Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows

文献类型: 外文期刊

第一作者: Ding, X.

作者: Ding, X.;Zhang, Z.;Li, X.;Wang, S.;Wu, X.;Sun, D.;Yu, Y.;Liu, J.;Wang, Y.;Zhang, Y.;Zhang, S.;Zhang, Y.;Zhang, Q.;Zhang, Z.

作者机构:

关键词: genomic selection;Chinese Holstein;cow;cross validation

期刊名称:JOURNAL OF DAIRY SCIENCE ( 影响因子:4.034; 五年影响因子:4.354 )

ISSN: 0022-0302

年卷期: 2013 年 96 卷 8 期

页码:

收录情况: SCI

摘要: Genomic selection using dense markers covering the whole genome is a tool for the genetic improvement of livestock and is revolutionizing the breeding system in dairy cattle. Progeny-tested bulls have been used to form reference populations in almost all countries where genomic selection has been implemented. In this study, the accuracy of genomic prediction when cows are used to form the reference population was investigated. The reference population consisted of 3,087 cows. All individuals were genotyped with Illumina BovineSNP50. After genotype imputation and editing, 48,676 single nucleotide polymorphisms were available for analysis. Two methods, genomic BLUP (GBLUP) and BayesB, were used to render genomic estimated breeding values (GEBV) for 5 milk production traits. Accuracies of GEBV were assessed in 3 ways: r(GEBV,EBV) (the correlation between GEBV and conventional EBV) in 67 progeny-tested bulls, r(GEBV,EBV) from a 5-fold cross validation in the 3,087 cow reference population, and the theoretical accuracy (for GBLUP) calculated in the same way as for conventional BLUP. The results showed that using GBLUP, the r(GEBV,EBV) and theoretical accuracy of genomic prediction in Chinese Holstein ranged from 0.59 to 0.76 and 0.70 to 0.80, respectively, which was 0.13 to 0.30 and 0.23 to 0.33 higher than the accuracies of conventional pedigree index, respectively. The results indicate that, as an alternative, genomic selection using cows in the reference population is feasible.

分类号:

  • 相关文献

[1]Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements. Zhang, Jing-Cheng,Wang, Ji-hua,Huang, Wen-jiang,Yuan, Lin,Luo, Ju-hua,Zhang, Jing-Cheng,Pu, Rui-liang,Zhang, Jing-Cheng,Yuan, Lin. 2012

[2]The patterns of genomic variances and covariances across genome for milk production traits between Chinese and Nordic Holstein populations. Li, Xiujin,Lund, Mogens Sando,Janss, Luc,Su, Guosheng,Li, Xiujin,Ding, Xiangdong,Zhang, Qin,Li, Xiujin,Wang, Chonglong. 2017

[3]Study on genetic variations of PPAR alpha gene and its effects on thermal tolerance in Chinese Holstein. Fang, Wenliang,He, Jianbin,Huang, Jinming,Ju, Zhihua,Wang, Changfa,Qi, Chao,Li, Jianbin,Li, Rongling,Zhong, Jifeng,Li, Qiuling,Fang, Wenliang,He, Jianbin.

[4]Genetic parameters for somatic cell score and production traits in the first three lactations of Chinese Holstein cows. Zhao Fu-ping,Guo Gang,Du Li-xin,Guo Gang,Guo Gang,Wang Ya-chun,Guo Xiang-yu,Zhang Yuan. 2015

[5]Solexa sequencing and custom microRNA chip reveal repertoire of microRNAs in mammary gland of bovine suffering from natural infectious mastitis. Ju, Zhihua,Jiang, Qiang,Wang, Xiuge,Luo, Guojing,Zhang, Yan,Huang, Jinming,Liu, Gang,Zhang, Jibin,Zhong, Jifeng,Huang, Jinming. 2018

[6]Novel splice variants of the bovine PCK1 gene. Zhang, Z. B.,Zhao, Z. S.,Huang, J. M.,Zhang, Z. B.,Zhang, W.,Li, R. L.,Li, J. B.,Zhong, J. F.. 2013

[7]Identification of CD14 transcript in blood polymorphonuclear neutrophil leukocytes and functional variation in Holsteins. Huang, J. M.,Hao, H. S.,Zhu, H. B.,Huang, J. M.,Wang, X. G.,Jiang, Q.,Sun, Y.,Yang, C. H.,Ju, Z. H.,Wang, C. F.,Zhong, J. F.. 2016

[8]Prophylactic strategy with herbal remedy to reduce puerperal metritis risk in dairy cows: A randomized clinical trial. Cui, Dongan,Wang, Shengyi,Wang, Lei,Wang, Hui,Li, Xia,Liu, Yongming.

