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Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Biobanque Québécoise de la Covid-19: Brent Richards’s institution has received investigator-initiated grant funding from Eli Lilly, GlaxoSmithKline and Biogen for projects unrelated to this research. He is the CEO of 5 Prime Sciences Inc (www.5primesciences.com). DeCOI: Oliver Witzke has received research grants for clinical studies, speaker’s fees, honoraria and travel expenses from Amgen, Alexion, Astellas, Basilea, Biotest, Bristol-Myers Squibb, Correvio, Chiesi, Gilead, Hexal, Janssen, Dr. F. Köhler Chemie, MSD, Novartis, Roche, Pfizer, Sanofi, Takeda, TEVA and UCB. Kerstin U. Ludwig is co-founder and holds equity in the LAMPseq Diagnostics GmbH. UP serves as ad hoc advisor for Sanofi-Pasteur, BioNtech and Sobi and is member of the SAB of Leukocare. Christoph D. Spinner reports grants, personal fees from AstraZeneca, personal fees and non-financial support from BBraun Melsungen, grants, personal fees and non-financial support from Gilead Sciences, grants and personal fees from Janssen-Cilag, personal fees from Eli Lilly, personal fees from Formycon, personal fees from Roche, other from Apeiron, grants and personal fees from MSD, grants from Cepheid, personal fees from GSK, personal fees from Molecular partners, other from Eli Lilly, personal fees from SOBI during the conduct of the study; personal fees from AbbVie, personal fees from MSD, grants and personal fees from ViiV Healthcare, outside the submitted work. Jochen Schneider received grants and/or personal fees from Gilead Sciences, Janssen-Cilag, and from AbbVie outside the submitted work. Philipp Koehler reports grants or contracts from German Federal Ministry of Research and Education (BMBF) B-FAST (Bundesweites Forschungsnetz Angewandte Surveillance und Testung) and NAPKON (Nationales Pandemie Kohorten Netz, German National Pandemic Cohort Network) of the Network University Medicine (NUM) and the State of North Rhine-Westphalia; Consulting fees Ambu GmbH, Gilead Sciences, Mundipharma Resarch Limited, Noxxon N.V. and Pfizer Pharma; Honoraria for lectures from Akademie für Infektionsmedizin e.V., Ambu GmbH, Astellas Pharma, BioRad Laboratories Inc., European Confederation of Medical Mycology, Gilead Sciences, GPR Academy Ruesselsheim, medupdate GmbH, MedMedia, MSD Sharp & Dohme GmbH, Pfizer Pharma GmbH, Scilink Comunicación Científica SC and University Hospital and LMU Munich; Participation on an Advisory Board from Ambu GmbH, Gilead Sciences, Mundipharma Resarch Limited and Pfizer Pharma; A pending patent currently reviewed at the German Patent and Trade Mark Office; Other non-financial interests from Elsevier, Wiley and Taylor & Francis online outside the submitted work. Oliver A. Cornely reports grants or contracts from Amplyx, Basilea, BMBF, Cidara, DZIF, EU-DG RTD (101037867), F2G, Gilead, Matinas, MedPace, MSD, Mundipharma, Octapharma, Pfizer, Scynexis; Consulting fees from Amplyx, Biocon, Biosys, Cidara, Da Volterra, Gilead, Matinas, MedPace, Menarini, Molecular Partners, MSG-ERC, Noxxon, Octapharma, PSI, Scynexis, Seres; Honoraria for lectures from Abbott, Al-Jazeera Pharmaceuticals, Astellas, Grupo Biotoscana/United Medical/Knight, Hikma, MedScape, MedUpdate, Merck/MSD, Mylan, Pfizer; Payment for expert testimony from Cidara; Participation on a Data Safety Monitoring Board or Advisory Board from Actelion, Allecra, Cidara, Entasis, IQVIA, Jannsen, MedPace, Paratek, PSI, Shionogi; A patent at the German Patent and Trade Mark Office (DE 10 2021 113 007.7); Other interests from DGHO, DGI, ECMM, ISHAM, MSG-ERC, Wiley. Genentech: Amy D Stockwell, Fang Cai, and Brian L Yaspan are, or were at the execution of the study, full time employees of Genentech with stock and stock options in Roche. Helix Exome+ and Healthy Nevada Project COVID-19 Phenotypes: Alexandre Bolze, Kelly M Schiabor Barrett, Simon White, Nicole L Washington, Francisco Tanudjaja, Stephen Riffle, Efren Sandoval, and Elizabeth T Cirulli are employees of Helix. Regeneron: Jack A Kosmicki and Manuel AR Ferreira are current employees and/or stockholders of Regeneron Genetics Center or Regeneron Pharmaceuticals. Vanda CALYPSO COVID-19: Bartlomiej Przychodzen and Sandra Smieszek are employees of Vanda Pharmaceuticals Inc.

