فهرست مطالب جک دکرز

In this study, body weight records of 19171 American Simmental beef cattle including 8758 bull and 10413 cows from 2000 to 2016 were used to estimate genetic parameters and trend. We conducted genetic evaluation of data from 5199, 10926, 7362 and 5636 genotyped beef cattle of gene-based single nucleotide polymorphisms (SNPs) for birth weight (BW), weaning weight direct (WWD), yearling weight (YW) and post-weaning weight gain (PWG), respectively. Genetic parameters estimated using univariate and bivariate animal models and BLUPf90 software. Heritabilities were estimated for BW, WW, YW and PWG 0.38 ± 0.02, 0.24 ± 0.01, 0.35 ± 0.01 and 0.22 ± 0.02 using univariate analysis, respectively. Genetic correlation between BW and PWG was 0.31. The highest and the lowest genomic accuracy were estimated for WWD (0.68) and PWG (0.27), respectively. Genetic trends were estimated for BW, WW, YW and PWG 0.13, 0.28, 3.43 and 0.05, respectively. In addition, slight changes were observed in the genetic trend of all traits, especially weaning weight direct and yearling weight across years of study which indicates the existence of specific selection goals and criteria in each of these traits. In general, three large-effect closely linked QTLs identified on BTA6 at 37 Mb, on BTA7 at 90 Mb and on BTA14 at 22 Mb. Because correlations were favorable between body weight traits, breeding program should be based on the simultaneous selection of these traits through the selection index.

The purpose of this study was to compare the accuracy of genomic evaluation for Bayes A, Bayes B, Bayes C and Bayes L multi-step methods and SSBR-C and SSBR-A single-step methods in the different values of π for predicting genomic breeding values of the genotyped and non-genotyped animals. A genome with 40000 SNPs on the 20 chromosom was simulated with the same distance (100cM). The π values that maximized the prediction accuracies in BayesC were 0.980 and 0.995 for the normal and gamma distributions of QTL, respectively, and were also used in SSBR-C method. Genomic prediction accuracy in the SSBR-C (π = 0.99) method was higher than multi step methods from 0.02 to 0.09 for gamma distribution. Results showed that considering mixture distribution and use of phenotype, genotype and pedigree information simultaneously, the SSBR-C (π = 0.99) method had higher accuracy than other methods and is considered a better choice in this scenario. Moreover, both single and multi-step methods showed similar prediction accuracy when the genetic architecture appeared to approach the normal distribution. Furthermore, SSBR-C (π = 0) method appeared to be more reliable choice that was due to regressions of true breeding value on estimated breeding value close to one in normal distribution. Generally, GEBV accuracy decreased as the distance increased between validations and training set, which was more sensitive for non-genotyped individuals compared to genotyped individuals.