Overcome the Limitation of Phenome-Wide Association Studies (PheWAS): Extension of PheWAS to Efficient and Robust Large-Scale ICD Codes Analysis.

The Phenome-wide association studies (PheWAS) have become widely used for efficient, high-throughput evaluation of relationship between a genetic factor and a large number of disease phenotypes, typically extracted from a DNA biobank linked with electronic medical records (EMR). Phecodes, billing code-derived disease case-control status, are usually used as outcome variables in PheWAS and logistic regression has been the standard choice of analysis method. Since the clinical diagnoses in EMR are often inaccurate with errors which can lead to biases in the odds ratio estimates, much effort has been put to accurately define the cases and controls to ensure an accurate analysis. Specifically in order to correctly classify controls in the population, an exclusion criteria list for each Phecode was manually compiled to obtain unbiased odds ratios. However, the accuracy of the list cannot be guaranteed without extensive data curation process. The costly curation process limits the efficiency of large-scale analyses that take full advantage of all structured phenotypic information available in EMR. Here, we proposed to estimate relative risks (RR) instead. We first demonstrated the desired nature of RR that overcomes the inaccuracy in the controls via theoretical formula. With simulation and real data application, we further confirmed that RR is unbiased without compiling exclusion criteria lists. With RR as estimates, we are able to efficiently extend PheWAS to a larger-scale, phenome construction agnostic analysis of phenotypes, using ICD 9/10 codes, which preserve much more disease-related clinical information than Phecodes.

Toward personalized skin cancer care: multiple skin cancer development in five cohorts.

Importance: Many patients will develop more than one skin cancer, however most research to date has examined only case status. Objective: Describe the frequency and timing of the treatment of multiple skin cancers in individual patients over time. Design: Longitudinal claims and electronic health record-based cohort study. Setting: Vanderbilt University Medical Center database called the Synthetic Derivative, VA, Medicare, Optum Clinformatics® Data Mart Database, IBM Marketscan. Participants: All patients with a Current Procedural Terminology code for the surgical management of a skin cancer in each of five cohorts. Exposures: None. Main Outcomes and Measures: The number of CPT codes for skin cancer treatment in each individual occurring on the same day as an ICD code for skin cancer over time. Results: Our cohort included 5,508,374 patients and 13,102,123 total skin cancers treated. Conclusions and Relevance: Nearly half of patients treated for skin cancer were treated for more than one skin cancer. Patients who have not developed a second skin cancer by 2 years after the first are unlikely to develop multiple skin cancers within the following 5 years. Better data formatting will allow for improved granularity in identifying individuals at high risk for multiple skin cancers and those unlikely to benefit from continued annual surveillance. Resource planning should take into account not just the number of skin cancer cases, but the individual burden of disease. Key points: Question: How many skin cancer patients are treated for more than one skin cancer and how soon after the first skin cancer do they occur?Findings: 43% of patients were treated for more than one skin cancer, the majority of which occurred within two years after the initial skin cancer. Just 3% of patients were treated for 10 or more skin cancers, but these patients accounted for 22% of all of the skin cancer treatments in the cohort Meaning: Nearly half of all skin cancer patients were treated for multiple skin cancers, while those without a second skin cancer after two years were less likely to be treated for a subsequent skin cancer within the next five years.

Clonal Hematopoiesis and Inflammation in the VasculaturE (CHIVE): a prospective, longitudinal cohort and biorepository.

