Simulation Theory
Interaction between genes and environments
Looking for best breeding programs
- In different environments, the relation between genotype and phenotype is changed.
- Hence, constructing a breeding program to achieve the best output is time-consuming and expensive via field trial experiments.
Quantitative genetic model
Factorial model
P = G + E + (GE) + R
P = phenotypic value
G = genotypic value
E = environmental effect
(GE) = genotype-by-environment effect
R = environment noise effect
The equation represents the standard quantitative genetic model.
Nested model
P = E + (GE) + R
This model is known as GE model. The GE term contains both the G and GE effects.
E(N:K) model
P = E (NK) + R
(NK) = each of E environments is the complete specification of all allelic effects of a set of N possibly interacting genes distributed across chromosomes using a recombination map.
QU-GENE is a software platform for stochastically simulating plant breeding programs and uses E(N:K) models to simulate genotype-by-environment interactions, where E is the number of environments, N is the number of genes, and K is the parameter for gene epistatic network (Podlich and Cooper, 1998). The E(N:K) models are a generalization of NK models (Kauffman, 1993). This capability of QU-GENE to simulate genotype-by-environment interaction provides an advantage over other similar plant breeding simulation programs (e.g. Faux et al. 2016; Lin et al., 2016).
QU-GENE structure
Consist of two components:
- QU-GENE engine to generate simulation inputs.
- Breeding modules used to conduct simulations of breeding strategies.
Four breeding modules:
- QuLine for self-pollinating species (Wang and Dieters, 2008a).
- QuLinePlus, an extension of QuLine, for cross pollinated species (Hoyos-Villegas et al, 2018).
- QuHybrid for hybrid development (Wang and Dieters, 2008b).
- QuMARS for marker-assisted recurrent selection (Li and Wang, 2011).