Comprehensive Characterization of Adaptive Regulatory Variation Linked to Human Disease

项目总结/摘要 在过去的十年中，全基因组关联研究（GWAS）迅速扩展， 以及英国生物银行（UKBioBank）和“我们所有人”（All of Us）项目等大型财团的发展。而 与人类特征和疾病相关的基因数量正在飙升，描述和解释这些基因的工具 缺少变量。实现基因组学潜力的一个挑战是，超过99%的人类基因组学是一个复杂的过程。 变异是非编码的调节序列。然而，"调节语法"-- 与转录因子相互作用以控制基因表达的序列知之甚少。组库 我们需要一个具有良好特征的因果变量来构建可推广的模型， 人类健康和历史的遗传基础。 自然选择是人类遗传变异的强大驱动力。当我们的物种遇到新的 气候、饮食的巨大变化和新的病原体，这些选择性的压力使数百名 我们基因组中的适应特征，反映在我们物种的疾病风险和形态多样性中。为 选择对它们起积极作用，这些适应性等位基因必须表现出相对较强的表型 这些基因会影响人类的健康，并继续导致现代性状和疾病（例如身高或镰状细胞性贫血）。突出 人类适应的例子包括免疫、代谢和形态，所有这些都具有广泛的， 未解决的GWAS信号。这使得最近进化的透镜成为一个强大的，但未被充分利用的工具， 在现代关联研究中识别有助于表型变异的等位基因。 该提案旨在通过A）使用进化来扩展良好表征的GWAS信号的库 优先考虑适应性变体，以及B）应用新颖的、高通量的实验和计算工具， 全面解读调控变体的功能。这些方法将确定急需的 因果变异，设计他们的研究范式，并告知未来的预测模型来表征他们。 在K99的指导阶段，我将首先开发选择和GWAS共定位信号的方法， 然后使用变异效应预测（VEP）来预测它们的功能。然后我会采用高穿透的方法 例如大规模平行报告基因测定和CRISPR非编码筛选，以在功能上表征它们 直接.从我们筛选出的适应性GWAS等位基因中，我们将使体内系统更深入地 在独立R00阶段进行表征。在此期间，我将部署各种基因组工具， 例如ChIP、ChIA-PET和RNA-seq，以了解适应性变体的分子病因。我将用 这些研究的经验数据和MPRA/HCR-FlowFISH屏幕，以建立更准确的VEP模型。 !

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. !