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

该项目研究受同种提供的环境影响的性状的遗传和进化潜力。 这些社会环境（例如，竞争环境）特别重要，因为它们源于个人特征，可能具有遗传基础。 理论模型已经表明，这些遗传为基础的环境影响，或间接遗传效应（IGEs），可以有重要的影响遗传结构（GA）和进化，但IGEs的实证研究是缺乏（与IGEs的研究所产生的父母对他们的后代的影响除外）。 该项目将为IGE在GA中的作用提供有价值的见解，有助于更好地理解复杂表型的遗传基础。 为了分析IGE对性状表达变异的贡献，拟议的研究将使用PI开发的实证方法。这种方法适用于性状表达的理论模型来解释在相互作用的个体群体的性状变异的组件。 使用这种方法，有可能将基因的直接和间接影响分开，这通常是混淆的，或者在IGE的情况下，隐藏在传统方法中。理论模型还可以用于分析基于观察到的GA的表型进化的动态，生成可以通过人工选择实验进行测试的预测，或者可以用于预测对选择的响应以改进农业系统。 这些方法将用于分析IGE对复杂性状发展的贡献（例如，种子数和总生物量）。这些性状的数量遗传学的数据将提供基础，为未来的建议，将检查实验进化IGEs的存在和分析IGE基因座的性状表达的分子图谱。 在这个未来的项目中，将通过人工选择来测试模型的预测。这些分歧线将近交和用于检查IGE基因座的基因型-表型图和进化动力学。因此，这一启动赠款的一个主要目标是提供必要的关键数据，以便在未来制定一个更大、更全面的提案。 这将大大有助于PI作为研究科学家的职业发展，并将有助于建立PI新的研究实验室。 这项研究计划的最终目标是研究IGE基因座的分子作图，建立在沃尔夫博士的NSF生物信息学博士后研究金的基础上，该研究金开发了分析母体效应基因座的基因型到表型作图的方法。当存在IGE时，GA的分子分析特别复杂，因为表型成为多个个体的基因型的属性，因此可以映射到多个个体的基因型。GA的这一新的方面已经很少探索，但PI以前的工作表明，它可以在某些类型的性状从基因型到表型的映射中发挥重要作用。为了实现这一目标，以前在理论和经验方法方面取得的进展将与新的理论模型的发展相结合，以最终了解基因型形成表型的所有途径。

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.