QU-GENE engine

APSIM

Yield value

Gene numberGene effectEpistasisEnvironmentGEI

Population

Selection in MET

Trait value

SoilWater CO2NitrogenRadiationTemperature

Computer Simulation in Plant Breeding

Introduction

a b

288 576 1152

3.0

3.4

3.8

4.2

Number of plants used in genomic selection

Re

spo

nse

128 markers 48 DHs128 markers 96 DHs256 markers 48 DHs256 markers 96 DHs

32 64 128 256 512 768

3.0

3.4

3.8

4.2

Number of markers used in two selection cycles

Re

spo

nse

20 QTL MARS20 QTL GS100 QTL MARS100 QTL GS

100 500 1000

0

1

2

3

Size of mapping population

Est

ima

ted

-sim

ula

ted

ad

diti

ve e

ffect

s

10 QTL h2=0.3

10 QTL h2=0.95

40 QTL h2=0.3

40 QTL h2=0.95

100 500 1000

0

20

40

60

80

100

Size of mapping population

Po

we

r (%

)

10 QTL h2=0.3

10 QTL h2=0.95

40 QTL h2=0.3

40 QTL h2=0.95

a b

Outline

Application I: Breeding Method

Application II: Gene Mapping

Application III: Genetic Modeling

Application IV: Crop Modeling

Key References

Acknowledgements

Xin Li1, Chengsong Zhu1, Jiankang Wang2, and Jianming Yu1

1Department of Agronomy, Kansas State University, Manhattan, KS, USA2Institute 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 ResearchEffects 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-

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Computer Simulation in

Plant Breeding

Breeding Method• Compare breeding

strategy• Assess factors influencing

marker assisted selection

Genetic Modeling• Combine genetic and

gene by environment interaction to simulate the whole plant breeding process

Crop Modeling• Use gene information as

model parameters• Predict crop

performance in target environments

Gene Mapping• Assess factors influencing

mapping power• Determine significant

threshold and confidence interval of QTL position

0 100 200 300 400

50

60

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Number of F2 plants

Ge

no

typ

e v

alu

e

(% o

f m

axim

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)

H2=0.1 ESH

2=0.1 SSD

H2=0.5 ES

H2=0.5 SSD

: : :

. . . . . . . . . . . . . . . . . . . . . . . .

P1 F1 P2

F2

P1×P2

F2

F3

F4

F5

F6

F7

F8

Plant breedingComputer simulation

Comparative simulation of ES and SSD

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. . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . .

F1

Phenotype value

Gen

otyp

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lue

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f max

)

c

Number of F2 plants

Den

sity

H=0.1 ESH=0.1 SSDH=0.5 ESH=0.5 SSD