Genome-Wide Association Studies of Schizophrenia and Bipolar Disorder in a Diverse Cohort of US Veterans.

BACKGROUND: Schizophrenia (SCZ) and bipolar disorder (BIP) are debilitating neuropsychiatric disorders, collectively affecting 2% of the world's population. Recognizing the major impact of these psychiatric disorders on the psychosocial function of more than 200 000 US Veterans, the Department of Veterans Affairs (VA) recently completed genotyping of more than 8000 veterans with SCZ and BIP in the Cooperative Studies Program (CSP) #572. METHODS: We performed genome-wide association studies (GWAS) in CSP #572 and benchmarked the predictive value of polygenic risk scores (PRS) constructed from published findings. We combined our results with available summary statistics from several recent GWAS, realizing the largest and most diverse studies of these disorders to date. RESULTS: Our primary GWAS uncovered new associations between CHD7 variants and SCZ, and novel BIP associations with variants in Sortilin Related VPS10 Domain Containing Receptor 3 (SORCS3) and downstream of PCDH11X. Combining our results with published summary statistics for SCZ yielded 39 novel susceptibility loci including CRHR1, and we identified 10 additional findings for BIP (28 326 cases and 90 570 controls). PRS trained on published GWAS were significantly associated with case-control status among European American (P < 10-30) and African American (P < .0005) participants in CSP #572. CONCLUSIONS: We have demonstrated that published findings for SCZ and BIP are robustly generalizable to a diverse cohort of US veterans. Leveraging available summary statistics from GWAS of global populations, we report 52 new susceptibility loci and improved fine-mapping resolution for dozens of previously reported associations.

Genome-wide association study of cognitive performance in U.S. veterans with schizophrenia or bipolar disorder.

Cognitive impairment is a frequent and serious problem in patients with various forms of severe mental illnesses (SMI), including schizophrenia (SZ) and bipolar disorder (BP). Recent research suggests genetic links to several cognitive phenotypes in both SMI and in the general population. Our goal in this study was to identify potential genomic signatures of cognitive functioning in veterans with severe mental illness and compare them to previous findings for cognition across different populations. Veterans Affairs (VA) Cooperative Studies Program (CSP) Study #572 evaluated cognitive and functional capacity measures among SZ and BP patients. In conjunction with the VA Million Veteran Program, 3,959 European American (1,095 SZ, 2,864 BP) and 2,601 African American (1,095 SZ, 2,864 BP) patients were genotyped using a custom Affymetrix Axiom Biobank array. We performed a genome-wide association study of global cognitive functioning, constructed polygenic scores for SZ and cognition in the general population, and examined genetic correlations with 2,626 UK Biobank traits. Although no single locus attained genome-wide significance, observed allelic effects were strongly consistent with previous studies. We observed robust associations between global cognitive functioning and polygenic scores for cognitive performance, intelligence, and SZ risk. We also identified significant genetic correlations with several cognition-related traits in UK Biobank. In a diverse cohort of U.S. veterans with SZ or BP, we demonstrate broad overlap of common genetic effects on cognition in the general population, and find that greater polygenic loading for SZ risk is associated with poorer cognitive performance.

Effect of the Tool to Reduce Inappropriate Medications on Medication Communication and Deprescribing.

OBJECTIVES: To examine the effect of the Tool to Reduce Inappropriate Medications (TRIM), a web tool linking an electronic health record (EHR) to a clinical decision support system, on medication communication and prescribing. DESIGN: Randomized clinical trial. SETTING: Primary care clinics at a Veterans Affairs Medical Center. PARTICIPANTS: Veterans aged 65 and older prescribed seven or more medications randomized to receipt of TRIM or usual care (N = 128). INTERVENTION: TRIM extracts information on medications and chronic conditions from the EHR and contains data entry screens for information obtained from brief chart review and telephonic patient assessment. These data serve as input for automated algorithms identifying medication reconciliation discrepancies, potentially inappropriate medications (PIMs), and potentially inappropriate regimens. Clinician feedback reports summarize discrepancies and provide recommendations for deprescribing. Patient feedback reports summarize discrepancies and self-reported medication problems. MEASUREMENTS: Primary: subscales of the Patient Assessment of Care for Chronic Conditions (PACIC) related to shared decision-making; clinician and patient communication. Secondary: changes in medications. RESULTS: 29.7% of TRIM participants and 15.6% of control participants provided the highest PACIC ratings; this difference was not significant. Adjusting for covariates and clustering of patients within clinicians, TRIM was associated with significantly more-active patient communication and facilitative clinician communication and with more medication-related communication among patients and clinicians. TRIM was significantly associated with correction of medication discrepancies but had no effect on number of medications or reduction in PIMs. CONCLUSION: TRIM improved communication about medications and accuracy of documentation. Although there was no association with prescribing, the small sample size provided limited power to examine medication-related outcomes.

