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.

Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs.

MOTIVATION: Making sense of networked multivariate association patterns is vitally important to many areas of high-dimensional analysis. Unfortunately, as the data-space dimensions grow, the number of association pairs increases in O(n2); this means that traditional visualizations such as heatmaps quickly become too complicated to parse effectively. RESULTS: Here, we present associationSubgraphs: a new interactive visualization method to quickly and intuitively explore high-dimensional association datasets using network percolation and clustering. The goal is to provide an efficient investigation of association subgraphs, each containing a subset of variables with stronger and more frequent associations among themselves than the remaining variables outside the subset, by showing the entire clustering dynamics and providing subgraphs under all possible cutoff values at once. Particularly, we apply associationSubgraphs to a phenome-wide multimorbidity association matrix generated from an electronic health record and provide an online, interactive demonstration for exploring multimorbidity subgraphs. AVAILABILITY AND IMPLEMENTATION: An R package implementing both the algorithm and visualization components of associationSubgraphs is available at https://github.com/tbilab/associationsubgraphs. Online documentation is available at https://prod.tbilab.org/associationsubgraphs_info/. A demo using a multimorbidity association matrix is available at https://prod.tbilab.org/associationsubgraphs-example/.

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.

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.

PheWAS-ME: a web-app for interactive exploration of multimorbidity patterns in PheWAS.

SUMMARY: Electronic health records (EHRs) linked with a DNA biobank provide unprecedented opportunities for biomedical research in precision medicine. The Phenome-wide association study (PheWAS) is a widely used technique for the evaluation of relationships between genetic variants and a large collection of clinical phenotypes recorded in EHRs. PheWAS analyses are typically presented as static tables and charts of summary statistics obtained from statistical tests of association between a genetic variant and individual phenotypes. Comorbidities are common and typically lead to complex, multivariate gene-disease association signals that are challenging to interpret. Discovering and interrogating multimorbidity patterns and their influence in PheWAS is difficult and time-consuming. We present PheWAS-ME: an interactive dashboard to visualize individual-level genotype and phenotype data side-by-side with PheWAS analysis results, allowing researchers to explore multimorbidity patterns and their associations with a genetic variant of interest. We expect this application to enrich PheWAS analyses by illuminating clinical multimorbidity patterns present in the data. AVAILABILITY AND IMPLEMENTATION: A demo PheWAS-ME application is publicly available at https://prod.tbilab.org/phewas_me/. Sample datasets are provided for exploration with the option to upload custom PheWAS results and corresponding individual-level data. Online versions of the appendices are available at https://prod.tbilab.org/phewas_me_info/. The source code is available as an R package on GitHub (https://github.com/tbilab/multimorbidity_explorer). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fulminant Myocarditis with Combination Immune Checkpoint Blockade.

Immune checkpoint inhibitors have improved clinical outcomes associated with numerous cancers, but high-grade, immune-related adverse events can occur, particularly with combination immunotherapy. We report the cases of two patients with melanoma in whom fatal myocarditis developed after treatment with ipilimumab and nivolumab. In both patients, there was development of myositis with rhabdomyolysis, early progressive and refractory cardiac electrical instability, and myocarditis with a robust presence of T-cell and macrophage infiltrates. Selective clonal T-cell populations infiltrating the myocardium were identical to those present in tumors and skeletal muscle. Pharmacovigilance studies show that myocarditis occurred in 0.27% of patients treated with a combination of ipilimumab and nivolumab, which suggests that our patients were having a rare, potentially fatal, T-cell-driven drug reaction. (Funded by Vanderbilt-Ingram Cancer Center Ambassadors and others.).

Snowball: resampling combined with distance-based regression to discover transcriptional consequences of a driver mutation.

