Coronary Artery Disease Genome-wide Association Studies Research Articles

Convergence of coronary artery disease genes onto endothelial cell programs.

Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.

Multi-ancestry genetic analysis of gene regulation in coronary arteries prioritizes disease risk loci

Genome-wide association studies (GWASs) have identified hundreds of risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in GWASs, and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotypes to identify quantitative trait loci for expression (eQTLs) and splicing (sQTLs) in coronary arteries from 138 ancestrally diverse Americans. Of 2,132 eQTL-associated genes (eGenes), 47% were previously unreported in coronary artery; 19% exhibited cell-type-specific expression. Colocalization revealed subgroups of eGenes unique to CAD and blood pressure GWAS. Fine-mapping highlighted additional eGenes, including TBX20 and IL5. We also identified sQTLs for 1,690 genes, among which TOR1AIP1 and ULK3 sQTLs demonstrated the importance of evaluating splicing to accurately identify disease-relevant isoform expression. Our work provides a patient-derived coronary artery eQTL resource and exemplifies the need for diverse study populations and multifaceted approaches to characterize gene regulation in disease processes.

Abstract 18642: GWAS to Function Highlights the Role of REST in Endothelial Biology and Atherosclerosis

Introduction: Despite the emergence of new lipid-lowering therapies, coronary artery disease (CAD) remains the leading cause of death worldwide. Genome-wide association studies (GWAS) for CAD have discovered multiple novel loci that contribute to CAD pathogenesis. However, limitations in the interpretability of those GWAS associations have hindered the clinical translation of those findings. Hypothesis: In this study, we sought to evaluate the mechanisms of the association between chromosomal locus 4q12 and CAD, replicated in multiple independent GWAS for CAD. Methods: We utilized a combination of computational methods and in vitro assays to achieve our objectives. Results: Meta-analysis of public expression quantitative trait locus (eQTL) data and multi-trait colocalization across atherosclerosis-relevant cell types revealed that the CAD 4q12 locus regulates the expression of REST, a transcriptional repressor, in human aortic endothelial cells (HAEC) and prioritized 2 enhancer variants as candidate causal variants for the GWAS association. CRISPR interference confirmed that the rs6853156 variant regulates REST expression in inducible human endothelial cells. Pathway enrichment analysis in HAEC after REST silencing showed that epithelial to mesenchymal transition was the most upregulated pathway suggesting a potential mechanism via which silencing REST contributes to atherosclerosis. Endothelial to mesenchymal transition (endMT) was confirmed in HAEC after REST silencing at the RNA and protein level by rt-qPCR, western blot, and immunofluorescence. Concordantly, REST silencing increased endothelial permeability and matrix metalloproteinase activity in endothelial cell supernatants in vitro. L1CAM, a known endMT activator was only expressed in HAEC upon REST silencing. Chromatin immunoprecipitation sequencing supports direct binding of REST to the L1CAM promoter. Simultaneous silencing of L1CAM and REST in HAEC inhibited the observed upregulation of mesenchymal genes induced by REST silencing. Conclusions: In summary, our data reveal a novel role of REST as a repressor that functions as a constitutive inhibitor of endMT in human arterial endothelial cells.

Abstract 11993: Integrative Analysis of Single-Cell Transcriptomics and Polygenic Risk Scores Uncovers Vascular Smooth Muscle Cell Heterogeneity in Human Aortic Aneurisms