Introduction: Severe COVID-19 leads to important changes in circulating immune-related proteins. To date it has been difficult to understand their temporal relationship and identify cytokines that are drivers of severe COVID-19 outcomes and underlie differences in outcomes between sexes. Here, we measured 147 immune-related proteins during acute COVID-19 to investigate these questions., Methods: We measured circulating protein abundances using the SOMAscan nucleic acid aptamer panel in two large independent hospital-based COVID-19 cohorts in Canada and the United States. We fit generalized additive models with cubic splines from the start of symptom onset to identify protein levels over the first 14 days of infection which were different between severe cases and controls, adjusting for age and sex. Severe cases were defined as individuals with COVID-19 requiring invasive or non-invasive mechanical respiratory support., Results: 580 individuals were included in the analysis. Mean subject age was 64.3 (sd 18.1), and 47% were male. Of the 147 proteins, 69 showed a significant difference between cases and controls (p < 3.4 × 10 -4 ). Three clusters were formed by 108 highly correlated proteins that replicated in both cohorts, making it difficult to determine which proteins have a true causal effect on severe COVID-19. Six proteins showed sex differences in levels over time, of which 3 were also associated with severe COVID-19: CCL26, IL1RL2, and IL3RA, providing insights to better understand the marked differences in outcomes by sex., Conclusions: Severe COVID-19 is associated with large changes in 69 immune-related proteins. Further, five proteins were associated with sex differences in outcomes. These results provide direct insights into immune-related proteins that are strongly influenced by severe COVID-19 infection., (© 2022. The Author(s).)

Although it has been more than 2 years since the start of the coronavirus disease 2019 (COVID-19) pandemic, COVID-19 continues to be a worldwide health crisis. Despite the development of preventive vaccines, therapies to treat COVID-19 and other inflammatory diseases remain a major unmet need in medicine. Our study sought to identify drivers of disease severity and mortality to develop tailored immunotherapy strategies to halt disease progression. We assembled the Mount Sinai COVID-19 Biobank, which was composed of almost 600 hospitalized patients followed longitudinally through the peak of the pandemic in 2020. Moderate disease and survival were associated with a stronger antigen presentation and effector T cell signature. In contrast, severe disease and death were associated with an altered antigen presentation signature, increased numbers of inflammatory immature myeloid cells, and extrafollicular activated B cells that have been previously associated with autoantibody formation. In severely ill patients with COVID-19, lung tissue-resident alveolar macrophages not only were drastically depleted but also had an altered antigen presentation signature, which coincided with an influx of inflammatory monocytes and monocyte-derived macrophages. In addition, we found that the size of the alveolar macrophage pool correlated with patient outcome and that alveolar macrophage numbers and functionality were restored to homeostasis in patients who recovered from COVID-19. These data suggest that local and systemic myeloid cell dysregulation are drivers of COVID-19 severity and modulation of alveolar macrophage numbers and activity in the lung may be a viable therapeutic strategy for the treatment of critical inflammatory lung diseases.

Recent efforts have identified genetic loci that are associated with coronavirus disease 2019 (COVID-19) infection rates and disease outcome severity. Translating these genetic findings into druggable genes that reduce COVID-19 host susceptibility is a critical next step. Using a translational genomics approach that integrates COVID-19 genetic susceptibility variants, multi-tissue genetically regulated gene expression (GReX), and perturbagen signatures, we identified IL10RB as the top candidate gene target for COVID-19 host susceptibility. In a series of validation steps, we show that predicted GReX upregulation of IL10RB and higher IL10RB expression in COVID-19 patient blood is associated with worse COVID-19 outcomes and that in vitro IL10RB overexpression is associated with increased viral load and activation of disease-relevant molecular pathways., (© 2022. The Author(s).)

Microglia are brain myeloid cells that play a critical role in neuroimmunity and the etiology of Alzheimer's disease (AD), yet our understanding of how the genetic regulatory landscape controls microglial function and contributes to AD is limited. Here, we performed transcriptome and chromatin accessibility profiling in primary human microglia from 150 donors to identify genetically driven variation and cell-specific enhancer-promoter (E-P) interactions. Integrative fine-mapping analysis identified putative regulatory mechanisms for 21 AD risk loci, of which 18 were refined to a single gene, including 3 new candidate risk genes (KCNN4, FIBP and LRRC25). Transcription factor regulatory networks captured AD risk variation and identified SPI1 as a key putative regulator of microglia expression and AD risk. This comprehensive resource capturing variation in the human microglia regulome provides insights into the etiology of neurodegenerative disease., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background and Objectives: Left ventricular ejection fraction is disrupted in patients on maintenance hemodialysis and can be estimated using deep learning models on electrocardiograms. Smaller sample sizes within this population may be mitigated using transfer learning., Design, Setting, Participants, & Measurements: We identified patients on hemodialysis with transthoracic echocardiograms within 7 days of electrocardiogram using diagnostic/procedure codes. We developed four models: ( 1 ) trained from scratch in patients on hemodialysis, ( 2 ) pretrained on a publicly available set of natural images (ImageNet), ( 3 ) pretrained on all patients not on hemodialysis, and ( 4 ) pretrained on patients not on hemodialysis and fine-tuned on patients on hemodialysis. We assessed the ability of the models to classify left ventricular ejection fraction into clinically relevant categories of ≤40%, 41% to ≤50%, and >50%. We compared performance by area under the receiver operating characteristic curve., Results: We extracted 705,075 electrocardiogram:echocardiogram pairs for 158,840 patients not on hemodialysis used for development of models 3 and 4 and n =18,626 electrocardiogram:echocardiogram pairs for 2168 patients on hemodialysis for models 1, 2, and 4. The transfer learning model achieved area under the receiver operating characteristic curves of 0.86, 0.63, and 0.83 in predicting left ventricular ejection fraction categories of ≤40% ( n =461), 41%-50% ( n =398), and >50% ( n =1309), respectively. For the same tasks, model 1 achieved area under the receiver operating characteristic curves of 0.74, 0.55, and 0.71, respectively; model 2 achieved area under the receiver operating characteristic curves of 0.71, 0.55, and 0.69, respectively, and model 3 achieved area under the receiver operating characteristic curves of 0.80, 0.51, and 0.77, respectively. We found that predictions of left ventricular ejection fraction by the transfer learning model were associated with mortality in a Cox regression with an adjusted hazard ratio of 1.29 (95% confidence interval, 1.04 to 1.59)., Conclusion: A deep learning model can determine left ventricular ejection fraction for patients on hemodialysis following pretraining on electrocardiograms of patients not on hemodialysis. Predictions of low ejection fraction from this model were associated with mortality over a 5-year follow-up period., Podcast: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022&#95;06&#95;06&#95;CJN16481221.mp3., (Copyright © 2022 by the American Society of Nephrology.)

