The Penn Medicine BioBank: Towards a Genomics-Enabled Learning Healthcare System to Accelerate Precision Medicine in a Diverse Population

CXCL12 drives natural variation in coronary artery anatomy across diverse populations

Coronary arteries have a specific branching pattern crucial for oxygenating heart muscle. Among humans, there is natural variation in coronary anatomy with respect to perfusion of the inferior/posterior left heart, which can branch from either the right arterial tree, the left, or both-a phenotype known as coronary dominance. Using angiographic data for >60,000 US veterans of diverse ancestry, we conducted a genome-wide association study of coronary dominance, revealing moderate heritability and identifying ten significant loci. The strongest association occurred near CXCL12 in both European- and African-ancestry cohorts, with downstream analyses implicating effects on CXCL12 expression. We show that CXCL12 is expressed in human fetal hearts at the time dominance is established. Reducing Cxcl12 in mice altered coronary dominance and caused septal arteries to develop away from Cxcl12 expression domains. These findings indicate that CXCL12 patterns human coronary arteries, paving the way for "medical revascularization" through targeting developmental pathways.

Common-variant and rare-variant genetic architecture of heart failure across the allele-frequency spectrum

Heart failure is a complex trait, influenced by environmental and genetic factors, affecting over 30 million individuals worldwide. Here we report common-variant and rare-variant association studies of all-cause heart failure and examine how different classes of genetic variation impact its heritability. We identify 176 common-variant risk loci at genome-wide significance in 2,358,556 individuals and cluster these signals into five broad modules based on pleiotropic associations with anthropomorphic traits/obesity, blood pressure/renal function, atherosclerosis/lipids, immune activity and arrhythmias. In parallel, we uncover exome-wide significant associations for heart failure and rare predicted loss-of-function variants in TTN, MYBPC3, FLNC and BAG3 using exome sequencing of 376,334 individuals. We find that total burden heritability of rare coding variants is highly concentrated in a small set of Mendelian cardiomyopathy genes, while common-variant heritability is diffusely spread throughout the genome. Finally, we show that common-variant background modifies heart failure risk among carriers of rare pathogenic truncating variants in TTN. Together, these findings discern genetic links between dysregulated metabolism and heart failure and highlight a polygenic component to heart failure not captured by current clinical genetic testing.

A genotype-first approach identifies high incidence of NF1 pathogenic variants with distinct disease associations

Loss of function variants in the NF1 gene cause neurofibromatosis type 1, a genetic disorder characterized by complete penetrance, characteristic physical exam findings, and a substantially increased risk for malignancy. However, our understanding of the disorder is based on patients ascertained through phenotype-first approaches, which estimate prevalence at 1 in 3000. Leveraging a genotype-first approach in multiple large patient cohorts including over one million individuals, we demonstrate an unexpectedly high prevalence (1 in 1,286) of NF1 pathogenic variants. Half are identified in individuals lacking clinical features of NF1, with many appearing to have post-zygotic mosaicism for the identified variant. Incidentally discovered variants are not associated with classic neurofibromatosis features but are associated with an increased incidence of malignancy compared to control populations. Our findings suggest that NF1 pathogenic variants are substantially more common than previously thought, often characterized by somatic mosaicism and reduced penetrance, and are important contributors to cancer risk in the general population.

A series of randomized trials of behavioral economic interventions to increase racial and ethnic diversity of research participants: Rationale and design of ITERATE

RATIONALE

Prospective clinical research studies are essential for determining the effectiveness and safety of drugs, medical devices, and healthcare delivery interventions. However, low enrollment, particularly among Black and Hispanic patients, challenges the generalizability of results and fairness of research. Leveraging insights from behavioral economics to modify the content of messages recruiting patients to join research studies may increase enrollment and representativeness of trial populations.

PRIMARY HYPOTHESIS

Method of outreach, source of outreach, message framing, and financial incentives will have important effects on enrollment fraction of Black and Hispanic patients electronically approached for participation in a prospective clinical research study.

DESIGN

ITERATE (NCT05827718) is a series of 4 randomized clinical trials (RCTs) designed to rigorously, systematically, and iteratively test the effects of different messaging strategies informed by behavioral economic theory on the enrollment of Black and Hispanic individuals into the Penn Medicine BioBank (PMBB), a prospective registry. For all 4 RCTs, we will identify patients eligible for enrollment in the PMBB (those with ≥ 1 encounter with the University of Pennsylvania Health System in the past 3 months, a phone number able to receive text messages or a valid email address on file, no history of consenting to or declining enrollment in the PMBB, and able to provide their own consent) and randomly assign them to receive different outreach messages. RCT 1 will test the method of outreach (email vs. text message vs. email + text message); RCT 2, source of outreach (research team vs. clinical team); RCT 3, message framing (appeal to altruism vs. appeal to social proof vs. control); and RCT 4, financial incentive (none vs. medium guarantee vs. small guarantee + small lottery vs. medium lottery vs. large lottery). In each RCT, at least 50% of the participants will be Black or Hispanic. The primary outcome of each RCT is enrollment fraction, defined as the number of participants who enroll in the PMBB divided by the total number of participants who received an outreach message, compared between arms among both Black and Hispanic patients. Secondary outcomes will include overall enrollment fraction and enrollment fraction among White patients. The "winning" strategies in earlier RCTs will be incorporated as the "standard of care" in the subsequent RCTs.

Improving Cardiovascular Health Through the Consideration of Social Factors in Genetics and Genomics Research: A Scientific Statement From the American Heart Association

Cardiovascular health (CVH) is affected by genetic, social, and genomic factors across the life course, yet little research has focused on the interrelationships among them. An extensive body of work has documented the impact of social determinants of health at both the structural and individual levels on CVH, highlighting pathways in which racism, housing, violence, and neighborhood environments adversely affect CVH and contribute to disparities in cardiovascular disease. Genetic factors have also been identified as contributors to risk for cardiovascular disease. Emerging evidence suggests that social factors can interact with genetic susceptibility to affect disease risk. Increasingly, social factors have been shown to affect epigenetic markers such as DNA methylation, which can regulate gene and protein expression. This is a potential biological mechanism through which exposure to poor social determinants of health becomes physically embodied at the molecular level, potentially contributing to the development of suboptimal CVH and chronic disease, thus reinforcing and propagating health disparities. The objective of this statement is to highlight and summarize key literature that has examined the joint associations between social, genetic, and genomic factors and CVH and cardiovascular disease.

Non-canonical splice variants in thoracic aortic dissection cases and Marfan syndrome with negative genetic testing

Individuals with heritable thoracic aortic disease (HTAD) face a high risk of deadly aortic dissections, but genetic testing identifies causative variants in only a minority of cases. We explored the contribution of non-canonical splice variants (NCVAS) to thoracic aortic disease (TAD) using SpliceAI and sequencing data from diverse cohorts, including 551 early-onset sporadic dissection cases and 437 HTAD probands with exome sequencing, 57 HTAD pedigrees with whole genome sequencing, and select sporadic cases with clinical panel testing. NCVAS were identified in syndromic HTAD genes such as FBN1, SMAD3, and COL3A1, including intronic variants in FBN1 in two Marfan syndrome (MFS) families. Validation in the Penn Medicine BioBank and UK Biobank showed enrichment of NCVAS in HTAD-associated genes among dissections. These findings suggest NCVAS are an underrecognized contributor to TAD, particularly in sporadic dissection and unsolved MFS cases, highlighting the potential of advanced splice prediction tools in genetic diagnostics.

