R PheWAS: data analysis and plotting tools for phenome-wide association studies in the R environment

Comorbidity Profiles of Posttraumatic Stress Disorder Across the Medical Phenome

Background

Previous epidemiological research has linked posttraumatic stress disorder (PTSD) with specific physical health problems, but the comprehensive landscape of medical conditions associated with PTSD remains uncharacterized. Electronic health records provide an opportunity to overcome clinical knowledge gaps and uncover associations with biological relevance that potentially vary by sex.

Methods

PTSD was defined among biobank participants (N = 145,959) in 3 major healthcare systems using 2 ICD code-based definitions: broad (≥1 PTSD or acute stress codes vs. 0; n cases = 16,706) and narrow (≥2 PTSD codes vs. 0; n cases = 3325). Using a phenome-wide association study design, we tested associations between each PTSD definition and all prevalent disease umbrella categories, i.e., phecodes. We also conducted sex-stratified phenome-wide association study analyses including a sex × diagnosis interaction term in each logistic regression.

Results

A substantial number of phecodes were significantly associated with PTSDNarrow (61%) and PTSDBroad (83%). While the strongest associations were shared between the 2 definitions, PTSDBroad captured 334 additional phecodes not significantly associated with PTSDNarrow and exhibited a wider range of significantly associated phecodes across various categories, including respiratory, genitourinary, and circulatory conditions. Sex differences were observed in that PTSDBroad was more strongly associated with osteoporosis, respiratory failure, hemorrhage, and pulmonary heart disease among male patients and with urinary tract infection, acute pharyngitis, respiratory infections, and overweight among female patients.

Conclusions

This study provides valuable insights into a diverse range of comorbidities associated with PTSD, including both known and novel associations, while highlighting the influence of sex differences and the impact of defining PTSD using electronic health records.

Clinical and genetic contributions to medical comorbidity in bipolar disorder: a study using electronic health records-linked biobank data

Bipolar disorder is a chronic and complex polygenic disease with high rates of comorbidity. However, the independent contribution of either diagnosis or genetic risk of bipolar disorder to the medical comorbidity profile of individuals with the disease remains unresolved. Here, we conducted a multi-step phenome-wide association study (PheWAS) of bipolar disorder using phenomes derived from the electronic health records of participants enrolled in the Mayo Clinic Biobank and the Mayo Clinic Bipolar Disorder Biobank. First, we explored the conditions associated with a diagnosis of bipolar disorder by conducting a phenotype-based PheWAS followed by LASSO-penalized regression to account for correlations within the phenome. Then, we explored the conditions associated with bipolar disorder polygenic risk score (BD-PRS) using a PRS-based PheWAS with a sequential exclusion approach to account for the possibility that diagnosis, instead of genetic risk, may drive such associations. 53,386 participants (58.7% women) with a mean age at analysis of 67.8 years (SD = 15.6) were included. A bipolar disorder diagnosis (n = 1479) was associated with higher rates of psychiatric conditions, injuries and poisonings, endocrine/metabolic and neurological conditions, viral hepatitis C, and asthma. BD-PRS was associated with psychiatric comorbidities but, in contrast, had no positive associations with general medical conditions. While our findings warrant confirmation with longitudinal-prospective studies, the limited associations between bipolar disorder genetics and medical conditions suggest that shared environmental effects or environmental consequences of diagnosis may have a greater impact on the general medical comorbidity profile of individuals with bipolar disorder than its genetic risk.

Adaptive selection at G6PD and disparities in diabetes complications

Diabetes complications occur at higher rates in individuals of African ancestry. Glucose-6-phosphate dehydrogenase deficiency (G6PDdef), common in some African populations, confers malaria resistance, and reduces hemoglobin A1c (HbA1c) levels by shortening erythrocyte lifespan. In a combined-ancestry genome-wide association study of diabetic retinopathy, we identified nine loci including a G6PDdef causal variant, rs1050828 -T (Val98Met), which was also associated with increased risk of other diabetes complications. The effect of rs1050828 -T on retinopathy was fully mediated by glucose levels. In the years preceding diabetes diagnosis and insulin prescription, glucose levels were significantly higher and HbA1c significantly lower in those with versus without G6PDdef. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, participants with G6PDdef had significantly higher hazards of incident retinopathy and neuropathy. At the same HbA1c levels, G6PDdef participants in both ACCORD and the Million Veteran Program had significantly increased risk of retinopathy. We estimate that 12% and 9% of diabetic retinopathy and neuropathy cases, respectively, in participants of African ancestry are due to this exposure. Across continentally defined ancestral populations, the differences in frequency of rs1050828 -T and other G6PDdef alleles contribute to disparities in diabetes complications. Diabetes management guided by glucose or potentially genotype-adjusted HbA1c levels could lead to more timely diagnoses and appropriate intensification of therapy, decreasing the risk of diabetes complications in patients with G6PDdef alleles.

Genome-wide association studies of coffee intake in UK/US participants of European ancestry uncover cohort-specific genetic associations

Coffee is one of the most widely consumed beverages. We performed a genome-wide association study (GWAS) of coffee intake in US-based 23andMe participants (N = 130,153) and identified 7 significant loci, with many replicating in three multi-ancestral cohorts. We examined genetic correlations and performed a phenome-wide association study across hundreds of biomarkers, health, and lifestyle traits, then compared our results to the largest available GWAS of coffee intake from the UK Biobank (UKB; N = 334,659). We observed consistent positive genetic correlations with substance use and obesity in both cohorts. Other genetic correlations were discrepant, including positive genetic correlations between coffee intake and psychiatric illnesses, pain, and gastrointestinal traits in 23andMe that were absent or negative in the UKB, and genetic correlations with cognition that were negative in 23andMe but positive in the UKB. Phenome-wide association study using polygenic scores of coffee intake derived from 23andMe or UKB summary statistics also revealed consistent associations with increased odds of obesity- and red blood cell-related traits, but all other associations were cohort-specific. Our study shows that the genetics of coffee intake associate with substance use and obesity across cohorts, but also that GWAS performed in different populations could capture cultural differences in the relationship between behavior and genetics.

A phenome-wide association and factorial Mendelian randomization study on the repurposing of uric acid-lowering drugs for cardiovascular outcomes

Uric acid has been linked to various disease outcomes. However, it remains unclear whether uric acid-lowering therapy could be repurposed as a treatment for conditions other than gout. We first performed both observational phenome-wide association study (Obs-PheWAS) and polygenic risk score PheWAS (PRS-PheWAS) to identify associations of uric acid levels with a wide range of disease outcomes. Then, trajectory analysis was conducted to explore temporal progression patterns of the observed disease outcomes. Finally, we investigated whether uric acid-lowering drugs could be repurposed using a factorial Mendelian randomization (MR) study design. A total of 41 overlapping phenotypes associated with uric acid levels were identified by both Obs- and PRS- PheWASs, primarily cardiometabolic diseases. The trajectory analysis illustrated how elevated uric acid levels contribute to cardiometabolic diseases, and finally death. Meanwhile, we found that uric acid-lowering drugs exerted a protective role in reducing the risk of coronary atherosclerosis (OR = 0.96, 95%CI: 0.93, 1.00, P = 0.049), congestive heart failure (OR = 0.64, 95%CI: 0.42, 0.99, P = 0.043), occlusion of cerebral arteries (OR = 0.93, 95%CI: 0.87, 1.00, P = 0.044) and peripheral vascular disease (OR = 0.60, 95%CI: 0.38, 0.94, P = 0.025). Furthermore, the combination of uric acid-lowering therapy (e.g. xanthine oxidase inhibitors) with antihypertensive treatment (e.g. calcium channel blockers) exerted additive effects and was associated with a 6%, 8%, 8%, 10% reduction in risk of coronary atherosclerosis, heart failure, occlusion of cerebral arteries and peripheral vascular disease, respectively. Our findings support a role of elevated uric acid levels in advancing cardiovascular dysfunction and identify potential repurposing opportunities for uric acid-lowering drugs in cardiovascular treatment.

