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 给定环境中的有机表型通常与基于预期的可能性不同 仅基因型或环境数据。这些基因型特异性的出发或基因环境相互作用 （GXE），可以构成表型变异的很大一部分，对于确定个人的重要性很重要 在给定环境中的福祉。适应特定环境的个人可以做出适当的反应 对于典型的局部压力和养分而言，但在新的或不断变化的环境中可能会导致不良。 GXE也是如此 使预测对环境的有机反应非常困难：大小和方向 GXE效应的效果取决于所涉及的基因座，等位基因，性状和环境。这使外推复杂 基因组预测模型成新的人群或环境。尽管已经确定GXE是一个 对表型变异的主要贡献者，对确定的分子机制知之甚少 个人对环境的不同反应。在复杂的现实世界环境中尤其如此 在实验室实验中不可能繁殖。基因表达的基因组，等位基因变异 累积影响有机体水平表型的GXE，但基因的复杂网络和模式 驾驶GXE的法规尚不清楚。在接下来的五年中，该项目将产生新的 数据集并分析现有数据集，以开始理解和建模全基因组模式 在现实世界环境中累积确定GXE的基因表达。在AIM 1中，组织样品 通过多环境实验将用于评估基因表达的景观 在各种环境中生长的遗传多样化的个体。具体来说，我们将调查如何改变 对于顺式调节序列（例如，转录因子结合基序）有助于GXE基因表达。 同时，我们将确定显示出表达水平GXE的基因，并模拟它们如何贡献 GXE用于有机体水平的表型。在AIM 2中，我们将使用现有的数据集独立但完整性 AIM 1中产生的那些是为了测试生物级表型中是否可以直接预测的GXE 序列变化。这项研究中的多尺度项目从序列级别到整个 生物。通过以多种量表和多种不同的数据类型研究GXE，本研究将 加强我们对等位基因序列变化如何改变基因调节网络和驱动的理解 局部适应。这些发现对于理解生物如何适应新环境很重要 并更好地预测对环境的有机反应。