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Review | Open Access

Genomic selection methods for crop improvement: Current status and prospects

Xin Wanga,b,c, Yang Xua, Zhongli Huc, Chenwu Xua( )
Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain Crops/Key Laboratory of Plant Functional Genomics of Ministry of Education, Yangzhou University, Yangzhou 225009, Jiangsu, China
College of Information Engineering, Yangzhou University, Yangzhou 225009, Jiangsu, China
State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan 430072, Hubei, China

Peer review under responsibility of Crop Science Society of China and Institute of Crop Science, CAAS.

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Abstract

With marker and phenotype information from observed populations, genomic selection (GS) can be used to establish associations between markers and phenotypes. It aims to use genome-wide markers to estimate the effects of all loci and thereby predict the genetic values of untested populations, so as to achieve more comprehensive and reliable selection and to accelerate genetic progress in crop breeding. GS models usually face the problem that the number of markers is much higher than the number of phenotypic observations. To overcome this issue and improve prediction accuracy, many models and algorithms, including GBLUP, Bayes, and machine learning have been employed for GS. As hot issues in GS research, the estimation of non-additive genetic effects and the combined analysis of multiple traits or multiple environments are also important for improving the accuracy of prediction. In recent years, crop breeding has taken advantage of the development of GS. The principles and characteristics of current popular GS methods and research progress in these methods for crop improvement are reviewed in this paper.

Keywords

PredictionAccuracyGenomic selectionCrop

References

[1]

L.N. Hazel, J.L. Lush, The efficiency of three methods of selection, J. Hered. 33 (1942) 393-399.
Crossref Google Scholar
[2]

C.R. Henderson, Best linear unbiased estimation and prediction under a selection model, Biometrics 31 (1975) 423-447.
Crossref Google Scholar
[3]

R. Lande, R. Thompson, Efficiency of marker-assisted selection in the improvement of quantitative traits, Genetics 124 (1990) 743-756.
Crossref Google Scholar
[4]

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
[5]

M. Goddard, Genomic selection: prediction of accuracy and maximisation of long term response, Genetica 136 (2009) 245-257.
Crossref Google Scholar
[6]

L. Chen, C. Li, M. Sargolzaei, F. Schenkel, Impact of genotype imputation on the performance of GBLUP and Bayesian methods for genomic prediction, PLoS One 9 (2014) e101544.
Crossref Google Scholar
[7]

Z. Zhang, M. Erbe, J. He, U. Ober, N. Gao, H. Zhang, H. Simianer, J. Li, Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix, G3-Genes Genomes Genet. 5 (2015) 615-627.
Crossref Google Scholar
[8]

E.L. Heffner, A.J. Lorenz, J.L. Jannink, M.E. Sorrells, Plant breeding with genomic selection: gain per unit time and cost, Crop Sci. 50 (2010) 1681-1690.
Crossref Google Scholar
[9]

R. Bernardo, J. Yu, Prospects for genomewide selection for quantitative traits in maize, Crop Sci. 47 (2007) 1082-1090.
Crossref Google Scholar
[10]

T. Albrecht, V. Wimmer, H. Auinger, M. Erbe, C. Knaak, M. Ouzunova, H. Simianer, C.C. Schön, Genome-based prediction of testcross values in maize, Theor. Appl. Genet. 123 (2011) 339-350.
Crossref Google Scholar
[11]

D.C. Kadam, S.M. Potts, M.O. Bohn, A.E. Lipka, A.J. Lorenz, Genomic prediction of single crosses in the early stages of a maize hybrid breeding pipeline, G3-Genes Genomes Genet. 6 (2016) 3443-3453.
Crossref Google Scholar
[12]

U. Beukert, Z. Li, G. Liu, Y. Zhao, N. Ramachandra, V. Mirdita, F. Pita, K. Pillen, J.C. Reif, Genome-based identification of heterotic patterns in rice, Rice 10 (2017) 22.
Crossref Google Scholar
[13]

