PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations

The broad impact of cell death genes on the human disease phenome

Cell death mediated by genetically defined signaling pathways influences the health and dynamics of all tissues, however the tissue specificity of cell death pathways and the relationships between these pathways and human disease are not well understood. We analyzed the expression profiles of an array of 44 cell death genes involved in apoptosis, necroptosis, and pyroptosis cell death pathways across 49 human tissues from GTEx, to elucidate the landscape of cell death gene expression across human tissues, and the relationship between tissue-specific genetically determined expression and the human phenome. We uncovered unique cell death gene expression profiles across tissue types, suggesting there are physiologically distinct cell death programs in different tissues. Using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank (n ~ 500,000), we evaluated 513 traits encompassing ICD-10 defined diagnoses and laboratory-derived traits. Our analysis revealed hundreds of significant (FDR < 0.05) associations between genetically regulated cell death gene expression and an array of human phenotypes encompassing both clinical diagnoses and hematologic parameters, which were independently validated in another large-scale DNA biobank (BioVU) at Vanderbilt University Medical Center (n = 94,474) with matching phenotypes. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits. Our findings are also concordant with independently published studies (e.g. associations between BCL2L11/BIM expression and platelet & lymphocyte counts). Overall, these results suggest that cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.

A multi-ancestry GWAS of Fuchs corneal dystrophy highlights the contributions of laminins, collagen, and endothelial cell regulation

Fuchs endothelial corneal dystrophy (FECD) is a leading indication for corneal transplantation, but its molecular etiology remains poorly understood. We performed genome-wide association studies (GWAS) of FECD in the Million Veteran Program followed by multi-ancestry meta-analysis with the previous largest FECD GWAS, for a total of 3970 cases and 333,794 controls. We confirm the previous four loci, and identify eight novel loci: SSBP3, THSD7A, LAMB1, PIDD1, RORA, HS3ST3B1, LAMA5, and COL18A1. We further confirm the TCF4 locus in GWAS for admixed African and Hispanic/Latino ancestries and show an enrichment of European-ancestry haplotypes at TCF4 in FECD cases. Among the novel associations are low frequency missense variants in laminin genes LAMA5 and LAMB1 which, together with previously reported LAMC1, form laminin-511 (LM511). AlphaFold 2 protein modeling, validated through homology, suggests that mutations at LAMA5 and LAMB1 may destabilize LM511 by altering inter-domain interactions or extracellular matrix binding. Finally, phenome-wide association scans and colocalization analyses suggest that the TCF4 CTG18.1 trinucleotide repeat expansion leads to dysregulation of ion transport in the corneal endothelium and has pleiotropic effects on renal function.

Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy

The Phenome-Wide Association Study (PheWAS) is increasingly used to broadly screen for potential treatment effects, e.g., IL6R variant as a proxy for IL6R antagonists. This approach offers an opportunity to address the limited power in clinical trials to study differential treatment effects across patient subgroups. However, limited methods exist to efficiently test for differences across subgroups in the thousands of multiple comparisons generated as part of a PheWAS. In this study, we developed an approach that maximizes the power to test for heterogeneous genotype-phenotype associations and applied this approach to an IL6R PheWAS among individuals of African (AFR) and European (EUR) ancestries. We identified 29 traits with differences in IL6R variant-phenotype associations, including a lower risk of type 2 diabetes in AFR (OR 0.96) vs EUR (OR 1.0, p-value for heterogeneity = 8.5 × 10-3), and higher white blood cell count (p-value for heterogeneity = 8.5 × 10-131). These data suggest a more salutary effect of IL6R blockade for T2D among individuals of AFR vs EUR ancestry and provide data to inform ongoing clinical trials targeting IL6 for an expanding number of conditions. Moreover, the method to test for heterogeneity of associations can be applied broadly to other large-scale genotype-phenotype screens in diverse populations.

Comparison of phenomic profiles in the All of Us Research Program against the US general population and the UK Biobank

IMPORTANCE

Knowledge gained from cohort studies has dramatically advanced both public and precision health. The All of Us Research Program seeks to enroll 1 million diverse participants who share multiple sources of data, providing unique opportunities for research. It is important to understand the phenomic profiles of its participants to conduct research in this cohort.

OBJECTIVES

More than 280 000 participants have shared their electronic health records (EHRs) in the All of Us Research Program. We aim to understand the phenomic profiles of this cohort through comparisons with those in the US general population and a well-established nation-wide cohort, UK Biobank, and to test whether association results of selected commonly studied diseases in the All of Us cohort were comparable to those in UK Biobank.

MATERIALS AND METHODS

We included participants with EHRs in All of Us and participants with health records from UK Biobank. The estimates of prevalence of diseases in the US general population were obtained from the Global Burden of Diseases (GBD) study. We conducted phenome-wide association studies (PheWAS) of 9 commonly studied diseases in both cohorts.

RESULTS

This study included 287 012 participants from the All of Us EHR cohort and 502 477 participants from the UK Biobank. A total of 314 diseases curated by the GBD were evaluated in All of Us, 80.9% (N = 254) of which were more common in All of Us than in the US general population [prevalence ratio (PR) >1.1, P < 2 × 10-5]. Among 2515 diseases and phenotypes evaluated in both All of Us and UK Biobank, 85.6% (N = 2152) were more common in All of Us (PR >1.1, P < 2 × 10-5). The Pearson correlation coefficients of effect sizes from PheWAS between All of Us and UK Biobank were 0.61, 0.50, 0.60, 0.57, 0.40, 0.53, 0.46, 0.47, and 0.24 for ischemic heart diseases, lung cancer, chronic obstructive pulmonary disease, dementia, colorectal cancer, lower back pain, multiple sclerosis, lupus, and cystic fibrosis, respectively.

DISCUSSION

Despite the differences in prevalence of diseases in All of Us compared to the US general population or the UK Biobank, our study supports that All of Us can facilitate rapid investigation of a broad range of diseases.

CONCLUSION

Most diseases were more common in All of Us than in the general US population or the UK Biobank. Results of disease-disease association tests from All of Us are comparable to those estimated in another well-studied national cohort.

A multi-ancestry genetic study of pain intensity in 598,339 veterans

Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects the quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids had a central role in precipitating the opioid crisis. Despite an estimated heritability of 25-50%, the genetic architecture of chronic pain is not well-characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 126 independent genetic loci, 69 of which are new. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level and cognitive traits. Integration of the genome-wide association studies findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, β-blockers and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.

Phenomewide Association Study of Health Outcomes Associated With the Genetic Correlates of 25 Hydroxyvitamin D Concentration and Vitamin D Binding Protein Concentration

While it is known that vitamin D deficiency is associated with adverse bone outcomes, it remains unclear whether low vitamin D status may increase the risk of a wider range of health outcomes. We had the opportunity to explore the association between common genetic variants associated with both 25 hydroxyvitamin D (25OHD) and the vitamin D binding protein (DBP, encoded by the GC gene) with a comprehensive range of health disorders and laboratory tests in a large academic medical center. We used summary statistics for 25OHD and DBP to generate polygenic scores (PGS) for 66,482 participants with primarily European ancestry and 13,285 participants with primarily African ancestry from the Vanderbilt University Medical Center Biobank (BioVU). We examined the predictive properties of PGS25OHD, and two scores related to DBP concentration with respect to 1322 health-related phenotypes and 315 laboratory-measured phenotypes from electronic health records. In those with European ancestry: (a) the PGS25OHD and PGSDBP scores, and individual SNPs rs4588 and rs7041 were associated with both 25OHD concentration and 1,25 dihydroxyvitamin D concentrations; (b) higher PGS25OHD was associated with decreased concentrations of triglycerides and cholesterol, and reduced risks of vitamin D deficiency, disorders of lipid metabolism, and diabetes. In general, the findings for the African ancestry group were consistent with findings from the European ancestry analyses. Our study confirms the utility of PGS and two key variants within the GC gene (rs4588 and rs7041) to predict the risk of vitamin D deficiency in clinical settings and highlights the shared biology between vitamin D-related genetic pathways a range of health outcomes.

