Computer Simulation in Plant Breeding Introduction Outline

Report
Computer Simulation in Plant Breeding
Xin Li1, Chengsong Zhu1, Jiankang Wang2, and Jianming Yu1
1Department
of Agronomy, Kansas State University, Manhattan, KS, USA
2Institute of Crop Science and CIMMYT China, Chinese Academy of Agricultural Sciences, Beijing, China
Introduction
Application I: Breeding Method
• 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 costeffectiveness.
• 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.
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.
b
F1
a
F2
F1
:::
F2
........................
F3
........................
F4
........................
4.2
4.2
3.8
3.8
20 QTL MARS
20 QTL GS
100 QTL MARS
100 QTL GS
3.4
F5
128
128
256
256
markers
markers
markers
markers
48
96
48
96
3.4
F7
F8
3.0
Phenotype value
Computer simulation
Plant breeding
3.0
32
64
128
256
512
768
288
Number of markers used
in two selection cycles
c
576
1152
Number of plants used
in genomic selection
90
Fig. 3 An Example of Computer Simulation in Breeding Method Research
Genotype value
value
Genotype
(%(%of of
maximum)
max)
QU-GENE
engine
Trait value
APSIM
Soil
Water
CO2
Nitrogen
Radiation
Temperature
Fig. 5 An Example of Computer Simulation in Crop Modeling Research
Application IV: Crop Modeling
DHs
DHs
DHs
DHs
F6
80
Comparison of Marker Assisted Recurrent Selection (MARS) and Genomic Selection (GS)
2
H=0.1
H =0.1ES
ES
2
H=0.1
H =0.1SSD
SSD
2
H=0.5
H =0.5ES
ES
2
H=0.5
H =0.5SSD
SSD
60
Gene number
Gene effect
Epistasis
Environment
GEI
Yield value
P1×P2
P2
70
Population
Linking QU-GENE with APSIM (Agricultural Production Systems sIMulator)
b
Response
Density
P1
 Vanoeveren, A. J., & Stam, P. 1992. Heredity, 69, 342-351.
 Tanksley, S. D., & Nelson, J. C. 1996. Theoretical and Applied Genetics, 92, 191203.
 Meuwissen, T. H. E. et al. 2001. Genetics, 157, 1819-1829.
 Bernardo, R., & Yu, J. 2007. Crop Science, 47, 1082-1090.
Response
a
Selection in MET
Application II: Gene Mapping
• 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.
 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.
50
200
300
400
plants
Number of FF22 plants
Comparative simulation of ES and SSD
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)
Outline
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.
• 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.





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.
a
b
100
Estimated-simulated additive effects
100
2
10 QTL h =0.3
2
10 QTL h =0.95
2
40 QTL h =0.3
2
40 QTL h =0.95
80
Power (%)
0
60
40
Breeding Method
•
•
Compare breeding
strategy
Assess factors influencing
marker assisted selection
20
0
100
500
Size of mapping population
Gene Mapping
Crop Modeling
• Assess factors influencing
mapping power
• Determine significant
threshold and confidence
interval of QTL position
Computer
Simulation in
Plant Breeding
•
•
Use gene information as
model parameters
Predict crop
performance in target
environments
Genetic Modeling
•
Combine genetic and
gene by environment
interaction to simulate
the whole plant breeding
process
Fig. 2 Various Applications of Computer Simulation in Plant Breeding
1000
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.
2
10 QTL h =0.3
2
10 QTL h =0.95
2
40 QTL h =0.3
2
40 QTL h =0.95
3
2
1
0
100
500
1000
Size of mapping population
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
Application III: Genetic Modeling
Plant breeding simulation platforms are potent tools which can simulate
the whole plant breeding process. They use genetic and geneenvironment interaction information to assist in decision making, e.g.
predicting cross performance and comparing selection methods.
 Podlich, D. W., & Cooper, M. 1998. Bioinformatics, 14, 632-653.
 Wang, J. K. et al. 2004. Crop Science, 44, 2006-2018.
Key References
 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.
Acknowledgements
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.

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