Characterizing and modeling the genomewide molecular basis of gene-environment interactions

项目总结/摘要 给定环境中的有机体表型经常与基于环境的预期不同。 基因型或环境数据。这些基因型特异性的偏差，或者说基因与环境的相互作用 (GxE)，可以构成表型变异的很大一部分，并且对于确定个体的 在特定的环境中保持健康。一个适应了特定环境的个体可以做出适当的反应 对典型的当地压力和营养，但可能不适应新的或不断变化的环境。GXE也 这使得预测生物体对环境的反应变得非常困难： GxE效应的影响取决于所涉及的基因座、等位基因、性状和环境。这使外推变得复杂， 基因组预测模型应用到新的种群或环境中。虽然GxE是一种 表型变异的主要贡献者，对决定表型变异的分子机制知之甚少。 个体对环境的不同反应。在复杂的、真实的世界环境中尤其如此 在实验室实验中是不可能复制的。全基因组，基因表达的等位基因变异 累积影响生物体水平表型的GxE，但基因的复杂网络和模式 GxE的调控机制还不太清楚。在未来五年内，该项目将产生新的 数据集，并分析现有的数据集，以开始理解和建模的基因组模式， 在真实的世界环境中累积决定GxE的基因表达。在目标1中，组织样本 多环境实验的结果将用于评估基因表达的景观， 在各种环境中生长的基因多样的个体。具体来说，我们将研究如何改变 顺式调节序列（例如转录因子结合基序）的结合有助于GxE的基因表达。 同时，我们将确定基因，显示GxE的表达水平和模型，他们如何有助于 生物体水平表型的GxE。在目标2中，我们将使用独立但互补的现有数据集， 目标1中生成的那些，以测试生物体水平表型中的GxE是否可以直接从 序列变异本研究中的多尺度项目范围从序列水平到整个 有机体通过研究GxE在多个尺度和各种不同的数据类型，这项研究将 加强我们对等位基因序列变异如何改变基因调控网络和驱动力的理解 地方适应。这些发现对于理解生物如何适应新环境很重要 以及更好地预测生物对环境的反应。

PROJECT SUMMARY/ABSTRACT Organismal phenotypes in a given environment frequently differ from what might be expected based on genotypic or environmental data alone. These genotype-specific deviations, or gene-environment interactions (GxE), can constitute a large portion of phenotypic variation and are important for determining an individual’s wellbeing in its given environment. An individual adapted to a particular environment can respond appropriately to typical local stresses and nutrients, but may be maladapted in new or changing environments. GxE also makes it exceedingly difficult to predict organismal response to the environment: the magnitude and direction of GxE effects depend on the loci, alleles, traits, and environments involved. This complicates extrapolation of genomic prediction models into new populations or environments. Although it is well established that GxE is a major contributor to phenotypic variation, much less is known about the molecular mechanisms determining individuals’ differential response to environments. This is particularly true in complex, real world environments that are impossible to reproduce in laboratory experiments. Genomewide, allelic variation for gene expression cumulatively influences GxE of organism-level phenotypes, but the complex networks and patterns of gene regulation driving GxE are not well understood. Over the coming five years, this project will generate new datasets and analyze existing datasets to begin understanding and modeling the genomewide patterns of gene expression that cumulatively determine GxE in real world environments. In Aim 1, tissue samples from multi-environmental experiments will be used to evaluate the landscape of gene expression among genetically diverse individuals grown in a variety of environments. Specifically, we will investigate how changes to cis-regulatory sequences (e.g. transcription factor binding motifs) contribute to GxE for gene expression. Simultaneously, we will identify genes that show GxE for expression levels and model how they contribute to GxE for organism-level phenotypes. In Aim 2, we will use existing datasets independent yet complementary to those generated in Aim 1 to test whether GxE in organism-level phenotypes can be predicted directly from sequence variation. Together the multi-scale projects in this study range from the sequence level to the entire organism. By studying GxE at multiple scales and with a variety of different data types, this study will strengthen our understanding of how allelic sequence variation changes gene regulatory networks and drives local adaptation. These findings are important for understanding how organisms adapt to new environments and for better predicting organismal response to the environment.