Clonal hematopoiesis (CH) is an age-associated phenomenon leading to increased risk of both hematologic malignancy and non-malignant organ dysfunction. Increasingly available genetic testing has made incidental discovery of CH clinically common, yet evidence-based guidelines and effective management strategies to prevent adverse CH health outcomes are lacking. To address this gap, the prospective CHIVE registry and biorepository was created to identify and monitor individuals at risk, support multidisciplinary CH clinics, and to refine standards of practice for CH risk mitigation. Data from the first 181 patients enrolled in this registry recapitulate the molecular epidemiology of CH from biobank scale retrospective studies, with DNMT3A, TET2, ASXL1, and TP53 as the most commonly mutated genes. CH patients had higher rates of end organ dysfunction, in particular chronic kidney disease (p=0.001). Among patients with CH, variant allele frequency was independently associated with presence of cytopenias (p=0.008) and progression to hematologic malignancy (p=0.010), while other common high-risk CH clone features were not clear. Notably, accumulation of multiple distinct high-risk clone features was also associated with cytopenias (p=0.013) and hematologic malignancy progression (p=0.004), supporting a recently published CH risk score. Surprisingly, ~30% of patients enrolled in CHIVE from CH clinics were adjudicated as not having CHIP, highlighting the need for molecular standards and purpose-built assays in this field. Maintenance of this well-annotated cohort and continued expansion of CHIVE to multiple institutions is underway and will be critical to understand how to thoughtfully care for this patient population.

Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems.

Background: Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administration raise questions about the consistency and reproducibility of EHR-based multimorbidity research. Methods: Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combing data from multiple sources for online multimorbidity analysis. Findings: Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies ( Kendall's τ = 0.643) and comorbidity strengths (Pearson ρ = 0.79). Consistent network statistics across EHRs suggest a similar structure of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically relevant disease relationships and provide novel insights. Interpretation: Our findings demonstrate the robustness of large-scale EHR data for studying complex disease interactions. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared etiology of diseases. The consistent core-periphery network structure offers a strategic approach to analyze disease clusters. This work also sets the stage for advanced disease modeling, with implications for precision medicine. Funding: VUMC Biostatistics Development Award, UL1 TR002243, R21DK127075, R01HL140074, P50GM115305, R01CA227481.

Cost-Effective and Scalable Clonal Hematopoiesis Assay Provides Insight into Clonal Dynamics.

Clonal hematopoiesis of indeterminate potential (CHIP) is a common age-related phenomenon in which hematopoietic stem cells acquire mutations in a select set of genes commonly mutated in myeloid neoplasia which then expand clonally. Current sequencing assays to detect CHIP mutations are not optimized for the detection of these variants and can be cost-prohibitive when applied to large cohorts or to serial sequencing. In this study, an affordable (approximately US $8 per sample), accurate, and scalable sequencing assay for CHIP is introduced and validated. The efficacy of the assay was demonstrated by identifying CHIP mutations in a cohort of 456 individuals with DNA collected at multiple time points in Vanderbilt University's biobank and quantifying clonal expansion rates over time. A total of 101 individuals with CHIP/clonal cytopenia of undetermined significance were identified, and individual-level clonal expansion rate was calculated using the variant allele fraction at both time points. Differences in clonal expansion rate by driver gene were observed, but there was also significant individual-level heterogeneity, emphasizing the multifactorial nature of clonal expansion. Additionally, the study explores mutation co-occurrence and clonal competition between multiple driver mutations.

