dc.contributor.author	Le Rouzic, Arnaud	en
dc.contributor.author	Siegel, Paul B.	en
dc.contributor.author	Carlborg, Örjan	en
dc.contributor.department	Animal and Poultry Sciences	en
dc.date.accessioned	2012-08-24T12:08:19Z	en
dc.date.available	2012-08-24T12:08:19Z	en
dc.date.issued	2007-11-14	en
dc.date.updated	2012-08-24T12:08:20Z	en
dc.description.abstract	Background The genetic architecture of a quantitative trait influences the phenotypic response to natural or artificial selection. One of the main objectives of genetic mapping studies is to identify the genetic factors underlying complex traits and understand how they contribute to phenotypic expression. Presently, we are good at identifying and locating individual loci with large effects, but there is a void in describing more complex genetic architectures. Although large networks of connected genes have been reported, there is an almost complete lack of information on how polymorphisms in these networks contribute to phenotypic variation and change. To date, most of our understanding comes from theoretical, model-based studies, and it remains difficult to assess how realistic their conclusions are as they lack empirical support. Results A previous study provided evidence that nearly half of the difference in eight-week body weight between two divergently selected lines of chickens was a result of four loci organized in a 'radial' network (one central locus interacting with three 'radial' loci that, in turn, only interacted with the central locus). Here, we study the relationship between phenotypic change and genetic polymorphism in this empirically detected network. We use a model-free approach to study, through individual-based simulations, the dynamic properties of this polymorphic and epistatic genetic architecture. The study provides new insights to how epistasis can modify the selection response, buffer and reveal effects of major loci leading to a progressive release of genetic variation. We also illustrate the difficulty of predicting genetic architecture from observed selection response, and discuss mechanisms that might lead to misleading conclusions on underlying genetic architectures from quantitative trait locus (QTL) experiments in selected populations. Conclusion Considering both molecular (QTL) and phenotypic (selection response) data, as suggested in this work, provides additional insights into the genetic mechanisms involved in the response to selection. Such dissection of genetic architectures and in-depth studies of their ability to contribute to short- or long-term selection response represents an important step towards a better understanding of the genetic bases of complex traits and, consequently, of the evolutionary properties of populations.	en
dc.description.version	Published version	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	BMC Biology. 2007 Nov 14;5(1):50	en
dc.identifier.doi	https://doi.org/10.1186/1741-7007-5-50	en
dc.identifier.uri	http://hdl.handle.net/10919/18913	en
dc.language.iso	en	en
dc.rights	Creative Commons Attribution 4.0 International	en
dc.rights.holder	Arnaud Le Rouzic et al.; licensee BioMed Central Ltd.	en
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	en
dc.title	Phenotypic evolution from genetic polymorphisms in a radial network architecture	en
dc.title.serial	BMC Biology	en
dc.type	Article - Refereed	en
dc.type.dcmitype	Text	en

Phenotypic evolution from genetic polymorphisms in a radial network architecture

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