Gene discovery and pleiotropic architecture of Chronic Pain in a Genome-wide Association Study of >1.2 million Individuals

Chronic pain is highly prevalent worldwide, and genome-wide association studies (GWAS) have identified a growing number of chronic pain loci. To further elucidate its genetic architecture, we leveraged data from 1,235,695 European ancestry individuals across three biobanks. In a meta-analytic GWAS, we identified 343 independent loci for chronic pain, 92 of which were new. Sex-specific meta-analyses revealed 115 independent loci (12 of which were new) for males (N = 583,066) and 12 loci (two of which were new) for females (N = 241,266). Multi-omics gene prioritization analyses highlighted 490 genes associated with chronic pain through their effects on brain- and blood-specific regulation. Loci associated with increased risk for chronic pain were also associated with increased risk for multiple other traits, with Mendelian randomization analyses showing that chronic pain was causally associated with psychiatric disorders, substance use disorders, and C-reactive protein levels. Chronic pain variants also exhibited pleiotropic associations with cortical area brain structures. This study expands our knowledge of the genetics of chronic pain and its pathogenesis, highlighting the importance of its pleiotropy with multiple disorders and elucidating its multi-omic pathophysiology.

Healthy lifestyle reduces cardiovascular risk in women with genetic predisposition to hypertensive disorders of pregnancy

The genetic risk for hypertensive disorders of pregnancy is linked with the development of atherosclerotic cardiovascular disease. However, the effects of lifestyle and metabolic syndrome on atherosclerotic cardiovascular disease have not been evaluated. Here, we assess the long-term association between these factors and atherosclerotic cardiovascular disease in women with genetic risk for hypertensive disorders of pregnancy. We evaluate the genetic risk for hypertensive disorders of pregnancy using a genome-wide polygenic risk score derived from a large-scale GWAS. The incidence of atherosclerotic cardiovascular disease is evaluated according to genetic risk, lifestyle, and metabolic syndrome. Individuals with a very high genetic risk for hypertensive disorders of pregnancy have a 53.0% higher chance of developing atherosclerotic cardiovascular disease than those with a low genetic risk. However, the risk of developing atherosclerotic cardiovascular disease is reduced by up to 64.6% through the maintenance of an ideal metabolic syndrome status and a healthy lifestyle in the high genetic risk group (top 20%), and by up to 65.4% in the low genetic risk group (bottom 20%). These findings emphasize that maintaining a healthy lifestyle in women is equally effective at reducing the risk of atherosclerotic cardiovascular disease independent of genetic risk for hypertensive disorders of pregnancy.

Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

Kidney dysfunction is a major cause of mortality, but its genetic architecture remains elusive. In this study, we conducted a multiancestry genome-wide association study in 2.2 million individuals and identified 1026 (97 previously unknown) independent loci. Ancestry-specific analysis indicated an attenuation of newly identified signals on common variants in European ancestry populations and the power of population diversity for further discoveries. We defined genotype effects on allele-specific gene expression and regulatory circuitries in more than 700 human kidneys and 237,000 cells. We found 1363 coding variants disrupting 782 genes, with 601 genes also targeted by regulatory variants and convergence in 161 genes. Integrating 32 types of genetic information, we present the "Kidney Disease Genetic Scorecard" for prioritizing potentially causal genes, cell types, and druggable targets for kidney disease.

Disease-associated Kv1.3 variants are energy compromised with impaired nascent chain folding

Human Kv1.3, encoded by KCNA3 , is expressed in neuronal and immune cells. Its impaired expression or function produces chronic inflammatory disease and autoimmune disorders, the severity of which correlates with Kv1.3 protein expression. The intersubunit recognition domain, T1, at the cytosolic N-terminus of Kv1.3, acquires secondary, tertiary, and quaternary structures during early biogenesis while the nascent protein is attached to the ribosome and/or the ER membrane. In this study, we ask whether native KCNA3 gene variants in T1 are associated with human disease and whether they manifest early-stage folding defects, energetic instabilities, and conformational distortion of subunits. We use three approaches: first, the unbiased "genome-first" approach to determine phenotype associations of specific KCNA3 rare variants. Second, we use biochemical assays to assess early-stage tertiary and quaternary folding and membrane association of these variants during early biogenesis. Third, we use all-atom molecular dynamics simulations of the T1 tetramer to assess structural macroscopic and energetic stability differences between wildtype (WT) Kv1.3 and a single-point variant, R114G. Measured folding probabilities and membrane associations are dramatically reduced in several of the native variants compared to WT. Simulations strikingly show that the R114G variant produces more energetically unstable and dynamic T1 domains, concomitant with tertiary unwinding and impaired formation of symmetrical tetramers. Our findings identify molecular mechanisms by which rare variants influence channel assembly, potentially contributing to diverse clinical phenotypes underlying human disease.

Strength of Genetic Associations with Thyrotropin Values Differs Between Populations with Similarity to African and European Reference Populations

Background: Epidemiological data suggest the population distribution of thyrotropin (TSH) values is shifted toward lower values in self-identified Black non-Hispanic individuals compared with self-identified White non-Hispanic individuals. It is unknown whether genetic differences between individuals with genetic similarities to African reference populations (GSA) and those with similarities to European reference populations (GSE) contribute to these observed differences. We aimed to compare genome-wide associations with TSH and putative causal TSH-associated variants between GSA and GSE groups. Methods: We performed genome-wide association studies (GWAS) in 9827 GSA individuals and 9827 GSE individuals with TSH values between 0.45 and 4.5 mU/L. We compared effect sizes and allele frequencies of previously reported putative causal TSH-associated variants and our power to detect associations with these variants between the two groups. We additionally focused on variants in PDE8B and PDE10A, loci that have been most strongly associated with TSH in previous GWAS in GSE populations. Results: Four loci attained genome-wide significance in the GSA group compared with seven in the GSE group. PDE8B was not significantly associated with TSH in the GSA group, despite its strong association in the GSE group. Eight putative causal variants had significantly different effect sizes between groups. There was ≥80% power in the GSA group to detect significant associations with variants in PDE8B, PDE10A, NFIA, and LOC105377480, with higher expected power than in the GSE group for variants in PDE8B, NFIA, and LOC105377480 and similar power for other variants in PDE8B and PDE10A. No additional putative causal variants in PDE8B and PDE10A had effect sizes that differed significantly between the groups; power to identify associations with additional putative causal variants in PDE8B and PDE10A was similar between the groups. Conclusions: Patterns of genetic associations with TSH differed between identically sized GSA and GSE groups. Failure to replicate the strongest associations previously reported in GSE individuals in our GSA population was not fully explained by differences in allele frequencies or power, assuming similar effect sizes. Larger GSA population GWAS are necessary to confirm our findings and further investigate the contribution of genetic factors to population differences in the distribution of TSH values.

Re-envisioning genetic predisposition to childhood and adolescent cancers

Although cancer is rare in children and adolescents, it remains a leading cause of death within this age range, and genetic predisposition is the main known risk factor. Since the discovery of retinoblastoma-predisposing RB1 pathogenic germline variants in 1985, several additional high-penetrance cancer predisposition genes (CPGs) have been identified. Although few clinically recognizable genetic conditions display moderate cancer phenotypes, burden testing has revealed low-to-moderate penetrance CPGs. In addition to germline pathogenic variants in CPGs, postzygotic somatic mosaic CPG pathogenic variants acquired during embryonic development are increasingly recognized as factors that predispose children and adolescents to malignancies. Genome-wide association studies of various childhood and adolescent cancer types have identified some common low-risk cancer susceptibility alleles. Although the clinical utility of polygenic risk scores is currently limited in children and adolescents, polygenic risk scores developed for adults can predict subsequent cancer risks in childhood and adolescent cancer survivors. In this Review, I describe our current knowledge of genetic predisposition to childhood and adolescent cancers. Survival rates in children and adolescents with cancer and CPGs are often poor, necessitating better integration of genomic testing into clinical care to improve cancer prevention, surveillance and therapies.

Exploring beyond diagnoses in electronic health records to improve discovery: a review of the phenome-wide association study

Objective

The phenome-wide association study (PheWAS) systematically examines the phenotypic spectrum extracted from electronic health records (EHRs) to uncover correlations between phenotypes and exposures. This review explores methodologies, highlights challenges, and outlines future directions for EHR-driven PheWAS.

Materials and Methods

We searched the PubMed database for articles spanning from 2010 to 2023, and we collected data regarding exposures, phenotypes, cohorts, terminologies, replication, and ancestry.

