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Lack of diversity regarding genetic and environmental backgrounds weakens the generalization and clinical applicability of research findings on psychotic disorders. Notably, Latin Americans have been generally neglected in genetic studies, comprising less than 2% of genome-wide association study samples. But Latin American populations represent a unique opportunity for research, given the exceptionally high ethnic admixture of this group. Increasing genetic diversity is essential to improve the fine mapping of known regions associated with psychotic disorders, discover novel genetic associations, and replicate studies. Additionally, Latin America is characterized by massive social, political, and economic inequalities, all known risk factors for mental health issues, including psychotic disorders. This article aims to 1) discuss the challenges and advantages of studying Latin America's particular genetic makeup and environmental context; 2) review previous studies conducted in the region; and 3) describe three Latin American research initiatives in progress: the Neuropsychiatric Genetics of Psychosis in Mexican Populations (NeuroMEX), the Paisa, and the Latin American Network for the Study of Early Psychosis (ANDES) studies.

Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n ~ 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders.

The pathophysiology of many neuropsychiatric disorders is still poorly understood. Identification of biomarkers for these diseases could benefit patients due to better classification and stratification. Exosomes excreted into the circulatory system can cross the blood–brain barrier and carry a cell type-specific set of molecules. Thus, exosomes are a source of potential biomarkers for many diseases, including neuropsychiatric disorders. Here, we investigated exosomal proteins produced from human-induced pluripotent stem cells (iPSCs) and iPSC-derived neural stem cells, neural progenitors, neurons, astrocytes, microglia-like cells, and brain capillary endothelial cells. Of the 31 exosome surface markers analyzed, a subset of biomarkers were significantly enriched in astrocytes (CD29, CD44, and CD49e), microglia-like cells (CD44), and neural stem cells (SSEA4). To identify molecular fingerprints associated with disease, circulating exosomes derived from healthy control (HC) individuals were compared against schizophrenia (SCZ) patients and late-onset Alzheimer's disease (LOAD) patients. A significant epitope pattern was identified for LOAD (CD1c and CD2) but not for SCZ compared to HC. Thus, analysis of cell type- and disease-specific exosome signatures of iPSC-derived cell cultures may provide a valuable model system to explore proteomic biomarkers for the identification of novel disease profiles. [ABSTRACT FROM AUTHOR]

Background: Very few predictive models in Psychiatry had their performance validated in independent external samples. A previously developed multivariable demographic model for attention‐deficit/hyperactivity disorder (ADHD) accurately predicted young adulthood ADHD using clinical and demographical information collected in childhood in three samples from developed countries, but failed to replicate its performance in a sample from a developing country. Furthermore, consolidated risk factors for ADHD were not included among its predictors. Methods: Participants were 1905 children and adolescents from a community‐based sample and followed from ages 6 to 14 years at baseline to ages 14 to 23 years (mean age 18) at follow‐up. We applied the intercept and weights of the original model to the data, calculating the predicted probability of each participant according to the set of predictors collected in childhood, and compared the estimates with the actual outcome (ADHD) collected during adolescence and young adulthood. We explored the performance of the original model, and of models including novel predictors (prematurity, family history of ADHD, and polygenic risk score for ADHD). Results: The observed area under the curve of the original model was.76 (95% Confidence Interval.70 to.82). The multivariable demographical model outperformed single variable models using only prematurity, family history, or the ADHD PRS. Adding either of these variables, or all at once, did not improve the performance of the original demographical model. Conclusions: Our findings suggest that the originally developed ADHD predictive model is suitable for use in different settings for clinical and research purposes. [ABSTRACT FROM AUTHOR]

Schizophrenia is a neurodevelopmental disorder with high heritability. Several lines of evidence indicate that the PRODH gene may be related to the disorder. Therefore, our study investigates the effects of 12 polymorphisms of PRODH on schizophrenia and its phenotypes. To further evaluate the roles of the associated variants in the disorder, we have conducted magnetic resonance imaging (MRI) scans to assess cortical volumes and thicknesses. A total of 192 patients were evaluated using the Structured Clinical Interview for DSM-IV (SCID), Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale, Global Assessment of Functioning (GAF) and Clinical Global Impression (CGI) instruments. The study included 179 controls paired by age and gender. The samples were genotyped using the real-time polymerase chain reaction (PCR), restriction fragment length polymorphism (RFLP)-PCR and Sanger sequencing methods. A sample of 138 patients and 34 healthy controls underwent MRI scans. One polymorphism was associated with schizophrenia (rs2904552), with the G-allele more frequent in patients than in controls. This polymorphism is likely functional, as predicted by PolyPhen and SIFT, but it was not associated with brain morphology in our study. In summary, we report a functional PRODH variant associated with schizophrenia that may have a neurochemical impact, altering brain function, but is not responsible for the cortical reductions found in the disorder. [ABSTRACT FROM AUTHOR]

Patient response to antipsychotic drugs varies and may be related to clinical and genetic heterogeneity. This study aimed to determine the performance of clinical, genetic, and hybrid models to predict the response of first episode of psychosis (FEP). patients to the antipsychotic risperidone. We evaluated 141 antipsychotic-naïve FEP patients before and after 10 weeks of risperidone treatment. Patients who had a response rate equal to or higher than 50% on the Positive and Negative Syndrome Scale were considered responders (n = 72; 51%). Analyses were performed using a support vector machine (SVM), k-nearest neighbors (kNN), and random forests (RF). Clinical and genetic (with single-nucleotide variants [SNVs]) models were created separately. Hybrid models (clinical+genetic factors) with and without feature selection were created. Clinical models presented greater balanced accuracy 63.3% (confidence interval [CI] 0.46-0.69) with the SVM algorithm than the genetic models (balanced accuracy: 58.5% [CI 0.41-0.76] - kNN algorithm). The hybrid model, which included duration of untreated psychosis, Clinical Global Impression-Severity scale scores, age, cannabis use, and 406 SNVs, showed the best performance (balanced accuracy: 72.9% [CI 0.62-0.84] - RF algorithm). A hybrid model, including clinical and genetic predictors, can provide enhanced predictions of response to antipsychotic treatment., Competing Interests: RAB declares personal fees and non-financial support from Janssen and Ache Laboratórios Farmacêuticos, grants, and personal fees from Roche, outside the submitted work. AG declares personal fees and non-financial support from Janssen, Daiichi Sankyo, Lundbeck, Teva, Cristalia, and Ache Laboratórios Farmacêuticos outside the submitted work. CN declares personal fees and non-financial support from Janssen, Daiichi Sankyo, Lundbeck, Teva, and Ache Laboratórios Farmacêuticos outside the submitted work. No other conflicts of interest declared concerning the publication of this article.

Lack of diversity regarding genetic and environmental backgrounds weakens the generalization and clinical applicability of research findings on psychotic disorders. Notably, Latin Americans have been generally neglected in genetic studies, comprising less than 2% of genome-wide association study samples. But Latin American populations represent a unique opportunity for research, given the exceptionally high ethnic admixture of this group. Increasing genetic diversity is essential to improve the fine mapping of known regions associated with psychotic disorders, discover novel genetic associations, and replicate studies. Additionally, Latin America is characterized by massive social, political, and economic inequalities, all known risk factors for mental health issues, including psychotic disorders. This article aims to 1) discuss the challenges and advantages of studying Latin America's particular genetic makeup and environmental context; 2) review previous studies conducted in the region; and 3) describe three Latin American research initiatives in progress: the Neuropsychiatric Genetics of Psychosis in Mexican Populations (NeuroMEX), the Paisa, and the Latin American Network for the Study of Early Psychosis (ANDES) studies.