MGDHGS: Gene-bridged metabolite-disease relationships prediction via GraphSAGE and self-attention mechanism

Metabolites represent the underlying information of biological systems. Revealing the links between metabolites and diseases can facilitate the development of targeted drugs. Traditional biological experiments can be used to validate the relationships of metabolite-disease, but these methods are time-consuming and labor-intensive. In contrast, the prevailing computational methods have improved efficiency but primarily rely on the metabolite-disease interactions, overlooking the impact of other biological components. To remedy the problem, we present a novel computational framework (MGDHGS) based on metabolite-gene-disease heterogeneous network to forecast potential associations. Specifically, we initially integrate data from multiple sources to construct metabolite-gene-disease heterogeneous network that includes known associations and computationally-derived similarities. Then, the GraphSAGE is harnessed to learn the low dimensional neighborhood representation in the heterogeneous network and self-attention mechanism is applied to effectively capture the connectivity patterns, which contributions to combine with nodes intrinsic and extrinsic features. Finally, the ultimate relationships probability scores are predicted by linear regression based on the these characteristics. The five-fold cross-validation showcases impressive AUC (0.9734) and PR (0.9718) for MGDHGS compared with five state-of-the-art methods, and the case studies validate that the metabolite-disease associations predicted by MGDHGS can be substantiated through pertinent biological experiments. The findings of this study show great potential contribution in the development of targeted drugs as well as offering solid support for our understanding of the complex interactions between metabolites, genes and diseases.

Improving prediction models of amyotrophic lateral sclerosis (ALS) using polygenic, pre-existing conditions, and survey-based risk scores in the UK Biobank

Background and Objectives

Amyotrophic lateral sclerosis (ALS) causes profound impairments in neurological function and a cure for this devastating disease remains elusive. Early detection and risk stratification are crucial for timely intervention and improving patient outcomes. This study aimed to identify predisposing genetic, phenotypic, and exposure-related factors for Amyotrophic lateral sclerosis using multi-modal data and assess their joint predictive potential.

Methods

Utilizing data from the UK Biobank, we analyzed an unrelated set of 292 ALS cases and 408,831 controls of European descent. Two polygenic risk scores (PRS) are constructed: "GWAS Hits PRS" and "PRS-CS," reflecting oligogenic and polygenic ALS risk profiles, respectively. Time-restricted phenome-wide association studies (PheWAS) were performed to identify pre-existing conditions increasing ALS risk, integrated into phenotypic risk scores (PheRS). A poly-exposure score ("PXS") captures the influence of environmental exposures measured through survey questionnaires. We evaluate the performance of these scores for predicting ALS incidence and stratifying risk, adjusting for baseline demographic covariates.

Results

Both PRSs modestly predicted ALS diagnosis, but with increased predictive power when combined (covariate-adjusted receiver operating characteristic [AAUC] = 0.584 [0.525, 0.639]). PheRS incorporated diagnoses 1 year before ALS onset (PheRS1) modestly discriminated cases from controls (AAUC = 0.515 [0.472, 0.564]). The "PXS" did not significantly predict ALS. However, a model incorporating PRSs and PheRS1 improved prediction of ALS (AAUC = 0.604 [0.547, 0.667]), outperforming a model combining all risk scores. This combined risk score identified the top 10% of risk score distribution with a 4-fold higher ALS risk (95% CI: [2.04, 7.73]) versus those in the 40%-60% range.

Discussions

By leveraging UK Biobank data, our study uncovers predisposing ALS factors, highlighting the improved effectiveness of multi-factorial prediction models to identify individuals at highest risk for ALS.

Iron Status and Risk of Heart Disease, Stroke, and Diabetes: A Mendelian Randomization Study in European Adults

BACKGROUND

The relevance of iron status biomarkers for coronary artery disease (CAD), heart failure (HF), ischemic stroke (IS), and type 2 diabetes (T2D) is uncertain. We compared the observational and Mendelian randomization (MR) analyses of iron status biomarkers and hemoglobin with these diseases.

METHODS AND RESULTS

Observational analyses of hemoglobin were compared with genetically predicted hemoglobin with cardiovascular diseases and diabetes in the UK Biobank. Iron biomarkers included transferrin saturation, serum iron, ferritin, and total iron binding capacity. MR analyses assessed associations with CAD (CARDIOGRAMplusC4D [Coronary Artery Disease Genome Wide Replication and Meta-Analysis Plus The Coronary Artery Disease Genetics], n=181 522 cases), HF (HERMES [Heart Failure Molecular Epidemiology for Therapeutic Targets), n=115 150 cases), IS (GIGASTROKE, n=62 100 cases), and T2D (DIAMANTE [Diabetes Meta-Analysis of Trans-Ethnic Association Studies], n=80 154 cases) genome-wide consortia. Observational analyses demonstrated J-shaped associations of hemoglobin with CAD, HF, IS, and T2D. In contrast, MR analyses demonstrated linear positive associations of higher genetically predicted hemoglobin levels with 8% higher risk per 1 SD higher hemoglobin for CAD, 10% to 13% for diabetes, but not with IS or HF in UK Biobank. Bidirectional MR analyses confirmed the causal relevance of iron biomarkers for hemoglobin. Further MR analyses in global consortia demonstrated modest protective effects of iron biomarkers for CAD (7%-14% lower risk for 1 SD higher levels of iron biomarkers), adverse effects for T2D, but no associations with IS or HF.

CONCLUSIONS

Higher levels of iron biomarkers were protective for CAD, had adverse effects on T2D, but had no effects on IS or HF. Randomized trials are now required to assess effects of iron supplements on risk of CAD in high-risk older people.

Polygenic risk score-based phenome-wide association study of head and neck cancer across two large biobanks

BACKGROUND

Numerous observational studies have highlighted associations of genetic predisposition of head and neck squamous cell carcinoma (HNSCC) with diverse risk factors, but these findings are constrained by design limitations of observational studies. In this study, we utilized a phenome-wide association study (PheWAS) approach, incorporating a polygenic risk score (PRS) derived from a wide array of genomic variants, to systematically investigate phenotypes associated with genetic predisposition to HNSCC. Furthermore, we validated our findings across heterogeneous cohorts, enhancing the robustness and generalizability of our results.

METHODS

We derived PRSs for HNSCC and its subgroups, oropharyngeal cancer and oral cancer, using large-scale genome-wide association study summary statistics from the Genetic Associations and Mechanisms in Oncology Network. We conducted a comprehensive investigation, leveraging genotyping data and electronic health records from 308,492 individuals in the UK Biobank and 38,401 individuals in the Penn Medicine Biobank (PMBB), and subsequently performed PheWAS to elucidate the associations between PRS and a wide spectrum of phenotypes.

RESULTS

We revealed the HNSCC PRS showed significant association with phenotypes related to tobacco use disorder (OR, 1.06; 95% CI, 1.05-1.08; P = 3.50 × 10-15), alcoholism (OR, 1.06; 95% CI, 1.04-1.09; P = 6.14 × 10-9), alcohol-related disorders (OR, 1.08; 95% CI, 1.05-1.11; P = 1.09 × 10-8), emphysema (OR, 1.11; 95% CI, 1.06-1.16; P = 5.48 × 10-6), chronic airway obstruction (OR, 1.05; 95% CI, 1.03-1.07; P = 2.64 × 10-5), and cancer of bronchus (OR, 1.08; 95% CI, 1.04-1.13; P = 4.68 × 10-5). These findings were replicated in the PMBB cohort, and sensitivity analyses, including the exclusion of HNSCC cases and the major histocompatibility complex locus, confirmed the robustness of these associations. Additionally, we identified significant associations between HNSCC PRS and lifestyle factors related to smoking and alcohol consumption.

CONCLUSIONS

The study demonstrated the potential of PRS-based PheWAS in revealing associations between genetic risk factors for HNSCC and various phenotypic traits. The findings emphasized the importance of considering genetic susceptibility in understanding HNSCC and highlighted shared genetic bases between HNSCC and other health conditions and lifestyles.

Disease progression strikingly differs in research and real-world Parkinson's populations

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data, and advances in natural language processing, particularly large language models, allow for a more granular comparison of populations than previously possible. This study includes two research populations and two real-world data-derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using a manually validated natural language processing with a large language model to extract measurements of PD progression. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p < 0.001; mini-mental state exam median decline 0.28 vs. 0.11, p < 0.001; and clinically recognized cognitive decline, p = 0.001). In real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p < 0.001). After diagnosis, in real-world cohorts, treatment with PD medications has initiated an average of 2.3 years later (95% CI: [2.1-2.4]; p < 0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real-world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

Pharmacogenetic and clinical risk factors for bevacizumab-related gastrointestinal hemorrhage in prostate cancer patients treated on CALGB 90401 (Alliance)

The objective of this study was to discover clinical and pharmacogenetic factors associated with bevacizumab-related gastrointestinal hemorrhage in Cancer and Leukemia Group B (Alliance) 90401. Patients with metastatic castration-resistant prostate cancer received docetaxel and prednisone ± bevacizumab. Patients were genotyped using Illumina HumanHap610-Quad and assessed using cause-specific risk for association between single nucleotide polymorphisms (SNPs) and gastrointestinal hemorrhage. In 1008 patients, grade 2 or higher gastrointestinal hemorrhage occurred in 9.5% and 3.8% of bevacizumab (n = 503) and placebo (n = 505) treated patients, respectively. Bevacizumab (P < 0.001) and age (P = 0.002) were associated with gastrointestinal hemorrhage. In 616 genetically estimated Europeans (n = 314 bevacizumab and n = 302 placebo treated patients), grade 2 or higher gastrointestinal hemorrhage occurred in 9.6% and 2.0% of patients, respectively. One SNP (rs1478947; HR 6.26; 95% CI 3.19-12.28; P = 9.40 × 10-8) surpassed Bonferroni-corrected significance. Grade 2 or higher gastrointestinal hemorrhage rate was 33.3% and 6.2% in bevacizumab-treated patients with the AA/AG and GG genotypes, versus 2.9% and 1.9% in the placebo arm, respectively. Prospective validation of these findings and functional analyses are needed to better understand the genetic contribution to treatment-related gastrointestinal hemorrhage.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

MOTIVATION

Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions.

