Study on estimation model of the genetic parameters for body measurements traits in Yu’nong black pigs
Journal Title: Journal of Henan Agricultural University - Year 2022, Vol 56, Issue 1
Abstract
[Objective] In order to improve the accuracy of genetic parameter estimation of body measurements traits in Yu’nong black pig and speed up the breeding progress in Yu’nong black pigs. [Method] In this study, the two methods of best linear unbiased prediction and genomic best linear unbiased prediction were used to construct three single-trait animal models, Model 1 based on BLUP method, Model 2 based on GBLUP method, and Model 3 based on GBLUP method with genome inbreeding coefficient. The average information restricted maximum likelihood algorithm (AIREML) was used to estimate the genetic parameters for body measurements traits in 702 Yu’nong black pigs. [Result] In the aspect of the accuracy of genetic parameter estimation, the accuracy of model 1 is lower than that of models 2 and 3. Compared with Model 2, Model 3 improves the accuracy of genetic parameter estimation of chest circumference, legs buttocks circumference, and loin muscle depth. Heritabilities estimated by Model 3 for body height, leg buttocks circumference, backfat thickness, and loin muscle depth were 0.566, 0.302, 0.467 and 0.652, which were of high heritability traits, while the heritability for body length, chest circumference, and cannon bone circumference were 0.152, 0.122 and 0.255, which were of middle heritability traits. Among body measurements traits, the genetic correlation ranged from -0.009 to 0.576, and the phenotypic correlation was varied from -0.108 to 0.985. [Conclusion] GBLUP model based on inbreeding coefficient can improve the accuracy of genetic evaluation when estimating genetic parameters of body measurements traits of Yu’nong black pigs. The results of this study have provided a scientific basis for accelerating genetic progress in production practice.
Authors and Affiliations
Cong LI, Dongdong DUAN, Mengyu LI, Benli ZHOU, Xiuling LI, Kejun WANG, Xuelei HAN, Xianwei WANG, Ruimin QIAO, Kai LI, Xinjian LI
Keywords
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- EP ID EP769017
- DOI 10.16445/j.cnki.1000-2340.20210817.001
- Views 5
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