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Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources.This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Consortium across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center).Predictive models were developed and validated with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015.In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82 - 0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites.In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Consortium website.

Objective We aimed to establish a comprehensive digital phenotype for postpartum hemorrhage (PPH). Current guidelines rely primarily on estimates of blood loss, which can be inaccurate and biased and ignore complementary information readily available in electronic medical records (EMR). Inaccurate and incomplete phenotyping contributes to ongoing challenges in tracking PPH outcomes, developing more accurate risk assessments, and identifying novel interventions. Materials and Methods We constructed a cohort of 71 944 deliveries from the Mount Sinai Health System. Estimates of postpartum blood loss, shifts in hematocrit, administration of uterotonics, surgical interventions, and diagnostic codes were combined to identify PPH, retrospectively. Clinical features were extracted from EMRs and mapped to common data models for maximum interoperability across hospitals. Blinded chart review was done by a physician on a subset of PPH and non-PPH patients and performance was compared to alternate PPH phenotypes. PPH was defined as clinical diagnosis of postpartum hemorrhage documented in the patient’s chart upon chart review. Results We identified 6639 PPH deliveries (9% prevalence) using our phenotype—more than 3 times as many as using blood loss alone (N = 1,747), supporting the need to incorporate other diagnostic and intervention data. Chart review revealed our phenotype had 89% accuracy and an F1-score of 0.92. Alternate phenotypes were less accurate, including a common blood loss-based definition (67%) and a previously published digital phenotype (74%). Conclusion We have developed a scalable, accurate, and valid digital phenotype that may be of significant use for tracking outcomes and ongoing clinical research to deliver better preventative interventions for PPH.

Objective Postpartum hemorrhage (PPH) remains a leading cause of preventable maternal mortality in the United States. We sought to develop a novel risk assessment tool and compare its accuracy to tools used in current practice. Materials and Methods We used a PPH digital phenotype that we developed and validated previously to identify 6639 PPH deliveries from our delivery cohort (N = 70 948). Using a vast array of known and potential risk factors extracted from electronic medical records available prior to delivery, we trained a gradient boosting model in a subset of our cohort. In a held-out test sample, we compared performance of our model with 3 clinical risk-assessment tools and 1 previously published model. Results Our 24-feature model achieved an area under the receiver-operating characteristic curve (AUROC) of 0.71 (95% confidence interval [CI], 0.69-0.72), higher than all other tools (research-based AUROC, 0.67 [95% CI, 0.66-0.69]; clinical AUROCs, 0.55 [95% CI, 0.54-0.56] to 0.61 [95% CI, 0.59-0.62]). Five features were novel, including red blood cell indices and infection markers measured upon admission. Additionally, we identified inflection points for vital signs and labs where risk rose substantially. Most notably, patients with median intrapartum systolic blood pressure above 132 mm Hg had an 11% (95% CI, 8%-13%) median increase in relative risk for PPH. Conclusions We developed a novel approach for predicting PPH and identified clinical feature thresholds that can guide intrapartum monitoring for PPH risk. These results suggest that our model is an excellent candidate for prospective evaluation and could ultimately reduce PPH morbidity and mortality through early detection and prevention.

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.

Preeclampsia (PE) is a heterogeneous and complex disease associated with rising morbidity and mortality in pregnant women and newborns in the US. Early recognition of patients at risk is a pressing clinical need to significantly reduce the risk of adverse pregnancy outcomes. We assessed whether information routinely collected and stored on women in their electronic medical records (EMR) could enhance the prediction of PE risk beyond what is achieved in standard of care assessments today. We developed a digital phenotyping algorithm to assemble and curate 108,557 pregnancies from EMRs across the Mount Sinai Health System (MSHS), accurately reconstructing pregnancy journeys and normalizing these journeys across different hospital EMR systems. We then applied machine learning approaches to a training dataset from Mount Sinai Hospital (MSH) (N = 60,879) to construct predictive models of PE across three major pregnancy time periods (ante-, intra-, and postpartum). The resulting models predicted PE with high accuracy across the different pregnancy periods, with areas under the receiver operating characteristic curves (AUC) of 0.92, 0.83 and 0.89 at 37 gestational weeks, intrapartum and postpartum, respectively. We observed comparable performance in two independent patient cohorts with diverse patient populations (MSH validation dataset N = 38,421 and Mount Sinai West dataset N = 9,257). While our machine learning approach identified known risk factors of PE (such as blood pressure, weight and maternal age), it also identified novel PE risk factors, such as complete blood count related characteristics for the antepartum time period and ibuprofen usage for the postpartum time period. Our model not only has utility for earlier identification of patients at risk for PE, but given the prediction accuracy substantially exceeds what is achieved today in clinical practice, our model provides a path for promoting personalized precision therapeutic strategies for patients at risk.