S.Z. Xu, D. Zhu, Q.F. 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
[14]

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
[15]

X. Wang, L.Z. Li, Z.F. Yang, X.F. Zheng, B. Yu, C.W. Xu, Z.L. 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
[16]

J.Y. Li, J. Wang, R.S. Zeigler, The 3,000 rice genomes project: new opportunities and challenges for future rice research, Gigascience 3 (2014) 8.
Crossref Google Scholar
[17]

H.H. Neves, R. Carvalheiro, S.A. Queiroz, A comparison of statistical methods for genomic selection in a mice population, BMC Genet. 13 (2012) 100.
Crossref Google Scholar
[18]

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
[19]

S.Z. Xu, Estimating polygenic effects using markers of the entire genome, Genetics 163 (2003) 789-801.
Crossref Google Scholar
[20]

R. Tibshirani, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. B (1996) 267-288.
Crossref Google Scholar
[21]

T. Park, G. Casella, The bayesian lasso, J. Am. Stat. Assoc. 103 (2008) 681-686.
Crossref Google Scholar
[22]

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
[23]

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
[24]

P. VanRaden, Efficient methods to compute genomic predictions, J. Dairy Sci. 91 (2008) 4414-4423.
Crossref Google Scholar
[25]

B.J. Hayes, P.M. Visscher, M.E. Goddard, Increased accuracy of artificial selection by using the realized relationship matrix, Genet. Res. 91 (2009) 47-60.
Crossref Google Scholar
[26]

D. Habier, J. Tetens, F.-R. Seefried, P. Lichtner, G. Thaller, The impact of genetic relationship information on genomic breeding values in German Holstein cattle, Genet. Sel. Evol. 42 (2010) 5.
Crossref Google Scholar
[27]

J. Yang, B. Benyamin, B.P. McEvoy, S. Gordon, A.K. Henders, D.R. Nyholt, P.A. Madden, A.C. Heath, N.G. Martin, G.W. Montgomery, Common SNPs explain a large proportion of the heritability for human height, Nat. Genet. 42 (2010) 565-569.
Crossref Google Scholar
[28]

M.E. Goddard, B.J. Hayes, T.H.E. Meuwissen, Using the genomic relationship matrix to predict the accuracy of genomic selection, J. Anim. Breed. Genet. 128 (2011) 409-421.
Crossref Google Scholar
[29]

O.F. Christensen, P. Madsen, B. Nielsen, T. Ostersen, G. Su, Single-step methods for genomic evaluation in pigs, Animal 6 (2012) 1565-1571.
Crossref Google Scholar
[30]

A. Legarra, I. Aguilar, I. Misztal, A relationship matrix including full pedigree and genomic information, J. Dairy Sci. 92 (2009) 4656-4663.
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]

N. Heslot, H.P. Yang, M.E. Sorrells, J.L. Jannink, Genomic selection in plant breeding: a comparison of models, Crop Sci. 52 (2012) 146-160.
Crossref Google Scholar
[33]

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 Google Scholar
[34]

D. Habier, R. Fernando, J. Dekkers, The impact of genetic relationship information on genome-assisted breeding values, Genetics 177 (2007) 2389-2397.
Crossref Google Scholar
[35]

H. Gao, G. Su, L. Janss, Y. Zhang, M.S. Lund, Model comparison on genomic predictions using high-density markers for different groups of bulls in the Nordic Holstein population, J. Dairy Sci. 96 (2013) 4678-4687.
Crossref Google Scholar
[36]

X. Wu, M.S. Lund, D. Sun, Q. Zhang, G. Su, Impact of relationships between test and training animals and among training animals on reliability of genomic prediction, J. Anim. Breed. Genet. 132 (2015) 366-375.
Crossref Google Scholar
[37]

