Defining Suicidal Thought and Behavior Phenotypes for Genetic Studies.

Background: Standardized definitions of suicidality phenotypes, including suicidal ideation (SI), attempt (SA), and death (SD) are a critical step towards improving understanding and comparison of results in suicide research. The complexity of suicidality contributes to heterogeneity in phenotype definitions, impeding evaluation of clinical and genetic risk factors across studies and efforts to combine samples within consortia. Here, we present expert and data-supported recommendations for defining suicidality and control phenotypes to facilitate merging current/legacy samples with definition variability and aid future sample creation. Methods: A subgroup of clinician researchers and experts from the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) reviewed existing PGC definitions for SI, SA, SD, and control groups and generated preliminary consensus guidelines for instrument-derived and international classification of disease (ICD) data. ICD lists were validated in two independent datasets (N = 9,151 and 12,394). Results: Recommendations are provided for evaluated instruments for SA and SI, emphasizing selection of lifetime measures phenotype-specific wording. Recommendations are also provided for defining SI and SD from ICD data. As the SA ICD definition is complex, SA code list recommendations were validated against instrument results with sensitivity (range = 15.4% to 80.6%), specificity (range = 67.6% to 97.4%), and positive predictive values (range = 0.59-0.93) reported. Conclusions: Best-practice guidelines are presented for the use of existing information to define SI/SA/SD in consortia research. These proposed definitions are expected to facilitate more homogeneous data aggregation for genetic and multisite studies. Future research should involve refinement, improved generalizability, and validation in diverse populations.

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.

PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis.

OBJECTIVES: To address the need for interactive visualization tools and databases in characterizing multimorbidity patterns across different populations, we developed the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME). This tool leverages three large-scale EHR systems to facilitate efficient analysis and visualization of disease multimorbidity, aiming to reveal both robust and novel disease associations that are consistent across different systems and to provide insight for enhancing personalized healthcare strategies. MATERIALS AND METHODS: PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities, utilizing data from Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. It offers interactive and multifaceted visualizations for exploring multimorbidity. Incorporating an enhanced version of associationSubgraphs, PheMIME also enables dynamic analysis and inference of disease clusters, promoting the discovery of complex multimorbidity patterns. A case study on schizophrenia demonstrates its capability for generating interactive visualizations of multimorbidity networks within and across multiple systems. Additionally, PheMIME supports diverse multimorbidity-based discoveries, detailed further in online case studies. RESULTS: The PheMIME is accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial and multiple case studies for demonstration are available at https://prod.tbilab.org/PheMIME_supplementary_materials/. The source code can be downloaded from https://github.com/tbilab/PheMIME. DISCUSSION: PheMIME represents a significant advancement in medical informatics, offering an efficient solution for accessing, analyzing, and interpreting the complex and noisy real-world patient data in electronic health records. CONCLUSION: PheMIME provides an extensive multimorbidity knowledge base that consolidates data from three EHR systems, and it is a novel interactive tool designed to analyze and visualize multimorbidities across multiple EHR datasets. It stands out as the first of its kind to offer extensive multimorbidity knowledge integration with substantial support for efficient online analysis and interactive visualization.

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, mutation co-occurrence and clonal competition between multiple driver mutations were explored.

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 administrative purposes 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 combining 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 similar structures 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 phenome-wide multimorbidities. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared biology of diseases. The consistent core-periphery structure offers analytical insights to discover complex disease interactions. This work also sets the stage for advanced disease modeling, with implications for precision medicine. Funding: VUMC Biostatistics Development Award, the National Institutes of Health, and the VA CSRD.

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.

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.