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

Associations between pathophysiological traits and symptom development in retrospective analysis of V30M and V122I transthyretin amyloidosis

Background

The Val30Met (V30M) and Val122Ile (V122I) transthyretin (TTR) mutations often beget hereditary amyloid transthyretin amyloidosis (hATTR). Since symptoms are progressively debilitating and potentially fatal if untreated, low survival rates result from late diagnoses of hATTR patients. This retrospective analysis of microarray and biobank data helped establish clinical biomarkers for early hATTR detection.

Methods

In a Portuguese sample of V30M carriers (n = 183), gene profiling identified dysregulated immune markers. Among African Americans (AA) and Hispanic/Latinx Americans (HA) from the Mount Sinai BioMe Biobank (n = 28,718), a case-control style Phenome-Wide Association Study (PheWAS; odds ratio [95% confidence interval]) of V122I for phenotypic and echocardiogram traits (β coefficients [95 % CI]) determined gene pleiotropy.

Results

Among V30M profiles, 96 (52.4%) were symptomatic, expressing upregulated neutrophil activity (p < 10-16), IL-6/JAK/STAT3 signaling (p < 10-3), and downregulated CD4+T cell expression (p = 0.009), compared to their asymptomatic counterparts. In BioMe, 562 (2.0%) were V122I carriers, demonstrating associations with heart failure (1.71 [1.23-2.39]; p = 0.0014), amyloidosis (20.79 [8.42-51.31]; p = 4.67 × 10-11), secondary/extrinsic cardiomyopathies (17.73 [7.25-43.37]; p = 2.97 × 10-10), peripheral nerve disorders (4.14 [2.42-7.09]; p = 2.26 × 10-7), primary angle-closure glaucoma (8.03 [3.15-20.46]; p = 1.27 × 10-5), malignant neoplasm of the female breast (4.48 [2.23-9.00]; p = 2.48 × 10-5), fracture of tibia and fibula (8.42 [3.25-21.89]; p = 1.19 × 10-5), and Carpal tunnel syndrome (2.62 [1.68-4.11]; p = 2.44 × 10-5). Echocardiographic presentations included higher LVEDV (15.87 [9.63-22.10]; p = 6.04 × 10-7) and LA length (1.52 [0.69-2.35]; p = 3.31 × 10-4). Race-stratified associations identified that AA presented more severe cardiac abnormalities than HA.

Conclusions

This study identified inflammatory biomarkers upregulated in symptomatic V30M carriers and phenotypic/echocardiographic traits associated with V122I, representing comorbidities of hATTR pathology. Such markers can provide the basis for future improvements in diagnostic regimes to deliver early therapies.

Clinical Characteristics associated with functional seizures in individuals with psychosis

BACKGROUND AND HYPOTHESIS

Functional seizures (FS) are episodes characterized by seizure-like events that are not caused by hypersynchronous neuronal activity. Prior studies have suggested an increased prevalence of psychotic disorders among patients with FS, but results have been inconsistent. We hypothesize that FS are associated with psychosis and that among patients with psychosis, the presence of FS may influence patient clinical characteristics, mortality, and medical resource utilization.

STUDY DESIGN

The association between FS and psychosis was assessed using electronic health records data from a total of 761,848 individuals receiving care at Vanderbilt University Medical Center between 1989 and 2023. Analyses of the association between FS and psychiatric outcomes, sexual trauma, healthcare utilization, and other clinical comorbidities were conducted in a subset of 5219 patients with psychosis.

STUDY RESULTS

Odds of FS were elevated among patients with psychosis compared to controls (OR = 10.09, 95 % CI = 8.40-12.13). Among patients with psychosis, those with FS exhibited higher rates of suicidality (OR = 2.18 95 % CI = 1.50-3.17), catatonia (OR = 2.15, 95 % CI = 1.33-3.45), sexual trauma history (OR = 2.93, 95 % CI = 2.00-4.29) and had a greater number of antipsychotic trials (4.63 versus 3.37, beta = 1.23, SE = 0.18, adjusted p < 0.001) than those without FS. Furthermore, patients with comorbid FS had more hospital presentations at one, three, five, and ten years after receiving a psychosis diagnosis (adjusted p < 0.001).

CONCLUSIONS

FS are more common among patients with psychosis and are associated with increased healthcare utilization as well as an increased prevalence of suicidality, catatonia, and certain psychiatric and medical comorbidities.

Ppp1r3b is a metabolic switch that shifts hepatic energy storage from lipid to glycogen

The PPP1R3B gene, encoding PPP1R3B protein, is critical for liver glycogen synthesis and maintaining blood glucose levels. Genetic variants affecting PPP1R3B expression are associated with several metabolic traits and liver disease, but the precise mechanisms are not fully understood. We studied the effects of both Ppp1r3b overexpression and deletion in mice and cell models and found that both changes in Ppp1r3b expression result in dysregulated metabolism and liver damage, with overexpression increasing liver glycogen stores, while deletion resulted in higher liver lipid accumulation. These patterns were confirmed in humans where variants increasing PPP1R3B expression had lower liver fat and decreased plasma lipids, whereas putative loss-of-function variants were associated with increased liver fat and elevated plasma lipids. These findings support that PPP1R3B is a crucial regulator of hepatic metabolism beyond glycogen synthesis and that genetic variants affecting PPP1R3B expression levels influence if hepatic energy is stored as glycogen or triglycerides.

A phenome-wide association study of uterine fibroids reveals a marked burden of comorbidities

BACKGROUND

The burden of comorbidities in those with uterine fibroids compared to those without fibroids is understudied. We performed a phenome-wide association study to systematically assess the association between fibroids and other conditions.

METHODS

Vanderbilt University Medical Center's Synthetic Derivative and Geisinger Health System Database, two electronic health record databases, were used for discovery and validation. Non-Hispanic Black and White females were included. Fibroid cases were identified through a previously validated algorithm. Race-stratified and multi-population phenome-wide association analyses, adjusting for age and body mass index, were performed before statistically significant, validated results were meta-analyzed.

RESULTS

There were 52,295 and 26,918 (9022 and 10,232 fibroid cases) females included in discovery and validation analyses. In multi-population meta-analysis, 389 conditions were associated with fibroid risk, with evidence of enrichment of circulatory, dermatologic, genitourinary, musculoskeletal, and sense organ conditions. The strongest associations within and across racial groups included conditions previously associated with fibroids. Numerous novel diagnoses, including cancers in female genital organs, were tied to fibroid status.

CONCLUSIONS

Overall, individuals with fibroids have a marked increase in comorbidities compared to those without fibroids. This approach to evaluate the health context of fibroids highlights the potential to understand fibroid etiology through studying the common biology of comorbid diagnoses and through disease networks.

Predisposition to hematopoietic malignancies by deleterious germline CHEK2 variants

The role of germline CHEK2 variants in hematopoietic malignancies (HMs) is poorly understood. We examined pathogenic/likely pathogenic (P/LP) CHEK2 variants in patients with hereditary HMs (HHMs), a solid tumor risk cohort, public datasets, and a knock-in mouse model. In the HHM cohort, 57 probands had germline P/LP CHEK2 variants, mostly p.I157T (53%, 30/57). Among CHEK2 p.I157T carriers, 43% (19/44) had myeloid malignancies, 32% (14/44) had lymphoid malignancies, and 2% (1/44) had both. Among those with other germline P/LP CHEK2 alleles, 36% (13/36) had myeloid malignancies, 28% (10/36) had lymphoid malignancies, and 6% (2/36) had both. CHEK2 p.I157T was enriched in HM patients (OR 6.44, 95%CI 3.68-10.73, P < 0.001). In a solid tumor risk cohort, 36% (15/42) of CHEK2 p.I157T patients had a HM family history. A genome wide association study showed enrichment of CHEK2 loss-of-function variants with myeloid leukemia (P = 5.78e-7). In public acute myeloid leukemia (AML) datasets, 1% (16/1348) of patients had P/LP CHEK2 variants. In a public myelodysplastic neoplasms (MDS) dataset, 2% (5/214) had P/LP CHEK2 variants. Chek2 p.I161T mice, homologous to human p.I157T, had worse survival as heterozygotes (P = 0.037) or homozygotes (P = 0.005), with fewer Lin-CD34+ and Lin-cKit+ cells. Our data suggest P/LP CHEK2 variants are HHM risk alleles.

Discovery of novel ancestry specific genes for androgens and hypogonadism in Million Veteran Program Men

Given the various roles of testosterone in men's health, we conducted a multi-ancestral genetic analysis of total testosterone, free testosterone, SHBG, and hypogonadism in men within the Million Veteran Program (MVP). Here we identified 157 significant testosterone genetic variants, of which 8 have significant ancestry-specific associations. These variants implicate several genes, including SERPINF2, PRPF8, BAIAP2L1, SHBG, PRMT6, and PPIF, related to liver function. Genetic regulators of testosterone have cell type-specific effects in the testes, liver, and adrenal gland and are associated with disease risk. We conducted a meta-analysis amongst ancestry groups to identify 188 variants significantly associated with testosterone, of which 22 are novel associations. We constructed genetic scores for total testosterone, SHBG levels, and hypogonadism and find that men with higher testosterone genetic scores have lower odds of diabetes, hyperlipidemia, gout, and cardiac disorders. These findings provide insight into androgen regulation and identify novel variants for disease risk stratification.