RESULTS

Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

AVAILABILITY AND IMPLEMENTATION

The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.

Making causal inferences from transactional data: A narrative review of opportunities and challenges when implementing the target trial framework

The target trial framework has emerged as a powerful tool for addressing causal questions in clinical practice and in public health. In the healthcare sector, where decision-making is increasingly data-driven, transactional databases, such as electronic health records (EHR) and insurance claims, present an untapped potential for answering complex causal questions. This narrative review explores the potential of the integration of the target trial framework with real-world data to enhance healthcare decision-making processes. We outline essential elements of the target trial framework, and identify pertinent challenges in data quality, privacy concerns, and methodological limitations, proposing solutions to overcome these obstacles and optimize the framework's application.

Predicting disease-gene associations through self-supervised mutual infomax graph convolution network

Illuminating associations between diseases and genes can help reveal the pathogenesis of syndromes and contribute to treatments, but a large number of associations remained unexplored. To identify novel disease-gene associations, many computational methods have been developed using disease and gene-related prior knowledge. However, these methods remain of relatively inferior performance due to the limited external data sources and the inevitable noise among the prior knowledge. In this study, we have developed a new method, Self-Supervised Mutual Infomax Graph Convolution Network (MiGCN), to predict disease-gene associations under the guidance of external disease-disease and gene-gene collaborative graphs. The noises within the collaborative graphs were eliminated by maximizing the mutual information between nodes and neighbors through a graphical mutual infomax layer. In parallel, the node interactions were strengthened by a novel informative message passing layer to improve the learning ability of graph neural network. The extensive experiments showed that our model achieved performance improvement over the state-of-art method by more than 8 % on AUC. The datasets, source codes and trained models of MiGCN are available at https://github.com/biomed-AI/MiGCN.

Disease progression strikingly differs in research and real-world Parkinson's populations

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data and recent advances in natural language processing, particularly large language models, allow for a more granular comparison of populations and the methods of data collection describing these populations than previously possible. This study includes two research populations and two real-world data derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using natural language processing with a large language model to extract measurements of PD progression. This extraction process is manually validated for accuracy. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p<0.001; mini-mental state exam median decline 0.28 vs. 0.11, p<0.001; and clinically recognized cognitive decline, p=0.001). In the real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p<0.001). After diagnosis, in real-world cohorts, treatment with PD medications is initiated 2.3 years later on average (95% CI: [2.1-2.4]; p<0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging using existing data. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

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.

To weight or not to weight? Studying the effect of selection bias in three large EHR-linked biobanks

Objective

To explore the role of selection bias adjustment by weighting electronic health record (EHR)-linked biobank data for commonly performed analyses.

Materials and methods

We mapped diagnosis (ICD code) data to standardized phecodes from three EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n=244,071), Michigan Genomics Initiative (MGI; n=81,243), and UK Biobank (UKB; n=401,167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to be more representative of the US adult population. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted four common descriptive and analytic tasks comparing unweighted and weighted results.

Results

For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB's estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted PheWAS for colorectal cancer, the strongest associations remained unaltered and there was large overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates.

Discussion

Weighting had limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation more. Results from untargeted association analyses should be followed by weighted analysis when effect size estimation is of interest for specific signals.

Conclusion

EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.

Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores

Importance

Large language models (LLMs) are crucial for medical tasks. Ensuring their reliability is vital to avoid false results. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR.

Objective

Evaluate ChatGPT and LlaMA-2 performance in extracting MMSE and CDR scores, including their associated dates.

Methods

Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA. After applying inclusion criteria 34,465 notes remained, of which 765 underwent ChatGPT (GPT-4) and LlaMA-2, and 22 experts reviewed the responses. ChatGPT successfully extracted MMSE and CDR instances with dates from 742 notes. We used 20 notes for fine-tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 each assigned to two reviewers simultaneously. Inter-rater-agreement (Fleiss' Kappa), precision, recall, true/false negative rates, and accuracy were calculated. Our study follows TRIPOD reporting guidelines for model validation.

Results

For MMSE information extraction, ChatGPT (vs. LlaMA-2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true-negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true-negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and LlaMA-2 on double-reviewed notes. LlaMA-2 errors included 27 cases of total hallucination, 19 cases of reporting other scores instead of MMSE, 25 missed scores, and 23 cases of reporting only the wrong date. In comparison, ChatGPT's errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date.

Conclusions

In this diagnostic/prognostic study of ChatGPT and LlaMA-2 for extracting cognitive exam dates and scores from clinical notes, ChatGPT exhibited high accuracy, with better performance compared to LlaMA-2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.

Age-Related RPE changes in Wildtype C57BL/6J Mice between 2 and 32 Months

Purpose

This study provides a systematic evaluation of age-related changes in RPE cell structure and function using a morphometric approach. We aim to better capture nuanced predictive changes in cell heterogeneity that reflect loss of RPE integrity during normal aging. Using C57BL6/J mice ranging from P60-P730, we sought to evaluate how regional changes in RPE shape reflect incremental losses in RPE cell function with advancing age. We hypothesize that tracking global morphological changes in RPE is predictive of functional defects over time.

Methods

We tested three groups of C57BL/6J mice (young: P60-180; Middle-aged: P365-729; aged: 730+) for function and structural defects using electroretinograms, immunofluorescence, and phagocytosis assays.

Results

The largest changes in RPE morphology were evident between the young and aged groups, while the middle-aged group exhibited smaller but notable region-specific differences. We observed a 1.9-fold increase in cytoplasmic alpha-catenin expression specifically in the central-medial region of the eye between the young and aged group. There was an 8-fold increase in subretinal, IBA-1-positive immune cell recruitment and a significant decrease in visual function in aged mice compared to young mice. Functional defects in the RPE corroborated by changes in RPE phagocytotic capacity.

Conclusions

The marked increase of cytoplasmic alpha-catenin expression and subretinal immune cell deposition, and decreased visual output coincide with regional changes in RPE cell morphometrics when stratified by age. These cumulative changes in the RPE morphology showed predictive regional patterns of stress associated with loss of RPE integrity.

Natural history of rare diseases using natural language processing of narrative unstructured electronic health records: The example of Dravet syndrome

OBJECTIVE

The increasing implementation of electronic health records allows the use of advanced text-mining methods for establishing new patient phenotypes and stratification, and for revealing outcome correlations. In this study, we aimed to explore the electronic narrative clinical reports of a cohort of patients with Dravet syndrome (DS) longitudinally followed at our center, to identify the capacity of this methodology to retrace natural history of DS during the early years.

METHODS

We used a document-based clinical data warehouse employing natural language processing to recognize the phenotype concepts in the narrative medical reports. We included patients with DS who have a medical report produced before the age of 2 years and a follow-up after the age of 3 years ("DS cohort," 56 individuals). We selected two control populations, a "general control cohort" (275 individuals) and a "neurological control cohort" (281 individuals), with similar characteristics in terms of gender, number of reports, and age at last report. To find concepts specifically associated with DS, we performed a phenome-wide association study using Cox regression, comparing the reports of the three cohorts. We then performed a qualitative analysis of the surviving concepts based on their median age at first appearance.

RESULTS

A total of 76 concepts were prevalent in the reports of children with DS. Concepts appearing during the first 2 years were mostly related with the epilepsy features at the onset of DS (convulsive and prolonged seizures triggered by fever, often requiring in-hospital care). Subsequently, concepts related to new types of seizures and to drug resistance appeared. A series of non-seizure-related concepts emerged after the age of 2-3 years, referring to the nonseizure comorbidities classically associated with DS.

SIGNIFICANCE

The extraction of clinical terms by narrative reports of children with DS allows outlining the known natural history of this rare disease in early childhood. This original model of "longitudinal phenotyping" could be applied to other rare and very rare conditions with poor natural history description.

