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This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM) – Genomics

Open Access Research

RNA-Seq analysis implicates dysregulation of the immune system in schizophrenia

Junzhe Xu123, Jingchun Sun45, Jingchun Chen6, Lily Wang7, Anna Li2, Matthew Helm2, Steven L Dubovsky1, Silviu-Alin Bacanu6, Zhongming Zhao458* and Xiangning Chen69*

Author affiliations

1 Department of psychiatry, School of Medicine, University at Buffalo, SUNY, Buffalo, NY 14260, USA

2 VA Western New York HealthCare System, Buffalo, NY 14215, USA

3 Buffalo Psychiatric Center, Buffalo, NY 14213, USA

4 Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

5 Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN 37212, USA

6 Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA

7 Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA

8 Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, TN 37232, USA

9 Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA

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Citation and License

BMC Genomics 2012, 13(Suppl 8):S2  doi:10.1186/1471-2164-13-S8-S2

Published: 17 December 2012

Abstract

Background

While genome-wide association studies identified some promising candidates for schizophrenia, the majority of risk genes remained unknown. We were interested in testing whether integration gene expression and other functional information could facilitate the identification of susceptibility genes and related biological pathways.

Results

We conducted high throughput sequencing analyses to evaluate mRNA expression in blood samples isolated from 3 schizophrenia patients and 3 healthy controls. We also conducted pooled sequencing of 10 schizophrenic patients and matched controls. Differentially expressed genes were identified by t-test. In the individually sequenced dataset, we identified 198 genes differentially expressed between cases and controls, of them 19 had been verified by the pooled sequencing dataset and 21 reached nominal significance in gene-based association analyses of a genome wide association dataset. Pathway analysis of these differentially expressed genes revealed that they were highly enriched in the immune related pathways. Two genes, S100A8 and TYROBP, had consistent changes in expression in both individual and pooled sequencing datasets and were nominally significant in gene-based association analysis.

Conclusions

Integration of gene expression and pathway analyses with genome-wide association may be an efficient approach to identify risk genes for schizophrenia.