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Review | Open Access

Genomic selection: A breakthrough technology in rice breeding

Yang Xua, Kexin Maa, Yue Zhaoa, Xin Wanga, Kai Zhoua, Guangning Yua, Cheng Lia, Pengcheng Lia, Zefeng Yanga, Chenwu Xua( ), Shizhong Xub( )
Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou 225009, Jiangsu, China
Department of Botany and Plant Sciences, University of California, Riverside, CA 92507, USA
Show Author Information

Abstract

Rice (Oryza sativa) provides a staple food source for more than half the world population. However, the current pace of rice breeding in yield growth is insufficient to meet the food demand of the ever-increasing global population. Genomic selection (GS) holds a great potential to accelerate breeding progress and is cost-effective via early selection before phenotypes are measured. Previous simulation and experimental studies have demonstrated the usefulness of GS in rice breeding. However, several affecting factors and limitations require careful consideration when performing GS. In this review, we summarize the major genetics and statistical factors affecting predictive performance as well as current progress in the application of GS to rice breeding. We also highlight effective strategies to increase the predictive ability of various models, including GS models incorporating functional markers, genotype by environment interactions, multiple traits, selection index, and multiple omic data. Finally, we envision that integrating GS with other advanced breeding technologies such as unmanned aerial vehicles and open-source breeding platforms will further improve the efficiency and reduce the cost of breeding.

Keywords

ModelHybridRiceGenomic selectionPredictive ability

References

[1]

L.T. Hickey, A.N. Hafeez, H. Robinson, S.A. Jackson, S.C.M. Leal-Bertioli, M. Tester, C. Gao, I.D. Godwin, B.J. Hayes, B.B.H. Wulff, Breeding crops to feed 10 billion, Nat. Biotechnol. 37 (2019) 744–754.
Crossref Google Scholar
[2]

Z.A. Desta, R. Ortiz, Genomic selection: Genome-wide prediction in plant improvement, Trends Plant Sci. 19 (2014) 592–601.
Crossref Google Scholar
[3]

T.H. Meuwissen, B.J. Hayes, M.E. Goddard, Prediction of total genetic value using genome-wide dense marker maps, Genetics 157 (2001) 1819–1829.
Crossref Google Scholar
[4]
J. Crossa, P. Perez-Rodriguez, J. Cuevas, O. Montesinos-Lopez, D. Jarquin, G. de los Campos, J. Burgueno, J.M. Gonzalez-Camacho, S. Perez-Elizalde, Y. Beyene, S. Dreisigacker, R. Singh, X.C. Zhang, M. Gowda, M. Roorkiwal, J. Rutkoski, R.K. Varshney, Genomic selection in plant breeding: methods, models, and perspectives, Trends Plant Sci. 22 (2017) 961–975.
Crossref
[5]

C. Riedelsheimer, A. Czedik-Eysenberg, C. Grieder, J. Lisec, F. Technow, R. Sulpice, T. Altmann, M. Stitt, L. Willmitzer, A.E. Melchinger, Genomic and metabolic prediction of complex heterotic traits in hybrid maize, Nat. Genet. 44 (2012) 217–220.
Crossref Google Scholar
[6]

J. Crossa, P. Pérez, J. Hickey, J. Burgueño, L. Ornella, J. Cerón-Rojas, X. Zhang, S. Dreisigacker, R. Babu, Y. Li, Genomic prediction in cimmyt maize and wheat breeding programs, Heredity 112 (2014) 48–60.
Crossref Google Scholar
[7]

X. Wang, L. Li, Z. Yang, X. Zheng, S. Yu, C. Xu, Z. Hu, Predicting rice hybrid performance using univariate and multivariate gblup models based on north carolina mating design Ⅱ, Heredity 118 (2017) 302–310.
Crossref Google Scholar
[8]