ObjectivePostpartum hemorrhage (PPH) remains a leading cause of preventable maternal mortality in the US. Our goal was to develop a novel risk assessment tool and compare its accuracy to those used in current practice.Materials and MethodsWe used a PPH digital phenotype we developed and validated previously to identify 6,639 cases from our delivery cohort (N=70,948). Using a vast array of known and potential risk factors extracted from electronic medical records available prior to delivery, we trained a gradient boosting model in a subset of our cohort. In a held-out test sample, we compared performance of our model to three clinical risk tools and one previously published model.ResultsOur 24-feature model achieved an area under the curve (AUC) of 0.71 (95% confidence interval [CI], 0.69-0.72), higher than all other tools (research-based AUC: 0.67 [95%CI, 0.66-0.69], clinical AUCs: 0.55 [95%CI, 0.54-0.56] to 0.61 [95%CI, 0.59-0.62]). Five features were novel including red blood cell indices and infection markers measured standardly upon admission. Additionally, we identified inflection points for several vital signs and labs where risk rose substantially. Most notably, patients with median intrapartum systolic blood pressure above 132mmHg had an 11% [interquartile range, 4%] median increase in relative risk for PPH.ConclusionsWe developed a novel approach for predicting PPH and identified clinical feature thresholds that can guide intrapartum monitoring for PPH risk. Our results suggest our model is an excellent candidate for prospective evaluation and could ultimately reduce PPH morbidity and mortality through early detection and prevention.

ObjectiveWe aimed to establish a comprehensive digital phenotype for postpartum hemorrhage (PPH). Current guidelines rely primarily on estimates of blood loss, which can be inaccurate and biased, and ignore a suite of complementary information readily available in electronic medical records (EMR). Inaccurate and incomplete phenotyping contribute to ongoing challenges to track PPH outcomes, develop more accurate risk assessments, and identify novel interventions.MethodsWe constructed a cohort of 71,944 deliveries from the Mount Sinai Health System, 2011-2019. Estimates of postpartum blood loss, shifts in hematocrit intra- and postpartum, administration of uterotonics, surgical treatments, and associated diagnostic codes were combined to identify PPH retrospectively. All clinical features were extracted from structured EMR data and mapped to common data models for maximum interoperability across hospitals. Blinded chart review was done on a randomly selected subset of cases and controls for validation and performance was compared to alternate PPH phenotypes.ResultsWe identified 6,639 cases (9% prevalence) using our phenotype – more than three times as many as using blood loss alone (N=1,747), supporting the need to incorporate other diagnostic and treatment data. Blinded chart review revealed our phenotype had 96% sensitivity, 89% precision, 77% specificity, and 89% accuracy to detect PPH. Alternate phenotypes were less accurate, including a common blood loss-based definition (67%) and a previously published digital phenotype (74%).ConclusionWe have developed a scalable, accurate, and valid digital phenotype that may be of significant use for tracking outcomes and ongoing clinical research to deliver better preventative interventions for PPH.

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.

ImportanceThere is an urgent need to understand patient characteristics of having COVID-19 disease and evaluate markers of critical illness and mortality.ObjectiveTo assess association of clinical features on patient outcomes.Design, Setting, and ParticipantsIn this observational case series, patient-level data were extracted from electronic medical records for 28,336 patients tested for SARS-CoV-2 at the Mount Sinai Health System from 2/24/ to 4/15/2020, including 6,158 laboratory-confirmed cases.ExposuresConfirmed COVID-19 diagnosis by RT-PCR assay from nasal swabs.Main Outcomes and MeasuresEffects of race on positive test rates and mortality were assessed. Among positive cases admitted to the hospital (N = 3,273), effects of patient demographics, hospital site and unit, social behavior, vital signs, lab results, and disease comorbidities on discharge and death were estimated.ResultsHispanics (29%) and African Americans (25%) had disproportionately high positive case rates relative to population base rates (ppConclusions and RelevanceWhile race was associated with higher risk of infection, we did not find a racial disparity in inpatient mortality suggesting that outcomes in a single tertiary care health system are comparable across races. We identified clinical features associated with reduced mortality and discharge. These findings could help to identify which COVID-19 patients are at greatest risk and evaluate the impact on survival.

