Abstract
For many plant and animal species, commercial products are hybrids between individuals from different genetic groups. For allogamous plant species such as maize, the breeding objective is to produce single-cross hybrid varieties from two inbred lines each selected in complementary groups. Efficient hybrid breeding requires methods that (1) quickly generate homozygous and homogeneous parental lines with high combining abilities, (2) efficiently choose among the large number of available parental lines the most promising ones, and (3) predict the performances of sets of non-phenotyped single-cross hybrids, or hybrids phenotyped in a limited number of environments, based on their relationship with another set of hybrids with known performances. The maize breeding community has been developing model-based prediction of hybrid performances well before the genomic era. This chapter (1) provides a reminder of the maize breeding scheme before the genomic era; (2) describes how genomic data were incorporated in the prediction models involved in different steps of genomic-based single-cross maize hybrid breeding; and (3) reviews factors affecting the accuracy of genomic prediction, approaches for optimizing GP-based single-cross maize hybrid breeding schemes, and ensuring the long-term sustainability of genomic selection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andorf C, Beavis WD, Hufford M, Smith S, Suza WP et al (2019) Technological advances in maize breeding: past, present and future. Theor Appl Genet 132:817–849
Edmeades GO, Trevisan W, Prasanna BM, Campos H (2017) Tropical maize (Zea mays L.). In: Campos H, Caligari PDS (eds) Genetic improvement of tropical crops. Springer, New York, pp 57–109
Bassi FM, Bentley AR, Charmet G, Ortiz R, Crossa J (2016) Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). Plant Sci 242:23–36
Heslot N, Akdemir D, Sorrells ME, Jannink JL (2014) Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions. Theor Appl Genet 127:463–480
Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829
Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D et al (2017) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22:961–975. https://doi.org/10.1016/j.tplants.2017.08.011
Dias KOG, Gezan SA, Guimarães CT, Nazarian A, e Silva LDC et al (2018) Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. Heredity 121:24–37
Dias KOG, Piepho HP, Guimarães LJM, Guimarães PDO, Parentoni SN et al (2020) Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data. Theor Appl Genet 133:443–455
Jarquín D, Howard R, Crossa J, Beyene Y, Gowda M et al (2020) Genomic prediction enhanced sparse testing for multi-environment trials. G3 10:2725–2739
Shull GH (1908) The composition of a field of maize. Report. Vol 4, issue 2. American Breeders Association, Washington, pp 296–301
Sprague GF, Eberhart AS (1977) Corn breeding. In: Sprague GF (ed) Corn and corn improvement, 2nd edn. American Society of Agronomy, Madison, pp 305–362
Kaeppler S (2012) Heterosis: many genes, many mechanisms end the search for an undiscovered unifying theory. Int Scholar Res Notices 2012:682824
Lee M (1995) DNA markers and plant breeding programs. Adv Agron 55:265–344
Hallauer AR, Russell WA, Lamkey KR (1988) Corn breeding. Corn Corn Improv 18:463–564
Bernardo R (2002) Breeding for quantitative traits in plants, vol 1. Stemma Press, Woodbury, MN, p 369
Odiyo O, Njoroge K, Chemining’wa GN, Beyene Y (2014) Performance and adaptability of doubled haploid maize testcross hybrids under drought stress and non-stress conditions. Field Crop Res 246:107693
Sserumaga JP, Oikeh SO, Mugo S, Asea G, Otim M et al (2016) Genotype by environment interactions and agronomic performance of doubled haploids testcross maize (Zea mays L.) hybrids. Euphytica 207:353–365
Meng D, Liu C, Chen S, Jin W (2021) Haploid induction and its application in maize breeding. Mol Breed 41:20. https://doi.org/10.1007/s11032-021-01204-5
Griffing B (1956) Concept of general and specific combining ability in relation to diallel crossing systems. Aust J Biol Sci 9:463–493
Lindstrom EW (1931) Prepotency of inbred sires on commercial varieties of maize. J Am Soc Agron 23:652-061
Jenkins MT, Brunson AM (1932) Methods of testing inbred lines of maize in crossbred combinations. J Am Soc Agron 24:523–530
Rawlings JO, Thompson DL (1962) Performance level as criterion for the choice of maize testers 1. Crop Sci 2:217–220