E.J. Millet, W. Kruijer, A. Coupel-Ledru, S. Alvarez Prado, L. Cabrera-Bosquet, S. Lacube, A. Charcosset, C. Welcker, F. van Eeuwijk, F. Tardieu, Genomic prediction of maize yield across european environmental conditions, Nat. Genet. 51 (2019) 952–956.
Crossref Google Scholar
[9]

S. Xu, D. Zhu, Q. Zhang, Predicting hybrid performance in rice using genomic best linear unbiased prediction, Proc. Natl. Acad. Sci. U. S. A. 111 (2014) 12456–12461.
Crossref Google Scholar
[10]

Y. Xu, X. Wang, X. Ding, X. Zheng, Z. Yang, C. Xu, Z. Hu, Genomic selection of agronomic traits in hybrid rice using an NCII population, Rice 11 (2018) 32.
Crossref Google Scholar
[11]

J. Spindel, H. Begum, D. Akdemir, P. Virk, B. Collard, E. Redona, G. Atlin, J.L. Jannink, S.R. McCouch, Genomic selection and association mapping in rice (oryza sativa L.): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines, PLoS Genet 11 (2015) e1004982.
Crossref Google Scholar
[12]

S. Yabe, H. Yoshida, H. Kajiya-Kanegae, M. Yamasaki, H. Iwata, K. Ebana, T. Hayashi, H. Nakagawa, Description of grain weight distribution leading to genomic selection for grain-filling characteristics in rice, PLoS ONE 13 (2018) e0207627.
Crossref Google Scholar
[13]

A. Bhandari, J. Bartholome, T.V. Cao-Hamadoun, N. Kumari, J. Frouin, A. Kumar, N. Ahmadi, Selection of trait-specific markers and multi environment models improve genomic predictive ability in rice, PLoS ONE 14 (2019) e0208871.
Crossref Google Scholar
[14]
J. Spindel, H. Iwata, Genomic selection in rice breeding, in: T. Sasaki, M. Ashikari (Eds.), Rice Genomics, Genetics and Breeding, Springer, Singapore, 2018, pp. 473–496.
[15]

S. Xu, Predicted residual error sum of squares of mixed models: an application for genomic prediction, G3-Genes Genomes Genet. 7 (2017) 895–909.
Crossref Google Scholar
[16]

Y. Xu, Y. Zhao, X. Wang, Y. Ma, P. Li, Z. Yang, X. Zhang, C. Xu, S. Xu, Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice, Plant Biotechnol. J. 19 (2021) 261–272.
Crossref Google Scholar
[17]

K.P. Voss-Fels, M. Cooper, B.J. Hayes, Accelerating crop genetic gains with genomic selection, Theor. Appl. Genet. 132 (2019) 669–686.
Crossref Google Scholar
[18]

T. Guo, X. Yu, X. Li, H. Zhang, C. Zhu, S. Flint-Garcia, M.D. McMullen, J.B. Holland, R.J. Wisser, J. Yu, Optimal designs for genomic selection in hybrid crops, Mol. Plant 12 (2019) 390–401.
Crossref Google Scholar
[19]

H.D. Daetwyler, U.K. Bansal, H.S. Bariana, M.J. Hayden, B.J. Hayes, Genomic prediction for rust resistance in diverse wheat landraces, Theor. Appl. Genet. 127 (2014) 1795–1803.
Crossref Google Scholar
[20]

X. Wang, Y. Xu, Z. Hu, C. Xu, Genomic selection methods for crop improvement: current status and prospects, Crop J. 6 (2018) 330–340.
Crossref Google Scholar
[21]

Y. Xu, X. Liu, J. Fu, H. Wang, J. Wang, C. Huang, B.M. Prasanna, M.S. Olsen, G. Wang, A. Zhang, Enhancing genetic gain through genomic selection: from livestock to plants, Plant Commun. 1 (2020) 100005.
Crossref Google Scholar
[22]

