Nonlinear regulation enhances the phenotypic expression of trans-acting genetic polymorphisms

Gjuvsland, Arne B., Hayes, Ben J., Meuwissen, Theo H. E., Plahte, Erik and Omholt, Stig W. (2007) Nonlinear regulation enhances the phenotypic expression of trans-acting genetic polymorphisms. BMC Systems Biology, 1 . doi:10.1186/1752-0509-1-32


Author Gjuvsland, Arne B.
Hayes, Ben J.
Meuwissen, Theo H. E.
Plahte, Erik
Omholt, Stig W.
Title Nonlinear regulation enhances the phenotypic expression of trans-acting genetic polymorphisms
Formatted title
Nonlinear regulation enhances the phenotypic expression of trans-acting genetic polymorphisms
Journal name BMC Systems Biology   Check publisher's open access policy
ISSN 1752-0509
Publication date 2007-07-25
Sub-type Article (original research)
DOI 10.1186/1752-0509-1-32
Open Access Status DOI
Volume 1
Total pages 12
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted abstract
Background: Genetic variation explains a considerable part of observed phenotypic variation in gene expression networks. This variation has been shown to be located both locally (cis) and distally (trans) to the genes being measured. Here we explore to which degree the phenotypic manifestation of local and distant polymorphisms is a dynamic feature of regulatory design.

Results: By combining mathematical models of gene expression networks with genetic maps and linkage analysis we find that very different network structures and regulatory motifs give similar cis/trans linkage patterns. However, when the shape of the cis-regulatory input functions is more nonlinear or threshold-like, we observe for all networks a dramatic increase in the phenotypic expression of distant compared to local polymorphisms under otherwise equal conditions.

Conclusion: Our findings indicate that genetic variation affecting the form of cis-regulatory input functions may reshape the genotype-phenotype map by changing the relative importance of cis and trans variation. Our approach combining nonlinear dynamic models with statistical genetics opens up for a systematic investigation of how functional genetic variation is translated into phenotypic variation under various systemic conditions.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Alliance for Agriculture and Food Innovation
 
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