Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Glycine max L.)

文献类型: 外文期刊

第一作者: Li, Wenbin

作者: 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

作者机构:

关键词: Genomic selection;Prediction accuracy;Glycine max;Sampling method

期刊名称:MOLECULAR BREEDING ( 影响因子:2.589; 五年影响因子:2.75 )

ISSN:

年卷期:

页码:

收录情况: SCI

摘要: Genomic selection is a promising molecular breeding strategy enhancing genetic gain per unit time. The objectives of our study were to (1) explore the prediction accuracy of genomic selection for plant height and yield per plant in soybean [Glycine max (L.) Merr.], (2) discuss the relationship between prediction accuracy and numbers of markers, and (3) evaluate the effect of marker preselection based on different methods on the prediction accuracy. Our study is based on a population of 235 soybean varieties which were evaluated for plant height and yield per plant at multiple locations and genotyped by 5361 single nucleotide polymorphism markers. We applied ridge regression best linear unbiased prediction coupled with fivefold cross-validations and evaluated three strategies of marker preselection. For plant height, marker density and marker preselection procedure impacted prediction accuracy only marginally. In contrast, for grain yield, prediction accuracy based on markers selected with a haplotype block analyses-based approach increased by approximately 4 % compared with random or equidistant marker sampling. Thus, applying marker preselection based on haplotype blocks is an interesting option for a cost-efficient implementation of genomic selection for grain yield in soybean breeding.

分类号: Q94

  • 相关文献

[1]Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models. Yuan, Huanhuan,Yang, Guijun,Wang, Yanjie,Liu, Jiangang,Yu, Haiyang,Feng, Haikuan,Xu, Bo,Zhao, Xiaoqing,Yang, Xiaodong,Yuan, Huanhuan,Li, Changchun,Wang, Yanjie,Yuan, Huanhuan,Yang, Guijun,Liu, Jiangang,Feng, Haikuan,Yang, Xiaodong,Yang, Guijun,Yu, Haiyang,Xu, Bo,Zhao, Xiaoqing,Yang, Xiaodong. 2017

[2]Population Forecasting Model of Nilaparvata lugens and Sogatella furcifera (Homoptera: Delphacidae) Based on Markov Chain Theory. Liu, Huai,Wang, Jin-Jun,Zhao, Zhi-Mo,Yan, Xiang-Hui,Huang, Fangneng,Cheng, Deng-Fa.

[3]Determination of Soil Parameters in Apple-Growing Regions by Near- and Mid-Infrared Spectroscopy. Dong Yi-Wei,Xu Chun-Ying,Li Yu-Zhong,Li Qiao-Zhen,Dong Yi-Wei,Yang Shi-Qi,Fan Zhong-Nan,Wang Ya-Nan,Bai Wei. 2011

[4]Identification of Empoasca onukii (Hemiptera: Cicadellidae) and Monitoring of its Populations in the Tea Plantations of South China. Shi, Long-Qing,Zeng, Zhao-Hua,Huang, Huo-Shui,Zhou, Yong-Mei,Vasseur, Liette,You, Min-Sheng,Shi, Long-Qing,Zhou, Yong-Mei,Vasseur, Liette,You, Min-Sheng,Zeng, Zhao-Hua,Huang, Huo-Shui,Vasseur, Liette.

[5]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

[6]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

[7]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

[8]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

[9]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

[10]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

[11]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

[12]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

[13]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

[14]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

[15]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

[16]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

[17]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..

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

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

[20]Bayesian methods for estimating GEBVs of threshold traits. 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.

作者其他论文 更多>>