Optimizing Selection Strategy Of Genomic Selection In Chinese Quality Chicken

Genomic selection is revolutionizing livestock and poultry breeding by obtaining a higher accuracy of selection and shorter generation interval. Chinese quality chicken breeding may benefit from increasing the accuracy of genomic selection. The objectives of this study were to investigate efficiency of genomic prediction in Chinese quality chicken. The results and coclusions of the current study are summarized as follow:(1) Genetic parameters were estimated using relationships between birds based on pedigree. Two growth traits and three carcass traits are analyzed, i.e. body weigh at 6th week, body weigh at 12th week, eviscarating percentage, breast muscle percentage and leg muscle percentage. The two growth traits had moderately low heritability, the heritability was 0.26 and 0.13, respectively. The three carcass traits had moderately high heritability, the heritability was 0.44,0.39 and 0.39. Three genomic prediction models, i.e. a genomic best linear unbiased prediction model, a Bayesian least absolute shrinkage and selection operator model, and a Bayesian mixture model with four distributions were compared to conventional prediction model for two growth traits and three carcass traits. The birds were genotyped using the Illumina Chicken 60K SNP Beadchip. Genomic prediction was assessed using a cross-validation procedure for two validation scenarios, i.e. a family sample scenario and a random sample scenario. In the family sample scenario,using the three genomic precition models, the correlations ranged from 0.448 to 0.468 for the two growth traits and from 0.176 to 0.255 for the three carcass traits, and the mean of the correlations for all traits was 0.319,but, in contrast to conventional prediction model, the mean was only 0.034. In the random sample scenario, the correlations were between 0.487 and 0.536 for growth traits and between 0.312 and 0.430 for carcass traits, and the mean of the correlations for all traits was 0.433, by contrast, it was only 0.236 using the conventional model. The results indicated that genomic selection could greatly improve the accuracy of selection in chickens, compared with conventional selection.(2) Genetic parameters were estimated based on pedigree for antibody response to Newcastle disease virus (Ab-NDV) and antibody response to Avian Influenza virus (Ab-AIV). Ab-NDV had a moderately high heritability of 0.478, and Ab-AIV had a moderate heritability of 0.301. Multiple-trait model was used to improve accuracy of genomic prediction for antibody response, Genomic prediction was also assessed using a cross-validation procedure for two validation scenarios. The estimated genetic correlation between the two traits was 0.438. In the family sample scenario, compared with the pedigree-based model, the accuracy of the genomic prediction increased from 0.086 to 0.237 for Ab-NDV and from 0.080 to 0.347 for Ab-AIV. In the random sample scenario, the accuracy was improved from 0.389 to 0.427 for Ab-NDV and from 0.281 to 0.367 for Ab-AIV. The multiple-trait GBLUP model led to a higher accuracy of genomic prediction by using the correlation between the two traits.(3) Using Monte-Carlo simulation method, the non-genomic reference scenario was compared to genomic breeding program to evaluate the economic profit in Chinese quality chicken three lines cross breeding program. After ten generation, line A, line B and line C increased 0.356,0.341 and 0.356 standard deviation extra genetic gain per generation, which could earn 401,414 and 4141 million yuan, respectively. The results shown genomic scenario increased the expected genetic gain and the economic profit of the breeding program. Genomic selection is shown to have the potential to improve genetic gain and economic profit in Chinese quality chicken breeding programs.