Developing a phenotype risk score for tic disorders in a large, clinical biobank

Tics are a common feature of early-onset neurodevelopmental disorders, characterized by involuntary and repetitive movements or sounds. Despite affecting up to 2% of children and having a genetic contribution, the underlying causes remain poorly understood. In this study, we leverage dense phenotype information to identify features (i.e., symptoms and comorbid diagnoses) of tic disorders within the context of a clinical biobank. Using de-identified electronic health records (EHRs), we identified individuals with tic disorder diagnosis codes. We performed a phenome-wide association study (PheWAS) to identify the EHR features enriched in tic cases versus controls (n = 1406 and 7030; respectively) and found highly comorbid neuropsychiatric phenotypes, including: obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorder, and anxiety (p < 7.396 × 10-5). These features (among others) were then used to generate a phenotype risk score (PheRS) for tic disorder, which was applied across an independent set of 90,051 individuals. A gold standard set of tic disorder cases identified by an EHR algorithm and confirmed by clinician chart review was then used to validate the tic disorder PheRS; the tic disorder PheRS was significantly higher among clinician-validated tic cases versus non-cases (p = 4.787 × 10-151; β = 1.68; SE = 0.06). Our findings provide support for the use of large-scale medical databases to better understand phenotypically complex and underdiagnosed conditions, such as tic disorders.

Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits

OBJECTIVE

Treatment-resistant depression (TRD) occurs in roughly one-third of all individuals with major depressive disorder (MDD). Although research has suggested a significant common variant genetic component of liability to TRD, with heritability estimated at 8% when compared with non-treatment-resistant MDD, no replicated genetic loci have been identified, and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. The objective of this study was to perform a well-powered genetic study of TRD.

METHODS

Using receipt of electroconvulsive therapy (ECT) as a surrogate for TRD, the authors applied standard machine learning methods to electronic health record data to derive predicted probabilities of receiving ECT. These probabilities were then applied as a quantitative trait in a genome-wide association study of 154,433 genotyped patients across four large biobanks.

RESULTS

Heritability estimates ranged from 2% to 4.2%, and significant genetic overlap was observed with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits, and body mass index. Two genome-wide significant loci were identified, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications.

CONCLUSIONS

This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.

To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice

OBJECTIVES

To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data.

MATERIALS AND METHODS

We mapped diagnosis (ICD code) data to standardized phecodes from 3 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 represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses 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 estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable 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 a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis.

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.

PYPE: A pipeline for phenome-wide association and Mendelian randomization in investigator-driven biobank scale analysis

Phenome-wide association studies (PheWASs) serve as a way of documenting the relationship between genotypes and multiple phenotypes, helping to uncover unexplored genotype-phenotype associations (known as pleiotropy). Secondly, Mendelian randomization (MR) can be harnessed to make causal statements about a pair of phenotypes by comparing their genetic architecture. Thus, approaches that automate both PheWASs and MR can enhance biobank-scale analyses, circumventing the need for multiple tools by providing a comprehensive, end-to-end tool to drive scientific discovery. To this end, we present PYPE, a Python pipeline for running, visualizing, and interpreting PheWASs. PYPE utilizes input genotype or phenotype files to automatically estimate associations between the chosen independent variables and phenotypes. PYPE can also produce a variety of visualizations and can be used to identify nearby genes and functional consequences of significant associations. Finally, PYPE can identify possible causal relationships between phenotypes using MR under a variety of causal effect modeling scenarios.

Patterns and correlates of mental healthcare utilization during the COVID-19 pandemic among individuals with pre-existing mental disorder

How did mental healthcare utilization change during the COVID-19 pandemic period among individuals with pre-existing mental disorder? Understanding utilization patterns of these at-risk individuals and identifying those most likely to exhibit increased utilization could improve patient stratification and efficient delivery of mental health services. This study leveraged large-scale electronic health record (EHR) data to describe mental healthcare utilization patterns among individuals with pre-existing mental disorder before and during the COVID-19 pandemic and identify correlates of high mental healthcare utilization. Using EHR data from a large healthcare system in Massachusetts, we identified three "pre-existing mental disorder" groups (PMD) based on having a documented mental disorder diagnosis within the 6 months prior to the March 2020 lockdown, related to: (1) stress-related disorders (e.g., depression, anxiety) (N = 115,849), (2) serious mental illness (e.g., schizophrenia, bipolar disorders) (N = 11,530), or (3) compulsive behavior disorders (e.g., eating disorder, OCD) (N = 5,893). We also identified a "historical comparison" group (HC) for each PMD (N = 113,604, 11,758, and 5,387, respectively) from the previous year (2019). We assessed the monthly number of mental healthcare visits from March 13 to December 31 for PMDs in 2020 and HCs in 2019. Phenome-wide association analyses (PheWAS) were used to identify clinical correlates of high mental healthcare utilization. We found the overall number of mental healthcare visits per patient during the pandemic period in 2020 was 10-12% higher than in 2019. The majority of increased visits was driven by a subset of high mental healthcare utilizers (top decile). PheWAS results indicated that correlates of high utilization (prior mental disorders, chronic pain, insomnia, viral hepatitis C, etc.) were largely similar before and during the pandemic, though several conditions (e.g., back pain) were associated with high utilization only during the pandemic. Limitations included that we were not able to examine other risk factors previously shown to influence mental health during the pandemic (e.g., social support, discrimination) due to lack of social determinants of health information in EHR data. Mental healthcare utilization among patients with pre-existing mental disorder increased overall during the pandemic, likely due to expanded access to telemedicine. Given that clinical correlates of high mental healthcare utilization in a major hospital system were largely similar before and during the COVID-19 pandemic, resource stratification based on known risk factor profiles may aid hospitals in responding to heightened mental healthcare needs during a pandemic.

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

Background

Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administrative purposes raise questions about the consistency and reproducibility of EHR-based multimorbidity research.

Methods

Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combining data from multiple sources for online multimorbidity analysis.

Findings

Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies (Kendall's τ = 0.643) and comorbidity strengths (Pearson ρ = 0.79). Consistent network statistics across EHRs suggest similar structures of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically relevant disease relationships and provide novel insights.

Interpretation

Our findings demonstrate the robustness of large-scale EHR data for studying phenome-wide multimorbidities. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared biology of diseases. The consistent core-periphery structure offers analytical insights to discover complex disease interactions. This work also sets the stage for advanced disease modeling, with implications for precision medicine.

Funding

VUMC Biostatistics Development Award, the National Institutes of Health, and the VA CSRD.

