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S002-1

ysregulation of Immunologic Genes and Transcription Regulators in Brain and
Blood in Schizophrenia

Stephen Glatt

Psychiatric Genetic Epidemiology & Neurobiology Laboratory Medical Genetics Research Center Department of
Psychiatry and Behavioral Sciences & Neuroscience and Physiology SUNY Upstate Medical University, USA

Background/Objective: The application of microarray technology in schizophrenia research was
heralded as paradigm-shifting, as it allowed for unbiased assessment of cell and tissue function. This
technology was widely adopted, initially in studies of postmortem brain tissue, and later in studies of
peripheral blood.

Method: The collective body of SZ microarray literature suffers from inconsistencies between
studies and failure to replicate top hits, in part due to small sample sizes, cohort-specific effects,
differences in array types, and other confounders. The major unanswered question after 15 years of
microarray studies of schizophrenia is: “What have we truly learned?” To answer this question, we
performed two mega-analyses of all available microarray data from postmortem prefrontal cortices
and from ex-vivo blood tissues sampled from schizophrenia cases and non-psychotic comparison
subjects. Our approach, using a maximized sample size and adjustment of regression models per
gene to remove non-significant covariates, provides best-estimates of transcripts dysregulated in SZ.

Result: We elucidated biological pathways, co-expression networks, and genetic factors associated
with the differentially expressed genes. The identities of the most significantly dysregulated genes
and their emergent biological functions were largely distinct for each tissue, but the findings were
consistent with shared regulatory factors (e.g., coordinate activation or inhibition of transcription
factors and miRNA species across tissues). Our network-based analyses converged upon similar
patterns of heightened innate immune gene expression in both brain and blood in schizophrenia. We
also constructed generalizable machine-learning classifiers using the blood-based microarray data.

Conclusion: Our study provides an informative atlas for future pathophysiologic and biomarker
studies of schizophrenia.