Correlates of Risk for Disinhibited Behaviors in the Million Veteran Program Cohort

Importance

Many psychiatric outcomes share a common etiologic pathway reflecting behavioral disinhibition, generally referred to as externalizing (EXT) disorders. Recent genome-wide association studies (GWASs) have demonstrated the overlap between EXT disorders and important aspects of veterans' health, such as suicide-related behaviors and substance use disorders (SUDs).

Objective

To explore correlates of risk for EXT disorders within the Veterans Health Administration (VA) Million Veteran Program (MVP).

Design, Setting, and Participants

A series of phenome-wide association studies (PheWASs) of polygenic risk scores (PGSs) for EXT disorders was conducted using electronic health records. First, ancestry-specific PheWASs of EXT PGSs were conducted in the African, European, and Hispanic or Latin American ancestries. Next, a conditional PheWAS, covarying for PGSs of comorbid psychiatric problems (depression, schizophrenia, and suicide attempt; European ancestries only), was performed. Lastly, to adjust for unmeasured confounders, a within-family analysis of significant associations from the main PheWAS was performed in full siblings (European ancestries only). This study included the electronic health record data from US veterans from VA health care centers enrolled in MVP. Analyses took place from February 2022 to August 2023 covering a period from October 1999 to January 2020.

Exposures

PGSs for EXT, depression, schizophrenia, and suicide attempt.

Main Outcomes and Measures

Phecodes for diagnoses derived from the International Statistical Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification, codes from electronic health records.

Results

Within the MVP (560 824 patients; mean [SD] age, 67.9 [14.3] years; 512 593 male [91.4%]), the EXT PGS was associated with 619 outcomes, of which 188 were independent of risk for comorbid problems or PGSs (from odds ratio [OR], 1.02; 95% CI, 1.01-1.03 for overweight/obesity to OR, 1.44; 95% CI, 1.42-1.47 for viral hepatitis C). Of the significant outcomes, 73 (11.9%) were significant in the African results and 26 (4.5%) were significant in the Hispanic or Latin American results. Within-family analyses uncovered robust associations between EXT PGS and consequences of SUDs, including liver disease, chronic airway obstruction, and viral hepatitis C.

Conclusions and Relevance

Results of this cohort study suggest a shared polygenic basis of EXT disorders, independent of risk for other psychiatric problems. In addition, this study found associations between EXT PGS and diagnoses related to SUDs and their sequelae. Overall, this study highlighted the potential negative consequences of EXT disorders for health and functioning in the US veteran population.

Development of a Polygenic Risk Score to Predict Diverticulitis

BACKGROUND

Despite its prevalence and associated morbidity, we remain limited in our ability to predict the course of a patient with diverticular disease. Although several clinical and genetic risk factors have been identified, we do not know how these factors relate to one another.

OBJECTIVE

Our aim was to determine whether a polygenic risk score could improve risk prediction for diverticulitis and recurrent diverticulitis compared with a model using only clinical factors.

DESIGN

This is an observational study.

SETTING

The study examines the predictive ability of a polygenic risk score for diverticulitis developed using prior genome-wide association studies and validated using the MyCode biobank.

PATIENTS

This study included patients of European ancestry in the Geisinger Health System who were enrolled in the MyCode Community Health biobanking program.

MAIN OUTCOME MEASURES

The ability of a polygenic risk score to predict diverticulosis, diverticulitis, and recurrent diverticulitis was the main outcome measure of this study.

RESULTS

A total of 60,861 patients were included, of whom 9912 (16.3%) had diverticulosis or diverticulitis (5015 with diverticulosis and 4897 with diverticulitis). When divided into deciles, our polygenic risk score stratified patients by risk of both diverticulosis and diverticulitis with a 2-fold difference in disease risk between the highest and lowest deciles for diverticulitis and a 4.8-fold difference for recurrent complicated diverticulitis. When compared with clinical factors alone, our polygenic risk score was able to improve risk prediction of recurrent diverticulitis.

LIMITATIONS

Our population is largely located in a single geographic region and were classified by disease status, using international classification of diseases codes.

CONCLUSIONS

This predictive model stratifies patients based on genetic risk for diverticular disease. The increased frequency of recurrent disease in our high-risk patients suggests that a polygenic risk score, in addition to other factors, may help guide the discussion regarding surgical intervention. See Video Abstract .

DESARROLLO DE UNA PUNTUACIN DE RIESGO POLIGNICO PARA PREDECIR LA DIVERTICULITIS

ANTECEDENTES:A pesar de su prevalencia y morbilidad asociada, nuestra capacidad para predecir el curso en un paciente con enfermedad diverticular sigue siendo limitada. Si bien se han identificado varios factores de riesgo clínicos y genéticos, no sabemos cómo se relacionan estos factores entre sí.OBJETIVO:Determinar si una puntuación de riesgo poligénico podría mejorar la predicción del riesgo de diverticulitis y diverticulitis recurrente en comparación con un modelo que utiliza solo factores clínicos.DISEÑO:Un estudio observacional que examina la capacidad predictiva de una puntuación de riesgo poligénico para la diverticulitis desarrollada usando estudios previos de asociación amplia del genoma y validada usando el biobanco MyCode.ÁMBITOS Y PACIENTES:Pacientes de ascendencia europea en el Sistema de Salud Geisinger que estaban inscritos en el programa de biobancos MyCode Community Health.PRINCIPALES MEDIDAS DE VALORACIÓN:La capacidad de una puntuación de riesgo poligénico para predecir diverticulosis, diverticulitis y diverticulitis recurrente.RESULTADOS:Se incluyeron un total de 60.861 pacientes, de los cuales 9.912 (16,3%) presentaban diverticulosis o diverticulitis (5.015 con diverticulosis y 4.897 con diverticulitis). Cuando se dividió en deciles, nuestra puntuación de riesgo poligénico estratificó a los pacientes según el riesgo de diverticulosis y diverticulitis con una diferencia de 2 veces en el riesgo de enfermedad entre los deciles más alto y más bajo para diverticulitis y una diferencia de 4,8 veces para diverticulitis complicada recurrente. En comparación con los factores clínicos solos, nuestra puntuación de riesgo poligénico pudo mejorar la predicción del riesgo de diverticulitis recurrente.LIMITACIONES:Nuestra población se encuentra en gran parte en una sola región geográfica y se clasificó por estado de enfermedad utilizando códigos de clasificación internacional de enfermedades.CONCLUSIONES:Este modelo predictivo estratifica a los pacientes en función del riesgo genético de enfermedad diverticular. La mayor frecuencia de enfermedad recurrente en nuestros pacientes de alto riesgo sugiere que un puntaje de riesgo poligénico, además de otros factores, puede ayudar a guiar la discusión sobre la intervención quirúrgica. (Traducción- Dr. Ingrid Melo ).

Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition

Implementing artificial intelligence to extract insights from large, real-world clinical data sets can supplement and enhance knowledge management efforts for health sciences research and clinical care. At Vanderbilt University Medical Center (VUMC), the in-house developed Word Cloud natural language processing system extracts coded concepts from patient records in VUMC's electronic health record repository using the Unified Medical Language System terminology. Through this process, the Word Cloud extracts the most prominent concepts found in the clinical documentation of a specific patient or population. The Word Cloud provides added value for clinical care decision-making and research. This viewpoint paper describes a use case for how the VUMC Center for Knowledge Management leverages the condition-disease associations represented by the Word Cloud to aid in the knowledge generation needed to inform the interpretation of phenome-wide association studies.

Phenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health

The human retina is a multilayered tissue that offers a unique window into systemic health. Optical coherence tomography (OCT) is widely used in eye care and allows the noninvasive, rapid capture of retinal anatomy in exquisite detail. We conducted genotypic and phenotypic analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed OCT layer cross-phenotype association analyses (OCT-XWAS), associating retinal thicknesses with 1866 incident conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association studies (GWASs), identifying inherited genetic markers that influence retinal layer thicknesses and replicated our associations among the LIFE-Adult Study (N = 6313). Last, we performed a comparative analysis of phenome- and genome-wide associations to identify putative causal links between retinal layer thicknesses and both ocular and systemic conditions. Independent associations with incident mortality were detected for thinner photoreceptor segments (PSs) and, separately, ganglion cell complex layers. Phenotypic associations were detected between thinner retinal layers and ocular, neuropsychiatric, cardiometabolic, and pulmonary conditions. A GWAS of retinal layer thicknesses yielded 259 unique loci. Consistency between epidemiologic and genetic associations suggested links between a thinner retinal nerve fiber layer with glaucoma, thinner PS with age-related macular degeneration, and poor cardiometabolic and pulmonary function with a thinner PS. In conclusion, we identified multiple inherited genetic loci and acquired systemic cardio-metabolic-pulmonary conditions associated with thinner retinal layers and identify retinal layers wherein thinning is predictive of future ocular and systemic conditions.

