Development of a large-scale de-identified DNA biobank to enable personalized medicine

Development of 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 (EHRs).

MATERIALS AND METHODS

We developed and validated electronic health record (EHR)-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in 3 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 1 of the following 3 criteria: (1) 2 or more dates with any DR ICD-9/10 code documented in the EHR, (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 examination. Criteria for controls included affirmative evidence for diabetes as well as an ophthalmology examination.

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 0.91 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, the algorithm for DR 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 6209 Hispanics with DR.

CONCLUSIONS/DISCUSSION

We demonstrate the robustness of the algorithms at 3 separate healthcare centers, with a minimum PPV of 0.84 and substantially improved NPV than existing automated methods. We strongly encourage independent validation and incorporation of features unique to each EHR to enhance algorithm performance for DR cases and controls.

A polygenic score for height identifies an unmeasured genetic predisposition among pediatric patients with idiopathic short stature

Background A subset of children with short stature do not have an identified clinical explanation and are assigned a diagnosis of idiopathic short stature (ISS). We hypothesized that a polygenic score for height (PGS height ) could identify children with ISS who have an unrecognized heritable predisposition to shorter height. Methods We examined 534 pediatric participants in an EHR-linked DNA biobank (BioVU) who had undergone an evaluation for short stature by an endocrinologist. We used a previously validated PGS height and standardized it to a standard deviation (SDS) of 1. PGS height differences between short stature subtypes was estimated using Tukey's HSD. The PGS height and mid-parental height (MPH) were then used to predict adult heights for each participant and these predictions were compared using Cohen's d stratifying by short stature subtype. The ability of the PGS height to discriminate between ISS and short stature due to underlying disease was evaluated using logistic regression models with area under the ROC curve (AUC) analyses and testing the incremental benefit (ΔAUC) of adding the PGS height to prediction models. Results Among the 534 participants, 22.1% had ISS (median [IQR] PGS height SDS = -1.31 [-2.15 to -0.47]), 6.6% had familial (genetic) short stature (FSS) (-1.62 [-2.13 to -0.54]), and 45.1% had short stature due to underlying pathology (-0.74 [-1.23 to -0.19]). Children with ISS had similar PGS height values as those with FSS (ΔPGS height [95% CI] = 0.19 [-0.31 to 0.70], p  = 0.75), but predicted heights generated by the PGS height were lower than the MPH estimate for children with ISS ( d = -0.64; p  = 4.0×10 - 18 ) but not FSS ( d  = 0.05; p  = 0.46), suggesting that MPH underestimates height in the ISS group. Children with ISS had lower PGS height values than children with pathology (ΔPGS height = -0.60 SDS [-0.89 to -0.31], p < 0.001), suggesting children with ISS have a larger predisposition to shorter height. In addition, the PGS height improved model discrimination between ISS and pathologic short stature (ΔAUC, + 0.07 [95% CI, 0.01 to 0.11]). Conclusions Some children with ISS have a clinically unrecognized polygenic predisposition to shorter height that is comparable to children with FSS and larger than those with underlying pathology. A PGS height could help clinicians identify children who have a benign predisposition to shorter height.

Dissecting the Reduced Penetrance of Putative Loss-of-Function Variants in Population-Scale Biobanks

Loss-of-function variants (LoFs) disrupt the activity of their impacted gene. They are often associated with clinical phenotypes, including autosomal dominant diseases driven by haploinsufficiency. Recent analyses using biobanks have suggested that LoF penetrance for some haploinsufficient disorders may be low, an observation that has important implications for population genomic screening. However, biobanks are also rife with missing data, and the reliability of these findings remains uncertain. Here, we examine the penetrance of putative LoFs (pLoFs) using a cohort of ≈24,000 carriers derived from two population-scale biobanks: the UK Biobank and the All of Us Research Program. We investigate several possible etiologies for reduced pLoF penetrance, including biobank recruitment biases, annotation artifacts, missed diagnoses, and incomplete clinical records. Systematically accounting for these factors increased penetrance, but widespread reduced penetrance remained. Therefore, we hypothesized that other factors must be driving this phenomenon. To test this, we trained machine learning models to identify pLoFs with high penetrance using the genomic features specific to each variant. These models were predictive of penetrance across a range of diseases and ploF types, including those with prior evidence for pathogenicity. This suggests that reduced ploF penetrance is in fact common, and care should be taken when counseling asymptomatic carriers.

Multiancestry transferability of a polygenic risk score for diverticulitis

OBJECTIVE

Polygenic risk scores (PRS) for diverticular disease must be evaluated in diverse cohorts. We sought to explore shared genetic predisposition across the phenome and to assess risk stratification in individuals genetically similar to European, African and Admixed-American reference samples.

METHODS

A 44-variant PRS was applied to the All of Us Research Program. Phenome-wide association studies (PheWAS) identified conditions linked with heightened genetic susceptibility to diverticular disease. To evaluate the PRS in risk stratification, logistic regression models for symptomatic and for severe diverticulitis were compared with base models with covariates of age, sex, body mass index, smoking and principal components. Performance was assessed using area under the receiver operating characteristic curves (AUROC) and Nagelkerke's R2.

RESULTS

The cohort comprised 181 719 individuals for PheWAS and 50 037 for risk modelling. PheWAS identified associations with diverticular disease, connective tissue disease and hernias. Across ancestry groups, one SD PRS increase was consistently associated with greater odds of severe (range of ORs (95% CI) 1.60 (1.27 to 2.02) to 1.86 (1.42 to 2.42)) and of symptomatic diverticulitis ((95% CI) 1.27 (1.10 to 1.46) to 1.66 (1.55 to 1.79)) relative to controls. European models achieved the highest AUROC and Nagelkerke's R2 (AUROC (95% CI) 0.78 (0.75 to 0.81); R2 0.25). The PRS provided a maximum R2 increase of 0.034 and modest AUROC improvement.

CONCLUSION

Associations between a diverticular disease PRS and severe presentations persisted in diverse cohorts when controlling for known risk factors. Relative improvements in model performance were observed, but absolute change magnitudes were modest.

