Project title: Multi-pronged approach to systematically identify and validate the transcriptional effects of non-coding genetic variation
PI: Chris Holmes
Up to 85% of genetic variants associated with human traits are in the ‘non-coding’ genome and alter gene expression levels rather than protein structure. It is challenging to decipher the precise effect of these variants. This project will benchmark statistical and machine learning models for linking ‘non-coding’ genetic variants to changes in gene expression and transcription. Using a combined model may reduce experimental costs, and improve the ability of models to capture long-range genome interactions.
