Gabrielle Shakt

BACKGROUND: Epigenetic mechanisms underlying susceptibility to abdominal aortic aneurysm (AAA) remain poorly understood. Identifying causal DNA methylation markers for AAA can elucidate the regulatory processes that drive aneurysm formation and accelerate translational applications. We leveraged the Veterans Affairs (VA) Million Veteran Program to identify methylation biomarkers and delineate underlying pathways. METHODS: We first conducted an epigenome-wide association study of incident AAA (1324 cases and 42 065 noncases), performed stratified analyses by population group and smoking status, and conducted Mendelian randomization to facilitate causal inference of the cytosine-phosphate-guanine (CpG)–AAA association. Chromatin state, island context, and transcription factor binding were implicated through functional annotation of identified CpGs. To identify genes impacted by change in methylation state, we aligned associations with transcriptional data obtained in blood, aorta, and liver. We performed expression quantitative trait methylation to capture CpG–gene-expression links across the genome. Network Mendelian randomization was used to test cardiometabolic and inflammatory mediation. Finally, we developed a methylation risk score for AAA using penalized regression and evaluated its performance against a clinical model in the Million Veteran Program and 2 external studies. RESULTS: Epigenome-wide association study identified 1253 CpGs associated with incident AAA, and Mendelian randomization supported a putative causal role for 143 of these associations. Functional annotation pointed to predominantly distal, enhancer-centered regulation and enrichment of inflammatory transcription factor programs (eg, activator protein-1 and nuclear factor κB). This distal architecture was consistent with expression quantitative trait methylation results, which showed a larger number of trans associations. Among 143 CpGs associated with AAA in Mendelian randomization analyses, we identified 213 CpG–gene expression pairs with strong colocalization support, including AGER, C2 , and DDR1 . Multi-trait colocalization further prioritized 8 CpG–gene expression–AAA links with evidence of a shared genetic signal. Network Mendelian randomization identified 135 putative mediation pathways linking CpGs to AAA, including 58 via cardiometabolic traits and 77 via inflammation-related traits. Blood lipid traits mediated >40% of the associations between AAA risk and CpGs near LDLR (cg19305903) and TRIB1 (cg14128479). Among inflammation-related mediators, circulating DAG1, NEXN, MMP9, IL1RN, and MMP13 were prioritized as potential intermediates. Additionally, a CpG selection process was used to develop a methylation risk score for AAA, which strongly associated with disease in both the discovery cohort and 2 external validation cohorts, even after adjustment for a full suite of clinical covariates. CONCLUSIONS: This study identified putative causal DNA methylation markers for AAA, and multi-omics analyses implicate activator protein-1–linked inflammatory transcriptional programs, blood lipids, and multiple inflammation-related proteins as key pathways underlying methylation-associated AAA risk.

Hernias affect millions of individuals worldwide and represent a significant public health burden, yet the genetic mechanisms underlying hernia development and the extent to which they are shared across anatomical subtypes remains incompletely understood. We performed a multi-population genome-wide association meta-analysis of five hernia subtypes and identified 243 genome-wide significant loci, including 173 novel associations. Gene prioritization implicated genes involved in extracellular matrix organization, elastic fiber assembly, and embryologic development as key effectors of hernia susceptibility. Further analyses demonstrated substantial overlap in the genomic architecture of hernia, including 30 causal variants that were shared across different hernia subtypes. We employed genomic structural equation modeling to formally model this relationship, which identified two distinct latent genetic factors corresponding to putative midline fusion defects (ventral, umbilical, diaphragmatic) and inguinofemoral hernias (inguinal, femoral). Mendelian randomization analyses confirmed causal roles for body mass index, visceral adipose tissue, and abdominal subcutaneous adipose tissue in hernia development while also identifying candidate therapeutic targets. Together, these findings delineate the shared and distinct genetic architecture of hernia subtypes providing a mechanistic foundation to enable precision risk stratification and inform the development of novel preventative and therapeutic strategies.