Sex-specific Mendelian randomization phenome-wide association study of basal metabolic rate

Observationally, higher basal metabolic rate (BMR) is associated with metabolism-related disorders, cancer, aging, and mortality. In this Mendelian randomization (MR) phenome-wide association study, using two-sample MR methods, we systematically and comprehensively investigated the health effects of genetically predicted BMR across the phenome sex-specifically. We obtained sex-specific genetic variants strongly (p < 5 × 10- 8) and independently (r2 < 0.001) predicting BMR from the UK Biobank and applied them to over 1,000 phenotypes within the same study. We combined genetic variant-specific Wald estimates using inverse-variance weighting, supplemented by sensitivity analysis. We used a false-discovery rate correction to allow for multiple comparisons as well as multivariable MR adjusted for body mass index and testosterone to investigate the independent effects of BMR on phenotypes with significant univariable associations. We obtained 217/219 genetic variants predicting BMR and applied them to 1,150/1,242 phenotypes in men/women, respectively. BMR was associated with 190/270 phenotypes in univariable analysis and 122/123 phenotypes in multivariable analysis in men/women. Examples of robust associations in multivariable analysis included those with neoplasms, diseases of the circulatory system, and growth and reproductive investment. In conclusion, BMR might affect a wide range of health-related outcomes. The underlying mechanisms and interactions between phenotypes warrant further study, as BMR is modifiable.

Genome-Wide Assessment of Pleiotropy Across >1000 Traits from Global Biobanks

Large-scale genetic association studies have identified thousands of trait-associated risk loci, establishing the polygenic basis for common complex traits and diseases. Although prior studies suggest that many trait-associated loci are pleiotropic, the extent to which this pleiotropy reflects shared causal variants or confounding by linkage disequilibrium remains poorly characterized. To define a set of candidate loci with potentially pleiotropic associations, we performed genome-wide association study (GWAS) meta-analyses of up to 1,167 clinically relevant traits and diseases across 1,789,365 diverse individuals genetically similar to Admixed American (AMR, NMax = 60,756), African (AFR, NMax = 128,361), East Asian (EAS, NMax = 307,465), European (EUR, NMax = 1,283,907), and South Asian (SAS, NMax = 8,876) reference populations from the VA Million Veteran Program (MVP), UK Biobank (UKB), FinnGen, Biobank Japan (BBJ), Tohoku Medical Megabank (ToMMO), and Korean Genome and Epidemiology Study (KoGES). We identified 27,193 genome-wide significant locus-trait pairs (1MB region with PGWAMA < 5 × 10-8) in within-population analysis and 29,139 in multi-population analysis (PMR-MEGA < 5 × 10-8). Among these, 11.5% (n = 3,149) of locus-trait pairs in population-wise and 6.4% (n = 1,875) in multi-population analyses did not reach genome-wide significance in previously published GWAS. In aggregate, the genome-wide significant loci fell within 2,624 non-overlapping autosomal genomic windows on average ~600kb in size. Each locus contained genome-wide significant signals for a median of 6 traits (IQR 2 to 18), including 2,110 (80%) pleiotropic loci associated with >1 trait. Multi-trait colocalization identified 1,902 (72%) loci with high-confidence (posterior probability > 0.9) evidence of a shared causal variant across two or more traits. Variants in pleiotropic loci were significantly enriched for a broad spectrum of functional annotations compared to non-pleiotropic counterparts. Polygenic scores (PGS) developed from these data generally improved prediction compared to existing PGS and were broadly associated with both on- and off-target phenotypes. These results provide a contemporary map of genetic pleiotropy across the spectrum of human traits/diseases and genetic backgrounds.

Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10-9), including two secondary signals. Notably, we identified and replicated a novel low-frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

Familial Renal Glucosuria and Potential Pharmacogenetic Impact on Sodium-Glucose Cotransporter-2 Inhibitors

Key Points

A significant knowledge gap exists in SLC5A2 's role in familial renal glycosuria and sodium-glucose cotransporter-2 inhibitors' efficacy. Two percent of individuals in the All-of-Us cohort harbor rare genetic variants in SLC5A2 , potentially increasing the risk of familial renal glycosuria. Our trial suggests differential responses to sodium-glucose cotransporter-2 inhibitors in individuals with rare SLC5A2 alleles compared with wild types.

Background

Renal glucosuria is a rare inheritable trait caused by loss-of-function variants in the gene that encodes sodium-glucose cotransporter-2 (SGLT2) (i.e ., SLC5A2 ). The genetics of renal glucosuria is poorly understood, and even less is known on how loss-of-function variants in SLC5A2 may affect response to SGLT2 inhibitors, a new class of medication gaining popularity to treat diabetes by artificially inducing glucosuria.

Methods

We used two biobanks that link genomic with electronic health record data to study the genetics of renal glucosuria. This included 245,394 participants enrolled in the All of Us Research Program and 11,011 enrolled in Marshfield Clinic's Personalized Research Project (PMRP). Association studies in All of Us and PMRP identified ten variants that reached an experiment-wise Bonferroni threshold in either cohort, of which nine were novel. PMRP was further used as a recruitment source for a prospective SGLT2 pharmacogenetic trial. During a glucose tolerance test, the trial measured urine glucose concentrations in 15 SLC5A2 variant–positive individuals and 15 matched wild types with and without an SGLT2 inhibitor.

Results

This trial demonstrated that carriers of SLC5A2 risk variants may be more sensitive to SGLT2 inhibitors compared with wild types (P = 0.075). On the basis of population data, 2% of an ethnically diverse population carried rare variants in SLC5A2 and are at risk of renal glucosuria.

Conclusions

As a result, 2% of individuals being treated with SGLT2 inhibitors may respond differently to this new class of medication compared with the general population, suggesting that a larger investigation into SLC5A2 variants and SGLT2 inhibitors is needed.

Linking epidemiology and genomics of maternal smoking during pregnancy in utero and in ageing: a population-based study using human foetuses and the UK Biobank cohort

BACKGROUND

Maternal smoking and foetal exposure to nicotine and other harmful chemicals in utero remains a serious public health issue with little knowledge about the underlying genetics and consequences of maternal smoking in ageing individuals. Here, we investigated the epidemiology and genomic architecture of maternal smoking in a middle-aged population and compare the results to effects observed in the developing foetus.

METHODS

In the current project, we included 351,562 participants from the UK Biobank (UKB) and estimated exposure to maternal smoking status during pregnancy through self-reporting from the UKB participants about the mother's smoking status around their birth. In addition, we analysed 64 foetal liver transcriptomic expression datasets collected from women seeking elective pregnancy terminations. Foetal maternal smoking exposure was confirmed through measurement of foetal plasma cotinine levels.

FINDINGS

Foetal exposure to maternal smoking had a greater impact on males than females, with more differentially expressed genes in liver tissue (3313 vs. 1163) and higher liver pathway activation. In the UKB, maternal smoking exposure was linked to an unhealthy lifestyle, lower education, and liver damage. In a genome-wide analysis in the UKB, we leveraged the shared genetic basis between affected offspring and their mothers and identified five genome-wide significant regions. We found a low heritability of the trait (∼4%) and implicated several disease-related genes in a transcriptome-wide association study. Maternal smoking increased all-cause mortality risk (Hazard ratio and 95% CI: 1.10 [1.04; 1.16], P = 4.04 × 10-4), which was attenuated in non-smoking males.

INTERPRETATION

Although male foetuses are more affected than females by maternal smoking in pregnancy, this effect was largely reduced in middle-aged individuals. Importantly, our results highlight that the overall 10% increased mortality due to maternal smoking in pregnancy was greatly attenuated in non-smokers. This study demonstrates the importance of campaigns promoting offspring smoking prevention in families where the parent(s) smoke.

FUNDING

Funding for this project was provided by the University of Aberdeen, the Science Initiative Panel of the Institute of Medical Science, the UK Medical Research Council, the Seventh Framework Programme of the European Union under Grant Agreement 212885 (REEF), NHS Grampian Endowments grants and the European Commission Horizon Europe research grant Agreement 101094099 (INITIALISE).

Health diagnosis associated with COVID-19 death in the United States: A retrospective cohort study using electronic health records

BACKGROUND

The United States has experienced high surge in COVID-19 cases since the dawn of 2020. Identifying the types of diagnoses that pose a risk in leading COVID-19 death casualties will enable our community to obtain a better perspective in identifying the most vulnerable populations and enable these populations to implement better precautionary measures.

OBJECTIVE

To identify demographic factors and health diagnosis codes that pose a high or a low risk to COVID-19 death from individual health record data sourced from the United States.

METHODS

We used logistic regression models to analyze the top 500 health diagnosis codes and demographics that have been identified as being associated with COVID-19 death.