Integrating Multiple Database Resources to Elucidate the Gene Flow in Southeast Asian Pig Populations

The domestic pig (Sus scrofa) and its subfamilies have experienced long-term and extensive gene flow, particularly in Southeast Asia. Here, we analyzed 236 pigs, focusing on Yunnan indigenous, European commercial, East Asian, and Southeast Asian breeds, using the Pig Genomics Reference Panel (PGRP v1) of Pig Genotype-Tissue Expression (PigGTEx) to investigate gene flow and associated complex traits by integrating multiple database resources. In this study, we discovered evidence of admixtures from European pigs into the genome of Yunnan indigenous pigs. Additionally, we hypothesized that a potential conceptual gene flow route that may have contributed to the genetic composition of the Diannan small-ear pig is a gene exchange from the Vietnamese pig. Based on the most stringent gene introgression scan using the fd statistic, we identified three specific loci on chromosome 8, ranging from 51.65 to 52.45 Mb, which exhibited strong signatures of selection and harbored the NAF1, NPY1R, and NPY5R genes. These genes are associated with complex traits, such as fat mass, immunity, and litter weight, in pigs, as supported by multiple bio-functionalization databases. We utilized multiple databases to explore the potential dynamics of genetic exchange in Southeast Asian pig populations and elucidated specific gene functionalities.

The landscape of rare genetic variation associated with inflammatory bowel disease and Parkinson's disease comorbidity

BACKGROUND

Inflammatory bowel disease (IBD) and Parkinson's disease (PD) are chronic disorders that have been suggested to share common pathophysiological processes. LRRK2 has been implicated as playing a role in both diseases. Exploring the genetic basis of the IBD-PD comorbidity through studying high-impact rare genetic variants can facilitate the identification of the novel shared genetic factors underlying this comorbidity.

METHODS

We analyzed whole exomes from the BioMe BioBank and UK Biobank, and whole genomes from a cohort of 67 European patients diagnosed with both IBD and PD to examine the effects of LRRK2 missense variants on IBD, PD and their co-occurrence (IBD-PD). We performed optimized sequence kernel association test (SKAT-O) and network-based heterogeneity clustering (NHC) analyses using high-impact rare variants in the IBD-PD cohort to identify novel candidate genes, which we further prioritized by biological relatedness approaches. We conducted phenome-wide association studies (PheWAS) employing BioMe BioBank and UK Biobank whole exomes to estimate the genetic relevance of the 14 prioritized genes to IBD-PD.

RESULTS

The analysis of LRRK2 missense variants revealed significant associations of the G2019S and N2081D variants with IBD-PD in addition to several other variants as potential contributors to increased or decreased IBD-PD risk. SKAT-O identified two significant genes, LRRK2 and IL10RA, and NHC identified 6 significant gene clusters that are biologically relevant to IBD-PD. We observed prominent overlaps between the enriched pathways in the known IBD, PD, and candidate IBD-PD gene sets. Additionally, we detected significantly enriched pathways unique to the IBD-PD, including MAPK signaling, LPS/IL-1 mediated inhibition of RXR function, and NAD signaling. Fourteen final candidate IBD-PD genes were prioritized by biological relatedness methods. The biological importance scores estimated by protein-protein interaction networks and pathway and ontology enrichment analyses indicated the involvement of genes related to immunity, inflammation, and autophagy in IBD-PD. Additionally, PheWAS provided support for the associations of candidate genes with IBD and PD.

CONCLUSIONS

Our study confirms and uncovers new LRRK2 associations in IBD-PD. The identification of novel inflammation and autophagy-related genes supports and expands previous findings related to IBD-PD pathogenesis, and underscores the significance of therapeutic interventions for reducing systemic inflammation.

Discovery of a polymorphic gene fusion via bottom-up chimeric RNA prediction

Gene fusions and their chimeric products are commonly linked with cancer. However, recent studies have found chimeric transcripts in non-cancer tissues and cell lines. Large-scale efforts to annotate structural variations have identified gene fusions capable of generating chimeric transcripts even in normal tissues. In this study, we present a bottom-up approach targeting population-specific chimeric RNAs, identifying 58 such instances in the GTEx cohort, including notable cases such as SUZ12P1-CRLF3, TFG-ADGRG7 and TRPM4-PPFIA3, which possess distinct patterns across different ancestry groups. We provide direct evidence for an additional 29 polymorphic chimeric RNAs with associated structural variants, revealing 13 novel rare structural variants. Additionally, we utilize the All of Us dataset and a large cohort of clinical samples to characterize the association of the SUZ12P1-CRLF3-causing variant with patient phenotypes. Our study showcases SUZ12P1-CRLF3 as a representative example, illustrating the identification of elusive structural variants by focusing on those producing population-specific fusion transcripts.

A new test for trait mean and variance detects unreported loci for blood-pressure variation

Variability in quantitative traits has clinical, ecological, and evolutionary significance. Most genetic variants identified for complex quantitative traits have only a detectable effect on the mean of trait. We have developed the mean-variance test (MVtest) to simultaneously model the mean and log-variance of a quantitative trait as functions of genotypes and covariates by using estimating equations. The advantages of MVtest include the facts that it can detect effect modification, that multiple testing can follow conventional thresholds, that it is robust to non-normal outcomes, and that association statistics can be meta-analyzed. In simulations, we show control of type I error of MVtest over several alternatives. We identified 51 and 37 previously unreported associations for effects on blood-pressure variance and mean, respectively, in the UK Biobank. Transcriptome-wide association studies revealed 633 significant unique gene associations with blood-pressure mean variance. MVtest is broadly applicable to studies of complex quantitative traits and provides an important opportunity to detect novel loci.

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

Background

Patients with schizophrenia have substantial comorbidity that contributes to reduced life expectancy of 10 to 20 years. Identifying modifiable comorbidities could improve rates of premature mortality. Conditions that frequently co-occur but lack shared genetic risk with schizophrenia are more likely to be products of treatment, behavior, or environmental factors and therefore are enriched for potentially modifiable associations.

Methods

Phenome-wide comorbidity was calculated from electronic health records of 250,000 patients across 2 independent health care institutions (Vanderbilt University Medical Center and Mass General Brigham); associations with schizophrenia polygenic risk scores were calculated across the same phenotypes in linked biobanks.

Results

Schizophrenia comorbidity was significantly correlated across institutions (r = 0.85), and the 77 identified comorbidities were consistent with prior literature. Overall, comorbidity and polygenic risk score associations were significantly correlated (r = 0.55, p = 1.29 × 10-118). However, directly testing for the absence of genetic effects identified 36 comorbidities that had significantly equivalent schizophrenia polygenic risk score distributions between cases and controls. This set included phenotypes known to be consequences of antipsychotic medications (e.g., movement disorders) or of the disease such as reduced hygiene (e.g., diseases of the nail), thereby validating the approach. It also highlighted phenotypes with less clear causal relationships and minimal genetic effects such as tobacco use disorder and diabetes.

Conclusions

This work demonstrates the consistency and robustness of electronic health record-based schizophrenia comorbidities across independent institutions and with the existing literature. It identifies known and novel comorbidities with an absence of shared genetic risk, indicating other causes that may be modifiable and where further study of causal pathways could improve outcomes for patients.

Abdominal CT metrics in 17,646 patients reveal associations between myopenia, myosteatosis, and medical phenotypes: a phenome-wide association study

BACKGROUND

Deep learning facilitates large-scale automated imaging evaluation of body composition. However, associations of body composition biomarkers with medical phenotypes have been underexplored. Phenome-wide association study (PheWAS) techniques search for medical phenotypes associated with biomarkers. A PheWAS integrating large-scale analysis of imaging biomarkers and electronic health record (EHR) data could discover previously unreported associations and validate expected associations. Here we use PheWAS methodology to determine the association of abdominal CT-based skeletal muscle metrics with medical phenotypes in a large North American cohort.