Background: Atherosclerosis is accompanied by distinct vascular phenotypes that underly its initiation and progression, though their contribution to its pathogenesis is currently unknown. By integrating polygenetic risk profiles from genome-wide association studies (GWAS) of vascular disease with single-cell transcriptional analyses, it is possible to implicate causal cellular subpopulations and functional states. Hypothesis: We sought to build a more precise understanding of the cellular mechanisms underlying vascular disease in humans by combining single-nuclear RNA-sequencing (snRNA-seq) data from vascular tissue with known polygenic risk loci associated with vascular disease. Methods: We performed snRNA-seq on five flash-frozen human aortas, implementing a novel dissociation method to isolate and sequence 14,228 quality-controlled nuclei from three thoracic aortic aneurysms and two samples without known aortopathy, obtained from patients aged 37-70 years. Cell types were defined using canonical gene markers, and non-linear dimensional reduction via tSNE was employed to identify transcriptional sub-phenotypes. The identified pathological programs were validated using polygenic risk and by integrating GWAS data from other vascular single-nuclear transcriptomic datasets. Results: Our analysis revealed ten distinct clusters representing all major vascular cell populations, with vascular smooth muscle cells (VSMCs) and myofibroblasts emerging as the most relevant cell types in relation to vascular disease based on polygenic risk. Within the VSMC population, two distinct subpopulations exhibited differential expression of genes related to the extracellular matrix, contractility, and proliferation. RNA-trajectory analysis further uncovered a continuum of gene expression linking VSMC1, VSMC2, and myofibroblast transcriptional clusters. Conclusions: This study validates novel methods for identifying pathologic VSMC states associated with vascular disease and implicates phenotypic switching as a pathogenic mechanism. We highlight genes and pathways associated with endothelial cell and VSMC state transitions that are enriched in GWAS of coronary artery disease (CAD).

Association rule mining for genome-wide association studies through Gibbs sampling

AbstractFinding associations between genetic markers and a phenotypic trait such as coronary artery disease (CAD) is of primary interest in genome-wide association studies (GWAS). A major challenge in GWAS is the involved genomic data often contain large number of genetic markers and the underlying genotype-phenotype relationship is mostly complex. Current statistical and machine learning methods lack the power to tackle this challenge with effectiveness and efficiency. In this paper, we develop a stochastic search method to mine the genotype-phenotype associations from GWAS data. The new method generalizes the well-established association rule mining (ARM) framework for searching for the most important genotype-phenotype association rules, where we develop a multinomial Gibbs sampling algorithm and use it together with the Apriori algorithm to overcome the overwhelming computing complexity in ARM in GWAS. Three simulation studies based on synthetic data are used to assess the performance of our developed method, delivering the anticipated results. Finally, we illustrate the use of the developed method through a case study of CAD GWAS.

The Genetics of Coronary Artery Disease: A Vascular Perspective.

Genome-wide association studies (GWAS) have identified a large number of genetic loci for coronary artery disease (CAD), with many located close to genes associated with traditional CAD risk pathways, such as lipid metabolism and inflammation. It is becoming evident with recent CAD GWAS meta-analyses that vascular pathways are also highly enriched and present an opportunity for novel therapeutics. This review examines GWAS-enriched vascular gene loci, the pathways involved and their potential role in CAD pathogenesis. The functionality of variants is explored from expression quantitative trait loci, massively parallel reporter assays and CRISPR-based gene-editing tools. We discuss how this research may lead to novel therapeutic tools to treat cardiovascular disorders.

The Associations of Genetically Predicted Plasma Alanine with Coronary Artery Disease and its Risk Factors: A Mendelian Randomization Study

BackgroundAlanine is an amino acid commonly used as a nutritional supplement and plays a key role in the glucose-alanine cycle. Plasma alanine has been associated in observational studies with a higher risk of coronary artery disease (CAD) and unhealthier lipid profiles. However, evidence from large randomized controlled trials is lacking. ObjectivesUsing Mendelian randomization (MR), we assessed the unconfounded associations of plasma alanine with CAD and CAD risk factors. MethodsWe applied single nucleotide polymorphisms that were strongly (P < 5 ×10-8) associated with plasma alanine as genetic instruments to large genome-wide association studies of CAD (63,108 cases; 296,901 controls), diabetes (90,612 cases; 583,493 controls), glucose (515,538 participants), lipids (low-density lipoprotein [LDL] cholesterol, high-density lipoprotein [HDL] cholesterol, triglycerides, total cholesterol, and apolipoprotein B) (>1.1 million participants), blood pressure (BP) (757,601 participants), and body mass index (682,137 participants). Given the potential sex disparity, we also conducted sex-specific analyses. MR estimates per standard deviation increase in alanine concentrations were obtained using inverse variance weighting followed by sensitivity analyses using weighted median, MR-Egger, MR-Pleiotropy RESidual Sum and Outlier, and MR-Robust Adjusted Profile Score. ResultsGenetically predicted plasma alanine was not associated with CAD but with a higher risk of diabetes (odds ratio [OR]: 1.35; 95% confidence interval [CI]: 1.06, 1.72), higher glucose (β: 0.11; 95% CI: 0.02, 0.19), LDL cholesterol (β: 0.08; 95% CI: 0.04, 0.12), triglycerides (β: 0.25; 95% CI: 0.13, 0.38), total cholesterol (β: 0.14; 95% CI: 0.08, 0.20), apolipoprotein B (β: 0.12; 95% CI: 0.03, 0.21), and BP (β: 1.17; 95% CI: 0.31, 2.04 for systolic BP: β: 0.97; 95% CI: 0.49, 1.45 for diastolic BP) overall. The positive associations of serum alanine with LDL cholesterol and triglycerides were more notable in women than in men. ConclusionsAlanine or factors affecting alanine may have causal effects on diabetes, blood glucose, lipid profiles, and BP but not on CAD. Further studies are needed to clarify possible mechanisms.

