Complex psychiatry最新文献

Introduction: Genome-wide association studies (GWAS) have played a critical role in identifying many thousands of loci associated with complex phenotypes and diseases. This has led to several translations of novel disease susceptibility genes into drug targets and care. This however has not been the case for analyses where sample sizes are small, which suffer from multiple comparisons testing. The present study examined the statistical impact of combining a burden test methodology, PrediXcan, with a multimodel meta-analysis, cross phenotype association (CPASSOC).

Methods: The analysis was conducted on 5 addiction traits: family alcoholism, cannabis craving, alcohol, nicotine, and cannabis dependence and 10 brain tissues: anterior cingulate cortex BA24, cerebellar hemisphere, cortex, hippocampus, nucleus accumbens basal ganglia, caudate basal ganglia, cerebellum, frontal cortex BA9, hypothalamus, and putamen basal ganglia. Our sample consisted of 1,640 participants from the University of California, San Francisco (UCSF) Family Alcoholism Study. Genotypes were obtained through low pass whole genome sequencing and the use of Thunder, a linkage disequilibrium variant caller.

Results: The post-PrediXcan, gene-phenotype association without aggregation resulted in 2 significant results, HCG27 and SPPL2B. Aggregating across phenotypes resulted no significant findings. Aggregating across tissues resulted in 15 significant and 5 suggestive associations: PPIE, RPL36AL, FOXN2, MTERF4, SEPTIN2, CIAO3, RPL36AL, ZNF304, CCDC66, SSPOP, SLC7A9, LY75, MTRF1L, COA5, and RRP7A; RPS23, GNMT, ERV3-1, APIP, and HLA-B, respectively.

Discussion: Given the relatively small size of the cohort, this multimodel approach was able to find over a dozen significant associations between predicted gene expression and addiction traits. Of our findings, 8 had prior associations with similar phenotypes through investigation of the GWAS Atlas. With the onset of improved transcriptome data, this approach should increase in efficacy.

Post-traumatic stress disorder is a prevalent disorder within the USA and worldwide with a yearly diagnosis rate of 2-4% and affecting women more than men. One of the primary methods for study of this stress disorder relies on animal models as there are few noninvasive methods and few replicated peripheral biomarkers for use in humans. One area of active research in psychiatric neuroscience is the field of epigenetics - how the chemical modifications of the genetic code regulate behavior. The dynamic changes in histone acetylation and deacetylation in the brain are not fully reflected by the study of peripheral biomarker. In this review, we aim to examine the role of histone acetylation and deacetylation in memory formation and fear memory learning. The studies discussed here focus largely on the role of histone deacetylases (HDACs) in animal models of trauma and fear response. Many studies used HDAC inhibitors to elucidate the effects after inhibition of these enzymes after trauma or stress. These studies of memory processing and cued fear extinction in animal can often shed light on human disorders of cued fear responses and memory dysregulation after stress or trauma such as in PTSD. These results provide strong evidence for a role of these enzymes in PTSD in humans. The few clinical studies that exist with HDAC inhibitors also suggest a fundamental role of these enzymes in the neurobiology of the stress response. Further study of these enzymes in both clinical and pre-clinical settings may help elucidate the neurobiology of stress-related pathology like PTSD and provide a foundation for novel therapy to treat these disorders.

Introduction: Opioid use disorders (OUDs) constitute a major public health issue, and we urgently need alternative methods for characterizing risk for OUD. Electronic health records (EHRs) are useful tools for understanding complex medical phenotypes but have been underutilized for OUD because of challenges related to underdiagnosis, binary diagnostic frameworks, and minimally characterized reference groups. As a first step in addressing these challenges, a new paradigm is warranted that characterizes risk for opioid prescription misuse on a continuous scale of severity, i.e., as a continuum.

Methods: Across sites within the PsycheMERGE network, we extracted prescription opioid data and diagnoses that co-occur with OUD (including psychiatric and substance use disorders, pain-related diagnoses, HIV, and hepatitis C) for over 2.6 million patients across three health registries (Vanderbilt University Medical Center, Mass General Brigham, Geisinger) between 2005 and 2018. We defined three groups based on levels of opioid exposure: no prescriptions, minimal exposure, and chronic exposure and then compared the comorbidity profiles of these groups to the full registries and to those with OUD diagnostic codes.

Results: Our results confirm that EHR data reflects known higher prevalence of substance use disorders, psychiatric disorders, medical, and pain diagnoses in patients with OUD diagnoses and chronic opioid use. Comorbidity profiles that distinguish opioid exposure are strikingly consistent across large health systems, indicating the phenotypes described in this new quantitative framework are robust to health systems differences.

Conclusion: This work indicates that EHR prescription opioid data can serve as a platform to characterize complex risk markers for OUD using existing data.