METHODS

An automated deep learning pipeline was used to measure skeletal muscle index (SMI; biomarker of myopenia) and skeletal muscle density (SMD; biomarker of myosteatosis) from abdominal CT scans of adults between 2012 and 2018. A PheWAS was performed with logistic regression using patient sex and age as covariates to assess for associations between CT-derived muscle metrics and 611 common EHR-derived medical phenotypes. PheWAS P values were considered significant at a Bonferroni corrected threshold (α = 0.05/1222).

FINDINGS

17,646 adults (mean age, 56 years ± 19 [SD]; 57.5% women) were included. CT-derived SMI was significantly associated with 268 medical phenotypes; SMD with 340 medical phenotypes. Previously unreported associations with the highest magnitude of significance included higher SMI with decreased cardiac dysrhythmias (OR [95% CI], 0.59 [0.55-0.64]; P < 0.0001), decreased epilepsy (OR, 0.59 [0.50-0.70]; P < 0.0001), and increased elevated prostate-specific antigen (OR, 1.84 [1.47-2.31]; P < 0.0001), and higher SMD with decreased decubitus ulcers (OR, 0.36 [0.31-0.42]; P < 0.0001), sleep disorders (OR, 0.39 [0.32-0.47]; P < 0.0001), and osteomyelitis (OR, 0.43 [0.36-0.52]; P < 0.0001).

INTERPRETATION

PheWAS methodology reveals previously unreported associations between CT-derived biomarkers of myopenia and myosteatosis and EHR medical phenotypes. The high-throughput PheWAS technique applied on a population scale can generate research hypotheses related to myopenia and myosteatosis and can be adapted to research possible associations of other imaging biomarkers with hundreds of EHR medical phenotypes.

FUNDING

National Institutes of Health, Stanford AIMI-HAI pilot grant, Stanford Precision Health and Integrated Diagnostics, Stanford Cardiovascular Institute, Stanford Center for Digital Health, and Stanford Knight-Hennessy Scholars.

Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases

Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

Genome-wide characterization of circulating metabolic biomarkers

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

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.

Childhood obesity and risk of Alzheimer's disease: a Mendelian randomization study

BACKGROUND

Midlife obesity is a modifiable risk factor for Alzheimer's disease. However, the association between childhood obesity and Alzheimer's disease remains largely unknown. Therefore, we conducted a mendelian randomization analysis (MR) to assess the causal link between childhood obesity and Alzheimer's disease.

METHODS

Using summary statistics from publicly available genome-wide association studies (GWAS) database, we explored the genetic link between childhood obesity and Alzheimer's disease through a two-sample MR. The primary analysis employed the inverse-variance weighted (IVW) method. To complement our findings, we also employed MR-Egger, weighted median, simple model, and weighted model methods for MR estimates. Furthermore, we conducted Cochrane's Q-statistic test, Egger intercept test, and a leave-one-out sensitivity test to ensure the robustness and reliability of our results.

RESULTS

The IVW analysis yielded non-significant results, indicating no significant genetic association between childhood obesity and Alzheimer's disease (OR = 0.958, 95% CI = 0.910-1.008, p = 0.095). Consistent with this, the results from MR-Egger, the weighted median, simple model, and weighted model approaches all supported these findings. Furthermore, we did not detect any signs of heterogeneity or pleiotropy, and our leave-one-out analysis confirmed that no single nucleotide polymorphisms had a substantial impact on the reliability of our results.

CONCLUSIONS

The evidence from our MR analyses suggests that there is no causal effect of childhood obesity on the risk of Alzheimer's disease.

Phenome-wide Mendelian randomization analysis reveals multiple health comorbidities of coeliac disease

BACKGROUND

Coeliac disease (CeD) has been associated with a broad range of diseases in observational data; however, whether these associations are causal remains undetermined. We conducted a phenome-wide Mendelian randomization analysis (MR-PheWAS) to investigate the comorbidities of CeD.

METHODS

Single nucleotide polymorphisms (SNPs) associated with CeD at the genome-wide significance threshold and without linkage disequilibrium (R2 <0.001) were selected from a genome-wide association study including 12,041 CeD cases as the instrumental variables. We first constructed a polygenic risk score for CeD and estimated its associations with 1060 unique clinical outcomes in the UK Biobank study (N = 385,917). We then used two-sample MR analysis to replicate the identified associations using data from the FinnGen study (N = 377,277). We performed a secondary analysis using a genetic instrument without extended MHC gene SNPs.

FINDINGS

Genetic liability to CeD was associated with 68 clinical outcomes in the UK Biobank, and 38 of the associations were replicated in the FinnGen study. Genetic liability to CeD was associated with a higher risk of several autoimmune diseases (type 1 diabetes and its complications, Graves' disease, Sjögren syndrome, chronic hepatitis, systemic and cutaneous lupus erythematosus, and sarcoidosis), non-Hodgkin's lymphoma, and osteoporosis and a lower risk of prostate diseases. The associations for type 1 diabetes and non-Hodgkin's lymphoma attenuated when excluding SNPs in the MHC region, indicating shared genetic aetiology.

INTERPRETATION

This study uncovers multiple clinical outcomes associated with genetic liability to CeD, which suggests the necessity of comorbidity monitoring among this population.

FUNDING

This project was funded by Karolinska Institutet and the Swedish Research Council.

Genetic investigation into the broad health implications of caffeine: evidence from phenome-wide, proteome-wide and metabolome-wide Mendelian randomization

BACKGROUND

Caffeine is one of the most utilized drugs in the world, yet its clinical effects are not fully understood. Circulating caffeine levels are influenced by the interplay between consumption behaviour and metabolism. This study aimed to investigate the effects of circulating caffeine levels by considering genetically predicted variation in caffeine metabolism.

METHODS

Leveraging genetic variants related to caffeine metabolism that affect its circulating levels, we investigated the clinical effects of plasma caffeine in a phenome-wide association study (PheWAS). We validated novel findings using a two-sample Mendelian randomization framework and explored the potential mechanisms underlying these effects in proteome-wide and metabolome-wide Mendelian randomization.

RESULTS

Higher levels of genetically predicted circulating caffeine among caffeine consumers were associated with a lower risk of obesity (odds ratio (OR) per standard deviation increase in caffeine = 0.97, 95% confidence interval (CI) CI: 0.95-0.98, p = 2.47 × 10-4), osteoarthrosis (OR = 0.97, 95% CI: 0.96-0.98, P=1.10 × 10-8) and osteoarthritis (OR: 0.97, 95% CI: 0.96 to 0.98, P = 1.09 × 10-6). Approximately one third of the protective effect of plasma caffeine on osteoarthritis risk was estimated to be mediated through lower bodyweight. Proteomic and metabolomic perturbations indicated lower chronic inflammation, improved lipid profiles, and altered protein and glycogen metabolism as potential biological mechanisms underlying these effects.

CONCLUSIONS

We report novel evidence suggesting that long-term increases in circulating caffeine may reduce bodyweight and the risk of osteoarthrosis and osteoarthritis. We confirm prior genetic evidence of a protective effect of plasma caffeine on risk of overweight and obesity. Further clinical study is warranted to understand the translational relevance of these findings before clinical practice or lifestyle interventions related to caffeine consumption are introduced.