Objective: To characterize continuous video electroencephalogram (VEEG) findings of hospitalized COVID-19 patients., Methods: We performed a retrospective chart review of patients admitted at three New York City hospitals who underwent VEEG at the peak of the COVID-19 pandemic. Demographics, comorbidities, neuroimaging, VEEG indications and findings, treatment, and outcomes were collected., Results: Of 93 patients monitored, 77% had severe COVID-19 and 40% died. Acute ischemic or hemorrhagic stroke was present in 26% and 15%, respectively. Most common VEEG indications were encephalopathy/coma (60%) and seizure-like movements (38%). Most common VEEG findings were generalized slowing (97%), generalized attenuation (31%), generalized periodic discharges (17%) and generalized sharp waves (15%). Epileptiform abnormalities were present in 43% and seizures in 8% of patients, all of whom had seizure risk factors. Factors associated with an epileptiform VEEG included increasing age (OR 1.07, p = 0.001) and hepatic/renal failure (OR 2.99, p = 0.03)., Conclusions: Most COVID-19 patients who underwent VEEG monitoring had severe COVID-19 and over one-third had acute cerebral injury (e.g., stroke, anoxia). Seizures were uncommon. VEEG findings were nonspecific., Significance: VEEG findings in this cohort of hospitalized COVID-19 patients were those often seen in critical illness. Seizures were uncommon and occurred in the setting of common seizure risk factors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objectives: This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population., Background: Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECGs) can assist diagnostic workflow. However, DL tools to estimate right ventricular (RV) function do not exist, whereas those to estimate left ventricular (LV) function are restricted to quantification of very low LV function only., Methods: A multicenter study was conducted with data from 5 New York City hospitals: 4 for internal testing and 1 serving as external validation. We created novel DL models to classify left ventricular ejection fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation., Results: We obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used natural language processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients. For LVEF classification in internal testing, area under curve (AUC) at detection of LVEF ≤40%, 40% < LVEF ≤50%, and LVEF >50% was 0.94 (95% CI: 0.94-0.94), 0.82 (95% CI: 0.81-0.83), and 0.89 (95% CI: 0.89-0.89), respectively. For external validation, these results were 0.94 (95% CI: 0.94-0.95), 0.73 (95% CI: 0.72-0.74), and 0.87 (95% CI: 0.87-0.88). For regression, the mean absolute error was 5.84% (95% CI: 5.82%-5.85%) for internal testing and 6.14% (95% CI: 6.13%-6.16%) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (95% CI: 0.84-0.84) in both internal testing and external validation., Conclusions: DL on ECG data can be used to create inexpensive screening, diagnostic, and predictive tools for both LV and RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease., Competing Interests: Funding Support and Author Disclosures This study was supported by the National Center for Advancing Translational Sciences, National Institutes of Health (U54 TR001433-05). This study has been approved by the institutional review board at the Icahn School of Medicine at Mount Sinai. Dr Nadkarni is supported by National Institutes of Health grants: R01DK127139 from NIDDK and R01HL155915 from NHLBI. Dr Somani is a co-founder of and owns equity in Monogram Orthopedics. Dr Johnson is an employee of Tempus, Inc. Dr Nadkarni is a scientific co-founder, consultant, advisory board member, and equity owner of Renalytix AI; a scientific co-founder and equity holder for Pensieve Health; a consultant for Variant Bio; and received grants from Goldfinch Bio and personal fees from Renalytix AI, BioVie, Reata, AstraZeneca, and GLG Consulting. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: The novel coronavirus disease 2019 (COVID-19) has heterogenous clinical courses, indicating that there might be distinct subphenotypes in critically ill patients. Although prior research has identified these subphenotypes, the temporal pattern of multiple clinical features has not been considered in cluster models. We aimed to identify temporal subphenotypes in critically ill patients with COVID-19 using a novel sequence cluster analysis and associate them with clinically relevant outcomes., Materials and Methods: We analyzed 1036 confirmed critically ill patients with laboratory-confirmed SARS-COV-2 infection admitted to the Mount Sinai Health System in New York city. The agglomerative hierarchical clustering method was used with Levenshtein distance and Ward's minimum variance linkage., Results: We identified four subphenotypes. Subphenotype I (N = 233 [22.5%]) included patients with rapid respirations and a rapid heartbeat but less need for invasive interventions within the first 24 hours, along with a relatively good prognosis. Subphenotype II (N = 418 [40.3%]) represented patients with the least degree of ailments, relatively low mortality, and the highest probability of discharge from the hospital. Subphenotype III (N = 259 [25.0%]) represented patients who experienced clinical deterioration during the first 24 hours of intensive care unit admission, leading to poor outcomes. Subphenotype IV (N = 126 [12.2%]) represented an acute respiratory distress syndrome trajectory with an almost universal need for mechanical ventilation., Conclusion: We utilized the sequence cluster analysis to identify clinical subphenotypes in critically ill COVID-19 patients who had distinct temporal patterns and different clinical outcomes. This study points toward the utility of including temporal information in subphenotyping approaches., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The coronaviruses responsible for severe acute respiratory syndrome (SARS-CoV), COVID-19 (SARS-CoV-2), Middle East respiratory syndrome-CoV, and other coronavirus infections express a nucleocapsid protein (N) that is essential for viral replication, transcription, and virion assembly. Phosphorylation of N from SARS-CoV by glycogen synthase kinase 3 (GSK-3) is required for its function and inhibition of GSK-3 with lithium impairs N phosphorylation, viral transcription, and replication. Here we report that the SARS-CoV-2 N protein contains GSK-3 consensus sequences and that this motif is conserved in diverse coronaviruses, raising the possibility that SARS-CoV-2 may be sensitive to GSK-3 inhibitors, including lithium. We conducted a retrospective analysis of lithium use in patients from three major health systems who were PCR-tested for SARS-CoV-2. We found that patients taking lithium have a significantly reduced risk of COVID-19 (odds ratio = 0.51 [0.35-0.74], P = 0.005). We also show that the SARS-CoV-2 N protein is phosphorylated by GSK-3. Knockout of GSK3A and GSK3B demonstrates that GSK-3 is essential for N phosphorylation. Alternative GSK-3 inhibitors block N phosphorylation and impair replication in SARS-CoV-2 infected lung epithelial cells in a cell-type-dependent manner. Targeting GSK-3 may therefore provide an approach to treat COVID-19 and future coronavirus outbreaks., Competing Interests: Competing interest statement: M.D.R. is on the Scientific Advisory Board for Goldfinch Bio and Cipherome., (Copyright © 2021 the Author(s). Published by PNAS.)

