Estimation of genetic parameters and breeding values in shrimp F enneropenaeus chinensis using the REML/BLUP procedure

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 estimating heritability and breeding values for BM.

Mapping and breeding value evaluation of a semi-dominant semidwarf gene in upland rice

Plant height is an important trait related to yield potential and plant architecture. A suitable plant height plays a crucial role in improvement of rice yield and lodging resistance. In this study, we found that the traditional upland landrace ＇Kaowenghan＇ （KWH） showed a special semi-dwarf phenotype. To identify the semi-dwarf gene from KWH, we raised BC2F4 semi-dwarf introgression lines （IL） by hybridization of the japonica rice cultivar ＇Dianjingyoul＇ （DJY1） and KWH in a DJY1 background. The plant height of the homozygous semi-dwarf IL （IL-87） was significantly reduced compared with that of DJY1. The phenotype of the F1 progeny of the semi-dwarf IL-87 and DJY1 showed that the semi-dwarf phenotype was semi- dominant. QTL mapping indicated that the semi-dwarf phenotype was controlled by a major QTL qDH1 and was localized between the markers RM6696 and RM12047 on chromosome 1. We also developed near-isogenic lines （NIL） from the BC3F3 population, and found that the yield of homozygous NIL （NIL-2） was not significantly different compared to DJY1. Breeding value evaluation through investigation of the plant height of the progeny of NIL （NIL-2） and cultivars from different genetic background indicate that the novel semi-dwarf gene shows potential as a genetic resource for rice breeding.

ASP-Based Programs of Best Linear Unbiased Prediction-Estimated Breeding Values in Breeding Stock

In order to improve the breeding effect of livestock, the data were read from an Excel file with Active Server Page （ASP） programs, and the breeding values of breeding stock were calculated by best linear unbiased prediction （BLUP） method.

Estimated Breeding Value in Iranian Buffaloes

This study was conducted from 1992-2002 on 65,534 individual milk records and fat percentage in 473 herds, in six provinces of Khuzestan, Mazndaran, W. Azerbaijan, E. Azerbaijan, Ardabil and Gilan. The data was analyzed by SAS sotfware using GLM procedure. The heritability and breeding value of 1,195 animals were calculated by DFRML procedure. The average milk yield per lactation, days of lactation, fat percentage and LSM of fat percentage were 1,513 kg, 202 days, 5.04 and 6.77, respectively. The estimated heritability of milk was 0.16. The LSM of average milk production in the provinces of Gilan, Mazandaran, E. Azerbaijan, W. Azerbaijan, Khuzestan and Ardabil were： 1,452, 1,586, 1,382, 1,183, 2,135 and 1,189 kg, respectively. These results indicated that Khuzestan province has the highest potential in the field of milk production. The top five highest breeding value bulls have been introduced to artificial insemination＇s station in the city of Uremia.

A comparison of genomic selection methods for breeding value prediction

Recent advances in molecular genetics techniques have made dense marker maps available, and the prediction of breeding value at the genome level has been employed in genetics research. However, an increasingly large number of markers raise both statistical and computational issues in genomic selection （GS）, and many methods have been developed for genomic prediction to address these problems, including ridge regression-best linear unbiased prediction （RR-BLUP）, genomic best linear unbiased prediction, BayesA, BayesB, BayesCπ, and Bayesian LASSO. In this paper, these methods were compared regarding inference under different conditions, using real data from a wheat data set and simulated scenarios with a small number of quantitative trait loci （QTL） （20）, a moderate number of QTL （60, 180） and an extreme number of QTL （540）. This study showed that the genetic architecture of a trait should be fully considered when a GS method is chosen. If a small amount of loci had a large effect on a trait, great differences were found between the predictive ability of various methods and BayesCπ was recommended. Although there was almost no significant difference between the predictive ability of BayesCπ andBayesB, BayesCπ is more feasible than BayesB for real data analysis. If a trait was controlled by a moderate number of genes, the absolute differences between the various methods were small, but BayesA was also found to be the most accurate method. Furthermore, BayesA was widely adaptable and could perform well with different numbers of QTL. If a trait was controlled by an extreme number of minor genes, almost no significant differences were detected between the predictive ability of various methods, but RR-BLUP slightly outperformed the others in both simulated scenarios and real data analysis, thus demonstrating its robustness and indicating that it was quite effective in this case.