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.

Multi-class Modeling Identifies Shared Genetic Risk for Late-onset Epilepsy and Alzheimer's Disease

Background

Previous studies have established a strong link between late-onset epilepsy (LOE) and Alzheimer's disease (AD). However, their shared genetic risk beyond the APOE gene remains unclear. Our study sought to examine the shared genetic factors of AD and LOE, interpret the biological pathways involved, and evaluate how AD onset may be mediated by LOE and shared genetic risks.

Methods

We defined phenotypes using phecodes mapped from diagnosis codes, with patients' records aged 60-90. A two-step Least Absolute Shrinkage and Selection Operator (LASSO) workflow was used to identify shared genetic variants based on prior AD GWAS integrated with functional genomic data. We calculated an AD-LOE shared risk score and used it as a proxy in a causal mediation analysis. We used electronic health records from an academic health center (UCLA Health) for discovery analyses and validated our findings in a multi-institutional EHR database (All of Us).

Results

The two-step LASSO method identified 34 shared genetic loci between AD and LOE, including the APOE region. These loci were mapped to 65 genes, which showed enrichment in molecular functions and pathways such as tau protein binding and lipoprotein metabolism. Individuals with high predicted shared risk scores have a higher risk of developing AD, LOE, or both in their later life compared to those with low-risk scores. LOE partially mediates the effect of AD-LOE shared genetic risk on AD (15% proportion mediated on average). Validation results from All of Us were consistent with findings from the UCLA sample.

Conclusions

We employed a machine learning approach to identify shared genetic risks of AD and LOE. In addition to providing substantial evidence for the significant contribution of the APOE-TOMM40-APOC1 gene cluster to shared risk, we uncovered novel genes that may contribute. Our study is one of the first to utilize All of Us genetic data to investigate AD, and provides valuable insights into the potential common and disease-specific mechanisms underlying AD and LOE, which could have profound implications for the future of disease prevention and the development of targeted treatment strategies to combat the co-occurrence of these two diseases.

Phenome-wide association study of ovarian cancer identifies common comorbidities and reveals shared genetics with complex diseases and biomarkers

BACKGROUND

Ovarian cancer (OC) is commonly diagnosed among older women who have comorbidities. This hypothesis-free phenome-wide association study (PheWAS) aimed to identify comorbidities associated with OC, as well as traits that share a genetic architecture with OC.

METHODS

We used data from 181,203 white British female UK Biobank participants and analysed OC and OC subtype-specific genetic risk scores (OC-GRS) for an association with 889 diseases and 43 other traits. We conducted PheWAS and colocalization analyses for individual variants to identify evidence for shared genetic architecture.

RESULTS

The OC-GRS was associated with 10 diseases, and the clear cell OC-GRS was associated with five diseases at the FDR threshold (p = 5.6 × 10-4 ). Mendelian randomizaiton analysis (MR) provided robust evidence for the association of OC with higher risk of "secondary malignant neoplasm of digestive systems" (OR 1.64, 95% CI 1.33, 2.02), "ascites" (1.48, 95% CI 1.17, 1.86), "chronic airway obstruction" (1.17, 95% CI 1.07, 1.29), and "abnormal findings on examination of the lung" (1.51, 95% CI 1.22, 1.87). Analyses of lung spirometry measures provided further support for compromised respiratory function. PheWAS on individual OC variants identified five genetic variants associated with other diseases, and seven variants associated with biomarkers (all, p ≤ 4.5 × 10-8 ). Colocalization analysis identified rs4449583 (from TERT locus) as the shared causal variant for OC and seborrheic keratosis.

CONCLUSIONS

OC is associated with digestive and respiratory comorbidities. Several variants affecting OC risk were associated with other diseases and biomarkers, with this study identifying a novel genetic locus shared between OC and skin conditions.

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.

Cerebrospinal Fluid C1-Esterase Inhibitor and Tie-1 Levels Affect Cognitive Performance: Evidence from Proteome-Wide Mendelian Randomization

OBJECTIVE

The association of cerebrospinal fluid (CSF) protein levels with cognitive function in the general population remains largely unexplored. We performed Mendelian randomization (MR) analyses to query which CSF proteins may have potential causal effects on cognitive performance.

METHODS AND ANALYSIS

Genetic associations with CSF proteins were obtained from a genome-wide association study conducted in up to 835 European-ancestry individuals and for cognitive performance from a meta-analysis of GWAS including 257,841 European-ancestry individuals. We performed Mendelian randomization (MR) analyses to test the effect of randomly allocated variation in 154 genetically predicted CSF protein levels on cognitive performance. Findings were validated by performing colocalization analyses and considering cognition-related phenotypes.

RESULTS

Genetically predicted C1-esterase inhibitor levels in the CSF were associated with a better cognitive performance (SD units of cognitive performance per 1 log-relative fluorescence unit (RFU): 0.23, 95% confidence interval: 0.12 to 0.35, p = 7.91 × 10-5), while tyrosine-protein kinase receptor Tie-1 (sTie-1) levels were associated with a worse cognitive performance (-0.43, -0.62 to -0.23, p = 2.08 × 10-5). These findings were supported by colocalization analyses and by concordant effects on distinct cognition-related and brain-volume measures.

CONCLUSIONS

Human genetics supports a role for the C1-esterase inhibitor and sTie-1 in cognitive performance.

Building a vertically integrated genomic learning health system: The biobank at the Colorado Center for Personalized Medicine

Precision medicine initiatives across the globe have led to a revolution of repositories linking large-scale genomic data with electronic health records, enabling genomic analyses across the entire phenome. Many of these initiatives focus solely on research insights, leading to limited direct benefit to patients. We describe the biobank at the Colorado Center for Personalized Medicine (CCPM Biobank) that was jointly developed by the University of Colorado Anschutz Medical Campus and UCHealth to serve as a unique, dual-purpose research and clinical resource accelerating personalized medicine. This living resource currently has more than 200,000 participants with ongoing recruitment. We highlight the clinical, laboratory, regulatory, and HIPAA-compliant informatics infrastructure along with our stakeholder engagement, consent, recontact, and participant engagement strategies. We characterize aspects of genetic and geographic diversity unique to the Rocky Mountain region, the primary catchment area for CCPM Biobank participants. We leverage linked health and demographic information of the CCPM Biobank participant population to demonstrate the utility of the CCPM Biobank to replicate complex trait associations in the first 33,674 genotyped individuals across multiple disease domains. Finally, we describe our current efforts toward return of clinical genetic test results, including high-impact pathogenic variants and pharmacogenetic information, and our broader goals as the CCPM Biobank continues to grow. Bringing clinical and research interests together fosters unique clinical and translational questions that can be addressed from the large EHR-linked CCPM Biobank resource within a HIPAA- and CLIA-certified environment.

Genetic Liability to Posttraumatic Stress Disorder Symptoms and Its Association With Cardiometabolic and Respiratory Outcomes

Importance

Posttraumatic stress disorder (PTSD) has been reported to be a risk factor for several physical and somatic symptoms. However, the genetics of PTSD and its potential association with medical outcomes remain unclear.

Objective

To examine disease categories and laboratory tests from electronic health records (EHRs) that are associated with PTSD polygenic scores.

