Network analysis of the post synaptic proteome and its implication for cognition

Abstract

The post synaptic proteome is a complex of more than 3,000 proteins that form modular molecular machines that are involved in signal transduction, information processing and learning in the central nervous system. Previous analyses have used techniques from network science such as community detection to show differential enrichment of network modules using over-representation analysis for functions and disorders.

In this thesis I extend these techniques by combining network analysis including community detection with Gene Set Analysis using the full polygenic signal available from summary GWA studies. Using large scale population discovery and replication samples I show that distinct communities in the post synaptic proteome have an enriched association for genes linked to human cognitive ability

No evidence is found for an effect of network centrality on the propensity of genes to have variants associated with differences in intelligence but these are found to be associated with increased loss of function intolerance.

This thesis describes a principled method in which network analysis of protein interactions can be used to understand human population genetic studies and could be extended to neuropsychiatric disorders affecting the synapse.