Comprehensive Characterization of Adaptive Regulatory Variation Linked to Human Disease

与人类疾病相关的适应性调节变异的综合表征

• 批准号: 10487545
• 负责人: Steven K. Reilly
• 金额: $ 24.57万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10487545
• 关键词: African; Alleles; Biological Assay; CCRL2 gene; CRISPR interference; CRISPR screen; CRISPR-mediated transcriptional activation; CRISPR/Cas technology; Cell Line; Cell model; Cells; Chromatin Interaction Analysis by Paired-End Tag Sequencing; Climate; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; Computer Models; Data; Databases; Deoxyribonucleases; Development; Diet; Disease; Epigenetic Process; Etiology; Evolution; Exhibits; Future; Gene Expression; Gene Targeting; Genes; Genetic; Genetic Transcription; Genetic Variation; Genome; Genomic Segment; Genomics; Health; Height; Histones; Human; Human Genetics; Immunity; Lassa Fever; Left; Link; Linkage Disequilibrium; Machine Learning; Measures; Mentors; Metabolism; Methods; Modeling; Modernization; Molecular; Morphology; National Human Genome Research Institute; Natural Selections; Neural Network Simulation; Pattern; Phase; Phenotype; Population; Preparation; Recording of previous events; Regulatory Element; Reporter; Reporting; Research; Scanning; Sickle Cell Anemia; Signal Transduction; System; Techniques; Testing; Time; Training; Untranslated RNA; Variant; biobank; causal variant; cell type; computerized tools; disorder risk; emerging pathogen; fitness; genetic association; genetic variant; genome editing; genome wide association study; genomic tools; human disease; improved; in vivo; in vivo Model; insight; lens; mouse model; novel; predictive modeling; pressure; tool; trait; transcription factor; transcriptome sequencing

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. !

项目概要/摘要 在过去的十年中，全基因组关联研究（GWAS）迅速扩展， 以及 UKBioBank 和 All of Us 项目等大型联盟的发展。 与人类特征和疾病的遗传关联的数量正在飙升，描述和解释这些遗传关联的工具 实现基因组学潜力的一项挑战是超过 99% 的人类遗传学。 变异是非编码的调节序列，然而，“调节语法”是复杂的模式。 人们对与转录因子相互作用以控制基因表达的序列知之甚少。 需要充分表征的因果变量来构建可推广的模型，从而解锁对 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，因为我们的物种遇到了新的物种。 气候、饮食的巨大变化以及新的病原体，这些选择压力使数百种 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上。 选择对它们产生积极作用，这些适应性等位基因必须表现出相对较强的表型 影响，并且它们继续导致现代特征和疾病（例如身高或镰状细胞性贫血）。 人类适应的例子包括免疫、新陈代谢和形态学，所有这些都具有广泛的、 未解决的 GWAS 信号使得最近的进化镜头成为一个强大但未得到充分利用的工具。 识别现代关联研究中导致表型变异的等位基因。 该提案旨在通过 A) 使用进化来扩展特征良好的 GWAS 信号库 优先考虑自适应变体，B) 应用新颖的、高通量的实验和计算工具 全面解读监管变体的功能将确定急需的方法。 因果变异，为他们的研究设计范式，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，我将首先开发将选择信号和 GWAS 共定位的方法， 然后使用变异效应预测（VEP）来预测它们的功能，然后我将采用高通量方法。 例如大规模并行报告分析和 CRISPR 非编码筛选，以对其进行功能表征 直接从我们的屏幕识别的自适应 GWAS 等位基因中，我们将使体内系统更加深入。 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。 例如 ChIP、ChIA-PET 和 RNA-seq 来了解适应性变异的分子病因学。 这些研究的经验数据以及 MPRA/HCR-FlowFISH 筛选可构建更准确的 VEP 模型。 ！