Comparative study of estimation methods for genomic breeding values

Estimation of genomic breeding values is important in genomic selection. Bayesian and BLUP methods are the main techniques employed. In this study,we conducted a comparative study of Bayes A, Bayes B,Bayes Cp and GBLUP methods in simulated data and real data of Chinese Holstein cattle. Results showed that, in simulated data, the accuracies of all methods were all similarly elevated with the increase of reference population size, but they made different responses to the changes of marker number or QTL number. In real data of Chinese Holstein cattle, Bayes A generated the highest accuracy almost for all six traits, and GBLUP performed as well as Bayes A for the traits of milk yield, fat yield and protein yield, while for the trait of fat percentage, protein percentage and somatic cell score, three Bayesian methods showed superior to GBLUP. Comprehensively analyzing above results, it can be speculated that accuracies of the three Bayesian methods are not only influenced by the absolute value of QTL number or marker number, but may also be influenced by the ratio of QTL number to marker number. And there is at least one kind of Bayesian methods performing better than GBLUP, when the ratio of QTL number versus marker number is very small or involving large-effect QTL.

Analysis of phenotypic and genetic parameters for growth-related traits in the half smooth tongue sole,Cynoglossus semilaevis

Phenotypic and genetic parameters for growth-related traits in the half-smooth tongue sole, Cynoglossus semilaevis, were estimated in 22 full-sib families produced by normal and neo-male breeding stocks. As phenotypic males with female genotypes, neo-males are harmful in C. semilaevis aquaculture because they reduce overall production. The present study evaluated the difference in the growth-related traits： total length （TL）, body weight （BW） and square root of body weight （SQ_BW） at the age of 570 days between normal and neo-male offspring （neo-males used as male parents）. The difference in the proportion of females between normal and neo-male offspring was also assessed. Based on the linear mixed model, restricted maximum likelihood （REML） and best linear unbiased prediction （BLUP） were used to estimate various （co）variance components and estimated breeding values （EBVs） of growth-related traits. As a result, all the mean values of the three studied traits were significantly larger in normal offspring than in neo-male offspring. Additionally, the female proportion was significantly larger in normal offspring than in neo-male offspring. Heritability was 0.128＋0.066 2 for TL, 0.128-4-0.065 5 for BW and 0.132~0.062 9 for SQBW, all of which were low level heritabilities. The correlation coefficients of EBVs and phenotypic values of the target traits were 0.516 for TL, 0.524 for BW and 0.506 for SQ_BW, all of which were highly significant （P〈0.01）. Genetic correlations among TL, BW and SQ_BW were positive high （0.921-0.969） and higher than those of phenotype （0.711-0.748）, both of which had low standard errors （0.063-0.123 for genotype, and 0.010-0.018 for phenotype）. Compared with normal offspring, neo-male offspring have lower breeding values for each studied trait through EBVs comparison. Therefore, neo-male offspring should not be used as broodstock in a C. semilaevis breeding programs.

Estimation of genetic parameters for growth trait of turbot using Bayesian and REML approaches

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.

