Advances in genomics are opening new windows into the biology of schizophrenia. Though common variants individually have small effects on disease risk, GWAS provide a powerful opportunity to explore pathways and mechanisms contributing to pathophysiology. Here, we highlight an underappreciated biological theme emerging from GWAS: the role of glycosylation in schizophrenia. The strongest coding variant in schizophrenia GWAS is a missense mutation in the manganese transporter SLC39A8, which is associated with altered glycosylation patterns in humans. Furthermore, variants near several genes encoding glycosylation enzymes are unambiguously associated with schizophrenia: FUT9, MAN2A1, TMTC1, GALNT10, and B3GAT1. Here, we summarize the known biological functions, target substrates, and expression patterns of these enzymes as a primer for future studies. We also highlight a subset of schizophrenia-associated proteins critically modified by glycosylation including glutamate receptors, voltage-gated calcium channels, the dopamine D2 receptor, and complement glycoproteins. We hypothesize that common genetic variants alter brain glycosylation and play a fundamental role in the development of schizophrenia. Leveraging these findings will advance our mechanistic understanding of disease and may provide novel avenues for treatment development.
Objective: Efforts to prevent depression, the leading cause of disability worldwide, have focused on a limited number of candidate factors. Using phenotypic and genomic data from over 100,000 UK Biobank participants, the authors sought to systematically screen and validate a wide range of potential modifiable factors for depression.
Methods: Baseline data were extracted for 106 modifiable factors, including lifestyle (e.g., exercise, sleep, media, diet), social (e.g., support, engagement), and environmental (e.g., green space, pollution) variables. Incident depression was defined as minimal depressive symptoms at baseline and clinically significant depression at follow-up. At-risk individuals for incident depression were identified by polygenic risk scores or by reported traumatic life events. An exposure-wide association scan was conducted to identify factors associated with incident depression in the full sample and among at-risk individuals. Two-sample Mendelian randomization was then used to validate potentially causal relationships between identified factors and depression.
Results: Numerous factors across social, sleep, media, dietary, and exercise-related domains were prospectively associated with depression, even among at-risk individuals. However, only a subset of factors was supported by Mendelian randomization evidence, including confiding in others (odds ratio=0.76, 95% CI=0.67, 0.86), television watching time (odds ratio=1.09, 95% CI=1.05, 1.13), and daytime napping (odds ratio=1.34, 95% CI=1.17, 1.53).
Conclusions: Using a two-stage approach, this study validates several actionable targets for preventing depression. It also demonstrates that not all factors associated with depression in observational research may translate into robust targets for prevention. A large-scale exposure-wide approach combined with genetically informed methods for causal inference may help prioritize strategies for multimodal prevention in psychiatry.
Keywords: Depression; Exposome; Mendelian Randomization; Polygenic Risk; Prevention; Protective Factors.
A common missense variant in SLC39A8 is convincingly associated with schizophrenia and several additional phenotypes. Homozygous loss-of-function mutations in SLC39A8 result in undetectable serum manganese (Mn) and a Congenital Disorder of Glycosylation (CDG) due to the exquisite sensitivity of glycosyltransferases to Mn concentration. Here, we identified several Mn-related changes in human carriers of the common SLC39A8 missense allele. Analysis of structural brain MRI scans showed a dose-dependent change in the ratio of T2w to T1w signal in several regions. Comprehensive trace element analysis confirmed a specific reduction of only serum Mn, and plasma protein N-glycome profiling revealed reduced complexity and branching. N-glycome profiling from two individuals with SLC39A8-CDG showed similar but more severe alterations in branching that improved with Mn supplementation, suggesting that the common variant exists on a spectrum of hypofunction with potential for reversibility. Characterizing the functional impact of this variant will enhance our understanding of schizophrenia pathogenesis and identify novel therapeutic targets and biomarkers.
Background: Whereas genetic susceptibility increases the risk for major depressive disorder (MDD), non-genetic protective factors may mitigate this risk. In a large-scale prospective study of US Army soldiers, we examined whether trait resilience and/or unit cohesion could protect against the onset of MDD following combat deployment, even in soldiers at high polygenic risk.
Methods: Data were analyzed from 3079 soldiers of European ancestry assessed before and after their deployment to Afghanistan. Incident MDD was defined as no MDD episode at pre-deployment, followed by a MDD episode following deployment. Polygenic risk scores were constructed from a large-scale genome-wide association study of major depression. We first examined the main effects of the MDD PRS and each protective factor on incident MDD. We then tested the effects of each protective factor on incident MDD across strata of polygenic risk.
Results: Polygenic risk showed a dose-response relationship to depression, such that soldiers at high polygenic risk had greatest odds for incident MDD. Both unit cohesion and trait resilience were prospectively associated with reduced risk for incident MDD. Notably, the protective effect of unit cohesion persisted even in soldiers at highest polygenic risk.
Conclusions: Polygenic risk was associated with new-onset MDD in deployed soldiers. However, unit cohesion - an index of perceived support and morale - was protective against incident MDD even among those at highest genetic risk, and may represent a potent target for promoting resilience in vulnerable soldiers. Findings illustrate the value of combining genomic and environmental data in a prospective design to identify robust protective factors for mental health.
Keywords: Depression; genetics; longitudinal; polygenic risk; resilience; social support.
