数字农科院2.0

Accuracies Of Genomic Prediction For T.wenty Economically Important Traits I n Chinese Simmental Beef Cattle

文献类型： 外文期刊

作者： Zhu, B; Guo, P; Wang, Z; Zhang, W; Chen, Y; Zhang, L; Gao, H; Gao, X; Xu, L; Li, J

作者机构：

关键词： accuracy; Bayesian methods; cross-validation; economic traits; prediction

期刊名称： ANIMAL GENETICS

ISSN： 0268-9146

年卷期： 2019 年 50 卷 6 期

页码：

收录情况： JCR（2021版）

摘要： Genomic prediction has been widely utilized to estimate genomic breeding values (GEBVs) in farm animals. In this study, we conducted genomic prediction for 20 economically important traits including growth, carcass and meat quality traits in Chinese Simmental beef cattle. Five approaches (GBLUP, BayesA, BayesB, BayesC pi and BayesR) were used to estimate the genomic breeding values. The predictive accuracies ranged from 0.159 (lean meat percentage estimated by BayesC pi) to 0.518 (striploin weight estimated by BayesR). Moreover, we found that the average predictive accuracies across 20 traits were 0.361, 0.361, 0.367, 0.367 and 0.378, and the averaged regression coefficients were 0.89, 0.86, 0.89, 0.94 and 0.95 for GBLUP, BayesA, BayesB, BayesC pi and BayesR respectively. The genomic prediction accuracies were mostly moderate and high for growth and carcass traits, whereas meat quality traits showed relatively low accuracies. We concluded that Bayesian regression approaches, especially for BayesR and BayesC pi, were slightly superior to GBLUP for most traits. Increasing with the sizes of reference population, these two approaches are feasible for future application of genomic selection in Chinese beef cattle.

分类号：

  • 相关文献

[1]Genome-Wide Detection Of Copy Number V.ariations In Polled Yak U sing The Illumina Bovinehd Beadchip. Jia, CJ, Wang, HB, Li, C, Wu, XY, Zan, LS, Ding, XZ, Guo, X, Bao, PJ, Pei, J, Chu, M, Liang, CN, Yan, P. 2019

[2]Development Of A Predictive Model T.o Determine Potato Flour C ontent In Potato-Wheat Blended Powders Using Near-Infrared Spectroscopy. Huang, YJ, Xu, F, Hu, HH, Dai, XF, Zhang, H. 2018

[3]Effects Of Marker Density And M.inor Allele Frequency On G enomic Prediction For Growth Traits In Chinese Simmental Beef Cattle. Zhu, B, Zhang, JJ, Niu, H, Guan, L, Guo, P, Xu, LY, Chen, Y, Zhang, LP, Gao, HJ, Gao, X, Li, JY. 2017

[4]Comparison Of The Efficiency Of Blup And Gblup In Genomic Prediction Of Immune Traits In Chickens. Zhang, J, Wang, J, Li, QH, Wang, Q, Wen, J, Zhao, GP. 2020

[5]Hybrid Breeding Of Rice Via Genomic Selection. 张帆,黎志康. 2020

[6]Prediction For Hog Prices Based O.n Similar Sub-Series Search A nd Support Vector Regression. Wang, Dongjie,Liu, Yiran,Zhang, Zhentao,Liu, Chunhong,Duan, Qingling. 2019

[7]Progress in Genomic Mating in Domestic Animals. Zhang P., Qiu X., Wang L., Zhao F.. 2022

[8]Estimation Of Nicotine Content In T.obacco Leaves Based On H yperspectral Imaging. Dou, YQ, Cheng, S, Li, XJ, Liu, Y, Yuan, XL. 2017

[9]Spatial patterns and driving factors of aboveground and belowground biomass over the eastern Eurasian steppe. Ding L., Li Z., Shen B., Wang X., Xu D., Yan R., Yan Y., Xin X., Xiao J., Li M., Wang P.. 2022

[10]Modelling and optimization of high-pressure homogenization of not-from-concentrate juice: Achieving better juice quality using sustainable production. Liu J., Bi J., Liu X., Liu D., Verkerk R., Dekker M., Lyu J., Wu X.. 2022

[11]Estimation of evapotranspiration using all-weather land surface temperature and variational trends with warming temperatures for the River Source Region in Southwest China. Ma Y., Zhou J., Liu S., Zhang W., Zhang Y., Xu Z., Song L., Zhao H.. 2022

[12]Effects of feeding level on enteric methane emissions and utilisation of energy and nitrogen in dry ewes of two genotypes offered fresh ryegrass. Yang C.T.,Wang C.M.,Zhao Y.G.,Chen T.B.,Aubry A.,Gordon A.W.,Yan T.. 2021

[13]Transfer Of Pesticide Residue During T.ea Brewing: Understanding The E ffects Of Pesticide'S Physico-Chemical Parameters On Its Transfer Behavior. Wang, XR, Zhou, L, Zhang, XZ, Luo, FJ, Chen, ZM. 2019

[14]Landscape Pattern Analysis And Prediction I.n Qingyang City Based O n Ca-Markov Model. Gai, AH, Di, LP, Tang, JM, Guo, LY, Qian, YL, Zhou, DM, Lu, Q, Song, JR, Cen, GZ. 2018

[15]Multivariate Financial Time-Series Prediction With Certified Robustness. 李慧,崔运鹏,王硕,刘娟. 2020

[16]Assess the Accuracy of the Globcover Cultivated Lan d Data in Northeast China. Zhang, Li,Wu, Wenbin,Zhou, Qingbo,Chen, Zhongxin,Li, Zhengguo,Wu, Wenbin,Zhou, Qingbo,Chen, Zhongxin,Li, Zhengguo. 2012

[17]Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties. Tao, FL,Yokozawa, M,Zhang, Z,Xu, YL,Hayashi, Y.

[18]A comparative analysis of five global cropland datasets in China. Lu, Miao,Wu, WenBin,Zhang, Li,Tang, HuaJun,Liao, AnPing,Peng, Shu. 2016

[19]A Non-Destructive Method for Identification of Tea Plant Cultivars Based on Deep Learning. Yi Ding,Haitao Huang,Hongchun Cui,Xinchao Wang,Yun Zhao. 2023

[20]Crop Mapping Based on Temporal and Spatial Sample Migrations: A Case Study Over Three Counties in Heilongjiang Province, Northeast China. Zuo, Hao-Nan,Leng, Pei,Li, Yu-Xuan,Song, Qian,Li, Zhao-Liang. 2024

作者其他论文 更多>>