Convergent functional genomics of anxiety disorders: translational identification of genes, biomarkers, pathways and mechanisms

Abstract

Anxiety disorders are prevalent and disabling yet understudied from a genetic standpoint, compared with other major psychiatric disorders such as bipolar disorder and schizophrenia. The fact that they are more common, diverse and perceived as embedded in normal life may explain this relative oversight. In addition, as for other psychiatric disorders, there are technical challenges related to the identification and validation of candidate genes and peripheral biomarkers. Human studies, particularly genetic ones, are susceptible to the issue of being underpowered, because of genetic heterogeneity, the effect of variable environmental exposure on gene expression, and difficulty of accrual of large, well phenotyped cohorts. Animal model gene expression studies, in a genetically homogeneous and experimentally tractable setting, can avoid artifacts and provide sensitivity of detection. Subsequent translational integration of the animal model datasets with human genetic and gene expression datasets can ensure cross-validatory power and specificity for illness. We have used a pharmacogenomic mouse model (involving treatments with an anxiogenic drug--yohimbine, and an anti-anxiety drug--diazepam) as a discovery engine for identification of anxiety candidate genes as well as potential blood biomarkers. Gene expression changes in key brain regions for anxiety (prefrontal cortex, amygdala and hippocampus) and blood were analyzed using a convergent functional genomics (CFG) approach, which integrates our new data with published human and animal model data, as a translational strategy of cross-matching and prioritizing findings. Our work identifies top candidate genes (such as FOS, GABBR1, NR4A2, DRD1, ADORA2A, QKI, RGS2, PTGDS, HSPA1B, DYNLL2, CCKBR and DBP), brain-blood biomarkers (such as FOS, QKI and HSPA1B), pathways (such as cAMP signaling) and mechanisms for anxiety disorders--notably signal transduction and reactivity to environment, with a prominent role for the hippocampus. Overall, this work complements our previous similar work (on bipolar mood disorders and schizophrenia) conducted over the last decade. It concludes our programmatic first pass mapping of the genomic landscape of the triad of major psychiatric disorder domains using CFG, and permitted us to uncover the significant genetic overlap between anxiety and these other major psychiatric disorders, notably the under-appreciated overlap with schizophrenia. PDE10A, TAC1 and other genes uncovered by our work provide a molecular basis for the frequently observed clinical co-morbidity and interdependence between anxiety and other major psychiatric disorders, and suggest schizo-anxiety as a possible new nosological domain.

Figure 1

Design of experiments and data analysis. (a) Pharmacological treatment paradigm. (b) Experimental design. (c) Venn diagram categorizing genes changed by the various drug treatments, and their classification into categories I, II, III and IV. (d) Multiple converging independent internal and external lines of evidence for cross-validation and prioritization of findings.

Figure 2

Behavioral correlates of diazepam and yohimbine treatment—time immobile in center zone. Analysis of mouse open field video-tracking behavioral phenotype data from 15 to 30 min after drug injections. Ratio of resting time in the center zone vs total time spent in the center zone. This measure reflects freezing behavior, an anxiety-driven phenomenon. Yohimbine increases freezing, diazepam reduces it, and co-treatment does not have an effect. One-tail t-tests are depicted. (^*) Statistically significant. The difference between diazepam and yohimbine is highly statistically significant (^**).

Figure 3

Top candidate genes for anxiety.

Figure 4

Top biological pathways for anxiety in different brain regions. Overlap between brain regions, and with the blood.

Figure 5

Anxiety disorders: reactivity to the environment.