J. Yu, G. Pressoir, W.H. Briggs, I. Vroh Bi, M. Yamasaki, J.F. Doebley, M.D. McMullen, B.S. Gaut, D.M. Nielsen, J.B. Holland, S. Kresovich, E.S. Buckler, A unified mixed-model method for association mapping that accounts for multiple levels of relatedness, Nat. Genet. 38 (2006) 203–208.
Crossref Google Scholar
[23]

Z. Guo, D.M. Tucker, C.J. Basten, H. Gandhi, E. Ersoz, B. Guo, Z. Xu, D. Wang, G. Gay, The impact of population structure on genomic prediction in stratified populations, Theor. Appl. Genet. 127 (2014) 749–762.
Crossref Google Scholar
[24]

Z. Jia, Controlling the overfitting of heritability in genomic selection through cross validation, Sci. Rep. 7 (2017) 13678.
Crossref Google Scholar
[25]

P.M. VanRaden, Efficient methods to compute genomic predictions, J. Dairy Sci. 91 (2008) 4414–4423.
Crossref Google Scholar
[26]

M. Goddard, Genomic selection: prediction of accuracy and maximisation of long term response, Genetica 136 (2009) 245–257.
Crossref Google Scholar
[27]

J. Friedman, T. Hastie, R. Tibshirani, Regularization paths for generalized linear models via coordinate descent, J. Stat. Softw. 33 (2010) 1–22.
Crossref Google Scholar
[28]

O. González-Recio, S. Forni, Genome-wide prediction of discrete traits using bayesian regressions and machine learning, Genet. Sel. Evol. 43 (2011) 1.
Crossref Google Scholar
[29]

G. Moser, S.H. Lee, B.J. Hayes, M.E. Goddard, N.R. Wray, P.M. Visscher, Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model, PLoS Genet. 11 (2015) e1004969.
Crossref Google Scholar
[30]

X. Wang, Z.F. Yang, C.W. Xu, A comparison of genomic selection methods for breeding value prediction, Sci. Bull. 60 (2015) 925–935.
Crossref Google Scholar
[31]

S. Maenhout, B. De Baets, G. Haesaert, E. Van Bockstaele, Support vector machine regression for the prediction of maize hybrid performance, Theor. Appl. Genet. 115 (2007) 1003–1013.
Crossref Google Scholar
[32]

G. de Los Campos, D. Gianola, D.B. Allison, Predicting genetic predisposition in humans: the promise of whole-genome markers, Nat. Rev. Genet. 11 (2010) 880–886.
Crossref Google Scholar
[33]

J.A. Holliday, T. Wang, S. Aitken, Predicting adaptive phenotypes from multilocus genotypes in sitka spruce (picea sitchensis) using random forest, G3-Genes Genomes Genet. 2 (2012) 1085–1093.
Crossref Google Scholar
[34]

P.E. Bayer, D. Edwards, Machine learning in agriculture: from silos to marketplaces, Plant Biotechnol. J. (2020) 648–650.
Crossref Google Scholar
[35]

M. Perez-Enciso, L.M. Zingaretti, A guide for using deep learning for complex trait genomic prediction, Genes 10 (2019) 553.
Crossref Google Scholar
[36]

L.L. Yin, H.H. Zhang, X. Zhou, X.H. Yuan, S.H. Zhao, X.Y. Li, X.L. Liu, Kaml: improving genomic prediction accuracy of complex traits using machine learning determined parameters, Genome Biol. 21 (2020) 146.
Crossref Google Scholar
[37]

Y. Xu, C. Xu, S. Xu, Prediction and association mapping of agronomic traits in maize using multiple omic data, Heredity 119 (2017) 174–184.
Crossref Google Scholar
[38]

A. Onogi, O. Ideta, Y. Inoshita, K. Ebana, T. Yoshioka, M. Yamasaki, H. Iwata, Exploring the areas of applicability of whole-genome prediction methods for asian rice (oryza sativa L.), Theor. Appl. Genet. 128 (2015) 41–53.
Crossref Google Scholar
[39]

