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. !

项目摘要/摘要 在过去的十年中，全基因组关联研究（GWAS）迅速扩张， 以及像Ukbiobank和我们所有人一样的大规模财团的发展。而 人类特征和疾病的遗传关联数量飙升，是表征和解释这些的工具 缺乏变体。意识到基因组学潜力的一个挑战是，超过99％的人类遗传学 变异是非编码的调节序列。但是，“监管语法” - 复杂的模式 与转录因子相互作用以控制基因表达的序列知之甚少。曲目 需要良好特征的因果变体来构建可概括的模型，以解锁洞察力 人类健康和历史的遗传基础。 自然选择是人类遗传变异的有力驱动力。由于我们的物种遇到了新的 气候，饮食的戏剧性改变以及新的病原体，这些选择性的压力留下了数百个 我们的基因组适应的特征反映在我们物种的疾病风险和形态多样性中。为了 这些自适应等位基因必须对它们进行积极作用，必须存在相对强的表型 影响，它们继续为现代性状和疾病（例如高度或镰状细胞贫血）做出贡献。突出 人类适应的例子包括免疫学，代谢和形态，所有这些都有广泛的 未解决的GWAS信号。这使最近进化的镜头成为有力但未充分利用的工具 确定在现代关联研究中有助于表型变异的等位基因。 该建议旨在通过a）使用进化来扩大良好的GWAS信号的曲目 优先考虑适应性变体，b）将新颖，高通量实验和计算工具应用于 全面破译监管变体的功能。这些方法将确定急需的 因果变体，为他们的研究设计范式，并为未来的预测模型提供了特征。 在K99的修改阶段，我将首先开发方法，以将选择和GWAS的信号共定位， 然后使用变体效应预测（VEP）来预测其功能。然后，我将员工高通道方法 例如大规模并行的记者测定法和CRIS不编码屏幕，以在功能上表征它们 直接地。从我们的屏幕识别的自适应GWA等位基因中，我们将使体内系统变得更深入 在独立的R00阶段表征它们。在此期间，我将部署各种基因组工具 例如芯片，chia-pet和RNA-seq，以了解适应性变体的分子病因。我将使用 这些研究的经验数据以及MPRA/HCR-FlowFish筛选以构建更准确的VEP模型。 呢