The recent explosion in the sample sizes and diversity of omics assays has created exciting new opportunities for biomedical scientists. However, connecting these omics data types in an interpretable way remains a challenge, especially when the data come from different sources and cohorts. One approach to this problem is to build regulatory networks that reflect the support of the data. In this talk, I will discuss a new method for estimating the effects of germline genetic variants on transcription factor (TF) regulatory events. This method, EGRET, integrates an individual's genotype with regulatory data to produce an individual-specific regulatory network. When we applied EGRET to RNA-seq and genotype data collected from 119 individuals in three cell types, we find TF regulatory disruptions that are disease-specific and occur only in the cell type relevant to the disease. Together this suggests that EGRET provides valuable insight into regulatory processes influencing disease.
1. Describe the effect size distribution and typical genomic locations of GWAS variants
2. Explain which data types the EGRET method uses as input