MOTIVATION: Large-scale cancer genomic studies, such as The Cancer Genome Atlas (TCGA), have profiled multidimensional genomic data, including mutation and expression profiles on a variety of cancer cell types, to uncover the molecular mechanism of cancerogenesis. More than a hundred driver mutations have been characterized that confer the advantage of cell growth. However, how driver mutations regulate the transcriptome to affect cellular functions remains largely unexplored. Differential analysis of gene expression relative to a driver mutation on patient samples could provide us with new insights in understanding driver mutation dysregulation in tumor genome and developing personalized treatment strategies. RESULTS: Here, we introduce the Snowball approach as a highly sensitive statistical analysis method to identify transcriptional signatures that are affected by a recurrent driver mutation. Snowball utilizes a resampling-based approach and combines a distance-based regression framework to assign a robust ranking index of genes based on their aggregated association with the presence of the mutation, and further selects the top significant genes for downstream data analyses or experiments. In our application of the Snowball approach to both synthesized and TCGA data, we demonstrated that it outperforms the standard methods and provides more accurate inferences to the functional effects and transcriptional dysregulation of driver mutations. AVAILABILITY AND IMPLEMENTATION: R package and source code are available from CRAN at http://cran.r-project.org/web/packages/DESnowball, and also available at http://bioinfo.mc.vanderbilt.edu/DESnowball/.

In-depth genomic data analyses revealed complex transcriptional and epigenetic dysregulations of BRAFV600E in melanoma.

BACKGROUND: The recurrent BRAF driver mutation V600E (BRAF (V600E)) is currently one of the most clinically relevant mutations in melanoma. However, the genome-wide transcriptional and epigenetic dysregulations induced by BRAF (V600E) are still unclear. The investigation of this driver mutation's functional consequences is critical to the understanding of tumorigenesis and the development of therapeutic strategies. METHODS AND RESULTS: We performed an integrative analysis of transcriptomic and epigenomic changes disturbed by BRAF (V600E) by comparing the gene expression and methylation profiles of 34 primary cutaneous melanoma tumors harboring BRAF (V600E) with those of 27 BRAF (WT) samples available from The Cancer Genome Atlas (TCGA). A total of 711 significantly differentially expressed genes were identified as putative BRAF (V600E) target genes. Functional enrichment analyses revealed the transcription factor MITF (p < 3.6 × 10(-16)) and growth factor TGFB1 (p < 3.1 × 10(-9)) were the most significantly enriched up-regulators, with MITF being significantly up-regulated, whereas TGFB1 was significantly down-regulated in BRAF (V600E), suggesting that they may mediate tumorigenesis driven by BRAF (V600E). Further investigation using the MITF ChIP-Seq data confirmed that BRAF (V600E) led to an overall increased level of gene expression for the MITF targets. Furthermore, DNA methylation analysis revealed a global DNA methylation loss in BRAF (V600E) relative to BRAF (WT). This might be due to BRAF dysregulation of DNMT3A, which was identified as a potential target with significant down-regulation in BRAF (V600E). Finally, we demonstrated that BRAF (V600E) targets may play essential functional roles in cell growth and proliferation, measured by their effects on melanoma tumor growth using a short hairpin RNA silencing experimental dataset. CONCLUSIONS: Our integrative analysis identified a set of BRAF (V600E) target genes. Further analyses suggested a complex mechanism driven by mutation BRAF (V600E) on melanoma tumorigenesis that disturbs specific cancer-related genes, pathways, and methylation modifications.

Unique DNA methylome profiles in CpG island methylator phenotype colon cancers.

A subset of colorectal cancers was postulated to have the CpG island methylator phenotype (CIMP), a higher propensity for CpG island DNA methylation. The validity of CIMP, its molecular basis, and its prognostic value remain highly controversial. Using MBD-isolated genome sequencing, we mapped and compared genome-wide DNA methylation profiles of normal, non-CIMP, and CIMP colon specimens. Multidimensional scaling analysis revealed that each specimen could be clearly classified as normal, non-CIMP, and CIMP, thus signifying that these three groups have distinctly different global methylation patterns. We discovered 3780 sites in various genomic contexts that were hypermethylated in both non-CIMP and CIMP colon cancers when compared with normal colon. An additional 2026 sites were found to be hypermethylated in CIMP tumors only; and importantly, 80% of these sites were located in CpG islands. These data demonstrate on a genome-wide level that the additional hypermethylation seen in CIMP tumors occurs almost exclusively at CpG islands and support definitively that these tumors were appropriately named. When these sites were examined more closely, we found that 25% were adjacent to sites that were also hypermethylated in non-CIMP tumors. Thus, CIMP is also characterized by more extensive methylation of sites that are already prone to be hypermethylated in colon cancer. These observations indicate that CIMP tumors have specific defects in controlling both DNA methylation seeding and spreading and serve as an important first step in delineating molecular mechanisms that control these processes.

