Network analysis of the post synaptic proteome and its implication for cognition
dc.contributor.advisor
Armstrong, Douglas
dc.contributor.advisor
Deary, Ian
dc.contributor.author
Robertson, Grant
dc.contributor.sponsor
Medical Research Council (MRC)
en
dc.date.accessioned
2022-12-15T16:46:44Z
dc.date.available
2022-12-15T16:46:44Z
dc.date.issued
2022-12-15
dc.description.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.
en
dc.identifier.uri
https://hdl.handle.net/1842/39620
dc.identifier.uri
http://dx.doi.org/10.7488/era/2869
dc.language.iso
en
en
dc.publisher
The University of Edinburgh
en
dc.subject
Network analysis
en
dc.subject
synapse
en
dc.subject
proteomics
en
dc.title
Network analysis of the post synaptic proteome and its implication for cognition
en
dc.type
Thesis or Dissertation
en
dc.type.qualificationlevel
Doctoral
en
dc.type.qualificationname
PhD Doctor of Philosophy
en
Files
Original bundle
1 - 1 of 1
- Name:
- RobertsonG-2022.pdf
- Size:
- 28.33 MB
- Format:
- Adobe Portable Document Format
- Description:
This item appears in the following Collection(s)

