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

这个项目研究受环境影响的性状的遗传学和进化潜力。这些社会环境(例如，竞争环境)特别重要，因为它们源于个人的特征，可能具有遗传基础。理论模型已经证明，这些基于遗传的环境影响或间接遗传效应(IGES)可以对遗传结构(GA)和进化产生重要影响，但缺乏对IGES的实证研究(除了对父母对其后代的影响而产生的IGES的研究)。该项目将为IGES在遗传算法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IGES对性状表达变异的贡献，建议的研究将使用PI开发的经验方法。该方法应用特征表达的理论模型来解释相互作用的个体群体中特征变异的组成部分。使用这种方法可以区分基因的直接和间接影响，而基因的直接和间接影响往往是混淆的，或者在IGES的情况下，传统方法是隐藏的。该理论模型还可用于基于观察到的遗传算法分析表型进化的动力学，产生可通过人工选择实验检验的预测，或可用于预测农业系统对选择改良的反应。这些方法将被用来分析IGES对快速循环油菜品种复杂性状(如种子数量和总生物量)发育的贡献。这些性状的数量遗传学数据将为未来的一项提案提供基础，该提案将在IGES存在的情况下审查实验进化，并分析IGE基因座与性状表达的分子图谱。在这个未来的项目中，将通过人工选择来获得表型发散的线条，以测试模型预测。这些不同的品系将被近交，并用于检查Ige基因座的基因型-表型图谱和进化动力学。因此，这笔初始拨款的一个主要目标是提供所需的关键数据，以便在未来制定一个更大、更全面的提案。这将大大促进国际和平研究所作为研究科学家的职业发展，并将有助于建立国际和平研究所新的研究实验室。这项研究计划的最终目标是检查免疫球蛋白E基因座的分子图谱，建立在沃尔夫博士的NSF生物信息学博士后研究成果的基础上，在那里开发了分析母体效应基因座的基因型到表型图谱的方法。当存在IGES时，GA的分子分析特别复杂，因为表型成为多个个体的属性，从而可以映射到多个个体的基因类型。遗传算法的这一新方面一直没有得到很好的探索，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，将把以前在理论和经验方法方面取得的进展与新的理论模型的开发结合起来，以最终了解基因型产生表型的所有途径。

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.