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Bioinformatics. 1998;14(7):632-53.

QU-GENE: a simulation platform for quantitative analysis of genetic models.

Author information

  • 1School of Land and Food, The University of Queensland, Brisbane, Queensland 4072, Australia.

Abstract

MOTIVATION:

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.

RESULTS:

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.

AVAILABILITY:

QU-GENE is available over the Internet at (http://pig.ag.uq.edu.au/qu-gene/)

CONTACT:

m.cooper@mailbox.uq.edu. au

PMID:
9730929
[PubMed - indexed for MEDLINE]
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