Transcriptomic context of DRD1 is associated with prefrontal activity and behavior during working memory

Abstract

Dopamine D receptor (DR) signaling shapes prefrontal cortex (PFC) activity during working memory (WM). Previous reports found higher WM performance associated with alleles linked to greater expression of the gene coding for DRs (). However, there is no evidence on the relationship between genetic modulation of expression in PFC and patterns of prefrontal activity during WM. Furthermore, previous studies have not considered that DRs are part of a coregulated molecular environment, which may contribute to DR-related prefrontal WM processing. Thus, we hypothesized a reciprocal link between a coregulated (i.e., coexpressed) molecular network including and PFC activity. To explore this relationship, we used three independent postmortem prefrontal mRNA datasets (total = 404) to characterize a coexpression network including Then, we indexed network coexpression using a measure (polygenic coexpression index--PCI) combining the effect of single nucleotide polymorphisms (SNPs) on coexpression. Finally, we associated the -PCI with WM performance and related brain activity in independent samples of healthy participants (total = 371). We identified and replicated a coexpression network including , whose coexpression was correlated with-PCI. We also found that -PCI was associated with lower PFC activity and higher WM performance. Behavioral and imaging results were replicated in independent samples. These findings suggest that genetically predicted expression of and of its coexpression partners stratifies healthy individuals in terms of WM performance and related prefrontal activity. They also highlight genes and SNPs potentially relevant to pharmacological trials aimed to test cognitive enhancers modulating signaling.

The genetic determinants of language network dysconnectivity in drug-naïve early stage schizophrenia

“…To test the hypothesis if there is a relation between the altered functional connectivity in language-related areas and language-related gene mutations, we carried out a genetic association analysis using the pathway-based polygenic risk scores 18 , 19 , as gene sets centering on putative core function, or belonging to specific biologically relevant pathways, may make a larger-than-expected contribution to polygenic risk 21 , 66 . In particular, we chose the candidate genes that are related to schizophrenia, development, and language by combing knowledge from multiple sources, including existing literature, gene ontology analysis, and brain gene expression data.…”

“…To this end we obtained pathway-specific polygenic risk scores (PRS) from language-related genes. Selecting a subset of genes that are grouped into the same pathway, or co-expressed with given candidate genes for calculating PRS, termed 'pathway-specific PRS' or biologically informed PRS [18][19][20] , is widely used in image genetics of psychiatric disorders including schizophrenia 21,22 . The goal of this work is to bridge the mechanistic gap between genetic variations and neuroimaging-based measure of brainwide dysconnectivity in schizophrenia.…”

Brain state stability during working memory is explained by network control theory, modulated by dopamine D1/D2 receptor function, and diminished in schizophrenia

“…Following from the dual-state theory of network function, the stability of task-related brain states should be related to prefrontal D1 receptor status. To estimate individual prefrontal D1 receptor expression, we utilized methods relating prefrontal cortex D1 and D2 receptor expression to genetic variation in their co-expression partner (Online Methods), thereby enabling us to predict individual dopamine receptor expression levels from genotype data across the whole genome (12,13). We found that D1 (but not the D2) expression-related gene score predicted stability of both states ( Fig.…”

A miR-137-related biological pathway of risk for Schizophrenia is associated with human brain emotion processing

“…We used SPM12 to perform multiple regression analyses for both tasks and for the two samples separately. For the co-expression analysis, we used the linear and quadratic terms of PCImiR-137 (30,66,78) as predictors and age, gender, and five genomic eigenvariates as covariates. For the PRSmiR-137 analyses, we used PRSs as predictors and the same covariates reported above.…”

“…In order to test systems-level phenotypes associated with the module of interest, we generated a Polygenic Co-expression Index (PCImiR-137). We identified SNPs in the module genes associated with the first principal component of module gene expression (a measure of co-expression of the whole module) and combined them into the PCImiR-137 (13,30,66) (SI-1.4). We estimated the effect of allelic dosage via a Robust Linear Model (rlm function -robust R package) and ranked SNPs according to their p-value (31).…”