eQTL mega-analysis for functional assessment of multi-enhancer gene regulation

用于多增强子基因调控功能评估的 eQTL 大分析

基本信息

批准号：
9330894
负责人：
GREGORY C GIBSON
金额：
$ 75.73万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-11 至 2019-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9330894
关键词：
Adult Affect Alleles Appearance Arthritis Autoimmune Diseases Autoimmune Process Bacterial Polysaccharides Bayesian Method Blood specimen CRISPR/Cas technology Cell Line Clinical Research Code Complex Computer Retrieval of Information on Scientific Projects Database Computer software Crohn&apos s disease Data Set Development Disease Drops Enhancers Evaluation Exposure to Gene Expression Gene Expression Profiling Gene Expression Regulation Genes Genetic Genetic Polymorphism Genetic Transcription Genome Genome engineering Genomic Segment Genotype HL60 Haplotypes Hour Human Immune Immune system Inflammation Inflammatory Inflammatory Bowel Diseases Instruction Joints Laboratory Research Lasso Learning Link Linkage Disequilibrium Lipopolysaccharides Lymphocyte Lymphoid Cell Maps Measures Mediating Methodology Methods Modeling Mutation Nonhomologous DNA End Joining Outcome Phenotype Play Polymorphism Analysis Probability Property Resources Rheumatoid Arthritis Role Sampling Sensitivity and Specificity Sentinel Signal Transduction Site Statistical Models Structure T-Lymphocyte TNF gene Technology Time Tissues Transcript Transcriptional Regulation Translations Untranslated RNA Variant Work base cell type clinically relevant combinatorial comparative genomics cytokine design digital disorder risk expectation functional genomics genome analysis genome wide association study genome-wide genomic data novel peripheral blood public health relevance response risk variant screening simulation transcriptome vector volunteer whole genome

项目摘要

DESCRIPTION (provided by applicant): Project Summary: eQTL Mega-analysis for Functional Assessment of Multi‐enhancer Gene Regulation This proposal is in response to RFA HG-13-013 "Interpreting Variation in Human Non-Coding Genomic Regions Using Computational Approaches and Experimental Assessment (R01)". It utilizes statistical modeling to identify multiple regulatory variants per transcript genome-wide, validates their actual function by genome engineering, and establishes their relevance in the context of inflammation. We propose to combine two parallel approaches to identification of regulatory polymorphisms in a unique resource of 10,000 peripheral blood transcriptome profiles linked to whole genome genotypes. Multivariate regression will then be used to fine map the highest probability common variants, focusing on those that play a critical role in transcriptional regulation specifically inthe context of inflammatory autoimmune diseases. CRISPR/Cas9 mediated site specific genome engineering will be used to experimentally confirm the predictions on a moderate-throughput basis for autoimmune loci in a lymphoid cell line. The computational approach will apply h hierarchical sparse learning (structured SL) models, informed by empirical measures of linkage disequilibrium, also incorporating evolutionary probabilities and ENCODE functional annotations to predict which variants are most likely to influence transcript abundance. Extensive simulations will be used to define parameters influencing the sensitivity and specificity of multivariate regulatory polymorphism detection, while also reducing the regulatory target for each transcript to just a dozen variants. Since a major objective of the RFA is not just to prioritize regulatory variants, but also to establish their influence on organismal phenotypes, we will profile their association with transcript abundance in T-lymphocytes isolated from peripheral blood samples exposed for 24 hours to lipopolysaccharide (LPS) or the inflammatory cytokine TNFα. Peripheral blood contains most of the relevant immune cell types, and our expectation is that genetic effects are modified in disease by the inflammatory agents, some variants losing their effect, other novel variants arising. Furthermore, direct demonstration of regulatory functio will be obtained for a set of up to 150 inflammatory autoimmune disease genes already identified by GWAS, using genome engineering. Non-homologous end joining will be used to disrupt each candidate site in a screening step, using drop digital PCR to measure the impact of mutations on gene expression, and then homology-directed replacement will be used for allele-specific replacement, in a handful of cases generating all possible haplotypes to experimentally confirm the predicted joint effects in a common genetic background. The computational and experimental approaches are expected to be extensible to many common diseases, and all code will be made publically available in conjunction with the MEGA suite of software for evolutionary genome analysis.

描述（由申请人提供）：项目摘要：用于多增强子基因调控功能评估的 eQTL 宏分析该提案是对 RFA HG-13-013“使用计算方法和实验解释人类非编码基因组区域的变异”的回应评估（R01）”。它利用统计模型来识别全基因组范围内每个转录本的多个调控变体，通过基因组工程验证它们的实际功能，并建立它们在我们建议结合两种并行方法来识别与全基因组基因型相关的 10,000 个外周血转录组图谱的独特资源，然后使用多元回归来精细绘制概率最高的常见变异，重点关注那些发挥作用的变异。 CRISPR/Cas9 介导的位点特异性基因组工程在转录调控中的关键作用，特别是在炎症性自身免疫性疾病中，将用于通过实验证实对自身免疫基因座的中等通量的预测。计算方法将应用 h 分层稀疏学习（结构化 SL）模型，根据连锁不平衡的经验测量，还结合进化概率和 ENCODE 功能注释来预测哪些变异最有可能影响转录本丰度。用于定义影响多变量调控多态性检测的敏感性和特异性的参数，同时还将每个转录本的调控目标减少到仅十几个变体，因为 RFA 的主要目标不仅仅是确定优先级。为了确定它们对生物体表型的影响，我们将分析它们与从暴露于脂多糖 (LPS) 或炎症细胞因子 TNFα 24 小时的外周血样本中分离出的 T 淋巴细胞转录本丰度的关系。相关的免疫细胞类型，我们的期望是炎症因子在疾病中改变遗传效应，一些变异失去其作用，其他新变异出现。此外，将获得调节功能的直接证明。 GWAS 已使用基因组工程鉴定出多达 150 个炎症性自身免疫性疾病基因，将在筛选步骤中使用非同源末端连接来破坏每个候选位点，并使用滴落数字 PCR 来测量突变对基因表达的影响，然后同源定向替换将用于等位基因特异性替换，在少数情况下生成所有可能的单倍型，以通过实验确认在共同遗传背景下预测的联合效应。计算和实验方法预计可扩展至许多常见疾病，所有代码将与用于进化基因组分析的 MEGA 软件套件一起公开提供。