H.H.R. Neves, R. Carvalheiro, A.M. Perez, Y.T. O'Brien, A.S. Utsunomiya, F.S. do Carmo, J. Schenkel, J.C. Soelkner, C.P. McEwan, J.B. Van Tassell, M.V.G.B. da Cole, S.A. Silva, T.S. Queiroz, J.F. Garcia Sonstegard, Accuracy of genomic predictions in Bos indicus (Nellore) cattle, Genet. Sel. Evol. 46 (2014) 17.
Crossref Google Scholar
[38]

Y. Xu, C.W. Xu, S.Z. Xu, Prediction and association mapping of agronomic traits in maize using multiple omic data, Heredity 119 (2017) 174-184.
Crossref Google Scholar
[39]

P. Perez-Rodriguez, D. Gianola, J.M. Gonzalez-Camacho, J. Crossa, Y. Manes, S. Dreisigacker, Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat, G3-Genes Genomes Genet. 2 (2012) 1595-1605.
Crossref Google Scholar
[40]

M. Roorkiwal, A. Rathore, R.R. Das, M.K. Singh, A. Jain, S. Srinivasan, P.M. Gaur, B. Chellapilla, S. Tripathi, Y. Li, Genome-enabled prediction models for yield related traits in chickpea, Front. Plant Sci. 7 (2016) 1666.
Crossref Google Scholar
[41]

O. Gonzalez-Recio, G.J.M. Rosa, D. Gianola, Machine learning methods and predictive ability metrics for genome-wide prediction of complex traits, Livest. Sci. 166 (2014) 217-231.
Crossref Google Scholar
[42]

D. Wang, I.S. El-Basyoni, P.S. Baenziger, J. Crossa, K.M. Eskridge, I. Dweikat, Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations, Heredity 109 (2012) 313-319.
Crossref Google Scholar
[43]

I. Misztal, T.J. Lawlor, R.L. Fernando, Dominance models with method R for stature of Holsteins, J. Dairy Sci. 80 (1997) 975-978.
Crossref Google Scholar
[44]

M. Denis, J.M. Bouvet, Efficiency of genomic selection with models including dominance effect in the context of eucalyptus breeding, Tree Genet. Genomes 9 (2013) 37-51.
Crossref Google Scholar
[45]

R. Wellmann, J. Bennewitz, Bayesian models with dominance effects for genomic evaluation of quantitative traits, Genet. Res. 94 (2012) 21-37.
Crossref Google Scholar
[46]

M. Balestre, R.G. Von Pinho, J.C. Souza, Prediction of maize single-cross performance by mixed linear models with microsatellite marker information, Genet. Mol. Res. 9 (2010) 1054-1068.
Crossref Google Scholar
[47]

N. Uwatoko, A. Onishi, Y. Ikeda, M. Kontani, A. Sasaki, K. Matsubara, Y. Itoh, Y. Sano, Epistasis among the three major flowering time genes in rice: coordinate changes of photoperiod sensitivity, basic vegetative growth and optimum photoperiod, Euphytica 163 (2008) 167-175.
Crossref Google Scholar
[48]

D. Mao, T. Liu, C. Xu, X. Li, Y. Xing, Epistasis and complementary gene action adequately account for the genetic bases of transgressive segregation of kilo-grain weight in rice, Euphytica 180 (2011) 261-271.
Crossref Google Scholar
[49]

J.W. Dudley, G.R. Johnson, Epistatic models improve prediction of performance in corn, Crop Sci. 49 (2009) 1533.
Crossref Google Scholar
[50]

T. Würschum, H.P. Maurer, F. Dreyer, J.C. Reif, Effect of inter- and intragenic epistasis on the heritability of oil content in rapeseed (Brassica napus L.), Theor. Appl. Genet. 126 (2013) 435-441.
Crossref Google Scholar
[51]

Z. Hu, Y. Li, X. Song, Y. Han, X. Cai, S. Xu, W. Li, Genomic value prediction for quantitative traits under the epistatic model, BMC Genet. 12 (2011) 15.
Crossref Google Scholar
[52]