Results

Our search yielded 690 articles. Following exclusion criteria, we identified 291 articles published between January 1, 2010, and December 31, 2023. A total number of 162 (55.6%) articles defined phenomes using phecodes, indicating that research is reliant on the organization of billing codes. Moreover, 72.8% of articles utilized exposures consisting of genetic data, and the majority (69.4%) of PheWAS lacked replication analyses.

Discussion

Existing literature underscores the need for deeper phenotyping, variability in PheWAS exposure variables, and absence of replication in PheWAS. Current applications of PheWAS mainly focus on cardiovascular, metabolic, and endocrine phenotypes; thus, applications of PheWAS in uncommon diseases, which may lack structured data, remain largely understudied.

Conclusions

With modern EHRs, future PheWAS should extend beyond diagnosis codes and consider additional data like clinical notes or medications to create comprehensive phenotype profiles that consider severity, temporality, risk, and ancestry. Furthermore, data interoperability initiatives may help mitigate the paucity of PheWAS replication analyses. With the growing availability of data in EHR, PheWAS will remain a powerful tool in precision medicine.

Risk factors affecting polygenic score performance across diverse cohorts

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI-covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects - across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

The interplay of sex and genotype in disease associations: a comprehensive network analysis in the UK Biobank

BACKGROUND

Disease comorbidities and longer-term complications, arising from biologically related associations across phenotypes, can lead to increased risk of severe health outcomes. Given that many diseases exhibit sex-specific differences in their genetics, our objective was to determine whether genotype-by-sex (GxS) interactions similarly influence cross-phenotype associations. Through comparison of sex-stratified disease-disease networks (DDNs)-where nodes represent diseases and edges represent their relationships-we investigate sex differences in patterns of polygenicity and pleiotropy between diseases.

RESULTS

Using UK Biobank summary statistics, we built male- and female-specific DDNs for 103 diseases. This revealed that male and female diseasomes have similar topology and central diseases (e.g., hypertensive, chronic respiratory, and thyroid-based disorders), yet some phenotypes exhibit sex-specific influence in cross-phenotype associations. Multiple sclerosis and osteoarthritis are central only in the female DDN, while cardiometabolic diseases and skin cancer are more prominent in the male DDN. Edge comparison indicated similar shared genetics between the two graphs relative to a random model of disease association, though notable discrepancies in embedding distances and clustering patterns imply a more expansive genetic influence on multimorbidity risk for females than males. Analysis of pleiotropic contributions of two sexually-dimorphic single-nucleotide polymorphisms related to thyroid disorders further validated a distinct genetic architecture across sexes that influences associations, confirmed through examination of corresponding gene expression profiles from the GTEx Portal.

CONCLUSIONS

Our analysis affirms the presence of GxS interactions in cross-phenotype associations, emphasizing the need to investigate the role of sex in disease onset and its importance in biomedical discovery and precision medicine research.

Evaluating Performance and Agreement of Coronary Heart Disease Polygenic Risk Scores

Importance

Polygenic risk scores (PRSs) for coronary heart disease (CHD) are a growing clinical and commercial reality. Whether existing scores provide similar individual-level assessments of disease susceptibility remains incompletely characterized.

Objective

To characterize the individual-level agreement of CHD PRSs that perform similarly at the population level.

Design, Setting, and Participants

Cross-sectional study of participants from diverse backgrounds enrolled in the All of Us Research Program (AOU), Penn Medicine BioBank (PMBB), and University of California, Los Angeles (UCLA) ATLAS Precision Health Biobank with electronic health record and genotyping data.

Exposures

Polygenic risk for CHD from published PRSs and new PRSs developed separately from testing samples.

Main Outcomes and Measures

PRSs that performed population-level prediction similarly were identified by comparing calibration and discrimination of models of prevalent CHD. Individual-level agreement was tested with intraclass correlation coefficient (ICC) and Light κ.

Results

A total of 48 PRSs were calculated for 171 095 AOU participants. The mean (SD) age was 56.4 (16.8) years. A total of 104 947 participants (61.3%) were female. A total of 35 590 participants (20.8%) were most genetically similar to an African reference population, 29 801 (17.4%) to an admixed American reference population, 100 493 (58.7%) to a European reference population, and the remaining to Central/South Asian, East Asian, and Middle Eastern reference populations. There were 17 589 participants (10.3%) with and 153 506 participants without (89.7%) CHD. When included in a model of prevalent CHD, 46 scores had practically equivalent Brier scores and area under the receiver operator curves (region of practical equivalence ±0.02). Twenty percent of participants had at least 1 score in both the top and bottom 5% of risk. Continuous agreement of individual predictions was poor (ICC, 0.373 [95% CI, 0.372-0.375]). Light κ, used to evaluate consistency of risk assignment, did not exceed 0.56. Analysis among 41 193 PMBB and 53 092 ATLAS participants yielded different sets of equivalent scores, which also lacked individual-level agreement.

Conclusions and Relevance

CHD PRSs that performed similarly at the population level demonstrated highly variable individual-level estimates of risk. Recognizing that CHD PRSs may generate incongruent individual-level risk estimates, effective clinical implementation will require refined statistical methods to quantify uncertainty and new strategies to communicate this uncertainty to patients and clinicians.

The Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) project: A team-based approach to clinically validated, research-ready electronic health record data

Introduction

The rapid adoption of electronic health record (EHR) systems has resulted in extensive archives of data relevant to clinical research, hospital operations, and the development of learning health systems. However, EHR data are not frequently available, cleaned, standardized, validated, and ready for use by stakeholders. We describe an in-progress effort to overcome these challenges with cooperative, systematic data extraction and validation.

Methods

A multi-disciplinary team of investigators collaborated to create the Complete Inpatient Record Using Comprehensive Electronic Data (CIRCE) Project dataset, which captures EHR data from six hospitals within the University of Pennsylvania Health System. Analysts and clinical researchers jointly iteratively reviewed SQL queries and their output to validate desired data elements. Data from patients aged ≥18 years with at least one encounter at an acute care hospital or hospice occurring since 7/1/2017 were included. The CIRCE Project includes three layers: (1) raw data comprised of direct SQL query output, (2) cleaned data with errors removed, and (3) transformed data with standardized implementations of commonly used case definitions and clinical scores.

Results

Between July 1, 2017 and December 31, 2023, the dataset captured 1 629 920 encounters from 740 035 patients. Most encounters were emergency department only visits (n = 965 834, 59.3%), followed by inpatient admissions without an intensive care unit admission (n = 518 367, 23.7%). The median age was 46.9 years (25th-75th percentiles = 31.1-64.7) at the time of the first encounter. Most patients were female (n = 418 303, 56.5%), a significant proportion were of non-White race (n = 272 018, 36.8%), and 54 625 (7.4%) were of Hispanic/Latino ethnicity.

Conclusions

The CIRCE Project represents a novel cooperative research model to capture clinically validated EHR data from a large diverse academic health system in the greater Philadelphia region and is designed to facilitate collaboration and data sharing to support learning health system activities. Ultimately, these data will be de-identified and converted to a publicly available resource.

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.

Expanding the genetic landscape of endometriosis: Integrative -omics analyses uncover key pathways from a multi-ancestry study of over 900,000 women

We report the findings of a genome-wide association study (GWAS) meta-analysis of endometriosis consisting of a large portion (31%) of non-European samples across 14 biobanks worldwide as part of the Global Biobank Meta-Analysis Initiative (GBMI). We identified 45 significant loci using a wide phenotype definition, seven of which are previously unreported and detected first genome-wide significant locus ( POLR2M ) among only African-ancestry. Our narrow phenotypes and surgically confirmed case definitions for endometriosis analyses replicated the known loci near CDC42 , SKAP1 , and GREB1 . Through this large ancestry stratified analyses, we document heritability estimates in range of 10-12% for all ancestral groups. Thirty-eight loci had at least one variant in the credible set after fine-mapping. An imputed transcriptome-wide association study (TWAS) identified 11 associated genes (two previously unreported), while the proteome-wide association study (PWAS) suggests significant association of R-spondin 3 (RSPO3) with wide endometriosis, which plays a crucial role in modulating the Wnt signaling pathway. Our diverse, comprehensive GWAS, coupled with integrative -omics analysis, identifies critical roles of immunopathogenesis, Wnt signaling, and balance between proliferation, differentiation, and migration of endometrial cells as hallmarks for endometriosis. These interconnected pathways and risk factors underscore a complex, multi-faceted etiology of endometriosis, suggesting multiple targets for precise and effective therapeutic interventions.

