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社区现在已经确定了数千种影响复杂特征的变体，但是有解释为什么这些变体很重要的能力仍然是一个关键的差距。虽然某些信号会影响基因直接参与相关疾病过程，由于间接反式，可能出现许多其他信号对核心疾病基因和途径的调节作用。近年来取得了巨大进展识别影响附近基因的顺式调节的变体的方法，但是很难很难测量和解释这些相关基因如何直接或通过直接影响疾病过程对其他基因的反式作用作用。在这笔赠款中，我们建议开发新的实验和计算有助于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扰动来绘制上游调节器和下游目标的技术感兴趣的基因。我们将研究这些方法的方法论改进，并将其应用于效应器 T细胞和调节性T细胞以这些重要的免疫细胞类型中绘制基因调节网络。我们将开发使用这些数据推断基因网络的新计算方法。最后，我们将发展并实施新方法来鉴于网络信息来解释自身免疫性疾病基因座。这项目将建立新的高通量技术，以阐明基因之间的调节关系用于解释GWAS数据。