Y. Jia, J.L. Jannink, Multiple-trait genomic selection methods increase genetic value prediction accuracy, Genetics 192 (2012) 1513-1522.
Crossref Google Scholar
[53]

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
[54]

A.W. Schulthess, Y. Wang, T. Miedaner, P. Wilde, J.C. Reif, Y. Zhao, Multiple-trait and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes, Theor. Appl. Genet. 129 (2016) 273-287.
Crossref Google Scholar
[55]

Z. Guo, D.M. Tucker, D. Wang, C.J. Basten, E. Ersoz, W.H. Briggs, J. Lu, M. Li, G. Gay, Accuracy of across-environment genome-wide prediction in maize nested association mapping populations, G3-Genes Genomes Genet. 3 (2013) 263-272.
Crossref Google Scholar
[56]

M. Scutari, P. Howell, D.J. Balding, I. Mackay, Multiple quantitative trait analysis using Bayesian networks, Genetics 198 (2014) 129-137.
Crossref Google Scholar
[57]

C. Henderson, R. Quaas, Multiple trait evaluation using relatives' records, J. Anim. Sci. 43 (1976) 1188-1197.
Crossref Google Scholar
[58]

T. Hayashi, H. Iwata, A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits, BMC Bioinf. 14 (2013) 34.
Crossref Google Scholar
[59]

S. Tsuruta, I. Misztal, I. Aguilar, T.J. Lawlor, Multiple-trait genomic evaluation of linear type traits using genomic and phenotypic data in US Holsteins, J. Dairy Sci. 94 (2011) 4198-4204.
Crossref Google Scholar
[60]

G. Guo, F. Zhao, Y. Wang, Y. Zhang, L. Du, G. Su, Comparison of single-trait and multiple-trait genomic prediction models, BMC Genet. 15 (2014) 30.
Crossref Google Scholar
[61]

Y. Bao, J.E. Kurle, G. Anderson, N.D. Young, Association mapping and genomic prediction for resistance to sudden death syndrome in early maturing soybean germplasm, Mol. Breed. 35 (2015) 128.
Crossref Google Scholar
[62]

J. Burgueño, G. de los Campos, K. Weigel, J. Crossa, Genomic prediction of breeding values when modeling genotype×environment interaction using pedigree and dense molecular markers, Crop Sci. 52 (2012) 707-719.
Crossref Google Scholar
[63]

N. Heslot, D. Akdemir, M.E. Sorrells, J.L. Jannink, Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions, Theor. Appl. Genet. 127 (2014) 463-480.
Crossref Google Scholar
[64]

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
[65]

O. Kempthorne, A.W. Nordskog, Restricted selection indices, Biometrics 15 (1959) 10-19.
Crossref Google Scholar
[66]

J.J. Cerón-Rojas, J. Crossa, F.H. Toledo, J. Sahagún-Castellanos, A predetermined proportional gains eigen selection index method, Crop Sci. 56 (2016) 2436-2447.
Crossref Google Scholar
[67]

J. Elgin, R. Hill, K. Zeiders, Comparison of four methods of multiple trait selection for five traits in alfalfa, Crop Sci. 10 (1970) 190-193.
Crossref Google Scholar
[68]

D.J. Dolan, D.D. Stuthman, F.L. Kolb, A.D. Hewings, Multiple trait selection in a recurrent selection population in oat (Avena sativa L.), Crop Sci. 36 (1996) 1207-1211.
Crossref Google Scholar
[69]

K. Suwantaradon, S. Eberhart, J. Mock, J. Owens, W. Guthrie, Index selection for several agronomic traits in the BSSS2 maize population, Crop Sci. 15 (1975) 827-833.
Crossref Google Scholar
[70]