RESULTS

Among 223,286 patients tested positive at least once, 218,831 (98%) patients were alive and 4,455 (2%) patients died during the duration of the study period. Through our logistic regression analysis, four demographic characteristics of patients; age, gender, race and region, were deemed to be associated with COVID-19 mortality. Patients from the West region of the United States: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming had the highest odds ratio of COVID-19 mortality across the United States. In terms of diagnoses, Complications mainly related to pregnancy (Adjusted Odds Ratio, OR:2.95; 95% Confidence Interval, CI:1.4 - 6.23) hold the highest odds ratio in influencing COVID-19 death followed by Other diseases of the respiratory system (OR:2.0; CI:1.84 - 2.18), Renal failure (OR:1.76; CI:1.61 - 1.93), Influenza and pneumonia (OR:1.53; CI:1.41 - 1.67), Other bacterial diseases (OR:1.45; CI:1.31 - 1.61), Coagulation defects, purpura and other hemorrhagic conditions(OR:1.37; CI:1.22 - 1.54), Injuries to the head (OR:1.27; CI:1.1 - 1.46), Mood [affective] disorders (OR:1.24; CI:1.12 - 1.36), Aplastic and other anemias (OR:1.22; CI:1.12 - 1.34), Chronic obstructive pulmonary disease and allied conditions (OR:1.18; CI:1.06 - 1.32), Other forms of heart disease (OR:1.18; CI:1.09 - 1.28), Infections of the skin and subcutaneous tissue (OR: 1.15; CI:1.04 - 1.27), Diabetes mellitus (OR:1.14; CI:1.03 - 1.26), and Other diseases of the urinary system (OR:1.12; CI:1.03 - 1.21).

CONCLUSION

We found demographic factors and medical conditions, including some novel ones which are associated with COVID-19 death. These findings can be used for clinical and public awareness and for future research purposes.

Efficient blockLASSO for polygenic scores with applications to all of us and UK Biobank

We develop a "block" LASSO (blockLASSO) approach for training polygenic scores (PGS) and demonstrate its use in All of Us (AoU) and the UK Biobank (UKB). blockLASSO utilizes the approximate block diagonal structure (due to chromosomal partition of the genome) of linkage disequilibrium (LD). The new implementation can be used for exploratory and methods research where repeated PGS training is necessary and expensive. For 11 different phenotypes, in two different biobanks, and across 5 different ancestry groups (African, American, East Asian, European, and South Asian) - we demonstrate that blockLASSO is generally as effective for training PGS as a (global) LASSO. Previous work has shown penalized regression methods produce competitive PGS to alternative approaches. It has been shown that some phenotypes are more/less polygenic than others. Using sparse algorithms, an accurate PGS can be trained for type 1 diabetes (T1D) using ∼ 100 single nucleotide variants (SNVs), but a PGS for body mass index (BMI) would need more than 10k SNVs. blockLASSO produces similar PGS for phenotypes while training with just a fraction of the variants per block. Within AoU (using only genetic information) block PGS for T1D reaches an AUC of 0 . 63 ± 0.02 and for BMI a correlation of 0 . 21 ± 0.01 , whereas a global LASSO approach which finds for T1D an AUC 0 . 65 ± 0.03 and BMI a correlation 0 . 19 ± 0.03 . This new block approach is more computationally efficient and scalable than naive global machine learning approaches and makes it ideal for exploratory methods investigations based on penalized regression.

Longitudinal Analysis of Electronic Health Records Reveals Medical Conditions Associated with Subsequent Alzheimer's Disease Development

Background

Several health conditions are known to increase the risk of Alzheimer's disease (AD). We aim to systematically identify medical conditions that are associated with subsequent development of AD by leveraging the growing resources of electronic health records (EHRs).

Methods

This retrospective cohort study used de-identified EHRs from two independent databases (MarketScan and VUMC) with 153 million individuals to identify AD cases and age- and gender-matched controls. By tracking their EHRs over a 10-year window before AD diagnosis and comparing the EHRs between AD cases and controls, we identified medical conditions that occur more likely in those who later develop AD. We further assessed the genetic underpinnings of these conditions in relation to AD genetics using data from two large-scale biobanks (BioVU and UK Biobank, total N=450,000).

Results

We identified 43,508 AD cases and 419,455 matched controls in MarketScan, and 1,320 AD cases and 12,720 matched controls in VUMC. We detected 406 and 102 medical phenotypes that are significantly enriched among the future AD cases in MarketScan and VUMC databases, respectively. In both EHR databases, mental disorders and neurological disorders emerged as the top two most enriched clinical categories. More than 70 medical phenotypes are replicated in both EHR databases, which are dominated by mental disorders (e.g., depression), neurological disorders (e.g., sleep orders), circulatory system disorders (e.g. cerebral atherosclerosis) and endocrine/metabolic disorders (e.g., type 2 diabetes). We identified 19 phenotypes that are either associated with individual risk variants of AD or a polygenic risk score of AD.

Conclusions

In this study, analysis of longitudinal EHRs from independent large-scale databases enables robust identification of health conditions associated with subsequent development of AD, highlighting potential opportunities of therapeutics and interventions to reduce AD risk.

Systematic method for classifying multiple congenital anomaly cases in electronic health records

PURPOSE

Congenital anomalies (CAs) affect approximately 3% of live births and are the leading cause of infant morbidity and mortality. Many individuals have multiple CAs (MCA), a constellation of 2 or more unrelated CAs; yet, there is no consensus on how to systematically identify these individuals in electronic health records (EHRs). We developed a scalable method to characterize MCA in the EHR, allowing for the dramatic improvement of our understanding of the genetic and epidemiologic underpinnings of MCA.

METHODS

From the Vanderbilt University Medical Center's anonymized EHR database, we evaluated 3 different approaches for classifying MCA, including a novel approach that removed minor vs major differentiation and their associated clinical utilization and population characteristics. Using phenome-wide association studies, we assessed the phenome associated with previously classified minor CAs.

RESULTS

Our proposed universal method for MCA identification in the EHR is accurate (positive predictive value = 97.1%), associated with heightened hospital utilization (41% receiving inpatient care), and captures granular patterns of CAs. A secondary application of our method was done in 2 separate cohorts.

CONCLUSION

We developed a method to comprehensively identify individuals with MCA in the EHR, allowing researchers to better investigate the genetic etiologies of MCA. This method can be applied across EHR databases with billing codes.

Atlas of genetic and phenotypic associations across 42 female reproductive health diagnoses

The genetic background of many female reproductive health diagnoses remains uncharacterized, compromising our understanding of the underlying biology. Here, we map the genetic architecture across 42 female-specific health conditions using data from up to 293,618 women from two large population-based cohorts, the Estonian Biobank and the FinnGen study. Our study illustrates the utility of genetic analyses in understanding women's health better. As specific examples, we describe genetic risk factors for ovarian cysts that elucidate the genetic determinants of folliculogenesis and, by leveraging population-specific variants, uncover new candidate genes for uterine fibroids. We find that most female reproductive health diagnoses have a heritable component, with varying degrees of polygenicity and discoverability. Finally, we identify pleiotropic loci and genes that function in genital tract development (WNT4, PAX8, WT1, SALL1), hormonal regulation (FSHB, GREB1, BMPR1B, SYNE1/ESR1) and folliculogenesis (CHEK2), underlining their integral roles in female reproductive health.

Transcriptomic imputation identifies tissue-specific genes associated with cervical myelopathy

BACKGROUND CONTEXT

Degenerative cervical myelopathy (DCM) is a progressive spinal condition that can lead to severe neurological dysfunction. Despite its degenerative pathophysiology, family history has shown to be a largely important factor in incidence and progression, suggesting that inherent genetic predisposition may play a role in pathophysiology.

PURPOSE

To determine the tissue-specific, functional genetic basis of hereditary predisposition to cervical myelopathy.

STUDY DESIGN

Retrospective case-control study using patient genetics and matched EHR from the Mount Sinai BioMe Biobank.

METHODS

In a large, diverse, urban biobank of 32,031 individuals, with 558 individuals with cervical myopathy, we applied transcriptomic imputation to identify genetically regulated gene expression signatures associated with DCM. We performed drug-repurposing analysis using the CMAP database to identify candidate therapeutic interventions to reverse the cervical myelopathy-associated gene signature.

RESULTS

We identified 16 genes significantly associated with DCM across 5 different tissues, suggesting tissue-specific manifestations of inherited genetic risk (upregulated: HES6, PI16, TMEM183A, BDH2, LINC00937, CLEC4D, USP43, SPATA1; downregulated: TTC12, CDK5, PAFAH1B2, RCSD1, KLHL29, PTPRG, RP11-620J15.3, C1RL). Drug repurposing identified 22 compounds with the potential to reverse the DCM-associated signature, suggesting points of therapeutic intervention.

CONCLUSIONS

The inherited genetic risk for cervical myelopathy is functionally associated with genes involved in tissue-specific nociceptive and proliferative processes. These signatures may be reversed by candidate therapeutics with nociceptive, calcium channel modulating, and antiproliferative effects.

CLINICAL SIGNIFICANCE

Understanding the genetic basis of DCM provides critical insights into the hereditary factors contributing to the disease, allowing for more personalized and targeted therapeutic approaches. The identification of candidate drugs through transcriptomic imputation and drug repurposing analysis offers potential new treatments that could significantly improve patient outcomes and quality of life by addressing the underlying genetic mechanisms of DCM.