Through copy number variations, the 16p13.11 locus has been consistently linked to mental disorders. This locus contains the NDE1 gene, which also encodes microRNA-484. Both of them have been highlighted to play a role in the etiology of mental illness. A 4-SNP haplotype spanning this locus has been shown to associate with schizophrenia in Finnish females. Here we set out to identify any functional variations implicated by this haplotype. We used a sequencing and genotyping study design to identify variations of interest in a Finnish familial cohort ascertained for schizophrenia. We identified 295 variants through sequencing, none of which were located directly within microRNA-484. Two variants were observed to associate with schizophrenia in a sex-dependent manner (females only) in the whole schizophrenia familial cohort (rs2242549 P = .00044; OR = 1.20, 95% CI 1.03–1.40; rs881803 P = .00021; OR = 1.20, 95% CI 1.02–1.40). Both variants were followed up in additional psychiatric cohorts, with neuropsychological traits, and gene expression data, in order to further examine their role. Gene expression data from the familial schizophrenia cohort demonstrated a significant association between rs881803 and 1504 probes (FDR q < 0.05). These were significantly enriched for genes that are predicted miR-484 targets (n = 54; P = .000193), and with probes differentially expressed between the sexes (n = 48; P = .000187). While both SNPs are eQTLs for NDE1, rs881803 is located in a predicted transcription factor binding site. Based on its location and association pattern, we conclude that rs881803 is the prime functional candidate under this locus, affecting the roles of both NDE1 and miR-484 in psychiatric disorders.

Summary Background Exercise is known to be associated with reduced risk of all-cause mortality, cardiovascular disease, stroke, and diabetes, but its association with mental health remains unclear. We aimed to examine the association between exercise and mental health burden in a large sample, and to better understand the influence of exercise type, frequency, duration, and intensity. Methods In this cross-sectional study, we analysed data from 1 237 194 people aged 18 years or older in the USA from the 2011, 2013, and 2015 Centers for Disease Control and Prevention Behavioral Risk Factors Surveillance System survey. We compared the number of days of bad self-reported mental health between individuals who exercised and those who did not, using an exact non-parametric matching procedure to balance the two groups in terms of age, race, gender, marital status, income, education level, body-mass index category, self-reported physical health, and previous diagnosis of depression. We examined the effects of exercise type, duration, frequency, and intensity using regression methods adjusted for potential confounders, and did multiple sensitivity analyses. Findings Individuals who exercised had 1·49 (43·2%) fewer days of poor mental health in the past month than individuals who did not exercise but were otherwise matched for several physical and sociodemographic characteristics (W=7·42 × 1010, p Interpretation In a large US sample, physical exercise was significantly and meaningfully associated with self-reported mental health burden in the past month. More exercise was not always better. Differences as a function of exercise were large relative to other demographic variables such as education and income. Specific types, durations, and frequencies of exercise might be more effective clinical targets than others for reducing mental health burden, and merit interventional study. Funding Cloud computing resources were provided by Microsoft.

Objective: Individuals at high risk for schizophrenia may benefit from early intervention, but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts. The authors sought to test the utility of this approach in clinical settings and to evaluate the broader health consequences of high genetic risk for schizophrenia. Methods: The authors used electronic health records for 106,160 patients from four health care systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1,359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites. Results: PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS, 1.55; 95% CI=1.4, 1.7), and patients in the highest risk decile of the PRS distribution had up to 4.6-fold higher odds of schizophrenia compared with those in the bottom decile (95% CI=2.9, 7.3). PRSs were also positively associated with other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity. Conclusions: The study demonstrates that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in health care settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. The results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in health care systems.