Repurposing N-acetylcysteine for management of non-acetaminophen induced acute liver failure: an evidence scan from a global health perspective

Background

The World Health Organization (WHO)'s Essential Medicines List (EML) plays an important role in advocating for access to key treatments for conditions affecting people in all geographic settings. We applied our established drug repurposing methods to one EML agent, N-acetylcysteine (NAC), to identify additional uses of relevance to the global health community beyond its existing EML indication (acetaminophen toxicity).

Methods

We undertook a phenome-wide association study (PheWAS) of a variant in the glutathione synthetase (GSS) gene in approximately 35,000 patients to explore novel indications for use of NAC, which targets glutathione. We then evaluated the evidence regarding biologic plausibility, efficacy, and safety of NAC use in the new phenotype candidates.

Results

PheWAS of GSS variant R418Q revealed increased risk of several phenotypes related to non-acetaminophen induced acute liver failure (ALF), indicating that NAC may represent a therapeutic option for treating this condition. Evidence review identified practice guidelines, systematic reviews, clinical trials, retrospective cohorts and case series, and case reports. This evidence suggesting benefit of NAC use in this subset of ALF patients. The safety profile of NAC in this literature was also concordant with existing evidence on safety of this agent in acetaminophen-induced ALF.

Conclusions

This body of literature indicates efficacy and safety of NAC in non-acetaminophen induced ALF. Given the presence of NAC on the EML, this medication is likely to be available across a range of resource settings; promulgating its use in this novel subset of ALF can provide healthcare professionals and patients with a valuable and safe complement to supportive care for this disease.

Polygenic scores for tobacco use provide insights into systemic health risks in a diverse EHR-linked biobank in Los Angeles

Tobacco use is a major risk factor for many diseases and is heavily influenced by environmental factors with significant underlying genetic contributions. Here, we evaluated the predictive performance, risk stratification, and potential systemic health effects of tobacco use disorder (TUD) predisposing germline variants using a European- ancestry-derived polygenic score (PGS) in 24,202 participants from the multi-ancestry, hospital-based UCLA ATLAS biobank. Among genetically inferred ancestry groups (GIAs), TUD-PGS was significantly associated with TUD in European American (EA) (OR: 1.20, CI: [1.16, 1.24]), Hispanic/Latin American (HL) (OR:1.19, CI: [1.11, 1.28]), and East Asian American (EAA) (OR: 1.18, CI: [1.06, 1.31]) GIAs but not in African American (AA) GIA (OR: 1.04, CI: [0.93, 1.17]). Similarly, TUD-PGS offered strong risk stratification across PGS quantiles in EA and HL GIAs and inconsistently in EAA and AA GIAs. In a cross-ancestry phenome-wide association meta-analysis, TUD-PGS was associated with cardiometabolic, respiratory, and psychiatric phecodes (17 phecodes at P < 2.7E-05). In individuals with no history of smoking, the top TUD-PGS associations with obesity and alcohol-related disorders (P = 3.54E-07, 1.61E-06) persist. Mendelian Randomization (MR) analysis provides evidence of a causal association between adiposity measures and tobacco use. Inconsistent predictive performance of the TUD-PGS across GIAs motivates the inclusion of multiple ancestry populations at all levels of genetic research of tobacco use for equitable clinical translation of TUD-PGS. Phenome associations suggest that TUD-predisposed individuals may require comprehensive tobacco use prevention and management approaches to address underlying addictive tendencies.

Medical and genetic correlates of long-term buprenorphine treatment in the electronic health records

Despite the benefits associated with longer buprenorphine treatment duration (i.e., >180 days) (BTD) for opioid use disorder (OUD), retention remains poor. Research on the impact of co-occurring psychiatric issues on BTD has yielded mixed results. It is also unknown whether the genetic risk in the form of polygenic scores (PGS) for OUD and other comorbid conditions, including problematic alcohol use (PAU) are associated with BTD. We tested the association between somatic and psychiatric comorbidities and long BTD and determined whether PGS for OUD-related conditions was associated with BTD. The study included 6686 individuals with a buprenorphine prescription that lasted for less than 6 months (short-BTD) and 1282 individuals with a buprenorphine prescription that lasted for at least 6 months (long-BTD). Recorded diagnosis of substance addiction and disorders (Odds Ratio (95% CI) = 22.14 (21.88-22.41), P = 2.8 × 10-116), tobacco use disorder (OR (95% CI) = 23.4 (23.13-23.68), P = 4.5 × 10-111), and bipolar disorder (OR(95% CI) = 9.70 (9.48-9.92), P = 1.3 × 10-91), among others, were associated with longer BTD. The PGS of OUD and several OUD co-morbid conditions were associated with any buprenorphine prescription. A higher PGS for OUD (OR per SD increase in PGS (95%CI) = 1.43(1.16-1.77), P = 0.0009), loneliness (OR(95% CI) = 1.39(1.13-1.72), P = 0.002), problematic alcohol use (OR(95%CI) = 1.47(1.19-1.83), P = 0.0004), and externalizing disorders (OR(95%CI) = 1.52(1.23 to 1.89), P = 0.0001) was significantly associated with long-BTD. Associations between BTD and the PGS of depression, chronic pain, nicotine dependence, cannabis use disorder, and bipolar disorder did not survive correction for multiple testing. Longer BTD is associated with diagnoses of psychiatric and somatic conditions in the EHR, as is the genetic score for OUD, loneliness, problematic alcohol use, and externalizing disorders.

CYP2A6 associates with respiratory disease risk and younger age of diagnosis: a phenome-wide association Mendelian Randomization study

CYP2A6, a genetically variable enzyme, inactivates nicotine, activates carcinogens, and metabolizes many pharmaceuticals. Variation in CYP2A6 influences smoking behaviors and tobacco-related disease risk. This phenome-wide association study examined associations between a reconstructed version of our weighted genetic risk score (wGRS) for CYP2A6 activity with diseases in the UK Biobank (N = 395 887). Causal effects of phenotypic CYP2A6 activity (measured as the nicotine metabolite ratio: 3'-hydroxycotinine/cotinine) on the phenome-wide significant (PWS) signals were then estimated in two-sample Mendelian Randomization using the wGRS as the instrument. Time-to-diagnosis age was compared between faster versus slower CYP2A6 metabolizers for the PWS signals in survival analyses. In the total sample, six PWS signals were identified: two lung cancers and four obstructive respiratory diseases PheCodes, where faster CYP2A6 activity was associated with greater disease risk (Ps < 1 × 10-6). A significant CYP2A6-by-smoking status interaction was found (Psinteraction < 0.05); in current smokers, the same six PWS signals were found as identified in the total group, whereas no PWS signals were found in former or never smokers. In the total sample and current smokers, CYP2A6 activity causal estimates on the six PWS signals were significant in Mendelian Randomization (Ps < 5 × 10-5). Additionally, faster CYP2A6 metabolizer status was associated with younger age of disease diagnosis for the six PWS signals (Ps < 5 × 10-4, in current smokers). These findings support a role for faster CYP2A6 activity as a causal risk factor for lung cancers and obstructive respiratory diseases among current smokers, and a younger onset of these diseases. This research utilized the UK Biobank Resource.

Development of Portable Electronic Health Record Based Algorithms to Identify Individuals with Diabetic Retinopathy

Objectives

To develop, validate and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHR)s. Methods : We developed and validated EHR-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in three independent EHR systems: Vanderbilt University Medical Center Synthetic Derivative (VUMC), the VA Northeast Ohio Healthcare System (VANEOHS), and Massachusetts General Brigham (MGB). Cases were required to meet one of three criteria: 1) two or more dates with any DR ICD-9/10 code documented in the EHR, or 2) at least one affirmative health-factor or EPIC code for DR along with an ICD9/10 code for DR on a different day, or 3) at least one ICD-9/10 code for any DR occurring within 24 hours of an ophthalmology exam. Criteria for controls included affirmative evidence for diabetes as well as an ophthalmology exam.

Results

The algorithms, developed and evaluated in VUMC through manual chart review, resulted in a positive predictive value (PPV) of 0.93 for cases and negative predictive value (NPV) of 0.97 for controls. Implementation of algorithms yielded similar metrics in VANEOHS (PPV=0.94; NPV=0.86) and lower in MGB (PPV=0.84; NPV=0.76). In comparison, use of DR definition as implemented in Phenome-wide association study (PheWAS) in VUMC, yielded similar PPV (0.92) but substantially reduced NPV (0.48). Implementation of the algorithms to the Million Veteran Program identified over 62,000 DR cases with genetic data including 14,549 African Americans and 6,209 Hispanics with DR.

Conclusions/Discussion

We demonstrate the robustness of the algorithms at three separate health-care centers, with a minimum PPV of 0.84 and substantially improved NPV than existing high-throughput methods. We strongly encourage independent validation and incorporation of features unique to each EHR to enhance algorithm performance for DR cases and controls.

