Publications by authors named "Smoller J"

Background: Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

Aims: We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

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Objective: Specific modifiable factors (e.g., screen time [ST], sleep duration, physical activity, or social connections) are targets for reducing depression risk in adults.

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Purpose: The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results. We performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) with genetic data to understand which decisions may affect performance.

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Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions.

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Thanks to methodological advances, large-scale data collections, and longitudinal designs, psychiatric neuroimaging is better equipped than ever to identify the neurobiological underpinnings of youth mental health problems. However, the complexity of such endeavors has become increasingly evident, as the field has been confronted by limited clinical relevance, inconsistent results, and small effect sizes. Some of these challenges parallel those historically encountered by psychiatric genetics.

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Importance: Leveraging real-world clinical biobanks to investigate the associations between genetic and environmental risk factors for mental illness may help direct clinical screening efforts and evaluate the portability of polygenic scores across environmental contexts.

Objective: To examine the associations between sexual trauma, polygenic liability to mental health outcomes, and clinical diagnoses of schizophrenia, bipolar disorder, and major depressive disorder in a clinical biobank setting.

Design, Setting, And Participants: This genetic association study was conducted using clinical and genotyping data from 96 002 participants across hospital-linked biobanks located at Vanderbilt University Medical Center (VUMC), Nashville, Tennessee (including 58 262 individuals with high genetic similarity to the 1000 Genomes Project [1KG] Northern European from Utah reference population [1KG-EU-clustered] and 11 047 with high genetic similarity to the 1KG African-ancestry reference population of Yoruba in Ibadan, Nigeria [1KG-YRI-clustered]), and Mass General Brigham (MGB), Boston, Massachusetts (26 693 individuals with high genetic similarity to the combined European-ancestry superpopulation [1KG-EU-clustered]).

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The protein glycome of individual cell types in the brain is unexplored, despite the critical function of these modifications in development and disease. In aggregate, the most abundant asparagine (N-) linked glycans in the adult brain are high mannose structures, and specifically ManGlcNAc (Man-5), which normally exits the ER for further processing in the Golgi. Mannose structures are uncommon in other organs and often overlooked or excluded in most studies.

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Article Synopsis
  • Subcortical brain structures play a crucial role in various developmental and psychiatric disorders, and a study analyzed brain volumes in 74,898 individuals, identifying 254 genetic loci linked to these volumes, which accounted for up to 35% of variation.
  • The research included exploring gene expression in specific neural cell types, focusing on genes involved in intracellular signaling and processes related to brain aging.
  • The findings suggest that certain genetic variants not only influence brain volume but also have potential causal links to conditions like Parkinson’s disease and ADHD, highlighting the genetic basis for risks associated with neuropsychiatric disorders.
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Genome-wide studies are yielding a growing catalog of common and rare variants that confer risk for psychopathology. However, despite representing unprecedented progress, emerging data also indicate that the full promise of psychiatric genetics-including understanding pathophysiology and improving personalized care-will not be fully realized by targeting traditional dichotomous diagnostic categories. The current article provides reflections on themes that emerged from a 2021 National Institute of Mental Health-sponsored conference convened to address strategies for the evolving field of psychiatric genetics.

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  • Current studies on electronic health records (EHR) using machine learning highlight the need for better patient representation to identify new medical patterns.
  • A novel unsupervised method was developed to embed high-dimensional EHR data and successfully predicts disease events and assesses patient diversity in complex diseases.
  • Validated on extensive datasets, the approach revealed distinct comorbidity patterns and variations in disease outcomes, demonstrating the effectiveness of representation learning in EHR analysis.
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  • The study investigates the genetic factors related to various alcohol use behaviors (AUBs) by analyzing large samples from the UK Biobank, recognizing the complexity and diversity of AUBs in its approach.
  • Researchers identified four latent genetic factors tied to AUBs, including how people consume alcohol and their drinking preferences, suggesting distinct genetic associations for each factor.
  • The findings emphasize the need for deep phenotyping and more sophisticated methods to better understand the genetics of AUBs, which remain poorly understood despite large sample sizes in previous genome-wide studies.
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Major depressive disorder (MDD) is highly prevalent in youth and generally characterized by psychiatric comorbidities. Secular trends in co-occurring diagnoses remain unclear, especially in healthcare settings. Using large-scale electronic health records data from a major U.

