Genomics yields biological and phenotypic insights into bipolar disorder.

Bipolar disorder is a leading contributor to the global burden of disease. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.

Unsupervised Ensemble Learning for Efficient Integration of Pre-trained Polygenic Risk Scores.

The growing availability of pre-trained polygenic risk score (PRS) models has enabled their integration into real-world applications, reducing the need for extensive data labeling, training, and calibration. However, selecting the most suitable PRS model for a specific target population remains challenging, due to issues such as limited transferability, data het-erogeneity, and the scarcity of observed phenotype in real-world settings. Ensemble learning offers a promising avenue to enhance the predictive accuracy of genetic risk assessments, but most existing methods often rely on observed phenotype data or additional genome-wide association studies (GWAS) from the target population to optimize ensemble weights, limiting their utility in real-time implementation.

Identifying genetic differences between bipolar disorder and major depression through multiple genome-wide association analyses.

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.

Evaluating hypothetical prevention strategies for internalizing symptoms in the general population and at-risk children.

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

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

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.

Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research.

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.

Psychiatric neuroimaging at a crossroads: Insights from psychiatric genetics.

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.

Sexual Trauma, Polygenic Scores, and Mental Health Diagnoses and Outcomes.

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]).

The restricted N-glycome of neurons is programmed during differentiation.

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.

Advancing Mental Health Research Through Strategic Integration of Transdiagnostic Dimensions and Genomics.

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.

Time Trends in Adolescent Diagnoses of Major Depressive Disorder and Co-occurring Psychiatric Conditions in Electronic Health Records.

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.

Cross-EHR validation of antidepressant response algorithm and links with genetics of psychiatric traits.

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.

Instability of high polygenic risk classification and mitigation by integrative scoring.

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.