BACKGROUND: Coronary artery disease (CAD) is a major contributor to cardiovascular morbidity (including myocardial infarction and heart failure) and mortality. Although the burden of CAD (number and degree of coronary artery stenosis) has been observationally linked to these outcomes, the causal contribution and independence from traditional cardiovascular risk factors have been poorly defined. METHODS: We developed a polygenic risk score for angiographic CAD burden using data from the VA Million Veteran Program (n=41 507) and validated this score using data from the Penn Medicine Biobank (n=41 660 with genotyping, N=3771 with angiogram data). We then used publicly available GWAS data and a mediation framework using Mendelian Randomization to investigate whether angiographic CAD burden contributes to adverse cardiovascular outcomes independent of traditional risk factors. RESULTS: We first demonstrated that increasing levels of the polygenic risk score were strongly associated with increased prevalence of nonobstructive and obstructive CAD on coronary angiography (odds ratio, 1.26 [95% CI, 1.14–1.39]; odds ratio, 2.23 [95% CI, 1.94–2.55], respectively) and was associated with other forms of cardiometabolic disease including peripheral artery disease and atherosclerotic risk factors including hyperlipidemia, hypercholesterolemia and hypertension. Through Mendelian randomization analyses, we found that lipid measures (apolipoprotein B, high-density lipoprotein, low-density lipoprotein, total cholesterol, triglycerides) and type 2 diabetes significantly influenced myocardial infarction risk through their effects on angiographic CAD burden. Furthermore, low-density lipoprotein and total cholesterol demonstrated significant indirect effects on heart failure through angiographic CAD burden, suggesting these lipids primarily influence heart failure through their impact on coronary atherosclerosis. CONCLUSIONS: Our findings indicate that angiographic burden of coronary atherosclerosis mediates a substantial proportion of the relationship between traditional cardiovascular risk factors and adverse outcomes. These results support prioritizing primary prevention efforts targeting modifiable risk factors to prevent the development and progression of coronary plaques before clinical disease manifestation.

Coronary artery disease (CAD) is a major contributor to cardiovascular morbidity (including myocardial infarction [MI] and heart failure [HF]), and mortality. Although the burden of CAD (number and degree of coronary artery stenosis) has been observationally linked to these outcomes, the causal contribution and independence from traditional cardiovascular risk factors has been poorly defined. Using publicly available genome wide association study (GWAS) data and individual level outcomes data from the Penn Medicine Biobank, we investigated whether angiographic CAD burden contributes to adverse cardiovascular outcomes independent of traditional risk factors. We developed and validated a polygenic risk score (PRS) for angiographic CAD burden, demonstrating that increasing levels of the PRS were strongly associated with increased prevalence of non-obstructive and obstructive CAD on coronary angiography, and was associated with other forms of cardiometabolic disease including peripheral artery disease and atherosclerotic risk factors including hyperlipidemia, hypercholesterolemia and hypertension. Through Mendelian randomization analyses, we found that lipid measures (ApoB, HDL, LDL, total cholesterol, triglycerides) and type 2 diabetes significantly influenced myocardial infarction risk through their effects on angiographic CAD burden. Furthermore, LDL and total cholesterol demonstrated significant indirect effects on heart failure through angiographic CAD burden, suggesting these lipids primarily influence heart failure through their impact on coronary atherosclerosis. Our findings indicate that angiographic burden of coronary atherosclerosis mediates a substantial proportion of the relationship between traditional cardiovascular risk factors and adverse outcomes. These results support prioritizing primary prevention efforts targeting modifiable risk factors to prevent development and progression of coronary plaques before clinical disease manifestation.

One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third ( n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.

Background: Major depressive disorder (MDD) has been identified as a causal risk factor for multiple forms of cardiovascular disease. Although observational evidence has linked MDD to peripheral artery disease (PAD), causal evidence of this relationship is lacking. Methods and Results: Inverse variance weighted 2‐sample Mendelian randomization was used to test the association the between genetic liability for MDD and genetic liability for PAD. Genetic liability for MDD was associated with increased genetic liability for PAD (odds ratio [OR], 1.17 [95% CI, 1.06–1.29]; P =2.6×10 ⁻³ ). Genetic liability for MDD was also associated with increased genetically determined lifetime smoking ( β =0.11 [95% CI, 0.078–0.14]; P =1.2×10 ⁻¹² ), decreased alcohol intake ( β =−0.078 [95% CI, −0.15 to 0]; P =0.043), and increased body mass index ( β =0.10 [95% CI, 0.02–0.19]; P =1.8×10 ⁻² ), which in turn were associated with genetic liability for PAD (smoking: OR, 2.81 [95% CI, 2.28–3.47], P =9.8×10 ⁻²² ; alcohol: OR, 0.77 [95% CI, 0.66–0.88]; P =1.8×10 ⁻⁴ ; body mass index: OR, 1.61 [95% CI, 1.52–1.7]; P =1.3×10 ⁻⁵⁷ ). Controlling for lifetime smoking index, alcohol intake, and body mass index with multivariable Mendelian randomization completely attenuated the association between genetic liability for MDD with genetic liability for PAD. Conclusions: This work provides evidence for a possible causal association between MDD and PAD that is dependent on intermediate risk factors, adding to the growing body of evidence suggesting that effective management and treatment of cardiovascular diseases may require a composite of physical and mental health interventions.