A path to personalized phenotypic prediction: unlocking the context-dependency of allelic effects

个性化表型预测之路：解锁等位基因效应的背景依赖性

• 批准号: 10552203
• 负责人: Julien Ayroles
• 金额: $ 43.74万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-18 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552203
• 关键词: Accounting; Address; Alleles; Complex; Dependence; Disease; Drosophila genus; Environment; Genetic; Genetic Variation; Genome; Genomic Segment; Genomics; Genotype; Goals; Health; Human; Human Genetics; Individual; Life; Medicine; Modeling; Modernization; Molecular; Persons; Phenotype; Physiological; Play; Population; Process; Recording of previous events; Research; Role; Shapes; Societies; Technology; Testing; Time; Variant; Work; disorder risk; environmental change; environmental stressor; genetic makeup; innovation; interest; lens; precision medicine; pressure; programs; resilience; tool; trait

PROJECT SUMMARY Many of our most onerous health burdens today are driven by complex and poorly understood interactions between a person’s genetic makeup and his or her environment. In other words, genetic variation may predispose individuals toward physiological sensitivity or resilience in the face of environmental perturbations. Our modern environments are deeply diverged from the ancestral selective pressures that have shaped human genetic variation through evolutionary time, raising the question of how much the “mismatch” between our genomes and modern life is responsible for the non-communicable diseases that plague modern societies. This concept, known as the evolutionary mismatch hypothesis, has become a central tenet of evolutionary medicine and has potentially profound implications for how we study, manage, and treat a long list of conditions thought to arise from genomes mismatched to our modern environments. However, this hypothesis has been difficult to robustly test in practice, perhaps in part because we do not fully understand the mechanistic basis underlying this phenomenon. Under a mismatch model, we would expect genetic variation that was neutral or beneficial in past environments to become disease-causing in modern environments; in other words, we would expect this mismatch to generate “genotype-by-environment” (GxE) interactions, such that some genetic effects on health are environmentally dependent. My lab’s goal is to advance our understanding of how and why some individuals are more sensitive to environmental stressors than others, and to identify the molecular and genetic drivers of this variation. Work in my group has clearly established that the contribution of GxE to phenotypic variation is pervasive and it is generally underestimated. We now want to move beyond accounting for the variance explained by GxE and gain a mechanistic understanding of how these interactions shape phenotypic variation and drive disease risk. Over the next 5 years, a major theme of my lab will be to use evolutionary mismatch as a lens to study GxE. We aim to understand: (1) What makes a specific genomic region sensitive to its environmental context? (2) What role does population evolutionary history play in this process? (3) Why and how some individuals appear to be more sensitive than others to environmental perturbation? We are particularly interested in studying a class of genetic effects that are only revealed by environmental change (also known as “cryptic genetic variation”). Understanding the mechanistic underpinnings of how this class of variants drive phenotypic variation is one of the major goals of my research program. Technological advances have fueled the ascent of personal genomics and the promise of precision medicine. However, to unlock this potential, we must first understand how the environmental and genetic interactions unique to each individual contribute to variation in complex traits. This is the primary goal of this application and the focus of my research program – my work leverages these tools to address questions related to the context-dependency of allelic effects using a powerful, evolutionary lens. We conduct this work using both Drosophila and humans as a model.

项目概要 我们今天许多最繁重的健康负担都是由复杂且知之甚少的相互作用造成的 一个人的基因构成和他或她的环境之间的关系。换句话说，遗传变异可能 使个体在面对环境扰动时具有生理敏感性或弹性。 我们的现代环境与塑造人类的祖先选择压力截然不同 随着进化时间的遗传变异，提出了一个问题：我们之间的“不匹配”程度有多大？ 基因组和现代生活是造成困扰现代社会的非传染性疾病的原因。这 被称为进化失配假说的概念已成为进化医学的核心原则 并且对我们如何研究、管理和治疗一长串的疾病有潜在的深远影响 来自与我们现代环境不匹配的基因组。然而，这个假说很难成立 在实践中进行稳健的测试，也许部分是因为我们没有完全理解其背后的机制基础 这种现象。在错配模型下，我们期望遗传变异是中性的或有益的 过去的环境在现代环境中成为致病因素；换句话说，我们期望这样 不匹配会产生“基因型与环境”（GxE）相互作用，从而对健康产生一些遗传影响 是环境依赖的。我实验室的目标是加深我们对某些人如何以及为何 比其他人对环境压力更敏感，并且能够识别环境压力的分子和遗传驱动因素 这种变化。我的团队的工作已经清楚地确定 GxE 对表型变异的贡献是 普遍存在，而且普遍被低估。我们现在想要超越方差的解释 由 GxE 解释并获得对这些相互作用如何塑造表型变异的机械理解 并增加疾病风险。在接下来的五年里，我实验室的一个主要主题将是利用进化失配作为 研究 GxE 的镜头。我们的目标是了解：（1）是什么使得特定的基因组区域对其敏感？ 环境背景？ （2）种群进化史在此过程中发挥什么作用？ (3) 为什么以及如何 有些人似乎比其他人对环境扰动更敏感？我们特别 有兴趣研究一类仅通过环境变化才能揭示的遗传效应（也称为 “神秘的遗传变异”）。了解此类变体如何驱动的机械基础 表型变异是我研究计划的主要目标之一。技术进步推动了 个人基因组学的崛起和精准医学的前景。然而，为了释放这种潜力，我们必须 首先了解每个个体独特的环境和遗传相互作用如何导致变异 在复杂的特征中。这是此应用程序的主要目标，也是我的研究计划的重点 - 我的工作 利用这些工具来解决与等位基因效应的上下文依赖性相关的问题，使用强大的、 进化镜头。我们使用果蝇和人类作为模型进行这项工作。