Improving disease-gene association testing using statistical priors on genetic regulation of gene expression

One popular approach to disease-gene association testing is a transcriptome-wide association study (TWAS). Conceptually, TWAS is a test for the genetic correlation between cis-regulated gene expression and disease. However, only half of the genetic regulation of gene expression is expected to be in cis, e.g. by genetic variation within 1 Mb of the gene. The goal of this project is to develop a novel statistical method that leverages priors on SNP-gene regulatory links beyond the cis-window to improve our understanding of the genetic regulation of gene expression. As a result, our ability to identify disease-associated genes via TWAS should substantially improve due to (1) the enhanced identification of genes regulated by genetic variation and (2) the increased accuracy with which we can predict an individual's gene expression.