Estimated clinical utility of multi-gene pharmacogenetic testing in a retrospective cohort of gynecology patients

OBJECTIVE

This study aimed to estimate the clinical utility of performing multi-gene pharmacogenetic testing on patients undergoing gynecologic surgery/procedure by evaluating the prescribing rate of Clinical Pharmacogenetics Implementation Consortium (CPIC) level A medications and frequency of drug-gene interactions (DGIs).

METHODS

The electronic health record was queried for 76 current procedural terminology codes to identify gynecologic surgeries/procedures that occurred between 1 January 2015 to 31 December 2020 in patients with at least one of 152 international classification of disease codes. Prescription data for CPIC level A medications was extracted. Those enrolled in the Penn Medicine Biobank were assessed for DGIs.

RESULTS

The cohort consisted of 7798 female patients and 682 were in the biobank. Up to 6 years following their surgery or procedure, 80% were ordered ≥1 CPIC level A medication. Over half (54%) of these medications were ordered within 3 days after their surgery or procedure. The most common CPIC level A medications ordered were ibuprofen (57%) and ondansetron (42%). Overall, 7% of the cohort had ≥1 known or predicted DGI with medications they were prescribed.

CONCLUSION

Multi-gene pharmacogenetic testing may be beneficial to gynecologic surgery/procedure patients by assisting clinicians with prescribing postoperative analgesics and future medications.

Thoracic Aortic Three-Dimensional Geometry

Background

Aortic structure impacts cardiovascular health through multiple mechanisms. Aortic structural degeneration occurs with aging, increasing left ventricular afterload and promoting increased arterial pulsatility and target organ damage. Despite the impact of aortic structure on cardiovascular health, three-dimensional (3D) aortic geometry has not been comprehensively characterized in large populations.

Methods

We segmented the complete thoracic aorta using a deep learning architecture and used morphological image operations to extract multiple aortic geometric phenotypes (AGPs, including diameter, length, curvature, and tortuosity) across various subsegments of the thoracic aorta. We deployed our segmentation approach on imaging scans from 54,241 participants in the UK Biobank and 8,456 participants in the Penn Medicine Biobank.

Conclusion

Our method provides a fully automated approach towards quantifying the three-dimensional structural parameters of the aorta. This approach expands the available phenotypes in two large representative biobanks and will allow large-scale studies to elucidate the biology and clinical consequences of aortic degeneration related to aging and disease states.

Common- and rare-variant genetic architecture of heart failure across the allele frequency spectrum

Heart failure (HF) is a complex trait, influenced by environmental and genetic factors, which affects over 30 million individuals worldwide. Historically, the genetics of HF have been studied in Mendelian forms of disease, where rare genetic variants have been linked to familial cardiomyopathies. More recently, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with risk of HF. However, the relative importance of genetic variants across the allele-frequency spectrum remains incompletely characterized. Here, we report the results of common- and rare-variant association studies of all-cause heart failure, applying recently developed methods to quantify the heritability of HF attributable to different classes of genetic variation. We combine GWAS data across multiple populations including 207,346 individuals with HF and 2,151,210 without, identifying 176 risk loci at genome-wide significance (P-value < 5×10-8). Signals at newly identified common-variant loci include coding variants in Mendelian cardiomyopathy genes (MYBPC3, BAG3) and in regulators of lipoprotein (LPL) and glucose metabolism (GIPR, GLP1R). These signals are enriched in myocyte and adipocyte cell types and can be clustered into 5 broad modules based on pleiotropic associations with anthropomorphic traits/obesity, blood pressure/renal function, atherosclerosis/lipids, immune activity, and arrhythmias. Gene burden studies across three biobanks (PMBB, UKB, AOU), including 27,208 individuals with HF and 349,126 without, uncover exome-wide significant (P-value < 1.57×10-6) associations for HF and rare predicted loss-of-function (pLoF) variants in TTN, MYBPC3, FLNC, and BAG3. Total burden heritability of rare coding variants (2.2%, 95% CI 0.99-3.5%) is highly concentrated in a small set of Mendelian cardiomyopathy genes, while common variant heritability (4.3%, 95% CI 3.9-4.7%) is more diffusely spread throughout the genome. Finally, we show that common-variant background, in the form of a polygenic risk score (PRS), significantly modifies the risk of HF among carriers of pathogenic truncating variants in the Mendelian cardiomyopathy gene TTN. Together, these findings provide a genetic link between dysregulated metabolism and HF, and suggest a significant polygenic component to HF exists that is not captured by current clinical genetic testing.

Population Performance and Individual Agreement of Coronary Artery Disease Polygenic Risk Scores

Importance

Polygenic risk scores (PRSs) for coronary artery disease (CAD) are a growing clinical and commercial reality. Whether existing scores provide similar individual-level assessments of disease liability is a critical consideration for clinical implementation that remains uncharacterized.

Objective

Characterize the reliability of CAD PRSs that perform equivalently at the population level at predicting individual-level risk.

Design

Cross-sectional Study.

Setting

All of Us Research Program (AOU), Penn Medicine Biobank (PMBB), and UCLA ATLAS Precision Health Biobank.

Participants

Volunteers of diverse genetic backgrounds enrolled in AOU, PMBB, and UCLA with available electronic health record and genotyping data.

Exposures

Polygenic risk for CAD from previously published PRSs and new PRSs developed separately from the testing cohorts.

Main Outcomes and Measures

Sets of CAD PRSs that perform population prediction equivalently were identified by comparing calibration and discrimination (Brier score and AUROC) of generalized linear models of prevalent CAD using Bayesian analysis of variance. Among equivalently performing scores, individual-level agreement between risk estimates was tested with intraclass correlation (ICC) and Light's Kappa, measures of inter-rater reliability.

Results

50 PRSs were calculated for 171,095 AOU participants. When included in a model of prevalent CAD, 48 scores had practically equivalent Brier scores and AUROCs (region of practical equivalence = 0.02). Across these scores, 84% of participants had at least one score in both the top and bottom risk quintile. Continuous agreement of individual risk predictions from the 48 scores was poor, with an ICC of 0.351 (95% CI; 0.349, 0.352). Agreement between two statistically equivalent scores was moderate, with an ICC of 0.649 (95% CI; 0.646, 0.652). Light's Kappa, used to evaluate consistency of assignment to high-risk thresholds, did not exceed 0.56 (interpreted as 'fair') across statistically and practically equivalent scores. Repeating the analysis among 41,193 PMBB and 50,748 UCLA participants yielded different sets of statistically and practically equivalent scores which also lacked strong individual agreement.

Conclusions and Relevance

Across three diverse biobanks, CAD PRSs that performed equivalently at the population level produced unreliable individual risk estimates. Approaches to clinical implementation of CAD PRSs must consider the potential for discordant individual risk estimates from otherwise indistinguishable scores.

