Meta-analysis of genome-wide associations and polygenic risk prediction for atrial fibrillation in more than 180,000 cases.

Atrial fibrillation (AF) is the most common heart rhythm abnormality and is a leading cause of heart failure and stroke. This large-scale meta-analysis of genome-wide association studies increased the power to detect single-nucleotide variant associations and found more than 350 AF-associated genetic loci. We identified candidate genes related to muscle contractility, cardiac muscle development and cell-cell communication at 139 loci.

Genetics of cardiovascular outcomes in individuals with chronic kidney disease: the Chronic Renal Insufficiency Cohort (CRIC) study.

Genome-wide association studies (GWAS) identified multiple loci for cardiovascular disease, but their relevance to individuals with chronic kidney disease (CKD), who are at higher risk of cardiovascular disease, is unknown. In this study, we performed GWAS analyses of coronary heart disease (CHD) or all-cause stroke in African (AFR) and European (EUR) American participants with CKD of the Chronic Renal Insufficiency Cohort (CRIC). Mixed- effect logistic regression models were race-stratified and adjusted for age, sex, site of recruitment, estimated glomerular filtration rate (eGFR), and principal components, followed by meta-analysis.

Dietary Cholesterol and Myocardial Infarction in the Million Veteran Program.

Background: Coronary artery disease is a leading cause of morbidity and mortality in the United States. Coronary artery disease can lead to major complications including myocardial infarction (MI). The association of dietary cholesterol with coronary artery disease remains inconsistent.

Large-scale multi-omics identifies drug targets for heart failure with reduced and preserved ejection fraction.

Heart failure (HF) has limited therapeutic options. In this study, we differentiated the pathophysiological underpinnings of the HF subtypes-HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF)-and uncovered subtype-specific therapeutic strategies. We investigated the causal roles of the human proteome and transcriptome using Mendelian randomization on more than 420,000 participants from the Million Veteran Program (27,799 HFrEF and 27,579 HFpEF cases).

A genetically based computational drug repurposing framework for rapid identification of candidate compounds: application to COVID-19.

Background: The development and approval of novel drugs are typically time-intensive and expensive. Leveraging a computational drug repurposing framework that integrates disease-relevant genetically regulated gene expression (GReX) and large longitudinal electronic medical record (EMR) databases can expedite the repositioning of existing medications. However, validating computational predictions of the drug repurposing framework remains a challenge.

ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis.

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features.

A semi-empirical Bayes approach for calibrating weak instrumental bias in sex-specific Mendelian randomization studies.

Strong sex differences exist in sleep phenotypes and also cardiovascular diseases (CVDs). However, sex-specific causal effects of sleep phenotypes on CVD-related outcomes have not been thoroughly examined. Mendelian randomization (MR) analysis is a useful approach for estimating the causal effect of a risk factor on an outcome of interest when interventional studies are not available.

Risk of Incident Cancer in Veterans with Diabetes Who Use Metformin Versus Sulfonylureas.

Prior research suggests metformin has anti-cancer effects, yet data are limited. We examined the association between diabetes treatment (metformin versus sulfonylurea) and risk of incident diabetes-related and non- diabetes-related cancers in US veterans. This retrospective cohort study included US veterans, without cancer, aged ≥ 55 years, who were new users of metformin or sulfonylureas for diabetes between 2001 to 2012.

DOME: Directional medical embedding vectors from Electronic Health Records.

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data.

The impact of common and rare genetic variants on bradyarrhythmia development.

To broaden our understanding of bradyarrhythmias and conduction disease, we performed common variant genome-wide association analyses in up to 1.3 million individuals and rare variant burden testing in 460,000 individuals for sinus node dysfunction (SND), distal conduction disease (DCD) and pacemaker (PM) implantation. We identified 13, 31 and 21 common variant loci for SND, DCD and PM, respectively.

Novel loci and biomedical consequences of iron homoeostasis variation.

Iron homoeostasis is tightly regulated, with hepcidin and soluble transferrin receptor (sTfR) playing significant roles. However, the genetic determinants of these traits and the biomedical consequences of iron homoeostasis variation are unclear. In a meta-analysis of 12 cohorts involving 91,675 participants, we found 43 genomic loci associated with either hepcidin or sTfR concentration, of which 15 previously unreported.

Cardiovascular Health Score and Atherosclerotic Cardiovascular Disease in the Million Veteran Program.

Importance: The American Heart Association proposed Life's Essential 8 (LE8) as an enhanced measurement tool for cardiovascular health.

Objective: To examine the association of LE8 with risk of atherosclerotic cardiovascular disease (ASCVD) incidence and prognosis in veterans.

Design, Setting, And Participants: This was a prospective cohort study of US veterans enrolled in the Department of Veterans Affairs (VA) Million Veteran Program (MVP) between 2011 and 2022.

A landmark federal interagency collaboration to promote data science in health care: Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now.

Objectives: In 2016, the Department of Veterans Affairs (VA) and the Department of Energy (DOE) established an Interagency Agreement (IAA), the Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP-CHAMPION) research collaboration.

Materials And Methods: Oversight fell under the VA Office of Research Development (VA ORD) and DOE headquarters. An Executive Committee and 2 senior scientific liaisons work with VA and DOE leadership to optimize efforts in the service of shared scientific goals.

Racial and Ethnic Disparities in Dietary Intake and Quality Among United States Veterans.

Background: Dietary quality plays an important role in disease development and prognosis, and diet is also a key contributor to disparities in many chronic diseases and health conditions.

Objectives: This study aimed to assess racial and ethnic disparities experienced by veterans; we examined food intake and dietary quality across different racial and ethnic groups of United States veterans.

Methods: The study included 420,730 males and females aged 19-107 y (91.

Comparison of methods for building polygenic scores for diverse populations.

Polygenic scores (PGSs) are a promising tool for estimating individual-level genetic risk of disease based on the results of genome-wide association studies (GWASs). However, their promise has yet to be fully realized because most currently available PGSs were built with genetic data from predominantly European-ancestry populations, and PGS performance declines when scores are applied to target populations different from the populations from which they were derived. Thus, there is a great need to improve PGS performance in currently under-studied populations.

Protein Identification for Stroke Progression via Mendelian Randomization in Million Veteran Program and UK Biobank.

Background: Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke.

Methods: We performed genome-wide association studies for subsequent major adverse cardiovascular events (MACE; n=51 929; n=39 980) and subsequent arterial ischemic stroke (AIS; n=45 120; n=46 789) after the first incident stroke within the Million Veteran Program and UK Biobank.