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Title: Gene expression data and neuropsychiatric disease
Author: Richards, Alexander
ISNI:       0000 0004 2749 969X
Awarding Body: Cardiff University
Current Institution: Cardiff University
Date of Award: 2010
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The overall aim of this study is to evaluate a diverse selection of methods for the analysis of large-scale gene expression data derived from human brain, and to apply them to furthering the understanding of heritable psychiatric disorders. One strand of research presented here focuses on using clustering algorithms to group genes according to their expression. Several methods for expression clustering were implemented and based upon brain expression datasets. Gene Ontology enrichment was then used to assess the concordance of the resulting clusters with current biological knowledge. Combining different clustering methods is the most effective strategy, as it allows the discovery of the widest range of clusters. Clusters produced by these methods were then investigated for enrichment with genes associated with, or differentially expressed in, bipolar disorder or schizophrenia. Particularly enriched clusters were further studied using the functional annotation database MetaCore. The second strand of this research focused on using control adult brain expression data and expression quantitative trait analysis to divide SNPs into those with a greater and lesser effect on global gene expression. This classification was used to enhance the prediction of schizophrenia affected status from genome-wide association study SNP data using polygenic score analysis, a method which aggregates information from a large number of loci. SNPs which have a larger effect on global gene expression are significantly superior at predicting schizophrenia affected status through polygenic score analysis, a novel finding which suggests that expression data from control adult brain can have relevance to the study of schizophrenia.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available