BackgroundAlthough depression is recognized as the leading cause of disability worldwide, decades of research have identified few actionable preventive factors. Using phenotypic and genomic data from the UK Biobank, we took advantage of a unique opportunity to screen a wide range of potentially modifiable factors that could offset known risk factors for depression.MethodsWe curated baseline data on more than 100 lifestyle and environmental factors in participants’ lives, including behavioral (e.g., exercise, sleep, media use, diet), social (e.g., support, activities), and environmental (e.g., greenspace, pollution) variables. In a follow-up survey, participants reported on their traumatic life experiences and mental health, including depression. Polygenic risk scores for depression were generated based on large-scale genome-wide association results. Excluding those meeting criteria for depression at baseline, we identified at-risk individuals at high predicted probability (> 90th percentile) for clinically significant depression at follow-up based on their (i) polygenic risk, or (ii) reported traumatic life events. Using a factors-wide design corrected for multiple testing and adjusted for potential confounders, we identified modifiable factors associated with follow-up depression in the full sample and among at-risk individuals. Using a two-sample Mendelian randomization (MR) design, we then examined which significant factors showed potential causal influences on depression risk, or vice versa.ResultsA range of baseline modifiable factors were prospectively associated with follow-up depression, including factors related to social engagement, physical activity, media use, and diet. MR follow-up analyses provided further support for the effects of social support-seeking, TV use, and other factors on depression risk.ConclusionAs the field increasingly quantifies the role of genetic factors in complex conditions such as depression, knowledge of modifiable factors that could offset one’s genetic risk has become highly relevant. Here, we present an approach to screening for potentially modifiable factors that may offset the risk of depression in general and among at-risk individuals. In light of the burden of disease associated with depression and the urgent need for actionable preventive strategies, this approach could help prioritize candidates for follow-up studies including clinical trials for depression prevention.

SummaryGenetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed a meta-analysis of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders identifying three groups of inter-related disorders. We detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning in the second trimester prenatally, and play prominent roles in a suite of neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

Schizophrenia Schizophrenia is a highly heritable, genetically complex, and heterogeneous psychiatric syndrome. Twin and family studies estimate 80–85% of variance in disease liability can be accounted for by genetic factors (Cannon, Kaprio, Lonnqvist, Huttunen, & Koskenvuo, 1998). Thus, substantial efforts have been dedicated towards uncovering genetic loci increasing risk for schizophrenia. Though many methodologies have been employed including genome-wide surveys of common genetic polymorphisms (Purcell et al., 2009; Ripke et al., 2013; Schizophrenia Working Group of the Psychiatric Genomics, 2014), exome sequencing investigating rare variants (Fromer et al., 2014; Purcell et al., 2014), gene expression analyses (Gardiner et al., 2013; Guillozet-Bongaarts et al., 2014; Gulsuner et al., 2013), and integration of multiple methods (Hertzberg, Katsel, Roussos, Haroutunian, & Domany, 2015; Luo et al., 2015; van Eijk et al., 2014), nearly all gene-finding efforts in schizophrenia to date have employed diagnostic level classification as the phenotype of interest. However, there is substantial evidence that risk-increasing genes confer risk via impacting intermediate levels of impairment, such as cognitive dysfunction, suggesting that these may also be useful phenotypic targets in gene discovery studies (Cannon & Keller, 2006; Greenwood, Light, Swerdlow, Radant, & Braff, 2012; Lencz et al., 2014; Tan, Callicott, & Weinberger, 2008; Toulopoulou et al., 2007). Compared to the diagnosis of schizophrenia, which could imply dysfunction across a host of brain systems and signaling pathways, a given endophenotype provides a more constrained framework within which to interpret the functional-biological relevance of statistically identified genes (Heck et al., 2014; Ibrahim-Verbaas et al., 2015; Lencz et al., 2014). One of the most reliable endophenotypes in schizophrenia – and most profound areas of cognitive impairment – is verbal memory (Cannon et al., 2000; Greenwood et al., 2013; Schaefer, Giangrande, Weinberger, & Dickinson, 2013; van Erp et al., 2008). The California Verbal Learning Test (CVLT), a list-learning exercise, is a robust measure of memory impairment in schizophrenia (Haut, in prep; Stone et al., 2011; van Erp et al., 2008), is heritable (Carmelli, Swan, DeCarli, & Reed, 2002; Greenwood et al., 2007; Kremen et al., 2014; Panizzon et al., 2011), and shows intermediate levels of affection among relatives of patients with schizophrenia in twin and family studies (Greenwood et al., 2013; van Erp et al., 2008), supporting its role as a candidate endophenotype in genetic studies. Initial molecular genetic investigations of endophenotypes for schizophrenia have provided evidence that genes associated with putative cognitive endophenotypes at least partially overlap with those associated with schizophrenia (Heck et al., 2014; Lencz et al., 2014). However, so far this approach has been used largely to demonstrate the broad, shared genetic etiology between these phenotypes, rather than to identify specific genetic loci impinging on both, which is critical for elucidating the functional-physiologic significance of the underlying genetic associations. Furthermore, all of these studies have assayed common polymorphisms. Over 90% of common polymorphisms previously related to psychiatric illness are located in regulatory regions of DNA, rather than in coding regions where mutations more directly affect protein structure, suggesting that many of these loci may exert their effects via regulation of gene expression (Kim et al., 2014; Maurano et al., 2012). Thus, examining gene expression in relation to a cognitive endophenotype for schizophrenia may yield insights into genotypic effects on disease status and help to elucidate mechanisms by which cognitive dysfunction manifests in this disease. In the current study, we used a discordant twin design to identify genes differentially expressed in relation to verbal memory performance, a cognitive endophenotype for schizophrenia. Gene expression was assayed from peripheral blood mononuclear cells (PBMCs) – the most feasible way to assess expression levels in living patients. Gene expression in PBMCs is broadly heritable and correlates with central nervous system expression patterns (Cheung et al., 2003). While it is likely that some genes influencing memory performance will not be schizophrenia-related, given the endophenotypic pattern of the CVLT, which suggests shared genetic etiology between memory and schizophrenia, we expected some proportion of these memory-related genes to show differential expression by diagnostic status (Glahn et al., 2007; Greenwood et al., 2007; van Erp et al., 2008). This subset would represent a selection of genes potentially involved in those systems underlying memory impairment in schizophrenia. A twin design further allowed us to test the heritability of expression for each of these genes, dissociating potentially etiologically relevant genetic alterations from disease-related secondary effects.

