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

Polygenic risk scores (PRS) have predominantly been derived from genome-wide association studies (GWAS) conducted in European ancestry (EUR) individuals. In this study, we present an in-depth evaluation of PRS based on multi-ancestry GWAS for five cardiometabolic phenotypes in the Penn Medicine BioBank (PMBB) followed by a phenome-wide association study (PheWAS). We examine the PRS performance across all individuals and separately in African ancestry (AFR) and EUR ancestry groups.

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.

Understanding Pain in Polycystic Ovary Syndrome: Health Risks and Treatment Effectiveness.

Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder in women, often accompanied by various symptoms including significant pain, such as dysmenorrhea, abdominal, and pelvic pain, which remains underexplored. This retrospective study examines electronic health records (EHR) data to assess the prevalence of pain in women with PCOS. Conducted on May 29, 2024, using data from 120 Health Care Organizations within the TriNetX Global Network, the study involved 76,859,666 women from diverse racial backgrounds.

Longitudinal method comparison: modeling polygenic risk for post-traumatic stress disorder over time in individuals of African and European ancestry.

Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression.

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.

Titin-Truncating variants Predispose to Dilated Cardiomyopathy in Diverse Populations.

Importance: The effect of high percentage spliced in (hiPSI) truncating variants (TTNtvs) on risk of dilated cardiomyopathy (DCM) has historically been studied among population subgroups defined by genetic similarity to European reference populations. This has raised questions about the effect of TTNtvs in diverse populations, especially among individuals genetically similar to African reference populations.

Objective: To determine the effect of TTNtvs on risk of DCM in diverse population as measured by genetic distance (GD) in principal component (PC) space.

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.

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

Polygenic risk scores (PRS) have predominantly been derived from genome-wide association studies (GWAS) conducted in European ancestry (EUR) individuals. In this study, we present an in-depth evaluation of PRS based on multi-ancestry GWAS for five cardiometabolic phenotypes in the Penn Medicine BioBank (PMBB) followed by a phenome-wide association study (PheWAS). We examine the PRS performance across all individuals and separately in African ancestry (AFR) and EUR ancestry groups.

Uncovering myocardial infarction genetic signatures using GWAS exploration in Saudi and European cohorts.

Genome-wide association studies (GWAS) have yielded significant insights into the genetic architecture of myocardial infarction (MI), although studies in non-European populations are still lacking. Saudi Arabian cohorts offer an opportunity to discover novel genetic variants impacting disease risk due to a high rate of consanguinity. Genome-wide genotyping (GWG), imputation and GWAS followed by meta-analysis were performed based on two independent Saudi Arabian studies comprising 3950 MI patients and 2324 non-MI controls.

Genome-Wide Association Study of Breast Density among Women of African Ancestry.

Breast density, the amount of fibroglandular versus fatty tissue in the breast, is a strong breast cancer risk factor. Understanding genetic factors associated with breast density may help in clarifying mechanisms by which breast density increases cancer risk. To date, 50 genetic loci have been associated with breast density, however, these studies were performed among predominantly European ancestry populations.

Leveraging electronic health record data for endometriosis research.

is a chronic, complex disease for which there are vast disparities in diagnosis and treatment between sociodemographic groups. Clinical presentation of endometriosis can vary from asymptomatic disease-often identified during (in)fertility consultations-to dysmenorrhea and debilitating pelvic pain. Because of this complexity, delayed diagnosis (mean time to diagnosis is 1.

Polygenic prediction of preeclampsia and gestational hypertension.

Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis.

Gene Interactions in Human Disease Studies-Evidence Is Mounting.

Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships.

Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets.

Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power.

Inference of Causal Relationships Between Genetic Risk Factors for Cardiometabolic Phenotypes and Female-Specific Health Conditions.

Background Cardiometabolic diseases are highly comorbid, but their relationship with female-specific or overwhelmingly female-predominant health conditions (breast cancer, endometriosis, pregnancy complications) is understudied. This study aimed to estimate the cross-trait genetic overlap and influence of genetic burden of cardiometabolic traits on health conditions unique to women. Methods and Results Using electronic health record data from 71 008 ancestrally diverse women, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index, coronary artery disease, type 2 diabetes, and hypertension) by performing 4 analyses: (1) cross-trait genetic correlation analyses to compare genetic architecture, (2) polygenic risk score-based association tests to characterize shared genetic effects on disease risk, (3) Mendelian randomization for significant associations to assess cross-trait causal relationships, and (4) chronology analyses to visualize the timeline of events unique to groups of women with high and low genetic burden for cardiometabolic traits and highlight the disease prevalence in risk groups by age.

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

The Penn Medicine BioBank (PMBB) is an electronic health record (EHR)-linked biobank at the University of Pennsylvania (Penn Medicine). A large variety of health-related information, ranging from diagnosis codes to laboratory measurements, imaging data and lifestyle information, is integrated with genomic and biomarker data in the PMBB to facilitate discoveries and translational science. To date, 174,712 participants have been enrolled into the PMBB, including approximately 30% of participants of non-European ancestry, making it one of the most diverse medical biobanks.

Session Introduction: SALUD: Scalable Applications of cLinical risk Utility and preDiction.

This PSB 2023 session discusses challenges in clinical implication and application of risk prediction models, which includes but is not limited to: implementation of risk models, responsible use of polygenic risk scores (PGS), and other risk prediction strategies. We focus on the development and use of new, scalable methods for harmonizing and refining risk prediction models by incorporating genetic and non-genetic risk factors, applying new phenotyping strategies, and integrating clinical factors and biomarkers. Lastly, we will discuss innovation in expanding the utility of these prediction models to underrepresented populations.