Utilizing patient data from the veterans administration electronic health record to support web-based clinical decision support: informatics challenges and issues from three clinical domains.

BACKGROUND: The US Veterans Administration (VA) has developed a robust and mature computational infrastructure in support of its electronic health record (EHR). Web technology offers a powerful set of tools for structuring clinical decision support (CDS) around clinical care. This paper describes informatics challenges and design issues that were confronted in the process of building three Web-based CDS systems in the context of the VA EHR. METHODS: Over the course of several years, we implemented three Web-based CDS systems that extract patient data from the VA EHR environment to provide patient-specific CDS. These were 1) the VACS (Veterans Aging Cohort Study) Index Calculator which estimates prognosis for HIV+ patients, 2) Neuropath/CDS which assists in the medical management of patients with neuropathic pain, and 3) TRIM (Tool to Reduce Inappropriate Medications) which identifies potentially inappropriate medications in older adults and provides recommendations for improving the medication regimen. RESULTS: The paper provides an overview of the VA EHR environment and discusses specific informatics issues/challenges that arose in the context of each of the three Web-based CDS systems. We discuss specific informatics methods and provide details of approaches that may be useful within this setting. CONCLUSIONS: Informatics issues and challenges relating to data access and data availability arose because of the particular architecture of the national VA infrastructure and the need to link to that infrastructure from local Web-based CDS systems. Idiosyncrasies of VA patient data, especially the medication data, also posed challenges. Other issues related to specific functional needs of individual CDS systems. The goal of this paper is to describe these issues so that our experience may serve as a useful foundation to assist others who wish to build such systems in the future.

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience.

Neuron modeling may be said to have originated with the Hodgkin and Huxley action potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over the ensuing decades, these approaches have led to a massive development of increasingly accurate and complex data-based models of neurons and neuronal circuits. ModelDB was founded in 1996 to support this new field and enhance the scientific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them. It has grown to include over 1100 published models covering more than 130 research topics. It is actively curated and developed to help researchers discover and understand models of interest. ModelDB also provides mechanisms to assist running models both locally and remotely, and has a graphical tool that enables users to explore the anatomical and biophysical properties that are represented in a model. Each of its capabilities is undergoing continued refinement and improvement in response to user experience. Large research groups (Allen Brain Institute, EU Human Brain Project, etc.) are emerging that collect data across multiple scales and integrate that data into many complex models, presenting new challenges of scale. We end by predicting a future for neuroscience increasingly fueled by new technology and high performance computation, and increasingly in need of comprehensive user-friendly databases such as ModelDB to provide the means to integrate the data for deeper insights into brain function in health and disease.

Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study.

A key step in genomic studies is to assess high throughput measurements across millions of markers for each participant's DNA, either using microarrays or sequencing techniques. Accurate genotype calling is essential for downstream statistical analysis of genotype-phenotype associations, and next generation sequencing (NGS) has recently become a more common approach in genomic studies. How the accuracy of variant calling in NGS-based studies affects downstream association analysis has not, however, been studied using empirical data in which both microarrays and NGS were available. In this article, we investigate the impact of variant calling errors on the statistical power to identify associations between single nucleotides and disease, and on associations between multiple rare variants and disease. Both differential and nondifferential genotyping errors are considered. Our results show that the power of burden tests for rare variants is strongly influenced by the specificity in variant calling, but is rather robust with regard to sensitivity. By using the variant calling accuracies estimated from a substudy of a Cooperative Studies Program project conducted by the Department of Veterans Affairs, we show that the power of association tests is mostly retained with commonly adopted variant calling pipelines. An R package, GWAS.PC, is provided to accommodate power analysis that takes account of genotyping errors (http://zhaocenter.org/software/).

Electronic approaches to making sense of the text in the adverse event reporting system.

