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Meta-analyses of expression profiling data in the postmortem human brain Mistry, Meeta

Abstract

Schizophrenia is a severe psychiatric illness for which the precise etiology remains unknown. Studies using postmortem human brain have become increasingly important in schizophrenia research, providing an opportunity to directly investigate the diseased brain tissue. Gene expression profiling technologies have been used by a number of groups to explore the postmortem human brain and seek genes which show changes in expression correlated with schizophrenia. While this has been a valuable means of generating hypotheses, there is a general lack of consensus in the findings across studies. Expression profiling of postmortem human brain tissue is difficult due to the effect of various factors that can confound the data. The first aim of this thesis was to use control postmortem human cortex for identification of expression changes associated with several factors, specifically: age, sex, brain pH and postmortem interval. I conducted a meta-analysis across the control arm of eleven microarray datasets (representing over 400 subjects), and identified a signature of genes associated with each factor. These genes provide critical information towards the identification of problematic genes when investigating postmortem human brain in schizophrenia and other neuropsychiatric illnesses. The second aim of this thesis was to evaluate gene expression patterns in the prefrontal cortex associated with schizophrenia by exploring two methods of analysis: differential expression and coexpression. Seven schizophrenia microarray studies of prefrontal cortex were combined for a total of 153 subjects with schizophrenia and 153 healthy controls. Meta-analysis was conducted with careful consideration for the effects of covariates, revealing a robust list of 98 differentially expressed ‘schizophrenia genes’. Using the same seven schizophrenia datasets, coexpression networks were generated for control and schizophrenia cohorts within each dataset and then combined across studies using a rank aggregation approach. Topological properties of our ‘schizophrenia genes’ were evaluated in the context of each network, highlighting differences in correlation structure of these genes in the control and schizophrenia brain. Together these results converge towards a general conclusion, emphasizing that the integration of postmortem human brain expression profiling data improves statistical power and is particularly useful in detecting subtle yet consistent changes in expression associated with schizophrenia

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