Ablation of IFNγ in myeloid cells suppresses liver inflammation and fibrogenesis in mice with hepatic small heterodimer partner (SHP) deletion.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a common complication of obesity and, in severe cases, progresses to metabolic dysfunction-associated steatohepatitis (MASH). Small heterodimer partner (SHP) is an orphan member of the nuclear receptor superfamily and regulates metabolism and inflammation in the liver via a variety of pathways. In this study, we investigate the molecular foundation of MASH progression in mice with hepatic SHP deletion and explore possible therapeutic means to reduce MASH. METHODS: Hepatic SHP knockout mice (SHPΔhep) and their wild-type littermates (SHPfl/fl) of both sexes were fed a fructose diet for 14 weeks and subjected to an oral glucose tolerance test. Then, plasma lipids were determined, and liver lipid metabolism and inflammation pathways were analyzed with immunoblotting, RNAseq, and qPCR assays. To explore possible therapeutic intersections of SHP and inflammatory pathways, SHPΔhep mice were reconstituted with bone marrow lacking interferon γ (IFNγ-/-) to suppress inflammation. RESULTS: Hepatic deletion of SHP in mice fed a fructose diet decreased liver fat and increased proteins for fatty acid oxidation and liver lipid uptake, including UCP1, CPT1α, ACDAM, and SRBI. Despite lower liver fat, hepatic SHP deletion increased liver inflammatory F4/80+ cells and mRNA levels of inflammatory cytokines (IL-12, IL-6, Ccl2, and IFNγ) in both sexes and elevated endoplasmic reticulum stress markers of Cox2 and CHOP in female mice. Liver bulk RNAseq data showed upregulation of genes whose protein products regulate lipid transport, fatty acid oxidation, and inflammation in SHPΔhep mice. The increased inflammation and fibrosis in SHPΔhep mice were corrected with bone marrow-derived IFNγ-/- myeloid cell transplantation. CONCLUSION: Hepatic deletion of SHP improves fatty liver but worsens hepatic inflammation possibly by driving excess fatty acid oxidation, which is corrected by deletion of IFNγ specifically in myeloid cells. This suggests that hepatic SHP limits fatty acid oxidation during fructose diet feeding but, in doing so, prevents pro-MASH pathways. The IFNγ-mediated inflammation in myeloid cells appears to be a potential therapeutic target to suppress MASH.

Integrating Electronic Health Records and Polygenic Risk to Identify Genetically Unrelated Comorbidities of Schizophrenia That May Be Modifiable.

Background: Patients with schizophrenia have substantial comorbidity that contributes to reduced life expectancy of 10 to 20 years. Identifying modifiable comorbidities could improve rates of premature mortality. Conditions that frequently co-occur but lack shared genetic risk with schizophrenia are more likely to be products of treatment, behavior, or environmental factors and therefore are enriched for potentially modifiable associations. Methods: Phenome-wide comorbidity was calculated from electronic health records of 250,000 patients across 2 independent health care institutions (Vanderbilt University Medical Center and Mass General Brigham); associations with schizophrenia polygenic risk scores were calculated across the same phenotypes in linked biobanks. Results: Schizophrenia comorbidity was significantly correlated across institutions (r = 0.85), and the 77 identified comorbidities were consistent with prior literature. Overall, comorbidity and polygenic risk score associations were significantly correlated (r = 0.55, p = 1.29 × 10-118). However, directly testing for the absence of genetic effects identified 36 comorbidities that had significantly equivalent schizophrenia polygenic risk score distributions between cases and controls. This set included phenotypes known to be consequences of antipsychotic medications (e.g., movement disorders) or of the disease such as reduced hygiene (e.g., diseases of the nail), thereby validating the approach. It also highlighted phenotypes with less clear causal relationships and minimal genetic effects such as tobacco use disorder and diabetes. Conclusions: This work demonstrates the consistency and robustness of electronic health record-based schizophrenia comorbidities across independent institutions and with the existing literature. It identifies known and novel comorbidities with an absence of shared genetic risk, indicating other causes that may be modifiable and where further study of causal pathways could improve outcomes for patients.

Patients with schizophrenia have many co-occurring diseases that contribute substantially to premature mortality of 10 to 20 years. Conditions that are comorbid but lack shared genetic risk with schizophrenia are likely to have causes that are more modifiable. Here, we calculated comorbidity from electronic health records from 2 independent health care institutions and associations with schizophrenia polygenic risk scores across the same phenotypes in linked biobanks. We identified known and novel diseases comorbid with schizophrenia, thereby validating our approach.

The association of infant urinary adrenal steroids with the risk of childhood asthma development.