Protein-truncating variant in APOL3 increases chronic kidney disease risk in epistasis with APOL1 risk alleles

BACKGROUNDTwo coding alleles within the APOL1 gene, G1 and G2, found almost exclusively in individuals genetically similar to West African populations, contribute substantially to the pathogenesis of chronic kidney disease (CKD). The APOL gene cluster on chromosome 22 contains a total of 6 APOL genes that have arisen as a result of gene duplication.METHODSUsing a genome-first approach in the Penn Medicine BioBank, we identified 62 protein-altering variants in the 6 APOL genes with a minor allele frequency of >0.1% in a population of participants genetically similar to African reference populations and performed population-specific phenome-wide association studies.RESULTSWe identified rs1108978, a stop-gain variant in APOL3 (p.Q58*), to be significantly associated with increased CKD risk, even after conditioning on APOL1 G1/G2 carrier status. These findings were replicated in the Veterans Affairs Million Veteran Program and the All of Us Research Program. APOL3 p.Q58* was also significantly associated with a number of quantitative traits linked to CKD, including decreased kidney volume. This truncating variant contributed the most risk for CKD in patients monoallelic for APOL1 G1/G2, suggesting an epistatic interaction and a potential protective effect of wild-type APOL3 against APOL1-induced kidney disease.CONCLUSIONThis study demonstrates the utility of targeting population-specific variants in a genome-first approach, even in the context of well-studied gene-disease relationships.FUNDINGNational Heart, Lung, and Blood Institute (F30HL172382, R01HL169378, R01HL169458), Doris Duke Foundation (grant 2023-2024), National Institute of Biomedical Imaging and Bioengineering (P41EB029460), and National Center for Advancing Translational Sciences (UL1-TR-001878).

Polygenic Scoring for Detection of Ascending Thoracic Aortic Dilation

BACKGROUND

Ascending thoracic aortic dilation is a complex heritable trait that involves modifiable and nonmodifiable risk factors. Polygenic scores (PGS) are increasingly used to assess risk for complex diseases. The degree to which a PGS can improve aortic diameter prediction in diverse populations is unknown. Presently, we tested whether adding a PGS to clinical prediction algorithms improves performance in a diverse biobank.

METHODS

The analytic cohort comprised 6235 Penn Medicine Biobank participants with available echocardiography and clinical data linked to genome-wide genotype data. Linear regression models were used to integrate PGS weights derived from a genome-wide association study of thoracic aortic diameter performed in the UK Biobank and were compared with the performance of the previously published aorta optimized regression for thoracic aneurysm (AORTA) score.

RESULTS

Cohort participants had a median age of 61 years (IQR, 53-70) and a mean ascending aortic diameter of 3.36 cm (SD, 0.49). Fifty-five percent were male, and 33% were genetically similar to an African reference population. Compared with the AORTA score, which explained 30.6% (95% CI, 29.9%-31.4%) of the variance in aortic diameter, AORTA score+UK Biobank-derived PGS explained 33.1%, (95% CI, 32.3%-33.8%), the reweighted AORTA score explained 32.5% (95% CI, 31.8%-33.2%), and the reweighted AORTA score+UK Biobank-derived PGS explained 34.9% (95% CI, 34.2%-35.6%). When stratified by population, models including the UK Biobank-derived PGS consistently improved upon the clinical AORTA score among individuals genetically similar to a European reference population but conferred minimal improvement among individuals genetically similar to an African reference population. Comparable performance disparities were observed in models developed to discriminate cases/noncases of thoracic aortic dilation (≥4.0 cm).

CONCLUSIONS

We demonstrated that inclusion of a UK Biobank-derived PGS to the AORTA score confers a clinically meaningful improvement in model performance only among individuals genetically similar to European reference populations and may exacerbate existing health care disparities.

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

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

Big data in sarcoidosis

PURPOSE OF REVIEW

This review provides an overview of recent advancements in sarcoidosis research, focusing on collaborative networks, phenotype characterization, and molecular studies. It highlights the importance of collaborative efforts, phenotype characterization, and the integration of multilevel molecular data for advancing sarcoidosis research and paving the way toward personalized medicine.

RECENT FINDINGS

Sarcoidosis exhibits heterogeneous clinical manifestations influenced by various factors. Efforts to define sarcoidosis endophenotypes show promise, while technological advancements enable extensive molecular data generation. Collaborative networks and biobanks facilitate large-scale studies, enhancing biomarker discovery and therapeutic protocols.

SUMMARY

Sarcoidosis presents a complex challenge due to its unknown cause and heterogeneous clinical manifestations. Collaborative networks, comprehensive phenotype delineation, and the utilization of cutting-edge technologies are essential for advancing our understanding of sarcoidosis biology and developing personalized medicine approaches. Leveraging large-scale epidemiological resources and biobanks and integrating multilevel molecular data offer promising avenues for unraveling the disease's heterogeneity and improving patient outcomes.

Genotype-First Approach Identifies an Association between rs28374544/FOG2S657G and Liver Disease through Alterations in mTORC1 Signaling

Metabolic dysfunction-associated Fatty Liver Disease (MAFLD) has emerged as one of the leading cardiometabolic diseases. Friend of GATA2 (FOG2) is a transcriptional co-regulator that has been shown to regulate hepatic lipid metabolism and accumulation. Using meta-analysis from several different biobank datasets, we identified a coding variant of FOG2 (rs28374544, A1969G, S657G) predominantly found in individuals of African ancestry (minor allele frequency~20%), which is associated with liver failure/cirrhosis phenotype and liver injury. To gain insight into potential pathways associated with this variant, we interrogated a previously published genomics dataset of 38 human induced pluripotent stem cell (iPSCs) lines differentiated into hepatocytes (iHeps). Using Differential Gene Expression Analysis and Gene Set Enrichment Analysis, we identified the mTORC1 pathway as differentially regulated between iHeps from individuals with and without the variant. Transient lipid-based transfections were performed on the human hepatoma cell line (Huh7) using wild-type FOG2 and FOG2S657G and demonstrated that FOG2S657G increased mTORC1 signaling, de novo lipogenesis, and cellular triglyceride synthesis and mass. In addition, we observed a significant downregulation of oxidative phosphorylation in FOG2S657G cells in fatty acid-loaded cells but not untreated cells, suggesting that FOG2S657G may also reduce fatty acid to promote lipid accumulation. Taken together, our multi-pronged approach suggests a model whereby the FOG2S657G may promote MAFLD through mTORC1 activation, increased de novo lipogenesis, and lipid accumulation. Our results provide insights into the molecular mechanisms by which FOG2S657G may affect the complex molecular landscape underlying MAFLD.

The common p.Ile291Val variant of ERLIN1 enhances TM6SF2 function and is associated with protection against MASLD

BACKGROUND

The ERLIN1 p.Ile291Val single-nucleotide polymorphism (rs2862954) is associated with protection from steatotic liver disease (SLD), but effects of this variant on metabolic phenotypes remain uncertain.

METHODS

Metabolic phenotypes and outcomes associated with ERLIN1 p.Ile291Val were analyzed by using a genome-first approach in the UK Biobank (UKB), Penn Medicine BioBank (PMBB), and All of Us cohort.

FINDINGS

ERLIN1 p.Ile291Val carriers exhibited significantly lower serum levels of alanine aminotransferase and aspartate aminotransferase as well as higher levels of triglycerides, low-density lipoprotein cholesterol, Apolipoprotein B, high-density lipoprotein cholesterol, and Apolipoprotein A1 in UKB, and these values were affected by ERLIN1 p.Ile291Val in an allele-dose-dependent manner. Homozygous ERLIN1 p.Ile291Val carriers had a significantly reduced risk of developing metabolic dysfunction-associated SLD (MASLD, adjusted odds ratio [aOR] = 0.92, 95% confidence interval [CI], 0.88-0.96). The protective effect of this variant was enhanced in patients with alcoholic liver disease. Our results were replicated in PMBB and the All of Us cohort. Strikingly, the protective effects of ERLIN1 p.Ile291Val were not apparent in individuals carrying the TM6SF2 p.Glu167Lys variant associated with increased risk of SLD. We analyzed the effects of predicted loss-of-function ERLIN1 variants and found that they had opposite effects, namely reduced plasma lipids, suggesting that ERLIN1 p.Ile291Val may be a gain-of-function variant.

CONCLUSION

Our study contributes to a better understanding of ERLIN1 by investigating a coding variant that has emerged as a potential gain-of-function mutation with protective effects against MASLD development.

