Effectiveness of adjusting for heterogeneity of variance in genetic evaluation of Japanese Black cattle.
Heterogeneity of variance among subclasses of an effect is a potential source of bias in genetic evaluation. The objectives of this study were to quantify the heterogeneity of variance in carcass weight in Japanese Black cattle, to develop an adjustment method to account for the heterogeneity, and to evaluate the effectiveness of the method. A total of 96,950 records were collected from steers and heifers slaughtered from 1997 to 2005. These records were grouped into 2,767 farm-market-year-sex subclasses. Fourteen log-linear models for the variances were set up to estimate the heterogeneous phenotypic variances within subclasses. Schwarz's Bayesian information criterion was used for model selection. The preadjustment of records to a baseline variance was based on maximum likelihood estimates obtained from the selected model. As a result of adjustment, the SD, the CV, and the Gini coefficient for the phenotypic variance decreased by 68.6, 69.8, and 70.1%, respectively. When the top 5% of sires and top 1% of dams were selected, Spearman's rank correlation coefficients between the adjusted and unadjusted data were 0.95 for the selected sires and 0.78 for the selected dams. The effectiveness of the adjustment was evaluated in terms of the ability to predict breeding values, using the results of the successive genetic evaluations. Mean squared error between the parent averages and actual predicted values of the genetic merit for the sires whose progeny had a carcass record only from 2003 to 2005 was significantly reduced by the adjustment (P < 0.05). The results suggest that the genetic evaluation becomes more accurate by adjusting the data using the procedure developed in this study.
Nakaoka
H
H
Laboratory of Animal Breeding and Genetics, Graduate School of Agriculture, Kyoto University, Kyoto, 606-8502, Japan. nak_1896@kais.kyoto-u.ac.jp
Narita
A
A
Ibi
T
T
Sasae
Y
Y
Miyake
T
T
Yamada
T
T
Sasaki
Y
Y
D000818:Animals; D001499:Bayes Theorem; D001947:Breeding; D002417:Cattle / Q000235:genetics*; D005260:Female; D014644:Genetic Variation; D007564:Japan; D016013:Likelihood Functions; D016014:Linear Models; D008297:Male; D008957:Models, Genetic; D010641:Phenotype; D011237:Predictive Value of Tests; D012727:Sex Characteristics; D018709:Statistics, Nonparametric