Design, Setting, and Participants

This genetic association study was conducted from July 15, 2021, to January 24, 2023, using EHR data from participants across 4 biobanks. The polygenic scores of PTSD symptom severity (PGS-PTSD) were tested with all available phecodes in Vanderbilt University Medical Center's biobank (BioVU), Mass General Brigham (MGB), Michigan Genomics Initiative (MGI), and UK Biobank (UKBB). The significant medical outcomes were tested for overrepresented disease categories and subsequently tested for genetic correlation and 2-sample mendelian randomization (MR) to determine genetically informed associations. Multivariable MR was conducted to assess whether PTSD associations with health outcomes were independent of the genetic effect of body mass index and tobacco smoking.

Exposures

Polygenic score of PTSD symptom severity.

Main Outcomes and Measures

A total of 1680 phecodes (ie, International Classification of Diseases, Ninth Revision- and Tenth Revision-based phenotypic definitions of health outcomes) across 4 biobanks and 490 laboratory tests across 2 biobanks (BioVU and MGB).

Results

In this study including a total of 496 317 individuals (mean [SD] age, 56.8 [8.0] years; 263 048 female [53%]) across the 4 EHR sites, meta-analyzing associations of PGS-PTSD with 1680 phecodes from 496 317 individuals showed significant associations to be overrepresented from mental health disorders (fold enrichment = 3.15; P = 5.81 × 10-6), circulatory system (fold enrichment = 3.32; P = 6.39 × 10-12), digestive (fold enrichment = 2.42; P = 2.16 × 10-7), and respiratory outcomes (fold enrichment = 2.51; P = 8.28 × 10-5). The laboratory measures scan with PGS-PTSD in BioVU and MGB biobanks revealed top associations in metabolic and immune domains. MR identified genetic liability to PTSD symptom severity as an associated risk factor for 12 health outcomes, including alcoholism (β = 0.023; P = 1.49 × 10-4), tachycardia (β = 0.045; P = 8.30 × 10-5), cardiac dysrhythmias (β = 0.016, P = 3.09 × 10-5), and acute pancreatitis (β = 0.049, P = 4.48 × 10-4). Several of these associations were robust to genetic effects of body mass index and smoking. We observed a bidirectional association between PTSD symptoms and nonspecific chest pain and C-reactive protein.

Conclusions and Relevance

Results of this study suggest the broad health repercussions associated with the genetic liability to PTSD across 4 biobanks. The circulatory and respiratory systems association was observed to be overrepresented in all 4 biobanks.

A wide landscape of morbidity and mortality risk associated with marital status in 0.5 million Chinese men and women: a prospective cohort study

Background

A comprehensive depiction of long-term health impacts of marital status is lacking.

Methods

Sex-stratified phenome-wide association analyses (PheWAS) of marital status (living with vs. without a spouse) were performed using baseline (2004-2008) and follow-up information (ICD10-coded events till Dec 31, 2017) from the China Kadoorie Biobank (CKB). We estimated adjusted hazard ratios (aHRs) to evaluate the associations of marital status with morbidity risks of phenome-wide significant diseases or sex-specific top-10 death causes in China documented in 2017. Additionally, the association between marital status and mortality risks among participants with major chronic diseases at baseline was assessed.

Findings

During up to 11.1 years of the median follow-up period, 1,946,380 incident health events were recorded among 210,202 men and 302,521 women aged 30-79. Marital status was found to have phenome-wide significant associations with thirteen diseases among men (p < 9.92 × 10-5) and nine diseases among women (p < 9.33 × 10-5), respectively. After adjusting for all disease-specific covariates in the final model, participants living without a spouse showed increased risks of schizophrenia, schizotypal and delusional disorders (aHR [95% CI]: 2.55, [1.83-3.56] for men; 1.49, [1.13-1.97] for women) compared with their counterparts. Additional higher risks in overall mental and behavioural disorder (1.31, 1.13-1.53), cardiovascular disease (1.07, 1.04-1.10) and cancer (1.06, 1.00-1.12) were only observed among men without a spouse, whereas women living without a spouse were at lower risks of developing genitourinary diseases (0.89, 0.85-0.93) and injury & poisoning (0.93, 0.88-0.97). Among 282,810 participants with major chronic diseases at baseline, 39,166 deaths were recorded. Increased mortality risks for those without a spouse were observed in 12 of 21 diseases among male patients and one of 23 among female patients. For patients with any self-reported disease at baseline, compared with those living with a spouse, the aHRs (95% CIs) of mortality risk were 1.29 (1.24-1.34) and 1.04 (1.00-1.07) among men and women without a spouse (pinteraction<0.0001), respectively.

Interpretation

Long-term associations of marital status with morbidity and mortality risks are diverse among middle-aged Chinese adults, and the adverse impacts due to living without a spouse are more profound among men. Marital status may be an influential factor for health needs.

Funding

The National Natural Science Foundation of China, the Kadoorie Charitable Foundation, the National Key R&D Program of China, the Chinese Ministry of Science and Technology, and the UK Wellcome Trust.

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.

Inferring disease architecture and predictive ability with LDpred2-auto

LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

OBJECTIVE

To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity.

METHODS

Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis.

RESULTS

The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection.

CONCLUSION

By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.

Using Multi-Modal Electronic Health Record Data for the Development and Validation of Risk Prediction Models for Long COVID Using the Super Learner Algorithm

BACKGROUND

Post-Acute Sequelae of COVID-19 (PASC) have emerged as a global public health and healthcare challenge. This study aimed to uncover predictive factors for PASC from multi-modal data to develop a predictive model for PASC diagnoses.

METHODS

We analyzed electronic health records from 92,301 COVID-19 patients, covering medical phenotypes, medications, and lab results. We used a Super Learner-based prediction approach to identify predictive factors. We integrated the model outputs into individual and composite risk scores and evaluated their predictive performance.

RESULTS

Our analysis identified several factors predictive of diagnoses of PASC, including being overweight/obese and the use of HMG CoA reductase inhibitors prior to COVID-19 infection, and respiratory system symptoms during COVID-19 infection. We developed a composite risk score with a moderate discriminatory ability for PASC (covariate-adjusted AUC (95% confidence interval): 0.66 (0.63, 0.69)) by combining the risk scores based on phenotype and medication records. The combined risk score could identify 10% of individuals with a 2.2-fold increased risk for PASC.

CONCLUSIONS

We identified several factors predictive of diagnoses of PASC and integrated the information into a composite risk score for PASC prediction, which could contribute to the identification of individuals at higher risk for PASC and inform preventive efforts.

Phenome-wide association study on miRNA-related sequence variants: the UK Biobank

BACKGROUND

Genetic variants in the coding region could directly affect the structure and expression levels of genes and proteins. However, the importance of variants in the non-coding region, such as microRNAs (miRNAs), remain to be elucidated. Genetic variants in miRNA-related sequences could affect their biogenesis or functionality and ultimately affect disease risk. Yet, their implications and pleiotropic effects on many clinical conditions remain unknown.

METHODS

Here, we utilised genotyping and hospital records data in the UK Biobank (N = 423,419) to investigate associations between 346 genetic variants in miRNA-related sequences and a wide range of clinical diagnoses through phenome-wide association studies. Further, we tested whether changes in blood miRNA expression levels could affect disease risk through colocalisation and Mendelian randomisation analysis.