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.

Evaluation of Genetic Associations with Clinical Phenotypes of Kidney Stone Disease.

Background and objective: Previous studies have reported a strong genetic contribution to kidney stone risk. This study aims to identify genetic associations of kidney stone disease within a large-scale electronic health record system. Methods: We performed genome-wide association studies (GWASs) for nephrolithiasis from genotyped samples of 5571 cases and 83 692 controls. This analysis included a primary GWAS focused on nephrolithiasis and subsequent subgroup GWASs stratified by stone composition types. For significant risk variants, we performed association analyses with stone composition and first-time 24-h urine parameters. To assess disease severity, we investigated the associations with age at first stone diagnosis, age at first stone-related procedure, and time between first and second stone-related procedures. Key findings and limitations: The primary GWAS analysis identified ten significant loci, all located on chromosome 16 within coding regions of the UMOD gene. The strongest signal was rs28544423 (odds ratio 1.17, 95% confidence interval 1.11-1.23, p = 2.7 × 10-9). In subgroup GWASs stratified by six kidney stone composition subtypes, 19 significant loci were identified including two loci in coding regions (brushite; NXPH1, rs79970906 and rs4725104). The UMOD single nucleotide polymorphism rs28544423 was associated with differences in 24-h excretion of urinary analytes, 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. Limitations include an omitted variable bias and a misclassification bias. Conclusions and clinical implications: 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-h urine parameters and stone composition, but not disease severity. Patient summary: We identify genetic variants linked to kidney stone disease within an electronic health record (EHR) system. These findings suggest a role for the EHR to enable a precision-medicine approach for stone disease.

Large Language Models Improve the Identification of Emergency Department Visits for Symptomatic Kidney Stones.

Background: Recent advancements of large language models (LLMs) like Generative Pre-trained Transformer 4 (GPT-4) have generated significant interest among the scientific community. Yet, the potential of these models to be utilized in clinical settings remains largely unexplored. This study investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were caused by symptomatic kidney stones. Methods: Leveraging a dataset of manually annotated ED reports, we developed strategies to enhance the performance of GPT-4, GPT-3.5, and Llama-2 including prompt optimization, zero- and few-shot prompting, fine-tuning, and prompt augmentation. Further, we implemented fairness assessment and bias mitigation methods to investigate the potential disparities by these LLMs with respect to race and gender. A clinical expert manually assessed the explanations generated by GPT-4 for its predictions to determine if they were sound, factually correct, unrelated to the input prompt, or potentially harmful. The evaluation includes a comparison between LLMs, traditional machine learning models (logistic regression, extreme gradient boosting, and light gradient boosting machine), and a baseline system utilizing International Classification of Diseases (ICD) codes for kidney stones. Results: The best results were achieved by GPT-4 (macro-F1=0.833, 95% confidence interval [CI]=0.826-0.841) and GPT-3.5 (macro-F1=0.796, 95% CI=0.796-0.796), both being statistically significantly better than the ICD-based baseline result (macro-F1=0.71). Ablation studies revealed that the initial pre-trained GPT-3.5 model benefits from fine-tuning when using the same parameter configuration. Adding demographic information and prior disease history to the prompts allows LLMs to make more accurate decisions. The evaluation of bias found that GPT-4 exhibited no racial or gender disparities, in contrast to GPT-3.5, which failed to effectively model racial diversity. The analysis of explanations provided by GPT-4 demonstrates advanced capabilities of this model in understanding clinical text and reasoning with medical knowledge.

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 before 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. In addition, 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.

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.

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.

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.

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.

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.