Molecular Breeding

, Volume 28, Issue 4, pp 421–436 | Cite as

The role and basics of computer simulation in support of critical decisions in plant breeding



A number of crucial decisions face the plant breeder before any field activities directed to crop genetic improvement are actually initiated, primarily related to choice of parents and breeding strategy options. Because of the impact, the complexity of these decisions, and the cost of implementing multiple options, computer simulation can be an important resource for the modern breeder. To maximize utility, the simulation tool must be based on effective models of the genome, the breeding process, and other ‘processes’ involved in genetic recombination, identification, and production of new cultivars. Additionally, the statistical methodology employed has ramifications for predicting performance and breeding outcome. The objective of this work is to highlight the role of computer simulation in the planning phases of crop genetic improvement, the basics of model building, statistical considerations, and key issues to be addressed. Examples of publicly available simulation software for plant breeding scenarios are described (features, functionalities, and assumptions) and new directions for improved/expanded approaches and tools are discussed.


Computer simulation Genome model Model building Choice of parents Breeding strategy Breeding method 



Best linear unbiased prediction


Doubled haploid


General combining ability


Genotype-by-environment interaction


Genomic selection


Genome-wide selection


Least absolute shrinkage and selection operator


Linkage disequilibrium


Linkage equilibrium


Least squares


Marker-assisted selection


Maize nested association mapping population


Quantitative trait locus or loci


Recombinant inbred line



This research was supported in part by a grant from Monsanto Company, St. Louis, MO, USA; X. Sun and T. Peng were also supported in their graduate studies as Monsanto Fellows in Plant Breeding through a gift of Monsanto Company to the University of Illinois. Many thanks to Drs. Andres Gordillo, Hans Peter Maurer, Nick Lauter, Nick Tinker, Jiankang Wang, and Edie Paul for valued input on their respective software programs. We also extend our appreciation to Drs. Jason Bull, Andrew Davis, G.R. Johnson, and John W. Dudley for their helpful review and valuable commentary on the manuscript. In addition, we wish to thank two anonymous reviewers whose comments helped to improve the manuscript.


