Transcriptomic Network Analysis Reveals Trauma Type– and Ancestry-Related Heterogeneity in Posttraumatic Stress Disorder

Abstract

OBJECTIVE: Posttraumatic stress disorder (PTSD) is a debilitating psychiatric disorder that frequently co-occurs with major depressive disorder (MDD), a comorbidity that is partly attributable to shared genetic heritability and common environmental risk factors. Despite extensive efforts to identify peripheral transcriptomic biomarkers, findings have been inconsistent, potentially reflecting the substantial biological heterogeneity underlying these disorders. The primary goal of this study was to identify molecular signatures that reflect the clinical and biological heterogeneity of PTSD and MDD. METHODS: The authors present a network analysis of blood-based transcriptomics from 466 pregnant women in South Africa, including 215 individuals with PTSD and/or MDD (mean age, 26.34 years [SD=5.77]) and 251 trauma-exposed control participants (mean age, 27.14 years [SD=5.82]). Gene expression modules associated with trauma-related phenotypes were identified, including childhood adversity, intimate partner violence, and recent life stress, as well as ancestry and depressive symptoms, highlighting multiple sources of molecular variation. To evaluate the predictive relevance of these transcriptomic features, random forest models were applied using a nested cross-validation framework with permutation testing. RESULTS: Models achieved moderate and statistically significant performance in predicting trauma-related phenotypes (mean area under the receiver operating characteristic curve [AUROC], ~0.61-0.69 across models [SD=0.07-0.10]), whereas prediction of PTSD or MDD diagnosis showed near-chance performance (mean AUROC=0.52, SD=0.03). CONCLUSIONS: These findings suggest that peripheral blood transcriptomic signatures are more strongly associated with trauma exposure profiles and population background than with diagnostic status. Collectively, the results support exposure-informed stratification as a complementary framework to traditional diagnostic categories for capturing biologically meaningful heterogeneity.