Integration of functional data and GWAS to elucidate genetic basis of diseases

整合功能数据和 GWAS 阐明疾病的遗传基础

• 批准号: 10370429
• 负责人: JONATHAN K PRITCHARD
• 金额: $ 70.63万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-23 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10370429
• 关键词: Affect; Autoimmune; Autoimmune Diseases; Biological Models; CRISPR screen; Cell Count; Cells; Clustered Regularly Interspaced Short Palindromic Repeats; Communities; Complex; Complex Genetic Trait; Computer software; Computing Methodologies; Data; Data Analyses; Development; Disease; Disease Outcome; Funding; Gene Proteins; Genes; Genetic; Genetic Variation; Grant; Guide RNA; Immune; Information Networks; Knock-out; Lead; Light; Link; Literature; Maps; Measures; Methodology; Methods; Molecular; Pathway interactions; Process; Property; Protocols documentation; RNA; RNA library; Regulation; Regulator Genes; Regulatory T-Lymphocyte; Research Personnel; Ribonucleoproteins; Signal Transduction; Supervision; T-Lymphocyte; Techniques; Technology; Variant; Work; base; cell type; cost; design; effector T cell; experience; experimental study; gene network; gene regulatory network; genetic variant; genome wide association study; interest; knockout gene; method development; single-cell RNA sequencing; tool; trait; transcriptome sequencing

ABSTRACT The GWAS community has now identified many thousands of variants that affect complex traits, but there is still a critical gap in our ability to interpret why these variants matter. While some signals affect genes that are directly involved in the relevant disease processes, many other signals likely arise due to indirect trans- regulatory effects on core disease genes and pathways. There has been huge progress in recent years on methods to identify variants that affect cis-regulation of nearby genes, but it remains extremely difficult to measure and interpret how and whether those associated genes may affect disease processes directly or via trans-acting effects on other genes. In this grant we propose to develop new experimental and computational methods to help bridge this critical gap. We have recently developed two complementary experimental techniques that use CRISPR-perturbations to map out the upstream regulators and downstream targets of a gene of interest. We will work on methodological improvements for these methods, and apply them in effector T cells and regulatory T cells to map out gene regulatory networks in these important immune cell types. We will develop new computational methods for inferring gene networks using these data. Finally, we will develop and implement new methods for interpreting autoimmune disease loci in light of the network information. This project will establish new high throughput techniques for elucidating regulatory relationships among genes and for using these to interpret GWAS data.

摘要 GWAS社区现在已经确定了数千种影响复杂性状的变体，但 这仍然是我们解释为什么这些变异很重要的能力上的一个关键差距。虽然有些信号会影响基因， 直接参与相关的疾病过程，许多其他信号可能会出现，由于间接trans- 对核心疾病基因和途径的调节作用。近年来， 方法来鉴定影响附近基因顺式调节的变异，但仍然非常困难， 测量和解释这些相关基因如何以及是否可以直接或通过 对其他基因的反式作用。在这项资助中，我们建议开发新的实验和计算 帮助弥合这一关键差距的方法。我们最近开发了两个互补的实验 使用CRISPR扰动来绘制上游调控因子和下游靶点的技术， 感兴趣的基因我们将对这些方法进行方法上的改进，并将其应用于效应器 T细胞和调节性T细胞来绘制这些重要免疫细胞类型中的基因调节网络。我们 将开发新的计算方法来推断基因网络使用这些数据。最后，我们将开发 并根据网络信息实施解释自身免疫性疾病位点的新方法。这 该项目将建立新的高通量技术，用于阐明基因之间的调控关系， 来解释GWAS数据。