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: 9382098
• 负责人: Julien Ayroles
• 金额: $ 37.4万
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9382098
• 关键词: Address; Alleles; Architecture; Biological; Biological Models; Communities; Complex; Dependence; Dependency; Disease; Environment; Environmental Exposure; Gene Frequency; Genes; Genetic; Genome; Genomics; Genotype; Goals; Hereditary Disease; Heritability; Human; Human Genetics; Inbred Strain; Individual; Knowledge; Link; Medical Genetics; Molecular; Phenotype; Population; Predisposition; Problem Solving; Research; Resources; Sample Size; Shapes; Variant; cost; disorder risk; fly; genetic association; innovation; precision medicine; programs; success; trait

The long-term goal of my research program is to understand the biological basis for individual variation. The genetic architecture of complex traits is not a static blueprint of the phenotype as it was previously thought; rather, it is highly dynamic and context-dependent. I seek to understand how genes interact with each other and their environment to shape variation between individuals and what factors control the degree of individual variability. Technological advances have recently fueled the ascent of personal genomics and the promise of precision medicine. The success of medical genetics will depend on its capacity to personalize, however, individualized prediction is a grand challenge. When the average effect of an allele does not capture a specific allelic contribution under certain conditions (whether due to genetic background or the environment), the link between genotype and phenotype will be missed. Given such context dependency, understanding how genotypic variation influences variation in an individual's phenotype demands a shift in focus from population averages to individual effects. Globally, we are witnessing the rise of complex diseases related to dramatic changes in our daily environments. These disorders have a clear environmental basis, but they also show strong familial correlations: susceptibility to these diseases is highly heritable. Despite considerable effort and resources, we have made little progress in understanding the genetic basis of these common conditions. This highlight the need for a different approach to identify the causal genetic factors underlying disorders characterized by non-additive interactions. To date, a key limitation to address this problem has been that small sample sizes and skewed allele frequency spectrum limit the power of detecting genetic associations. We have solved this problem by creating a new community resource made of large, synthetic outbred populations. This enables us to break away from traditional, artificial and underpowered approaches that have relied on inbred strains. In parallel, we have developed a molecular and analytical pipeline allowing us to sequence thousands of single flies at high throughput with very low cost and reliable accuracy. With this new and versatile resource, we can rear thousands of genetically unique flies drawn from a common genetic pool, expose them to a range of different environments, and contrast the ensuing genetic architectures. Our inability to make progress in human genetics for diseases with strong environmental components suggests a fundamental knowledge gap that my research addresses in a powerful model system. Given that in humans there is extreme variation and stochasticity in environmental exposure, we need a predictive framework that can accommodate these individual-specific impacts. My research program paves a path to personalized phenotypic prediction by unlocking the context dependence of allelic effects.

我的研究计划的长期目标是了解个体的生物学基础， 变化量复杂性状的遗传结构并不是表型的静态蓝图， 以前认为，它是高度动态的和依赖于上下文的。我想知道 基因如何相互作用和环境塑造个体之间的变异 以及哪些因素控制着个体差异的程度。技术进步 最近推动了个人基因组学的兴起和精准医疗的前景。的 医学遗传学的成功将取决于其个性化的能力，然而， 预测是一个巨大的挑战。当一个等位基因的平均效应不能捕获一个特定的 等位基因的贡献在某些条件下（无论是由于遗传背景或 环境），基因型和表型之间的联系将被错过。鉴于这种背景 依赖性，了解基因型变异如何影响个体的变异 表型要求将焦点从群体平均转移到个体效应。在全球，我们 正在目睹与我们日常生活中的巨大变化有关的复杂疾病的增加， 环境.这些疾病有明确的环境基础，但它们也表现出强烈的 家族相关性：对这些疾病的易感性是高度遗传的。尽管有相当大 尽管付出了巨大的努力和资源，我们在了解这些疾病的遗传基础方面进展甚微。 共同条件。这突出了需要一种不同的方法来确定因果遗传 以非加性相互作用为特征的疾病的潜在因素。到目前为止，一个关键的限制 为了解决这个问题，小样本量和倾斜的等位基因频谱 限制了检测基因关联的能力。我们已经解决了这个问题， 由大量的、合成的远系繁殖种群组成的社区资源。这使我们能够打破 远离传统的、人工的和动力不足的方法，这些方法依赖于近交系。 与此同时，我们已经开发了一个分子和分析管道，使我们能够测序 以极低的成本和可靠的精度高通量地处理数千个单蝇。与此 新的和多功能的资源，我们可以饲养成千上万的遗传独特的苍蝇从一个 共同的基因库，将它们暴露在一系列不同的环境中，并对比 随后的遗传结构。我们无法在人类遗传学方面取得进展， 强烈的环境因素表明了一个基本的知识差距， 在一个强大的模型系统中。考虑到在人类中有极端的变异， 由于环境暴露的随机性，我们需要一个预测框架， 这些个体特有的影响。我的研究计划为个性化的表型 通过解锁等位基因效应的上下文依赖性来预测。