Interpreting SNP heritability in admixed populations

SNP heritabilityh s n p 2 is defined as the proportion of phenotypic variance explained by genotyped SNPs and is believed to be a lower bound of heritability (h 2 ), being equal to it if all causal variants are known. Despite the simple intuition behindh s n p 2 , its interpretation and equivalence toh 2 is unclear, particularly in the presence of population structure and assortative mating. It is well known that population structure can lead to inflation inh ˆ s n p 2 estimates because of confounding due to linkage disequilibrium (LD) or shared environment. Here we use analytical theory and simulations to demonstrate thath s n p 2 estimates can be biased in admixed populations, even in the absence of confounding and even if all causal variants are known. This is because admixture generates LD, which contributes to the genetic variance, and therefore to heritability. Genome-wide restricted maximum likelihood (GREML) does not capture this contribution leading to under- or over-estimates ofh s n p 2 relative toh 2 , depending on the genetic architecture. In contrast, Haseman-Elston (HE) regression exaggerates the LD contribution leading to biases in the opposite direction. For the same reason, GREML and HE estimates of local ancestry heritabilityh γ 2 are also biased. We describe this bias inh ˆ s n p 2 andh ˆ γ 2 as a function of admixture history and the genetic architecture of the trait and show that it can be recovered under some conditions. We clarify the interpretation ofh ˆ s n p 2 in admixed populations and discuss its implication for genome-wide association studies and polygenic prediction.

Centralized and Federated Models for the Analysis of Clinical Data

The progress of precision medicine research hinges on the gathering and analysis of extensive and diverse clinical datasets. With the continued expansion of modalities, scales, and sources of clinical datasets, it becomes imperative to devise methods for aggregating information from these varied sources to achieve a comprehensive understanding of diseases. In this review, we describe two important approaches for the analysis of diverse clinical datasets, namely the centralized model and federated model. We compare and contrast the strengths and weaknesses inherent in each model and present recent progress in methodologies and their associated challenges. Finally, we present an outlook on the opportunities that both models hold for the future analysis of clinical data.

Assessing the Utility of a Genotype-Guided Tacrolimus Equation in African American Kidney Transplant Recipients: A Single Institution Retrospective Study

Tacrolimus metabolism is heavily influenced by the CYP3A5 genotype, which varies widely among African Americans (AA). We aimed to assess the performance of a published genotype-informed tacrolimus dosing model in an independent set of adult AA kidney transplant (KTx) recipients. CYP3A5 genotypes were obtained for all AA KTx recipients (n = 232) from 2010 to 2019 who met inclusion criteria at a single transplant center in Philadelphia, Pennsylvania, USA. Medical record data were used to calculate predicted tacrolimus clearance using the published AA KTx dosing equation and two modified iterations. Observed and model-predicted trough levels were compared at 3 days, 3 months, and 6 months post-transplant. The mean prediction error at day 3 post-transplant was 3.05 ng/mL, indicating that the model tended to overpredict the tacrolimus trough. This bias improved over time to 1.36 and 0.78 ng/mL at 3 and 6 months post-transplant, respectively. Mean absolute prediction error-a marker of model precision-improved with time to 2.33 ng/mL at 6 months. Limiting genotype data in the model decreased bias and improved precision. The bias and precision of the published model improved over time and were comparable to studies in previous cohorts. The overprediction observed by the published model may represent overfitting to the initial cohort, possibly limiting generalizability.

Genetic predictors of blood pressure traits are associated with preeclampsia

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

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

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

CT imaging-derived phenotypes for abdominal muscle and their association with age and sex in a medical biobank

The study of muscle mass as an imaging-derived phenotype (IDP) may yield new insights into determining the normal and pathologic variations in muscle mass in the population. This can be done by determining 3D abdominal muscle mass from 12 distinct abdominal muscle regions and groups using computed tomography (CT) in a racially diverse medical biobank. To develop a fully automatic technique for assessment of CT abdominal muscle IDPs and preliminarily determine abdominal muscle IDP variations with age and sex in a clinically and racially diverse medical biobank. This retrospective study was conducted using the Penn Medicine BioBank (PMBB), a research protocol that recruits adult participants during outpatient visits at hospitals in the Penn Medicine network. We developed a deep residual U-Net (ResUNet) to segment 12 abdominal muscle groups including the left and right psoas, quadratus lumborum, erector spinae, gluteus medius, rectus abdominis, and lateral abdominals. 110 CT studies were randomly selected for training, validation, and testing. 44 of the 110 CT studies were selected to enrich the dataset with representative cases of intra-abdominal and abdominal wall pathology. The studies were divided into non-overlapping training, validation and testing sets. Model performance was evaluated using the Sørensen-Dice coefficient. Volumes of individual muscle groups were plotted to distribution curves. To investigate associations between muscle IDPs, age, and sex, deep learning model segmentations were performed on a larger abdominal CT dataset from PMBB consisting of 295 studies. Multivariable models were used to determine relationships between muscle mass, age and sex. The model's performance (Dice scores) on the test data was the following: psoas: 0.85 ± 0.12, quadratus lumborum: 0.72 ± 0.14, erector spinae: 0.92 ± 0.07, gluteus medius: 0.90 ± 0.08, rectus abdominis: 0.85 ± 0.08, lateral abdominals: 0.85 ± 0.09. The average Dice score across all muscle groups was 0.86 ± 0.11. Average total muscle mass for females was 2041 ± 560.7 g with a high of 2256 ± 560.1 g (41-50 year old cohort) and a change of - 0.96 g/year, declining to an average mass of 1579 ± 408.8 g (81-100 year old cohort). Average total muscle mass for males was 3086 ± 769.1 g with a high of 3385 ± 819.3 g (51-60 year old cohort) and a change of - 1.73 g/year, declining to an average mass of 2629 ± 536.7 g (81-100 year old cohort). Quadratus lumborum was most highly correlated with age for both sexes (correlation coefficient of - 0.5). Gluteus medius mass in females was positively correlated with age with a coefficient of 0.22. These preliminary findings show that our CNN can automate detailed abdominal muscle volume measurement. Unlike prior efforts, this technique provides 3D muscle segmentations of individual muscles. This technique will dramatically impact sarcopenia diagnosis and research, elucidating its clinical and public health implications. Our results suggest a peak age range for muscle mass and an expected rate of decline, both of which vary between genders. Future goals are to investigate genetic variants for sarcopenia and malnutrition, while describing genotype-phenotype associations of muscle mass in healthy humans using imaging-derived phenotypes. It is feasible to obtain 3D abdominal muscle IDPs with high accuracy from patients in a medical biobank using fully automated machine learning methods. Abdominal muscle IDPs showed significant variations in lean mass by age and sex. In the future, this tool can be leveraged to perform a genome-wide association study across the medical biobank and determine genetic variants associated with early or accelerated muscle wasting.

PMS2CL interference leading to erroneous identification of a pathogenic PMS2 variant in Black patients

This study investigates the frequency of a clinically reported variant in PMS2, NM_000535.7:c.2523G>A p.(W841∗), from next-generation sequencing studies in 2 racially diverse cohorts. We identified clinical reports of the PMS2 c.2523G>A p.(W841∗) variant in the National Precision Oncology Program's somatic testing database (n = 25,168). We determined frequency of the variant in germline exome sequencing from the Penn Medicine BioBank (n = 44,256) and in gnomAD. The PMS2 c.2523G>A p.(W841∗) was identified as a homozygous variant on tumor testing in an adult patient of self-identified Black race/ethnicity with no evidence of constitutional mismatch repair deficiency. The variant was clinically reported on 35 total tumor and liquid biopsy tests (0.1%), and all individuals with the variant were of self-identified Black race/ethnicity (0.6% of n = 5787). In individuals of African genetic ancestry (AFR), the variant's germline frequency was reported to be 0.2% and 1.3% in the Penn Medicine BioBank (PMBB) and gnomAD, respectively. The variant cannot be found in any individuals of European genetic ancestry (EUR) from either of the databases. The variant is found in a region of PMS2 with 100% homology to the PMS2CL pseudogene. PMS2 c.2523G>A p.(W841∗), when identified, is typically an African-ancestry-specific PMS2CL pseudogene variant, which should be recognized to prevent misdiagnosis of Lynch syndrome in Blacks.