Russell WA, Eberhart SA, Urbano AVO (1973) Recurrent selection for specific combining ability for yield in two maize populations 1. Crop Sci 13:257–261
Wegenast T, Longin CFH, Utz HF, Melchinger AE, Maurer HP, Reif JC (2008) Hybrid maize breeding with doubled haploids: IV. Number versus size of crosses and importance of parental selection in two-stage selection for testcross performance. Theor Appl Genet 117:2
Rhoades MM (1931) Cytoplasmic inheritance of male sterility in Zea mays. Science 73:340–341
Gabay-Laughnan S, Laughnan JR (1994) Male sterility and restorer genes in maize. In: The maize handbook. Springer, New York, NY, pp 418–423
Feng PC, Qi Y, Chiu T, Stoecker MA, Schuster CL et al (2014) Improving hybrid seed production in corn with glyphosate-mediated male sterility. Pest Manag Sci 70:212–218
Wu Y, Fox TW, Trimnell MR, Wang L, Xu RJ et al (2016) Development of a novel recessive genetic male sterility system for hybrid seed production in maize and other cross-pollinating crops. Plant Biotechnol J 14:1046–1054
Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423–447
Henderson CR (1976) A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32:69–83
Bernardo R (1994) Prediction of maize single-cross performance using RFLPs and information from related hybrids. Crop Sci 34:20–25
Panter DM, Allen FL (1995) Using best linear unbiased predictions to enhance breeding for yield in soybean: I. Choosing parents. Crop Sci 35:397–405
Bernardo R (1995) Genetic models for predicting maize single-cross performance in unbalanced yield trial data. Crop Sci 35:141–147
Bernardo R (1996) Best linear unbiased prediction of the performance of crosses between untested maize inbreds. Crop Sci 36:872–876
Crossa J, Burgueño J, Cornelius PL, McLaren G, Trethowan R, Krishnamachari A (2006) Modeling genotype × environment interaction using additive genetic covariances of relatives for predicting breeding values of wheat genotypes. Crop Sci 46:1722–1733
Smith AB, Cullis BR, Thompson R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. J Agric Sci 143:449–462
Malosetti M, Ribaut JM, Vargas M, Crossa J, Van Eeuwijk FA (2008) A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.). Euphytica 161:241–257
Viana JMS, Pereira HD, Mundim GB, Piepho HP, Silva FF (2018) Efficiency of genomic prediction of non-assessed single crosses. Heredity 120:283–295. https://doi.org/10.1038/s41437-017-0027-0
Persa R, Bernardeli A, Jarquín D (2020) Prediction strategies for leveraging information of associated traits under single- and multi-trait approaches in soybeans. Agriculture 10:308. https://doi.org/10.3390/agriculture10080308
Bernardo R, Yu J (2006) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090. https://doi.org/10.2135/cropsci2006.11.0690
Beyene Y, Gowda M, Pérez-Rodriguez P, Olsen M, Robbins KR et al (2021) Application of genomic selection at the early stage of breeding pipeline in tropical maize. Front Plant Sci 12:685488
Lee E, Ash M, Good B (2007) Re-examining the relationship between degree of relatedness, genetic effects, and heterosis in maize. Crop Sci 47:629–635
Melchinger A (1999) Genetic diversity and heterosis. In: The genetics and exploitation of heterosis in crops. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Madison, WI, pp 99–118
Technow F, Riedelsheimer C, Schrag TA, Melchinger AE (2012) Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects. Theor Appl Genet 125:1181–1194
Massman JM, Gordillo A, Lorenzana RE, Bernardo R (2013) Genomewide predictions from maize single-cross data. Theor Appl Genet 126:13–22
Technow F, Schrag TA, Schipprack W, Bauer E, Simianer H, Melchinger AE (2014) Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize. Genetics 197:1343–1355
Kadam DC, Potts SM, Bohn MO, Lipka AE, Lorenz AJ (2016) Genomic prediction of single crosses in the early stages of a maize hybrid breeding pipeline. G3 6:3443–3453
Zhao Y, Mette MF, Reif JC (2015) Genomic selection in hybrid breeding. Plant Breed 134:1–10
Almeida Filho JE, Guimarães JFR, e Silva FF, De Resende MDV, Muñoz P et al (2016) The contribution of dominance to phenotype prediction in a pine breeding and simulated population. Heredity 117:33–41
Basnet BR, Crossa J, Dreisigacker S, Pérez-Rodríguez P, Manes Y et al (2019) Hybrid wheat prediction using genomic, pedigree, and environmental covariables interaction models. Plant Genome 12:180051
VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423
Calus MPL (2010) Genomic breeding value prediction: methods and procedures. Animal 4:157–164
Acosta-Pech R, Crossa J, de Los Campos G, Teyssèdre S, Claustres B et al (2017) Genomic models with genotype× environment interaction for predicting hybrid performance: an application in maize hybrids. Theor Appl Genet 130:1431–1440
Costa-Neto G, Fritsche-Neto R, Crossa J (2021) Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity 126:92–106
Alves FC, Granato I, Galli G, Lyra DH, Fritsche-Neto R, de los Campos G (2019) Bayesian analysis and prediction of hybrid performance. Plant Methods 15:14. https://doi.org/10.1186/s13007-019-0388-
Zhao W, Lai X, Liu D, Zhang Z, Ma P et al (2020) Applications of support vector machine in genomic prediction in pig and maize populations. Front Genet 11:598318. https://doi.org/10.3389/fgene.2020.598318
Crossa J, Pérez P, de los Campos G, Mahuku G, Dreisigacker S, Magorokosho C (2011) Genomic selection and prediction in plant breeding. J Crop Improv 25:239–261
de los Campos G, Naya H, Gianola D, Crossa J, Legarra A et al (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182:375–385
Heslot N, Yang HP, Sorrells ME, Jannink JL (2012) Genomic selection in plant breeding: a comparison of models. Crop Sci 52:146–160
Pérez-Rodríguez P, Gianola D, González-Camacho JM, Crossa J, Manès Y, Dreisigacker S (2012) Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3 2:1595–1605
Cerrudo D, Cao S, Yuan Y, Martinez C, Suarez EA et al (2018) Genomic selection outperforms marker assisted selection for grain yield and physiological traits in a maize doubled haploid population across water treatments. Front Plant Sci 9:366
Schopp P, Müller D, Technow F, Melchinger AE (2017) Accuracy of genomic prediction in synthetic populations depending on the number of parents, relatedness, and ancestral linkage disequilibrium. Genetics 205:441–454
Zhang H, Yin L, Wang M, Yuan X, Liu X (2019) Factors affecting the accuracy of genomic selection for agricultural economic traits in maize, cattle, and pig populations. Front Genet 10:189
Gowda M, Zhao Y, Würschum T, Longin CF, Miedaner T et al (2013) Relatedness severely impacts accuracy of marker-assisted selection for disease resistance in hybrid wheat. Heredity 112:552–561
Albrecht T, Wimmer V, Auinger H-J, Erbe M, Knaak C et al (2011) Genome-based prediction of testcross values in maize. Theor Appl Genet 123:339–350
Miedaner T, Zhao Y, Gowda M, Longin CF, Korzun V et al (2013) Genetic architecture of resistance to Septoria tritici blotch in European wheat. BMC Genomics 14:858
Windhausen VS, Atlin GA, Hickey JM, Crossa J, Jannink JL et al (2012) Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3 2:1427–1436
Zhao Y, Gowda M, Liu W, Würschum T, Maurer HP et al (2012) Accuracy of genomic selection in European maize elite breeding populations. Theor Appl Genet 124:769–776
Wang Y, Mette MF, Miedaner T, Gottwald M, Wilde P et al (2014) The accuracy of prediction of genomic selection in elite hybrid rye populations surpasses the accuracy of marker assisted selection and is equally augmented by multiple field evaluation locations and test years. BMC Genomics 15:556
Würschum T, Reif JC, Kraft R, Janssen G, Zhao Y (2013) Genomic selection in sugar beet breeding populations. BMC Genet 14:85
Albrecht T, Auinger HJ, Wimmer V, Ogutu JO, Knaak C et al (2014) Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and years. Theor Appl Genet 27:1375–1386
Rincent R, Laloe D, Nicolas S, Altmann T, Brunel D et al (2012) Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics 192:715–728
Fristche-Neto R, Akdemir D, Jannink J-L (2018) Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs. Theor Appl Genet 131:1153–1162. https://doi.org/10.1007/s00122-018-3068-8
Heslot N, Feoktistov V (2020) Optimization of selective phenotyping and population design for genomic prediction. J Agric Biol Environ Stat 25:579–600. https://doi.org/10.1007/s13253-020-00415-1
Kadam DC, Rodriguez OR, Lorenz AJ (2021) Optimization of training sets for genomic prediction of early-stage single crosses in maize. Theor Appl Genet 134(2):687–699. https://doi.org/10.1007/s00122-020-03722-w
Hickey JM, Dreisigacker S, Crossa J, Hearne S, Babu R et al (2014) Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation. Crop Sci 54:1476–1488