INTRODUCTION: Health care organizations working to eliminate preventable harm and to improve patient safety must have robust programs to collect and to analyze data on adverse events in order to use the information to affect improvement. Such adverse event reporting systems are based on frontline personnel reporting issues that arise in the course of their daily work. Limitations in how existing software systems handle these reports mean that use of this potentially rich information is resource intensive and prone to variable results. AIM: The aim of this study was to develop an electronic approach to processing the text in medical event reports that would be reliable enough to be used to improve patient safety. METHODS: At Connecticut Children's Medical Center, staff manually enter reports of adverse events into a web-based software tool. We evaluated the ability of 2 electronic methods-rule-based query and semi-supervised machine learning-to identify specific types of events ("use cases") versus a reference standard. Rule-based query was tested on 5 use cases and machine learning on a subset of 2 using 9164 events reported from February 2012-January 2014. RESULTS: Machine learning found 93% of the weight-based errors and 92% of the errors in patient-identification. Rule-based query had accuracy of 99% or greater, high precision, and high recall for all use cases. CONCLUSIONS: Electronic approaches to streamlining the use of adverse event reports are feasible to automate and valuable for categorizing this important data for use in improving patient safety.

Development of the Tool to Reduce Inappropriate Medications (TRIM): A Clinical Decision Support System to Improve Medication Prescribing for Older Adults.

STUDY OBJECTIVE: To create a clinical decision support system (CDSS) for evaluating problems with medications among older outpatients based on a broad set of criteria. DESIGN: Web-based CDSS development. SETTING: Primary care clinics at a Veterans Affairs medical center. PARTICIPANTS: Forty veterans 65 years and older who were prescribed seven or more medications that included those for treatment of diabetes mellitus and hypertension. MEASUREMENTS AND MAIN RESULTS: The Tool to Reduce Inappropriate Medications (TRIM) uses a program to extract age, medications, and chronic conditions from the electronic health record to identify high-risk patients and as input for evaluating the medication regimen. Additional health variables obtained through chart review and direct patient assessment are entered into a Web-based program. Based on a series of algorithms, TRIM generates feedback reports for clinicians. TRIM identified medication reconciliation discrepancies in 98% (39/40) of veterans, potentially inappropriate medications in 58% (23/40), potential problems with feasibility (based on poor adherence and/or cognitive impairment) in 25% (10/40), potential overtreatment of hypertension in 50% (20/40), potential overtreatment of diabetes in 43% (17/40), inappropriate dosing of renally excreted medications in 5% (2/40), and patient-reported adverse reactions in 5% (2/40). CONCLUSION: This evaluation of TRIM demonstrated that data elements can be extracted from the electronic health record to identify older primary care patients at risk for potentially problematic medication regimens. Supplemented with chart review and direct patient assessment, these data can be processed through clinical algorithms that identify potential problems and generate patient-specific feedback reports. Additional work is necessary to assess the effects of TRIM on medication deprescribing.

YPED: an integrated bioinformatics suite and database for mass spectrometry-based proteomics research.

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database (YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a single laboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography-tandem mass spectrometry (LC-MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring (MRM)/selective reaction monitoring (SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.

Extending the NIF DISCO framework to automate complex workflow: coordinating the harvest and integration of data from diverse neuroscience information resources.

This paper describes how DISCO, the data aggregator that supports the Neuroscience Information Framework (NIF), has been extended to play a central role in automating the complex workflow required to support and coordinate the NIF's data integration capabilities. The NIF is an NIH Neuroscience Blueprint initiative designed to help researchers access the wealth of data related to the neurosciences available via the Internet. A central component is the NIF Federation, a searchable database that currently contains data from 231 data and information resources regularly harvested, updated, and warehoused in the DISCO system. In the past several years, DISCO has greatly extended its functionality and has evolved to play a central role in automating the complex, ongoing process of harvesting, validating, integrating, and displaying neuroscience data from a growing set of participating resources. This paper provides an overview of DISCO's current capabilities and discusses a number of the challenges and future directions related to the process of coordinating the integration of neuroscience data within the NIF Federation.

The genetics of functional disability in schizophrenia and bipolar illness: Methods and initial results for VA cooperative study #572.

