Research Starter Grant: Contribution of Indirect Genetic Effects to Genetic Architecture and Evolution of Complex Phenotypes

University Of Tennessee Knoxville, Knoxville TN

Investigators

Abstract

This project investigates the genetics and evolutionary potential of traits that are influenced by the environment provided by conspecifics. These social environments (e.g., the competitive environment) are particularly important because, since they originate from features of individuals, they can have a genetic basis. Theoretical models have demonstrated that these genetically based environmental influences, or indirect genetic effects (IGEs), can have important impacts on genetic architecture (GA) and evolution, but empirical studies of IGEs are lacking (with the exception of studies of IGEs arising from the influence of parents on their offspring). This project will provide valuable insights into the role of IGEs in GA, contributing to a better understanding of the genetic basis of complex phenotypes. To analyze the contribution of IGEs to variation in trait expression the proposed research will use empirical methods developed by the PI. This approach applies a theoretical model of trait expression to interpret components of trait variation in a population of interacting individuals. Using this approach it is possible to separate direct and indirect effects of genes, which are often confounded or, in the case of IGEs, hidden to traditional methods. The theoretical models can also be used to analyze the dynamics of phenotypic evolution based on observed GA, generating predictions that can be tested by artificial selection experiments or can be used to predict response to selection for improvement in agricultural systems. These methods will be used to analyze the contribution of IGEs to the development of complex traits (e.g., seed number and total biomass) in a rapid-cycling variety of Brassica rapa. Data on the quantitative genetics of these traits will provide the foundation for a future proposal that will examine experimental evolution in the presence of IGEs and analyze molecular mapping of IGE loci to trait expression. In this future project phenotypically divergent lines will be derived by artificial selection to test model predictions. These divergent lines will be inbred and used to examine the genotype-to-phenotype map and evolutionary dynamics of IGE loci. Thus, a major goal of this starter grant is to provide the critical data needed to develop a much larger and more comprehensive proposal in the future. This will contribute significantly to the development of the career of the PI as a research scientist and will help establish the PIs new research lab. The ultimate goal of this research program, to examine the molecular mapping of IGE loci, builds on the work from Dr. Wolf's NSF Postdoctoral Fellowship in Biological Informatics, where methods to analyze the genotype to phenotype mapping of maternal effect loci were developed. Molecular analysis of GA is particularly complex when IGEs are present because the phenotype becomes the property of, and thus can map to, the genotypes of multiple individuals. This novel aspect of GA has been poorly explored but previous work by the PI demonstrates that it can play an important role in mapping from genotype to phenotype for some types of traits. To achieve this goal the previous advances made in theoretical and empirical methods will be combined with the development of new theoretical models to ultimately understand all of the pathways through which genotypes make phenotypes.