No Increased Risk of Major Adverse Cardiovascular Events following Nicotinamide Exposure

IMPORTANCE

Nicotinamide metabolites have recently been implicated in increased risk of major cardiovascular events (MACE). Supportive data about clinical risk of MACE for nicotinamide users is lacking.

OBJECTIVE

To determine whether nicotinamide use results in an increase of MACE.

DESIGN SETTING PARTICIPANTS

Retrospective cohort study of two patient cohorts, Vanderbilt University Medical Center (VUMC) and Military Veteran Program (MVP). The risk of MACE in patients exposed to nicotinamide was compared to the risk of MACE in unexposed patients. In the VUMC cohort, 1228 patients were exposed to nicotinamide based on keyword entry for "nicotinamide" or "niacinamide" and hand-review of charts, while 253 were unexposed but had documented recommendation for use. In the MVP cohort, there were 1594 with exposure to nicotinamide propensity score matched to 2694 without exposure.

EXPOSURES

The primary exposure for the VUMC cohort was a confirmed exposure to nicotinamide in chart review. The primary exposure for the MVP cohort was medication entry for "nicotinamide" or "niacinamide".

MAIN OUTCOMES AND MEASURES

The primary outcome was development of MACE based on a validated phenotype.

RESULTS

Between both cohorts, 6039 patients were included, of whom 5125 were male with a mean age of 63.2 years. Neither cohort had significant differences in mean age, sex, race and ethnicity between the nicotinamide exposed and unexposed groups. In the VUMC cohort, there was no significant association between nicotinamide exposure and the primary outcome of MACE (HR 0.76, 95% CI 0.46 - 1.25, p = 0.28). MACE prior to nicotinamide exposure was strongly associated with subsequent MACE (HR 9.01, 95% CI 5.90 - 13.70, p < 0.001). In the MVP cohort, we adjusted for MACE risk factors as potential confounding variables and saw no significant association between nicotinamide exposure and MACE (HR 1.00 95% CI 0.75 - 1.32), while history of prior MACE remained strongly associated with subsequent MACE (HR 9.50, 95% CI 6.38 - 14.1).

CONCLUSIONS AND RELEVANCE

In this retrospective cohort study of 6039 adults from two different patient populations, we found no increased risk of MACE in patients with nicotinamide exposure.

Impact of metabolic and weight components on incident asthma using a real-world cohort

BACKGROUND

Obesity and metabolic dysregulation (MetD) have increasing prevalence and adversely affect asthma morbidity and therapeutic response.

OBJECTIVE

To determine the role of weight and MetD on incident asthma in adulthood.

METHODS

In a retrospective, longitudinal cohort of patients, we performed a time-to-asthma diagnosis analysis after a 3-year landmark period (t0-t3) during which weight and MetD components were evaluated. We assessed incident asthma risk with MetD components and weight.

RESULTS

In total, 90,081 patients met the inclusion criteria, with 836 cases (0.93%) of incident asthma in our primary cohort. Diabetes present at t0, but no other MetD components, was associated with increased risk of asthma (adjusted hazard ratio = 1.85, 95% CI: 1.27-2.71, P = .0002). The effect of weight on asthma risk, independent of other MetD components, identified individuals with overweight or obesity as having a 10-year attributable risk of 15.4%. Metformin was prescribed more frequently, and hemoglobin A1c levels were lower in patients with diabetes in whom asthma did not develop (P < .0001).

CONCLUSION

Weight and diabetes prevention and management represent modifiable risk factors for adult asthma development.

Enhancing genetic association power in endometriosis through unsupervised clustering of clinical subtypes identified from electronic health records

Endometriosis is a complex and heterogeneous condition affecting 10% of reproductive-age women, and yet, it often goes undiagnosed for several years. Limited observed heritability (7%) of large genetic association studies may be attributable to underlying heterogeneity of disease mechanisms. Therefore, we conducted this study to investigate genetic associations across sub-phenotypes of endometriosis. We performed unsupervised clustering of 4,078 women with endometriosis based on known endometriosis risk factors, symptoms, and concomitant conditions. The clusters were characterized by examining electronic health record (EHR) data and comprehensive chart reviews. We then performed genetic association for each cluster with 39 endometriosis-associated loci (Total Nendometriosis cases = 12,350). We identified five sub-phenotype clusters: (1) pain comorbidities, (2) uterine disorders, (3) pregnancy complications, (4) cardiometabolic comorbidities, and (5) HER-asymptomatic. Bonferroni significant loci included PDLIM5 for the cluster 1, GREB1 for cluster 2, WNT4 for cluster 3, RNLS for cluster 4, and ABO for cluster 5. The difference in associations between the groups suggests complex and varied genetic mechanisms of endometriosis and its symptoms. This study enhances our understanding of the clinical patterns of endometriosis sub-phenotypes, showcasing the innovative approach employed to investigate this complex disease.

Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale

An understanding of human brain individuality requires the integration of data on brain organization across people and brain regions, molecular and systems scales, as well as healthy and clinical states. Here, we help advance this understanding by leveraging methods from computational genomics to integrate large-scale genomic, transcriptomic, neuroimaging, and electronic-health record data sets. We estimated genetically regulated gene expression (gr-expression) of 18,647 genes, across 10 cortical and subcortical regions of 45,549 people from the UK Biobank. First, we showed that patterns of estimated gr-expression reflect known genetic-ancestry relationships, regional identities, as well as inter-regional correlation structure of directly assayed gene expression. Second, we performed transcriptome-wide association studies (TWAS) to discover 1,065 associations between individual variation in gr-expression and gray-matter volumes across people and brain regions. We benchmarked these associations against results from genome-wide association studies (GWAS) of the same sample and found hundreds of novel associations relative to these GWAS. Third, we integrated our results with clinical associations of gr-expression from the Vanderbilt Biobank. This integration allowed us to link genes, via gr-expression, to neuroimaging and clinical phenotypes. Fourth, we identified associations of polygenic gr-expression with structural and functional MRI phenotypes in the Human Connectome Project (HCP), a small neuroimaging-genomic data set with high-quality functional imaging data. Finally, we showed that estimates of gr-expression and magnitudes of TWAS were generally replicable and that the p-values of TWAS were replicable in large samples. Collectively, our results provide a powerful new resource for integrating gr-expression with population genetics of brain organization and disease.