Given the prominence of cognitive impairments and disability associated with schizophrenia and bipolar disorder, substantial interest has arisen in identifying determinants of the diseases and their features. Genetic variation has been linked to skills that underlie disability ("functional capacity" or FC), highlighting need for understanding of these relationships. We describe the design and methods of a large, multisite, observational study focusing on the genetics of functional disability in schizophrenia and bipolar disorder, presenting initial data on recruitment, and characterization of the sample. Known as Veterans Affairs (VA) Cooperative Studies Program (CSP)#572, this study is recruiting, diagnosing, and assessing U.S. Veterans with either schizophrenia or bipolar I disorder. Assessments include neuropsychological (NP) testing, FC, suicidality, and co-morbid conditions such as posttraumatic stress disorder (PTSD). A sample of "psychiatrically healthy" Veterans from another project serves as a comparison group. An interim total of 8,140 participants (42.1% schizophrenia) have been recruited and assessed as of September 30, 2013, with 9 months of enrollment remaining and with a target sample size of 9,500. Veterans with schizophrenia were more likely to never have married, whereas lifetime PTSD and suicidality were more common in the bipolar veterans. Performance on the FC measures and NP tests was consistent with previous results, with mean t-scores of 35 (-1.5 SD) for schizophrenia and 41 (-0.9 SD) for the bipolar Veterans. This large population is representative of previous studies in terms of patient performance and co-morbidities. Subsequent genomic analyses will examine the genomic correlates of performance-based measures. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Exploring a clinically friendly web-based approach to clinical decision support linked to the electronic health record: design philosophy, prototype implementation, and framework for assessment.

BACKGROUND: Computer-based clinical decision support (CDS) is an important component of the electronic health record (EHR). As an increasing amount of CDS is implemented, it will be important that this be accomplished in a fashion that assists in clinical decision making without imposing unacceptable demands and burdens upon the provider's practice. OBJECTIVE: The objective of our study was to explore an approach that allows CDS to be clinician-friendly from a variety of perspectives, to build a prototype implementation that illustrates features of the approach, and to gain experience with a pilot framework for assessment. METHODS: The paper first discusses the project's design philosophy and goals. It then describes a prototype implementation (Neuropath/CDS) that explores the approach in the domain of neuropathic pain and in the context of the US Veterans Administration EHR. Finally, the paper discusses a framework for assessing the approach, illustrated by a pilot assessment of Neuropath/CDS. RESULTS: The paper describes the operation and technical design of Neuropath/CDS, as well as the results of the pilot assessment, which emphasize the four areas of focus, scope, content, and presentation. CONCLUSIONS: The work to date has allowed us to explore various design and implementation issues relating to the approach illustrated in Neuropath/CDS, as well as the development and pilot application of a framework for assessment.

Databases in SenseLab for the genomics, proteomics, and function of olfactory receptors.

We present here, the salient aspects of three databases: Olfactory Receptor Database (ORDB) is a repository of genomics and proteomics information of ORs; OdorDB stores information related to odorous compounds, specifically identifying those that have been shown to interact with olfactory rectors; and OdorModelDB disseminates information related to computational models of olfactory receptors (ORs). The data stored among these databases is integrated. Presented in this chapter are descriptions of these resources, which are part of the SenseLab suite of databases, a discussion of the computational infrastructure that enhances the efficacy of information storage, retrieval, dissemination, and automated data population from external sources.

Using a pharmacokinetic model to relate an individual's susceptibility to alcohol dependence to genotypes.

BACKGROUND/AIMS: Population-based studies have successfully identified genes affecting common diseases, but have not provided a molecular mechanism. We describe an approach for alcohol dependence connecting a mechanistic model at the molecular level with disease risk at the population level, and investigate how this model implies statistical gene-gene interactions that affect disease risk. METHODS: We develop a pharmacokinetic model describing how genetic variations in ADH1B, ADH1C, ADH7, ALDH2, and TAS2R38 affect consumption behavior, and alcohol and acetaldehyde levels over time in various tissues of individuals with a particular genotype to predict their susceptibility to alcohol dependence. RESULTS: We show that there is good agreement between the observed genotype/haplotype frequencies and those predicted by the model among cases and controls. Based on this framework, we show that we expect to observe statistical interactions among these genes for a reasonably large sample size when logistic regression models are used to relate genotype effects and disease risk. CONCLUSION: Our model exemplifies mechanistic modeling of how genes interact to influence an individual's susceptibility to alcohol dependence. We anticipate that this general approach could also be applied to study other diseases at the molecular level.

The NIF DISCO Framework: facilitating automated integration of neuroscience content on the web.

This paper describes the capabilities of DISCO, an extensible approach that supports integrative Web-based information dissemination. DISCO is a component of the Neuroscience Information Framework (NIF), an NIH Neuroscience Blueprint initiative that facilitates integrated access to diverse neuroscience resources via the Internet. DISCO facilitates the automated maintenance of several distinct capabilities using a collection of files 1) that are maintained locally by the developers of participating neuroscience resources and 2) that are "harvested" on a regular basis by a central DISCO server. This approach allows central NIF capabilities to be updated as each resource's content changes over time. DISCO currently supports the following capabilities: 1) resource descriptions, 2) "LinkOut" to a resource's data items from NCBI Entrez resources such as PubMed, 3) Web-based interoperation with a resource, 4) sharing a resource's lexicon and ontology, 5) sharing a resource's database schema, and 6) participation by the resource in neuroscience-related RSS news dissemination. The developers of a resource are free to choose which DISCO capabilities their resource will participate in. Although DISCO is used by NIF to facilitate neuroscience data integration, its capabilities have general applicability to other areas of research.