RESULTS

We identified 122 associations for six variants in the seed region of miRNAs, nine variants in the mature region of miRNAs, and 27 variants in the precursor miRNAs. These included associations with hypertension, dyslipidaemia, immune-related disorders, and others. Nineteen miRNAs were associated with multiple diagnoses, with six of them associated with multiple disease categories. The strongest association was reported between rs4285314 in the precursor of miR-3135b and celiac disease risk (odds ratio (OR) per effect allele increase = 0.37, P = 1.8 × 10-162). Colocalisation and Mendelian randomisation analysis highlighted potential causal role of miR-6891-3p in dyslipidaemia.

CONCLUSIONS

Our study demonstrates the pleiotropic effect of miRNAs and offers insights to their possible clinical importance.

Identification of 13 Novel Loci in a Genome-Wide Association Study on Taiwanese with Hepatocellular Carcinoma

Liver cancer is caused by complex interactions among genetic factors, viral infection, alcohol abuse, and metabolic diseases. We conducted a genome-wide association study and polygenic risk score (PRS) model in Taiwan, employing a nonspecific etiology approach, to identify genetic risk factors for hepatocellular carcinoma (HCC). Our analysis of 2836 HCC cases and 134,549 controls revealed 13 novel associated loci such as the FAM66C gene, noncoding genes, liver-fibrosis-related genes, metabolism-related genes, and HCC-related pathway genes. We incorporated the results from the UK Biobank and Japanese database into our study for meta-analysis to validate our findings. We also identified specific subtypes of the major histocompatibility complex that influence both viral infection and HCC progression. Using this data, we developed a PRS to predict HCC risk in the general population, patients with HCC, and HCC-affected families. The PRS demonstrated higher risk scores in families with multiple HCCs and other cancer cases. This study presents a novel approach to HCC risk analysis, identifies seven new genes associated with HCC development, and introduces a reproducible PRS model for risk assessment.

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.

Impact of selection bias on polygenic risk score estimates in healthcare settings

BACKGROUND

Hospital-based biobanks are being increasingly considered as a resource for translating polygenic risk scores (PRS) into clinical practice. However, since these biobanks originate from patient populations, there is a possibility of bias in polygenic risk estimation due to overrepresentation of patients with higher frequency of healthcare interactions.

METHODS

PRS for schizophrenia, bipolar disorder, and depression were calculated using summary statistics from the largest available genomic studies for a sample of 24 153 European ancestry participants in the Mass General Brigham (MGB) Biobank. To correct for selection bias, we fitted logistic regression models with inverse probability (IP) weights, which were estimated using 1839 sociodemographic, clinical, and healthcare utilization features extracted from electronic health records of 1 546 440 non-Hispanic White patients eligible to participate in the Biobank study at their first visit to the MGB-affiliated hospitals.

RESULTS

Case prevalence of bipolar disorder among participants in the top decile of bipolar disorder PRS was 10.0% (95% CI 8.8-11.2%) in the unweighted analysis but only 6.2% (5.0-7.5%) when selection bias was accounted for using IP weights. Similarly, case prevalence of depression among those in the top decile of depression PRS was reduced from 33.5% (31.7-35.4%) to 28.9% (25.8-31.9%) after IP weighting.

CONCLUSIONS

Non-random selection of participants into volunteer biobanks may induce clinically relevant selection bias that could impact implementation of PRS in research and clinical settings. As efforts to integrate PRS in medical practice expand, recognition and mitigation of these biases should be considered and may need to be optimized in a context-specific manner.

Decoding Genetics, Ancestry, and Geospatial Context for Precision Health

Mass General Brigham, an integrated healthcare system based in the Greater Boston area of Massachusetts, annually serves 1.5 million patients. We established the Mass General Brigham Biobank (MGBB), encompassing 142,238 participants, to unravel the intricate relationships among genomic profiles, environmental context, and disease manifestations within clinical practice. In this study, we highlight the impact of ancestral diversity in the MGBB by employing population genetics, geospatial assessment, and association analyses of rare and common genetic variants. The population structures captured by the genetics mirror the sequential immigration to the Greater Boston area throughout American history, highlighting communities tied to shared genetic and environmental factors. Our investigation underscores the potency of unbiased, large-scale analyses in a healthcare-affiliated biobank, elucidating the dynamic interplay across genetics, immigration, structural geospatial factors, and health outcomes in one of the earliest American sites of European colonization.

GWAS for the composite traits of hematuria and albuminuria

Our GWAS of hematuria in the UK Biobank identified 6 loci, some of which overlap with loci for albuminuria suggesting pleiotropy. Since clinical syndromes are often defined by combinations of traits, generating a combined phenotype can improve power to detect loci influencing multiple characteristics. Thus the composite trait of hematuria and albuminuria was chosen to enrich for glomerular pathologies. Cases had both hematuria defined by ICD codes and albuminuria defined as uACR > 3 mg/mmol. Controls had neither an ICD code for hematuria nor an uACR > 3 mg/mmol. 2429 cases and 343,509 controls from the UK Biobank were included. eGFR was lower in cases compared to controls, with the exception of the comparison in females using CKD-EPI after age adjustment. Variants at 4 loci met genome-wide significance with the following nearest genes: COL4A4, TRIM27, ETV1 and CUBN. TRIM27 is part of the extended MHC locus. All loci with the exception of ETV1 were replicated in the Geisinger MyCode cohort. The previous GWAS of hematuria reported COL4A3-COL4A4 variants and HLA-B*0801 within MHC, which is in linkage disequilibrium with the TRIM27 variant (D' = 0.59). TRIM27 is highly expressed in the tubules. Additional loci included a coding sequence variant in CUBN (p.Ala2914Val, MAF = 0.014 (A), p = 3.29E-8, OR = 2.09, 95% CI = 1.61-2.72). Overall, GWAS for the composite trait of hematuria and albuminuria identified 4 loci, 2 of which were not previously identified in a GWAS of hematuria.

Genome-wide Association Study Identifies Novel Risk Loci for Apical Periodontitis

INTRODUCTION

Apical periodontitis (AP) is a common consequence of root canal infection leading to periapical bone resorption. Microbial and host genetic factors and their interactions have been shown to play a role in AP development and progression. Variations in a few genes have been reported in association with AP; however, the lack of genome-wide studies has hindered progress in understanding the molecular mechanisms involved. Here, we report the first genome-wide association study of AP in a large and well-characterized population.

METHODS

Male and female adults (n = 932) presenting with deep caries and AP (cases), or deep caries without AP (controls) were included. Genotyping was performed using the Illumina Expanded Multi-Ethnic Genotyping Array (MEGA). Single-variant association testing was performed adjusting for sex and 5 principal components. Subphenotype association testing, analyses of genetically regulated gene expression, polygenic risk score, and phenome-wide association (PheWAS) analyses were also conducted.

RESULTS

Eight loci reached near genome-wide significant association with AP (P < 5 × 10-6); gene-focused analyses replicated 3 previously reported associations (P < 8.9 × 10-5). Sex-specific and subphenotype-specific analyses revealed additional significant associations with variants genome-wide. Functionally oriented gene-based analyses revealed 8 genes significantly associated with AP (P < 5 × 10-5), and PheWAS analysis revealed 33 phecodes associated with AP risk score (P < 3.08 × 10-5).

CONCLUSIONS

This study identified novel genes/loci contributing to AP and specific contributions to AP risk in men and women. Importantly, we identified additional systemic conditions significantly associated with AP risk. Our findings provide strong evidence for host-mediated effects on AP susceptibility.