BACKGROUND: Adrenal steroids play important roles in early-life development. However, our understanding of the effects of perinatal adrenal steroids on the development of childhood asthma is incomplete. OBJECTIVE: To evaluate the associations between early-life adrenal steroid levels and childhood asthma. METHODS: Participants included the Infant Susceptibility to Pulmonary Infections and Asthma following Respiratory Syncytial Virus Exposure birth cohort children with untargeted urinary metabolomics data measured during early infancy (n = 264) and nasal immune mediator data measured concurrently at age 2 to 6 months (n = 76). A total of 11 adrenal steroid compounds were identified using untargeted metabolomics and 6 asthma-relevant nasal immune mediators from multiplex assays were a priori selected. Current asthma at ages 5 and 6 years was ascertained using validated questionnaires. Associations were tested using logistic and linear regression with confounders adjustment. RESULTS: Pregnenetriol disulfate (adjusted odds ratio [aOR] = 0.20, 95% CI = 0.06-0.68) and 3a,21-dihydroxy-5b-pregnane-11,20-dione-21-glucuronide (aOR = 0.34, 95% CI = 0.14-0.75) were inversely associated with childhood asthma at 5 and 6 years after multiple testing adjustment. There was a significant interaction effect of pregnanediol-3-glucuronide by biological sex assigned at birth (aOR = 0.11, 95% CI = 0.02-0.51, for those with female sex) on childhood asthma. Pregnenetriol disulfate was inversely associated with granulocyte-macrophage colony-stimulating factor (ß = -0.45, q-value = 0.05). 3a,21-dihydroxy-5b-pregnane-11,20-dione 21-glucuronide was inversely associated with interleukin [IL]-4 (ß = -0.29, q-value = 0.02), IL-5 (ß = -0.35, q-value = 0.006), IL-13 (ß = -0.26, q-value = 0.02), granulocyte-macrophage colony-stimulating factor (ß = -0.35, q-value = 0.006), and fibroblast growth factor-ß (ß = -0.24, q-value = 0.01) after multiple testing adjustment. CONCLUSION: The inverse association between adrenal steroids downstream of progesterone and 17-hydroxypregnenolone and the odds of childhood asthma and nasopharyngeal type 2 immune biomarkers suggest that increased early-life adrenal steroids may suppress type 2 inflammation and protect against the development of childhood asthma.

Solid organ transplant recipients exhibit more TET2-mutant clonal hematopoiesis of indeterminate potential not driven by increased transplantation risk.

PURPOSE: Solid organ transplant recipients comprise a unique population of immunosuppressed patients with increased risk of malignancy, including hematologic neoplasms. Clonal hematopoiesis of indeterminate potential (CHIP) represents a known risk factor for hematologic malignancy and this study describes the prevalence and patterns of CHIP mutations across several types of solid organ transplants. EXPERIMENTAL DESIGN: We use two national biobank cohorts comprised of >650,000 participants with linked genomic and longitudinal phenotypic data to describe the features of CHIP across 2,610 individuals who received kidney, liver, heart, or lung allografts. RESULTS: We find individuals with an allograft prior to their biobank enrollment had an increased prevalence of TET2 mutations (OR 1.90; p = 4.0e-4), but individuals who received transplants post-enrollment had a CHIP mutation spectrum similar to that of the general population, without enrichment of TET2. Additionally, we do not observe an association between CHIP and risk of incident transplantation among the overall population (HR 1.02; p = 0.91). And in an exploratory analysis, we do not find evidence for a strong association between CHIP and rates of transplant complications such as rejection or graft failure. CONCLUSIONS: These results demonstrate that recipients of solid organ transplants display a unique pattern of clonal hematopoiesis with enrichment of TET2 driver mutations, the causes of which remain unclear and are deserving of further study.

Polygenic risk score for ulcerative colitis predicts immune checkpoint inhibitor-mediated colitis.

