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).

Simulation flow

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