Document Detail


The GP problem: quantifying gene-to-phenotype relationships.
MedLine Citation:
PMID:  12066839     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In this paper we refer to the gene-to-phenotype modeling challenge as the GP problem. Integrating information across levels of organization within a genotype-environment system is a major challenge in computational biology. However, resolving the GP problem is a fundamental requirement if we are to understand and predict phenotypes given knowledge of the genome and model dynamic properties of biological systems. Organisms are consequences of this integration, and it is a major property of biological systems that underlies the responses we observe. We discuss the E(NK) model as a framework for investigation of the GP problem and the prediction of system properties at different levels of organization. We apply this quantitative framework to an investigation of the processes involved in genetic improvement of plants for agriculture. In our analysis, N genes determine the genetic variation for a set of traits that are responsible for plant adaptation to E environment-types within a target population of environments. The N genes can interact in epistatic NK gene-networks through the way that they influence plant growth and development processes within a dynamic crop growth model. We use a sorghum crop growth model, available within the APSIM agricultural production systems simulation model, to integrate the gene-environment interactions that occur during growth and development and to predict genotype-to-phenotype relationships for a given E(NK) model. Directional selection is then applied to the population of genotypes, based on their predicted phenotypes, to simulate the dynamic aspects of genetic improvement by a plant-breeding program. The outcomes of the simulated breeding are evaluated across cycles of selection in terms of the changes in allele frequencies for the N genes and the genotypic and phenotypic values of the populations of genotypes.
Authors:
Mark Cooper; Scott C Chapman; Dean W Podlich; Graeme L Hammer
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  In silico biology     Volume:  2     ISSN:  1386-6338     ISO Abbreviation:  In Silico Biol. (Gedrukt)     Publication Date:  2002  
Date Detail:
Created Date:  2002-06-17     Completed Date:  2003-02-27     Revised Date:  2009-11-19    
Medline Journal Info:
Nlm Unique ID:  9815902     Medline TA:  In Silico Biol     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  151-64     Citation Subset:  IM    
Affiliation:
School of Land and Food Sciences, The University of Queensland, Brisbane, Australia. mark.cooper@pioneer.com
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MeSH Terms
Descriptor/Qualifier:
Computer Simulation
Crops, Agricultural / genetics
Gene Frequency
Genes, Plant
Genome
Genotype*
Phenotype*
Poaceae / genetics,  physiology
Selection, Genetic

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