Bayesian methods for estimating GEBVs of threshold traits

文献类型： 外文期刊

第一作者： Wang, C-L

作者： Wang, C-L;Ding, X-D;Wang, J-Y;Liu, J-F;Fu, W-X;Zhang, Z.;Yin, Z-J;Zhang, Q.;Wang, C-L

作者机构：

关键词： Bayesian methods;genomic selection;MCMC;threshold traits

期刊名称：HEREDITY （ 影响因子：3.821； 五年影响因子：4.553 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Estimation of genomic breeding values is the key step in genomic selection (GS). Many methods have been proposed for continuous traits, but methods for threshold traits are still scarce. Here we introduced threshold model to the framework of GS, and specifically, we extended the three Bayesian methods BayesA, BayesB and BayesC pi on the basis of threshold model for estimating genomic breeding values of threshold traits, and the extended methods are correspondingly termed BayesTA, BayesTB and BayesTC pi. Computing procedures of the three BayesT methods using Markov Chain Monte Carlo algorithm were derived. A simulation study was performed to investigate the benefit of the presented methods in accuracy with the genomic estimated breeding values (GEBVs) for threshold traits. Factors affecting the performance of the three BayesT methods were addressed. As expected, the three BayesT methods generally performed better than the corresponding normal Bayesian methods, in particular when the number of phenotypic categories was small. In the standard scenario (number of categories = 2, incidence = 30%, number of quantitative trait loci = 50, h(2) = 0.3), the accuracies were improved by 30.4%, 2.4%, and 5.7% points, respectively. In most scenarios, BayesTB and BayesTC pi generated similar accuracies and both performed better than BayesTA. In conclusion, our work proved that threshold model fits well for predicting GEBVs of threshold traits, and BayesTC pi is supposed to be the method of choice for GS of threshold traits. Heredity (2013) 110, 213-219; doi:10.1038/hdy.2012.65; published online 14 November 2012

分类号： Q3

  • 相关文献

[1]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

[2]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

[3]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

[4]Genome-Wide Association Study and QTL Mapping Reveal Genomic Loci Associated with Fusarium Ear Rot Resistance in Tropical Maize Germplasm. Chen, Jiafa,Ding, Junqiang,Wu, Jianyu,Chen, Jiafa,Ding, Junqiang,Wu, Jianyu,Wu, Jianyu,Chen, Jiafa,Shrestha, Rosemary,Zheng, Hongjian,Mu, Chunhua,Mahuku, George,Zheng, Hongjian,Mu, Chunhua,Mahuku, George. 2016

[5]Improving accuracy of genomic prediction by genetic architecture based priors in a Bayesian model. Gao, Ning,Li, Jiaqi,He, Jinlong,Xiao, Guang,Luo, Yuanyu,Zhang, Hao,Chen, Zanmou,Zhang, Zhe,Gao, Ning,Zhang, Zhe. 2015

[6]Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows. 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.. 2013

[7]Genetic parameters and trends for production and reproduction traits of a Landrace herd in China. Zhang Zhe,Zhang Hao,Pan Rong-yang,Wu Long,Li Ya-lan,Chen Zan-mou,Cai Geng-yuan,Li Jia-qi,Wu Zhen-fang. 2016

[8]Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Glycine max L.). Li, Wenbin,Ma, Yansong,Liu, Zhangxiong,Guo, Yong,Qiu, Lijuan,Ma, Yansong,Luan, Xiaoyan,Reif, Jochen C.,Jiang, Yong,Wen, Zixiang,Wang, Dechun,Han, Tianfu,Wu, Cunxiang,Sun, Shi,Wei, Shuhong,Wang, Shuming,Yang, Chunming,Wang, Huicai,Yang, Chunming,Zhang, Mengchen,Lu, Weiguo,Xu, Ran,Zhou, Rong,Zhou, Xinan,Wang, Ruizhen,Sun, Zudong,Chen, Huaizhu,Zhang, Wanhai,Sun, Bincheng,Wu, Jian,Han, Dezhi,Yan, Hongrui,Hu, Guohua,Liu, Chunyan,Fu, Yashu,Chen, Weiyuan,Guo, Tai,Zhang, Lei,Yuan, Baojun.

[9]The strategy and potential utilization of temperate germplasm for tropical germplasm improvement: a case study of maize (Zea mays L.). Wen, Weiwei,Tovar, Victor H. Chavez,Taba, Suketoshi,Wen, Weiwei,Yan, Jianbing,Guo, Tingting,Li, Huihui. 2012

[10]Transcriptome Analysis Suggests That Chromosome Introgression Fragments from Sea Island Cotton (Gossypium barbadense) Increase Fiber Strength in Upland Cotton (Gossypium hirsutum). Quanwei Lu,Yuzhen Shi,Huang, Jinling,Yuan, Youlu,Xianghui Xiao,Pengtao Li,Juwu Gong,Wankui Gong,Aiying Liu,Haihong Shang,Junwen Li,Qun Ge,Weiwu Song,Shaoqi Li,Zhen Zhang,Md Harun or Rashid,Renhai Peng,Youlu Yuan,Jinling Huang. 2017

[11]Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations. Zhang, Ao,Liu, Yubo,Cui, Zhenhai,Ruan, Yanye,Yu, Haiqiu,Zhang, Ao,Wang, Hongwu,Liu, Yubo,Burgueno, Juan,San Vicente, Felix,Crossa, Jose,Zhang, Xuecai,Wang, Hongwu,Beyene, Yoseph,Semagn, Kassa,Olsen, Michael,Prasanna, Boddupalli M.,Cao, Shiliang,Semagn, Kassa. 2017

[12]Comparison of single-trait and multiple-trait genomic prediction models. Guo, Gang,Zhao, Fuping,Du, Lixin,Guo, Gang,Guo, Gang,Wang, Yachun,Zhang, Yuan,Guo, Gang,Su, Guosheng. 2014

[13]Genome-Wide SNP Discovery and Analysis of Genetic Diversity in Farmed Sika Deer (Cervus nippon) in Northeast China Using Double-Digest Restriction Site-Associated DNA Sequencing. Ba, Hengxing,Jia, Boyin,Wang, Guiwu,Yang, Yifeng,Li, Chunyi,Kedem, Gilead. 2017

[14]Accuracy of genomic prediction using low-density marker panels. Zhang, Z.,Ding, X.,Liu, J.,Zhang, Q.,Zhang, Z.,de Koning, D. -J.,Zhang, Z.,de Koning, D. -J.,de Koning, D. -J..

[15]Whole-genome strategies for marker-assisted plant breeding. Xu, Yunbi,Lu, Yanli,Gao, Shibin,Prasanna, Boddupalli M.. 2012

[16]A novel genomic selection method combining GBLUP and LASSO. Li, Hengde,Wang, Jingwei,Li, Hengde,Bao, Zhenmin,Wang, Jingwei.

作者其他论文 更多>>

微信小程序