OBJECTIVEIndividuals at high risk for schizophrenia may benefit from early intervention but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts, but its utility in clinical settings remains largely unexplored. Moreover, the broad health consequences of a high genetic risk of schizophrenia are poorly understood, despite being relevant to treatment decisions.METHODWe used electronic health records for 106,160 patients from four healthcare systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites.RESULTSPRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS=1.55 [95% confidence interval (CI), 1.4-1.7], p=4.48 x 10-16) and patients in the highest risk decile of the PRS distribution had up to 4.6-fold increased odds of schizophrenia compared to those in the bottom decile (95% CI, 2.9-7.3, p=1.37 x 10-10). PRSs were also positively associated with a range of other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity.CONCLUSIONSWe demonstrate that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in healthcare settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. Our results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in healthcare systems.

Objective: Even though safe and effective treatments for depression are available, many individuals with a diagnosis of depression do not obtain treatment. This study aimed to develop a tool to identify persons who might not initiate treatment among those who acknowledge a need.Methods: Data were aggregated from the 2008–2014 U.S. National Survey on Drug Use and Health (N=391,753), including 20,785 adults given a diagnosis of depression by a health care provider in the 12 months before the survey. Machine learning was applied to self-report survey items to develop strategies for identifying individuals who might not get needed treatment.Results: A derivation cohort aggregated between 2008 and 2013 was used to develop a model that identified the 30.6% of individuals with depression who reported needing but not getting treatment. When applied to independent responses from the 2014 cohort, the model identified 72% of those who did not initiate treatment (pConclusions: Considerable work is needed to improve follow-up and retention rates after the critical initial meeting in which a patient is given a diagnosis of depression. Routinely collected information about patients with depression could identify those at risk of not obtaining needed treatment, which may inform the development and implementation of interventions to reduce the prevalence of untreated depression.

Genetic risk variants for schizophrenia have been linked to many related clinical and biological phenotypes with the hopes of delineating how individual variation across thousands of variants corresponds to the clinical and etiologic heterogeneity within schizophrenia. This has primarily been done using risk score profiling, which aggregates effects across all variants into a single predictor. While effective, this method lacks flexibility in certain domains: risk scores cannot capture nonlinear effects and do not employ any variable selection. We used random forest, an algorithm with this flexibility designed to maximize predictive power, to predict 6 cognitive endophenotypes in a combined sample of psychiatric patients and controls (N = 739) using 77 genetic variants strongly associated with schizophrenia. Tenfold cross-validation was applied to the discovery sample and models were externally validated in an independent sample of similar ancestry (N = 336). Linear approaches, including linear regression and task-specific polygenic risk scores, were employed for comparison. Random forest models for processing speed (P = .019) and visual memory (P = .036) and risk scores developed for verbal (P = .042) and working memory (P = .037) successfully generalized to an independent sample with similar predictive strength and error. As such, we suggest that both methods may be useful for mapping a limited set of predetermined, disease-associated SNPs to related phenotypes. Incorporating random forest and other more flexible algorithms into genotype-phenotype mapping inquiries could contribute to parsing heterogeneity within schizophrenia; such algorithms can perform as well as standard methods and can capture a more comprehensive set of potential relationships.