  1. Austin DF, Lee M, Veldboom LR, Hallauer AR (2000) Genetic mapping in maize with hybrid progeny across testers and generations: grain yield and grain moisture. Crop Sci 40:30–39CrossRefGoogle Scholar
  2. Bernardo R (1990) Identifying populations useful for improving parents of a single cross based on net transfer of alleles. Theor Appl Genet 80:349–352Google Scholar
  3. Bernardo R (1993) Estimation of coefficient of coancestry using molecular markers in maize. Theor Appl Genet 85:1055–1062CrossRefGoogle Scholar
  4. Bernardo R (1994) Prediction of maize single-cross performance using RFLPs and information from related hybrids. Crop Sci 34:20–25CrossRefGoogle Scholar
  5. Bernardo R (2002) Breeding for quantitative traits in plants. Stemma Press, Woodbury, MN, USAGoogle Scholar
  6. Bernardo R (2009) Genomewide selection for rapid introgression of exotic germplasm in maize. Crop Sci 49:419–425CrossRefGoogle Scholar
  7. Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090CrossRefGoogle Scholar
  8. Blanc G, Charcosset A, Mangin B, Gallais A, Moreau L (2006) Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize. Theor Appl Genet 113:206–224PubMedCrossRefGoogle Scholar
  9. Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz JC (2009) The genetic architecture of maize flowering time. Science 325:714–718PubMedCrossRefGoogle Scholar
  10. Bulmer MG (1985) The mathematical theory of quantitative genetics. Oxford University Press, OxfordGoogle Scholar
  11. Burkhamer RL, Lanning SP, Martens RJ, Martin JM, Talbert LE (1998) Predicting progeny variance from parental divergence in hard red spring wheat. Crop Sci 38:243–248CrossRefGoogle Scholar
  12. Burrows PM (1975) Expected selection differentials for directional selection. Biometrics 28:1091–1100CrossRefGoogle Scholar
  13. Caldwell KS, Russell J, Langridge P, Powell W (2006) Extreme population-dependent linkage disequilibrium detected in an inbreeding plant species, Hordeum vulgare. Genetics 172:557–567PubMedCrossRefGoogle Scholar
  14. Calus MPL, Veerkamp RF (2007) Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM. J Anim Breed Genet 124:362–368PubMedCrossRefGoogle Scholar
  15. Coburn JR, Temnykh SV, Paul EM, McCouch SR (2002) Design and application of microsatellite marker panels for semiautomated genotyping of rice (Oryza sativa L.). Crop Sci 42:2092–2099CrossRefGoogle Scholar
  16. Cockerham CC (1954) An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Genetics 39:859–882PubMedGoogle Scholar
  17. Cooper M, Podlich DW (2002) The E (NK) model: extending the NK model to incorporate gene-by-environment interactions and epistasis for diploid genomes. Complexity 7:31–47CrossRefGoogle Scholar
  18. Cooper M, Podlich DW, Luo L (2007) Modeling QTL effects and MAS in plant breeding. In: Genomics-assisted crop improvement. Springer, Dordrecht, pp 57–95Google Scholar
  19. Crosby JL (1973) Computer simulation in genetics. Wiley, HobokenGoogle Scholar
  20. Crow JF, Kimura M (2009) An introduction to population genetics theory. Blackburn Press, CaldwellGoogle Scholar
  21. de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes JM (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigrees. Genetics 182:375–385CrossRefGoogle Scholar
  22. Dudley JW (1982) Theory for transfer of alleles. Crop Sci 22:631–637CrossRefGoogle Scholar
  23. Dudley JW (1984) A method of identifying lines for use in improving parents of a single cross. Crop Sci 24:355–357CrossRefGoogle Scholar
  24. Dudley JW (1987) Modification of methods for identifying populations to be used for improving parents of elite single crosses. Crop Sci 27:940–943CrossRefGoogle Scholar
  25. Dudley JW (2004) Breeding: choice of parents. In: Goodman RM (ed) Encyclopedia of plant and crop science. Taylor & Francis, London, pp 215–217Google Scholar
  26. Dudley JW, Johnson GR (2010) Epistatic models improve between year prediction and prediction of testcross performance in corn. Crop Sci 50:763–769CrossRefGoogle Scholar
  27. Dudley JW, Maroof MAS, Rufener GK (1992) Molecular marker information and selection of parents in corn breeding programs. Crop Sci 32:301–304CrossRefGoogle Scholar
  28. Eathington SR, Crosbie TM, Edwards MD, Reiter RS, Bull JK (2007) Molecular markers in a commercial breeding program. Crop Sci 47:154–163CrossRefGoogle Scholar
  29. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. Longman and Company, EssexGoogle Scholar
  30. Frisch M, Bohn M, Melchinger AE (2000) Computer note. PLABSIM: software for simulation of marker-assisted backcrossing. J Hered 91:86–87PubMedCrossRefGoogle Scholar
  31. Frisch M, Thiemann A, Fu J, Schrag T, Scholten S, Melchinger AE (2010) Transcriptome-based distance measures for grouping of germplasm and prediction of hybrid performance in maize. Theor Appl Genet 120:441–450PubMedCrossRefGoogle Scholar
  32. Gordillo GA, Geiger HH (2008a) Alternative recurrent selection strategies using doubled haploid lines in hybrid maize breeding. Crop Sci 48:911–922CrossRefGoogle Scholar
