Comprehensive Characterization of Adaptive Regulatory Variation Linked to Human Disease

项目摘要/摘要 在过去的十年中，全基因组关联研究(GWAS)得到了迅速发展， 以及像英国生物银行和我们所有人项目这样的大型财团的发展。而当 与人类特征和疾病相关的基因数量正在飙升，描述和解释这些特征的工具 缺乏变种。实现基因组学潜力的一个挑战是，超过99%的人类基因 变异是一种非编码的调节序列。然而，“调节语法”--复杂的句型 与转录因子相互作用以控制基因表达的序列，目前还知之甚少。一套曲目 建立可推广的模型，以便深入了解 人类健康和历史的遗传基础。 自然选择是人类基因变异的强大驱动力。因为我们这个物种遇到了新的 气候，饮食的戏剧性变化，以及新的病原体，这些选择性的压力已经留下了数百 我们基因组中的适应特征，反映在我们物种疾病风险和形态的多样性上。为 选择要对它们起积极作用，这些适应等位基因必须表现出相对较强的表型 影响，并继续促进现代特征和疾病(如身高或镰状细胞性贫血)。显著 人类适应的例子包括免疫、新陈代谢和形态，所有这些都有广泛的， 未解析的GWAS信号。这使得最近进化的镜头成为一种强大的、但未得到充分利用的工具 在现代关联研究中识别导致表型变异的等位基因。 这一建议旨在通过A)利用进化来扩展具有良好特征的GWAS信号的曲目 确定自适应变量的优先顺序，以及B)应用新的高通量实验和计算工具 全面破译监管变种的功能。这些方法将确定急需的 因果变异，为它们的研究设计范例，并为未来的预测模型提供信息来表征它们。 在K99的指导阶段，我将首先开发共同定位选择信号和GWAs的方法， 然后使用变量效应预测(VEP)来预测它们的功能。然后我将采用高通方法 例如大规模平行报告分析和CRISPR非编码筛选，以从功能上表征它们 直接去吧。从我们的屏幕识别的适应性GWA型等位基因中，我们将使体内系统更深入 在独立R00阶段描述它们的特征。在此期间，我将部署各种基因组工具 如CHIP、CHIA-PET和RNA-SEQ，以了解适应性变异体的分子病因学。我将使用 来自这些研究的经验数据，以及MPRA/HCR-FlowFISH筛选，以建立更准确的VEP模型。 好了！

Project Summary/ Abstract Over the past decade there has been a rapid expansion of genome-wide association studies (GWAS), as well as the development of large-scale consortia like the UKBioBank and the All of Us project. While the number of genetic associations to human traits and disease is soaring, tools to characterize and interpret these variants are lacking. One challenge to realizing the potential of genomics is that over 99% of human genetic variation is non-coding, regulatory sequences. However, ‘regulatory grammar’ – the complex pattern of sequences that interact with transcription factors to control gene expression, is poorly understood. A repertoire of well-characterized causal variants is needed to build generalizable models with which to unlock insights into the genetic basis of human health and history. Natural selection is a powerful driver of human genetic variation. As our species has encountered new climates, dramatic alterations in diet, and novel pathogens, these selective pressures have left hundreds of signatures of adaptation in our genomes, reflected in our species’ diversity of disease risk and morphology. For selection to have acted positively on them, these adaptive alleles must exhibit relatively strong phenotypic effects, and they continue to contribute to modern traits and disease (e.g. height or sickle cell anemia). Salient examples of human adaptation include immunity, metabolism, and morphology, all of which have extensive, unresolved GWAS signals. This renders the lens of recent evolution a powerful, but underutilized, tool for identifying alleles that contribute to phenotypic variation in modern association studies. This proposal aims to expand the repertoire of well-characterized GWAS signals, by A) using evolution to prioritize adaptive variants, and B) applying novel, high-throughput experimental and computational tools to comprehensively decipher the functions of regulatory variants. These approaches will identify much needed causal variants, devise paradigms for their study, and inform future predictive models to characterize them. During the mentored phase of the K99, I will first develop methods to colocalize signals of selection and GWAS, and then use Variant Effect Predictions (VEP) to predict their function. I will then employ high-through methods such as a the massively parallel reporter assay and CRISPR non-coding screen to functionally characterize them directly. From the adaptive GWAS alleles our screens identify, we will make in-vivo system to more deeply characterize them during the Independent R00 phase. During this time I will deploy a variety of genomic tools such as ChIP, ChIA-PET, and RNA-seq to understand the adaptive variants’ molecular etiology. I will use the empirical data fro these studies, and the MPRA/HCR-FlowFISH screens to build more accurate VEP models. !