Evaluation of polygenic scores for hypertrophic cardiomyopathy in the general population and across clinical settings

Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality, with pathogenic variants found in about a third of cases. Large-scale genome-wide association studies (GWAS) demonstrate that common genetic variation contributes to HCM risk. Here we derive polygenic scores (PGS) from HCM GWAS and genetically correlated traits and test their performance in the UK Biobank, 100,000 Genomes Project, and clinical cohorts. We show that higher PGS significantly increases the risk of HCM in the general population, particularly among pathogenic variant carriers, where HCM penetrance differs 10-fold between those in the highest and lowest PGS quintiles. Among relatives of HCM probands, PGS stratifies risks of developing HCM and adverse outcomes. Finally, among HCM cases, PGS strongly predicts the risk of adverse outcomes and death. These findings support the broad utility of PGS across clinical settings, enabling tailored screening and surveillance and stratification of risk of adverse outcomes.

Unsupervised Learning-Derived Complex Metabolic Signatures Refine Cardiometabolic Risk

BACKGROUND

Cardiometabolic diseases have become a leading cause of morbidity and mortality globally. Nuclear magnetic resonance metabolomics represents a precise tool for assessing metabolic individuality.

OBJECTIVES

This study aimed to use unsupervised learning to decode plasma metabolomic profiles, providing new insights into the etiology of cardiometabolic diseases.

METHODS

We applied unsupervised learning to generate robust metabolic signatures from the plasma profiles of 118,001 UK Biobank participants. Phenome-wide and genome-wide association studies were conducted to reveal their phenomic and genetic architectures. Integrated prospective cohort analyses and Mendelian randomization clarified their roles in cardiometabolic risks.

RESULTS

Eleven metabolic clusters were identified, linked to 101 loci and 445 phenotypes, mostly cardiometabolic diseases. These novel signatures partially outperformed traditional lipids in cardiometabolic risk prediction. Triglyceride-rich lipoproteins demonstrated superior predictive power for ischemic heart disease, type 2 diabetes, and hypertension, compared with apolipoprotein B and lipoprotein(a). Non-high-density lipoprotein cholesterol was found to increase the risk of hyperlipidemia and ischemic heart disease while offering a protective effect against type 2 diabetes. Furthermore, different high-density lipoprotein clusters showed heterogeneous associations with cardiometabolic diseases, with high-density lipoprotein subpopulations enriched in free cholesterol or triglyceride increasing risk, and those enriched in cholesterol esters providing protection.

CONCLUSIONS

These metabolic signatures extract comprehensive information from the metabolomic profile while maintaining clarity and interpretability, facilitating clinical translation. The findings emphasize the crucial roles of lipid subpopulations in cardiometabolic risks, encouraging clinicians to take a more nuanced approach to managing blood lipids and balancing different disease risks.

Towards Scalable Biomarker Discovery in Posttraumatic Stress Disorder: Triangulating Genomic and Phenotypic Evidence from a Health System Biobank

Importance

Biomarkers can potentially improve the diagnosis, monitoring, and treatment of posttraumatic stress disorder (PTSD). However, PTSD biomarkers that are scalable and easily integrated into real-world clinical settings have not been identified.

Objective

To triangulate phenotypic and genomic evidence from a health system biobank with a goal of identifying scalable and clinically relevant biomarkers for PTSD.

Design setting and participants

The analysis was conducted between June to November 2024 using genomic samples and laboratory test results recorded in the Mass General Brigham (MGB) Health System. The analysis included 23,743 European ancestry participants from the nested MGB Biobank study.

Exposures

The first exposure was polygenic risk score (PRS) for PTSD, calculated using the largest available European ancestry genome-wide association study (GWAS), employing a Bayesian polygenic scoring method. The second exposure was a clinical diagnosis of PTSD, determined by the presence of two or more qualifying PTSD phecodes in the longitudinal electronic health records (EHR).

Main outcomes and measures

The primary outcomes were the inverse normal quantile transformed, median lab values of 241 laboratory traits with non-zero h 2 SNP estimates.

Results

Sixteen unique laboratory traits across the cardiometabolic, hematologic, hepatic, and immune systems were implicated in both genomic and phenotypic lab-wide association scans (LabWAS). Two-sample Mendelian randomization analyses provided evidence of potential unidirectional causal effects of PTSD liability on five laboratory traits.

Conclusion and relevance

These findings demonstrate the potential of a triangulation approach to uncover scalable and clinically relevant biomarkers for PTSD.

Insights from the largest diverse ancestry sex-specific disease map for genetically predicted height

We performed ancestry and sex specific Phenome Wide Association Studies (PheWAS) to explore disease related outcomes associated with genetically predicted height. This is the largest PheWAS on genetically predicted height involving up to 840,000 individuals of diverse ancestry. We explored European, African, East Asian ancestries and Hispanic population groups. Increased genetically predicted height is associated with hyperpotassemia and autism in the male cross-ancestry analysis. We report male-only European ancestry associations with anxiety disorders, post-traumatic stress and substance addiction and disorders. We identify a signal with benign neoplasm of other parts of digestive system in females. We report associations with a series of disorders, several with no prior evidence of association with height, involving mental disorders and the endocrine system. Our study suggests that increased genetically predicted height is associated with higher prevalence of many clinically relevant traits which has important implications for epidemiological and clinical disease surveillance and risk stratification.

Large-Scale Pharmacogenomics Analysis of Patients With Cancer Within the 100,000 Genomes Project Combining Whole-Genome Sequencing and Medical Records to Inform Clinical Practice

PURPOSE

As part of the 100,000 Genomes Project, we set out to assess the potential viability and clinical impact of reporting genetic variants associated with drug-induced toxicity for patients with cancer recruited for whole-genome sequencing (WGS) as part of a genomic medicine service.

METHODS

Germline WGS from 76,805 participants was analyzed for pharmacogenetic (PGx) variants in four genes (DPYD, NUDT15, TPMT, UGT1A1) associated with toxicity induced by five drugs used in cancer treatment (capecitabine, fluorouracil, mercaptopurine, thioguanine, irinotecan). Linking genomic data with prescribing and hospital incidence records, a phenome-wide association study (PheWAS) was performed to identify whether phenotypes indicative of adverse drug reactions (ADRs) were enriched in drug-exposed individuals with the relevant PGx variants. In a subset of 7,081 patients with cancer, DPYD variants were reported back to clinicians and outcomes were collected.

RESULTS

We identified clinically relevant PGx variants across the four genes in 62.7% of participants in our cohort. Extending this to annual prescription numbers in England for the drugs affected by these PGx variants, approximately 14,540 patients per year could potentially benefit from a reduced dose or alternative drug to reduce the risk of ADRs. Validating PGx associations in a real-world data set, we found a significant association between PGx variants in DPYD and toxicity-related phenotypes in patients treated with capecitabine or fluorouracil. Reported DPYD variants were deemed informative for clinical decision making in a majority of cases.

CONCLUSION

Reporting PGx variants from germline WGS relevant to patients with cancer alongside primary findings related to their cancer can be clinically informative, informing prescribing to reduce the risk of ADRs. Extending the range of actionable variants to those found in patients of non-European ancestry is important and will extend the potential clinical impact.

Type 2 diabetes polygenic risk score demonstrates context-dependent effects and associations with type 2 diabetes-related risk factors and complications across diverse populations

Polygenic risk scores (PRS) hold prognostic value for identifying individuals at higher risk of type 2 diabetes (T2D). However, further characterization is needed to understand the generalizability of T2D PRS in diverse populations across various contexts. We characterized a multi-ancestry T2D PRS among 244,637 cases and 637,891 controls across eight populations from the Population Architecture Genomics and Epidemiology (PAGE) Study and 13 additional biobanks and cohorts. PRS performance was context dependent, with better performance in those who were younger, male, with a family history of T2D, without hypertension, and not obese or overweight. Additionally, the PRS was associated with various diabetes-related cardiometabolic traits and T2D complications, suggesting its utility for stratifying risk of complications and identifying shared genetic architecture between T2D and other diseases. These findings highlight the need to account for context when evaluating PRS as a tool for T2D risk prognostication and potentially generalizable associations of T2D PRS with diabetes-related traits despite differential performance in T2D prediction across diverse populations.

Distinguishing clinical and genetic risk factors for suicidal ideation and behavior in a diverse hospital population

Suicidal ideation (SI) and behavior (SB) are major public health concerns, but risk factors for their development and progression are poorly understood. We used ICD codes and a natural language processing algorithm to identify individuals in a hospital biobank with SI-only, SB, and controls without either. We compared the profiles of SB and SI-only patients to controls, and each other, using phenome-wide association studies (PheWAS) and polygenic risk scores (PRS). PheWAS identified many risk factors for SB and SI-only, plus specific psychiatric disorders which may be involved in progression from SI-only to SB. PRS for suicide attempt were only associated with SB, and even after accounting for psychiatric disorder PRS. SI PRS were only associated with SI-only, although not after accounting for psychiatric disorder PRS. These findings advance understanding of distinct genetic and clinical risk factors for SB and SI-only, which will aid in early detection and intervention efforts.