C.C. Holbrook, J.W. Burton, T.E. Carter, Evaluation of recurrent restricted index selection for increasing yield while holding seed protein constant in soybean, Crop Sci. 29 (1989) 324-329.
Crossref Google Scholar
[71]

J.C.M. Dekkers, Prediction of response to marker-assisted and genomic selection using selection index theory, J. Anim. Breed. Genet. 124 (2007) 331-341.
Crossref Google Scholar
[72]

J.J. Cerón-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
[73]

D.H. Lyra, L. de Freitas Mendonça, G. Galli, F.C. Alves, Í.S.C. Granato, R. Fritsche-Neto, Multi-trait genomic prediction for nitrogen response indices in tropical maize hybrids, Mol. Breed. 37 (2017) 80.
Crossref Google Scholar
[74]

H.M. Nielsen, A.K. Sonesson, H. Yazdi, T.H.E. Meuwissen, Comparison of accuracy of genome-wide and BLUP breeding value estimates in sib based aquaculture breeding schemes, Aquaculture 289 (2009) 259-264.
Crossref Google Scholar
[75]

S.A. Clark, J.M. Hickey, H.D. Daetwyler, J.H.J. van der Werf, The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes, Genet. Sel. Evol. 44 (2012) 4.
Crossref Google Scholar
[76]

G. Su, R.F. Brondum, P. Ma, B. Guldbrandtsen, G.R. Aamand, M.S. Lund, Comparison of genomic predictions using medium-density (similar to 54,000) and high-density (similar to 777,000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations, J. Dairy Sci. 95 (2012) 4657-4665.
Crossref Google Scholar
[77]

E.L. Heffner, J.L. Jannink, H. Iwata, E. Souza, M.E. Sorrells, Genomic selection accuracy for grain quality traits in biparental wheat populations, Crop Sci. 51 (2011) 2597.
Crossref Google Scholar
[78]

M.P.L. Calus, T.H.E. Meuwissen, A.P.W. de Roos, R.F. Veerkamp, Accuracy of genomic selection using different methods to define haplotypes, Genetics 178 (2008) 553-561.
Crossref Google Scholar
[79]

T.M. Villumsen, L. Janss, M.S. Lund, The importance of haplotype length and heritability using genomic selection in dairy cattle, J. Anim. Breed. Genet. 126 (2009) 3-13.
Crossref Google Scholar
[80]

S.Q. Zhong, J.C.M. Dekkers, R.L. Fernando, J.L. Jannink, Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study, Genetics 182 (2009) 355-364.
Crossref Google Scholar
[81]

J.L. Jannink, A.J. Lorenz, H. Iwata, Genomic selection in plant breeding: from theory to practice, Brief. Funct. Genomics 9 (2010) 166-177.
Crossref Google Scholar
[82]

N.A. Alimi, M.C.A.M. Bink, J.A. Dieleman, J.J. Magán, A.M. Wubs, A. Palloix, F.A.V. Eeuwijk, Multi-trait and multi-environment QTL analyses of yield and a set of physiological traits in pepper, Theor. Appl. Genet. 126 (2013) 2597-2625.
Crossref Google Scholar
[83]

T. Gartner, M. Steinfath, S. Andorf, J. Lisec, R.C. Meyer, T. Altmann, L. Willmitzer, J. Selbig, Improved heterosis prediction by combining information on DNA- and metabolic markers, PLoS One 4 (2009) e5220.
Crossref Google Scholar
The Crop Journal
Volume 6 Issue 4,
August 2018
Pages 330-340
DOI: 10.1016/j.cj.2018.03.001
Cite this article:
Wang X, Xu Y, Hu Z, et al. Genomic selection methods for crop improvement: Current status and prospects. The Crop Journal, 2018, 6(4): 330-340. https://doi.org/10.1016/j.cj.2018.03.001

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Received: 04 February 2018
Revised: 25 March 2018
Accepted: 09 April 2018
Published: 15 April 2018
© 2018 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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