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.

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.

Evaluation of Genetic Associations with Clinical Phenotypes of Kidney Stone Disease

Background and objective

Previous studies have reported a strong genetic contribution to kidney stone risk. This study aims to identify genetic associations of kidney stone disease within a large-scale electronic health record system.

Methods

We performed genome-wide association studies (GWASs) for nephrolithiasis from genotyped samples of 5571 cases and 83 692 controls. This analysis included a primary GWAS focused on nephrolithiasis and subsequent subgroup GWASs stratified by stone composition types. For significant risk variants, we performed association analyses with stone composition and first-time 24-h urine parameters. To assess disease severity, we investigated the associations with age at first stone diagnosis, age at first stone-related procedure, and time between first and second stone-related procedures.

Key findings and limitations

The primary GWAS analysis identified ten significant loci, all located on chromosome 16 within coding regions of the UMOD gene. The strongest signal was rs28544423 (odds ratio 1.17, 95% confidence interval 1.11-1.23, p = 2.7 × 10-9). In subgroup GWASs stratified by six kidney stone composition subtypes, 19 significant loci were identified including two loci in coding regions (brushite; NXPH1, rs79970906 and rs4725104). The UMOD single nucleotide polymorphism rs28544423 was associated with differences in 24-h excretion of urinary analytes, and the minor allele was positively associated with calcium oxalate dihydrate stone composition (p < 0.05). No associations were found between UMOD variants and disease severity. Limitations include an omitted variable bias and a misclassification bias.

Conclusions and clinical implications

We replicated germline variants associated with kidney stone disease risk at UMOD and reported novel variants associated with stone composition. Genetic variants of UMOD are associated with differences in 24-h urine parameters and stone composition, but not disease severity.

Patient summary

We identify genetic variants linked to kidney stone disease within an electronic health record (EHR) system. These findings suggest a role for the EHR to enable a precision-medicine approach for stone disease.

Detection of distant relatedness in biobanks to identify undiagnosed cases of Mendelian disease as applied to Long QT syndrome

Rare genetic diseases are typically studied in referral populations, resulting in underdiagnosis and biased assessment of penetrance and phenotype. To address this, we develop a generalizable method of genotype inference based on distant relatedness and deploy this to identify undiagnosed Type 5 Long QT Syndrome (LQT5) rare variant carriers in a non-referral population. We identify 9 LQT5 families referred to a single specialty clinic, each carrying p.Asp76Asn, the most common LQT5 variant. We uncover recent common ancestry and a single shared haplotype among probands. Application to a non-referral population of 69,819 BioVU biobank subjects identifies 22 additional subjects sharing this haplotype, which we confirm to carry p.Asp76Asn. Referral and non-referral carriers have prolonged QT interval corrected for heart rate (QTc) compared to controls, and, among carriers, the QTc polygenic score is independently associated with QTc prolongation. Thus, our innovative analysis of shared chromosomal segments identifies undiagnosed cases of genetic disease and refines the understanding of LQT5 penetrance and phenotype.

Genome-wide association study of hospitalized patients and acute kidney injury

Acute kidney injury (AKI) is a common and devastating complication of hospitalization. Here, we identified genetic loci associated with AKI in patients hospitalized between 2002-2019 in the Million Veteran Program and data from Vanderbilt University Medical Center's BioVU. AKI was defined as meeting a modified KDIGO Stage 1 or more for two or more consecutive days or kidney replacement therapy. Control individuals were required to have one or more qualifying hospitalizations without AKI and no evidence of AKI during any other observed hospitalizations. Genome-wide association studies (GWAS), stratified by race, adjusting for sex, age, baseline estimated glomerular filtration rate (eGFR), and the top ten principal components of ancestry were conducted. Results were meta-analyzed using fixed effects models. In total, there were 54,488 patients with AKI and 138,051 non-AKI individuals included in the study. Two novel loci reached genome-wide significance in the meta-analysis: rs11642015 near the FTO locus on chromosome 16 (obesity traits) (odds ratio 1.07 (95% confidence interval, 1.05-1.09)) and rs4859682 near the SHROOM3 locus on chromosome 4 (glomerular filtration barrier integrity) (odds ratio 0.95 (95% confidence interval, 0.93-0.96)). These loci colocalized with previous studies of kidney function, and genetic correlation indicated significant shared genetic architecture between AKI and eGFR. Notably, the association at the FTO locus was attenuated after adjustment for BMI and diabetes, suggesting that this association may be partially driven by obesity. Both FTO and the SHROOM3 loci showed nominal evidence of replication from diagnostic-code-based summary statistics from UK Biobank, FinnGen, and Biobank Japan. Thus, our large GWA meta-analysis found two loci significantly associated with AKI suggesting genetics may explain some risk for AKI.

Detection and Correction of Sample Misidentifications in a Biobank Using the MassARRAY System and Genomic Information

With the number of samples increasing in many biobanks, one of the most pressing tasks is recording the correct relationships between information and the specimens. Genomic information is useful in determining the identity of these specimens. The Tohoku Medical Megabank Organization is running one of the largest biobanks in Japan. Here, we introduce a management system, which includes the development of a new probe set for the MassARRAY system for use during the production of proliferating T cells (T cells) and lymphoblastoid cell lines (LCLs). We selected single nucleotide variants that could be detected by next-generation sequencing and showed high resolution with ∼0.5 minor allele frequencies. After checking the set of probes against 96 samples from 48 people, we obtained no contradictory results in comparison with our genome sequence information. When we applied the set to our 3035 LCLs and 2256 T cells, the result showed 98.93% consistency with the corresponding genomic information. We surveyed the handling records of the 1.07% of samples that showed inconsistencies, and found that most had resulted from human errors (ID swapping between samples) during manual operations. After improving a few error-prone protocols, the error rate dropped to 0.47% for LCLs and 0% for T cells. Overall, the system that we developed shows high accuracy with easy and fast operability, and provides a good opportunity to improve the validation procedure to facilitate high-quality banking, especially in cases involving genomic information.