Polygenic risk scores (PRS) summarize genetic liability to a disease at the individual level, and the aim is to use them as biomarkers of disease and poor outcomes in real-world clinical practice. To date, few studies have assessed the prognostic value of PRS relative to standards of care. Schizophrenia (SCZ), the archetypal psychotic illness, is an ideal test case for this because the predictive power of the SCZ PRS exceeds that of most other common diseases. Here, we analyzed clinical and genetic data from two multi-ethnic cohorts totaling 8,541 adults with SCZ and related psychotic disorders, to assess whether the SCZ PRS improves the prediction of poor outcomes relative to clinical features captured in a standard psychiatric interview. For all outcomes investigated, the SCZ PRS did not improve the performance of predictive models, an observation that was generally robust to divergent case ascertainment strategies and the ancestral background of the study participants., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and pathology of multiple organs in individuals under 21 years of age in the weeks following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although an autoimmune pathogenesis has been proposed, the genes, pathways and cell types causal to this new disease remain unknown. Here we perform RNA sequencing of blood from patients with MIS-C and controls to find disease-associated genes clustered in a co-expression module annotated to CD56 dim CD57 + natural killer (NK) cells and exhausted CD8 + T cells. A similar transcriptome signature is replicated in an independent cohort of Kawasaki disease (KD), the related condition after which MIS-C was initially named. Probing a probabilistic causal network previously constructed from over 1,000 blood transcriptomes both validates the structure of this module and reveals nine key regulators, including TBX21, a central coordinator of exhausted CD8 + T cell differentiation. Together, this unbiased, transcriptome-wide survey implicates downregulation of NK cells and cytotoxic T cell exhaustion in the pathogenesis of MIS-C., (© 2021. The Author(s).)

Background and Objectives: AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited., Design, Setting, Participants, & Measurements: Using data from adult patients hospitalized with COVID-19 from five hospitals from the Mount Sinai Health System who were admitted between March 10 and December 26, 2020, we developed and validated several models (logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme GradientBoosting [XGBoost; with and without imputation]) for predicting treatment with dialysis or death at various time horizons (1, 3, 5, and 7 days) after hospital admission. Patients admitted to the Mount Sinai Hospital were used for internal validation, whereas the other hospitals formed part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vital signs within 12 hours of hospital admission., Results: A total of 6093 patients (2442 in training and 3651 in external validation) were included in the final cohort. Of the different modeling approaches used, XGBoost without imputation had the highest area under the receiver operating characteristic (AUROC) curve on internal validation (range of 0.93-0.98) and area under the precision-recall curve (AUPRC; range of 0.78-0.82) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range of 0.85-0.87, and AUPRC range of 0.27-0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04; mean difference in AUPRC of 0.15). Features of creatinine, BUN, and red cell distribution width were major drivers of the model's prediction., Conclusions: An XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in patients positive for COVID-19 had the best performance, as compared with standard and other machine learning models., Podcast: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021&#95;07&#95;09&#95;CJN17311120.mp3., (Copyright © 2021 by the American Society of Nephrology.)

