Integrating Evolutionary Game Theory into Mechanistic Genotype-Phenotype Mapping

Xuli Zhu, Libo Jiang, Meixia Ye, Lidan Sun, Claudia Gragnoli, Rongling Wu

Research output: Contribution to journalReview articlepeer-review

12 Scopus citations

Abstract

Natural selection has shaped the evolution of organisms toward optimizing their structural and functional design. However, how this universal principle can enhance genotype-phenotype mapping of quantitative traits has remained unexplored. Here we show that the integration of this principle and functional mapping through evolutionary game theory gains new insight into the genetic architecture of complex traits. By viewing phenotype formation as an evolutionary system, we formulate mathematical equations to model the ecological mechanisms that drive the interaction and coordination of its constituent components toward population dynamics and stability. Functional mapping provides a procedure for estimating the genetic parameters that specify the dynamic relationship of competition and cooperation and predicting how genes mediate the evolution of this relationship during trait formation. The past two decades have been fertile for the development and application of genetic mapping and genome-wide association studies, driven by advances in the quality and cost of high-throughput genotyping and sequencing.Traditional mapping strategies that simply associate molecular markers with phenotypic values can be best utilized when the phenotype of one individual is independent of those of other members of the same mapping population.Game theory can be used to model the effect of individual-individual interactions on phenotypic variation and further identify quantitative trait loci that modulate these ecological interactions.The integration of game theory and functional mapping can not only improve the precision and efficiency of complex trait mapping through the mechanistic modeling of phenotype formation but can also provide an emergent platform for biological and biomedical research.

Original languageEnglish (US)
Pages (from-to)256-268
Number of pages13
JournalTrends in Genetics
Volume32
Issue number5
DOIs
StatePublished - May 1 2016

All Science Journal Classification (ASJC) codes

  • Genetics

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