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Introduction: The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare. Methods: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively. Results: Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare. Conclusions: Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.
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The differential performance of polygenic risk scores (PRSs) by group is one of the major ethical barriers to their clinical use. It is also one of the main practical challenges for any implementation effort. The social repercussions of how people are grouped in PRS research must be considered in communications with research participants, including return of results. Here, we outline the decisions faced and choices made by a large multi-site clinical implementation study returning PRSs to diverse participants in handling this issue of differential performance. Our approach to managing the complexities associated with the differential performance of PRSs serves as a case study that can help future implementers of PRSs to plot an anticipatory course in response to this issue.
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Predisposición Genética a la Enfermedad , Herencia Multifactorial , Humanos , Herencia Multifactorial/genética , Factores de Riesgo , Estudio de Asociación del Genoma Completo , Medición de Riesgo , Pruebas Genéticas/métodos , Puntuación de Riesgo GenéticoRESUMEN
As large-scale genomic screening becomes increasingly prevalent, understanding the influence of actionable results on healthcare utilization is key to estimating the potential long-term clinical impact. The eMERGE network sequenced individuals for actionable genes in multiple genetic conditions and returned results to individuals, providers, and the electronic health record. Differences in recommended health services (laboratory, imaging, and procedural testing) delivered within 12 months of return were compared among individuals with pathogenic or likely pathogenic (P/LP) findings to matched individuals with negative findings before and after return of results. Of 16,218 adults, 477 unselected individuals were found to have a monogenic risk for arrhythmia (n = 95), breast cancer (n = 96), cardiomyopathy (n = 95), colorectal cancer (n = 105), or familial hypercholesterolemia (n = 86). Individuals with P/LP results more frequently received services after return (43.8%) compared to before return (25.6%) of results and compared to individuals with negative findings (24.9%; p < 0.0001). The annual cost of qualifying healthcare services increased from an average of $162 before return to $343 after return of results among the P/LP group (p < 0.0001); differences in the negative group were non-significant. The mean difference-in-differences was $149 (p < 0.0001), which describes the increased cost within the P/LP group corrected for cost changes in the negative group. When stratified by individual conditions, significant cost differences were observed for arrhythmia, breast cancer, and cardiomyopathy. In conclusion, less than half of individuals received billed health services after monogenic return, which modestly increased healthcare costs for payors in the year following return.
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Neoplasias de la Mama , Cardiomiopatías , Adulto , Humanos , Femenino , Estudios Prospectivos , Aceptación de la Atención de Salud , Arritmias Cardíacas , Neoplasias de la Mama/genética , Cardiomiopatías/genéticaRESUMEN
Influenza A Virus (IAV) is a recurring respiratory virus with limited availability of antiviral therapies. Understanding host proteins essential for IAV infection can identify targets for alternative host-directed therapies (HDTs). Using affinity purification-mass spectrometry and global phosphoproteomic and protein abundance analyses using three IAV strains (pH1N1, H3N2, H5N1) in three human cell types (A549, NHBE, THP-1), we map 332 IAV-human protein-protein interactions and identify 13 IAV-modulated kinases. Whole exome sequencing of patients who experienced severe influenza reveals several genes, including scaffold protein AHNAK, with predicted loss-of-function variants that are also identified in our proteomic analyses. Of our identified host factors, 54 significantly alter IAV infection upon siRNA knockdown, and two factors, AHNAK and coatomer subunit COPB1, are also essential for productive infection by SARS-CoV-2. Finally, 16 compounds targeting our identified host factors suppress IAV replication, with two targeting CDK2 and FLT3 showing pan-antiviral activity across influenza and coronavirus families. This study provides a comprehensive network model of IAV infection in human cells, identifying functional host targets for pan-viral HDT.
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COVID-19 , Subtipo H5N1 del Virus de la Influenza A , Virus de la Influenza A , Gripe Humana , Humanos , Virus de la Influenza A/genética , Gripe Humana/genética , Subtipo H5N1 del Virus de la Influenza A/genética , Subtipo H3N2 del Virus de la Influenza A/metabolismo , Proteómica , Replicación Viral/genética , SARS-CoV-2 , Antivirales/metabolismo , Interacciones Huésped-Patógeno/genéticaRESUMEN
Polygenic risk scores (PRS) have potential to improve health care by identifying individuals that have elevated risk for common complex conditions. Use of PRS in clinical practice, however, requires careful assessment of the needs and capabilities of patients, providers, and health care systems. The electronic Medical Records and Genomics (eMERGE) network is conducting a collaborative study which will return PRS to 25,000 pediatric and adult participants. All participants will receive a risk report, potentially classifying them as high risk (â¼2-10% per condition) for 1 or more of 10 conditions based on PRS. The study population is enriched by participants from racial and ethnic minority populations, underserved populations, and populations who experience poorer medical outcomes. All 10 eMERGE clinical sites conducted focus groups, interviews, and/or surveys to understand educational needs among key stakeholders-participants, providers, and/or study staff. Together, these studies highlighted the need for tools that address the perceived benefit/value of PRS, types of education/support needed, accessibility, and PRS-related knowledge and understanding. Based on findings from these preliminary studies, the network harmonized training initiatives and formal/informal educational resources. This paper summarizes eMERGE's collective approach to assessing educational needs and developing educational approaches for primary stakeholders. It discusses challenges encountered and solutions provided.