Genetic predictors of blood pressure traits are associated with preeclampsia

Preeclampsia, a pregnancy complication characterized by hypertension after 20 gestational weeks, is a major cause of maternal and neonatal morbidity and mortality. Mechanisms leading to preeclampsia are unclear; however, there is evidence of high heritability. We evaluated the association of polygenic scores (PGS) for blood pressure traits and preeclampsia to assess whether there is shared genetic architecture. Non-Hispanic Black and White reproductive age females with pregnancy indications and genotypes were obtained from Vanderbilt University's BioVU, Electronic Medical Records and Genomics network, and Penn Medicine Biobank. Preeclampsia was defined by ICD codes. Summary statistics for diastolic blood pressure (DBP), systolic blood pressure (SBP), and pulse pressure (PP) PGS were acquired from Giri et al. Associations between preeclampsia and each PGS were evaluated separately by race and data source before subsequent meta-analysis. Ten-fold cross validation was used for prediction modeling. In 3504 Black and 5009 White included individuals, the rate of preeclampsia was 15.49%. In cross-ancestry meta-analysis, all PGSs were associated with preeclampsia (ORDBP = 1.10, 95% CI 1.02-1.17, p = 7.68 × 10-3; ORSBP = 1.16, 95% CI 1.09-1.23, p = 2.23 × 10-6; ORPP = 1.14, 95% CI 1.07-1.27, p = 9.86 × 10-5). Addition of PGSs to clinical prediction models did not improve predictive performance. Genetic factors contributing to blood pressure regulation in the general population also predispose to preeclampsia.

Defining Suicidal Thought and Behavior Phenotypes for Genetic Studies

Background

Standardized definitions of suicidality phenotypes, including suicidal ideation (SI), attempt (SA), and death (SD) are a critical step towards improving understanding and comparison of results in suicide research. The complexity of suicidality contributes to heterogeneity in phenotype definitions, impeding evaluation of clinical and genetic risk factors across studies and efforts to combine samples within consortia. Here, we present expert and data-supported recommendations for defining suicidality and control phenotypes to facilitate merging current/legacy samples with definition variability and aid future sample creation.

Methods

A subgroup of clinician researchers and experts from the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) reviewed existing PGC definitions for SI, SA, SD, and control groups and generated preliminary consensus guidelines for instrument-derived and international classification of disease (ICD) data. ICD lists were validated in two independent datasets (N = 9,151 and 12,394).

Results

Recommendations are provided for evaluated instruments for SA and SI, emphasizing selection of lifetime measures phenotype-specific wording. Recommendations are also provided for defining SI and SD from ICD data. As the SA ICD definition is complex, SA code list recommendations were validated against instrument results with sensitivity (range = 15.4% to 80.6%), specificity (range = 67.6% to 97.4%), and positive predictive values (range = 0.59-0.93) reported.

Conclusions

Best-practice guidelines are presented for the use of existing information to define SI/SA/SD in consortia research. These proposed definitions are expected to facilitate more homogeneous data aggregation for genetic and multisite studies. Future research should involve refinement, improved generalizability, and validation in diverse populations.

Multivariate, Multi-omic Analysis in 799,429 Individuals Identifies 134 Loci Associated with Somatoform Traits

Somatoform traits, which manifest as persistent physical symptoms without a clear medical cause, are prevalent and pose challenges to clinical practice. Understanding the genetic basis of these disorders could improve diagnostic and therapeutic approaches. With publicly available summary statistics, we conducted a multivariate genome-wide association study (GWAS) and multi-omic analysis of four somatoform traits-fatigue, irritable bowel syndrome, pain intensity, and health satisfaction-in 799,429 individuals genetically similar to Europeans. Using genomic structural equation modeling, GWAS identified 134 loci significantly associated with a somatoform common factor, including 44 loci not significant in the input GWAS and 8 novel loci for somatoform traits. Gene-property analyses highlighted an enrichment of genes involved in synaptic transmission and enriched gene expression in 12 brain tissues. Six genes, including members of the CD300 family, had putatively causal effects mediated by protein abundance. There was substantial polygenic overlap (76-83%) between the somatoform and externalizing, internalizing, and general psychopathology factors. Somatoform polygenic scores were associated most strongly with obesity, Type 2 diabetes, tobacco use disorder, and mood/anxiety disorders in independent biobanks. Drug repurposing analyses suggested potential therapeutic targets, including MEK inhibitors. Mendelian randomization indicated potentially protective effects of gut microbiota, including Ruminococcus bromii. These biological insights provide promising avenues for treatment development.

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.

MICOS Complex Loss Governs Age-Associated Murine Mitochondrial Architecture and Metabolism in the Liver, While Sam50 Dictates Diet Changes

The liver, the largest internal organ and a metabolic hub, undergoes significant declines due to aging, affecting mitochondrial function and increasing the risk of systemic liver diseases. How the mitochondrial three-dimensional (3D) structure changes in the liver across aging, and the biological mechanisms regulating such changes confers remain unclear. In this study, we employed Serial Block Face-Scanning Electron Microscopy (SBF-SEM) to achieve high-resolution 3D reconstructions of murine liver mitochondria to observe diverse phenotypes and structural alterations that occur with age, marked by a reduction in size and complexity. We also show concomitant metabolomic and lipidomic changes in aged samples. Aged human samples reflected altered disease risk. To find potential regulators of this change, we examined the Mitochondrial Contact Site and Cristae Organizing System (MICOS) complex, which plays a crucial role in maintaining mitochondrial architecture. We observe that the MICOS complex is lost during aging, but not Sam50. Sam50 is a component of the sorting and assembly machinery (SAM) complex that acts in tandem with the MICOS complex to modulate cristae morphology. In murine models subjected to a high-fat diet, there is a marked depletion of the mitochondrial protein SAM50. This reduction in Sam50 expression may heighten the susceptibility to liver disease, as our human biobank studies corroborate that Sam50 plays a genetically regulated role in the predisposition to multiple liver diseases. We further show that changes in mitochondrial calcium dysregulation and oxidative stress accompany the disruption of the MICOS complex. Together, we establish that a decrease in mitochondrial complexity and dysregulated metabolism occur with murine liver aging. While these changes are partially be regulated by age-related loss of the MICOS complex, the confluence of a murine high-fat diet can also cause loss of Sam50, which contributes to liver diseases. In summary, our study reveals potential regulators that affect age-related changes in mitochondrial structure and metabolism, which can be targeted in future therapeutic techniques.