The Role of Circadian Rhythms and Sleep in Anorexia Nervosa

Importance

Observational studies have associated anorexia nervosa with circadian rhythms and sleep traits. However, the direction of causality and the extent of confounding by psychosocial comorbidities in these associations are unknown.

Objectives

To investigate the association between anorexia nervosa and circadian and sleep traits through mendelian randomization and to test the associations between a polygenic risk score (PRS) for anorexia nervosa and sleep disorders in a clinical biobank.

Design, Setting, and Participants

This genetic association study used bidirectional 2-sample mendelian randomization with summary-level genetic associations between anorexia nervosa (from the Psychiatric Genomics Consortium) and chronotype and sleep traits (primarily from the UK Biobank). The inverse-variance weighted method, in addition to other sensitivity approaches, was used. From the clinical Mass General Brigham (MGB) Biobank (n = 47 082), a PRS for anorexia nervosa was calculated for each patient and associations were tested with prevalent sleep disorders derived from electronic health records. Patients were of European ancestry. All analyses were performed between February and August 2023.

Exposures

Genetic instruments for anorexia nervosa, chronotype, daytime napping, daytime sleepiness, insomnia, and sleep duration.

Main Outcomes and Measures

Chronotype, sleep traits, risk of anorexia nervosa, and sleep disorders derived from a clinical biobank.

Results

The anorexia nervosa genome-wide association study included 16 992 cases (87.7%-97.4% female) and 55 525 controls (49.6%-63.4% female). Genetic liability for anorexia nervosa was associated with a more morning chronotype (β = 0.039; 95% CI, 0.006-0.072), and conversely, genetic liability for morning chronotype was associated with increased risk of anorexia nervosa (β = 0.178; 95% CI, 0.042-0.315). Associations were robust in sensitivity and secondary analyses. Genetic liability for insomnia was associated with increased risk of anorexia nervosa (β = 0.369; 95% CI, 0.073-0.666); however, sensitivity analyses indicated bias due to horizontal pleiotropy. The MGB Biobank analysis included 47 082 participants with a mean (SD) age of 60.4 (17.0) years and 25 318 (53.8%) were female. A PRS for anorexia nervosa was associated with organic or persistent insomnia in the MGB Biobank (odds ratio, 1.10; 95% CI, 1.03-1.17). No associations were evident for anorexia nervosa with other sleep traits.

Conclusions and Relevance

The results of this study suggest that in contrast to other metabo-psychiatric diseases, anorexia nervosa is a morningness eating disorder and further corroborate findings implicating insomnia in anorexia nervosa. Future studies in diverse populations and with subtypes of anorexia nervosa are warranted.

Subset scanning for multi-trait analysis using GWAS summary statistics

MOTIVATION

Multi-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits, which heavily rely on domain knowledge.

RESULTS

To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that identifies potential pleiotropic traits from a moderate or large number of traits (e.g. dozens to thousands) and tests the association between one genetic variant and the selected traits. TraitScan can handle either individual-level or summary-level GWAS data. We evaluated TraitScan using extensive simulations and found that it outperformed existing methods in terms of both testing power and trait selection when sparsity was low or modest. We then applied it to search for traits associated with Ewing Sarcoma, a rare bone tumor with peak onset in adolescence, among 754 traits in UK Biobank. Our analysis revealed a few promising traits worthy of further investigation, highlighting the use of TraitScan for more effective multi-trait analysis as biobanks emerge. We also extended TraitScan to search and test association with a polygenic risk score and genetically imputed gene expression.

AVAILABILITY AND IMPLEMENTATION

Our algorithm is implemented in an R package "TraitScan" available at https://github.com/RuiCao34/TraitScan.

Polygenic risk scores for cardiometabolic traits demonstrate importance of ancestry for predictive precision medicine

Polygenic risk scores (PRS) have predominantly been derived from genome-wide association studies (GWAS) conducted in European ancestry (EUR) individuals. In this study, we present an in-depth evaluation of PRS based on multi-ancestry GWAS for five cardiometabolic phenotypes in the Penn Medicine BioBank (PMBB) followed by a phenome-wide association study (PheWAS). We examine the PRS performance across all individuals and separately in African ancestry (AFR) and EUR ancestry groups. For AFR individuals, PRS derived using the multi-ancestry LD panel showed a higher effect size for four out of five PRSs (DBP, SBP, T2D, and BMI) than those derived from the AFR LD panel. In contrast, for EUR individuals, the multi-ancestry LD panel PRS demonstrated a higher effect size for two out of five PRSs (SBP and T2D) compared to the EUR LD panel. These findings underscore the potential benefits of utilizing a multi-ancestry LD panel for PRS derivation in diverse genetic backgrounds and demonstrate overall robustness in all individuals. Our results also revealed significant associations between PRS and various phenotypic categories. For instance, CAD PRS was linked with 18 phenotypes in AFR and 82 in EUR, while T2D PRS correlated with 84 phenotypes in AFR and 78 in EUR. Notably, associations like hyperlipidemia, renal failure, atrial fibrillation, coronary atherosclerosis, obesity, and hypertension were observed across different PRSs in both AFR and EUR groups, with varying effect sizes and significance levels. However, in AFR individuals, the strength and number of PRS associations with other phenotypes were generally reduced compared to EUR individuals. Our study underscores the need for future research to prioritize 1) conducting GWAS in diverse ancestry groups and 2) creating a cosmopolitan PRS methodology that is universally applicable across all genetic backgrounds. Such advances will foster a more equitable and personalized approach to precision medicine.

Identification of Clinical Drivers of Left Atrial Enlargement Through Genomics of Left Atrial Size

BACKGROUND

Greater left atrial size is associated with a higher incidence of cardiovascular disease and mortality, but the full spectrum of diagnoses associated with left atrial enlargement in sex-stratified clinical populations is not well known. Our study sought to identify genetic risk mechanisms affecting left atrial diameter (LAD) in a clinical cohort.

METHODS

Using Vanderbilt deidentified electronic health record, we studied 6163 females and 5993 males of European ancestry who had at least 1 LAD measure and available genotyping. A sex-stratified polygenic score was constructed for LAD variation and tested for association against 1680 International Classification of Diseases code-based phenotypes. Two-sample univariable and multivariable Mendelian randomization approaches were used to assess etiologic relationships between candidate associations and LAD.

RESULTS

A phenome-wide association study identified 25 International Classification of Diseases code-based diagnoses in females and 11 in males associated with a polygenic score of LAD (false discovery rate q<0.01), 5 of which were further evaluated by Mendelian randomization (waist circumference [WC], atrial fibrillation, heart failure, systolic blood pressure, and coronary artery disease). Sex-stratified differences in the genetic associations between risk factors and a polygenic score for LAD were observed (WC for females; heart failure, systolic blood pressure, atrial fibrillation, and WC for males). By multivariable Mendelian randomization, higher WC remained significantly associated with larger LAD in females, whereas coronary artery disease, WC, and atrial fibrillation remained significantly associated with larger LAD in males.

CONCLUSIONS

In a clinical population, we identified, by genomic approaches, potential etiologic risk factors for larger LAD. Further studies are needed to confirm the extent to which these risk factors may be modified to prevent or reverse adverse left atrial remodeling and the extent to which sex modifies these risk factors.

EVALUATING THE RELATIONSHIPS BETWEEN GENETIC ANCESTRY AND THE CLINICAL PHENOME

There is a desire in research to move away from the concept of race as a clinical factor because it is a societal construct used as an imprecise proxy for geographic ancestry. In this study, we leverage the biobank from Vanderbilt University Medical Center, BioVU, to investigate relationships between genetic ancestry proportion and the clinical phenome. For all samples in BioVU, we calculated six ancestry proportions based on 1000 Genomes references: eastern African (EAFR), western African (WAFR), northern European (NEUR), southern European (SEUR), eastern Asian (EAS), and southern Asian (SAS). From PheWAS, we found phecode categories significantly enriched neoplasms for EAFR, WAFR, and SEUR, and pregnancy complication in SEUR, NEUR, SAS, and EAS (p < 0.003). We then selected phenotypes hypertension (HTN) and atrial fibrillation (AFib) to further investigate the relationships between these phenotypes and EAFR, WAFR, SEUR, and NEUR using logistic regression modeling and non-linear restricted cubic spline modeling (RCS). For EAS and SAS, we chose renal failure (RF) for further modeling. The relationships between HTN and AFib and the ancestries EAFR, WAFR, and SEUR were best fit by the linear model (beta p < 1x10-4 for all) while the relationships with NEUR were best fit with RCS (HTN ANOVA p = 0.001, AFib ANOVA p < 1x10-4). For RF, the relationship with SAS was best fit with a linear model (beta p < 1x10-4) while RCS model was a better fit for EAS (ANOVA p < 1x10-4). In this study, we identify relationships between genetic ancestry and phenotypes that are best fit with non-linear modeling techniques. The assumption of linearity for regression modeling is integral for proper fitting of a model and there is no knowing a priori to modeling if the relationship is truly linear.