In a recent report of the North American Prodrome Longitudinal Study (NAPLS), clinical high-risk individuals who converted to psychosis showed a steeper rate of cortical gray matter reduction compared with non-converters and healthy controls, and the rate of cortical thinning was correlated with levels of proinflammatory cytokines at baseline. These findings suggest a critical role for microglia, the resident macrophages in the brain, in perturbations of cortical maturation processes associated with onset of psychosis. Elucidating gene expression pathways promoting microglial action prior to disease onset would inform potential preventative intervention targets. Here we used a forward stepwise regression algorithm to build a classifier of baseline microRNA expression in peripheral leukocytes associated with annualized rate of cortical thinning in a subsample of the NAPLS cohort (N=74). Our cortical thinning classifier included nine microRNAs, p=3.63 × 10-08, R2=0.358, permutation-based p=0.039, the gene targets of which were enriched for intracellular signaling pathways that are important to coordinating inflammatory responses within immune cells (p

Recent work suggests that genes encoding complement proteins that are active in the innate immune system may confer risk for schizophrenia by disrupting typical synaptic pruning in late adolescence. Alterations in the complement pathway may contribute to aberrant cortical thinning in schizophrenia prodromes and reduced prefrontal cortical thickness in chronic schizophrenia patients; however, this theory needs to be translated to humans. We conducted a series of analyses in a sample of adult Swedish twins enriched for schizophrenia (N=129) to assess the plausibility of a relationship between complement gene expression and cortical thickness that could go awry in the etiology of schizophrenia. First, we identified that peripheral mRNA expression levels of two complement genes (C5, SERPING1) made unique contributions to the variance in superior frontal cortical thickness among all participants. Vertex-wise maps of the association between gene expression levels and thickness across the cortex suggested that this relationship was especially strong with SERPING1 in the superior frontal region, consistent with the pattern of disruption in cortical thickness observed in schizophrenia. Additional analyses identified that these genes are expressed in the human superior frontal cortex, that heritable genetic factors influence SERPING1 gene expression levels, and that these associations are observed regardless of case status. These findings provide initial evidence linking the complement system with cortical thinning in humans, a process potentially involved in the pathogenesis of schizophrenia.

Background: Genes involved in the complement pathway of the innate immune system have been implicated in risk for schizophrenia. Emerging evidence suggests that this pathway is involved in synaptic pruning, a phenomenon that likely influences cortical thinning and may underlie reduction of cortical thickness in prefrontal regions in schizophrenia. However, a link between complement genes and cortical thickness has not yet been established in humans.

Background: In a recent report of the North American Prodrome Longitudinal Study (NAPLS), clinical high-risk individuals who converted to psychosis showed a steeper rate of cortical gray matter reduction compared with nonconverters and healthy controls, and the rate of cortical thinning was correlated with levels of proinflammatory cytokines at baseline. These findings suggest a critical role for microglia, the resident macrophages in the brain, in perturbations of cortical maturation processes associated with onset of psychosis. Elucidating gene expression pathways promoting microglial action prior to disease onset would inform potential preventative intervention targets. Methods: We used a forward stepwise regression algorithm to build a classifier of baseline microRNA expression in peripheral leukocytes associated with annualized rate of cortical thinning in a subsample of the NAPLS cohort (N=74). Results: Our cortical thinning classifier included 9 microRNAs, P=3.63 × 108, R2=0.358, permutation-based P=.039, the gene targets of which were enriched for intracellular signaling pathways that are important to coordinating inflammatory responses within immune cells (P < .05, Benjamini-Hochberg corrected). The classifier was also related to proinflammatory cytokine levels in serum (P=.038). Furthermore, miRNAs that predicted conversion status were found to do so in a manner partially mediated by rate of cortical thinning (point estimate=0.078 [95% CIs: 0.003, 0.168], P=.03). Conclusion: Many of the miRNAs identified here have been previously implicated in brain development, synaptic plasticity, immune function, and/or schizophrenia, showing some convergence across studies and methodologies. Altered intracellular signaling within the immune system may interact with cortical maturation in individuals at high risk for schizophrenia promoting disease onset.

