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

基本信息

批准号：
10612857
负责人：
JONATHAN K PRITCHARD
金额：
$ 71.29万
依托单位：
STANFORD UNIVERSITY
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：
2016-06-23 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10612857
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 Genes Genetic Genetic Variation Grant Guide RNA Immune Information Networks Knock-out Light Link Literature Maps Measures Methodology Methods Molecular Pathway interactions Process Property Proteins Protocols documentation RNA library Regulation Regulator Genes Regulatory T-Lymphocyte Research Personnel Ribonucleoproteins Signal Transduction 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 improved interest knockout gene method development single-cell RNA sequencing tool trait transcriptome sequencing

项目摘要

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.

抽象的 GWAS 社区现已识别出数千种影响复杂性状的变异，但仍有我们解释这些变异为何重要的能力仍然存在重大差距。虽然有些信号会影响基因直接参与相关疾病过程，许多其他信号可能由于间接反式而产生对核心疾病基因和通路的调节作用。近年来在这方面取得了巨大进展识别影响附近基因顺式调节的变异的方法，但仍然极其困难测量和解释这些相关基因如何以及是否可能直接或通过影响疾病过程对其他基因的反式作用。在这笔赠款中，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 扰动来绘制某个基因的上游调控因子和下游靶标的技术感兴趣的基因。我们将致力于这些方法的方法改进，并将其应用到效应器中 T 细胞和调节性 T 细胞在这些重要的免疫细胞类型中绘制基因调节网络。我们将开发新的计算方法来使用这些数据推断基因网络。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来阐明基因和使用这些来解释 GWAS 数据。