Liu G, Zhao Y, Gowda M, Longin CFH, Reif JC, Mette MF (2016) Predicting hybrid performances for quality traits through genomic-assisted approaches in central European wheat. PLoS One 11:e0158635
Zhang A, Wang H, Beyene Y, Semagn K, Liu Y et al (2017) Effect of trait heritability, training population size and marker density on genomic prediction accuracy estimation in 22 bi-parental tropical maize populations. Front Plant Sci 8:1916
Cao S, Loladze A, Yuan Y, Wu Y, Zhang A et al (2017) Genome-wide analysis of tar spot complex resistance in maize using genotyping-by-sequencing SNPs and whole-genome prediction Plant Genome
Liu X, Wang H, Wang H, Guo Z, Xu X et al (2018) Factors affecting genomic selection revealed by empirical evidence in maize. Crop J 6:341–352
Voss-Fels KP, Cooper M, Hayes BJ (2019) Accelerating crop genetic gains with genomic selection. Theor Appl Genet 132:669–686
MacLeod IM, Bowman PJ, Vander CJ, Haile-Mariam M, Kemper KE et al (2016) Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genomics 17:1–21
Isidro J, Jannink JL, Akdemir D, Poland J, Heslot N, Sorrells ME (2015) Training set optimization under population structure in genomic selection. Theor Appl Genet 128:145–158. https://doi.org/10.1007/s00122-014-2418-4
Rio S, Mary-Huard T, Moreau L, Charcosset A (2019) Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel. Theor Appl Genet 132:81–96. https://doi.org/10.1007/s00122-018-3196-1
Jiang S, Cheng Q, Yan J, Fu R, Wang X (2020) Genome optimization for improvement of maize breeding. Theor Appl Genet 133:1491–1502. https://doi.org/10.1007/s00122-019-03493-z
Atanda SA, Olsen M, Burgueño J, Crossa J, Dzidzienyo D et al (2021) Maximizing efficiency of genomic selection in CIMMYT’s tropical maize breeding program. Theor Appl Genet 134:279–294. https://doi.org/10.1007/s00122-020-03696-9
Guo Z, Tucker DM, Basten CJ, Gandhi H, Ersoz E et al (2014) The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet 127:749–762. https://doi.org/10.1007/s00122-013-2255-x
Guo T, Yu X, Li X, Zhang H, Zhu C et al (2019) Optimal designs for genomic selection in hybrid crops. Mol Plant 12:390–401
Kaufman L, Rousseeuw P (1987) Clustering by means of medoids. North-Holland, Amsterdam
Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. Wiley, New York
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31:264–323
Leskovec J, Faloutsos C (2006) Sampling from large graphs. In: KDD 2006, pp. 631–636. https://doi.org/10.1145/1150402.1150479
Bernardo R (2014) Genomewide selection of parental inbreds: classes of loci and virtual biparental populations. Crop Sci 54:1–33
Liu X, Wang H, Hu X, Li K, Liu Z et al (2019) Improving genomic selection with quantitative trait loci and nonadditive effects revealed by empirical evidence in maize. Front Plant Sci 10:1129
Wang X, Li L, Yang Z, Zheng X, Yu S (2017) Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II. Heredity 118:302–310. https://doi.org/10.1038/hdy.2016.87
Bandeira e Sousa M, Cuevas J, de Oliveira Couto EG, Pérez-Rodríguez P, Jarquín D et al (2017) Predição genômica em milho usando modelos de kernel com interação genótipo × ambiente. G3 7:1995–2014
Zhang X, Pérez-Rodríguez P, Semagn K, Beyene Y, Babu R et al (2015) Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity 114:291–299
Fritsche-Neto R, Galli G, Alves FC, Sabadin F, Lyra DH et al (2021) Optimizing genomic-enabled prediction in small-scale low budged maize hybrid breeding programs: a roadmap review. Front Plant Sci 12:1058
Xu Y (2016) Envirotyping for deciphering environmental impacts on crop plants. Theor Appl Genet 129:653–673
Riedelsheimer C, Technow F, Melchinger AE (2012) Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines. BMC Genomics 13:452
Resende RT, Piepho HP, Rosa GJM, Silva-junior OB, e Silva FF et al (2021) Enviromics in breeding: applications and perspectives on envirotypic-assisted selection. Theor Appl Genet 134:95–112. https://doi.org/10.1007/s00122-020-03684-z
Zhao Y, Gowda M, Liu W, Würschum T, Maurer HP et al (2013) Choice of shrinkage parameter and prediction of genomic breeding values in elite maize breeding populations. Plant Breed 132:99–106
Piepho H, Ogutu J, Schulz-Streeck T, Estaghvirou B, Gordillo A, Technow F (2012) Efficient computation of ridge-regression best linear unbiased prediction in genomic selection in plant breeding. Crop Sci 52:1093–1104
