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Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.

Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African–European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P  values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants. Tractor is a statistical framework that facilitates the inclusion of admixed individuals in association studies by leveraging local ancestry. Tractor generates accurate ancestry-specific effect-size estimates and improves the resolution of association signals.

Background Understanding the factors that determine distinct courses of anxiety symptoms throughout development will better guide interventions. There are scarce data‐driven longitudinal studies, using multi‐modal predictors, investigating the chronicity of anxiety symptoms from childhood to young adulthood, particularly in a middle‐income country. Methods 2033 youths (ages 6–14 years [Mean age = 10.4 ± 1.94) at Baseline] were enrolled in the Brazilian High‐Risk Cohort for Mental Conditions longitudinal study, and assessed at three timepoints, between 2010 and 2019, using the Screen for Child Anxiety Related Disorders. Confirmatory Factor Analysis provided input to Growth Mixture Models to identify the best fitting trajectory model. Multinomial logistic regression analyses tested the effects of intelligence quotient (IQ), environmental factors and polygenic risk scores on internalizing symptomatology within trajectory class membership. Results The best model solution identified three classes: high‐decreasing, moderate/low‐stable and low‐increasing symptoms over time. The high‐decreasing class showed a higher incidence of anxiety symptoms at the second time point (Mean age = 13.8 ± 1.93); while anxiety symptoms were highest in the low‐increasing class at the third timepoint (Mean age = 18.35 ± 2.03). Further, lower IQ predicted membership in the high‐decreasing trajectory class (OR = 0.68, 95% CI [0.55, 0.85]), while higher IQ predicted membership in the low‐increasing trajectory class (OR = 1.95, 95% CI [1.42, 2.67]). Finally, females were more likely than males to be in the low‐increasing trajectory class. Polygenic risk scores were not associated with anxiety trajectory class membership. Conclusion Recognizing that anxiety symptoms follow diverse paths over time will allow for more effective intervention strategies. Specifically, interventions could accommodate children for greater anxiety risk in early childhood (i.e., lower IQ) versus late adolescence (i.e., higher IQ). That said, the emotional needs of girls in late adolescence should be monitored, regardless of their cognitive abilities or high achievements. Three distinct trajectories of anxiety symptoms were identified across three assessments spanning from childhood to young adulthood: a moderate/low stable class, a high‐decreasing class, and a low‐increasing class. Female sex and higher IQ predicted the low‐increasing trajectory, while a lower IQ predicted the high‐decreasing trajectory. These findings have significant implications for understanding how anxiety symptoms evolve over time and the contributing factors.

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.

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.

Schizophrenia (SCZ) is a mental disorder arising from a complex interaction of genetic and environmental factors. It has been suggested that treatment-resistant schizophrenia (TRS) is a distinct, more severe, and homogenous subgroup of schizophrenia that could present specific biological markers. Our aim was to characterize expression of target genes in blood of TRS patients compared with non-TRS (NTRS) patients and healthy controls (HC). TRS has been defined using failure to respond to two previous antipsychotic trials. We hypothesized that genes involved in neurodevelopment, myelination, neuroplasticity, neurotransmission, and miRNA processing could be involved in treatment resistance; then, we investigated 13 genes related to those processes in 256 subjects, being 94 healthy controls and 162 schizophrenia patients treated with antipsychotics. Of those, 78 were TRS patients and 84 were NTRS patients. Peripheral blood samples were collected from all subjects and RNA was isolated. Gene expression analysis was performed using the TaqMan low-density array (TLDA) technology. To verify the influence of expression quantitative trait loci (eQTLs), we evaluated single-nucleotide polymorphism (SNP) of all genes using data from GTEx Project. SNP genotypes were obtained from HumanOmniExpress BeadChip. We did not detect gene expression differences between TRS and NTRS subjects, indicating candidate genes specific to treatment resistance. We detected an upregulation of CNR1 and UFD1L gene expression in patients (TRS and NTRS groups) when compared to controls, that may be associated with the release of neurotransmitters, which can influence neuronal plasticity, or with a stress response-activating protein degradation. DICER1 and AKT1 expression increased slightly across the groups and could differentiate only the extreme opposite groups, HC and TRS. Both genes act in heterogeneous pathways, such as cell signaling and miRNA processing, and seem to have an increased demand in the TRS group. We did not detect any eQTLs in our sample that could explain differences in mRNA levels, suggesting a possible regulation by other mechanism, not driven by genotypes. Our data strengthen the importance of several biological pathways involved in the schizophrenia refractoriness and severity, adding knowledge to develop more effective treatments in the future.