Immune checkpoint inhibitor-mediated colitis (IMC) is a common adverse event of treatment with immune checkpoint inhibitors (ICI). We hypothesize that genetic susceptibility to Crohn's disease (CD) and ulcerative colitis (UC) predisposes to IMC. In this study, we first develop a polygenic risk scores for CD (PRSCD) and UC (PRSUC) in cancer-free individuals and then test these PRSs on IMC in a cohort of 1316 patients with ICI-treated non-small cell lung cancer and perform a replication in 873 ICI-treated pan-cancer patients. In a meta-analysis, the PRSUC predicts all-grade IMC (ORmeta=1.35 per standard deviation [SD], 95% CI = 1.12-1.64, P = 2×10-03) and severe IMC (ORmeta=1.49 per SD, 95% CI = 1.18-1.88, P = 9×10-04). PRSCD is not associated with IMC. Furthermore, PRSUC predicts severe IMC among patients treated with combination ICIs (ORmeta=2.20 per SD, 95% CI = 1.07-4.53, P = 0.03). Overall, PRSUC can identify patients receiving ICI at risk of developing IMC and may be useful to monitor patients and improve patient outcomes.

Unbiased characterization of atrial fibrillation phenotypic architecture provides insight to genetic liability and clinically relevant outcomes.

Background: Atrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. Methods: We identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping. The phenotypic architecture in the AF cohort was defined using principal component analysis of 35 expertly curated and uncorrelated clinical features. We applied an unsupervised co-clustering machine learning algorithm to the 35 features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention in the EHR. Polygenic risk scores (PRS) for AF and cytokine levels were used to assess genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from EHR up to the last medical contact. Results: The analysis included 23,271 subjects with AF, of which 6,023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal dysfunction, and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbid cluster, while there was no difference between groups in genetically predicted levels of inflammatory biomarkers. Subgroup assignment was associated with multiple clinical outcomes including mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. Conclusion: Patient subgroups identified by unsupervised clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes. Polygenic liability to AF across clusters was greatest in the low comorbidity subgroup. Clinical inflammation, as reflected by measured biomarkers, was lowest in the subgroup with lowest comorbidities. However, there were no differences in genetically predicted levels of inflammatory biomarkers, suggesting associations between AF and inflammation is driven by acquired comorbidities rather than genetic predisposition.

Evaluation of genetic associations with clinical phenotypes of kidney stone disease.

Introduction and Objective: We sought to replicate and discover genetic associations of kidney stone disease within a large-scale electronic health record (EHR) system. Methods: We performed genome-wide association studies (GWASs) for nephrolithiasis from genotyped samples of 5,571 cases and 83,692 controls. Among the significant risk variants, we performed association analyses of stone composition and first-time 24-hour urine parameters. To assess disease severity, we investigated the associations of risk variants with age at first stone diagnosis, age at first procedure, and time from first to second procedure. Results: The main GWAS analysis identified 10 significant loci, each located on chromosome 16 within coding regions of the UMOD gene, which codes for uromodulin, a urine protein with inhibitory activity for calcium crystallization. The strongest signal was from SNP 16:20359633-C-T (odds ratio [OR] 1.17, 95% CI 1.11-1.23), with the remaining significant SNPs having similar effect sizes. In subgroup GWASs by stone composition, 19 significant loci were identified, of which two loci were located in coding regions (brushite; NXPH1 , rs79970906 and rs4725104). The UMOD SNP 16:20359633-C-T was associated with differences in 24-hour excretion of urinary calcium, uric acid, phosphorus, sulfate; and the minor allele was positively associated with calcium oxalate dihydrate stone composition (p<0.05). No associations were found between UMOD variants and disease severity. Conclusions: We replicated germline variants associated with kidney stone disease risk at UMOD and reported novel variants associated with stone composition. Genetic variants of UMOD are associated with differences in 24-hour urine parameters and stone composition, but not disease severity.

Driver mutation zygosity is a critical factor in predicting clonal hematopoiesis transformation risk.

