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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：
9072104
负责人：
GREGORY C GIBSON
金额：
$ 79.64万
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/9072104
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 Diseases Immune system Inflammation Inflammatory Inflammatory Bowel Diseases Joints Laboratory Research Lasso Lead 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.