Project description:Ex vivo large-scale proteomic analysis using LC-MS/MS in postmortem brains of patients with substance use disorder and controls. Brain tissue was collected postmortem after consent from the next of kin. As part of the clinical information, medical records were obtained and a detailed psychological autopsy was performed on all participants by interviewing the next-of-kin. Information regarding the psychiatric clinical phenotypes (evidence of depression, mania, and psychosis) stressful life events, age of drug use onset, types of drugs used, smoking and drinking history, and any co-morbidities, was obtained. A diagnosis of substance use disorder was confirmed based on the psychological autopsy, detailed medical records, and review of all relevant case information by three psychiatrists at a consensus meeting. The cause of death was obtained from the medical examiner’s report and toxicological findings after death.

2021-09-10 | PXD025269 | Pride

Genetic analysis of Substance Use Disorder using the Indiana Biobank data

Project description:Genetic analysis of Substance Use Disorder using the Indiana Biobank data

| PRJNA869819 | ENA

Genetic analysis of Substance Use Disorder using the Indiana Biobank data

Project description:Genetic analysis of Substance Use Disorder using the Indiana Biobank data

| PRJNA869818 | ENA

Demographically Diverse Substance Use Disorder Cohorts of Dr. Stanley H. Weiss

Project description:Demographically Diverse Substance Use Disorder Cohorts of Dr. Stanley H. Weiss

| PRJNA671286 | ENA

Demographically Diverse Substance Use Disorder Cohorts of Dr. Stanley H. Weiss

Project description:Demographically Diverse Substance Use Disorder Cohorts of Dr. Stanley H. Weiss

| PRJNA671287 | ENA

Systems biology of prostate cancer

Project description:Systems biology of prostate cancer

| PRJEB34826 | ENA

Analysis of association between LPHN3 markers and substance use disorder

Project description:Genetic factors are strongly implicated in the susceptibility to develop externalizing syndromes such as attention deficit/hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, and substance use disorder (SUD). Variants in the ADGRL3 (LPHN3) gene predispose to ADHD and predict ADHD severity, disruptive behaviors comorbidity, long-term outcome, and response to treatment. In this study, we investigated whether variants within ADGRL3 are associated with SUD, a disorder that is frequently co-morbid with ADHD. Using family-based, case-control, and longitudinal samples from disparate regions of the world, recruited either for clinical, genetic epidemiological or pharmacogenomic studies of ADHD, we assembled recursive-partitioning frameworks (classification tree analyses) with clinical, demographic, and ADGRL3 genetic information to predict SUD susceptibility. Our results indicate that SUD can be efficiently and robustly predicted in ADHD participants. The genetic models used remained highly efficient in predicting SUD in a large sample of individuals with severe SUD from a psychiatric institution that were not ascertained on the basis of ADHD diagnosis, thus identifying ADGRL3 as a risk gene for SUD. Recursive partitioning analyses revealed that rs4860437 was the predominant predictive variant. This new methodological approach offers novel insights into higher order predictive interactions and offers a unique opportunity for translational application in the clinical assessment of patients at high risk for SUD

2020-04-01 | GSE112652 | GEO

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