Defining interaction quantitative trait loci (iQTLs) in the human genome

定义人类基因组中的相互作用数量性状位点 (iQTL)

• 批准号: 10603644
• 负责人: Ferhat Ay
• 金额: $ 9.7万
• 依托单位: LA JOLLA INSTITUTE FOR IMMUNOLOGY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10603644
• 关键词: 3-Dimensional; Address; Affect; Asthma; Binding; Biological; Biological Assay; Cell physiology; Chromatin; Chromatin Structure; Code; Collaborations; Computer Analysis; Computing Methodologies; DNA; DNA Sequence; Dependence; Dimensions; Disease; Disease susceptibility; Distal; Experimental Designs; Gene Expression; Gene Expression Regulation; Gene Structure; Genes; Genetic; Genetic Variation; Genotype; Goals; Human; Human Genome; Immune; Institutes; Knowledge; Laboratories; Machine Learning; Molecular Biology Techniques; Population; Positioning Attribute; Predisposition; Quantitative Trait Loci; Research; Resources; Role; Sampling; Specificity; Susceptibility Gene; Uncertainty; Untranslated RNA; Variant; Work; base; cell type; chromosome conformation capture; genetic variant; genome wide association study; genome-wide; genome-wide analysis; graph theory; histone modification; novel; predictive modeling; programs; promoter; statistics; transcription factor

Abstract My research aims to understand the role of three-dimensional (3D) chromatin structure in gene regulation. This involves studying associations among genotype, histone modifications, transcription factor binding, non-coding RNAs, chromatin interactions and gene expression. In order to transform this genome-wide information into new biological discoveries, my laboratory develops scalable and interpretable computational methods based on statistics, graph theory and machine learning. Our recent focus is to address an important gap in the current knowledge of the role of 3D chromatin structure in gene regulation. That is, we aim to define how genotypic variation affects 3D organization of gene promoters, and in turn, their expression. To achieve this at a genome-wide scale is an ambitious goal, because it requires having at a minimum, genotype, gene expression and chromatin interaction profiles in pure populations of specific cell types from a large number of donors. However, my laboratory is uniquely positioned to perform this research because: i) we are involved in a study at the La Jolla Institute (LJI-R24AI108564) that has already genotyped ~100 donors and expression-profiled more than 15 different pure populations of human immune cell types, and we have access to the same samples for chromatin interaction mapping, ii) in collaboration with other groups at LJI, we have already discovered a prototypical example of an interaction quantitative trait locus (iQTL) that alters and rewires interactions from the promoter of a specific gene that is associated with asthma susceptibility, iii) we have the necessary expertise and proven track record in experimental design and computational analyses of various chromatin conformation capture assays. Leveraging the resources available at LJI and our expertise in the field, we will build a unique research program around the novel concept of iQTLs. The emerging set of three main questions we propose to address within the next five years are: Q1) How do we define cell-type-specific iQTLs for common genetic variants? Q2) What is the extent of overlap between iQTLs and GWAS SNPs? Q3) Can we build predictive models for the cell-type specificity of chromatin interactions and iQTLs? Although we propose to define iQTLs only in two abundant, easily accessible, and highly disease-relevant immune cell types, the concept of iQTLs is equally important in other cell types implicated in diseases with a genetic component. Hence, the proof-of-concept developed by this work, without a doubt, will open up a new field in studying a previously uncharacterized role for disease-susceptibility variants, specifically non-coding SNPs, from genome-wide association studies (GWAS) in gene regulation.

摘要 我的研究旨在了解三维（3D）染色质结构在基因表达中的作用。 调控这包括研究基因型，组蛋白修饰， 转录因子结合、非编码RNA、染色质相互作用和基因表达。在 为了将这些全基因组信息转化为新的生物学发现，我的实验室 开发可扩展和可解释的计算方法，基于统计，图论和 机器学习我们最近的重点是解决目前知识中的一个重要空白， 3D染色质结构在基因调控中的作用。也就是说，我们的目标是确定基因型 变异影响基因启动子的3D组织，进而影响它们的表达。实现这一 在全基因组范围内是一个雄心勃勃的目标，因为它需要至少有基因型， 基因表达和染色质相互作用谱的纯群体的特定细胞类型， 大量的捐赠者。然而，我的实验室是唯一的定位来执行这项研究 因为：i）我们参与了拉霍亚研究所的一项研究（LJI-R24 AI 108564），该研究已经 对约100名供体进行基因分型，并对超过15个不同的纯人群进行表达谱分析。 人类免疫细胞类型，并且我们可以获得相同的样本进行染色质相互作用 映射，ii）与LJI的其他团队合作，我们已经发现了一个原型 一个相互作用数量性状基因座（iQTL）的例子，改变和重新连接的相互作用，从 与哮喘易感性相关的特定基因的启动子，iii）我们有 在实验设计和计算分析方面具有必要的专业知识和良好的业绩记录 各种染色质构象捕获分析。利用LJI的可用资源， 我们在该领域的专业知识，我们将建立一个独特的研究计划，围绕新的概念， iQTL。我们建议在今后五年内解决的三个主要问题 Q1）我们如何定义常见遗传变异的细胞类型特异性iQTL？Q2）什么是 iQTL和GWAS SNP之间的重叠程度？Q3）我们可以建立预测模型， 染色质相互作用和iQTL的细胞类型特异性？虽然我们建议定义iQTL 只有在两种丰富的、容易获得的和高度疾病相关的免疫细胞类型中， iQTL的概念在涉及具有遗传缺陷的疾病的其他细胞类型中同样重要。 成分因此，这项工作开发的概念验证，毫无疑问，将开辟一个 这是研究疾病易感性变异体以前未被描述的作用的新领域， 特别是非编码SNP，来自基因调控的全基因组关联研究（GWAS）。