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.
In the last decade there has been huge progress in identifying the genetic bases of complex diseases. However, it has been much more challenging to interpret the molecular mechanisms through which these genetic variants act to affect disease outcomes. In this project we propose to develop new techniques for studying how disease-associated genes may affect gene regulatory networks in cells, as these are one important step linking genetic variation to disease.