  33. Gordillo GA, Geiger HH (2008b) MBP (Version 1.0): a software package to optimize maize breeding procedures based on doubled haploid lines. J Hered 99:227–231PubMedCrossRefGoogle Scholar
  34. Grapes L, Dekkers JCM, Rothschild MF, Fernando RL (2004) Comparing linkage disequilibrium-based methods for fine mapping quantitative trait loci. Genetics 166:1561–1570PubMedCrossRefGoogle Scholar
  35. Griffing B (1956) A generalized treatment of the use of diallel crosses in quantitative inheritance. Heredity 10:31–50CrossRefGoogle Scholar
  36. Haldane JBS (1919) The combination of linkage values and the calculation of distances between the loci of linked factors. J Genet 8:299–309CrossRefGoogle Scholar
  37. Hallauer AR, Pandey S (2006) Defining and achieving plant-breeding goals. In: Lamkey KR, Lee M (eds) Plant breeding: The Arnel R Hallauer International Symposium. Blackwell Publishing, Ames, pp 73–89Google Scholar
  38. Hamblin MT, Salas Fernandez MG, Casa AM, Mitchell SE, Paterson AH, Kresovich S (2005) Equilibrium processes cannot explain high levels of short- and medium-range linkage disequilibrium in the domesticated grass sorghum bicolor. Genetics 171:1247–1256PubMedCrossRefGoogle Scholar
  39. Heckenberger M, Maurer HP, Melchinger AE, Frisch M (2008) The Plabsoft database: a comprehensive database management system for integrating phenotypic and genomic data in academic and commercial plant breeding programs. Euphytica 161:173–179CrossRefGoogle Scholar
  40. Heffner EL, Sorrells ME, Jannink J-L (2009) Genomic selection for crop improvement. Crop Sci 49:1–12CrossRefGoogle Scholar
  41. Hessel DA, Lawrence CJ, Lauter N (2010) COGENFITO: a composite genotype finder tool for optimizing isoline selection in maize breeding schemes. In: Proceedings of the 46th Illinois Corn Breeders School, University of Illinois. Urbana-Champaign, IL, pp 28–39Google Scholar
  42. Holland JB (2007) Genetic architecture of complex traits in plants. Curr Opin Plant Biol 10:156–161PubMedCrossRefGoogle Scholar
  43. Hyten DL, Choi I-Y, Song Q, Shoemaker RC, Nelson RL, Costa JM, Specht JE, Cregan PB (2007) Highly variable patterns of linkage disequilibrium in multiple soybean populations. Genetics 175:1937–1944PubMedCrossRefGoogle Scholar
  44. Ihaka R, Gentleman R (1996) A language for data analysis and graphics. J Comput Graph Stat 5:299–314CrossRefGoogle Scholar
  45. Ishii T, Yonezawa K (2007) Optimization of the marker-based procedures for pyramiding genes from multiple donor lines: II. Strategies for selecting the objective homozygous plant. Crop Sci 47:1878–1886CrossRefGoogle Scholar
  46. Karlin KS, Liberman UL (1978) Classifications and comparisons of multilocus recombination distributions. Proc Natl Acad Sci USA 75:332–336CrossRefGoogle Scholar
  47. Kearsey MJ, Farquhar AGL (1998) QTL analysis in plants; where are we now? Heredity 80:137–142PubMedCrossRefGoogle Scholar
  48. Kharkwal MC, Roy D (2004) Plant Breeding—Mendelian to molecular approaches: 2. A century of advances in plant breeding methodologies. Narosa Publishing House, New DelhiGoogle Scholar
  49. Kuchel H, Ye G, Fox R, Jefferies S (2005) Genetic and economic analysis of a targeted marker-assisted wheat breeding strategy. Mol Breed 16:67–78CrossRefGoogle Scholar
  50. Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756PubMedGoogle Scholar
  51. Laurie CC, Chasalow SD, LeDeaux JR, McCarroll R, Bush D, Hauge B, Lai C, Clark D, Rocheford TR, Dudley JW (2004) The genetic architecture of response to long-term artificial selection for oil concentration in the maize kernel. Genetics 168:2141–2155PubMedCrossRefGoogle Scholar
  52. Lawrence CJ, Schaeffer ML, Seigfried TE, Campbell DA, Harper LC (2007) MaizeGDB’s new data types, resources and activities. Nucleic Acids Res 35:895–900CrossRefGoogle Scholar
  53. Longin C, Utz H, Reif JC, Schipprack W, Melchinger AE (2006) Hybrid maize breeding with doubled haploids: I. One-stage versus two-stage selection for testcross performance. Theor Appl Genet 112:903–912PubMedCrossRefGoogle Scholar
  54. Luan T, Woolliams JA, Lien S, Kent M, Svendsen M, Meuwissen THE (2009) The accuracy of genomic selection in Norwegian red cattle assessed by cross validation. Genetics 183:1119–1126PubMedCrossRefGoogle Scholar
  55. Mackay TFC (2001) The genetic architecture of quantitative traits. Annu Rev Genet 35:303–339PubMedCrossRefGoogle Scholar
  56. Malysheva-Otto LV, Ganal MW, Roder MS (2006) Analysis of molecular diversity, population structure and linkage disequilibrium in a worldwide survey of cultivated barley germplasm (Hordeum vulgare L.). BMC Genet 7:6PubMedCrossRefGoogle Scholar
  57. Maurer HP, Melchinger AE, Frisch M (2007) An incomplete enumeration algorithm for an exact test of Hardy–Weinberg proportions with multiple alleles. Theor Appl Genet 115:193–398CrossRefGoogle Scholar
  58. Maurer HP, Melchinger AE, Frisch M (2008) Population genetic simulation and data analysis with Plabsoft. Euphytica 161:133–139CrossRefGoogle Scholar
  59. Melchinger AE, Schmidt W, Geiger HH (1988) Comparison of testcrosses produced from F2 and first backcross populations in maize. Crop Sci 28:743–749CrossRefGoogle Scholar