Interstitial Cystitis: a phenotype and rare variant exome sequencing study: Interstitial Cystitis: a phenotype and exome sequencing study

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a poorly understood and underdiagnosed syndrome of chronic bladder/pelvic pain with urinary frequency and urgency. Though IC/BPS can be hereditary, little is known of its genetic etiology. Using the eMERGE data, we confirmed known phenotypic associations such as gastroesophageal reflux disease and irritable bowel syndrome and detected new associations, including osteoarthrosis/osteoarthritis and Barrett's esophagus. An exome wide ultra-rare variants analysis in 348 IC/BPS and 11,981 controls extended the previously reported association with ATP2C1 and ATP2A2, implicated in Mendelian desquamating skin disorders, but did not provide evidence for other previously proposed pathogenic pathways such as bladder development, nociception or inflammation. Pathway analysis detected new associations with "anaphase-promoting complex-dependent catabolic process", the "regulation of MAPK cascade" and "integrin binding". These findings suggest perturbations in biological networks for epithelial integrity and cell cycle progression in IC/BPS pathogenesis, and provide a roadmap for its future investigation.

A phenome-wide association study of polygenic scores for selected childhood cancer: Results from the UK Biobank

The aim of this study was to scan phenotypes in adulthood associated with polygenic risk scores (PRS) for childhood cancers with well-articulated genetic architectures-acute lymphoblastic leukemia (ALL), Ewing sarcoma, and neuroblastoma-to examine genetic pleiotropy. Furthermore, we aimed to determine which SNPs could drive associations. Per-SNP summary statistics were extracted for PRS calculation. Participants with white British ancestry were exclusively included for analyses. SNPs were queried from the UK Biobank genotype imputation data. Records from the cancer registry, death registry, and inpatient diagnoses were abstracted for phenome-wide scans. Firth logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) alongside corresponding p values, adjusting for age at recruitment and sex. A total of 244,332 unrelated white British participants were included. We observed a significant association between ALL-PRS and ALL (OR: 1.20e+24, 95% CI: 9.08e+14-1.60e+33). In addition, we observed a significant association between high-risk neuroblastoma PRS and nonrheumatic aortic valve disorders (OR: 43.9, 95% CI: 7.42-260). There were no significant phenotype associations with Ewing sarcoma and neuroblastoma PRS. Regarding individual SNPs, rs17607816 increased the risk of ALL (OR: 6.40, 95% CI: 3.26-12.57). For high-risk neuroblastoma, rs80059929 elevated the risk of atrioventricular block (OR: 3.04, 95% CI: 1.85-4.99). Our findings suggest that individuals with genetic susceptibility to ALL may face a lifelong risk for developing ALL, along with a genetic pleiotropic association between high-risk neuroblastoma and circulatory diseases.

A genome-wide association study of high-sensitivity C-reactive protein in a large Korean population highlights its genetic relationship with cholesterol metabolism

High-sensitivity C-reactive protein (hsCRP) is a representative biomarker of systemic inflammation and is associated with numerous chronic diseases. To explore the biological pathways and functions underlying chronic inflammation, we conducted a genome-wide association study (GWAS) and several post-GWAS analyses of the hsCRP levels. This study was performed on data from 71,019 Koreans and is one of the largest East Asian studies. Overall, 69 independent single nucleotide polymorphisms (SNPs) were identified, including 13 novel variants. The implicated genes and pathways are primarily involved in cholesterol metabolism and the immune response. A phenome-wide association study was performed based on a polygenic risk score (PRS) constructed using 69 hsCRP-associated SNPs. Notably, the alleles associated with higher hsCRP levels appeared to be associated with lower low-density lipoprotein cholesterol levels (P = 1.69 × 10-33, β = -1.47) and higher γ -glutamyl transpeptidase (P = 8.30 × 10-8, β = 0.84). It suggests that increase in genetically determined hsCRP may contribute to a decrease in cholesterol level and a strong oxidative environment in the blood vessel. Thus, individuals with higher hsCRP-PRS may have a greater risk of cardiovascular diseases. Our findings suggest the genetic association between cholesterol and hsCRP, as well as the clinical importance of hsCRP-PRS for predicting the potential risk of cardiovascular diseases.

Machine learning derived retinal pigment score from ophthalmic imaging shows ethnicity is not biology

Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as a surrogate marker for biological variability. We derived a continuous, measured metric, the retinal pigment score (RPS), that quantifies the degree of pigmentation from a colour fundus photograph of the eye. RPS was validated using two large epidemiological studies with demographic and genetic data (UK Biobank and EPIC-Norfolk Study) and reproduced in a Tanzanian, an Australian, and a Chinese dataset. A genome-wide association study (GWAS) of RPS from UK Biobank identified 20 loci with known associations with skin, iris and hair pigmentation, of which eight were replicated in the EPIC-Norfolk cohort. There was a strong association between RPS and ethnicity, however, there was substantial overlap between each ethnicity and the respective distributions of RPS scores. RPS decouples traditional demographic variables from clinical imaging characteristics. RPS may serve as a useful metric to quantify the diversity of the training, validation, and testing datasets used in the development of AI algorithms to ensure adequate inclusion and explainability of the model performance, critical in evaluating all currently deployed AI models. The code to derive RPS is publicly available at: https://github.com/uw-biomedical-ml/retinal-pigmentation-score .

Sexual Trauma, Polygenic Scores, and Mental Health Diagnoses and Outcomes

Importance

Leveraging real-world clinical biobanks to investigate the associations between genetic and environmental risk factors for mental illness may help direct clinical screening efforts and evaluate the portability of polygenic scores across environmental contexts.

Objective

To examine the associations between sexual trauma, polygenic liability to mental health outcomes, and clinical diagnoses of schizophrenia, bipolar disorder, and major depressive disorder in a clinical biobank setting.

Design, Setting, and Participants

This genetic association study was conducted using clinical and genotyping data from 96 002 participants across hospital-linked biobanks located at Vanderbilt University Medical Center (VUMC), Nashville, Tennessee (including 58 262 individuals with high genetic similarity to the 1000 Genomes Project [1KG] Northern European from Utah reference population [1KG-EU-clustered] and 11 047 with high genetic similarity to the 1KG African-ancestry reference population of Yoruba in Ibadan, Nigeria [1KG-YRI-clustered]), and Mass General Brigham (MGB), Boston, Massachusetts (26 693 individuals with high genetic similarity to the combined European-ancestry superpopulation [1KG-EU-clustered]). Clinical data analyzed included diagnostic billing codes and clinical notes spanning from 1976 to 2023. Data analysis was performed from 2022 to 2024.

Exposures

Clinically documented sexual trauma disclosures and polygenic scores for schizophrenia, bipolar disorder, and major depressive disorder.

Main Outcomes and Measures

Diagnoses of schizophrenia, bipolar disorder, and major depressive disorder, determined by aggregating related diagnostic billing codes, were the dependent variables in logistic regression models including sexual trauma disclosure status, polygenic scores, and their interactions as the independent variables.

Results

Across the VUMC and MGB biobanks, 96 002 individuals were included in analyses (VUMC 1KG-EU-clustered: 33 011 [56.7%] female; median [range] age, 56.8 [10.0 to >89] years; MGB 1KG-EU-clustered: 14 647 [54.9%] female; median [range] age, 58.0 [10.0 to >89] years; VUMC 1KG-YRI-clustered: 6961 [63.0%] female; median [range] age, 44.6 [10.1 to >89] years). Sexual trauma history was associated with all mental health conditions across institutions (ORs ranged from 8.83 [95% CI, 5.50-14.18] for schizophrenia in the VUMC 1KG-YRI-clustered cohort to 17.65 [95% CI, 12.77-24.40] for schizophrenia in the VUMC 1KG-EU-clustered cohort). Sexual trauma history and polygenic scores jointly explained 3.8% to 8.8% of mental health phenotypic variance. Schizophrenia and bipolar disorder polygenic scores had greater associations with mental health outcomes in individuals with no documented disclosures of sexual trauma (schizophrenia interaction: OR, 0.70 [95% CI, 0.56-0.88]; bipolar disorder interaction: OR, 0.83 [95% CI, 0.74-0.94]).

Conclusions and Relevance

Sexual trauma and mental health polygenic scores, while correlated with one another, were independent and joint risk factors for severe mental illness in a large, diverse hospital biobank population. Furthermore, associations of schizophrenia and bipolar disorder polygenic scores with respective diagnoses were greater in those without disclosures, suggesting that genetic predisposition to mental illness as measured by polygenic scores may be less impactful in the presence of this severe environmental risk factor.

PheWAS analysis on large-scale biobank data with PheTK

SUMMARY

With the rapid growth of genetic data linked to electronic health record (EHR) 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 EHR data. Previous PheWAS packages were developed mostly with smaller datasets 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, on GitHub under GNU General Public License (GPL-3) at https://github.com/nhgritctran/PheTK, and on Zenodo, DOI 10.5281/zenodo.14217954, at https://doi.org/10.5281/zenodo.14217954. PheTK is implemented in Python and platform independent.

