Transcriptome analysis of cortical tissue reveals shared sets of downregulated genes in autism and schizophrenia

Autism (AUT), schizophrenia (SCZ) and bipolar disorder (BPD) are three highly heritable neuropsychiatric conditions. Clinical similarities and genetic overlap between the three disorders have been reported; however, the causes and the downstream effects of this overlap remain elusive. By analyzing transcriptomic RNA-sequencing data generated from post-mortem cortical brain tissues from AUT, SCZ, BPD and control subjects, we have begun to characterize the extent of gene expression overlap between these disorders. We report that the AUT and SCZ transcriptomes are significantly correlated (P<0.001), whereas the other two cross-disorder comparisons (AUT–BPD and SCZ–BPD) are not. Among AUT and SCZ, we find that the genes differentially expressed across disorders are involved in neurotransmission and synapse regulation. Despite the lack of global transcriptomic overlap across all three disorders, we highlight two genes, IQSEC3 and COPS7A, which are significantly downregulated compared with controls across all three disorders, suggesting either shared etiology or compensatory changes across these neuropsychiatric conditions. Finally, we tested for enrichment of genes differentially expressed across disorders in genetic association signals in AUT, SCZ or BPD, reporting lack of signal in any of the previously published genome-wide association study (GWAS). Together, these studies highlight the importance of examining gene expression from the primary tissue involved in neuropsychiatric conditions—the cortical brain. We identify a shared role for altered neurotransmission and synapse regulation in AUT and SCZ, in addition to two genes that may more generally contribute to neurodevelopmental and neuropsychiatric conditions.

Assessing the significance for Correlations of Cross-Disorder Transcriptomic Similarity: For each cross-disorder comparison, density plots for the correlations of the 1000 null permutations are plotted in black. The cross-disorder correlation derived from the data are plotted in red. (a) The correlation between AUT and SCZ is more extreme than the correlation in any of the 1000 null permutations (p<0.001). (b) The correlation between differential gene expression AUT and BPD is not significant relative to the null correlations (p=0.246). (c) The correlation between SCZ and BPD is similarly not significant (p=0.405).
Supplemental Figure 4 a b Accounting for Unknown Covariates Affects Correlation: The correlation between differential gene expression in SCZ and BPD reported in this paper in which the linear model included SVs to account for unknown covariates (a) relative to an analysis in which these covariates were not included (b). The lack of SV inclusion in the linear model to detect differential gene expression leads to an artificially inflated correlation.

Upregulated Downregulated
Supplemental Figure 5 NONE GO Analysis of DCEGs : Genes either concordantly upregulated (left) or concordantly downregulated (right) were analyzed for ontological enrichment of biological processes, developmental processes, and cellular component. Onotological categories with at least five genes and an adjusted p-value < 0.001 are highlighted in red. This abundance of ontological enrichment in those genes concordantly downregulated highlights the role for downregulation of genes differentially expressed in both AUT and SCZ. Enrichment of DEGs among GWAS signal : QQ plots assess enrichment of differential gene expression signal (red) among suggestive GWAS results (p<0.05). Data for 100 null permutations are plotted in gray. Each row corresponds to GWAS data from a separate disorder (AUT, BPD, SCZ from top to bottom) and each column a different cross-disorder comparison (AUT-SCZ, AUT-BPD, and SCZ-BPD from left to right).

Cross-Disorder DEGs
Enrichment of DEGs among GWAS at a more permissive p-value cutoff (p<0.1) : QQ plots assess enrichment of differential gene expression signal (red) among suggestive GWAS results (p<0.1). Data for 100 null permutations are plotted in gray. Each row corresponds to GWAS data from a separate disorder (AUT, BPD, SCZ from top to bottom) and each column a different crossdisorder comparison (AUT-SCZ, AUT-BPD, and SCZ-BPD from left to right).
Enrichment of DEGs among GWAS signal at a more stringent p-value cutoff (p<0.01) : QQ plots assess enrichment of differential gene expression signal (red) among suggestive GWAS results (p<0.01). Data for 100 null permutations are plotted in gray. Each row corresponds to GWAS data from a separate disorder (AUT, BPD, SCZ from top to bottom) and each column a different cross-disorder comparison (AUT-SCZ, AUT-BPD, and SCZ-BPD from left to right).

Enrichment of DEGs among all genes (no gene based GWAS p-value cutoff imposed) :
QQ plots demonstrate that inflation of the teststatistic is present in the data regardless of genebased p-value cut off. Data for 100 null permutations are plotted in gray. Each row corresponds to GWAS data from a separate disorder (AUT, BPD, SCZ from top to bottom) and each column a different cross-disorder comparison (AUT-SCZ, AUT-BPD, and SCZ-BPD from left to right).