1. Computer Simulation in Plant Breeding Introduction Outline Application I: Breeding Method Application II: Gene Mapping Application III: Genetic Modeling Application IV: Crop Modeling Key References Acknowledgements Xin Li 1, Chengsong Zhu 1, Jiankang Wang 2, and Jianming Yu 1 1 Department of Agronomy, Kansas State University, Manhattan, KS, USA 2 Institute of Crop Science and CIMMYT China, Chinese Academy of Agricultural Sciences, Beijing, China As a bridge between theory and experimentation, computer simulation has become a powerful tool in scientific research. It provides not only preliminary validation of theories, but also guidelines for empirical experiments. Plant breeding is to develop superior genotypes with available genetic and non-genetic resources, during which selecting the best breeding strategy would maximize genetic gain and achieve cost- effectiveness. Computer simulation can establish the breeding process in silico and identify candidates of the optimum combination of various factors, which can then be validated empirically. Insights gained from empirical studies, in turn, can be further incorporated into computer simulations. Fig. 1 Joining Computer Simulation with Plant Breeding For example, computer simulation can be used to compare two breeding methods, Early Selection (ES) and Single Seed Descent (SSD) In this review, we discussed the application of computer simulation in different aspects of plant breeding. First, we briefly summarized the history of plant breeding and computer simulation, and how computer simulation can be used to facilitate the breeding process. Next, we partitioned the utility of computer simulation into different research areas of plant breeding, including breeding method comparison, gene mapping, genetic modeling, and crop modeling. Then we discussed computational issues involved in the simulation process. Finally, the application of computer simulation in the future was discussed. This work was supported by the Plant Feedstock Genomics Program of USDA and DOE, the Plant Genome Program of NSF, the Targeted Excellence Program of Kansas State University, and the Great Plains Sorghum Improvement and Utilization Center. Computer simulation can integrate crop physiological models, environmental information, and genetic compositions of different crops to fill the gap between genotype and phenotype. We can use computer simulation to predict the performance of different cultivars in the target population of environments, thus facilitate the plant breeding process. When coupled with climate simulation models, crop models can be used to predict the possible influences from climate change on crop production, which can subsequently provide guidelines for plant breeding. Computer simulation can be employed to compare different breeding strategies, incorporating various factors simultaneously, such as gene information, cross scheme, propagation method, population size, selection intensity, and number of generations. Thus, we can use computer simulation to decide which breeding strategy could lead to higher selection gain. Computer simulation can be applied to gene mapping study to validate the effectiveness of new mapping methods or assess the factors influencing mapping power, such as population type and size, marker number and density, heritability, and number of QTL. Computer simulation can also help us determine the significant threshold (LOD score) and confidence interval, which otherwise are difficult to obtain. Plant breeding simulation platforms are potent tools which can simulate the whole plant breeding process. They use genetic and gene- environment interaction information to assist in decision making, e.g. predicting cross performance and comparing selection methods. Fig. 2 Various Applications of Computer Simulation in Plant Breeding Fig. 3 An Example of Computer Simulation in Breeding Method Research Comparison of Marker Assisted Recurrent Selection (MARS) and Genomic Selection (GS) Fig. 4 An Example of Computer Simulation in Gene Mapping Research Effects of heritability and sample size on the power, precision and accuracy of QTL study Fig. 5 An Example of Computer Simulation in Crop Modeling Research Linking QU-GENE with APSIM (Agricultural Production Systems sIMulator) Perspectives Research in establishing genotype-phenotype relationship, and developing new breeding methods, have been proposed as key factors to realize the potential brought by ultrahigh throughput genomic technologies in plant breeding, and computer simulation, undoubtedly, will play a key role in this process. As a tool to aid decision making and resource allocation, computer simulation would undertake the responsibility of transferring the experimental outcome from laboratory to realistic agriculture production, predicting the outcome of breeding decision, directing gene mapping, and tackling genotype by environment interaction and climate change.  Vanoeveren, A. J., & Stam, P. 1992. Heredity, 69, 342-351.  Tanksley, S. D., & Nelson, J. C. 1996. Theoretical and Applied Genetics, 92, 191- 203.  Meuwissen, T. H. E. et al. 2001. Genetics, 157, 1819-1829.  Bernardo, R., & Yu, J. 2007. Crop Science, 47, 1082-1090.  Lander, E. S., & Botstein, D. 1989. Genetics, 121, 185-199.  Churchill, G. A., & Doerge, R. W. 1994. Genetics, 138, 963-971.  Zeng, Z. B. 1994. Genetics, 136, 1457-1468.  Beavis, W. D. 1998. Molecular Dissection of Complex Traits, 145-162.  Yu, J. et al. 2006. Nature Genetics, 38, 203-208.  Podlich, D. W., & Cooper, M. 1998. Bioinformatics, 14, 632-653.  Wang, J. K. et al. 2004. Crop Science, 44, 2006-2018.  Chapman, S. et al. 2003. Agronomy Journal, 95, 99-113.  Yin, X. Y. et al. 2005. Journal of Experimental Botany, 56, 967-976.  Hodson, D., & White, J. 2010. Climate Change and Crop Production, 245-262.  Allard, R. W. 1960. Principles of plant breeding.  Falconer, D. S., & Mackay, T. F. C. 1996. Introduction to quantitative genetics.  Hartl, D. L., & Clark, A. G. 1997. Principles of population genetics.  Lynch, M., & Walsh, B. 1997. Genetics and analysis of quantitative traits.  Mackay, T. F. C. 2001. The genetic architecture of quantitative traits. Annual Review of Genetics, 35, 303-339.  Bernardo, R. 2002. Breeding for quantitative traits in plants.  Doerge, R. W. 2002. Mapping and analysis of quantitative trait loci in experimental populations. Nature Reviews Genetics, 3, 43-52.  Holland, J. B. 2007. Genetic architecture of complex traits in plants. Current Opinion in Plant Biology, 10, 156-161.