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

• 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
• 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' 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

 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：eQTL Mega-analysis for Functional Assessment of Multi-enhancer Gene Regulation本提案是对RFA HG-13-013“Interpreting Variation in Human Non-Coding Genomic Regions Using Computational Approaches and Experimental Assessment（R 01）”的回应。它利用统计建模来识别每个转录本基因组范围内的多个调控变体，通过基因组工程验证其实际功能，并建立其在炎症背景下的相关性。我们建议将联合收割机两种平行的方法结合起来，以鉴定与全基因组基因型相关的10，000个外周血转录组谱的独特资源中的调节多态性。然后，将使用多变量回归来精细映射最高概率的常见变体，重点关注那些在转录调控中起关键作用的变体，特别是在炎症性自身免疫性疾病的背景下。CRISPR/Cas9介导的位点特异性基因组工程将用于在中等通量的基础上通过实验确认淋巴细胞系中自身免疫基因座的预测。计算方法将应用h分层稀疏学习（结构化SL）模型，通过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体最有可能影响转录本丰度。广泛的模拟将用于定义影响多变量调控多态性检测的灵敏度和特异性的参数，同时还将每个转录本的调控靶点减少到十几个变体。由于RFA的主要目标不仅是优先考虑调节变体，而且还要确定它们对生物体表型的影响，因此我们将分析它们与从暴露于脂多糖（LPS）或炎性细胞因子TNFα 24小时的外周血样本中分离的T淋巴细胞中转录本丰度的相关性。外周血含有大多数相关的免疫细胞类型，我们的预期是，遗传效应在疾病中被炎症因子改变，一些变体失去了它们的作用，其他新的变体出现。此外，GWAS已使用基因组工程鉴定出的一组多达150个炎症性自身免疫疾病基因将直接证明其调节功能。非同源末端连接将用于在筛选步骤中破坏每个候选位点，使用滴式数字PCR测量突变对基因表达的影响，然后同源定向置换将用于等位基因特异性置换，在少数情况下产生所有可能的单倍型，以实验证实在共同遗传背景中预测的联合效应。预计计算和实验方法可扩展到许多常见疾病，所有代码将与MEGA软件套件一起用于进化基因组分析。