Optimising the use of electronic medical records for large scale research in psychiatry

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.

Accelerating Genome- and Phenome-Wide Association Studies using GPUs - A case study using data from the Million Veteran Program

The expansion of biobanks has significantly propelled genomic discoveries yet the sheer scale of data within these repositories poses formidable computational hurdles, particularly in handling extensive matrix operations required by prevailing statistical frameworks. In this work, we introduce computational optimizations to the SAIGE (Scalable and Accurate Implementation of Generalized Mixed Model) algorithm, notably employing a GPU-based distributed computing approach to tackle these challenges. We applied these optimizations to conduct a large-scale genome-wide association study (GWAS) across 2,068 phenotypes derived from electronic health records of 635,969 diverse participants from the Veterans Affairs (VA) Million Veteran Program (MVP). Our strategies enabled scaling up the analysis to over 6,000 nodes on the Department of Energy (DOE) Oak Ridge Leadership Computing Facility (OLCF) Summit High-Performance Computer (HPC), resulting in a 20-fold acceleration compared to the baseline model. We also provide a Docker container with our optimizations that was successfully used on multiple cloud infrastructures on UK Biobank and All of Us datasets where we showed significant time and cost benefits over the baseline SAIGE model.

Combining Transdiagnostic and Disorder-Level GWAS Enhances Precision of Psychiatric Genetic Risk Profiles in a Multi-Ancestry Sample

The etiology of substance use disorders (SUDs) and psychiatric disorders reflects a combination of both transdiagnostic (i.e., common) and disorder-level (i.e., independent) genetic risk factors. We applied genomic structural equation modeling to examine these genetic factors across SUDs, psychotic, mood, and anxiety disorders using genome-wide association studies (GWAS) of European- (EUR) and African-ancestry (AFR) individuals. In EUR individuals, transdiagnostic genetic factors represented SUDs (143 lead single nucleotide polymorphisms [SNPs]), psychotic (162 lead SNPs), and mood/anxiety disorders (112 lead SNPs). We identified two novel SNPs for mood/anxiety disorders that have probable regulatory roles on FOXP1, NECTIN3, and BTLA genes. In AFR individuals, genetic factors represented SUDs (1 lead SNP) and psychiatric disorders (no significant SNPs). The SUD factor lead SNP, although previously significant in EUR- and cross-ancestry GWAS, is a novel finding in AFR individuals. Shared genetic variance accounted for overlap between SUDs and their psychiatric comorbidities, with second-order GWAS identifying up to 12 SNPs not significantly associated with either first-order factor in EUR individuals. Finally, common and independent genetic effects showed different associations with psychiatric, sociodemographic, and medical phenotypes. For example, the independent components of schizophrenia and bipolar disorder had distinct associations with affective and risk-taking behaviors, and phenome-wide association studies identified medical conditions associated with tobacco use disorder independent of the broader SUDs factor. Thus, combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for psychiatric disorders that demonstrate considerable symptom and etiological overlap.

The 14th International Podocyte Conference 2023: from podocyte biology to glomerular medicine

The 14th International Podocyte Conference took place in Philadelphia, Pennsylvania, USA from May 23 to 26, 2023. It commenced with an early-career researchers' meeting on May 23, providing young scientists with a platform to present and discuss their research findings. Throughout the main conference, 29 speakers across 9 sessions shared their insights on podocyte biology, glomerular medicine, novel technologic advancements, and translational approaches. Additionally, the event featured 3 keynote lectures addressing engineered chimeric antigen receptor T cell- and mRNA-based therapies and the use of biobanks for enhanced disease comprehension. Furthermore, 4 brief oral abstract sessions allowed scientists to present their findings to a broad audience. The program also included a panel discussion addressing the challenges of conducting human research within the American Black community. Remarkably, after a 5-year hiatus from in-person conferences, the 14th International Podocyte Conference successfully convened scientists from around the globe, fostering the presentation and discussion of crucial research findings, as summarized in this review. Furthermore, to ensure continuous and sustainable education, research, translation, and trial medicine related to podocyte and glomerular diseases for the benefit of patients, the International Society of Glomerular Disease was officially launched during the conference.

Enhancing Genetic Association Power in Endometriosis through Unsupervised Clustering of Clinical Subtypes Identified from Electronic Health Records

Background

Endometriosis affects 10% of reproductive-age women, and yet, it goes undiagnosed for 3.6 years on average after symptoms onset. Despite large GWAS meta-analyses (N > 750,000), only a few dozen causal loci have been identified. We hypothesized that the challenges in identifying causal genes for endometriosis stem from heterogeneity across clinical and biological factors underlying endometriosis diagnosis.

Methods

We extracted known endometriosis risk factors, symptoms, and concomitant conditions from the Penn Medicine Biobank (PMBB) and performed unsupervised spectral clustering on 4,078 women with endometriosis. The 5 clusters were characterized by utilizing additional electronic health record (EHR) variables, such as endometriosis-related comorbidities and confirmed surgical phenotypes. From four EHR-linked genetic datasets, PMBB, eMERGE, AOU, and UKBB, we extracted lead variants and tag variants 39 known endometriosis loci for association testing. We meta-analyzed ancestry-stratified case/control tests for each locus and cluster in addition to a positive control (Total N endometriosis cases = 10,108).

Results

We have designated the five subtype clusters as pain comorbidities, uterine disorders, pregnancy complications, cardiometabolic comorbidities, and EHR-asymptomatic based on enriched features from each group. One locus, RNLS , surpassed the genome-wide significant threshold in the positive control. Thirteen more loci reached a Bonferroni threshold of 1.3 x 10 -3 (0.05 / 39) in the positive control. The cluster-stratified tests yielded more significant associations than the positive control for anywhere from 5 to 15 loci depending on the cluster. Bonferroni significant loci were identified for four out of five clusters, including WNT4 and GREB1 for the uterine disorders cluster, RNLS for the cardiometabolic cluster, FSHB for the pregnancy complications cluster, and SYNE1 and CDKN2B-AS1 for the EHR-asymptomatic cluster. This study enhances our understanding of the clinical presentation patterns of endometriosis subtypes, showcasing the innovative approach employed to investigate this complex disease.

Distinct genomic and immunologic tumor evolution in germline TP53-driven breast cancers

Pathogenic germline TP53 alterations cause Li-Fraumeni Syndrome (LFS), and breast cancer is the most common cancer in LFS females. We performed first of its kind multimodal analysis of LFS breast cancer (LFS-BC) compared to sporadic premenopausal BC. Nearly all LFS-BC underwent biallelic loss of TP53 with no recurrent oncogenic variants except ERBB2 (HER2) amplification. Compared to sporadic BC, in situ and invasive LFS-BC exhibited a high burden of short amplified aneuploid segments (SAAS). Pro-apoptotic p53 target genes BAX and TP53I3 failed to be up-regulated in LFS-BC as was seen in sporadic BC compared to normal breast tissue. LFS-BC had lower CD8+ T-cell infiltration compared to sporadic BC yet higher levels of proliferating cytotoxic T-cells. Within LFS-BC, progression from in situ to invasive BC was marked by an increase in chromosomal instability with a decrease in proliferating cytotoxic T-cells. Our study uncovers critical events in mutant p53-driven tumorigenesis in breast tissue.