Clonal hematopoiesis (CH) can be caused by either single gene mutations (eg point mutations in JAK2 causing CHIP) or mosaic chromosomal alterations (e.g., loss of heterozygosity at chromosome 9p). CH is associated with a significantly increased risk of hematologic malignancies. However, the absolute rate of transformation on an annualized basis is low. Improved prognostication of transformation risk is urgently needed for routine clinical practice. We hypothesized that the co-occurrence of CHIP and mCAs at the same locus (e.g., transforming a heterozygous JAK2 CHIP mutation into a homozygous mutation through concomitant loss of heterozygosity at chromosome 9) might have important prognostic implications for malignancy transformation risk. We tested this hypothesis using our discovery cohort, the UK Biobank (n = 451,180), and subsequently validated it in the BioVU cohort (n = 91,335). We find that individuals with a concurrent somatic mutation and mCA were at significantly increased risk of hematologic malignancy (for example, In BioVU cohort incidence of hematologic malignancies is higher in individuals with co-occurring JAK2 V617F and 9p CN-LOH; HR = 54.76, 95% CI = 33.92-88.41, P < 0.001 vs. JAK2 V617F alone; HR = 44.05, 95% CI = 35.06-55.35, P < 0.001). Currently, the 'zygosity' of the CHIP mutation is not routinely reported in clinical assays or considered in prognosticating CHIP transformation risk. Based on these observations, we propose that clinical reports should include 'zygosity' status of CHIP mutations and that future prognostication systems should take mutation 'zygosity' into account.

Evidence of recent and ongoing admixture in the U.S. and influences on health and disparities.

Many researchers in genetics and social science incorporate information about race in their work. However, migrations (historical and forced) and social mobility have brought formerly separated populations of humans together, creating younger generations of individuals who have more complex and diverse ancestry and race profiles than older age groups. Here, we sought to better understand how temporal changes in genetic admixture influence levels of heterozygosity and impact health outcomes. We evaluated variation in genetic ancestry over 100 birth years in a cohort of 35,842 individuals with electronic health record (EHR) information in the Southeastern United States. Using the software STRUCTURE, we analyzed 2,678 ancestrally informative markers relative to three ancestral clusters (African, East Asian, and European) and observed rising levels of admixture for all clinically-defined race groups since 1990. Most race groups also exhibited increases in heterozygosity and long-range linkage disequilibrium over time, further supporting the finding of increasing admixture in young individuals in our cohort. These data are consistent with United States Census information from broader geographic areas and highlight the changing demography of the population. This increased diversity challenges classic approaches to studies of genotype-phenotype relationships which motivated us to explore the relationship between heterozygosity and disease diagnosis. Using a phenome-wide association study approach, we explored the relationship between admixture and disease risk and found that increased admixture resulted in protective associations with female reproductive disorders and increased risk for diseases with links to autoimmune dysfunction. These data suggest that tendencies in the United States population are increasing ancestral complexity over time. Further, these observations imply that, because both prevalence and severity of many diseases vary by race groups, complexity of ancestral origins influences health and disparities.

Cost-effective and scalable clonal hematopoiesis assay provides insight into clonal dynamics.

Clonal hematopoiesis of indeterminate potential (CHIP) is a common age-related phenomenon that occurs when hematopoietic stem cells acquire mutations in a select set of genes commonly mutated in myeloid neoplasia which then expand clonally. Current sequencing assays to detect CHIP are not optimized for the detection of these variants and can be cost-prohibitive when applied to large cohorts or serial sequencing. Here, we present and validate a CHIP targeted sequencing assay that is affordable (∼$8/sample), accurate and highly scalable. To demonstrate the utility of this assay, we detected CHIP in a cohort of 456 individuals with DNA collected at multiple timepoints in the Vanderbilt BioVU biobank and quantified clonal expansion rates over time. A total of 101 individuals with CHIP were identified, and individual-level clonal expansion rate was calculated using the variant allele fraction (VAF) at both timepoints. Differences in clonal expansion rate by driver gene were observed, but there was also significant individual-level heterogeneity, emphasizing the multifactorial nature of clonal expansion. We further describe the mutation co-occurrence and clonal competition between multiple driver mutations.