A study of impulsivity and adverse childhood experiences in a population health setting

As complex mental health traits and life histories are often poorly captured in hospital systems, the utility of using the Barratt Impulsivity Scale (BIS) and Adverse Childhood Experiences (ACEs) for assessing adult disease risks is unknown. Here, we use participants from the Healthy Nevada Project (HNP) to determine if two standard self-assessments could predict the incidence and onset of disease. We conducted a retrospective cohort study involving adult participants who completed the Behavioral and Mental Health Self-Assessment (HDSA) between September 2018 and March 2024. Impulsivity levels were measured using the BIS-15, and retrospective self-reports of ACEs were collected through a standardized questionnaire. In total, 17,482 HNP participants completed the HDSA. Our findings indicate that ACEs were significantly associated with impulsivity. Disease associations with impulsivity and ACEs were evaluated using a phenome-wide association study, identifying 230 significant associations with impulsivity. Among these, 44 were related to mental health diagnoses, including major depressive disorder (MDD). Kaplan-Meier survival estimates characterized the differences in the lifetime predicted probability between high and low impulsivity for major depressive disorder and essential hypertension. This analysis showed that having both high ACEs and high impulsivity confer substantial risk of MDD diagnosis (hazard ratios 2.81, 2.17, respectively). Additionally, lifetime predicted probability of MDD was approximately 40% higher for high ACEs and high impulsivity compared to no ACEs and low impulsivity. Essential hypertension demonstrated similar trends, with an approximate 20% increase in predicted lifetime probability of diagnosis. These results demonstrate that high ACES and elevated impulsivity scores are associated with a range of negative health outcomes and a simple self-assessment of complex traits and life history may significantly impact clinical risk assessments.

Phenome-wide diagnostic comparison among suicide deaths and living individuals with chronic pain diagnoses

BACKGROUND

Chronic pain, regardless of its type, is a significant risk factor for suicide. However, not all individuals with chronic pain also experience suicidal thoughts and behaviors. Better characterization of clinical risk profiles and comorbidities across the medical spectrum among people with chronic pain who die by suicide is urgently needed to aid treatment and prevention strategies.

METHODS

This case-control study leverages population-based data from the Utah Suicide Mortality Risk Study. Specifically, we identify clinical phenotypes from diagnostic data that differentiate between individuals that died by suicide with chronic pain diagnoses (N = 1,410) and living control individuals who also had chronic pain diagnoses (N = 4,664). Medical diagnostic codes were aggregated via phecodes to perform a phenotype-based phenome-wide association study. Using multivariable logistic regression analysis adjusting for covariates and multiple testing, differences in 1,727 common clinical phenotypes (phecodes) were assessed between suicide deaths and controls with chronic pain diagnoses. Models were also stratified by sex.

RESULTS

Chronic pain diagnoses were nearly three times more prevalent in individuals who died by suicide compared with those who did not. Sixty-five phecodes were significantly overrepresented among suicide deaths with chronic pain diagnoses compared with controls with chronic pain diagnoses. Utah suicide deaths with chronic pain had significantly more psychiatric diagnoses (mood disorders, anxiety disorders, attention deficit hyperactivity disorder, posttraumatic stress disorder, personality disorders, schizophrenia/psychosis, substance use related traits and prior overdoses, and diagnoses related to previous suicidal thoughts and behaviors) in addition to insomnia and specific pain related diagnoses compared to Utah controls with chronic pain (odds ratios ranged from 1.40-7.10). Twenty-five phecodes were overrepresented in controls with chronic pain compared to suicides. These were related to preventative care, cancer, obesity and other conditions (odds ratios ranged from 0.16-0.73). Sex-specific analyses largely replicated the combined analyses, yet the strength of the association was stronger for women with phecodes related to prior self-harm.

CONCLUSIONS

Results identified multiple clinical comorbidities with chronic pain that differentiate suicide deaths from living control individuals with a history of diagnosed chronic pain. Our findings may help discern individuals with chronic pain who may be at greater risk for suicide death.

Genome-wide association analyses identify distinct genetic architectures for age-related macular degeneration across ancestries

To effectively reduce vision loss due to age-related macular generation (AMD) on a global scale, knowledge of its genetic architecture in diverse populations is necessary. A critical element, AMD risk profiles in African and Hispanic/Latino ancestries, remains largely unknown. We combined data in the Million Veteran Program with five other cohorts to conduct the first multi-ancestry genome-wide association study of AMD and discovered 63 loci (30 novel). We observe marked cross-ancestry heterogeneity at major risk loci, especially in African-ancestry populations which demonstrate a primary signal in a major histocompatibility complex class II haplotype and reduced risk at the established CFH and ARMS2/HTRA1 loci. Dissecting local ancestry in admixed individuals, we find significantly smaller marginal effect sizes for CFH risk alleles in African ancestry haplotypes. Broadening efforts to include ancestrally distinct populations helped uncover genes and pathways that boost risk in an ancestry-dependent manner and are potential targets for corrective therapies.

Bidirectional Risk Modulator and Modifier Variant of Dilated and Hypertrophic Cardiomyopathy in BAG3

Importance

The genetic factors that modulate the reduced penetrance and variable expressivity of heritable dilated cardiomyopathy (DCM) are largely unknown. BAG3 genetic variants have been implicated in both DCM and hypertrophic cardiomyopathy (HCM), nominating BAG3 as a gene that harbors potential modifier variants in DCM.

Objective

To interrogate the clinical traits and diseases associated with BAG3 coding variation.

Design, Setting, and Participants

This was a cross-sectional study in the Penn Medicine BioBank (PMBB) enrolling patients of the University of Pennsylvania Health System's clinical practice sites from 2014 to 2023. Whole-exome sequencing (WES) was linked to electronic health record (EHR) data to associate BAG3 coding variants with EHR phenotypes. This was a health care population-based study including individuals of European and African genetic ancestry in the PMBB with WES linked to EHR phenotypes, with replication studies in BioVU, UK Biobank, MyCode, and DCM Precision Medicine Study.

Exposures

Carrier status for BAG3 coding variants.

Main Outcomes and Measures

Association of BAG3 coding variation with clinical diagnoses, echocardiographic traits, and longitudinal outcomes.

Results

In PMBB (n = 43 731; median [IQR] age, 65 [50-76] years; 21 907 female [50.1%]), among 30 324 European and 11 198 African individuals, the common C151R variant was associated with decreased risk for DCM (odds ratio [OR], 0.85; 95% CI, 0.78-0.92) and simultaneous increased risk for HCM (OR, 1.59; 95% CI, 1.25-2.02), which was confirmed in the replication cohorts. C151R carriers exhibited improved longitudinal outcomes compared with noncarriers as assessed by age at death (hazard ratio [HR], 0.85; 95% CI, 0.74-0.96; median [IQR] age, 71.8 [63.1-80.7] in carriers and 70.3 [61.6-79.2] in noncarriers) and heart transplant (HR, 0.81; 95% CI, 0.66-0.99; median [IQR] age, 56.7 [46.1-63.1] in carriers and 55.6 [45.2-62.9] in noncarriers). C151R was associated with reduced risk of DCM (OR, 0.42; 95% CI, 0.24-0.74) and heart failure (OR, 0.27; 95% CI, 0.14-0.50) among individuals harboring truncating TTN variants in exons with high cardiac expression (n = 358).

Conclusions and Relevance

BAG3 C151R was identified as a bidirectional modulator of risk along the DCM-HCM spectrum, as well as an important genetic modifier variant in TTN-mediated DCM. This work expands on the understanding of the etiology and penetrance of DCM, suggesting that BAG3 C151R is an important genetic modifier variant contributing to the variable expressivity of DCM, warranting further exploration of its mechanisms and of genetic modifiers in DCM more broadly.

Genome-wide association analysis provides insights into the molecular etiology of dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is a leading cause of heart failure and cardiac transplantation. We report a genome-wide association study and multi-trait analysis of DCM (14,256 cases) and three left ventricular traits (36,203 UK Biobank participants). We identified 80 genomic risk loci and prioritized 62 putative effector genes, including several with rare variant DCM associations (MAP3K7, NEDD4L and SSPN). Using single-nucleus transcriptomics, we identify cellular states, biological pathways, and intracellular communications that drive pathogenesis. We demonstrate that polygenic scores predict DCM in the general population and modify penetrance in carriers of rare DCM variants. Our findings may inform the design of genetic testing strategies that incorporate polygenic background. They also provide insights into the molecular etiology of DCM that may facilitate the development of targeted therapeutics.

Sex-Specific Association Between Genetic Risk of Psychiatric Disorders and Cardiovascular Diseases

BACKGROUND

Though epidemiological studies show increased cardiovascular disease (CVD) risks among individuals with psychiatric disorders, findings on sex differences in comorbidity have been inconsistent.

METHODS

This genetic epidemiology study examined the sex-specific association between the genetic risk of 3 psychiatric disorders (major depression [MD], schizophrenia, and bipolar disorder), estimated using polygenic scores (PGSs), and risks of 3 CVDs (atrial fibrillation [AF], coronary artery disease [CAD], and heart failure [HF]) in 345 169 European-ancestry individuals (UK Biobank), with analyses replicated in an independent BioVU cohort (n=49 057). Mediation analysis was conducted to determine whether traditional CVD risk factors could explain any observed sex difference.

