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

基因-环境相互作用的全基因组分子基础的表征和建模

• 批准号: 10712927
• 负责人: Joseph Lee Gage
• 金额: $ 38万
• 依托单位: NORTH CAROLINA STATE UNIVERSITY RALEIGH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712927
• 关键词: Alleles; Automobile Driving; Binding; Complex; Data; Data Set; Environment; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Genes; Genetic; Genome; Genotype; Individual; Modeling; Molecular; Nutrient; Organism; Pattern; Personal Satisfaction; Phenotype; Population; Research; Stress; Testing; Tissue Sample; Variant; experimental study; gene environment interaction; gene expression variation; gene regulatory network; genome-wide; genomic predictors; laboratory experiment; phenotypic data; predictive modeling; response; trait; transcription factor

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.

项目摘要/摘要 特定环境中的生物表型经常与基于以下因素的预期不同 单独的基因型别或环境数据。这些特定于基因型的偏差，或基因与环境的相互作用 (GxE)，可以构成很大一部分表型变异，并且对于决定个体的 在特定环境中的幸福感。适应于特定环境的个体可以做出适当的反应 对典型的局部压力和营养物质，但可能不适应新的或变化的环境。GxE也 这使得预测生物体对环境的反应变得极其困难：大小和方向 GxE效应的大小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 基因组预测模型进入新的种群或环境。尽管众所周知，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。在复杂的现实世界环境中尤其如此 在实验室实验中是不可能复制的。基因表达的全基因组、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 推动GxE的监管并未得到很好的理解。在未来的五年里，这个项目将产生新的 数据集和分析现有数据集，以开始了解和建模全基因组模式 在真实世界环境中累积决定GxE的基因表达。在目标1中，组织样本 来自多个环境的实验将被用来评估基因在 在不同环境中生长的基因不同的个体。具体地说，我们将调查如何改变 顺式调控序列(如转录因子结合基序)有助于基因表达的GxE。 同时，我们将识别显示GxE表达水平的基因，并建立它们对 GxE用于生物体水平的表型。在目标2中，我们将使用独立但互补的现有数据集 在目标1中产生的那些用于测试生物体水平表型中的GxE是否可以直接从 序列变异。总而言之，本研究中的多尺度项目范围从序列层面到整体 有机体。通过在多个尺度和各种不同数据类型下对GxE进行研究，本研究将 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的理解 地方性适应。这些发现对于理解生物体如何适应新环境很重要。 以及更好地预测生物体对环境的反应。