J. Isidro, J.L. Jannink, D. Akdemir, J. Poland, N. Heslot, M.E. Sorrells, Training set optimization under population structure in genomic selection, Theor. Appl. Genet. 128 (2015) 145–158.
Crossref Google Scholar
[40]

H. Iwata, K. Ebana, Y. Uga, T. Hayashi, Genomic prediction of biological shape: elliptic fourier analysis and kernel partial least squares (pls) regression applied to grain shape prediction in rice (oryza sativa L.), PLoS ONE 10 (2015) e0120610.
Crossref Google Scholar
[41]

C. Grenier, T.V. Cao, Y. Ospina, C. Quintero, M.H. Chatel, J. Tohme, B. Courtois, N. Ahmadi, Accuracy of genomic selection in a rice synthetic population developed for recurrent selection breeding, PLoS ONE 10 (2015) e0136594.
Crossref Google Scholar
[42]

M. Ben Hassen, T.V. Cao, J. Bartholome, G. Orasen, C. Colombi, J. Rakotomalala, L. Razafinimpiasa, C. Bertone, C. Biselli, A. Volante, F. Desiderio, L. Jacquin, G. Vale, N. Ahmadi, Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel, Theor. Appl. Genet. 131 (2018) 417–435.
Crossref Google Scholar
[43]

M. Huang, E.G. Balimponya, E.M. Mgonja, L.K. McHale, A. Luzi-Kihupi, G.L. Wang, C.H. Sneller, Use of genomic selection in breeding rice (oryza sativa L.) for resistance to rice blast (magnaporthe oryzae), Mol. Breed. 39 (2019) 114.
Crossref Google Scholar
[44]

T. Jumin, Z. Guoan, D. Karabi, X. Caiguo, H. Yuqing, Z. Qifa, Field performance of transgenic elite commercial hybrid rice expressing bacillus thuringiensis δ endotoxin, Nat. Biotechnol. 18 (2000) 1101–1104.
Crossref Google Scholar
[45]

W. Wang, R. Mauleon, Z. Hu, D. Chebotarov, S. Tai, Z. Wu, M. Li, T. Zheng, R.R. Fuentes, F. Zhang, L. Mansueto, D. Copetti, M. Sanciangco, K.C. Palis, J. Xu, C. Sun, B. Fu, H. Zhang, Y. Gao, X. Zhao, F. Shen, X. Cui, H. Yu, Z. Li, M. Chen, J. Detras, Y. Zhou, X. Zhang, Y. Zhao, D. Kudrna, C. Wang, R. Li, B. Jia, J. Lu, X. He, Z. Dong, J. Xu, Y. Li, M. Wang, J. Shi, J. Li, D. Zhang, S. Lee, W. Hu, A. Poliakov, I. Dubchak, V.J. Ulat, F.N. Borja, J.R. Mendoza, J. Ali, J. Li, Q. Gao, Y. Niu, Z. Yue, M.E.B. Naredo, J. Talag, X. Wang, J. Li, X. Fang, Y. Yin, J.C. Glaszmann, J. Zhang, J. Li, R.S. Hamilton, R.A. Wing, J. Ruan, G. Zhang, C. Wei, N. Alexandrov, K.L. McNally, Z. Li, H. Leung, Genomic variation in 3010 diverse accessions of asian cultivated rice, Nature 557 (2018) 43–49.
Crossref Google Scholar
[46]

Y. Cui, R. Li, G. Li, F. Zhang, T. Zhu, Q. Zhang, J. Ali, Z. Li, S. Xu, Hybrid breeding of rice via genomic selection, Plant Biotechnol. J. 18 (2020) 57–67.
Crossref Google Scholar
[47]

S.A. Atanda, M. Olsen, J. Burgueño, J. Crossa, D. Dzidzienyo, Y. Beyene, M. Gowda, K. Dreher, X. Zhang, B.M. Prasanna, P. Tongoona, E.Y. Danquah, G. Olaoye, K.R. Robbins, Maximizing efficiency of genomic selection in cimmyt’s tropical maize breeding program, Theor. Appl. Genet. 134 (2021) 279–294.
Crossref Google Scholar
[48]

