PheWAS analysis on large-scale biobank data with PheTK.

Summary: With the rapid growth of genetic data linked to electronic health record data in huge cohorts, large-scale phenome-wide association study (PheWAS), have become powerful discovery tools in biomedical research. PheWAS is an analysis method to study phenotype associations utilizing longitudinal electronic health record (EHR) data. Previous PheWAS packages were developed mostly in the days of smaller biobanks and with earlier PheWAS approaches. PheTK was designed to simplify analysis and efficiently handle biobank-scale data. PheTK uses multithreading and supports a full PheWAS workflow including extraction of data from OMOP databases and Hail matrix tables as well as PheWAS analysis for both phecode version 1.2 and phecodeX. Benchmarking results showed PheTK took 64% less time than the R PheWAS package to complete the same workflow. PheTK can be run locally or on cloud platforms such as the All of Us Researcher Workbench ( All of Us ) or the UK Biobank (UKB) Research Analysis Platform (RAP). Availability and implementation: The PheTK package is freely available on the Python Package Index (PyPi) and on GitHub under GNU Public License (GPL-3) at https://github.com/nhgritctran/PheTK . It is implemented in Python and platform independent. The demonstration workspace for All of Us will be made available in the future as a featured workspace. Contact: PheTK@mail.nih.gov.

Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics.

MOTIVATION: Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure. RESULTS: Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research. AVAILABILITY AND IMPLEMENTATION: phecodeX is available at https://github.com/PheWAS/phecodeX.

The All of Us Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research.

The All of Us Research Program's Data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in All of Us, 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers. In this review, we first describe how the DRC is organized to meet the needs of this broad group of stakeholders. We then outline guiding principles, common challenges, and innovative approaches used to build the All of Us data ecosystem. Finally, we share lessons learned to help others navigate important decisions and trade-offs in building a modern biomedical data platform.

The phenotype-genotype reference map: Improving biobank data science through replication.

Population-scale biobanks linked to electronic health record data provide vast opportunities to extend our knowledge of human genetics and discover new phenotype-genotype associations. Given their dense phenotype data, biobanks can also facilitate replication studies on a phenome-wide scale. Here, we introduce the phenotype-genotype reference map (PGRM), a set of 5,879 genetic associations from 523 GWAS publications that can be used for high-throughput replication experiments. PGRM phenotypes are standardized as phecodes, ensuring interoperability between biobanks. We applied the PGRM to five ancestry-specific cohorts from four independent biobanks and found evidence of robust replications across a wide array of phenotypes. We show how the PGRM can be used to detect data corruption and to empirically assess parameters for phenome-wide studies. Finally, we use the PGRM to explore factors associated with replicability of GWAS results.

Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program.

Genome-wide association studies (GWAS) have underrepresented individuals from non-European populations, impeding progress in characterizing the genetic architecture and consequences of health and disease traits. To address this, we present a population-stratified phenome-wide GWAS followed by a multi-population meta-analysis for 2,068 traits derived from electronic health records of 635,969 participants in the Million Veteran Program (MVP), a longitudinal cohort study of diverse U.S. Veterans genetically similar to the respective African (121,177), Admixed American (59,048), East Asian (6,702), and European (449,042) superpopulations defined by the 1000 Genomes Project. We identified 38,270 independent variants associating with one or more traits at experiment-wide P<4.6×10-11 significance; fine-mapping 6,318 signals identified from 613 traits to single-variant resolution. Among these, a third (2,069) of the associations were found only among participants genetically similar to non-European reference populations, demonstrating the importance of expanding diversity in genetic studies. Our work provides a comprehensive atlas of phenome-wide genetic associations for future studies dissecting the architecture of complex traits in diverse populations.

Antibacterial and Cytocompatible pH-Responsive Peptide Hydrogel.

A short peptide, FHHF-11, was designed to change stiffness as a function of pH due to changing degree of protonation of histidines. As pH changes in the physiologically relevant range, G' was measured at 0 Pa (pH 6) and 50,000 Pa (pH 8). This peptide-based hydrogel is antimicrobial and cytocompatible with skin cells (fibroblasts). It was demonstrated that the incorporation of unnatural AzAla tryptophan analog residue improves the antimicrobial properties of the hydrogel. The material developed can have a practical application and be a paradigm shift in the approach to wound treatment, and it will improve healing outcomes for millions of patients each year.

Correlates of Taxane-Induced Neuropathy, an Electronic Health Record Based Observational Study.

BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) is a common therapeutic complication affecting cancer patients' quality-of-life. We evaluated clinical characteristics, demographics, and lifestyle factors in association with CIPN following taxane treatment. METHODS: Data were extracted from the electronic health record of 3387 patients diagnosed with a primary cancer and receiving taxane (i.e., paclitaxel or docetaxel) at Vanderbilt University Medical Center. Neuropathy was assessed via a validated computer algorithm. Univariate and multivariate regression models were applied to evaluate odds ratios (ORs) and 95% confidence intervals (CIs) of CIPN-associated factors. RESULTS: Female sex (OR = 1.28, 95% CI = 1.01-1.62), high body-mass index (BMI) (OR = 1.31, 95% CI = 1.06-1.61 for overweight, and OR = 1.49, 95% CI = 1.21-1.83 for obesity), diabetes (OR = 1.66, 95% CI = 1.34-2.06), high mean taxane dose (OR = 1.05, 95% CI = 1.03-1.08 per 10 mg/m2), and more treatment cycles (1.12, 95% CI = 1.10-1.14) were positively associated with CIPN. Concurrent chemotherapy (OR = 0.74, 95% CI = 0.58-0.94) and concurrent radiotherapy (OR = 0.77, 95% CI = 0.59-1.00) were inversely associated with CIPN. Obesity and diabetes both had a stronger association with docetaxel CIPN compared to paclitaxel, although interaction was only significant for diabetes and taxane (p = 0.019). Increased BMI was associated with CIPN only among non-diabetic patients (OR:1.34 for overweight and 1.68 for obesity), while diabetes increased CIPN risk across all BMI strata (ORs were 2.65, 2.41, and 2.15 for normal weight, overweight, and obese, respectively) compared to normal-weight non-diabetic patients (p for interaction = 0.039). CONCLUSIONS: Female sex, obesity, and diabetes are significantly associated with taxine-induced CIPN. Further research is needed to identify clinical and pharmacologic strategies to prevent and mitigate CIPN in at-risk patient populations.

Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10-8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.

Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics.

OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

The All of Us Research Program: Data quality, utility, and diversity.

The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools.

Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies.

Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics.

Comparing medical history data derived from electronic health records and survey answers in the All of Us Research Program.

OBJECTIVE: A participant's medical history is important in clinical research and can be captured from electronic health records (EHRs) and self-reported surveys. Both can be incomplete, EHR due to documentation gaps or lack of interoperability and surveys due to recall bias or limited health literacy. This analysis compares medical history collected in the All of Us Research Program through both surveys and EHRs. MATERIALS AND METHODS: The All of Us medical history survey includes self-report questionnaire that asks about diagnoses to over 150 medical conditions organized into 12 disease categories. In each category, we identified the 3 most and least frequent self-reported diagnoses and retrieved their analogues from EHRs. We calculated agreement scores and extracted participant demographic characteristics for each comparison set. RESULTS: The 4th All of Us dataset release includes data from 314 994 participants; 28.3% of whom completed medical history surveys, and 65.5% of whom had EHR data. Hearing and vision category within the survey had the highest number of responses, but the second lowest positive agreement with the EHR (0.21). The Infectious disease category had the lowest positive agreement (0.12). Cancer conditions had the highest positive agreement (0.45) between the 2 data sources. DISCUSSION AND CONCLUSION: Our study quantified the agreement of medical history between 2 sources-EHRs and self-reported surveys. Conditions that are usually undocumented in EHRs had low agreement scores, demonstrating that survey data can supplement EHR data. Disagreement between EHR and survey can help identify possible missing records and guide researchers to adjust for biases.

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space.

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) was developed to address a widespread community need for a unified computing environment for genomics data storage, management, and analysis. In this perspective, we present AnVIL, describe its ecosystem and interoperability with other platforms, and highlight how this platform and associated initiatives contribute to improved genomic data sharing efforts. The AnVIL is a federated cloud platform designed to manage and store genomics and related data, enable population-scale analysis, and facilitate collaboration through the sharing of data, code, and analysis results. By inverting the traditional model of data sharing, the AnVIL eliminates the need for data movement while also adding security measures for active threat detection and monitoring and provides scalable, shared computing resources for any researcher. We describe the core data management and analysis components of the AnVIL, which currently consists of Terra, Gen3, Galaxy, RStudio/Bioconductor, Dockstore, and Jupyter, and describe several flagship genomics datasets available within the AnVIL. We continue to extend and innovate the AnVIL ecosystem by implementing new capabilities, including mechanisms for interoperability and responsible data sharing, while streamlining access management. The AnVIL opens many new opportunities for analysis, collaboration, and data sharing that are needed to drive research and to make discoveries through the joint analysis of hundreds of thousands to millions of genomes along with associated clinical and molecular data types.