Systematic replication of smoking disease associations using survey responses and EHR data in the All of Us Research Program

OBJECTIVE

The All of Us Research Program (All of Us) aims to recruit over a million participants to further precision medicine. Essential to the verification of biobanks is a replication of known associations to establish validity. Here, we evaluated how well All of Us data replicated known cigarette smoking associations.

MATERIALS AND METHODS

We defined smoking exposure as follows: (1) an EHR Smoking exposure that used International Classification of Disease codes; (2) participant provided information (PPI) Ever Smoking; and, (3) PPI Current Smoking, both from the lifestyle survey. We performed a phenome-wide association study (PheWAS) for each smoking exposure measurement type. For each, we compared the effect sizes derived from the PheWAS to published meta-analyses that studied cigarette smoking from PubMed. We defined two levels of replication of meta-analyses: (1) nominally replicated: which required agreement of direction of effect size, and (2) fully replicated: which required overlap of confidence intervals.

RESULTS

PheWASes with EHR Smoking, PPI Ever Smoking, and PPI Current Smoking revealed 736, 492, and 639 phenome-wide significant associations, respectively. We identified 165 meta-analyses representing 99 distinct phenotypes that could be matched to EHR phenotypes. At P < .05, 74 were nominally replicated and 55 were fully replicated. At P < 2.68 × 10-5 (Bonferroni threshold), 58 were nominally replicated and 40 were fully replicated.

DISCUSSION

Most phenotypes found in published meta-analyses associated with smoking were nominally replicated in All of Us. Both survey and EHR definitions for smoking produced similar results.

CONCLUSION

This study demonstrated the feasibility of studying common exposures using All of Us data.

The citrate transporter SLC13A5 as a therapeutic target for kidney disease: evidence from Mendelian randomization to inform drug development

BACKGROUND

Solute carrier family 13 member 5 (SLC13A5) is a Na+-coupled citrate co-transporter that mediates entry of extracellular citrate into the cytosol. SLC13A5 inhibition has been proposed as a target for reducing progression of kidney disease. The aim of this study was to leverage the Mendelian randomization paradigm to gain insight into the effects of SLC13A5 inhibition in humans, towards prioritizing and informing clinical development efforts.

METHODS

The primary Mendelian randomization analyses investigated the effect of SLC13A5 inhibition on measures of kidney function, including creatinine and cystatin C-based measures of estimated glomerular filtration rate (creatinine-eGFR and cystatin C-eGFR), blood urea nitrogen (BUN), urine albumin-creatinine ratio (uACR), and risk of chronic kidney disease and microalbuminuria. Secondary analyses included a paired plasma and urine metabolome-wide association study, investigation of secondary traits related to SLC13A5 biology, a phenome-wide association study (PheWAS), and a proteome-wide association study. All analyses were compared to the effect of genetically predicted plasma citrate levels using variants selected from across the genome, and statistical sensitivity analyses robust to the inclusion of pleiotropic variants were also performed. Data were obtained from large-scale genetic consortia and biobanks, with sample sizes ranging from 5023 to 1,320,016 individuals.

RESULTS

We found evidence of associations between genetically proxied SLC13A5 inhibition and higher creatinine-eGFR (p = 0.002), cystatin C-eGFR (p = 0.005), and lower BUN (p = 3 × 10-4). Statistical sensitivity analyses robust to the inclusion of pleiotropic variants suggested that these effects may be a consequence of higher plasma citrate levels. There was no strong evidence of associations of genetically proxied SLC13A5 inhibition with uACR or risk of CKD or microalbuminuria. Secondary analyses identified evidence of associations with higher plasma calcium levels (p = 6 × 10-13) and lower fasting glucose (p = 0.02). PheWAS did not identify any safety concerns.

CONCLUSIONS

This Mendelian randomization analysis provides human-centric insight to guide clinical development of an SLC13A5 inhibitor. We identify plasma calcium and citrate as biologically plausible biomarkers of target engagement, and plasma citrate as a potential biomarker of mechanism of action. Our human genetic evidence corroborates evidence from various animal models to support effects of SLC13A5 inhibition on improving kidney function.

Differences in set-based tests for sparse alternatives when testing sets of outcomes compared to sets of explanatory factors in genetic association studies

Set-based association tests are widely popular in genetic association settings for their ability to aggregate weak signals and reduce multiple testing burdens. In particular, a class of set-based tests including the Higher Criticism, Berk-Jones, and other statistics have recently been popularized for reaching a so-called detection boundary when signals are rare and weak. Such tests have been applied in two subtly different settings: (a) associating a genetic variant set with a single phenotype and (b) associating a single genetic variant with a phenotype set. A significant issue in practice is the choice of test, especially when deciding between innovated and generalized type methods for detection boundary tests. Conflicting guidance is present in the literature. This work describes how correlation structures generate marked differences in relative operating characteristics for settings (a) and (b). The implications for study design are significant. We also develop novel power bounds that facilitate the aforementioned calculations and allow for analysis of individual testing settings. In more concrete terms, our investigation is motivated by translational expression quantitative trait loci (eQTL) studies in lung cancer. These studies involve both testing for groups of variants associated with a single gene expression (multiple explanatory factors) and testing whether a single variant is associated with a group of gene expressions (multiple outcomes). Results are supported by a collection of simulation studies and illustrated through lung cancer eQTL examples.

Polygenic risk alters the penetrance of monogenic kidney disease

Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease.

Mouse and human studies support DSTYK loss of function as a low-penetrance and variable expressivity risk factor for congenital urinary tract anomalies

PURPOSE

Previous work identified rare variants in DSTYK associated with human congenital anomalies of the kidney and urinary tract (CAKUT). Here, we present a series of mouse and human studies to clarify the association, penetrance, and expressivity of DSTYK variants.

METHODS

We phenotypically characterized Dstyk knockout mice of 3 separate inbred backgrounds and re-analyzed the original family segregating the DSTYK c.654+1G>A splice-site variant (referred to as "SSV" below). DSTYK loss of function (LOF) and SSVs were annotated in individuals with CAKUT, epilepsy, or amyotrophic lateral sclerosis vs controls. A phenome-wide association study analysis was also performed using United Kingdom Biobank (UKBB) data.

RESULTS

Results demonstrate ∼20% to 25% penetrance of obstructive uropathy, at least, in C57BL/6J and FVB/NJ Dstyk-/- mice. Phenotypic penetrance increased to ∼40% in C3H/HeJ mutants, with mild-to-moderate severity. Re-analysis of the original family segregating the rare SSV showed low penetrance (43.8%) and no alternative genetic causes for CAKUT. LOF DSTYK variants burden showed significant excess for CAKUT and epilepsy vs controls and an exploratory phenome-wide association study supported association with neurological disorders.

CONCLUSION

These data support causality for DSTYK LOF variants and highlights the need for large-scale sequencing studies (here >200,000 cases) to accurately assess causality for genes and variants to lowly penetrant traits with common population prevalence.

Phenome-wide association study and precision medicine of cardiovascular diseases in the post-COVID-19 era

SARS-CoV-2 infection causes injuries of not only the lungs but also the heart and endothelial cells in vasculature of multiple organs, and induces systemic inflammation and immune over-reactions, which makes COVID-19 a disease phenome that simultaneously affects multiple systems. Cardiovascular diseases (CVD) are intrinsic risk and causative factors for severe COVID-19 comorbidities and death. The wide-spread infection and reinfection of SARS-CoV-2 variants and the long-COVID may become a new common threat to human health and propose unprecedented impact on the risk factors, pathophysiology, and pharmacology of many diseases including CVD for a long time. COVID-19 has highlighted the urgent demand for precision medicine which needs new knowledge network to innovate disease taxonomy for more precise diagnosis, therapy, and prevention of disease. A deeper understanding of CVD in the setting of COVID-19 phenome requires a paradigm shift from the current phenotypic study that focuses on the virus or individual symptoms to phenomics of COVID-19 that addresses the inter-connectedness of clinical phenotypes, i.e., clinical phenome. Here, we summarize the CVD manifestations in the full clinical spectrum of COVID-19, and the phenome-wide association study of CVD interrelated to COVID-19. We discuss the underlying biology for CVD in the COVID-19 phenome and the concept of precision medicine with new phenomic taxonomy that addresses the overall pathophysiological responses of the body to the SARS-CoV-2 infection. We also briefly discuss the unique taxonomy of disease as Zheng-hou patterns in traditional Chinese medicine, and their potential implications in precision medicine of CVD in the post-COVID-19 era.