PheMIME: An Interactive Web App and Knowledge Base for Phenome-Wide, Multi-Institutional Multimorbidity Analysis.

Motivation: Multimorbidity, characterized by the simultaneous occurrence of multiple diseases in an individual, is an increasing global health concern, posing substantial challenges to healthcare systems. Comprehensive understanding of disease-disease interactions and intrinsic mechanisms behind multimorbidity can offer opportunities for innovative prevention strategies, targeted interventions, and personalized treatments. Yet, there exist limited tools and datasets that characterize multimorbidity patterns across different populations. To bridge this gap, we used large-scale electronic health record (EHR) systems to develop the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME), which facilitates research in exploring and comparing multimorbidity patterns among multiple institutions, potentially leading to the discovery of novel and robust disease associations and patterns that are interoperable across different systems and organizations. Results: PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities. These are currently derived from three major institutions: Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. PheMIME offers interactive exploration of multimorbidity through multi-faceted visualization. Incorporating an enhanced version of associationSubgraphs, PheMIME enables dynamic analysis and inference of disease clusters, promoting the discovery of multimorbidity patterns. Once a disease of interest is selected, the tool generates interactive visualizations and tables that users can delve into multimorbidities or multimorbidity networks within a single system or compare across multiple systems. The utility of PheMIME is demonstrated through a case study on schizophrenia. Availability and implementation: The PheMIME knowledge base and web application are accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial, including a use-case example, is available at https://prod.tbilab.org/PheMIME_supplementary_materials/. Furthermore, the source code for PheMIME can be freely downloaded from https://github.com/tbilab/PheMIME. Data availability statement: The data underlying this article are available in the article and in its online web application or supplementary material.

Polygenic risk score for ulcerative colitis predicts immune checkpoint inhibitor-mediated colitis.

Immune checkpoint inhibitors (ICIs) are a remarkable advancement in cancer therapeutics; however, a substantial proportion of patients develop severe immune-related adverse events (irAEs). Understanding and predicting irAEs is a key to advancing precision immuno-oncology. Immune checkpoint inhibitor-mediated colitis (IMC) is a significant complication from ICI and can have life-threatening consequences. Based on clinical presentation, IMC mimics inflammatory bowel disease, however the link is poorly understood. We hypothesized that genetic susceptibility to Crohn's disease (CD) and ulcerative colitis (UC) may predispose to IMC. We developed and validated polygenic risk scores for CD (PRSCD) and UC (PRSUC) in cancer-free individuals and assessed the role of each of these PRSs on IMC in a cohort of 1,316 patients with non-small cell lung cancer who received ICIs. Prevalence of all-grade IMC in our cohort was 4% (55 cases), and for severe IMC, 2.5% (32 cases). The PRSUC predicted the development of all-grade IMC (HR=1.34 per standard deviation [SD], 95% CI=1.02-1.76, P=0.04) and severe IMC (HR=1.62 per SD, 95% CI=1.12-2.35, P=0.01). PRSCD was not associated with IMC or severe IMC. The association between PRSUC and IMC (all-grade and severe) was consistent in an independent pan-cancer cohort of patients treated with ICIs. Furthermore, PRSUC predicted severe IMC among patients treated with combination ICIs (OR = 2.20 per SD, 95% CI = 1.07-4.53, P=0.03). This is the first study to demonstrate the potential clinical utility of a PRS for ulcerative colitis in identifying patients receiving ICI at high risk of developing IMC, where risk reduction and close monitoring strategies could help improve overall patient outcomes.

Identifying modifiable comorbidities of schizophrenia by integrating electronic health records and polygenic risk.