[9]Accumulation and Depletion of Cadmium in the Blood, Milk, Hair, Feces, and Urine of Cows During and After Treatment. Su, Chuanyou,Zhang, Junmin,Zhao, Qingyu,Su, Chuanyou,Li, Zhentian,Liu, Kaidong,Sun, Youde,Wang, Jianhua.

[10]Comparative proteomics of milk fat globule membrane in different species reveals variations in lactation and nutrition. Lu, Jing,Zhang, Weiqing,Liu, Lu,Pang, Xiaoyang,Zhang, Shuwen,Lv, Jiaping,Wang, Xinyu,Wang, Xinyu.

[11]Proteome profile of bovine ruminal epithelial tissue based on GeLC-MS/MS. Yang, Yongxin,Wang, Jiaqi,Yuan, Tingjie,Bu, Dengpan,Yang, Jinhui,Sun, Peng,Yang, Yongxin.

[12]The administration of Sheng Hua Tang immediately after delivery to reduce the incidence of retained placenta in Holstein dairy cows. Cui, Dongan,Wang, Xuezhi,Wang, Lei,Wang, Xurong,Zhang, Jingyan,Qin, Zhe,Li, Jianxi,Yang, Zhiqiang.

[13]Identification of Wohlfahrtiimonas chitiniclastica isolated from an infected cow with hoof fetlow, China. Qi, Jing,Li, Lu-lu,Du, Yi-jun,Liu, Yu-qing,Gao, Yang,Zhao, Xiao-min,Wang, Gui-sheng,Li, Lan-bo.

[14]A rapid analytical method of major milk proteins by reversed-phase high-performance liquid chromatography. Ma, Lu,Yang, Yongxin,Chen, Jingting,Wang, Jiaqi,Bu, Dengpan,Ma, Lu,Bu, Dengpan,Bu, Dengpan. 2017

[15]Efficacy of progesterone supplementation during early pregnancy in cows: A meta-analysis. Yan, Leyan,Shi, Zhendan,Robinson, Robert,Mann, George.

[16]Proteomic analysis of physiological function response to hot summer in liver from lactating dairy cows. Wang, Qiangjun,Zhao, Xiaowei,Zhao, Huiling,Huang, Dongwei,Cheng, Guanglong,Yang, Yongxin,Wang, Qiangjun,Zhang, Zijun.

[17]Analysis of 22 Elements in Milk, Feed, and Water of Dairy Cow, Goat, and Buffalo from Different Regions of China. Zhou, Xuewei,Qu, Xueyin,Zhao, Shengguo,Wang, Jiaqi,Li, Songli,Zheng, Nan,Zhou, Xuewei,Qu, Xueyin,Zhao, Shengguo,Wang, Jiaqi,Li, Songli,Zheng, Nan.

[18]Comparative study of estimation methods for genomic breeding values. Wang, Chonglong,Qian, Rong,Wang, Chonglong,Zhang, Qin,Jiang, Li,Ding, Xiangdong,Wang, Chonglong,Zhao, Yaofeng. 2016

[19]Achievements and prospects of genomics-assisted breeding in three legume crops of the semi-arid tropics. Varshney, Rajeev K.,Mohan, S. Murali,Gaur, Pooran M.,Pandey, Manish K.,Sawargaonkar, Shrikant L.,Chitikineni, Annapurna,Janila, Pasupuleti,Saxena, K. B.,Sharma, Mamta,Rathore, Abhishek,Mallikarjuna, Nalini,Gowda, C. L. L.,Varshney, Rajeev K.,Varshney, Rajeev K.,Varshney, Rajeev K.,Liang, Xuanqiang,Gangarao, N. V. P. R.,Pandey, Manish K.,Bohra, Abhishek,Pratap, Aditya,Datta, Subhojit,Chaturvedi, S. K.,Nadarajan, N.,Kimurto, Paul K.,Fikre, Asnake,Tripathi, Shailesh,Bharadwaj, Ch.,Anuradha, G.,Babbar, Anita,Choudhary, Arbind K.,Mhase, M. B.,Mannur, D. M.. 2013

[20]Incorporating Gene Annotation into Genomic Prediction of Complex Phenotypes. Gao, Ning,Zhang, Zhe,Yuan, Xiaolong,Zhang, Hao,Li, Jiaqi,Gao, Ning,Martini, Johannes W. R.,Simianer, Henner. 2017

作者其他论文 更多>>