Background: Low maternal vitamin D levels [serum 25-hydroxyvitamin D (25(OH)D)] during pregnancy have been linked to offspring neuropsychiatric outcomes such as schizophrenia and autism, but studies on depression are lacking. We examined the association between maternal vitamin D status during pregnancy and offspring depression during childhood and adolescence and investigated whether any associations were modified by offspring genetic risk for depression.
Methods: Mother-singleton birth offspring pairs in the Avon Longitudinal Study of Parents and Children (ALSPAC) that had maternal 25(OH)D measurements, offspring genetic data, and offspring depression measures collected in childhood (mean age=10.6 years; n = 2938) and/or adolescence (mean age=13.8 years; n = 2485) were included in the analyses. Using multivariable logistic regression, we assessed associations between maternal vitamin D status and offspring polygenic risk score (PRS) for depression on childhood/adolescent depression risk.
Results: There was no evidence for an association between maternal vitamin D status during pregnancy and offspring depression in childhood (p = 0.72) or adolescence (p = 0.07). Offspring depression PRS were independently associated with childhood depression (p = 0.003), but did not interact with maternal vitamin D status. These results were robust to adjustments for potential confounders and different cut-offs for vitamin D insufficiency/deficiency.
Limitations: 25(OH)D measurements were only available at a single time point during pregnancy.
Conclusion: These findings suggest that maternal vitamin D status during pregnancy does not affect an offspring's risk for early life depression.
Keywords: ALSPAC; Depression; Epidemiology; Gene-environment interactions; Polygenic risk; Vitamin D.
Importance: Suicide is a leading cause of mortality, with suicide-related deaths increasing in recent years. Automated methods for individualized risk prediction have great potential to address this growing public health threat. To facilitate their adoption, they must first be validated across diverse health care settings.
Objective: To evaluate the generalizability and cross-site performance of a risk prediction method using readily available structured data from electronic health records in predicting incident suicide attempts across multiple, independent, US health care systems.
Design, setting, and participants: For this prognostic study, data were extracted from longitudinal electronic health record data comprising International Classification of Diseases, Ninth Revision diagnoses, laboratory test results, procedures codes, and medications for more than 3.7 million patients from 5 independent health care systems participating in the Accessible Research Commons for Health network. Across sites, 6 to 17 years' worth of data were available, up to 2018. Outcomes were defined by International Classification of Diseases, Ninth Revision codes reflecting incident suicide attempts (with positive predictive value >0.70 according to expert clinician medical record review). Models were trained using naive Bayes classifiers in each of the 5 systems. Models were cross-validated in independent data sets at each site, and performance metrics were calculated. Data analysis was performed from November 2017 to August 2019.
Main outcomes and measures: The primary outcome was suicide attempt as defined by a previously validated case definition using International Classification of Diseases, Ninth Revision codes. The accuracy and timeliness of the prediction were measured at each site.
Results: Across the 5 health care systems, of the 3 714 105 patients (2 130 454 female [57.2%]) included in the analysis, 39 162 cases (1.1%) were identified. Predictive features varied by site but, as expected, the most common predictors reflected mental health conditions (eg, borderline personality disorder, with odds ratios of 8.1-12.9, and bipolar disorder, with odds ratios of 0.9-9.1) and substance use disorders (eg, drug withdrawal syndrome, with odds ratios of 7.0-12.9). Despite variation in geographical location, demographic characteristics, and population health characteristics, model performance was similar across sites, with areas under the curve ranging from 0.71 (95% CI, 0.70-0.72) to 0.76 (95% CI, 0.75-0.77). Across sites, at a specificity of 90%, the models detected a mean of 38% of cases a mean of 2.1 years in advance.
Conclusions and relevance: Across 5 diverse health care systems, a computationally efficient approach leveraging the full spectrum of structured electronic health record data was able to detect the risk of suicidal behavior in unselected patients. This approach could facilitate the development of clinical decision support tools that inform risk reduction interventions.
Background: Physical activity is increasingly recognized as an important modifiable factor for depression. However, the extent to which individuals with stable risk factors for depression, such as high genetic vulnerability, can benefit from the protective effects of physical activity, remains unknown. Using a longitudinal biobank cohort integrating genomic data from 7,968 individuals of European ancestry with high-dimensional electronic health records and lifestyle survey responses, we examined whether physical activity was prospectively associated with reduced risk for incident depression in the context of genetic vulnerability.
Methods: We identified individuals with incident episodes of depression, based on two or more diagnostic billing codes for a depressive disorder within 2 years following their lifestyle survey, and no such codes in the year prior. Polygenic risk scores were derived based on large-scale genome-wide association results for major depression. We tested main effects of physical activity and polygenic risk scores on incident depression, and effects of physical activity within stratified groups of polygenic risk.
Results: Polygenic risk was associated with increased odds of incident depression, and physical activity showed a protective effect of similar but opposite magnitude, even after adjusting for BMI, employment status, educational attainment, and prior depression. Higher levels of physical activity were associated with reduced odds of incident depression across all levels of genetic vulnerability, even among individuals at highest polygenic risk.
Conclusions: Real-world data from a large healthcare system suggest that individuals with high genetic vulnerability are more likely to avoid incident episodes of depression if they are physically active.
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