Bioinformatics-Driven Identification and Prioritization of PTSD Targets Based on Published Multi-omic Data

Efforts in recent years to uncover neurobiological mechanisms underlying post-traumatic stress disorder (PTSD) have yielded an expanding candidate pool of targets from genomic and transcriptomic data. However, not all candidates are disease-causing, related to pathological mechanisms, clinically relevant, nor druggable by conventional means. An effective method to systematically identify and prioritize high-confidence, high-impact targets in the central nervous system (CNS) is required to de-risk resource-intensive experimental validation of disease mechanisms and accelerate the development of novel treatments. Here, we describe methods and implementation of a novel 3-phased, biologically rationalized, and quantitative prioritization strategy to identify and rank PTSD-associated targets based on confidence of association to PTSD and estimated CNSrelevant pathogenicity. Phase 1 was designed to identify and advance targets confidently associated with PTSD through their expression in CNS tissues. Putative targets derived from 29 transcriptomic and genomic analyses of PTSD were evaluated for: 1. Replication in independent cohorts, 2. Observation of differential expression in PTSD CNS tissues, and 3. Demonstration of consistent direction of effect. This strategy resulted in 177 targets that passed criteria for advancement. Phase 1-selected targets were enriched for PTSD relevant traits including irritability, emotional symptoms, and insomnia (FDR <0.05). Phase 2 advanced targets with additional evidence of association to pathological CNS phenotypes. DisGeNET gene-disease association scores were applied to each Phase 1-selected target to assign a confidence score indicating that a target was associated with CNS-relevant pathology using criteria for moderate or strong evidence of CNS disease association. Phase 2 advanced 55 of the 177 (31.1%) targets. The Phase 2 target pool was enriched for CNS phenotypic abnormalities (FDR<0.05). Finally, Phase 3 enabled target prioritization by annotating targets with a composite pathogenicity score. Components of the pathogenicity score included metrics derived from drug trial databases, predicted loss-of-function intolerance, and connectivity within a protein-protein interaction network defined by PTSD-associated targets. The resulting 55 targets were ultimately prioritized by the sum of Phase 2 and Phase 3 scores, where top-ranked targets had strong evidence to support both association with PTSD in brain and high pathogenicity estimates in a CNS-relevant context. Biologically, top-ranked targets implicate transmitter systems (GABA, histamine, and estrogen), structural regulation of neurites, and protein homeostasis. Future work will be required to experimentally validate the utility of the high priority PTSD targets we identified as well as to demonstrate the general applicability of this methodology. Ultimately, we anticipate that the three phased approach will enable efficient de-risking of PTSD and other poorly understood CNS disorders.