A Gene Co-Expression Network in Whole Blood of Schizophrenia Patients Is Independent of Antipsychotic-Use and Enriched for Brain-Expressed Genes

Despite large-scale genome-wide association studies (GWAS), the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood of schizophrenia patients and controls. We applied a systems biology approach to genome-wide expression data from whole blood of 92 medicated and 29 antipsychotic-free schizophrenia patients and 118 healthy controls. We show that gene expression profiling in whole blood can identify twelve large gene co-expression modules associated with schizophrenia. Several of these disease related modules are likely to reflect expression changes due to antipsychotic medication. However, two of the disease modules could be replicated in an independent second data set involving antipsychotic-free patients and controls. One of these robustly defined disease modules is significantly enriched with brain-expressed genes and with genetic variants that were implicated in a GWAS study, which could imply a causal role in schizophrenia etiology. The most highly connected intramodular hub gene in this module (ABCF1), is located in, and regulated by the major histocompatibility (MHC) complex, which is intriguing in light of the fact that common allelic variants from the MHC region have been implicated in schizophrenia. This suggests that the MHC increases schizophrenia susceptibility via altered gene expression of regulatory genes in this network

Modifier genes in SCN1A-related epilepsy syndromes

Background: SCN1A is one of the most important epilepsy-related genes, with pathogenic variants leading to a range of phenotypes with varying disease severity. Different modifying factors have been hypothesized to influence SCN1A-related phenotypes. We investigate the presence of rare and more common variants in epilepsy-related genes as potential modifiers of SCN1A-related disease severity. Methods: 87 patients with SCN1A-related epilepsy were investigated. Whole-exome sequencing was performed by the Beijing Genomics Institute (BGI). Functional variants in 422 genes associated with epilepsy and/or neuronal excitability were investigated. Differences in proportions of variants between the epilepsy genes and four control gene sets were calculated, and compared to the proportions of variants in the same genes in the ExAC database. Results: Statistically significant excesses of variants in epilepsy genes were observed in the complete cohort and in the combined group of mildly and severely affected patients, particularly for variants with minor allele frequencies of <0.05. Patients with extreme phenotypes showed much greater excesses of epilepsy gene variants than patients with intermediate phenotypes. Conclusion: Our results indicate that relatively common variants in epilepsy genes, which would not necessarily be classified as pathogenic, may play a large role in modulating SCN1A phenotypes. They may modify the phenotypes of both severely and mildly affected patients. Our results may be a first step toward meaningful testing of modifier gene variants in regular diagnostics for individual patients, to provide a better estimation of disease severity for newly diagnosed patients

Assessment of parental mosaicism in SCN1A -related epilepsy by single-molecule molecular inversion probes and next-generation sequencing

Background: Dravet syndrome is a severe genetic encephalopathy, caused by pathogenic variants in SCN1A. Low-grade parental mosaicism occurs in a substantial proportion of families (7%-13%) and has important implications for recurrence risks. However, parental mosaicism can remain undetected by methods regularly used in diagnostics. In this study, we use single-molecule molecular inversion probes (smMIP), a technique with high sensitivity for detecting low-grade mosaic variants and high cost-effectiveness, to investigate the incidence of parental mosaicism of SCN1A variants in a cohort of 90 families and assess the feasibility of this technique. Methods: Deep sequencing of SCN1A was performed using smMIPs. False positive rates for each of the proband's pathogenic variants were determined in 145 unrelated samples. If parents showed corresponding variant alleles at a significantly higher rate than the established noise ratio, mosaicism was confirmed by droplet digital PCR (ddPCR). Results: Sequence coverage of at least 100× at the location of the corresponding pathogenic variant was reached for 80 parent couples. The variant ratio was significantly higher than the established noise ratio in eight parent couples, of which four (5%) were regarded as true mosaics, based on ddPCR results. The false positive rate of smMIP analysis without ddPCR was therefore 50%. Three of these variants had previously been considered de novo in the proband by Sanger sequencing. Conclusion: smMIP technology combined withnext generation sequencing (NGS) performs better than Sanger sequencing in the detection of parental mosaicism. Because parental mosaicism has important implications for genetic counselling and recurrence risks, we stress the importance of implementing high-sensitivity NGS-based assays in standard diagnostics

A gene co-expression network in whole blood of schizophrenia patients is independent of antipsychotic-use and enriched for brain-expressed genes.

Despite large-scale genome-wide association studies (GWAS), the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood of schizophrenia patients and controls. We applied a systems biology approach to genome-wide expression data from whole blood of 92 medicated and 29 antipsychotic-free schizophrenia patients and 118 healthy controls. We show that gene expression profiling in whole blood can identify twelve large gene co-expression modules associated with schizophrenia. Several of these disease related modules are likely to reflect expression changes due to antipsychotic medication. However, two of the disease modules could be replicated in an independent second data set involving antipsychotic-free patients and controls. One of these robustly defined disease modules is significantly enriched with brain-expressed genes and with genetic variants that were implicated in a GWAS study, which could imply a causal role in schizophrenia etiology. The most highly connected intramodular hub gene in this module (ABCF1), is located in, and regulated by the major histocompatibility (MHC) complex, which is intriguing in light of the fact that common allelic variants from the MHC region have been implicated in schizophrenia. This suggests that the MHC increases schizophrenia susceptibility via altered gene expression of regulatory genes in this network

Mosaicism of de novo pathogenic SCN1A variants in epilepsy is a frequent phenomenon that correlates with variable phenotypes

