Network Propagation to Enhance the Identification of Genetic Modifiers of Rett Syndrome (DSI-SRP)
This DSI-SRP fellowship funded Chetan Immanneni to work in the laboratory of Dr. Jeffrey L. Neul in the Department of Pediatric Neurology during the summer of 2021. Chetan is a senior graduating in May 2022 with majors in Neuroscience and Medicine, Health, and Society and a minors in Data Science.
Chetan’s project focused on developing methods to aid in identifying genetic modifiers (genes that could affect a condition) of Rett Syndrome (RTT). RTT is caused primarily by mutations in a gene called Methyl-CpG Binding Protein 2 (MECP2) (Zoghbi et. al. 1999). While specific mutations in MECP2 correlate with varying disease severity (Neul et. al. 2008), some individuals have a clinical presentation atypical of that expected for a given mutation. The central hypothesis for this project is that genetic variation beyond MECP2 may modify phenotypes of MECP2 dysfunction. Initial association testing of exome sequence data (sequencing of known coding regions of the genome) obtained from individuals with mild and severe RTT identified a limited set of candidate genes that may impact RTT severity. To amplify the sensitivity of these association tests and potentially detect additional underrepresented candidate modifiers, Chetan focused on applying network propagation algorithms to data from the exome sequencing cohort. Network propagation allows for the integration of existing biological information (e.g. known protein-protein interactions) with genetic sequence data to infer subnetworks of genes that correlate with a phenotype of interest (see Cover Figure). This is beneficial as this approach has the power to identify candidate genetic modifiers that might be missed by traditional association testing alone. With support from the DSI-SRP fellowship and a post-doctoral student (Dr. Jonathan K. Merritt), Chetan was able to implement network propagation methods and demonstrate that this method can successfully identify clusters of genes that associate with RTT severity. Beyond these proof of concept studies, Chetan plans to continue developing network propagation methods that integrate data from additional “omics” data streams (i.e. RNAseq and metabolomics data) during the 2021 academic year. Ultimately, identifying the causes of atypical severity in RTT could lead to improved diagnostics and reveal new targets for therapeutic intervention.
In addition to receiving support through a DSI-SRP fellowship, this project was supported and facilitated by the DSI Data Science Team through their regular summer workshops and demo sessions.