X. Zhang, P. Pérez-Rodríguez, J. Burgueño, M. Olsen, E. Buckler, G. Atlin, B.M. Prasanna, M. Vargas, F. San Vicente, J. Crossa, Rapid cycling genomic selection in a multiparental tropical maize population, G3-Genes Genomes Genet. 7 (2017) 2315–2326.
Crossref Google Scholar
[49]

R. Bernardo, Genomewide selection when major genes are known, Crop Sci. 54 (2014) 68–75.
Crossref Google Scholar
[50]

Z. Zhang, U. Ober, M. Erbe, H. Zhang, N. Gao, J. He, J. Li, H. Simianer, Improving the accuracy of whole genome prediction for complex traits using the results of genome wide association studies, PLoS ONE 9 (2014) e93017.
Crossref Google Scholar
[51]

J.E. Spindel, H. Begum, D. Akdemir, B. Collard, E. Redoña, J.L. Jannink, S. McCouch, Genome-wide prediction models that incorporate de novo gwas are a powerful new tool for tropical rice improvement, Heredity 116 (2016) 395–408.
Crossref Google Scholar
[52]

Y. Bian, J.B. Holland, Enhancing genomic prediction with genome-wide association studies in multiparental maize populations, Heredity 118 (2017) 585–593.
Crossref Google Scholar
[53]

B. Rice, A.E. Lipka, Evaluation of RR-Blup genomic selection models that incorporate peak genome-wide association study signals in maize and sorghum, Plant Genome 12 (2019) 180052.
Crossref Google Scholar
[54]

J. Crossa, From genotype × environment interaction to gene × environment interaction, Curr. Genomics 13 (2012) 225–244.
Crossref Google Scholar
[55]

M. Lopez-Cruz, J. Crossa, D. Bonnett, S. Dreisigacker, J. Poland, J.L. Jannink, R.P. Singh, E. Autrique, G. de los Campos, Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model, G3-Genes Genomes Genet. 5 (2015) 569–582.
Crossref Google Scholar
[56]

J. Cuevas, J. Crossa, V. Soberanis, S. Pérez-Elizalde, P. Pérez-Rodríguez, G.L. Campos, O.A. Montesinos-López, J. Burgueño, Genomic prediction of genotype × environment interaction kernel regression models, Plant Genome 9 (2016) 1–20.
Crossref Google Scholar
[57]

J. Crossa, G. de los Campos, M. Maccaferri, R. Tuberosa, J. Burgueno, P. Perez-Rodriguez, Extending the marker × environment interaction model for genomic-enabled prediction and genome-wide association analysis in durum wheat, Crop Sci. 56 (2016) 2193–2209.
Crossref Google Scholar
[58]

J. Cuevas, J. Crossa, O.A. Montesinos-Lopez, J. Burgueno, P. Perez-Rodriguez, G. de los Campos, Bayesian genomic prediction with genotype × environment interaction kernel models, G3-Genes Genomes Genet. 7 (2017) 41–53.
Crossref Google Scholar
[59]

M. Ben Hassen, J. Bartholome, G. Vale, T.V. Cao, N. Ahmadi, Genomic prediction accounting for genotype by environment interaction offers an effective framework for breeding simultaneously for adaptation to an abiotic stress and performance under normal cropping conditions in rice, G3-Genes Genomes Genet. 8 (2018) 2319–2332.
Crossref Google Scholar
[60]

S. Wang, Y. Xu, H. Qu, Y. Cui, R. Li, J.M. Chater, L. Yu, R. Zhou, R. Ma, Y. Huang, Y. Qiao, X. Hu, W. Xie, Z. Jia, Boosting predictabilities of agronomic traits in rice using bivariate genomic selection, Brief. Bioinform. (2020), https://doi.org/10.1093/bib/bbaa103.
Crossref Google Scholar
[61]