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.

Genome-wide association studies of lifetime and frequency cannabis use in 131,895 individuals

Cannabis is one of the most widely used drugs globally. Decriminalization of cannabis is further increasing cannabis consumption. We performed genome-wide association studies (GWASs) of lifetime (N=131,895) and frequency (N=73,374) of cannabis use. Lifetime cannabis use GWAS identified two loci, one near CADM2 (rs11922956, p=2.40E-11) and another near GRM3 (rs12673181, p=6.90E-09). Frequency of use GWAS identified one locus near CADM2 (rs4856591, p=8.10E-09; r2 =0.76 with rs11922956). Both traits were heritable and genetically correlated with previous GWASs of lifetime use and cannabis use disorder (CUD), as well as other substance use and cognitive traits. Polygenic scores (PGSs) for lifetime and frequency of cannabis use associated cannabis use phenotypes in AllofUs participants. Phenome-wide association study of lifetime cannabis use PGS in a hospital cohort replicated associations with substance use and mood disorders, and uncovered associations with celiac and infectious diseases. This work demonstrates the value of GWASs of CUD transition risk factors.

Voriconazole metabolism is associated with the number of skin cancers per patient

Voriconazole exposure is associated with skin cancer, but it is unknown how the full spectrum of its metabolizer phenotypes impacts this association. We conducted a retrospective cohort study to determine how variation in metabolism of voriconazole as measured by metabolizer status of CYP2C19 is associated with the total number of skin cancers a patient develops and the rate of development of the first skin cancer after treatment. There were 1,739 organ transplant recipients with data on CYP2C19 phenotype. Of these, 134 were exposed to voriconazole. There was a significant difference in the number of skin cancers after transplant based on exposure to voriconazole, metabolizer phenotype, and the interaction of these two (p < 0.01 for all three). This increase was driven primarily by number of squamous cell carcinomas among rapid metabolizes with voriconazole exposure (p < 0.01 for both). Patients exposed to voriconazole developed skin cancers more rapidly than those without exposure (Fine-Grey hazard ratio 1.78, 95% confidence interval 1.19-2.66). This association was similarly driven by development of SCC (Fine-Grey hazard ratio 1.83, 95% confidence interval 1.14-2.94). Differences in voriconazoles metabolism are associated with an increase in the number of skin cancers developed after transplant, particularly SCC.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

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

Introduction: 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. Methods: We examined the association between polygenic scores for 724 metabolites with 1,247 clinical phenotypes in the BioVU DNA biobank, comprising 57,735 European ancestry and 15,754 African ancestry participants. We applied Mendelian randomization (MR) to probe significant relationships and validated significant MR associations using independent GWAS of candidate phenotypes. Results and Discussion: We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes in African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolitephenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p < 0.05). These included associations between bilirubin and X-21796 with cholelithiasis, phosphatidylcholine (16:0/22:5n3,18:1/20:4) and arachidonate 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.

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.

Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits

Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P < 5 × 10-8) from the largest single-stage blood pressure (BP) genome-wide association study to date (n = 1,028,980 European individuals). These associations explain more than 60% of single nucleotide polymorphism-based BP heritability. Comparing top versus bottom deciles of polygenic risk scores (PRSs) reveals clinically meaningful differences in BP (16.9 mmHg systolic BP, 95% CI, 15.5-18.2 mmHg, P = 2.22 × 10-126) and more than a sevenfold higher odds of hypertension risk (odds ratio, 7.33; 95% CI, 5.54-9.70; P = 4.13 × 10-44) in an independent dataset. Adding PRS into hypertension-prediction models increased the area under the receiver operating characteristic curve (AUROC) from 0.791 (95% CI, 0.781-0.801) to 0.826 (95% CI, 0.817-0.836, ∆AUROC, 0.035, P = 1.98 × 10-34). We compare the 2,103 loci results in non-European ancestries and show significant PRS associations in a large African-American sample. Secondary analyses implicate 500 genes previously unreported for BP. Our study highlights the role of increasingly large genomic studies for precision health research.

Clinical associations with a polygenic predisposition to benign lower white blood cell counts

Polygenic variation unrelated to disease contributes to interindividual variation in baseline white blood cell (WBC) counts, but its clinical significance is uncharacterized. We investigated the clinical consequences of a genetic predisposition toward lower WBC counts among 89,559 biobank participants from tertiary care centers using a polygenic score for WBC count (PGSWBC) comprising single nucleotide polymorphisms not associated with disease. A predisposition to lower WBC counts was associated with a decreased risk of identifying pathology on a bone marrow biopsy performed for a low WBC count (odds-ratio = 0.55 per standard deviation increase in PGSWBC [95%CI, 0.30-0.94], p = 0.04), an increased risk of leukopenia (a low WBC count) when treated with a chemotherapeutic (n = 1724, hazard ratio [HR] = 0.78 [0.69-0.88], p = 4.0 × 10-5) or immunosuppressant (n = 354, HR = 0.61 [0.38-0.99], p = 0.04). A predisposition to benign lower WBC counts was associated with an increased risk of discontinuing azathioprine treatment (n = 1,466, HR = 0.62 [0.44-0.87], p = 0.006). Collectively, these findings suggest that there are genetically predisposed individuals who are susceptible to escalations or alterations in clinical care that may be harmful or of little benefit.

Voriconazole Metabolism is Associated with the Number of Skin Cancers Per Patient

Voriconazole exposure is associated with skin cancer, but it is unknown how the full spectrum of its metabolizer phenotypes impacts this association. We conducted a retrospective cohort study to determine how variation in metabolism of voriconazole as measured by metabolizer status of CYP2C19 is associated with the total number of skin cancers a patient develops and the rate of development of the first skin cancer after treatment. There were 1,739 organ transplant recipients with data on CYP2C19 phenotype. Of these, 134 were exposed to voriconazole. There was a significant difference in the number of skin cancers after transplant based on exposure to voriconazole, metabolizer phenotype, and the interaction of these two (p < 0.01 for all three). This increase was driven primarily by number of squamous cell carcinomas among rapid metabolizes with voriconazole exposure (p < 0.01 for both). Patients exposed to voriconazole developed skin cancers more rapidly than those without exposure (Fine-Grey hazard ratio 1.78, 95% confidence interval 1.19-2.66). This association was similarly driven by development of SCC (Fine-Grey hazard ratio 1.83, 95% confidence interval 1.14-2.94). Differences in voriconazoles metabolism are associated with an increase in the number of skin cancers developed after transplant, particularly SCC.

