Gene-to-phenotype models and complex trait genetics

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 x 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 x environment interactions, and pleiotropy. Some promising G --> P modelling directions are discussed.