M.P.L. Calus, R.F. Veerkamp, Accuracy of multi-trait genomic selection using different methods, Genet. Sel. Evol. 43 (2011) 26.
Crossref Google Scholar
[62]

Y. Jia, J.L. Jannink, Multiple-trait genomic selection methods increase genetic value prediction accuracy, Genetics 192 (2012) 1513–1522.
Crossref Google Scholar
[63]

H. Cheng, K. Kizilkaya, J. Zeng, D. Garrick, R. Fernando, Genomic prediction from multiple-trait bayesian regression methods using mixture priors, Genetics 209 (2018) 89–103.
Crossref Google Scholar
[64]

J. Sun, J.E. Rutkoski, J.A. Poland, J. Crossa, J.L. Jannink, M.E. Sorrells, Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield, Plant Genome 10 (2017) 1–12.
Crossref Google Scholar
[65]

J.J. Cerón-Rojas, J. Crossa, Efficiency of a constrained linear genomic selection index to predict the net genetic merit in plants, G3-Genes Genomes Genet. 9 (2019) 3981–3994.
Crossref Google Scholar
[66]

J.J. Ceron-Rojas, J. Crossa, V.N. Arief, K. Basford, J. Rutkoski, D. Jarquin, G. Alvarado, Y. Beyene, K. Semagn, I. DeLacy, A genomic selection index applied to simulated and real data, G3-Genes Genomes Genet. 5 (2015) 2155–2164.
Crossref Google Scholar
[67]

X. Wang, Y. Xu, P. Li, M. Liu, C. Xu, Z. Hu, Efficiency of linear selection index in predicting rice hybrid performance, Mol. Breed. 39 (2019) 77.
Crossref Google Scholar
[68]

M.D. Ritchie, E.R. Holzinger, R.W. Li, S.A. Pendergrass, D. Kim, Methods of integrating data to uncover genotype-phenotype interactions, Nat. Rev. Genet. 16 (2015) 85–97.
Crossref Google Scholar
[69]

M. Westhues, T.A. Schrag, Omics-based hybrid prediction in maize, Theor. Appl. Genet. 130 (2017) 1927–1939.
Crossref Google Scholar
[70]

M. Frisch, A. Thiemann, J. Fu, T.A. Schrag, S. Scholten, A.E. Melchinger, Transcriptome-based distance measures for grouping of germplasm and prediction of hybrid performance in maize, Theor. Appl. Genet. 120 (2010) 441–450.
Crossref Google Scholar
[71]

J. Fu, K.C. Falke, A. Thiemann, T.A. Schrag, A.E. Melchinger, S. Scholten, M. Frisch, Partial least squares regression, support vector machine regression, and transcriptome-based distances for prediction of maize hybrid performance with gene expression data, Theor. Appl. Genet. 124 (2012) 825–833.
Crossref Google Scholar
[72]

C. Zenke-Philippi, M. Frisch, A. Thiemann, F. Seifert, T. Schrag, A.E. Melchinger, S. Scholten, E. Herzog, Transcriptome-based prediction of hybrid performance with unbalanced data from a maize breeding programme, Plant Breed. 136 (2017) 331–337.
Crossref Google Scholar
[73]

C. Zenke-Philippi, A. Thiemann, F. Seifert, T. Schrag, A.E. Melchinger, S. Scholten, M. Frisch, Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mrna transcription profiles, BMC Genomics 17 (2016) 262.
Crossref Google Scholar
[74]

R.C. Meyer, M. Steinfath, J. Lisec, M. Becher, H. Witucka-Wall, O. Törjék, O. Fiehn, A. Eckardt, L. Willmitzer, J. Selbig, T. Altmann, The metabolic signature related to high plant growth rate in arabidopsis thaliana, Proc. Natl. Acad. Sci. U. S. A. 104 (2007) 4759–4764.
Crossref Google Scholar
[75]

S. Xu, Y. Xu, L. Gong, Q. Zhang, Metabolomic prediction of yield in hybrid rice, Plant J. 88 (2016) 219–227.
Crossref Google Scholar
[76]

