Beyond being one the most widely used psychoactive drugs in the world, cannabis has been identified as an environmental risk factor for psychosis. Though the relationship between cannabis use and psychiatric disorders remains controversial, consistent association between early adolescent cannabis use and the subsequent risk of psychosis suggested adolescence may be a particularly vulnerable period. Previous findings on gene by environment interactions indicated that cannabis use may only increase the risk for psychosis in the subjects who have a specific genetic vulnerability. The type 1 cannabinoid receptor (CB1), encoded by the CNR1 gene, is a key component of the endocannabinoid system. As the primary endocannabinoid receptor in the brain, CB1 is the main molecular target of the endocannabinoid ligand, as well as tetrahydrocannabinol (THC), the principal psychoactive ingredient of cannabis. In this study, we have examined mRNA expression and DNA methylation of CNR1 in human prefrontal cortex (PFC), hippocampus, and caudate samples. The expression of CNR1 is higher in fetal PFC and hippocampus, then drops down dramatically after birth. The lifespan trajectory of CNR1 expression in the DLPFC differentially correlated with age by allelic variation at rs4680, a functional polymorphism in the COMT gene. Compared with COMT methionine¹⁵⁸ carriers, Caucasian carriers of the COMT valine¹⁵⁸ allele have a stronger negative correlation between the expression of CNR1 in DLPFC and age. In contrast, the methylation level of cg02498983, which is negatively correlated with the expression of CNR1 in PFC, showed the strongest positive correlation with age in PFC of Caucasian carriers of COMT valine¹⁵⁸. Additionally, we have observed decreased mRNA expression of CNR1 in the DLPFC of patients with schizophrenia. Further analysis revealed a positive eQTL SNP, rs806368, which predicted the expression of a novel transcript of CNR1 in human DLPFC, hippocampus and caudate. This SNP has been associated with addiction and other psychiatric disorders. THC or ethanol are each significantly associated with dysregulated expression of CNR1 in the PFC of patients with affective disorder, and the expression of CNR1 is significantly upregulated in the PFC of schizophrenia patients who completed suicide. Our results support previous studies that have implicated the endocannabinoid system in the pathology of schizophrenia and provided additional insight into the mechanism of increasing risk for schizophrenia in the adolescent cannabis users.

Genomic data production is at its highest level and continues to increase, making available novel primary data and existing public data to researchers for exploration. Here we explore the consequences of “batch” correction for biological discovery in two publicly available expression datasets. We consider this to include the estimation of and adjustment for wide-spread systematic heterogeneity in genomic measurements that is unrelated to the effects under study, whether it be technical or biological in nature. We present three illustrative data analyses using surrogate variable analysis (SVA) and describe how to perform artifact discovery in light of natural heterogeneity within biological groups, secondary biological questions of interest, and non-linear treatment effects in a dataset profiling differentiating pluripotent cells (GSE32923) and another from human brain tissue (GSE30272). Careful specification of biological effects of interest is very important to factor-based approaches like SVA. We demonstrate greatly sharpened global and gene-specific differential expression across treatment groups in stem cell systems. Similarly, we demonstrate how to preserve major non-linear effects of age across the lifespan in the brain dataset. However, the gains in precisely defining known effects of interest come at the cost of much other information in the “cleaned” data, including sex, common copy number effects and sample or cell line-specific molecular behavior. Our analyses indicate that data “cleaning” can be an important component of high-throughput genomic data analysis when interrogating explicitly defined effects in the context of data affected by robust technical artifacts. However, caution should be exercised to avoid removing biological signal of interest. It is also important to note that open data exploration is not possible after such supervised “cleaning”, because effects beyond those stipulated by the researcher may have been removed. With the goal of making these statistical algorithms more powerful and transparent to researchers in the biological sciences, we provide exploratory plots and accompanying R code for identifying and guiding “cleaning” process ( https://github.com/andrewejaffe/StemCellSVA ). The impact of these methods is significant enough that we have made newly processed data available for the brain data set at http://braincloud.jhmi.edu/plots/ and GSE30272.

Anorexia nervosa (AN), bulimia nervosa (BN) and obsessive-compulsive disorder (OCD) are complex psychiatric disorders with shared obsessive features, thought to arise from the interaction of multiple genes of small effect with environmental factors. Potential candidate genes for AN, BN and OCD have been identified through clinical association and neuroimaging studies; however, recent genome-wide association studies of eating disorders (ED) so far have failed to report significant findings. In addition, few, if any, studies have interrogated postmortem brain tissue for evidence of expression quantitative trait loci (eQTLs) associated with candidate genes, which has particular promise as an approach to elucidating molecular mechanisms of association. We therefore selected single-nucleotide polymorphisms (SNPs) based on candidate gene studies for AN, BN and OCD from the literature, and examined the association of these SNPs with gene expression across the lifespan in prefrontal cortex of a nonpsychiatric control cohort (N=268). Several risk-predisposing SNPs were significantly associated with gene expression among control subjects. We then measured gene expression in the prefrontal cortex of cases previously diagnosed with obsessive psychiatric disorders, for example, ED (N=15) and OCD/obsessive-compulsive personality disorder or tics (OCD/OCPD/Tic; N=16), and nonpsychiatric controls (N=102) and identified 6 and 286 genes that were differentially expressed between ED compared with controls and OCD cases compared with controls, respectively (false discovery rate (FDR) <5%). However, none of the clinical risk SNPs were among the eQTLs and none were significantly associated with gene expression within the broad obsessive cohort, suggesting larger sample sizes or other brain regions may be required to identify candidate molecular mechanisms of clinical association in postmortem brain data sets.

We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).