Bayesian and restricted maximum likelihood （REML） approaches were used to estimate the genetic parameters in a cultured turbot Scophthalmus maximus stock. The data set consisted of harvest body weight from 2 462 progenies （17 months old） from 28 families that were produced through artificial insemination using 39 parent fish. An animal model was applied to partition each weight value into a fixed effect, an additive genetic effect, and a residual effect. The average body weight of each family, which was measured at 110 days post-hatching, was considered as a covariate. For Bayesian analysis, heritability and breeding values were estimated using both the posterior mean and mode from the joint posterior conditional distribution. The results revealed that for additive genetic variance, the posterior mean estimate （σa^2 =9 320） was highest but with the smallest residual variance, REML estimates （σa^28 088） came second and the posterior mode estimate （σa^2=7 849） was lowest. The corresponding three heritability estimates followed the same trend as additive genetic variance and they were all high. The Pearson correlations between each pair of the three estimates of breeding values were all high, particularly that between the posterior mean and REML estimates （0.996 9）. These results reveal that the differences between Bayesian and REML methods in terms of estimation of heritability and breeding values were small. This study provides another feasible method of genetic parameter estimation in selective breeding programs of turbot.
GUAN JiantaoWANG WeijiHU YulongWANG MosangTIAN TaoKONG Jie
Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis is placed on comparing the properties of two LMM approaches: restricted maximum likelihood (REML) and minimum norm quadratic unbiased estimation (MINQUE) with and without resampling techniques being included. Bias, testing power, Type I error, and computing time were compared between REML and MINQUE approaches with and without Jackknife technique based on 500 simulated data sets. Results showed that MINQUE and REML methods performed equally regarding bias, Type I error, and power. Jackknife-based MINQUE and REML greatly improved power compared to non-Jackknife based linear mixed model approaches. Results also showed that MINQUE is more time-saving compared to REML, especially with the use of resampling techniques and large data set analysis. Results from the actual cotton data analysis were in agreement with our simulated results. Therefore, Jackknife-based MINQUE approaches could be recommended to achieve desirable power with reduced time for a large data analysis and model simulations.
An analysis of a selection experiment was used to assess the impact of various animal model struc- tures on REML estimates of variance components. The analyses were carried out based on 162 d body mass （BM） of 1 287 animals from 21 paternal half-sib groups of Fenneropenaeus chinensis. Estimated breeding values （EBV） of BM of all individuals were estimated using eight statistical models （A, AB, ABC, ABDC, ABMFC, ABMDC, ABFDC and ABMFDC） and BLUP （best linear unbiased prediction）. These models were designed involving factors such as sex, spawn date as fixed effects, maternal genetic effects, full-sib family effects as random effects, mean BM of families at tagging and age at recording （covariate）. The results demonstrate the importance of correct interpretation of effects in the data set, particularly those that can influence resemblance between relatives. The data structure and the particular model that was applied markedly influenced the magnitude of variance component estimates. Models based on few effects obtained upward biased estimates of additive genetic variance. The accuracy of genetic parameters and breeding value es- timated by ABFDC model was higher than other models. The results imply that additive genetic direct value, full-sib family effects, and covariance effects besides sex and spawn date as fixed effects were very important for estimating genetic parameters and breeding value of body mass. This model had a heritability estimate of 162 d BM of 0.44. The comparison of the efficiency of selection based on breeding values or phenotypic value revealed great difference： average breeding value of the best 24 families selected by the 162 d BM breeding value and phenotype were 0.577 g and 0.366 g, respectively, representing a 36.57% higher efficiency in the former. In conclusion, selection based on breeding value was more effective than selection based on phenotypic value. Our results indicate that effects influencing the magnitude of estimates should be taken into account when estim