Under-specification as the source of ambiguity and vagueness in narrative phenotype algorithm definitions.

INTRODUCTION: Currently, one of the commonly used methods for disseminating electronic health record (EHR)-based phenotype algorithms is providing a narrative description of the algorithm logic, often accompanied by flowcharts. A challenge with this mode of dissemination is the potential for under-specification in the algorithm definition, which leads to ambiguity and vagueness. METHODS: This study examines incidents of under-specification that occurred during the implementation of 34 narrative phenotyping algorithms in the electronic Medical Record and Genomics (eMERGE) network. We reviewed the online communication history between algorithm developers and implementers within the Phenotype Knowledge Base (PheKB) platform, where questions could be raised and answered regarding the intended implementation of a phenotype algorithm. RESULTS: We developed a taxonomy of under-specification categories via an iterative review process between two groups of annotators. Under-specifications that lead to ambiguity and vagueness were consistently found across narrative phenotype algorithms developed by all involved eMERGE sites. DISCUSSION AND CONCLUSION: Our findings highlight that under-specification is an impediment to the accuracy and efficiency of the implementation of current narrative phenotyping algorithms, and we propose approaches for mitigating these issues and improved methods for disseminating EHR phenotyping algorithms.

Extracellular Vimentin as a Target Against SARS-CoV-2 Host Cell Invasion.

Infection of human cells by pathogens, including SARS-CoV-2, typically proceeds by cell surface binding to a crucial receptor. The primary receptor for SARS-CoV-2 is the angiotensin-converting enzyme 2 (ACE2), yet new studies reveal the importance of additional extracellular co-receptors that mediate binding and host cell invasion by SARS-CoV-2. Vimentin is an intermediate filament protein that is increasingly recognized as being present on the extracellular surface of a subset of cell types, where it can bind to and facilitate pathogens' cellular uptake. Biophysical and cell infection studies are done to determine whether vimentin might bind SARS-CoV-2 and facilitate its uptake. Dynamic light scattering shows that vimentin binds to pseudovirus coated with the SARS-CoV-2 spike protein, and antibodies against vimentin block in vitro SARS-CoV-2 pseudovirus infection of ACE2-expressing cells. The results are consistent with a model in which extracellular vimentin acts as a co-receptor for SARS-CoV-2 spike protein with a binding affinity less than that of the spike protein with ACE2. Extracellular vimentin may thus serve as a critical component of the SARS-CoV-2 spike protein-ACE2 complex in mediating SARS-CoV-2 cell entry, and vimentin-targeting agents may yield new therapeutic strategies for preventing and slowing SARS-CoV-2 infection.

Inference-based correction of multi-site height and weight measurement data in the All of Us research program.

OBJECTIVE: Measurement and data entry of height and weight values are error prone. Aggregation of medical record data from multiple sites creates new challenges prompting the need to identify and correct errant values. We sought to characterize and correct issues with height and weight measurement values within the All of Us (AoU) Research Program. MATERIALS AND METHODS: Using the AoU Researcher Workbench, we assessed site-level measurement value distributions to infer unit types. We also used plausibility checks with exceptions for conditions with possible outlier values, eg obesity, and assessed for excess deviation within individual participant's records. RESULTS: 15.8% of height and 22.4% of weight values had missing unit type information. DISCUSSION: We identified several measurement unit related issues: the use of different units of measure within and between sites, missing units, and incorrect labeling of units. Failure to account for these in patient data repositories may lead to erroneous study results and conclusions. CONCLUSION: Discrepancies in height and weight measurement data may arise from missing or mislabeled units. Using site- and participant-level analyses while accounting for outlier value-associated clinical conditions, we can infer measurement units and apply corrections. These methods are adaptable and expandable within AoU and other data repositories.

Spreading rates of bacterial colonies depend on substrate stiffness and permeability.

The ability of bacteria to colonize and grow on different surfaces is an essential process for biofilm development. Here, we report the use of synthetic hydrogels with tunable stiffness and porosity to assess physical effects of the substrate on biofilm development. Using time-lapse microscopy to track the growth of expanding Serratia marcescens colonies, we find that biofilm colony growth can increase with increasing substrate stiffness, unlike what is found on traditional agar substrates. Using traction force microscopy-based techniques, we find that biofilms exert transient stresses correlated over length scales much larger than a single bacterium, and that the magnitude of these forces also increases with increasing substrate stiffness. Our results are consistent with a model of biofilm development in which the interplay between osmotic pressure arising from the biofilm and the poroelastic response of the underlying substrate controls biofilm growth and morphology.