Prospective, multi-site study of healthcare utilization after actionable monogenic findings from clinical sequencing

As large-scale genomic screening becomes increasingly prevalent, understanding the influence of actionable results on healthcare utilization is key to estimating the potential long-term clinical impact. The eMERGE network sequenced individuals for actionable genes in multiple genetic conditions and returned results to individuals, providers, and the electronic health record. Differences in recommended health services (laboratory, imaging, and procedural testing) delivered within 12 months of return were compared among individuals with pathogenic or likely pathogenic (P/LP) findings to matched individuals with negative findings before and after return of results. Of 16,218 adults, 477 unselected individuals were found to have a monogenic risk for arrhythmia (n = 95), breast cancer (n = 96), cardiomyopathy (n = 95), colorectal cancer (n = 105), or familial hypercholesterolemia (n = 86). Individuals with P/LP results more frequently received services after return (43.8%) compared to before return (25.6%) of results and compared to individuals with negative findings (24.9%; p < 0.0001). The annual cost of qualifying healthcare services increased from an average of $162 before return to $343 after return of results among the P/LP group (p < 0.0001); differences in the negative group were non-significant. The mean difference-in-differences was $149 (p < 0.0001), which describes the increased cost within the P/LP group corrected for cost changes in the negative group. When stratified by individual conditions, significant cost differences were observed for arrhythmia, breast cancer, and cardiomyopathy. In conclusion, less than half of individuals received billed health services after monogenic return, which modestly increased healthcare costs for payors in the year following return.

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 genetic contribution to the comorbidity of depression and anxiety: a multi-site electronic health records study of almost 178 000 people

BACKGROUND

Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation.

METHODS

Diagnostic codes were extracted from electronic health records for four biobanks [N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry.

RESULTS

In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity.

CONCLUSIONS

This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations

Circulating metabolites act as biomarkers of dysregulated metabolism, and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. We examined the association between polygenic scores for 726 metabolites (derived from OMICSPRED) with 1,247 clinical phenotypes in 57,735 European ancestry and 15,754 African ancestry participants from the BioVU DNA Biobank. We probed significant relationships through Mendelian randomization (MR) using genetic instruments constructed from the METSIM Study, and validated significant MR associations using independent GWAS of candidate phenotypes. We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes among African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolite-phenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p<0.05). Validated findings included the metabolites bilirubin and X-21796 with cholelithiasis, phosphatidylcholine(16:0/22:5n3,18:1/20:4) and arachidonate(20:4n6) with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations

Circulating metabolites act as biomarkers of dysregulated metabolism, and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. We examined the association between polygenic scores for 726 metabolites (derived from OMICSPRED) with 1,247 clinical phenotypes in 57,735 European ancestry and 15,754 African ancestry participants from the BioVU DNA Biobank. We probed significant relationships through Mendelian randomization (MR) using genetic instruments constructed from the METSIM Study, and validated significant MR associations using independent GWAS of candidate phenotypes. We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes among African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolite-phenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p<0.05). Validated findings included the metabolites bilirubin and X-21796 with cholelithiasis, phosphatidylcholine(16:0/22:5n3,18:1/20:4) and arachidonate(20:4n6) with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

Exploring shared genetics between maximal oxygen uptake and disease: the HUNT study

Low cardiorespiratory fitness, measured as maximal oxygen uptake (V̇o2max), is associated with all-cause mortality and disease-specific morbidity and mortality and is estimated to have a large genetic component (∼60%). However, the underlying mechanisms explaining the associations are not known, and no association study has assessed shared genetics between directly measured V̇o2max and disease. We believe that identifying the mechanisms explaining how low V̇o2max is related to increased disease risk can contribute to prevention and therapy. We used a phenome-wide association study approach to test for shared genetics. A total of 64,479 participants from the Trøndelag Health Study (HUNT) were included. Genetic variants previously linked to V̇o2max were tested for association with diseases related to the cardiovascular system, diabetes, dementia, mental disorders, and cancer as well as clinical measurements and biomarkers from HUNT. In the total population, three single-nucleotide polymorphisms (SNPs) in and near the follicle-stimulating hormone receptor gene (FSHR) were found to be associated (false discovery rate < 0.05) with serum creatinine levels and one intronic SNP in the Rap-associating DIL domain gene (RADIL) with diabetes type 1 with neurological manifestations. In males, four intronic SNPs in the PBX/knotted homeobox 2 gene (PKNOX2) were found to be associated with endocarditis. None of the association tests in the female population reached overall statistical significance; the associations with the lowest P values included other cardiac conduction disorders, subdural hemorrhage, and myocarditis. The results might suggest shared genetics between V̇o2max and disease. However, further effort should be put into investigating the potential shared genetics between inborn V̇o2max and disease in larger cohorts to increase statistical power.NEW & NOTEWORTHY To our knowledge, this is the first genetic association study exploring how genes linked to cardiorespiratory fitness (CRF) relate to disease risk. By investigating shared genetics, we found indications that genetic variants linked to directly measured CRF also affect the level of blood creatinine, risk of diabetes, and endocarditis. Less certain findings showed that genetic variants of high CRF might cause lower body mass index, healthier HDL cholesterol, and lower resting heart rate.

The collaborative study on the genetics of alcoholism: Genetics

This review describes the genetic approaches and results from the family-based Collaborative Study on the Genetics of Alcoholism (COGA). COGA was designed during the linkage era to identify genes affecting the risk for alcohol use disorder (AUD) and related problems, and was among the first AUD-focused studies to subsequently adopt a genome-wide association (GWAS) approach. COGA's family-based structure, multimodal assessment with gold-standard clinical and neurophysiological data, and the availability of prospective longitudinal phenotyping continues to provide insights into the etiology of AUD and related disorders. These include investigations of genetic risk and trajectories of substance use and use disorders, phenome-wide association studies of loci of interest, and investigations of pleiotropy, social genomics, genetic nurture, and within-family comparisons. COGA is one of the few AUD genetics projects that includes a substantial number of participants of African ancestry. The sharing of data and biospecimens has been a cornerstone of the COGA project, and COGA is a key contributor to large-scale GWAS consortia. COGA's wealth of publicly available genetic and extensive phenotyping data continues to provide a unique and adaptable resource for our understanding of the genetic etiology of AUD and related traits.

Relevance of body composition in phenotyping the obesities

Obesity is the most extended metabolic alteration worldwide increasing the risk for the development of cardiometabolic alterations such as type 2 diabetes, hypertension, and dyslipidemia. Body mass index (BMI) remains the most frequently used tool for classifying patients with obesity, but it does not accurately reflect body adiposity. In this document we review classical and new classification systems for phenotyping the obesities. Greater accuracy of and accessibility to body composition techniques at the same time as increased knowledge and use of cardiometabolic risk factors is leading to a more refined phenotyping of patients with obesity. It is time to incorporate these advances into routine clinical practice to better diagnose overweight and obesity, and to optimize the treatment of patients living with obesity.

PheWAS and cross-disorder analysis reveal genetic architecture, pleiotropic loci and phenotypic correlations across 11 autoimmune disorders

Introduction

Autoimmune disorders (ADs) are a group of about 80 disorders that occur when self-attacking autoantibodies are produced due to failure in the self-tolerance mechanisms. ADs are polygenic disorders and associations with genes both in the human leukocyte antigen (HLA) region and outside of it have been described. Previous studies have shown that they are highly comorbid with shared genetic risk factors, while epidemiological studies revealed associations between various lifestyle and health-related phenotypes and ADs.

Methods

Here, for the first time, we performed a comparative polygenic risk score (PRS) - Phenome Wide Association Study (PheWAS) for 11 different ADs (Juvenile Idiopathic Arthritis, Primary Sclerosing Cholangitis, Celiac Disease, Multiple Sclerosis, Rheumatoid Arthritis, Psoriasis, Myasthenia Gravis, Type 1 Diabetes, Systemic Lupus Erythematosus, Vitiligo Late Onset, Vitiligo Early Onset) and 3,254 phenotypes available in the UK Biobank that include a wide range of socio-demographic, lifestyle and health-related outcomes. Additionally, we investigated the genetic relationships of the studied ADs, calculating their genetic correlation and conducting cross-disorder GWAS meta-analyses for the observed AD clusters.

Results

In total, we identified 508 phenotypes significantly associated with at least one AD PRS. 272 phenotypes were significantly associated after excluding variants in the HLA region from the PRS estimation. Through genetic correlation and genetic factor analyses, we identified four genetic factors that run across studied ADs. Cross-trait meta-analyses within each factor revealed pleiotropic genome-wide significant loci.

Discussion

Overall, our study confirms the association of different factors with genetic susceptibility for ADs and reveals novel observations that need to be further explored.