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Registros Electrónicos de Salud , Etnicidad , Adulto , Humanos , Niño , Grupos Minoritarios , Factores de Riesgo , GenómicaRESUMEN
OBJECTIVE: Diverticular disease (DD) is one of the most prevalent conditions encountered by gastroenterologists, affecting ~50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with DD, leveraging multiple electronic health record (EHR) data sources of 91,166 multi-ancestry participants with a Natural Language Processing (NLP) technique. MATERIALS AND METHODS: We developed a NLP-enriched phenotyping algorithm that incorporated colonoscopy or abdominal imaging reports to identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of DD in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in clinical phenotypes. RESULTS: Our developed algorithm showed a significant improvement in patient classification performance for DD analysis (algorithm PPVs ≥ 0.94), with up to a 3.5 fold increase in terms of the number of identified patients than the traditional method. Ancestry-stratified analyses of diverticulosis and diverticulitis of the identified subjects replicated the well-established associations between ARHGAP15 loci with DD, showing overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients. Our PheWAS analyses identified significant associations between the DD GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes. DISCUSSION: As the first multi-ancestry GWAS-PheWAS study, we showcased that heterogenous EHR data can be mapped through an integrative analytical pipeline and reveal significant genotype-phenotype associations with clinical interpretation. CONCLUSION: A systematic framework to process unstructured EHR data with NLP could advance a deep and scalable phenotyping for better patient identification and facilitate etiological investigation of a disease with multilayered data.
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Enfermedades Diverticulares , Diverticulitis , Divertículo , Humanos , Registros Electrónicos de Salud , Estudio de Asociación del Genoma Completo/métodos , Procesamiento de Lenguaje Natural , Fenotipo , Algoritmos , Polimorfismo de Nucleótido SimpleRESUMEN
The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.
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Bases de Datos Genéticas , Proteínas , Ontología de Genes , Proteínas/genética , Anotación de Secuencia Molecular , Biología ComputacionalRESUMEN
PURPOSE: Assessing the risk of common, complex diseases requires consideration of clinical risk factors as well as monogenic and polygenic risks, which in turn may be reflected in family history. Returning risks to individuals and providers may influence preventive care or use of prophylactic therapies for those individuals at high genetic risk. METHODS: To enable integrated genetic risk assessment, the eMERGE (electronic MEdical Records and GEnomics) network is enrolling 25,000 diverse individuals in a prospective cohort study across 10 sites. The network developed methods to return cross-ancestry polygenic risk scores, monogenic risks, family history, and clinical risk assessments via a genome-informed risk assessment (GIRA) report and will assess uptake of care recommendations after return of results. RESULTS: GIRAs include summary care recommendations for 11 conditions, education pages, and clinical laboratory reports. The return of high-risk GIRA to individuals and providers includes guidelines for care and lifestyle recommendations. Assembling the GIRA required infrastructure and workflows for ingesting and presenting content from multiple sources. Recruitment began in February 2022. CONCLUSION: Return of a novel report for communicating monogenic, polygenic, and family history-based risk factors will inform the benefits of integrated genetic risk assessment for routine health care.
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Genoma , Genómica , Humanos , Estudios Prospectivos , Genómica/métodos , Factores de Riesgo , Medición de RiesgoRESUMEN
The chr12q24.13 locus encoding OAS1-OAS3 antiviral proteins has been associated with coronavirus disease 2019 (COVID-19) susceptibility. Here, we report genetic, functional and clinical insights into this locus in relation to COVID-19 severity. In our analysis of patients of European (n = 2,249) and African (n = 835) ancestries with hospitalized versus nonhospitalized COVID-19, the risk of hospitalized disease was associated with a common OAS1 haplotype, which was also associated with reduced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clearance in a clinical trial with pegIFN-λ1. Bioinformatic analyses and in vitro studies reveal the functional contribution of two associated OAS1 exonic variants comprising the risk haplotype. Derived human-specific alleles rs10774671-A and rs1131454 -A decrease OAS1 protein abundance through allele-specific regulation of splicing and nonsense-mediated decay (NMD). We conclude that decreased OAS1 expression due to a common haplotype contributes to COVID-19 severity. Our results provide insight into molecular mechanisms through which early treatment with interferons could accelerate SARS-CoV-2 clearance and mitigate against severe COVID-19.
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COVID-19 , 2',5'-Oligoadenilato Sintetasa/genética , 2',5'-Oligoadenilato Sintetasa/metabolismo , Alelos , COVID-19/genética , Hospitalización , Humanos , SARS-CoV-2/genéticaRESUMEN
The use of race and ethnicity in medicine has become a matter of intense debate. Currently the social construct of race and ethnicity is far from precise, serving as a poor proxy for ancestry. In this issue of Cell, Belbin et al. provide an interesting framework for better characterizing population substructure in a diverse urban environment.