Comparison of late-onset and non-late-onset systemic lupus erythematosus individuals in a real-world electronic health record cohort

Objective: Late-onset systemic lupus erythematosus (LO-SLE) is defined as SLE diagnosed at age 50 years or later. Current studies on LO-SLE are small and have conflicting results.Methods: Using a large, electronic health record (EHR)-based cohort of SLE individuals, we compared demographics, disease characteristics, SLE-specific antibodies, and medication prescribing practices in LO (n = 123) vs. NLO-SLE (n = 402) individuals.Results: The median age (interquartile range) at SLE diagnosis was 60 (56-67) years for LO-SLE and 28 (20-38) years for NLO-SLE. Both groups were predominantly female (85% vs. 91%, p = 0.10). LO-SLE individuals were more likely to be White than NLO-SLE individuals (74% vs. 60%, p = 0.005) and less likely to have positive dsDNA (39% vs. 58%, p = 0.001) and RNP (17% vs. 32%, p = 0.02) with no differences in Smith, SSA, and SSB. Autoantibody positivity declined with increasing age at SLE diagnosis. LO-SLE individuals were less likely to develop SLE nephritis (9% vs. 29%, p < 0.001) and less likely to be prescribed multiple classes of SLE medications including antimalarials (90% vs. 95%, p = 0.04), azathioprine (17% vs. 31%, p = 0.002), mycophenolate mofetil (12% vs. 38%, p < 0.001), and belimumab (2% vs. 8%, p = 0.02).Conclusion: LO-SLE individuals may be less likely to fit an expected course for SLE with less frequent positive autoantibodies at diagnosis and lower rates of nephritis, even after adjusting for race. Understanding how age impacts SLE disease presentation could help reduce diagnostic delays in SLE.

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.

Individualized Dose-Response to Statins Associated with Cardiovascular Disease Outcomes

Background

Statins reduce low-density lipoprotein cholesterol (LDL-C) and are efficacious in the prevention of atherosclerotic cardiovascular disease (ASCVD). Dose-response to statins varies among patients and can be modeled using three distinct pharmacological properties: (1) E0 (baseline LDL-C), (2) ED50 (potency: median dose achieving 50% reduction in LDL-C); and (3) Emax (efficacy: maximum LDL-C reduction). However, individualized dose-response and its association with ASCVD events remains unknown.

Objective

We analyze the relationship between ED50 and Emax with real-world cardiovascular disease outcomes.

Method

We leveraged de-identified electronic health record data to identify individuals exposed to multiple doses of the three most commonly prescribed statins (atorvastatin, simvastatin, or rosuvastatin) within the context of their longitudinal healthcare. We derived ED50 and Emax to quantify the relationship with a composite outcome of ASCVD events and all-cause mortality.

Results

We estimated ED50 and Emax for 3,033 unique individuals (atorvastatin: 1,632, simvastatin: 1,089, and rosuvastatin: 312) using a nonlinear, mixed effects dose-response model. Time-to-event analyses revealed that ED50 and Emax are independently associated with the primary endpoint. Hazard ratios were 0.85 (p < 0.01), 0.83 (p < 0.01), and 0.87 (p = 0.10) for ED50 and 1.13 (p < 0.001), 1.06 (p < 0.001), and 1.15 (p = 0.009) for Emax in the atorvastatin, simvastatin, and rosuvastatin cohorts, respectively.

Conclusion

The class-wide association of ED50 and Emax with clinical outcomes indicates that these measures influence the risk for ASCVD events in patients on statins.

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

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

Identifying antinuclear antibody positive individuals at risk for developing systemic autoimmune disease: development and validation of a real-time risk model

Objective

Positive antinuclear antibodies (ANAs) cause diagnostic dilemmas for clinicians. Currently, no tools exist to help clinicians interpret the significance of a positive ANA in individuals without diagnosed autoimmune diseases. We developed and validated a risk model to predict risk of developing autoimmune disease in positive ANA individuals.

Methods

Using a de-identified electronic health record (EHR), we randomly chart reviewed 2,000 positive ANA individuals to determine if a systemic autoimmune disease was diagnosed by a rheumatologist. A priori, we considered demographics, billing codes for autoimmune disease-related symptoms, and laboratory values as variables for the risk model. We performed logistic regression and machine learning models using training and validation samples.

Results

We assembled training (n = 1030) and validation (n = 449) sets. Positive ANA individuals who were younger, female, had a higher titer ANA, higher platelet count, disease-specific autoantibodies, and more billing codes related to symptoms of autoimmune diseases were all more likely to develop autoimmune diseases. The most important variables included having a disease-specific autoantibody, number of billing codes for autoimmune disease-related symptoms, and platelet count. In the logistic regression model, AUC was 0.83 (95% CI 0.79-0.86) in the training set and 0.75 (95% CI 0.68-0.81) in the validation set.

Conclusion

We developed and validated a risk model that predicts risk for developing systemic autoimmune diseases and can be deployed easily within the EHR. The model can risk stratify positive ANA individuals to ensure high-risk individuals receive urgent rheumatology referrals while reassuring low-risk individuals and reducing unnecessary referrals.

Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma

BACKGROUND

Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma.

METHODS

The subjects included asthma patients of European ancestry (n = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest.

RESULTS

The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67-0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70-0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma (TPSAB1, FBXL16), asthma symptoms and severity (ABCA7, CNN2, PTRN3, and BSG/CD147), airway remodeling (ELANE, FSTL3), mucin production (GAL3ST), leukotriene synthesis (GPX4), allergic asthma (ZFPM1, SBNO2), and others.

CONCLUSIONS

An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction.

Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation

Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness globally, shows disparity in prevalence and manifestations across ancestries. We perform meta-analysis across 15 biobanks (of the Global Biobank Meta-analysis Initiative) (n = 1,487,441: cases = 26,848) and merge with previous multi-ancestry studies, with the combined dataset representing the largest and most diverse POAG study to date (n = 1,478,037: cases = 46,325) and identify 17 novel significant loci, 5 of which were ancestry specific. Gene-enrichment and transcriptome-wide association analyses implicate vascular and cancer genes, a fifth of which are primary ciliary related. We perform an extensive statistical analysis of SIX6 and CDKN2B-AS1 loci in human GTEx data and across large electronic health records showing interaction between SIX6 gene and causal variants in the chr9p21.3 locus, with expression effect on CDKN2A/B. Our results suggest that some POAG risk variants may be ancestry specific, sex specific, or both, and support the contribution of genes involved in programmed cell death in POAG pathogenesis.

Microvascular insulin resistance associates with enhanced muscle glucose disposal in CD36 deficiency

Dysfunction of endothelial insulin delivery to muscle associates with insulin resistance. CD36, a fatty acid transporter and modulator of insulin signaling is abundant in endothelial cells, especially in capillaries. Humans with inherited 50% reduction in CD36 expression have endothelial dysfunction but whether it is associated with insulin resistance is unclear. Using hyperinsulinemic/euglycemic clamps in Cd36-/- and wildtype mice, and in 50% CD36 deficient humans and matched controls we found that Cd36-/- mice have enhanced systemic glucose disposal despite unaltered transendothelial insulin transfer and reductions in microvascular perfusion and blood vessel compliance. Partially CD36 deficient humans also have better glucose disposal than controls with no capillary recruitment by insulin. CD36 knockdown in primary human-derived microvascular cells impairs insulin action on AKT, endothelial nitric oxide synthase, and nitric oxide release. Thus, insulin resistance of microvascular function in CD36 deficiency paradoxically associates with increased glucose utilization, likely through a remodeling of muscle gene expression.

Composite CYP3A (CYP3A4 and CYP3A5) phenotypes and influence on tacrolimus dose adjusted concentrations in adult heart transplant recipients

CYP3A5 genetic variants are associated with tacrolimus metabolism. Controversy remains on whether CYP3A4 increased [*1B (rs2740574), *1 G (rs2242480)] and decreased function [*22 (rs35599367)] genetic variants provide additional information. This retrospective cohort study aims to address whether tacrolimus dose-adjusted trough concentrations differ between combined CYP3A (CYP3A5 and CYP3A4) phenotype groups. Heart transplanted patients (n = 177, between 2008 and 2020) were included and median age was 54 years old. Significant differences between CYP3A phenotype groups in tacrolimus dose-adjusted trough concentrations were found in the early postoperative period and continued to 6 months post-transplant. In CYP3A5 nonexpressers, carriers of CYP3A4*1B or *1 G variants (Group 3) compared to CYP3A4*1/*1 (Group 2) patients were found to have lower tacrolimus dose-adjusted trough concentrations at 2 months. In addition, significant differences were found among CYP3A phenotype groups in the dose at discharge and time to therapeutic range while time in therapeutic range was not significantly different. A combined CYP3A phenotype interpretation may provide more nuanced genotype-guided TAC dosing in heart transplant recipients.

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

Background

Atrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear.

Methods

We identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping. The phenotypic architecture in the AF cohort was defined using principal component analysis of 35 expertly curated and uncorrelated clinical features. We applied an unsupervised co-clustering machine learning algorithm to the 35 features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention in the EHR. Polygenic risk scores (PRS) for AF and cytokine levels were used to assess genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from EHR up to the last medical contact.

Results

The analysis included 23,271 subjects with AF, of which 6,023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal dysfunction, and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbid cluster, while there was no difference between groups in genetically predicted levels of inflammatory biomarkers. Subgroup assignment was associated with multiple clinical outcomes including mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis.

Conclusion

Patient subgroups identified by unsupervised clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes. Polygenic liability to AF across clusters was greatest in the low comorbidity subgroup. Clinical inflammation, as reflected by measured biomarkers, was lowest in the subgroup with lowest comorbidities. However, there were no differences in genetically predicted levels of inflammatory biomarkers, suggesting associations between AF and inflammation is driven by acquired comorbidities rather than genetic predisposition.

Evaluation of genetic associations with clinical phenotypes of kidney stone disease

Introduction and Objective

We sought to replicate and discover genetic associations of kidney stone disease within a large-scale electronic health record (EHR) system.

Methods

We performed genome-wide association studies (GWASs) for nephrolithiasis from genotyped samples of 5,571 cases and 83,692 controls. Among the significant risk variants, we performed association analyses of stone composition and first-time 24-hour urine parameters. To assess disease severity, we investigated the associations of risk variants with age at first stone diagnosis, age at first procedure, and time from first to second procedure.

Results

The main GWAS analysis identified 10 significant loci, each located on chromosome 16 within coding regions of the UMOD gene, which codes for uromodulin, a urine protein with inhibitory activity for calcium crystallization. The strongest signal was from SNP 16:20359633-C-T (odds ratio [OR] 1.17, 95% CI 1.11-1.23), with the remaining significant SNPs having similar effect sizes. In subgroup GWASs by stone composition, 19 significant loci were identified, of which two loci were located in coding regions (brushite; NXPH1 , rs79970906 and rs4725104). The UMOD SNP 16:20359633-C-T was associated with differences in 24-hour excretion of urinary calcium, uric acid, phosphorus, sulfate; and the minor allele was positively associated with calcium oxalate dihydrate stone composition (p<0.05). No associations were found between UMOD variants and disease severity.

Conclusions

We replicated germline variants associated with kidney stone disease risk at UMOD and reported novel variants associated with stone composition. Genetic variants of UMOD are associated with differences in 24-hour urine parameters and stone composition, but not disease severity.

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.

Developing and evaluating pediatric phecodes (Peds-Phecodes) for high-throughput phenotyping using electronic health records

OBJECTIVE

Pediatric patients have different diseases and outcomes than adults; however, existing phecodes do not capture the distinctive pediatric spectrum of disease. We aim to develop specialized pediatric phecodes (Peds-Phecodes) to enable efficient, large-scale phenotypic analyses of pediatric patients.