Semantic SenseLab: Implementing the vision of the Semantic Web in neuroscience.

OBJECTIVE: Integrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases. METHODS: Based on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources. CONCLUSION: We demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/.

Entrez Neuron RDFa: a pragmatic semantic web application for data integration in neuroscience research.

The amount of biomedical data available in Semantic Web formats has been rapidly growing in recent years. While these formats are machine-friendly, user-friendly web interfaces allowing easy querying of these data are typically lacking. We present "Entrez Neuron", a pilot neuron-centric interface that allows for keyword-based queries against a coherent repository of OWL ontologies. These ontologies describe neuronal structures, physiology, mathematical models and microscopy images. The returned query results are organized hierarchically according to brain architecture. Where possible, the application makes use of entities from the Open Biomedical Ontologies (OBO) and the 'HCLS knowledgebase' developed by the W3C Interest Group for Health Care and Life Science. It makes use of the emerging RDFa standard to embed ontology fragments and semantic annotations within its HTML-based user interface. The application and underlying ontologies demonstrate how Semantic Web technologies can be used for information integration within a curated information repository and between curated information repositories. It also demonstrates how information integration can be accomplished on the client side, through simple copying and pasting of portions of documents that contain RDFa markup.

Synergies and distinctions between computational disciplines in biomedical research: perspective from the Clinical andTranslational Science Award programs.

Clinical and translational research increasingly requires computation. Projects may involve multiple computationally oriented groups including information technology (IT) professionals, computer scientists, and biomedical informaticians. However, many biomedical researchers are not aware of the distinctions among these complementary groups, leading to confusion, delays, and suboptimal results. Although written from the perspective of Clinical and Translational Science Award (CTSA) programs within academic medical centers, this article addresses issues that extend beyond clinical and translational research. The authors describe the complementary but distinct roles of operational IT, research IT, computer science, and biomedical informatics using a clinical data warehouse as a running example. In general, IT professionals focus on technology. The authors distinguish between two types of IT groups within academic medical centers: central or administrative IT (supporting the administrative computing needs of large organizations) and research IT (supporting the computing needs of researchers). Computer scientists focus on general issues of computation such as designing faster computers or more efficient algorithms, rather than specific applications. In contrast, informaticians are concerned with data, information, and knowledge. Biomedical informaticians draw on a variety of tools, including but not limited to computers, to solve information problems in health care and biomedicine. The paper concludes with recommendations regarding administrative structures that can help to maximize the benefit of computation to biomedical research within academic health centers.

Approaches to neuroscience data integration.

As the number of neuroscience databases increases, the need for neuroscience data integration grows. This paper reviews and compares several approaches, including the Neuroscience Database Gateway (NDG), Neuroscience Information Framework (NIF) and Entrez Neuron, which enable neuroscience database annotation and integration. These approaches cover a range of activities spanning from registry, discovery and integration of a wide variety of neuroscience data sources. They also provide different user interfaces for browsing, querying and displaying query results. In Entrez Neuron, for example, four different facets or tree views (neuron, neuronal property, gene and drug) are used to hierarchically organize concepts that can be used for querying a collection of ontologies. The facets are also used to define the structure of the query results.

The NIF LinkOut broker: a web resource to facilitate federated data integration using NCBI identifiers.

This paper describes the NIF LinkOut Broker (NLB) that has been built as part of the Neuroscience Information Framework (NIF) project. The NLB is designed to coordinate the assembly of links to neuroscience information items (e.g., experimental data, knowledge bases, and software tools) that are (1) accessible via the Web, and (2) related to entries in the National Center for Biotechnology Information's (NCBI's) Entrez system. The NLB collects these links from each resource and passes them to the NCBI which incorporates them into its Entrez LinkOut service. In this way, an Entrez user looking at a specific Entrez entry can LinkOut directly to related neuroscience information. The information stored in the NLB can also be utilized in other ways. A second approach, which is operational on a pilot basis, is for the NLB Web server to create dynamically its own Web page of LinkOut links for each NCBI identifier in the NLB database. This approach can allow other resources (in addition to the NCBI Entrez) to LinkOut to related neuroscience information. The paper describes the current NLB system and discusses certain design issues that arose during its implementation.