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the associated Coronavirus Disease 2019 (COVID-19) is a public health emergency. Acute kidney injury (AKI) is a common complication in hospitalized patients with COVID-19 although mechanisms underlying AKI are yet unclear. There may be a direct effect of SARS-CoV-2 virus on the kidney; however, there is currently no data linking SARS-CoV-2 viral load (VL) to AKI. We explored the association of SARS-CoV-2 VL at admission to AKI in a large diverse cohort of hospitalized patients with COVID-19., Methods and Findings: We included patients hospitalized between March 13th and May 19th, 2020 with SARS-CoV-2 in a large academic healthcare system in New York City (N = 1,049) with available VL at admission quantified by real-time RT-PCR. We extracted clinical and outcome data from our institutional electronic health records (EHRs). AKI was defined by KDIGO guidelines. We fit a Fine-Gray competing risks model (with death as a competing risk) using demographics, comorbidities, admission severity scores, and log10 transformed VL as covariates and generated adjusted hazard ratios (aHR) and 95% Confidence Intervals (CIs). VL was associated with an increased risk of AKI (aHR = 1.04, 95% CI: 1.01-1.08, p = 0.02) with a 4% increased hazard for each log10 VL change. Patients with a viral load in the top 50th percentile had an increased adjusted hazard of 1.27 (95% CI: 1.02-1.58, p = 0.03) for AKI as compared to those in the bottom 50th percentile., Conclusions: VL is weakly but significantly associated with in-hospital AKI after adjusting for confounders. This may indicate the role of VL in COVID-19 associated AKI. This data may inform future studies to discover the mechanistic basis of COVID-19 associated AKI., Competing Interests: GNN is a scientific co-founder in Renalytix AI and owns equity, is on the scientific advisory board and receives consulting fees. GNN is also a scientific co-founder in Pensieve health, owns equity and is on the scientific advisory board. GNN has also received consulting fees from AstraZeneca, Reata, BioVie, Variant Bio and GLG consulting and has received operational funding from Goldfinch Bio. There is no specific competing interest related to this work. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability., Objective: We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days., Methods: Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator., Results: The LASSO federated model outperformed the LASSO local model at 3 hospitals, and the MLP federated model performed better than the MLP local model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSO pooled model outperformed the LASSO federated model at all hospitals, and the MLP federated model outperformed the MLP pooled model at 2 hospitals., Conclusions: The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy., (©Akhil Vaid, Suraj K Jaladanki, Jie Xu, Shelly Teng, Arvind Kumar, Samuel Lee, Sulaiman Somani, Ishan Paranjpe, Jessica K De Freitas, Tingyi Wanyan, Kipp W Johnson, Mesude Bicak, Eyal Klang, Young Joon Kwon, Anthony Costa, Shan Zhao, Riccardo Miotto, Alexander W Charney, Erwin Böttinger, Zahi A Fayad, Girish N Nadkarni, Fei Wang, Benjamin S Glicksberg. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 27.01.2021.)

Elevated plasma cholesterol and type 2 diabetes (T2D) are associated with coronary artery disease (CAD). Individuals treated with cholesterol-lowering statins have increased T2D risk, while individuals with hypercholesterolemia have reduced T2D risk. We explore the relationship between lipid and glucose control by constructing network models from the STARNET study with sequencing data from seven cardiometabolic tissues obtained from CAD patients during coronary artery by-pass grafting surgery. By integrating gene expression, genotype, metabolomic, and clinical data, we identify a glucose and lipid determining (GLD) regulatory network showing inverse relationships with lipid and glucose traits. Master regulators of the GLD network also impact lipid and glucose levels in inverse directions. Experimental inhibition of one of the GLD network master regulators, lanosterol synthase (LSS), in mice confirms the inverse relationships to glucose and lipid levels as predicted by our model and provides mechanistic insights.

Background: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associated with worse outcomes. However, AKI among hospitalized patients with COVID-19 in the United States is not well described., Methods: This retrospective, observational study involved a review of data from electronic health records of patients aged ≥18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality., Results: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patients with AKI required dialysis. The proportions with stages 1, 2, or 3 AKI were 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up., Conclusions: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge., (Copyright © 2021 by the American Society of Nephrology.)

Objective: The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive., Design: Demographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive., Setting: All patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system., Participants: Participants over the age of 18 years were included., Primary Outcomes: We investigated in-hospital mortality during the study period., Results: A total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 μg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 μg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL., Conclusions: In our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged., Competing Interests: Competing interests: GNN reports grants, personal fees and non-financial support from Renalytix AI, non-financial support from Pensieve Health, personal fees from AstraZeneca, BioVie, GLG Consulting, from outside the submitted work. AL reports personal fees from Zoll, outside the submitted work. ZAF reports grants from Daiichi Sankyo, grants from Amgen, Bristol Myers Squibb and Siemens Healthineers, personal fees from Alexion, GlaxoSmithKline, Trained Therapeutix Discovery, outside the submitted work. In addition, ZAF has patents licensed to Trained Therapeutix Discovery. The other authors have nothing to disclose., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Initially, children were thought to be spared from disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, a month into the epidemic, a novel multisystem inflammatory syndrome in children (MIS-C) emerged. Herein, we report on the immune profiles of nine MIS-C cases. All MIS-C patients had evidence of prior SARS-CoV-2 exposure, mounting an antibody response with intact neutralization capability. Cytokine profiling identified elevated signatures of inflammation (IL-18 and IL-6), lymphocytic and myeloid chemotaxis and activation (CCL3, CCL4, and CDCP1), and mucosal immune dysregulation (IL-17A, CCL20, and CCL28). Immunophenotyping of peripheral blood revealed reductions of non-classical monocytes, and subsets of NK and T lymphocytes, suggesting extravasation to affected tissues. Finally, profiling the autoantigen reactivity of MIS-C plasma revealed both known disease-associated autoantibodies (anti-La) and novel candidates that recognize endothelial, gastrointestinal, and immune-cell antigens. All patients were treated with anti-IL-6R antibody and/or IVIG, which led to rapid disease resolution., Competing Interests: Declaration of Interests DB reports ownership in Lab11 Therapeutics. S. Gnjatic reports consultancy and/or advisory roles for Merck, Neon Therapeutics and OncoMed and research funding from Bristol-Myers Squibb, Genentech, Immune Design, Agenus, Janssen R&D, Pfizer, Takeda, and Regeneron., (Published by Elsevier Inc.)

Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking., Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points., Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions., Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction., Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes., (©Akhil Vaid, Sulaiman Somani, Adam J Russak, Jessica K De Freitas, Fayzan F Chaudhry, Ishan Paranjpe, Kipp W Johnson, Samuel J Lee, Riccardo Miotto, Felix Richter, Shan Zhao, Noam D Beckmann, Nidhi Naik, Arash Kia, Prem Timsina, Anuradha Lala, Manish Paranjpe, Eddye Golden, Matteo Danieletto, Manbir Singh, Dara Meyer, Paul F O'Reilly, Laura Huckins, Patricia Kovatch, Joseph Finkelstein, Robert M. Freeman, Edgar Argulian, Andrew Kasarskis, Bethany Percha, Judith A Aberg, Emilia Bagiella, Carol R Horowitz, Barbara Murphy, Eric J Nestler, Eric E Schadt, Judy H Cho, Carlos Cordon-Cardo, Valentin Fuster, Dennis S Charney, David L Reich, Erwin P Bottinger, Matthew A Levin, Jagat Narula, Zahi A Fayad, Allan C Just, Alexander W Charney, Girish N Nadkarni, Benjamin S Glicksberg. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.11.2020.)

Background: Thromboembolic disease is common in coronavirus disease-2019 (COVID-19). There is limited evidence on the association of in-hospital anticoagulation (AC) with outcomes and postmortem findings., Objectives: The purpose of this study was to examine association of AC with in-hospital outcomes and describe thromboembolic findings on autopsies., Methods: This retrospective analysis examined the association of AC with mortality, intubation, and major bleeding. Subanalyses were also conducted on the association of therapeutic versus prophylactic AC initiated ≤48 h from admission. Thromboembolic disease was contextualized by premortem AC among consecutive autopsies., Results: Among 4,389 patients, median age was 65 years with 44% women. Compared with no AC (n = 1,530; 34.9%), therapeutic AC (n = 900; 20.5%) and prophylactic AC (n = 1,959; 44.6%) were associated with lower in-hospital mortality (adjusted hazard ratio [aHR]: 0.53; 95% confidence interval [CI]: 0.45 to 0.62 and aHR: 0.50; 95% CI: 0.45 to 0.57, respectively), and intubation (aHR: 0.69; 95% CI: 0.51 to 0.94 and aHR: 0.72; 95% CI: 0.58 to 0.89, respectively). When initiated ≤48 h from admission, there was no statistically significant difference between therapeutic (n = 766) versus prophylactic AC (n = 1,860) (aHR: 0.86; 95% CI: 0.73 to 1.02; p = 0.08). Overall, 89 patients (2%) had major bleeding adjudicated by clinician review, with 27 of 900 (3.0%) on therapeutic, 33 of 1,959 (1.7%) on prophylactic, and 29 of 1,530 (1.9%) on no AC. Of 26 autopsies, 11 (42%) had thromboembolic disease not clinically suspected and 3 of 11 (27%) were on therapeutic AC., Conclusions: AC was associated with lower mortality and intubation among hospitalized COVID-19 patients. Compared with prophylactic AC, therapeutic AC was associated with lower mortality, although not statistically significant. Autopsies revealed frequent thromboembolic disease. These data may inform trials to determine optimal AC regimens., (Copyright © 2020. Published by Elsevier Inc.)

Several studies have revealed that the hyper-inflammatory response induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major cause of disease severity and death. However, predictive biomarkers of pathogenic inflammation to help guide targetable immune pathways are critically lacking. We implemented a rapid multiplex cytokine assay to measure serum interleukin (IL)-6, IL-8, tumor necrosis factor (TNF)-α and IL-1β in hospitalized patients with coronavirus disease 2019 (COVID-19) upon admission to the Mount Sinai Health System in New York. Patients (n = 1,484) were followed up to 41 d after admission (median, 8 d), and clinical information, laboratory test results and patient outcomes were collected. We found that high serum IL-6, IL-8 and TNF-α levels at the time of hospitalization were strong and independent predictors of patient survival (P < 0.0001, P = 0.0205 and P = 0.0140, respectively). Notably, when adjusting for disease severity, common laboratory inflammation markers, hypoxia and other vitals, demographics, and a range of comorbidities, IL-6 and TNF-α serum levels remained independent and significant predictors of disease severity and death. These findings were validated in a second cohort of patients (n = 231). We propose that serum IL-6 and TNF-α levels should be considered in the management and treatment of patients with COVID-19 to stratify prospective clinical trials, guide resource allocation and inform therapeutic options.

