Podlich D W, Cooper M
School of Land and Food, The University of Queensland, Brisbane, Queensland 4072, Australia.
Bioinformatics. 1998;14(7):632-53. doi: 10.1093/bioinformatics/14.7.632.
Classical quantitative genetics theory makes a number of simplifying assumptions in order to develop mathematical expressions that describe the mean and variation (genetic and phenotypic) within and among populations, and to predict how these are expected to change under the influence of external forces. These assumptions are often necessary to render the development of many aspects of the theory mathematically tractable. The availability of high-speed computers today provides opportunity for the use of computer simulation methodology to investigate the implications of relaxing many of the assumptions that are commonly made.
QU-GENE (QUantitative-GENEtics) was developed as a flexible computer simulation platform for the quantitative analysis of genetic models. Three features of the QU-GENE software that contribute to its flexibility are (i) the core E(N:K) genetic model, where E is the number of types of environment, N is the number of genes, K indicates the level of epistasis and the parentheses indicate that different N:K genetic models can be nested within types of environments, (ii) the use of a two-stage architecture that separates the definition of the genetic model and genotype-environment system from the detail of the individual simulation experiments and (iii) the use of a series of interactive graphical windows that monitor the progress of the simulation experiments. The E(N:K) framework enables the generation of families of genetic models that incorporate the effects of genotype-by-environment (G x E) interactions and epistasis. By the design of appropriate application modules, many different simulation experiments can be conducted for any genotype-environment system. The structure of the QU-GENE simulation software is explained and demonstrated by way of two examples. The first concentrates on some aspects of the influence of G x E interactions on response to selection in plant breeding, and the second considers the influence of multiple-peak epistasis on the evolution of a four-gene epistatic network.
QU-GENE is available over the Internet at (http://pig.ag.uq.edu.au/qu-gene/)
经典数量遗传学理论做出了一些简化假设，以便推导出描述种群内部和种群之间的均值与变异（遗传和表型）的数学表达式，并预测在外力影响下这些均值与变异预期将如何变化。这些假设对于使该理论许多方面的发展在数学上易于处理通常是必要的。如今高速计算机的可用性为使用计算机模拟方法来研究放宽许多常见假设的影响提供了机会。
QU - GENE（数量遗传学）被开发为一个用于遗传模型定量分析的灵活计算机模拟平台。QU - GENE软件有助于其灵活性的三个特性是：（i）核心E(N:K)遗传模型，其中E是环境类型的数量，N是基因数量，K表示上位性水平，括号表示不同的N:K遗传模型可以嵌套在环境类型中；（ii）使用两阶段架构，将遗传模型和基因型 - 环境系统的定义与单个模拟实验的细节分开；（iii）使用一系列交互式图形窗口来监控模拟实验的进展。E(N:K)框架能够生成包含基因型×环境（G×E）互作和上位性效应的遗传模型家族。通过设计适当的应用模块，可以针对任何基因型 - 环境系统进行许多不同的模拟实验。通过两个例子解释并演示了QU - GENE模拟软件的结构。第一个例子专注于G×E互作对植物育种中选择响应的影响的某些方面，第二个例子考虑多峰上位性对四基因上位性网络进化的影响。
可通过互联网在（http://pig.ag.uq.edu.au/qu - gene/）获取QU - GENE。