  60. Metz G (1994) Probability of net gain of favorable alleles for improving an elite single cross. Crop Sci 34:668–672CrossRefGoogle Scholar
  61. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedGoogle Scholar
  62. Mihaljevic R, Utz HF, Melchinger AE (2005) No evidence for epistasis in hybrid and per se performance of elite European flint maize inbreds from generation means and QTL analyses. Crop Sci 45:2605–2613CrossRefGoogle Scholar
  63. Moose SP, Mumm RH (2008) Molecular plant breeding as the foundation for 21st century crop improvement. Plant Physiol 147:969–977PubMedCrossRefGoogle Scholar
  64. Mumm RH (2007) Backcross versus forward breeding in the development of transgenic maize hybrids: theory and practice. Crop Sci 47(Suppl 3):S164–S171Google Scholar
  65. Nei M (1973) Analysis of gene diversity in subdivided populations. Proc Natl Acad Sci USA 70:3321–3323PubMedCrossRefGoogle Scholar
  66. Nei M, Li WH (1979) Mathematical models for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci USA 76:5268–5371CrossRefGoogle Scholar
  67. Panter DM, Allen FL (1995) Using best linear unbiased predictions to enhance breeding for yield in soybean: I. Choosing parents. Crop Sci 35:397–405CrossRefGoogle Scholar
  68. Piepho HP (2009) Ridge regression and extensions for genomewide selection in maize. Crop Sci 49:1165–1176CrossRefGoogle Scholar
  69. Podlich DW, Cooper M (1998) QU-GENE: a simulation platform for quantitative analysis of genetic models. Bioinformatics 14:632–653PubMedCrossRefGoogle Scholar
  70. Prigge V, Maurer HP, Mackill DJ, Melchinger AE, Frisch M (2008) Comparison of the observed with the simulated distributions of the parental genome contribution in two marker-assisted backcross programs in rice. Theor Appl Genet 116:739–744PubMedCrossRefGoogle Scholar
  71. Remington DL, Thornsberry JM, Matsuoka Y, Wilson LM, Whitt SR, Doebley J, Kresovich S, Goodman MM, Buckler ES (2001) Structure of linkage disequilibrium and phenotypic associations in the maize genome. Proc Natl Acad Sci USA 98:11479–11484PubMedCrossRefGoogle Scholar
  72. Ribaut J-M, Jiang C, Hoisington D (2002) Simulation experiments on efficiencies of gene introgression by backcrossing. Crop Sci 42:557–565CrossRefGoogle Scholar
  73. Santiago E, Caballero A (1995) Effective size of populations under selection. Genetics 139:1013–1030PubMedGoogle Scholar
  74. Schon C, Utz H, Groh S, Truberg B, Openshaw S, Melchinger A (2004) Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits. Genetics 167:485PubMedCrossRefGoogle Scholar
  75. Senior ML, Chin ECL, Lee M, Smith JSC, Stuber CW (1996) Simple sequence repeat markers developed from maize sequences found in the GENBANK database: map construction. Crop Sci 36:1676–1683CrossRefGoogle Scholar
  76. Tinker NA, Mather DE (1993) GREGOR: software for genetic simulation. J Hered 84:237Google Scholar
  77. Wang J, van Ginkel M, Podlich D, Ye G, Trethowan R, Pfeiffer W, DeLacy IH, Cooper M, Rajaram S (2003) Comparison of two breeding strategies by computer simulation. Crop Sci 43:1764–1773CrossRefGoogle Scholar
  78. Wang J, van Ginkel M, Trethowan R, Ye G, DeLacy I, Podlich D, Cooper M (2004) Simulating the effects of dominance and epistasis on selection response in the CIMMYT wheat breeding program using QuCim. Crop Sci 44:2006–2018CrossRefGoogle Scholar
  79. Wang J, Eagles HA, Trethowan R, Van Ginkel M (2005) Using computer simulation of the selection process and known gene information to assist in parental selection in wheat quality breeding. Aust J Agric Res 56:465–473CrossRefGoogle Scholar
  80. Wang J, Chapman SC, Bonnett DG, Rebetzke GJ, Crouch J (2007) Application of population genetic theory and simulation models to efficiently pyramid multiple genes via marker-assisted selection. Crop Sci 47:582–590CrossRefGoogle Scholar
  81. Wong CK, Bernardo R (2008) Genomewide selection in oil palm: increasing selection gain per unit time and cost with small populations. Theor Appl Genet 116:815–824PubMedCrossRefGoogle Scholar
  82. Wright S (1965) The interpretation of population structure by F-Statistics with special regard to systems of mating. Evolution 19:395–420CrossRefGoogle Scholar
  83. Wright S (1978) Evolution and the genetics of populations, vol 4: variability within and among natural populations. University of Chicago Press, ChicagoGoogle Scholar
  84. Xu Y (2010) Molecular plant breeding. CABI, CambridgeCrossRefGoogle Scholar
  85. Xu Y, Crouch JH (2008) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48:391–407CrossRefGoogle Scholar
  86. Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551PubMedCrossRefGoogle Scholar
  87. Zhong S, Jannink JL (2007) Using quantitative trait loci results to discriminate among crosses on the basis of their progeny mean and variance. Genetics 177:567–576PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Crop Sciences, Illinois Plant Breeding CenterUniversity of Illinois at Urbana-ChampaignUrbanaUSA

Personalised recommendations