Racial disparity in taxane-induced neutropenia among cancer patients.

BACKGROUND: Large interindividual variations have been reported in chemotherapy-induced toxicities. Little is known whether racial disparities exist in neutropenia associated with taxanes. METHODS: Patients with a diagnosis of primary cancer who underwent chemotherapy with taxanes were identified from Vanderbilt University Medical Center's Synthetic Derivative. Multinomial regression models were applied to evaluate odds ratios (ORs) and 95% confidence intervals (CIs) of neutropenia associated with race, with adjustments for demographic variables, baseline neutrophil count, chemotherapy-related information, prior treatments, and cancer site. RESULTS: A total of 3492 patients were included in the study. Compared with White patients, grade 2 or higher neutropenia was more frequently recorded among Black patients who received taxanes overall (42.2% vs. 32.7%, p < 0.001) or paclitaxel (43.0% vs. 36.7%, p < 0.001) but not among those who received docetaxel (32.0% vs. 30.2%, p = 0.821). After adjustments for multiple covariates, Black patients who received chemotherapy with any taxanes had significantly higher risk of grade 2 (OR = 1.53; 95% CI = 1.09-2.14) and grade 3 (OR = 1.91; 95% CI = 1.36-2.67) neutropenia but comparable risk of grade 4 neutropenia (OR = 1.19; 95% CI = 0.79-1.79). Similar association patterns were observed for Black patients who specifically received paclitaxel, but a null association was found for those treated with docetaxel. CONCLUSION: Black cancer patients treated with taxanes for any cancer had a higher risk of neutropenia compared with their White counterparts, especially those who received paclitaxel. More research is needed to understand the mechanism(s) underlying this racial disparity in order to enhance the delivery of patient-centered oncology.

Comparison of family health history in surveys vs electronic health record data mapped to the observational medical outcomes partnership data model in the All of Us Research Program.

OBJECTIVE: Family health history is important to clinical care and precision medicine. Prior studies show gaps in data collected from patient surveys and electronic health records (EHRs). The All of Us Research Program collects family history from participants via surveys and EHRs. This Demonstration Project aims to evaluate availability of family health history information within the publicly available data from All of Us and to characterize the data from both sources. MATERIALS AND METHODS: Surveys were completed by participants on an electronic portal. EHR data was mapped to the Observational Medical Outcomes Partnership data model. We used descriptive statistics to perform exploratory analysis of the data, including evaluating a list of medically actionable genetic disorders. We performed a subanalysis on participants who had both survey and EHR data. RESULTS: There were 54 872 participants with family history data. Of those, 26% had EHR data only, 63% had survey only, and 10.5% had data from both sources. There were 35 217 participants with reported family history of a medically actionable genetic disorder (9% from EHR only, 89% from surveys, and 2% from both). In the subanalysis, we found inconsistencies between the surveys and EHRs. More details came from surveys. When both mentioned a similar disease, the source of truth was unclear. CONCLUSIONS: Compiling data from both surveys and EHR can provide a more comprehensive source for family health history, but informatics challenges and opportunities exist. Access to more complete understanding of a person's family health history may provide opportunities for precision medicine.

Extracellular vimentin as a target against SARS-CoV-2 host cell invasion.

Infection of human cells by pathogens, including SARS-CoV-2, typically proceeds by cell surface binding to a crucial receptor. In the case of SARS-CoV-2, angiotensin-converting enzyme 2 (ACE2) has been identified as a necessary receptor, but not all ACE2-expressing cells are equally infected, suggesting that other extracellular factors are involved in host cell invasion by SARS-CoV-2. Vimentin is an intermediate filament protein that is increasingly recognized as being present on the extracellular surface of a subset of cell types, where it can bind to and facilitate pathogens' cellular uptake. Here, we present evidence that extracellular vimentin might act as a critical component of the SARS-CoV-2 spike protein-ACE2 complex in mediating SARS-CoV-2 cell entry. We demonstrate direct binding between vimentin and SARS-CoV-2 pseudovirus coated with the SARS-CoV-2 spike protein and show that antibodies against vimentin block in vitro SARS-CoV-2 pseudovirus infection of ACE2-expressing cells. Our results suggest new therapeutic strategies for preventing and slowing SARS-CoV-2 infection, focusing on targeting cell host surface vimentin.