test-status’ and 9600 animals in the pedigree file. Results from bivariate analysis showed unbiased estimates of variance components and genetic parameters when true rE = 0.0. For rE = 0.5, variance components (13–19% bias) and especially inline image (50–80%) were underestimated for the continuous trait, while heritability estimates were unbiased. For rE = −0.5, heritability estimates of test-status were unbiased, while genetic variance and heritability of the continuous trait together with inline image were overestimated (25–50%). The bias was larger for the higher culling frequency. Culling always reduced genetic progress from selection, but the genetic progress was found to be robust to the use of wrong parameter values of the true environmental correlation between test-status and the continuous trait. Use of a bivariate linear-linear model reduced bias in genetic evaluations, when data were subject to culling.