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Etnicidad , Grupos Raciales , Etnicidad/genética , Humanos , Grupos Raciales/genéticaRESUMEN
Implementing an effective software solution is an important step in managing a portfolio of core facilities. Though commercial options are available, developing or adopting a custom platform is a viable path for many institutions. At Northwestern University (NU), the cores program was reorganized beginning in 2008, and we pursued the latter path in order to retain control of the development priorities and to ensure integration with other enterprise systems. This manuscript describes our experience and results after a decade of effort. The platform, named NUcore, began enrollment in 2011, and full enrollment was achieved in 2019. Key features of NUcore include a stable and secure environment, a responsive and intuitive interface for users and core staff, seamless integration with the university financial system, rules and restrictions to ensure compliance, and both core-specific and enterprise reporting. NUcore now supports nearly half of all sponsored award dollars at NU at a cost of only 1 cent per dollar of business transacted. On average, there are over 4000 active users each year. NUcore is managed as an open-source project, available at no cost to any organization. Five academic organizations currently use the NUcore code base. For NU, NUcore has been a substantial success, and continuous development will ensure that it meets the future needs of our university and its cores.
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Comercio , Programas Informáticos , Humanos , UniversidadesRESUMEN
CONTEXT: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. OBJECTIVE: Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment. DESIGN, PATIENTS, AND METHODS: Leveraging the electronic health records (EHRs) of 124â 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS. RESULTS: The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity", "type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension", and "sleep apnea" reaching phenome-wide significance. CONCLUSIONS: Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome-phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.
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Algoritmos , Estudio de Asociación del Genoma Completo , Herencia Multifactorial/genética , Fenómica/métodos , Fenotipo , Síndrome del Ovario Poliquístico/diagnóstico , Adolescente , Anciano , Estudios de Casos y Controles , Niño , Registros Electrónicos de Salud , Femenino , Estudios de Seguimiento , Predisposición Genética a la Enfermedad , Humanos , Persona de Mediana Edad , Síndrome del Ovario Poliquístico/epidemiología , Síndrome del Ovario Poliquístico/genética , Pronóstico , Factores de RiesgoRESUMEN
After serving the Dictyostelium community for many years, the first version of dictyBase (Chisholm et al., 2006; Fey et al., 2006) was in need of a decisive update. The original dictyBase software was not adaptable to more current demands such as handling the import of large-scale data from recently sequenced genomes, keeping up with changes in the Gene Ontology (GO), or handling the automatic annotation of over 20,000 new strains. Therefore, we have embarked on a complete overhaul of dictyBase. The new infrastructure will allow the introduction of new data, such as more expressive GO annotations and Dictyostelium disease orthologs. A modern user interface aims to streamline usage of the database including orders from the Dicty Stock Center (DSC). New displays will allow novel views including the combination of data in two new tools. With the underlying software infrastructure now in place, dictyBase software engineers and curators are currently adding the user interfaces, new tools and content pages for the evolving version 2.0 of dictyBase. This review highlights the emerging status of the new dictyBase, updated pages and annotations that will soon be available in the new environment, an overview of our annotation procedures, and plans to involve the community in curation efforts.
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Bancos de Muestras Biológicas , Bases de Datos Genéticas , Dictyostelium/genética , Dictyostelium/fisiología , Animales , Genes Protozoarios , Genoma de Protozoos , Almacenamiento y Recuperación de la Información , Internet , Mutación , Fenotipo , Plásmidos/genética , Programas Informáticos , Integración de Sistemas , Interfaz Usuario-ComputadorRESUMEN
Advances in technologies for assessing genomic variation and an increasing understanding of the effects of genomic variants on health and disease are driving the transition of genomics from the research laboratory into clinical care. Genomic medicine, or the use of an individual's genomic information as part of their clinical care, is increasingly gaining acceptance in routine practice, including in assessing disease risk in individuals and their families, diagnosing rare and undiagnosed diseases, and improving drug safety and efficacy. We describe the major types and measurement tools of genomic variation that are currently of clinical importance, review approaches to interpreting genomic sequence variants, identify publicly available tools and resources for genomic test interpretation, and discuss several key barriers in using genomic information in routine clinical practice.
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Genómica/métodos , Medicina de Precisión/métodos , Predisposición Genética a la Enfermedad , Humanos , Variantes FarmacogenómicasRESUMEN
Existing approaches to managing genetic and genomic test results from external laboratories typically include filing of text reports within the electronic health record, making them unavailable in many cases for clinical decision support. Even when structured computable results are available, the lack of adopted standards requires considerations for processing the results into actionable knowledge, in addition to storage and management of the data. Here, we describe the design and implementation of an ancillary genomics system used to receive and process heterogeneous results from external laboratories, which returns a descriptive phenotype to the electronic health record in support of pharmacogenetic clinical decision support.