Statistical epistasis is a generic feature of gene regulatory networks

Gjuvsland, Arne B., Hayes, Ben J., Omholt, Stig W. and Carlborg, Orjan (2007) Statistical epistasis is a generic feature of gene regulatory networks. Genetics, 175 1: 411-420. doi:10.1534/genetics.106.058859


Author Gjuvsland, Arne B.
Hayes, Ben J.
Omholt, Stig W.
Carlborg, Orjan
Title Statistical epistasis is a generic feature of gene regulatory networks
Journal name Genetics   Check publisher's open access policy
ISSN 0016-6731
1943-2631
Publication date 2007-01
Sub-type Article (original research)
DOI 10.1534/genetics.106.058859
Open Access Status Not yet assessed
Volume 175
Issue 1
Start page 411
End page 420
Total pages 10
Place of publication Bethesda, MD, United States
Publisher Genetics Society of America
Language eng
Abstract Functional dependencies between genes are a defining characteristic of gene networks underlying quantitative traits. However, recent studies show that the proportion of the genetic variation that can be attributed to statistical epistasis varies from almost zero to very high. It is thus of fundamental as well as instrumental importance to better understand whether different functional dependency patterns among polymorphic genes give rise to distinct statistical interaction patterns or not. Here we address this issue by combining a quantitative genetic model approach with genotype-phenotype models capable of translating allelic variation and regulatory principles into phenotypic variation at the level of gene expression. We show that gene regulatory networks with and without feedback motifs can exhibit a wide range of possible statistical genetic architectures with regard to both type of effect explaining phenotypic variance and number of apparent loci underlying the observed phenotypic effect. Although all motifs are capable of harboring significant interactions, positive feedback gives rise to higher amounts and more types of statistical epistasis. The results also suggest that the inclusion of statistical interaction terms in genetic models will increase the chance to detect additional QTL as well as functional dependencies between genetic loci over a broad range of regulatory regimes. This article illustrates how statistical genetic methods can fruitfully be combined with nonlinear systems dynamics to elucidate biological issues beyond reach of each methodology in isolation. Copyright
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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