Complex psychiatry最新文献

Introduction: Suicidal thoughts and behaviors have partially distinct genetic etiologies.

Methods: We used PRS-CS to create polygenic risk scores (PRSs) from GWAS of non-suicidal self-injury, broad-sense self-harm ideation, nonfatal suicide attempt, death by suicide, and depression. Using mixed-effect models, we estimated whether these PRSs were associated with a range of suicidal thoughts and behaviors in the Collaborative Study on the Genetics of Alcoholism (N = 7,526).

Results: All PRSs were significantly associated with suicidal ideation and suicide attempt (betas = 0.08-0.44, false discovery rate [FDR] <0.023). All PRSs except non-suicidal self-injury PRS were associated with active suicidal ideation (betas = 0.14-0.22, FDR <0.003). Several associations remained significant in models where all significant PRSs were included as simultaneous predictors, and when all PRSs predicted suicide attempt, the PRS together explained 6.2% of the variance in suicide attempt. Significant associations were also observed between some PRSs and persistent suicidal ideation, non-suicidal self-injury, compounded suicide attempt, and desire to die.

Conclusion: Our findings suggest that PRS for depression does not explain the entirety of the variance in suicidal thoughts and behaviors, with PRS specifically for suicidal thoughts and behaviors making additional and sometimes unique contributions.

Introduction: Large somatic deletions of mitochondrial DNA (mtDNA) accumulate with aging in metabolically active tissues such as the brain. We have cataloged the breakpoints and frequencies of large mtDNA deletions in the human brain.

Methods: We quantified 112 high-frequency mtDNA somatic deletions across four human brain regions with the Splice-Break2 pipeline. In addition, we utilized PLINK/Seq to test the association of mitochondrial genotypes with the abundance of these high-frequency mtDNA deletions. A conservative p value threshold of 5E-08 was used to find the significant loci.

Results: One mtDNA SNP (T14798C) was significantly associated with mtDNA deletions in two brain regions, the dorsolateral prefrontal cortex (DLPFC) and the superior temporal gyrus. Since the DLPFC showed the most robust association between T14798C and two deletion breakpoints (7816-14807 and 5462-14807), this association was tested in the DLPFC of a replication sample and validated the first results. Incorporating the C allele at 14,798 bp increased the perfect/imperfect length of the repeat at the 3' breakpoint of the two associated deletions.

Conclusion: This is the first study to identify the association of mtDNA SNP with large mtDNA deletions in the human brain. The T14798C allele located in the MT-CYB gene is a common polymorphism that occurs in several mitochondrial haplogroups. We hypothesize that the T14798C association with two deletions occurs by extending the repeat length around the 3' deletion breakpoints. This simple mechanism suggests that mtDNA SNPs can affect the mitochondrial genome structure, especially in brain where high levels of reactive oxygen species lead to deletion accumulation with aging.

Introduction: Sexual assault is an urgent public health concern with both immediate and long-lasting health consequences, affecting 44% of women and 25% of men during their lifetimes. Large studies are needed to understand the unique healthcare needs of this patient population.

Methods: We mined clinical notes to identify patients with a history of sexual assault in the electronic health record (EHR) at Vanderbilt University Medical Center (VUMC), a large university hospital in the Southeastern USA, from 1989 to 2021 (N = 3,376,424). Using a phenome-wide case-control study, we identified diagnoses co-occurring with disclosures of sexual assault. We performed interaction tests to examine whether sex modified any of these associations. Association analyses were restricted to a subset of patients receiving regular care at VUMC (N = 833,185).

Results: The phenotyping approach identified 14,496 individuals (0.43%) across the VUMC-EHR with documentation of sexual assault and achieved a positive predictive value of 93.0% (95% confidence interval = 85.6-97.0%), determined by manual patient chart review. Out of 1,703 clinical diagnoses tested across all subgroup analyses, 465 were associated with sexual assault. Sex-by-trauma interaction analysis revealed 55 sex-differential associations and demonstrated increased odds of psychiatric diagnoses in male survivors.

Discussion: This case-control study identified associations between disclosures of sexual assault and hundreds of health conditions, many of which demonstrated sex-differential effects. The findings of this study suggest that patients who have experienced sexual assault are at risk for developing wide-ranging medical and psychiatric comorbidities and that male survivors may be particularly vulnerable to developing mental illness.

Introduction: Genetic correlations between brain and behavioral phenotypes in analyses from major genetic consortia have been weak and mostly nonsignificant. fMRI models of systems-level brain patterns may help improve our ability to link genes, brains, and behavior by identifying reliable and reproducible endophenotypes. Work using connectivity-based predictive modeling has generated brain-based proxies of behavioral and neuropsychological variables. If such models capture activity in inherited brain systems, they may offer a more powerful link between genes and behavior.

Method: As a proof of concept, we develop models predicting intelligence (IQ) based on fMRI connectivity and test their effectiveness as endophenotypes. We link brain and IQ in a model development dataset of N = 3,000 individuals and test the genetic correlations between brain models and measured IQ in a genetic validation sample of N = 13,092 individuals from the UK Biobank. We compare an additive connectivity-based model to multivariate LASSO and ridge models phenotypically and genetically. We also compare these approaches to single "candidate" brain areas.

Results: We found that predictive brain models were significantly phenotypically correlated with IQ and showed much stronger correlations than individual edges. Further, brain models were more heritable (h2 = 0.155-0.181) than single brain regions (h2 = 0.038-0.118) and captured about half of the genetic variance in IQ (rG = 0.422-0.576), while rGs with single brain measures were smaller and nonsignificant. For the different approaches, LASSO and ridge were similarly predictive, with slightly weaker performance of the additive model. LASSO model weights were highly theoretically interpretable and replicated known brain IQ associations. Finally, functional connectivity models trained in midlife showed genetic correlations with early life correlates of IQ, suggesting some stability in the prediction of fMRI models.

Conclusion: Multisystem predictive models hold promise as imaging endophenotypes that offer complex and theoretically relevant conclusions for future imaging genetics research.

Introduction: Chronic pain is a common condition with high socioeconomic and public health burden. A wide range of psychiatric conditions are often comorbid with chronic pain and chronic pain conditions, negatively impacting successful treatment of either condition. The psychiatric condition receiving most attention in the past with regard to chronic pain comorbidity has been major depressive disorder, despite the fact that many other psychiatric conditions also demonstrate epidemiological and genetic overlap with chronic pain. Further understanding potential mechanisms involved in psychiatric and chronic pain comorbidity could lead to new treatment strategies both for each type of disorder in isolation and in scenarios of comorbidity.

Methods: This article provides an overview of relationships between DSM-5 psychiatric diagnoses and chronic pain, with particular focus on PTSD, ADHD, and BPD, disorders which are less commonly studied in conjunction with chronic pain. We also discuss potential mechanisms that may drive comorbidity, and present new findings on the genetic overlap of chronic pain and ADHD, and chronic pain and BPD using linkage disequilibrium score regression analyses.

Results: Almost all psychiatric conditions listed in the DSM-5 are associated with increased rates of chronic pain. ADHD and BPD are significantly genetically correlated with chronic pain. Psychiatric conditions aside from major depression are often under-researched with respect to their relationship with chronic pain.

Conclusion: Further understanding relationships between psychiatric conditions other than major depression (such as ADHD, BPD, and PTSD as exemplified here) and chronic pain can positively impact understanding of these disorders, and treatment of both psychiatric conditions and chronic pain.

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.