RESULTS

In the UK Biobank, a 1-SD increase in PGSMD was significantly associated with the incident risks of all 3 CVDs in females after multiple testing corrections (hazard ratio [HR]AF-female=1.04 [95% CI, 1.02-1.06]; P=1.5×10-4; HRCAD-female=1.07 [95% CI, 1.04-1.11]; P=2.6×10-6; and HRHF-female=1.09 [95% CI, 1.06-1.13]; P=9.7×10-10), but not in males. These female-specific associations remained even in the absence of any psychiatric disorder diagnosis or psychiatric medication use. Although mediation analysis demonstrated that the association between PGSMD and CVDs in females was partly mediated by baseline body mass index, hypercholesterolemia, hypertension, and smoking, these risk factors did not explain the higher risk compared with males. The association between PGSMD and CAD was consistent between females who were premenopausal and postmenopausal at baseline, while the association with AF and HF was only observed in the baseline postmenopausal cohort. No significant association with CVD risks was observed for the PGS of schizophrenia or bipolar disorder. The female-specific positive association of PGSMD with CAD risk was replicated in BioVU.

CONCLUSIONS

Genetic predisposition to MD confers a greater risk of CVDs in females versus males, even in the absence of any depression diagnosis. This study warrants further investigation into whether genetic predisposition to depression could be useful for improving cardiovascular risk prediction, especially in women.

Diagnostic profiles among suicide decedents with and without borderline personality disorder

BACKGROUND

Borderline personality disorder (BPD) is a debilitating condition characterized by pervasive instability across multiple major domains of functioning. The majority of persons with BPD engage in self-injury and up to 10% die by suicide - rendering persons with this condition at exceptionally elevated risk of comorbidity and premature mortality. Better characterization of clinical risk factors among persons with BPD who die by suicide is urgently needed.

METHODS

We examined patterns of medical and psychiatric diagnoses (1580 to 1700 Phecodes) among persons with BPD who died by suicide (n = 379) via a large suicide death data resource and biobank. In phenotype-based phenome-wide association tests, we compared these individuals to three other groups: (1) persons who died by suicide without a history of BPD (n = 9468), (2) persons still living with a history of BPD diagnosis (n = 280), and (3) persons who died by suicide with a different personality disorder (other PD n = 589).

RESULTS

Multivariable logistic regression models revealed that persons with BPD who died by suicide were more likely to present with co-occurring psychiatric diagnoses, and have a documented history of self-harm in the medical system prior to death, relative to suicides without BPD. Posttraumatic stress disorder was more elevated among those with BPD who died by suicide relative to the other PD group.

CONCLUSIONS

We found significant differences among persons with BPD who died by suicide and all other comparison groups. Such differences may be clinically informative for identifying high-risk subtypes and providing targeted intervention approaches.

Identifying the genetic association between severe autoimmune type 2 diabetes and the risk of focal epilepsy

Background

Observational studies suggested a bidirectional relationship between severe autoimmune type 2 diabetes and focal epilepsy. However, it remains debated whether and in which direction a causal association exists. This genetics-based study aimed to explore the relationships of severe autoimmune type 2 diabetes (T2DM) and focal epilepsy outcomes with two sample Mendelian randomization (TSMR) method.

Methods

Genetic instruments were obtained from large-scale genome-wide meta-analysis of severe autoimmune T2DM (Ncase = 452, Ncontrol = 2,744), and focal epilepsy (Ncase = 929, Ncontrol = 212,532) of European ancestry. A series of analyses were performed to select eligible genetic instruments robustly associated with each of the traits using summary-level statistics. Inverse variance weighted was used for primary analysis, with alternative 11 MR methods. A scatter plot was utilized to illustrate the association between single nucleotide polymorphism (SNP) effects on the exposure and SNP effects on the outcome. The Wald ratio for individual SNPs and their cumulative effects was depicted using a forest plot. And diagnostics and sensitivity analyses were used to evaluate if the causal estimates are robust to violations of MR underlying assumptions, including pleiotropy, heterogeneity assessment, and leave-one-out analysis. Then the results were validated using CURATED database of DisGeNET platform.

Results

For forward analysis, genetic predisposition to severe autoimmune T2DM was associated with an increased risk of focal epilepsy (Inverse variance weighted (IVW) method: OR = 1.11, 95% CI = 1.03-1.18, p = 0.012). For reverse analysis, there was no enough instrument variables of focal epilepsy on severe autoimmune T2DM. Further, the interrelation between severe autoimmune T2DM and focal epilepsy was demonstrated via variant-disease association network analysis using the instrument SNPs.

Discussion

This MR study supports a causal link between severe autoimmune T2DM and focal epilepsy. More effort should be made to screen seizure in severe autoimmune T2DM, unravel its clinical implications, and explore its role as a putative modifiable risk factor.

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.

Novel Genes Associated With Atrial Fibrillation and the Predictive Models for AF Incorporating Polygenic Risk Score and PheWAS-Derived Risk Factors

BACKGROUND

Atrial fibrillation (AF), the most common atrial arrhythmia, presents with varied clinical manifestations. Despite the identification of genetic loci associated with AF, particularly in specific populations, research within Asian ethnicities remains limited. In this study we aimed to develop predictive models for AF using AF-associated single-nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) on a substantial cohort of Taiwanese individuals, to evaluate the predictive efficacy of the model.

METHODS

There were 75,121 subjects, that included 5694 AF patients and 69,427 normal control subjects with GWAS data, and we merged polygenic risk scores from AF-associated SNPs with phenome-wide association study-derived risk factors. Advanced statistical and machine learning techniques were used to develop and evaluate AF predictive models for discrimination and calibration.

RESULTS

The study identified the top 30 significant SNPs associated with AF, predominantly on chromosomes 10 and 16, implicating genes like NEURL1, SH3PXD2A, INA, NT5C2, STN1, and ZFHX3. Notably, INA, NT5C2, and STN1 were newly linked to AF. The GWAS predictive power using polygenic risk score-continuous shrinkage analysis for AF exhibited an area under the curve of 0.600 (P < 0.001), which improved to 0.855 (P < 0.001) after adjusting for age and sex. Phenome-wide association study analysis showed the top 10 diseases associated with these genes were circulatory system diseases.

CONCLUSIONS

Integrating genetic and phenotypic data enhanced the accuracy and clinical relevance of AF predictive models. The findings suggest promise for refining AF risk assessment, enabling personalized interventions, and reducing AF-related morbidity and mortality burdens.

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

OBJECTIVES

To address the need for interactive visualization tools and databases in characterizing multimorbidity patterns across different populations, we developed the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME). This tool leverages three large-scale EHR systems to facilitate efficient analysis and visualization of disease multimorbidity, aiming to reveal both robust and novel disease associations that are consistent across different systems and to provide insight for enhancing personalized healthcare strategies.

MATERIALS AND METHODS

PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities, utilizing data from Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. It offers interactive and multifaceted visualizations for exploring multimorbidity. Incorporating an enhanced version of associationSubgraphs, PheMIME also enables dynamic analysis and inference of disease clusters, promoting the discovery of complex multimorbidity patterns. A case study on schizophrenia demonstrates its capability for generating interactive visualizations of multimorbidity networks within and across multiple systems. Additionally, PheMIME supports diverse multimorbidity-based discoveries, detailed further in online case studies.

RESULTS

The PheMIME is accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial and multiple case studies for demonstration are available at https://prod.tbilab.org/PheMIME_supplementary_materials/. The source code can be downloaded from https://github.com/tbilab/PheMIME.

DISCUSSION

PheMIME represents a significant advancement in medical informatics, offering an efficient solution for accessing, analyzing, and interpreting the complex and noisy real-world patient data in electronic health records.

CONCLUSION

PheMIME provides an extensive multimorbidity knowledge base that consolidates data from three EHR systems, and it is a novel interactive tool designed to analyze and visualize multimorbidities across multiple EHR datasets. It stands out as the first of its kind to offer extensive multimorbidity knowledge integration with substantial support for efficient online analysis and interactive visualization.

Genetic insights into the association between inflammatory bowel disease and Alzheimer's disease

Myeloid cells, including monocytes, macrophages, microglia, dendritic cells and neutrophils are a part of innate immune system, playing a major role in orchestrating innate and adaptive immune responses. Both Alzheimer's disease (AD) and inflammatory bowel disease (IBD) susceptibility loci are enriched for genes expressed in myeloid cells, but it is not clear whether these myeloid risk factors are shared between the two diseases. Leveraging results of genome-wide association studies, we investigated the causal effect of IBD (including ulcerative colitis (UC) and Crohn's disease (CD)) variants on AD and its endophenotypes. Microglia and monocyte expression Quantitative Trait Locus (eQTLs) were used to examine the functional consequences of IBD and AD variants. Our results revealed distinct sets of genes and pathways of AD and IBD susceptibility loci. Specifically, AD loci are enriched for microglial eQTLs, while IBD loci are enriched for monocyte eQTLs. However, we also found that genetically determined IBD is associated with a protective effect against AD (p<0.03). Yet, a genetic propensity for the CD subtype is associated with increased amyloid accumulation (beta=7.14, p-value=0.02) and susceptibility to AD. Susceptibility to UC was associated with increased deposition of TDP-43 (beta=7.58, p-value=6.11×10-4). The relation of these gastrointestinal inflammatory disease to AD is therefore complex; while the different subsets of susceptibility variants preferentially affect different myeloid cell subtypes, there do appear to be certain shared pathways and the possible protective effect of IBD susceptibility on the risk of AD which may provide therapeutic insights.