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Article Synopsis
  • Subcortical brain structures play a crucial role in various disorders, and a study analyzed the genetic basis of brain volumes in nearly 75,000 individuals of European ancestry, revealing 254 loci linked to these volumes.
  • The research identified significant gene expression in neural cells, relating to brain aging and signaling, and found that polygenic scores could predict brain volumes across different ancestries.
  • The study highlights genetic connections between brain volumes and conditions like Parkinson's disease and ADHD, suggesting specific gene expression patterns could be involved in neuropsychiatric disorders.
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Objective: Antidepressants are commonly prescribed medications in the United States, however, factors underlying response are poorly understood. Electronic health records (EHRs) provide a cost-effective way to create and test response algorithms on large, longitudinal cohorts. We describe a new antidepressant response algorithm, validation in two independent EHR databases, and genetic associations with antidepressant response.

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  • Major depressive disorder (MDD) is a common illness that causes difficulties in life, and scientists are studying how genetics and environment influence it.
  • This study looked at 10,032 people in Nepal to understand the genetic factors related to MDD and found that both genetics and a person's life experiences matter.
  • Although the genetic factors for MDD in Nepal were similar to those found in European studies, the methods used for predicting MDD based on European data did not work well for Nepalese people, suggesting more research is needed.
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  • Prior research was limited in understanding how cardiometabolic risk factors (CVDRFs), like hypertension and diabetes, mediate the link between anxiety/depression and cardiovascular disease (CVD).
  • The study followed over 71,000 participants for a decade to assess the impact of anxiety/depression on the development of CVDRFs and the role of neuro-immune mechanisms, with findings showing a clear association and effect differences based on age and sex.
  • Results indicated that anxiety/depression significantly increases the risk of developing CVDRFs, especially in younger women, and this relationship may be influenced by neuro-immune pathways.
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Polygenic risk scores (PRS) continue to improve with novel methods and expanding genome-wide association studies. Healthcare and commercial laboratories are increasingly deploying PRS reports to patients, but it is unknown how the classification of high polygenic risk changes across individual PRS. Here, we assessed association and classification performance of cataloged PRS for three complex traits.

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  • The study looked at how to find people who were newly diagnosed with bipolar disorder and had episodes of mania by using a big database in Massachusetts.
  • Researchers found 583 young individuals who seemed to have new bipolar disorder diagnoses in 2016, and many had seen doctors for other issues before their diagnosis.
  • The research showed that it's important to look at a lot of medical history from different sources to correctly identify new cases of bipolar disorder with mania.
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  • PheMIME is an interactive visualization tool developed to analyze and characterize multimorbidity patterns across different populations using data from large-scale electronic health record (EHR) systems.
  • It integrates data from institutions like Vanderbilt University and Mass General Brigham, allowing users to explore complex disease relationships through dynamic, multi-faceted visualizations and analyses.
  • The tool enhances our understanding of patient health by making it easier to identify disease associations, ultimately contributing to more personalized healthcare strategies.
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Background: Previous epidemiological research has linked posttraumatic stress disorder (PTSD) with specific physical health problems, but the comprehensive landscape of medical conditions associated with PTSD remains uncharacterized. Electronic health records provide an opportunity to overcome clinical knowledge gaps and uncover associations with biological relevance that potentially vary by sex.

Methods: PTSD was defined among biobank participants ( = 145,959) in 3 major healthcare systems using 2 ICD code-based definitions: broad (≥1 PTSD or acute stress codes vs.

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Article Synopsis
  • * A genome-wide association meta-analysis of nearly 122,000 ANX cases revealed 58 significant genetic variants and 66 related genes, with many of these findings replicated in a larger independent sample.
  • * The findings indicate a substantial genetic overlap between ANX and other conditions like depression, emphasizing GABAergic signaling as a key mechanism, thereby enhancing our understanding of the genetic basis of ANX for future research.
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