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Originally published as Genetics Published Articles Ahead of Print on October 8, 2006.

Genetics, Vol. 175, 411-420, January 2007, Copyright © 2007
doi:10.1534/genetics.106.058859

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Statistical Epistasis Is a Generic Feature of Gene Regulatory Networks

Arne B. Gjuvsland*,1, Ben J. Hayes{dagger}, Stig W. Omholt* and Örjan Carlborg{ddagger}

* Centre for Integrative Genetics (CIGENE) and Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, N-1432 Aas, Norway, {dagger} Animal Genetics and Genomics, Department of Primary Industries, Attwood, Victoria, Australia 3049 and {ddagger} Linnaeus Centre for Bioinformatics, Uppsala University, SE-751 24 Uppsala, Sweden

1 Corresponding author: Centre for Integrative Genetics (CIGENE), Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Aas, Norway.
E-mail: arne.gjuvsland{at}cigene.no

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.




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