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.