Importance of “connectedness” between herds for effective across herd genetic evaluation

“Connectedness” is an essential component of genetic evaluations. The degree of connectedness affects the accuracy of comparing estimated breeding values (EBVs) from one herd or contemporary group to the other. It can be measured through Connectedness Rating (CR) which is based on variances and covariance among the estimates of contemporary group effects. A computing algorithm and a computer program for estimating CR is available. The minimum required level of connectedness depends upon the size of the contemporary groups, the level of accuracy and the residual variance. About 48% CR is required to detect differences between EBVs that are greater than 20% of the standard deviation in the trait, for group sizes of about 100 animals. Higher levels are necessary for smaller group sizes and for more accurate comparisons. Breeders participating in a common genetic evaluation program should therefore exchange their superior genetics and possibly use some common testing facilities for meaningful estimates of breeding values. Maintaining a good connectedness level will make the genetic evaluation program more useful for selection of superior breeding animals and achieving faster rate of genetic progress.

A Method for Accuracy of Genetic Evaluation by Utilization of Canadian Genetic Evaluation Information to Improve Heilongjiang Holstein Herds

The objectives of this study were to set up a new genetic evaluation procedure to predict the breeding values of Holstein herds in Heilongjiang Province of China for milk and fat production by utilizing Canadian pedigree and genetic evaluation information and to compare the breeding values of the sires from different countries. The data used for evaluating young sires for the Chinese Holstein population consisted of records selected from 21 herds in Heilongjiang Province. The first lactation records of 2 496 daughters collected in 1989 and 2000 were analyzed. A single-trait animal model including a fixed herd-year effect, random animal and residual effects was used by utilizing Canadian pedigree and genetic evaluation information of 5 126 sires released from the Canadian Dairy Network in August 2000. The BLUP procedure was used to evaluate all cattle in this study and the Estimated Breeding Values (EBV)for milk and fat production of 6 697 cattle (including 673 sires and 6 024 cows) were predicted. The genetic levels of the top 100 sires originated from different countries were compared. Unlike the BLUP procedure that is being used in conjunction with the single-trait sire model in Heilongjiang Province of China now, the genetic evaluation procedure used in this study not only can be used simultaneously to evaluate sires and cows but also increase the accuracy of evaluation due to using the relationships and genetic values of the Canadian evaluated sires with more daughters. The results showed that the new procedure was useful for genetic evaluation of dairy herds and the comparison of the breeding values of these sires imported from different countries showed that a significant genetic improvement has been achieved for milk production of the Heilongjiang Holstein dairy population by importing sires from foreign countries, especially from the United States due to the higher breeding values.

Influence of Allele Frequency on Predicting Animal Phenotype Using Back-Propagation Artificial Neural Networks

To overcome the obstacle of the fascinating relation in predicting animal phenotype value, we have developed a neural network model to detect the complex non-linear relationships between the genotypes and phenotypes and the possible interactions that cannot be expressed with equations. In this paper, back-propagation neural network is used to discuss the influences of different allele frequencies on estimating the polygenic phenotype value. To ensure the precision of prediction, normalization was needed to train the prediction model. The results show that back-propagation artificial neural networks can be used to predict the phenotype value and perform very well in allele frequency from 0.2 to 0.8, when the allele frequency is very small （less than 0.2） or big （more than 0.8）; however, the prediction model was not reliable and the predicted value should be carefully tested.

Statistical considerations for genomic selection

Genomic selection is becoming increasingly important in animal and plant breeding, and is attracting greater attention for human disease risk prediction. This review covers the most commonly used statistical methods and some extensions of them, i.e., ridge regression and genomic best linear unbiased prediction, Bayesian alphabet, and least absolute shrinkage and selection operator.Then it discusses the measurement of the performance of genomic selection and factors affecting the prediction of performance. Among the measurements of prediction performance, the most important and commonly used measurement is prediction accuracy. In simulation studies where true breeding values are available, accuracy of genomic estimated breeding value can be calculated directly. In real or industrial data studies, either trainingtesting approach or k-fold cross-validation is commonly employed to validate methods. Factors influencing the accuracy of genomic selection include linkage disequilibrium between markers and quantitative trait loci, genetic architecture of the trait, and size and composition of the training population. Genomic selection has been implemented in the breeding programs of dairy cattle, beef cattle, pigs and poultry. Genomic selection in other species has also been intensively researched, and is likely to be implemented in the near future.