Patients with schizophrenia have substantial comorbidity contributing to reduced life expectancy of 10-20 years. Identifying which comorbidities might be modifiable could improve rates of premature mortality in this population. We hypothesize that conditions that frequently co-occur but lack shared genetic risk with schizophrenia are more likely to be products of treatment, behavior, or environmental factors and therefore potentially modifiable. To test this hypothesis, we calculated phenome-wide comorbidity from electronic health records (EHR) in 250,000 patients in each of two independent health care institutions (Vanderbilt University Medical Center and Mass General Brigham) and association with schizophrenia polygenic risk scores (PRS) across the same phenotypes (phecodes) in linked biobanks. Comorbidity with schizophrenia was significantly correlated across institutions (r = 0.85) and consistent with prior literature. After multiple test correction, there were 77 significant phecodes comorbid with schizophrenia. Overall, comorbidity and PRS association were highly correlated (r = 0.55, p = 1.29×10-118), however, 36 of the EHR identified comorbidities had significantly equivalent schizophrenia PRS distributions between cases and controls. Fifteen of these lacked any PRS association and were enriched for phenotypes known to be side effects of antipsychotic medications (e.g., "movement disorders", "convulsions", "tachycardia") or other schizophrenia related factors such as from smoking ("bronchitis") or reduced hygiene (e.g., "diseases of the nail") highlighting the validity of this approach. Other phenotypes implicated by this approach where the contribution from shared common genetic risk with schizophrenia was minimal included tobacco use disorder, diabetes, and dementia. This work demonstrates the consistency and robustness of EHR-based schizophrenia comorbidities across independent institutions and with the existing literature. It identifies comorbidities with an absence of shared genetic risk indicating other causes that might be more modifiable and where further study of causal pathways could improve outcomes for patients.

A practical approach to curate clonal hematopoiesis of indeterminate potential in human genetic data sets.

Clonal hematopoiesis of indeterminate potential (CHIP) is a common form of age-related somatic mosaicism that is associated with significant morbidity and mortality. CHIP mutations can be identified in peripheral blood samples that are sequenced using approaches that cover the whole genome, the whole exome, or targeted genetic regions; however, differentiating true CHIP mutations from sequencing artifacts and germ line variants is a considerable bioinformatic challenge. We present a stepwise method that combines filtering based on sequencing metrics, variant annotation, and population-based associations to increase the accuracy of CHIP calls. We apply this approach to ascertain CHIP in ∼550 000 individuals in the UK Biobank complete whole exome cohort and the All of Us Research Program initial whole genome release cohort. CHIP ascertainment on this scale unmasks recurrent artifactual variants and highlights the importance of specialized filtering approaches for several genes, including TET2 and ASXL1. We show how small changes in filtering parameters can considerably increase CHIP misclassification and reduce the effect size of epidemiological associations. Our high-fidelity call set refines previous population-based associations of CHIP with incident outcomes. For example, the annualized incidence of myeloid malignancy in individuals with small CHIP clones is 0.03% per year, which increases to 0.5% per year among individuals with very large CHIP clones. We also find a significantly lower prevalence of CHIP in individuals of self-reported Latino or Hispanic ethnicity in All of Us, highlighting the importance of including diverse populations. The standardization of CHIP calling will increase the fidelity of CHIP epidemiological work and is required for clinical CHIP diagnostic assays.

Walk before you run: Feasibility challenges and lessons learned from the PROCLAIM study, a multicenter randomized controlled trial of misoprostol for prevention of recurrent Clostridioides difficile during COVID-19.

We analyzed our challenging experience with a randomized controlled trial of misoprostol for prevention of recurrent C. difficile. Despite careful prescreening and thoughtful protocol modifications to facilitate enrollment, we closed the study early after enrolling just 7 participants over 3 years. We share lessons learned, noting the importance of feasibility studies, inclusion of biomarker outcomes, and dissemination of such findings to inform future research design and implementation successes.