Birth weights (4,155) and weaning weights (3,884) of Line 1 Herefords collected at the Fort Keogh Livestock and Range Research Laboratory in Miles City, MT, between the years of 1935 to 1989 were available. To study the effect of misidentification on estimates of genetic parameters, the sire identification of calf was randomly replaced by the identification of another sire based on the fraction of progeny each sire contributed to a yearly calf crop. Misidentification rates ranged from 5 to 50% with increments of 5%. For each rate of misidentification, 100 replicates were obtained and analyzed with single-trait and two-trait analyses with a restricted maximum likelihood (REML) algorithm. Two different models were used. Both models contained year x sex combinations and ages of dam as fixed effects, calendar birth date as a fixed covariate, and random animal and maternal genetic effects and maternal permanent environment effects. Model 2 also included sire x year combinations as random effects. As the rate of misidentification increased, estimates of the direct-maternal genetic correlation increased for both traits, with both models, for all analyses. With singletrait analyses, estimates of the fraction of variance that were due to sire x year interaction effects increased slightly for birth weight (near zero) and decreased slightly (0.015 to 0.004) for weaning weight as misidentification increased. With two-trait analyses, estimates of fraction of variance that were due to sire x year effects gradually decreased for weaning weight as misidentification increased. With the two-trait analyses, and with both models, as the level of sire misidentification increased, estimates of the genetic correlation between direct effects gradually increased, and estimates of the correlation between maternal effects gradually decreased. Estimates of the direct-maternal genetic correlation were more positive with Model 2 than with Model 1 for all levels of misidentification. Results of this study indicate that misidentification of sires would severely bias estimates of genetic parameters and would reduce genetic gain from selection.
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