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The application of microarray technology in schizophrenia research was heralded as
paradigm-shifting, as it allowed for high-throughput 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. The collective body of schizophrenic
microarray literature contains apparent, inconsistencies between studies, with failures
to replicate top hits, in part due to small sample sizes, cohort-specific effects,
differences in array types, and other confounders. In an attempt to summarize
existing studies of schizophrenia cases and non-related comparison subjects, we i
performed two mega-analyses of a combined set of microarray data from postmortem
prefrontal cortices (n = 315) and from ex-vivo blood tissues (n = 578). We adjusted
regression models per gene to remove non-significant covariates, providing bestestimates of transcripts dysregulated in schizophrenia. We also examined
dysregulation of functionally related gene sets and gene co-expression modules, and
assessed enrichment of cell types and genetic risk factors. The identities of the most
significantly dysregulated genes were largely distinct for each tissue, but the findings
indicated common emergent biological functions (e.g. immunity) and regulatory
factors (e.g., predicted targets of transcription factors and miRNA species across !
i
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-learnjng classifiers using the blood-based
microarray data. Our stu^y provides an informative atlas for future pathophysiologic
and biomarker studies of schizophrenia.