Objective: Phenotypes caused by de novo SCN1A pathogenic variants are very variable, ranging from severely affected patients with Dravet syndrome to much milder genetic epilepsy febrile seizures plus cases. The most important determinant of disease severity is the type of variant, with variants that cause a complete loss of function of the SCN1A protein (α-subunit of the neuronal sodium channel Nav1.1) being detected almost exclusively in Dravet syndrome patients. However, even within Dravet syndrome disease severity ranges greatly, and consequently other disease modifiers must exist. A better prediction of disease severity is very much needed in daily practice to improve counseling, stressing the importance of identifying modifying factors in this patient group. We evaluated 128 participants with de novo, pathogenic SCN1A variants to investigate whether mosaicism, caused by postzygotic mutation, is a major modifier in SCN1A-related epilepsy. Methods: Mosaicism was investigated by reanalysis of the pathogenic SCN1A variants using single molecule molecular inversion probes and next generation sequencing with high coverage. Allelic ratios of pathogenic variants were used to determine whether mosaicism was likely. Selected mosaic variants were confirmed by droplet digital polymerase chain reaction and sequencing of different tissues. Developmental outcome was classified based on available data on intelligence quotient and school functioning/education. Results: Mosaicism was present for 7.5% of de novo pathogenic SCN1A variants in symptomatic patients. Mosaic participants were less severely affected than nonmosaic participants if only participants with truncating variants are considered (distribution of developmental outcome scores, Mann-Whitney U, P =.023). Significance: Postzygotic mutation is a common phenomenon in SCN1A-related epilepsies. Participants with mosaicism have on average milder phenotypes, suggesting that mosaicism can be a major modifier of SCN1A-related diseases. Detection of mosaicism has important implications for genetic counseling and can be achieved by deep sequencing of unique reads

Mosaicism of de novo pathogenic SCN1A variants in epilepsy is a frequent phenomenon that correlates with variable phenotypes

Objective: Phenotypes caused by de novo SCN1A pathogenic variants are very variable, ranging from severely affected patients with Dravet syndrome to much milder genetic epilepsy febrile seizures plus cases. The most important determinant of disease severity is the type of variant, with variants that cause a complete loss of function of the SCN1A protein (α-subunit of the neuronal sodium channel Nav1.1) being detected almost exclusively in Dravet syndrome patients. However, even within Dravet syndrome disease severity ranges greatly, and consequently other disease modifiers must exist. A better prediction of disease severity is very much needed in daily practice to improve counseling, stressing the importance of identifying modifying factors in this patient group. We evaluated 128 participants with de novo, pathogenic SCN1A variants to investigate whether mosaicism, caused by postzygotic mutation, is a major modifier in SCN1A-related epilepsy. Methods: Mosaicism was investigated by reanalysis of the pathogenic SCN1A variants using single molecule molecular inversion probes and next generation sequencing with high coverage. Allelic ratios of pathogenic variants were used to determine whether mosaicism was likely. Selected mosaic variants were confirmed by droplet digital polymerase chain reaction and sequencing of different tissues. Developmental outcome was classified based on available data on intelligence quotient and school functioning/education. Results: Mosaicism was present for 7.5% of de novo pathogenic SCN1A variants in symptomatic patients. Mosaic participants were less severely affected than nonmosaic participants if only participants with truncating variants are considered (distribution of developmental outcome scores, Mann-Whitney U, P =.023). Significance: Postzygotic mutation is a common phenomenon in SCN1A-related epilepsies. Participants with mosaicism have on average milder phenotypes, suggesting that mosaicism can be a major modifier of SCN1A-related diseases. Detection of mosaicism has important implications for genetic counseling and can be achieved by deep sequencing of unique reads

Description of datasets.

<p>For this study, three datasets were used; schizophrenia cases and controls, an antipsychotic-free set and a control dataset. Age and gender information is given for cases and controls separately. Gene expression data was generated in two batches (batch 1: Illumina H-8 and batch 2: Illumina H-12) and collected at different sites, information given in the fourth and fifth row). The batch effect resulting from the use of different arrays on different time points in the latter set was removed using the SampleNetwork R package <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039498#pone.0039498-Oldham3" target="_blank">[62]</a>. The number of expressed genes is given in the last row. *DK  =  Denmark and NL  =  The Netherlands.</p

Visual representation of connections of genes in the Tan schizophrenia module.

<p>This figure shows target genes of the probes in the Tan schizophrenia module with the strongest connections only (<i>r</i> >0.64). Blue-colored nodes represent brain-expressed genes. Square-shape nodes indicate <i>cis</i>-regulation. Node size is related to the number of connections of that particular gene; a highly connected gene (i.e. ‘hub gene’) is therefore larger than genes with fewer connections. Red text indicates genes previously implicated in schizophrenia. Image created using Cytoscape software <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039498#pone.0039498-Smoot1" target="_blank">[69]</a>.</p

Module eigengene significance for co-expression modules.

<p>The modules that were found by WGCNA in the first dataset are listed together with the number of genes they contain (shown in the second column). Differences in cases and controls were tested using a linear model with FDR correction. Results for the medicated cases versus controls are presented in column three and four. The modules that were found to be differentially expressed were also tested for significance between cases and controls in the antipsychotic-free set, and results are presented in the fifth and sixth column. The last column indicates the percentage of module content that was also found to be expressed in brain (log₂>4). For all genes in the other modules, this was found to be 45%. For the Tan module, this was significantly higher (Fisher <i>p</i> = 4.3×10⁻⁴).</p

Network construction identifies distinct modules of co-expressed genes.

<p>The network was constructed using gene expression data of 92 medicated schizophrenia cases and 78 controls (dataset 1). The dendrogram was produced by average linkage hierarchical clustering of genes using 1-topological overlap as dissimilarity measure (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0039498#s4" target="_blank">methods</a> section). Modules of co-expressed genes were assigned colors corresponding to the branches indicated by the horizontal bar beneath the dendrogram.</p