MATERIALS AND METHODS

We adopted a hybrid data- and knowledge-driven approach leveraging electronic health records (EHRs) and genetic data from Vanderbilt University Medical Center to modify the most recent version of phecodes to better capture pediatric phenotypes. First, we compared the prevalence of patient diagnoses in pediatric and adult populations to identify disease phenotypes differentially affecting children and adults. We then used clinical domain knowledge to remove phecodes representing phenotypes unlikely to affect pediatric patients and create new phecodes for phenotypes relevant to the pediatric population. We further compared phenome-wide association study (PheWAS) outcomes replicating known pediatric genotype-phenotype associations between Peds-Phecodes and phecodes.

RESULTS

The Peds-Phecodes aggregate 15 533 ICD-9-CM codes and 82 949 ICD-10-CM codes into 2051 distinct phecodes. Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 vs 192 out of 687 SNPs, P < .001).

DISCUSSION

We introduce Peds-Phecodes, a high-throughput EHR phenotyping tool tailored for use in pediatric populations. We successfully validated the Peds-Phecodes using genetic replication studies. Our findings also reveal the potential use of Peds-Phecodes in detecting novel genotype-phenotype associations for pediatric conditions. We expect that Peds-Phecodes will facilitate large-scale phenomic and genomic analyses in pediatric populations.

CONCLUSION

Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior PheWAS outcomes compared to phecodes.

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

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

LATTE: Label-efficient incident phenotyping from longitudinal electronic health records

Electronic health record (EHR) data are increasingly used to support real-world evidence studies but are limited by the lack of precise timings of clinical events. Here, we propose a label-efficient incident phenotyping (LATTE) algorithm to accurately annotate the timing of clinical events from longitudinal EHR data. By leveraging the pre-trained semantic embeddings, LATTE selects predictive features and compresses their information into longitudinal visit embeddings through visit attention learning. LATTE models the sequential dependency between the target event and visit embeddings to derive the timings. To improve label efficiency, LATTE constructs longitudinal silver-standard labels from unlabeled patients to perform semi-supervised training. LATTE is evaluated on the onset of type 2 diabetes, heart failure, and relapses of multiple sclerosis. LATTE consistently achieves substantial improvements over benchmark methods while providing high prediction interpretability. The event timings are shown to help discover risk factors of heart failure among patients with rheumatoid arthritis.

Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning

Background

Traditional approaches to modeling suicide-related thoughts and behaviors focus on few data types from often-siloed disciplines. While psychosocial aspects of risk for these phenotypes are frequently studied, there is a lack of research assessing their impact in the context of biological factors, which are important in determining an individual's fulsome risk profile. To directly test this biopsychosocial model of suicide and identify the relative importance of predictive measures when considered together, a transdisciplinary, multivariate approach is needed. Here, we systematically review the emerging literature on large-scale studies using machine learning to integrate measures of psychological, social, and biological factors simultaneously in the study of suicide.

Methods

We conducted a systematic review of studies that used machine learning to model suicide-related outcomes in human populations including at least one predictor from each of biological, psychological, and sociological data domains. Electronic databases MEDLINE, EMBASE, PsychINFO, PubMed, and Web of Science were searched for reports published between August 2013 and August 30, 2023. We evaluated populations studied, features emerging most consistently as risk or resilience factors, methods used, and strength of evidence for or against the biopsychosocial model of suicide.

Results

Out of 518 full-text articles screened, we identified a total of 20 studies meeting our inclusion criteria, including eight studies conducted in general population samples and 12 in clinical populations. Common important features identified included depressive and anxious symptoms, comorbid psychiatric disorders, social behaviors, lifestyle factors such as exercise, alcohol intake, smoking exposure, and marital and vocational status, and biological factors such as hypothalamic-pituitary-thyroid axis activity markers, sleep-related measures, and selected genetic markers. A minority of studies conducted iterative modeling testing each data type for contribution to model performance, instead of reporting basic measures of relative feature importance.

Conclusion

Studies combining biopsychosocial measures to predict suicide-related phenotypes are beginning to proliferate. This literature provides some early empirical evidence for the biopsychosocial model of suicide, though it is marred by harmonization challenges. For future studies, more specific definitions of suicide-related outcomes, inclusion of a greater breadth of biological data, and more diversity in study populations will be needed.

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.

The Use of Electronic Health Records for Behavioral Phenotyping of School-Age Children With Unilateral Hearing Loss: A Methodological Approach

PURPOSE

This methodological study describes a technique for extracting information from de-identified electronic health records (EHRs) to identify occurrences of permanent unilateral hearing loss (UHL) and associated educational comorbidities.

METHOD

This was an exploratory methodological study utilizing approximately 3.3 million de-identified medical records. Structured and unstructured data were extracted using both automated and manual methods. When both methods were available, positive and negative predictive values were calculated to evaluate the utility of using automated methods.

RESULTS

We defined a cohort of 471 records that met our criteria of school-age children with permanent UHL and no additional significant disabilities/diagnoses. Fifty-one percent of the children reflected in this cohort had indicators of adverse educational progress, defined as documentation of receiving educational services, speech-language therapy, and/or parental/teacher concern, with 12% of records reflecting overlapping services/concerns. Negative predictive values were generally high and positive predictive values were generally low, suggesting automated searches are useful for excluding factors of interest, but not finding them.

CONCLUSIONS

This study demonstrates the feasibility of using EHRs in examining UHL in school-age children. By restricting our cohort to individuals who were seen in audiology clinic, we were able to capture variables such as educational difficulty that are not routinely ascertained in medical contexts. The proportion of children in this cohort demonstrating a marker of adverse educational progress is consistent with numerous prior observational studies, thus providing validity to this ascertainment approach. We describe challenges encountered in creating this cohort and detail our hybrid approach to ascertaining key variables accurately.

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.

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.

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.

Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology

Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a standardized framework for the step-by-step implementation of artificial intelligence into the clinical practice of radiology that focuses on three key components: problem identification, stakeholder alignment, and pipeline integration. A review of the recent literature and empirical evidence in radiologic imaging applications justifies this approach and offers a discussion on structuring implementation efforts to help other hospital practices leverage ML to improve patient care. Clinical trial registration no. 04242667 © RSNA, 2024 Supplemental material is available for this article.