Background: Data on patients with coronavirus disease 2019 (COVID-19) who return to hospital after discharge are scarce. Characterization of these patients may inform post-hospitalization care., Objective: To describe clinical characteristics of patients with COVID-19 who returned to the emergency department (ED) or required readmission within 14 days of discharge., Design: Retrospective cohort study of SARS-COV-2-positive patients with index hospitalization between February 27 and April 12, 2020, with ≥ 14-day follow-up. Significance was defined as P < 0.05 after multiplying P by 125 study-wide comparisons., Participants: Hospitalized patients with confirmed SARS-CoV-2 discharged alive from five New York City hospitals., Main Measures: Readmission or return to ED following discharge., Results: Of 2864 discharged patients, 103 (3.6%) returned for emergency care after a median of 4.5 days, with 56 requiring inpatient readmission. The most common reason for return was respiratory distress (50%). Compared with patients who did not return, there were higher proportions of COPD (6.8% vs 2.9%) and hypertension (36% vs 22.1%) among those who returned. Patients who returned also had a shorter median length of stay (LOS) during index hospitalization (4.5 [2.9,9.1] vs 6.7 [3.5, 11.5] days; P adjusted = 0.006), and were less likely to have required intensive care on index hospitalization (5.8% vs 19%; P adjusted = 0.001). A trend towards association between absence of in-hospital treatment-dose anticoagulation on index admission and return to hospital was also observed (20.9% vs 30.9%, P adjusted = 0.06). On readmission, rates of intensive care and death were 5.8% and 3.6%, respectively., Conclusions: Return to hospital after admission for COVID-19 was infrequent within 14 days of discharge. The most common cause for return was respiratory distress. Patients who returned more likely had COPD and hypertension, shorter LOS on index-hospitalization, and lower rates of in-hospital treatment-dose anticoagulation. Future studies should focus on whether these comorbid conditions, longer LOS, and anticoagulation are associated with reduced readmissions.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Though discovered over 100 years ago, the molecular foundation of sporadic Alzheimer's disease (AD) remains elusive. To better characterize the complex nature of AD, we constructed multiscale causal networks on a large human AD multi-omics dataset, integrating clinical features of AD, DNA variation, and gene- and protein-expression. These probabilistic causal models enabled detection, prioritization and replication of high-confidence master regulators of AD-associated networks, including the top predicted regulator, VGF. Overexpression of neuropeptide precursor VGF in 5xFAD mice partially rescued beta-amyloid-mediated memory impairment and neuropathology. Molecular validation of network predictions downstream of VGF was also achieved in this AD model, with significant enrichment for homologous genes identified as differentially expressed in 5xFAD brains overexpressing VGF. Our findings support a causal role for VGF in protecting against AD pathogenesis and progression.

Psychiatry is constructed around a taxonomy of several hundred diagnoses differentiated by nuances in the timing, co-occurrence, and severity of symptoms. Bipolar disorder (BD) is notable among these diagnoses for manic, depressive, and psychotic symptoms all being core features. Here, we trace current understanding of the neurobiological origins of BD and related diagnoses. To provide context, we begin by exploring the historical origins of psychiatric taxonomy. We then illustrate how key discoveries in pharmacology and neuroscience gave rise to a generation of neurobiological hypotheses about the origins of these disorders that facilitated therapeutic innovation but failed to explain disease pathogenesis. Lastly, we examine the extent to which genetics has succeeded in filling this void and contributing to the construction of an objective classification of psychiatric disturbance.

Background: Genetic risk for bipolar disorder (BD) is conferred through many common alleles, while a role for rare copy number variants (CNVs) is less clear. Subtypes of BD including schizoaffective disorder bipolar type (SAB), bipolar I disorder (BD I), and bipolar II disorder (BD II) differ according to the prominence and timing of psychosis, mania, and depression. The genetic factors contributing to the combination of symptoms among these subtypes are poorly understood., Methods: Rare large CNVs were analyzed in 6353 BD cases (3833 BD I [2676 with psychosis, 850 without psychosis, and 307 with unknown psychosis history], 1436 BD II, 579 SAB, and 505 BD not otherwise specified) and 8656 controls. CNV burden and a polygenic risk score (PRS) for schizophrenia were used to evaluate the relative contributions of rare and common variants to risk of BD, BD subtypes, and psychosis., Results: CNV burden did not differ between BD and controls when treated as a single diagnostic entity. However, burden in SAB was increased relative to controls (p = .001), BD I (p = .0003), and BD II (p = .0007). Burden and schizophrenia PRSs were increased in SAB compared with BD I with psychosis (CNV p = .0007, PRS p = .004), and BD I without psychosis (CNV p = .0004, PRS p = 3.9 × 10 -5 ). Within BD I, psychosis was associated with increased schizophrenia PRSs (p = .005) but not CNV burden., Conclusions: CNV burden in BD is limited to SAB. Rare and common genetic variants may contribute differently to risk for psychosis and perhaps other classes of psychiatric symptoms., (Copyright © 2019 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10 -4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10 -8 ) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.

The genetic architecture of schizophrenia (SCZ) includes numerous risk loci across a range of frequencies and sizes, including common and rare single-nucleotide variants and insertions/deletions (indels), as well as rare copy number variants (CNVs). Despite the clear heritability of the disease, monozygotic twins are discordant for SCZ at a significant rate. Somatic variants-genetic changes that arise after fertilization rather than through germline inheritance-are widespread in the human brain and known to contribute to risk for both rare and common neuropsychiatric conditions. The contribution of somatic variants in the brain to risk of SCZ remains to be determined. In this study, we surveyed somatic single-nucleotide variants (sSNVs) in the brains of controls and individuals with SCZ (n = 10 and n = 9, respectively). From each individual, whole-exome sequencing (WES) was performed on DNA from neuronal and non-neuronal nuclei isolated by fluorescence activated nuclear sorting (FANS) from frozen postmortem prefrontal cortex (PFC) samples, as well as DNA extracted from temporal muscle as a reference. We identified an increased burden of sSNVs in cases compared to controls (SCZ rate = 2.78, control rate = 0.70; P = 0.0092, linear mixed effects model), that included a higher rate of non-synonymous and loss-of-function variants (SCZ rate = 1.33, control rate = 0.50; P = 0.047, linear mixed effects model). Our findings suggest sSNVs in the brain may constitute an additional component of the complex genetic architecture of SCZ. This perspective argues for the need to further investigate somatic variation in the brain as an explanation of the discordance in monozygotic twins and a potential guide to the identification of novel therapeutic targets.

Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. Am J Psychiatry 172:363-372, 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3330 BD cases and 3952 controls of European ancestry were used to estimate SNP-based heritability (h 2 g ) and genetic correlation (r g ) between EHR-based phenotype definitions and traditionally ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and three rule-based algorithms using codified EHR with decreasing levels of stringency-"coded-strict", "coded-broad", and "coded-broad based on a single clinical encounter" (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1968 coded-strict, 2581 coded-broad, 408 coded-broad-SV BD cases, and 3 952 controls. The estimated h 2 g were 0.24 (p = 0.015), 0.09 (p = 0.064), 0.13 (p = 0.003), 0.00 (p = 0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h 2 g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h 2 g ) was 0.12 (p = 0.004). These h 2 g were lower or similar to the h 2 g observed by the ICCBD + PGCBD (0.23, p = 3.17E-80, total N = 33,181). However, the r g between ICCBD + PGCBD and the EHR-based cases were high for 95-NLP (0.66, p = 3.69 × 10 -5 ), coded-strict (1.00, p = 2.40 × 10 -4 ), and coded-broad (0.74, p = 8.11 × 10 -7 ). The r g between EHR-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.

Lithium is the mainstay prophylactic treatment for bipolar disorder (BD), but treatment response varies considerably across individuals. Patients who respond well to lithium treatment might represent a relatively homogeneous subtype of this genetically and phenotypically diverse disorder. Here, we performed genome-wide association studies (GWAS) to identify (i) specific genetic variations influencing lithium response and (ii) genetic variants associated with risk for lithium-responsive BD. Patients with BD and controls were recruited from Sweden and the United Kingdom. GWAS were performed on 2698 patients with subjectively defined (self-reported) lithium response and 1176 patients with objectively defined (clinically documented) lithium response. We next conducted GWAS comparing lithium responders with healthy controls (1639 subjective responders and 8899 controls; 323 objective responders and 6684 controls). Meta-analyses of Swedish and UK results revealed no significant associations with lithium response within the bipolar subjects. However, when comparing lithium-responsive patients with controls, two imputed markers attained genome-wide significant associations, among which one was validated in confirmatory genotyping (rs116323614, P=2.74 × 10(-8)). It is an intronic single-nucleotide polymorphism (SNP) on chromosome 2q31.2 in the gene SEC14 and spectrin domains 1 (SESTD1), which encodes a protein involved in regulation of phospholipids. Phospholipids have been strongly implicated as lithium treatment targets. Furthermore, we estimated the proportion of variance for lithium-responsive BD explained by common variants ('SNP heritability') as 0.25 and 0.29 using two definitions of lithium response. Our results revealed a genetic variant in SESTD1 associated with risk for lithium-responsive BD, suggesting that the understanding of BD etiology could be furthered by focusing on this subtype of BD.

Background: Therapeutic treatments for schizophrenia do not alleviate symptoms for all patients and efficacy is limited by common, often severe, side-effects. Genetic studies of disease can identify novel drug targets, and drugs for which the mechanism has direct genetic support have increased likelihood of clinical success. Large-scale genetic studies of schizophrenia have increased the number of genes and gene sets associated with risk. We aimed to examine the overlap between schizophrenia risk loci and gene targets of a comprehensive set of medications to potentially inform and improve treatment of schizophrenia., Methods: We defined schizophrenia risk loci as genomic regions reaching genome-wide significance in the latest Psychiatric Genomics Consortium schizophrenia genome-wide association study (GWAS) of 36 989 cases and 113 075 controls and loss of function variants observed only once among 5079 individuals in an exome-sequencing study of 2536 schizophrenia cases and 2543 controls (Swedish Schizophrenia Study). Using two large and orthogonally created databases, we collated drug targets into 167 gene sets targeted by pharmacologically similar drugs and examined enrichment of schizophrenia risk loci in these sets. We further linked the exome-sequenced data with a national drug registry (the Swedish Prescribed Drug Register) to assess the contribution of rare variants to treatment response, using clozapine prescription as a proxy for treatment resistance., Findings: We combined results from testing rare and common variation and, after correction for multiple testing, two gene sets were associated with schizophrenia risk: agents against amoebiasis and other protozoal diseases (106 genes, p=0·00046, pcorrected =0·024) and antipsychotics (347 genes, p=0·00078, pcorrected=0·046). Further analysis pointed to antipsychotics as having independent enrichment after removing genes that overlapped these two target sets. We noted significant enrichment both in known targets of antipsychotics (70 genes, p=0·0078) and novel predicted targets (277 genes, p=0·019). Patients with treatment-resistant schizophrenia had an excess of rare disruptive variants in gene targets of antipsychotics (347 genes, p=0·0067) and in genes with evidence for a role in antipsychotic efficacy (91 genes, p=0·0029)., Interpretation: Our results support genetic overlap between schizophrenia pathogenesis and antipsychotic mechanism of action. This finding is consistent with treatment efficacy being polygenic and suggests that single-target therapeutics might be insufficient. We provide evidence of a role for rare functional variants in antipsychotic treatment response, pointing to a subset of patients where their genetic information could inform treatment. Finally, we present a novel framework for identifying treatments from genetic data and improving our understanding of therapeutic mechanism., Funding: US National Institutes of Health., (Copyright © 2016 Elsevier Ltd. All rights reserved.)