A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data

BACKGROUND

MicroRNAs (miRNAs) are small non-coding RNAs that post-transcriptionally regulate gene expression. Perturbations in plasma miRNA levels are known to impact disease risk and have potential as disease biomarkers. Exploring the genetic regulation of miRNAs may yield new insights into their important role in governing gene expression and disease mechanisms.

RESULTS

We present genome-wide association studies of 2083 plasma circulating miRNAs in 2178 participants of the Rotterdam Study to identify miRNA-expression quantitative trait loci (miR-eQTLs). We identify 3292 associations between 1289 SNPs and 63 miRNAs, of which 65% are replicated in two independent cohorts. We demonstrate that plasma miR-eQTLs co-localise with gene expression, protein, and metabolite-QTLs, which help in identifying miRNA-regulated pathways. We investigate consequences of alteration in circulating miRNA levels on a wide range of clinical conditions in phenome-wide association studies and Mendelian randomisation using the UK Biobank data (N = 423,419), revealing the pleiotropic and causal effects of several miRNAs on various clinical conditions. In the Mendelian randomisation analysis, we find a protective causal effect of miR-1908-5p on the risk of benign colon neoplasm and show that this effect is independent of its host gene (FADS1).

CONCLUSIONS

This study enriches our understanding of the genetic architecture of plasma miRNAs and explores the signatures of miRNAs across a wide range of clinical conditions. The integration of population-based genomics, other omics layers, and clinical data presents opportunities to unravel potential clinical significance of miRNAs and provides tools for novel miRNA-based therapeutic target discovery.

HIV persists in late coronary atheroma and is associated with increased local inflammation and disease progression

Chronic inflammation contributes to the prevalence of cardiovascular disease in people living with HIV (PLWH). The immune mechanisms driving atherosclerosis progression in PLWH remain unclear. This study conducted comprehensive assessments of medium-sized coronary arteries and aorta from deceased PLWH and controls without HIV using DNA/RNA assays, spatial transcriptomics, and high-resolution mass spectrometry. Findings revealed more significant inflammation correlated with higher HIV copy numbers in late atheroma of PLWH. Enhanced CXCL12 and decreased ABCA1/ABCG1 expression in CD163+ macrophages were co-localized in coronaries of PLWH, suggesting a reduction in plasma lipoprotein clearance compared to controls. Spatial analyses identified potential therapeutic targets by revealing inflammatory changes in medium-sized arteries and the aorta. We examined the relationship between atherosclerotic phenotypes and inflammatory gene expression in Vanderbilts Biobank to study these findings in a larger clinical cohort. This established a significant association between ABCA1 and CXCL12 gene expressions with atherosclerosis, partly influenced by HIV.

Phenome-Wide Analysis of Coffee Intake on Health over 20 Years of Follow-Up Among Adults in Hong Kong Osteoporosis Study

BACKGROUND/OBJECTIVES

There has been limited evidence on the long-term impacts of coffee intake on health. We aimed to investigate the association between coffee intake and the incidence of diseases and mortality risk over 20 years among community-dwelling Chinese adults.

METHODS

Participants were from the Hong Kong Osteoporosis Study who attended baseline assessments during 1995-2010. Coffee intake was self-reported through a food frequency questionnaire and was previously validated. Disease diagnoses, which were mapped into 1795 distinct phecodes, and mortality data were obtained from linkage with territory-wide electronic health records. Cox models were used to estimate the association between coffee intake and the incidence of each disease outcome and mortality among individuals without a history of the respective medical condition at baseline. All models were adjusted for age, sex, body mass index, smoking, alcohol drinking, and education.

RESULTS

Among the 7420 included participants (mean age 53.2 years, 72.2% women), 54.0% were non-coffee drinkers, and only 2.7% consumed more than one cup of coffee per day. Over a median follow-up of 20.0 years, any coffee intake was associated with a reduced risk of dementia, atrial fibrillation, painful respirations, infections, atopic dermatitis, and dizziness at a false discovery rate (FDR) of <0.05. Furthermore, any coffee intake was associated with an 18% reduced risk of all-cause mortality (95% confidence interval = 0.73-0.93).

CONCLUSION

In a population with relatively low coffee consumption, any coffee intake is linked to a lower risk of several neurological, circulatory, and respiratory diseases and symptoms, as well as mortality.

Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk

Lung cancer remains the leading cause of cancer mortality, despite declining smoking rates. Previous lung cancer GWAS have identified numerous loci, but separating the genetic risks of lung cancer and smoking behavioral susceptibility remains challenging. Here, we perform multi-ancestry GWAS meta-analyses of lung cancer using the Million Veteran Program cohort (approximately 95% male cases) and a previous study of European-ancestry individuals, jointly comprising 42,102 cases and 181,270 controls, followed by replication in an independent cohort of 19,404 cases and 17,378 controls. We then carry out conditional meta-analyses on cigarettes per day and identify two novel, replicated loci, including the 19p13.11 pleiotropic cancer locus in squamous cell lung carcinoma. Overall, we report twelve novel risk loci for overall lung cancer, lung adenocarcinoma, and squamous cell lung carcinoma, nine of which are externally replicated. Finally, we perform PheWAS on polygenic risk scores for lung cancer, with and without conditioning on smoking. The unconditioned lung cancer polygenic risk score is associated with smoking status in controls, illustrating a reduced predictive utility in non-smokers. Additionally, our polygenic risk score demonstrates smoking-independent pleiotropy of lung cancer risk across neoplasms and metabolic traits.

PheW2P2V: a phenome-wide prediction framework with weighted patient representations using electronic health records

Objective

Electronic health records (EHRs) provide opportunities for the development of computable predictive tools. Conventional machine learning methods and deep learning methods have been widely used for this task, with the approach of usually designing one tool for one clinical outcome. Here we developed PheW2P2V, a Phenome-Wide prediction framework using Weighted Patient Vectors. PheW2P2V conducts tailored predictions for phenome-wide phenotypes using numeric representations of patients' past medical records weighted based on their similarities with individual phenotypes.

Materials and Methods

PheW2P2V defines clinical disease phenotypes using Phecode mapping based on International Classification of Disease codes, which reduces redundancy and case-control misclassification in real-life EHR datasets. Through upweighting medical records of patients that are more relevant to a phenotype of interest in calculating patient vectors, PheW2P2V achieves tailored incidence risk prediction of a phenotype. The calculation of weighted patient vectors is computationally efficient, and the weighting mechanism ensures tailored predictions across the phenome. We evaluated prediction performance of PheW2P2V and baseline methods with simulation studies and clinical applications using the MIMIC-III database.

Results

Across 942 phenome-wide predictions using the MIMIC-III database, PheW2P2V has median area under the receiver operating characteristic curve (AUC-ROC) 0.74 (baseline methods have values ≤0.72), median max F1-score 0.20 (baseline methods have values ≤0.19), and median area under the precision-recall curve (AUC-PR) 0.10 (baseline methods have values ≤0.10).

Discussion

PheW2P2V can predict phenotypes efficiently by using medical concept embeddings and upweighting relevant past medical histories. By leveraging both labeled and unlabeled data, PheW2P2V reduces overfitting and improves predictions for rare phenotypes, making it a useful screening tool for early diagnosis of high-risk conditions, though further research is needed to assess the transferability of embeddings across different databases.

Conclusions

PheW2P2V is fast, flexible, and has superior prediction performance for many clinical disease phenotypes across the phenome of the MIMIC-III database compared to that of several popular baseline methods.

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.

Time Trends in Adolescent Diagnoses of Major Depressive Disorder and Co-occurring Psychiatric Conditions in Electronic Health Records

Major depressive disorder (MDD) is highly prevalent in youth and generally characterized by psychiatric comorbidities. Secular trends in co-occurring diagnoses remain unclear, especially in healthcare settings. Using large-scale electronic health records data from a major U.S. healthcare system, we examined the prevalence of MDD diagnoses and co-occurring psychiatric conditions during adolescence (12-18 years; N = 133,753) across four generations (birth years spanning 1985 to 2002) and by sex. Then using a phenome-wide association analysis, we explored which of 67 psychiatric conditions were associated with adolescent MDD diagnosis in earlier versus recent generations. Adolescent MDD diagnosis prevalence increased (8.9 to 11.4%) over time. Over 60% with an MDD diagnosis had co-occurring psychiatric diagnoses, especially neurodevelopmental and anxiety disorders. Co-occurring diagnoses generally increased over time, especially for anxiety disorders (14 to 50%) and suicidal behaviors (6 to 23%), across both sexes. Eight comorbidities interacted with generation, showing stronger associations with MDD diagnosis in earlier (e.g., conduct disorder) versus more recent (e.g., suicidal ideation and behaviors) generations. The findings underscore the importance of assessing psychiatric complexity in adolescents diagnosed with MDD, applying transdiagnostic approaches to address co-occurring presentations, and further investigating potential causes for generational increases.

Transforming Clinical Research: The Power of High-Throughput Omics Integration

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.