Z. Dan, J. Hu, W. Zhou, G. Yao, R. Zhu, Y. Zhu, W. Huang, Metabolic prediction of important agronomic traits in hybrid rice (oryza sativa L.), Sci. Rep. 6 (2016) 21732.
Crossref Google Scholar
[77]

Z. Dan, Y. Chen, Y. Xu, J. Huang, J. Huang, J. Hu, G. Yao, Y. Zhu, W. Huang, A metabolome-based core hybridisation strategy for the prediction of rice grain weight across environments, Plant Biotechnol. J. 17 (2019) 906–913.
Crossref Google Scholar
[78]

S. Wang, J. Wei, R. Li, H. Qu, J.M. Chater, R.Y. Ma, Y. Li, W. Xie, Z. Jia, Identification of optimal prediction models using multi-omic data for selecting hybrid rice, Heredity 123 (2019) 395–406.
Crossref Google Scholar
[79]

T.A. Schrag, M. Westhues, W. Schipprack, F. Seifert, A. Thiemann, S. Scholten, A.E. Melchinger, Beyond genomic prediction: combining different types of omics data can improve prediction of hybrid performance in maize, Genetics 208 (2018) 1373–1385.
Crossref Google Scholar
[80]

W. Yang, H. Feng, X. Zhang, J. Zhang, J.H. Doonan, W.D. Batchelor, L. Xiong, J. Yan, Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives, Mol. Plant 13 (2020) 187–214.
Crossref Google Scholar
[81]

W.H. Maes, K. Steppe, Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture, Trends Plant Sci. 24 (2019) 152–164.
Crossref Google Scholar
[82]

P. Juliana, O.A. Montesinos-López, J. Crossa, S. Mondal, L. González Pérez, J. Poland, J. Huerta-Espino, L. Crespo-Herrera, V. Govindan, S. Dreisigacker, S. Shrestha, P. Pérez-Rodríguez, F. Pinto Espinosa, R.P. Singh, Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat, Theor. Appl. Genet. 132 (2019) 177–194.
Crossref Google Scholar
[83]

Y. Xu, P. Li, C. Zou, Y. Lu, C. Xie, X. Zhang, B.M. Prasanna, M.S. Olsen, Enhancing genetic gain in the era of molecular breeding, J. Exp. Bot. 68 (2017) 2641–2666.
Crossref Google Scholar
[84]

H. Chen, W. Xie, H. He, H. Yu, W. Chen, J. Li, R. Yu, Y. Yao, W. Zhang, Y. He, X. Tang, F. Zhou, X.W. Deng, Q. Zhang, A high-density snp genotyping array for rice biology and molecular breeding, Mol. Plant 7 (2014) 541–553.
Crossref Google Scholar
[85]

F. Zhang, C. Wang, M. Li, Y. Cui, Y. Shi, Z. Wu, Z. Hu, W. Wang, J. Xu, Z. Li, The landscape of gene-cds-haplotype diversity in rice: properties, population organization, footprints of domestication and breeding, and implications for genetic improvement, Mol. Plant (2021), https://doi.org/10.1016/j.molp.2021.02.003.
Crossref Google Scholar
[86]

J.E. Spindel, S.R. McCouch, When more is better: how data sharing would accelerate genomic selection of crop plants, New Phytol. 212 (2016) 814–826.
Crossref Google Scholar
The Crop Journal
Volume 9 Issue 3,
June 2021
Pages 669-677
DOI: 10.1016/j.cj.2021.03.008
Cite this article:
Xu Y, Ma K, Zhao Y, et al. Genomic selection: A breakthrough technology in rice breeding. The Crop Journal, 2021, 9(3): 669-677. https://doi.org/10.1016/j.cj.2021.03.008

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Received: 10 January 2021
Revised: 14 March 2021
Accepted: 29 March 2021
Published: 22 April 2021
© 2021 Crop Science Society of China and Institute of Crop Science, CAAS.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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