A path to personalized phenotypic prediction: unlocking the context-dependency 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 变种。复杂性状的遗传结构不是表型的静态蓝图，因为它 它是以前认为的；相反，它是高度动态的和依赖于上下文的。我试图理解 基因如何相互作用以及它们的环境如何形成个体之间的差异 以及哪些因素控制着个体变异性的程度。技术的进步已经 最近推动了个人基因组学的兴起和精准医学的前景。这个 医学遗传学的成功将取决于它的个性化能力，然而，个性化 预测是一个巨大的挑战。当等位基因的平均效应不能捕捉到特定的 特定条件下的等位基因贡献(无论是由于遗传背景还是由于 环境)，则将忽略基因型和表型之间的联系。在这样的背景下 依赖性，了解基因变异如何影响个体的变异 表型需要将重点从群体平均转移到个体影响。在全球范围内，我们 正在见证与我们日常生活中的戏剧性变化相关的复杂疾病的兴起 环境。这些疾病有明确的环境基础，但它们也显示出很强的 家族相关性：对这些疾病的易感性具有高度的遗传性。尽管有相当大的 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。 常见情况。这突显了需要一种不同的方法来识别因果遗传 以非相加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 解决这个问题的方法是样本量小和等位基因频谱不对称 限制检测遗传关联的能力。我们已经解决了这个问题，创造了一个新的 由大的、合成的远交种群组成的群落资源。这使我们能够突破 远离依赖近亲繁殖品系的传统、人工和动力不足的方法。 与此同时，我们已经开发了一种分子和分析管道，使我们能够对 数以千计的单只苍蝇以极低的成本和可靠的准确性在高吞吐量下运行。有了这个 新的和多才多艺的资源，我们可以饲养数以千计的遗传独特的苍蝇从一个 共同的基因库，将他们暴露在一系列不同的环境中，并将 随之而来的是遗传结构。我们无法在人类疾病遗传学方面取得进展 强烈的环境成分表明，我的研究存在基本的知识鸿沟 一个强大的模型系统中的地址。鉴于人类中存在极端的变异， 环境暴露的随机性，我们需要一个能够适应 这些特定于个人的影响。我的研究计划为个性化表型铺平了道路 通过解锁等位基因效应的上下文相关性进行预测。

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.