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

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

Universal Exome Sequencing in Critically Ill Adults: A Diagnostic Yield of 25% and Race-Based Disparities in Access to Genetic Testing

Numerous studies have underscored the diagnostic and therapeutic potential of exome or genome sequencing in critically ill pediatric populations. However, an equivalent investigation in critically ill adults remains conspicuously absent. We retrospectively analyzed whole exome sequencing (WES) data available through the PennMedicine Biobank (PMBB) from all 365 young adult patients, aged 18-40 years, with intensive care unit (ICU) admissions at the University of Pennsylvania Health System who met inclusion criteria for our study. For each participant, two Medical Genetics and Internal Medicine-trained clinicians reviewed WES reports and patient charts for variant classification, result interpretation, and identification of genetic diagnoses related to their critical illness. Of the 365 individuals in our study, 90 (24.7%) were found to have clearly diagnostic results on WES; an additional 40 (11.0%) had a suspicious variant of uncertain significance (VUS) identified; and an additional 16 (4.4%) had a medically actionable incidental finding. The diagnostic rate of exome sequencing did not decrease with increasing patient age. Affected genes were primarily involved in cardiac function (18.8%), vascular health (16.7%), cancer (16.7%), and pulmonary disease (11.5%). Only half of all diagnostic findings were known and documented in the patient chart at the time of ICU admission. Significant disparities emerged in subgroup analysis by EHR-reported race, with genetic diagnoses known/documented for 63.5% of White patients at the time of ICU admission but only for 28.6% of Black or Hispanic patients. There was a trend towards patients with undocumented genetic diagnoses having a 66% increased mortality rate, making these race-based disparities in genetic diagnosis even more concerning. Altogether, universal exome sequencing in ICU-admitted adult patients was found to yield a new definitive diagnosis in 11.2% of patients. Of these diagnoses, 76.6% conferred specific care-altering medical management recommendations. Our study suggests that the diagnostic utility of exome sequencing in critically ill young adults is similar to that observed in neonatal and pediatric populations and is age-independent. The high diagnostic rate and striking race-based disparities we find in genetic diagnoses argue for broad and universal approaches to genetic testing for critically ill adults. The widespread implementation of comprehensive genetic sequencing in the adult population promises to enhance medical care for all individuals and holds the potential to rectify disparities in genetic testing referrals, ultimately promoting more equitable healthcare delivery.

Association between genetic risk and adherence to healthy lifestyle for developing age-related hearing loss

BACKGROUND

Previous studies have shown that lifestyle/environmental factors could accelerate the development of age-related hearing loss (ARHL). However, there has not yet been a study investigating the joint association among genetics, lifestyle/environmental factors, and adherence to healthy lifestyle for risk of ARHL. We aimed to assess the association between ARHL genetic variants, lifestyle/environmental factors, and adherence to healthy lifestyle as pertains to risk of ARHL.

METHODS

This case-control study included 376,464 European individuals aged 40 to 69 years, enrolled between 2006 and 2010 in the UK Biobank (UKBB). As a replication set, we also included a total of 26,523 individuals considered of European ancestry and 9834 individuals considered of African-American ancestry through the Penn Medicine Biobank (PMBB). The polygenic risk score (PRS) for ARHL was derived from a sensorineural hearing loss genome-wide association study from the FinnGen Consortium and categorized as low, intermediate, high, and very high. We selected lifestyle/environmental factors that have been previously studied in association with hearing loss. A composite healthy lifestyle score was determined using seven selected lifestyle behaviors and one environmental factor.

RESULTS

Of the 376,464 participants, 87,066 (23.1%) cases belonged to the ARHL group, and 289,398 (76.9%) individuals comprised the control group in the UKBB. A very high PRS for ARHL had a 49% higher risk of ARHL than those with low PRS (adjusted OR, 1.49; 95% CI, 1.36-1.62; P < .001), which was replicated in the PMBB cohort. A very poor lifestyle was also associated with risk of ARHL (adjusted OR, 3.03; 95% CI, 2.75-3.35; P < .001). These risk factors showed joint effects with the risk of ARHL. Conversely, adherence to healthy lifestyle in relation to hearing mostly attenuated the risk of ARHL even in individuals with very high PRS (adjusted OR, 0.21; 95% CI, 0.09-0.52; P < .001).

CONCLUSIONS

Our findings of this study demonstrated a significant joint association between genetic and lifestyle factors regarding ARHL. In addition, our analysis suggested that lifestyle adherence in individuals with high genetic risk could reduce the risk of ARHL.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Genetic Associations of Circulating Cardiovascular Proteins With Gestational Hypertension and Preeclampsia

Importance

Hypertensive disorders of pregnancy (HDPs), including gestational hypertension and preeclampsia, are important contributors to maternal morbidity and mortality worldwide. In addition, women with HDPs face an elevated long-term risk of cardiovascular disease.

Objective

To identify proteins in the circulation associated with HDPs.

Design, Setting, and Participants

Two-sample mendelian randomization (MR) tested the associations of genetic instruments for cardiovascular disease-related proteins with gestational hypertension and preeclampsia. In downstream analyses, a systematic review of observational data was conducted to evaluate the identified proteins' dynamics across gestation in hypertensive vs normotensive pregnancies, and phenome-wide MR analyses were performed to identify potential non-HDP-related effects associated with the prioritized proteins. Genetic association data for cardiovascular disease-related proteins were obtained from the Systematic and Combined Analysis of Olink Proteins (SCALLOP) consortium. Genetic association data for the HDPs were obtained from recent European-ancestry genome-wide association study meta-analyses for gestational hypertension and preeclampsia. Study data were analyzed October 2022 to October 2023.

Exposures

Genetic instruments for 90 candidate proteins implicated in cardiovascular diseases, constructed using cis-protein quantitative trait loci (cis-pQTLs).

Main Outcomes and Measures

Gestational hypertension and preeclampsia.

Results

Genetic association data for cardiovascular disease-related proteins were obtained from 21 758 participants from the SCALLOP consortium. Genetic association data for the HDPs were obtained from 393 238 female individuals (8636 cases and 384 602 controls) for gestational hypertension and 606 903 female individuals (16 032 cases and 590 871 controls) for preeclampsia. Seventy-five of 90 proteins (83.3%) had at least 1 valid cis-pQTL. Of those, 10 proteins (13.3%) were significantly associated with HDPs. Four were robust to sensitivity analyses for gestational hypertension (cluster of differentiation 40, eosinophil cationic protein [ECP], galectin 3, N-terminal pro-brain natriuretic peptide [NT-proBNP]), and 2 were robust for preeclampsia (cystatin B, heat shock protein 27 [HSP27]). Consistent with the MR findings, observational data revealed that lower NT-proBNP (0.76- to 0.88-fold difference vs no HDPs) and higher HSP27 (2.40-fold difference vs no HDPs) levels during the first trimester of pregnancy were associated with increased risk of HDPs, as were higher levels of ECP (1.60-fold difference vs no HDPs). Phenome-wide MR analyses identified 37 unique non-HDP-related protein-disease associations, suggesting potential on-target effects associated with interventions lowering HDP risk through the identified proteins.

Conclusions and Relevance

Study findings suggest genetic associations of 4 cardiovascular disease-related proteins with gestational hypertension and 2 associated with preeclampsia. Future studies are required to test the efficacy of targeting the corresponding pathways to reduce HDP risk.

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

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

A multi-cohort genome-wide association study in African ancestry individuals reveals risk loci for primary open-angle glaucoma

Primary open-angle glaucoma (POAG), the leading cause of irreversible blindness worldwide, disproportionately affects individuals of African ancestry. We conducted a genome-wide association study (GWAS) for POAG in 11,275 individuals of African ancestry (6,003 cases; 5,272 controls). We detected 46 risk loci associated with POAG at genome-wide significance. Replication and post-GWAS analyses, including functionally informed fine-mapping, multiple trait co-localization, and in silico validation, implicated two previously undescribed variants (rs1666698 mapping to DBF4P2; rs34957764 mapping to ROCK1P1) and one previously associated variant (rs11824032 mapping to ARHGEF12) as likely causal. For individuals of African ancestry, a polygenic risk score (PRS) for POAG from our mega-analysis (African ancestry individuals) outperformed a PRS from summary statistics of a much larger GWAS derived from European ancestry individuals. This study quantifies the genetic architecture similarities and differences between African and non-African ancestry populations for this blinding disease.