Background Psychiatric symptomatology during late childhood and early adolescence tends to persist later in life. In the present longitudinal study, we aimed to identify changes in genome-wide DNA methylation patterns that were associated with the emergence of psychopathology in youths from the Brazilian High-Risk Cohort (HRC) for psychiatric disorders. Moreover, for the differentially methylated genes, we verified whether differences in DNA methylation corresponded to differences in mRNA transcript levels by analyzing the gene expression levels in the blood and by correlating the variation of DNA methylation values with the variation of mRNA levels of the same individuals. Finally, we examined whether the variations in DNA methylation and mRNA levels were correlated with psychopathology measurements over time. Methods We selected 24 youths from the HRC who presented with an increase in dimensional psychopathology at a 3-year follow-up as measured by the Child Behavior Checklist (CBCL). The DNA methylation and gene expression data were compared in peripheral blood samples ( n  = 48) obtained from the 24 youths before and after developing psychopathology. We implemented a methodological framework to reduce the effect of chronological age on DNA methylation using an independent population of 140 youths and the effect of puberty using data from the literature. Results We identified 663 differentially methylated positions (DMPs) and 90 differentially methylated regions (DMRs) associated with the emergence of psychopathology. We observed that 15 DMPs were mapped to genes that were differentially expressed in the blood; among these, we found a correlation between the DNA methylation and mRNA levels of RB1CC1 and a correlation between the CBCL and mRNA levels of KMT2E. Of the DMRs, three genes were differentially expressed: ASCL2 , which is involved in neurogenesis; HLA-E , which is mapped to the MHC loci; and RPS6KB1 , the gene expression of which was correlated with an increase in the CBCL between the time points. Conclusions We observed that changes in DNA methylation and, consequently, in gene expression in the peripheral blood occurred concurrently with the emergence of dimensional psychopathology in youths. Therefore, epigenomic modulations might be involved in the regulation of an individual’s development of psychopathology.

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.Human brain structure changes throughout the lifespan. Brouwer et al. identified genetic variants that affect rates of brain growth and atrophy. The genes are linked to early brain development and neurodegeneration and suggest involvement of metabolic processes.

Admixed populations are routinely excluded from medical genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulations and empirical data focused on admixed African-European individuals. Tractor generates ancestry-specific effect size estimates, can boost GWAS power, and improves the resolution of association signals. Using a local ancestry aware regression model, we replicate known hits for blood lipids in admixed populations, discover novel hits missed by standard GWAS procedures, and localize signals closer to putative causal variants. Competing Interest Statement M.J.D. is a founder of Maze Therapeutics. A.R.M. serves as a consultant for 23andMe and is a member of the Precise.ly Scientific Advisory Board. B.M.N. is a member of the Deep Genomics Scientific Advisory Board and serves as a consultant for the Camp4 Therapeutics Corporation, Takeda Pharmaceutical and Biogen. The remaining authors declare no competing interests. Footnotes * https://github.com/eatkinson/Tractor

Introduction Obstructive sleep apnea (OSA) is a sleep-related breathing disorder that has a complex phenotype. Currently, few genes have been linked with OSA in cross-sectional studies. Thus, the aim of this study was to conduct a genome-wide association study (GWAS) of the apnea-hypopnea index (AHI) variation along time in a prospective cohort, and to correlate its possible alleles frequencies with the development of new cases of OSA. Methods We used data derived from EPISONO follow-up cohort. Our phenotype of interest was delta-AHI and incident OSA. DNA was genotyped for 730,525 SNPs. Our final GWAS model used delta-AHI as a dependent variable and the SNPs and covariates as independent variables. We also performed a gene-set and pathway analysis using Magma software. Results We found 2 significant and 23 suggestive loci associated with delta-AHI. The strongest association (rs12415421, (SE)=0.2782 (0.04932), p=3.36×10-8) was observed at ST8SIA6 gene and the other significant hit (rs4731117, β (SE)=0.2782 (0.04932), p=3.36×10-8) was in an intergenic region in linkage disequilibrium with our third hit (rs12669165, β (SE)=0.2799 (0.05009) mins/allele, p=4.41×10-8) in the ASB15gene. We found an independent effect of the allele rs12415421 for the incidence of OSA (OR=5.1, CI=1.7-15.4, p=0.004). Additionally, we observed that individuals with both risk alleles presented a higher incidence of OSA when compared to those with one (OR=17.3, CI=1.4-216.7, p=0.027) or without any risk alleles (OR=20.1, CI=1.7-232.7, p=0.016). Conclusion In conclusion, this study found two novel genomic regions significantly associated with the increase in AHI that seem to be involved in the growth and stability of the muscle and bone; and the allele frequencies of these SNPs were independent risk factors for the incidence of OSA in the EPISONO longitudinal cohort. Support (If Any) This work was supported by grants from AFIP, FAPESP and CAPES.