Heterozygous variants in TBCK cause a mild neurologic syndrome in humans and mice

TBCK-related encephalopathy is a rare pediatric neurodegenerative disorder caused by biallelic loss-of-function variants in the TBCK gene. After receiving anecdotal reports of neurologic phenotypes in both human and mouse TBCK heterozygotes, we quantified if TBCK haploinsufficiency causes a phenotype in mice and humans. Using the tbck+/- mouse model, we performed a battery of behavioral assays and mTOR pathway analysis to investigate potential alterations in neurophysiology. We conducted as well a phenome-wide association study (PheWAS) analysis in a large adult biobank to determine the presence of potential phenotypes associated to this variant. The tbck+/- mouse model demonstrates a reduction of exploratory behavior in animals with significant sex and genotype interactions. The concurrent PheWAS analysis of 10,900 unrelated individuals showed that patients with one copy of a TBCK loss-of-function allele had a significantly higher rate of acquired toe and foot deformities, likely indicative of a mild peripheral neuropathy phenotype. This study presents an example of what may be the underappreciated occurrence of mild neurogenic symptoms in heterozygote individuals of recessive neurogenetic syndromes.

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.

Genome-Wide Association Studies of Coffee Intake in UK/US Participants of European Ancestry Uncover Gene-Cohort Influences

Coffee is one of the most widely consumed beverages. We performed a genome-wide association study (GWAS) of coffee intake in US-based 23andMe participants (N=130,153) and identified 7 significant loci, with many replicating in three multi-ancestral cohorts. We examined genetic correlations and performed a phenome-wide association study across thousands of biomarkers and health and lifestyle traits, then compared our results to the largest available GWAS of coffee intake from UK Biobank (UKB; N=334,659). The results of these two GWAS were highly discrepant. We observed positive genetic correlations between coffee intake and psychiatric illnesses, pain, and gastrointestinal traits in 23andMe that were absent or negative in UKB. Genetic correlations with cognition were negative in 23andMe but positive in UKB. The only consistent observations were positive genetic correlations with substance use and obesity. Our study shows that GWAS in different cohorts could capture cultural differences in the relationship between behavior and genetics.

Characterizing the phenotypic and genetic structure of psychopathology in UK Biobank

Mental conditions exhibit a higher-order transdiagnostic factor structure which helps to explain the widespread comorbidity observed in psychopathology. However, the phenotypic and genetic structures of psychopathology may differ, raising questions about the validity and utility of these factors. Here, we study the phenotypic and genetic factor structures of ten psychiatric conditions using UK Biobank and public genomic data. Although the factor structure of psychopathology was generally genetically and phenotypically consistent, conditions related to externalizing (e.g., alcohol use disorder) and compulsivity (e.g., eating disorders) exhibited cross-level disparities in their relationships with other conditions, plausibly due to environmental influences. Domain-level factors, especially thought disorder and internalizing factors, were more informative than a general psychopathology factor in genome-wide association and polygenic index analyses. Collectively, our findings enhance the understanding of comorbidity and shared etiology, highlight the intricate interplay between genes and environment, and offer guidance for psychiatric research using polygenic indices.

Association of Helicobacter pylori Positivity With Risk of Disease and Mortality

INTRODUCTION

Helicobacter pylori colonizes the human stomach. Infection causes chronic gastritis and increases the risk of gastroduodenal ulcer and gastric cancer. Its chronic colonization in the stomach triggers aberrant epithelial and inflammatory signals that are also associated with systemic alterations.

METHODS

Using a PheWAS analysis in more than 8,000 participants in the community-based UK Biobank, we explored the association of H. pylori positivity with gastric and extragastric disease and mortality in a European country.

RESULTS

Along with well-established gastric diseases, we dominantly found overrepresented cardiovascular, respiratory, and metabolic disorders. Using multivariate analysis, the overall mortality of H. pylori -positive participants was not altered, while the respiratory and Coronovirus 2019-associated mortality increased. Lipidomic analysis for H. pylori -positive participants revealed a dyslipidemic profile with reduced high-density lipoprotein cholesterol and omega-3 fatty acids, which may represent a causative link between infection, systemic inflammation, and disease.

DISCUSSION

Our study of H. pylori positivity demonstrates that it plays an organ- and disease entity-specific role in the development of human disease and highlights the importance of further research into the systemic effects of H. pylori infection.

Association Between Fuchs Endothelial Corneal Dystrophy, Diabetes Mellitus, and Multimorbidity

PURPOSE

The aim of this study was to assess risk for demographic variables and other health conditions that are associated with Fuchs endothelial corneal dystrophy (FECD).

METHODS

We developed a FECD case-control algorithm based on structured electronic health record data and confirmed accuracy by individual review of charts at 3 Veterans Affairs (VA) Medical Centers. This algorithm was applied to the Department of VA Million Veteran Program cohort from whom sex, genetic ancestry, comorbidities, diagnostic phecodes, and laboratory values were extracted. Single-variable and multiple variable logistic regression models were used to determine the association of these risk factors with FECD diagnosis.

RESULTS

Being a FECD case was associated with female sex, European genetic ancestry, and a greater number of comorbidities. Of 1417 diagnostic phecodes evaluated, 213 had a significant association with FECD, falling in both ocular and nonocular conditions, including diabetes mellitus (DM). Five of 69 laboratory values were associated with FECD, with the direction of change for 4 being consistent with DM. Insulin dependency and type 1 DM raised risk to a greater degree than type 2 DM, like other microvascular diabetic complications.

CONCLUSIONS

Female sex, European ancestry, and multimorbidity increased FECD risk. Endocrine/metabolic clinic encounter codes and altered patterns of laboratory values support DM increasing FECD risk. Our results evoke a threshold model in which the FECD phenotype is intensified by DM and potentially other health conditions that alter corneal physiology. Further studies to better understand the relationship between FECD and DM are indicated and may help identify opportunities for slowing FECD progression.

Comorbidity Profiles of Posttraumatic Stress Disorder Across the Medical Phenome

Background

Prior epidemiological research has linked PTSD with specific physical health problems, but the comprehensive landscape of medical conditions associated with PTSD remains uncharacterized. Electronic health records (EHR) provide an opportunity to overcome prior clinical knowledge gaps and uncover associations with biological relevance that potentially vary by sex.

Methods

PTSD was defined among biobank participants (total N=123,365) in a major healthcare system using two ICD code-based definitions: broad (1+ PTSD or acute stress codes versus 0; NCase=14,899) and narrow (2+ PTSD codes versus 0; NCase=3,026). Using a phenome-wide association (PheWAS) design, we tested associations between each PTSD definition and all prevalent disease umbrella categories, i.e., phecodes. We also conducted sex-stratified PheWAS analyses including a sex-by-diagnosis interaction term in each logistic regression.

Results

A substantial number of phecodes were significantly associated with PTSDNarrow (61%) and PTSDBroad (83%). While top associations were shared between the two definitions, PTSDBroad captured 334 additional phecodes not significantly associated with PTSDNarrow and exhibited a wider range of significantly associated phecodes across various categories, including respiratory, genitourinary, and circulatory conditions. Sex differences were observed, in that PTSDBroad was more strongly associated with osteoporosis, respiratory failure, hemorrhage, and pulmonary heart disease among male patients, and with urinary tract infection, acute pharyngitis, respiratory infections, and overweight among female patients.

Conclusions

This study provides valuable insights into a diverse range of comorbidities associated with PTSD, including both known and novel associations, while highlighting the influence of sex differences and the impact of defining PTSD using EHR.