Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality
RESEARCH ARTICLE

Gene-to-phenotype models and complex trait genetics

Mark Cooper A B , Dean W. Podlich A and Oscar S. Smith A
+ Author Affiliations
- Author Affiliations

A Pioneer Hi-Bred International Inc., 7250 N.W. 62nd Avenue, PO Box 552, Johnston, IA 50131, USA.

B Corresponding author. Email: mark.cooper@pioneer.com

Australian Journal of Agricultural Research 56(9) 895-918 https://doi.org/10.1071/AR05154
Submitted: 9 May 2005  Accepted: 20 June 2005   Published: 28 September 2005

Abstract

The premise that is explored in this paper is that in some cases, in order to make progress in the design of molecular breeding strategies for complex traits, we will need a theoretical framework for quantitative genetics that is grounded in the concept of gene-networks. We seek to develop a gene-to-phenotype (G→P) modelling framework for quantitative genetics that explicitly deals with the context-dependent gene effects that are attributed to genes functioning within networks, i.e. epistasis, gene × environment interactions, and pleiotropy. The E(NK) model is discussed as a starting point for building such a theoretical framework for complex trait genetics. Applying this framework to a combination of theoretical and empirical G→P models, we find that although many of the context-dependent effects of genetic variation on phenotypic variation can reduce the rate of genetic progress from breeding, it is possible to design molecular breeding strategies for complex traits that on average will outperform phenotypic selection. However, to realise these potential advantages, empirical G→P models of the traits will need to take into consideration the context-dependent effects that are a consequence of epistasis, gene × environment interactions, and pleiotropy. Some promising G→P modelling directions are discussed.

Additional keywords: gene-networks, interaction, prediction, validation, complexity.


Acknowledgments

An earlier version of this paper was published in the Proceedings of the 4th International Crop Science Congress, held in Brisbane, 26 September–1 October 2004. We thank the Congress organisers for permission to publish this updated manuscript. Our many colleagues at Pioneer have contributed much to the ideas reviewed in this paper. We thank Stu Kauffman for many stimulating and thought-provoking discussions over the years. Bill Muir provided many valuable comments and suggestions on early and advanced drafts of this manuscript. The innovative research atmosphere that exists at the Santa Fe Institute and the discussions we have had with other researchers via this highly connected intellectual network node have contributed to the ideas reviewed in this paper. We also benefited from the free-ranging discussions with Graeme Hammer, Scott Chapman, Stephen Welch, François Tardieu, Bruce Walsh, and Fred van Eeuwijk, held in Brisbane during the 4th International Crop Science Congress.


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