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

摘要 Gwas社区现在已经确定了数千种影响复杂特征的变异，但有 在我们解释这些变异为什么重要的能力方面，仍然存在一个严重的差距。虽然有些信号会影响那些 直接参与相关的疾病过程，许多其他信号可能是由于间接反式- 对核心疾病基因和途径的调控作用。近年来，在这方面取得了巨大的进展 识别影响附近基因顺式调控的变异的方法，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能直接或通过 对其他基因的反式作用效应。在这项拨款中，我们建议开发新的实验和计算方法 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摄动来绘制出上游调节器和下游目标 关注的基因。我们将致力于这些方法的方法学改进，并将它们应用于效应器 T细胞和调节性T细胞，以绘制出这些重要免疫细胞类型的基因调控网络。我们 将开发利用这些数据推断基因网络的新计算方法。最后，我们将发展 并根据网络信息实现了解释自身免疫性疾病基因座的新方法。这 该项目将建立新的高通量技术，以阐明基因和 使用这些数据来解释全球气候变化数据。

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.