Understanding molecular processes that link comorbid traits such as addictions and mental disorders can provide novel therapeutic targets. Neuregulin signaling pathway (NSP) has previously been implicated in schizophrenia, a neurodevelopmental disorder with high comorbidity to smoking. Using a Finnish twin family sample, we have previously detected association between nicotine dependence and ERBB4 (a neuregulin receptor), and linkage for smoking initiation at the ERBB4 locus on 2q33. Further, Neuregulin3 has recently been shown to associate with nicotine withdrawal in a behavioral mouse model. In this study, we scrutinized association and linkage between 15 036 common, low frequency and rare genetic variants in 10 NSP genes and phenotypes encompassing smoking and alcohol use. Using the Finnish twin family sample (N=1998 from 740 families), we detected 66 variants (representing 23 LD blocks) significantly associated (false discovery rate P, published version, peerReviewed

Genetic studies of familial schizophrenia in Finland have observed significant associations with a group of biologically related genes, DISC1 , NDE1 , NDEL1 , PDE4B and PDE4D , the ‘DISC1 network’. Here, we use gene expression and psychoactive medication use data to study their biological consequences and potential treatment implications. Gene expression levels were determined in 64 individuals from 18 families, while prescription medication information has been collected over a 10-year period for 931 affected individuals. We demonstrate that the NDE1 SNP rs2242549 associates with significant changes in gene expression for 2908 probes (2542 genes), of which 794 probes (719 genes) were replicable. A significant number of the genes altered were predicted targets of microRNA-484 ( p = 3.0 × 10 −8 ), located on a non-coding exon of NDE1 . Variants within the NDE1 locus also displayed significant genotype by gender interaction to early cessation of psychoactive medications metabolized by CYP2C19. Furthermore, we demonstrate that miR-484 can affect the expression of CYP2C19 in a cell culture system. Thus, variation at the NDE1 locus may alter risk of mental illness, in part through modification of miR-484, and such modification alters treatment response to specific psychoactive medications, leading to the potential for use of this locus in targeting treatment.

Brain phenotypes showing environmental influence may help clarify unexplained associations between urban exposure and psychiatric risk. Heritable prefrontal fMRI activation during working memory (WM) is such a phenotype. We hypothesized that urban upbringing (childhood urbanicity) would alter this phenotype and interact with dopamine genes that regulate prefrontal function during WM. Further, dopamine has been hypothesized to mediate urban-associated factors like social stress. WM-related prefrontal function was tested for main effects of urbanicity, main effects of three dopamine genes-catechol-O-methyltransferase (COMT), dopamine receptor D1 (DRD1), and dopamine receptor D2 (DRD2)-and, importantly, dopamine gene-by-urbanicity interactions. For COMT, three independent human samples were recruited (total n = 487). We also studied 253 subjects genotyped for DRD1 and DRD2. 3T fMRI activation during the N-back WM task was the dependent variable, while childhood urbanicity, dopamine genotype, and urbanicity-dopamine interactions were independent variables. Main effects of dopamine genes and of urbanicity were found. Individuals raised in an urban environment showed altered prefrontal activation relative to those raised in rural or town settings. For each gene, dopamine genotype-by-urbanicity interactions were shown in prefrontal cortex-COMT replicated twice in two independent samples. An urban childhood upbringing altered prefrontal function and interacted with each gene to alter genotype-phenotype relationships. Gene-environment interactions between multiple dopamine genes and urban upbringing suggest that neural effects of developmental environmental exposure could mediate, at least partially, increased risk for psychiatric illness in urban environments via dopamine genes expressed into adulthood.

This study tested the hypothesis that enhanced neural arousal in response to performance errors would predict poor affect and coping behaviors in everyday life. Participants were preselected as either low-depressed (LD) or high-depressed (HD) based on a screening questionnaire, and they then completed a laboratory Stroop task while EEG was recorded, followed by a 2-week period of daily reports of affect and coping behaviors. The EEG measure of arousal response to errors was the degree of error-related alpha suppression (ERAS) in the intertrial interval, that is the reduction in alpha power following errors compared with correct responses. ERAS was relatively heightened at frontal sites for the HD versus the LD group, and frontal ERAS predicted lower positive affect, higher negative affect, and less adaptive coping behaviors in the daily reports. Together, the results imply that heightened arousal following mistakes is associated with suboptimal emotion and coping with stressors.