Ren D, Teng J, Diao S, Lin Q, Li J, Zhang Z (2021) Impact of marker pruning strategies based on different measurements of marker distance on genomic prediction in dairy cattle. Animals 11:1992
Sousa MB, Galli G, Lyra DH, Granato ÍSC, Matias FI et al (2019) Increasing accuracy and reducing costs of genomic prediction by marker selection. Euphytica 215:18
Zhang Z, Erbe M, He J, Ober U, Gao N et al (2015) Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix. G3 5:615–627
Ma Y, Reif JC, Jiang Y, Wen Z, Wang D et al (2016) Potential of marker selection to increase prediction accuracy of genomic selection in soybean (Glycine max L.). Mol Breed 36:1–10
Subedi S, Feng Z, Deardon R, Schenkel FS (2013) SNP selection for predicting a quantitative trait. J Appl Stat 40:600–613
DoVale JC, Carvalho HF, Sabadin F, Fritsche-Neto R (2021) Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops. bioRxiv. https://doi.org/10.1101/2021.03.05.434084
Osthushenrich T, Frisch M, Herzog E (2017) Genomic selection of crossing partners on basis of the expected mean and variance of their derived lines. PLoS One 12:e0188839
Hofheinz N, Borchardt D, Weissleder K, Frisch M (2012) Genome-based prediction of test cross performance in two subsequent breeding cycles. Theor Appl Genet 125:1639–1645
Zenke-Philippi C, Thiemann A, Seifert F, Schrag T, Melchinger AE et al (2016) Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mRNA transcription profiles. BMC Genomics 17:1–8
Longin CFH, Utz HF, Melchinger AE, Reif JC (2007) Hybrid maize breeding with doubled haploids: II. Optimum number and type of testers in two-stage selection for general combining ability. Theor Appl Genet 114:393–402
Lorenz AJ (2013) Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: a simulation experiment. G3 3:481–491
Podlich DW, Winkler CR, Cooper M (2004) Mapping as you go: an effective approach for marker-assisted selection of complex traits. Crop Sci 44:1560–1571
Seye AI, Bauland C, Charcosset A, Moreau L (2020) Revisiting hybrid breeding designs using genomic predictions: simulations highlight the superiority of incomplete factorials between segregating families over topcross designs. Theor Appl Genet 133:1995–2010. https://doi.org/10.1007/s00122-020-03573-5
Jannink J-L (2010) Dynamics of long-term genomic selection. Genet Sel Evol 42:35. https://doi.org/10.1186/1297-9686-42-35
Goddard M (2009) Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136:245–257
Daetwyler H, Hayden M, Spangenberg G, Hayes B (2015) Selection on optimal haploid value increases genetic gain and preserves more genetic diversity relative to genomic selection. Genetics 200:1341–1348
Goiffon M, Kusmec A, Wang L, Hu G, Schnable P (2017) Improving response in genomic selection with a population-based selection strategy: optimal population value selection. Genetics 206:1675–1682. https://doi.org/10.1534/genetics.116.197103
Moeinizade S, Hu G, Wang L, Schnable PS (2019) Optimizing selection and mating in genomic selection with a look-ahead approach: an operations research framework. G3 9:2123–2133. https://doi.org/10.1534/g3.118.200842
Allier A, Teyssèdre S, Lehermeier C, Claustres B, Maltese S et al (2019) Assessment of breeding programs sustainability: application of phenotypic and genomic indicators to a North European grain maize program. Theor Appl Genet 132:1321–1334
Santantonio N, Atanda SA, Beyene Y, Varshney RK, Olsen M et al (2020) Strategies for effective use of genomic information in crop breeding programs Serving Africa and South Asia. Front Plant Sci 11:353. https://doi.org/10.3389/fpls.2020.00353
Goiffon M, Kusmec A, Wang L, Hu G, Schnable PS (2017) Improving Response in Genomic Selection with a Population-Based Selection Strategy: Optimal Population Value Selection. Genetics 206(3):1675–1682. https://doi.org/10.1534/genetics.116.197103
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Martins Oliveira, I.C., Bernardeli, A., Soler Guilhen, J.H., Pastina, M.M. (2022). Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids. In: Ahmadi, N., Bartholomé, J. (eds) Genomic Prediction of Complex Traits. Methods in Molecular Biology, vol 2467. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2205-6_20
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2205-6_20
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-2204-9
Online ISBN: 978-1-0716-2205-6
eBook Packages: Springer Protocols