A multivariate genome-wide association study of psycho-cardiometabolic multimorbidity.

Coronary artery disease (CAD), type 2 diabetes (T2D) and depression are among the leading causes of chronic morbidity and mortality worldwide. Epidemiological studies indicate a substantial degree of multimorbidity, which may be explained by shared genetic influences. However, research exploring the presence of pleiotropic variants and genes common to CAD, T2D and depression is lacking. The present study aimed to identify genetic variants with effects on cross-trait liability to psycho-cardiometabolic diseases. We used genomic structural equation modelling to perform a multivariate genome-wide association study of multimorbidity (Neffective = 562,507), using summary statistics from univariate genome-wide association studies for CAD, T2D and major depression. CAD was moderately genetically correlated with T2D (rg = 0.39, P = 2e-34) and weakly correlated with depression (rg = 0.13, P = 3e-6). Depression was weakly correlated with T2D (rg = 0.15, P = 4e-15). The latent multimorbidity factor explained the largest proportion of variance in T2D (45%), followed by CAD (35%) and depression (5%). We identified 11 independent SNPs associated with multimorbidity and 18 putative multimorbidity-associated genes. We observed enrichment in immune and inflammatory pathways. A greater polygenic risk score for multimorbidity in the UK Biobank (N = 306,734) was associated with the co-occurrence of CAD, T2D and depression (OR per standard deviation = 1.91, 95% CI = 1.74-2.10, relative to the healthy group), validating this latent multimorbidity factor. Mendelian randomization analyses suggested potentially causal effects of BMI, body fat percentage, LDL cholesterol, total cholesterol, fasting insulin, income, insomnia, and childhood maltreatment. These findings advance our understanding of multimorbidity suggesting common genetic pathways.

Abstract 145: CRISPRi-Perturb-seq In Endothelial Cells Links Atherosclerosis Risk Loci To Novel Klf2/4 Regulatory Genes

Introduction: Genome-wide association studies (GWAS) have discovered &gt;300 associations for CAD. Few loci are functionally characterized and may represent new mechanisms of disease. Hypothesis: Can an unbiased transcriptional screen for the &gt;2000 candidate GWAS genes in endothelial cells (ECs) identify causal genes and disease-relevant pathways at CAD GWAS loci. Methods: We applied CRISPRi-Perturb-seq to knock down expression of genes within 500 Kb of coronary artery disease GWAS loci (2,300 genes in total) and measure their transcriptomic effects using single-cell RNA-seq. Validation of candidate causal genes was conducted in primary human ECs and zebrafish. Results: We identified 50 programs of co-expressed genes, which represent core cellular pathways (i.e. ribosome biogenesis) and 13 EC-specific pathways such as flow response and angiogenesis. The EC-specific programs show the greatest contribution to CAD heritability. 5 EC-specific programs have the greatest number of GWAS candidate genes. We find that 43 of 306 CAD GWAS signals converge onto these 5 gene programs, and all are regulated by genes in the cerebral cavernous malformations (CCM) pathway—which coordinates KLF2/4 transcriptional responses in endothelial cells, but has not been linked to CAD risk. The strongest regulator of these programs is TLNRD1 , which we show is a new CAD gene and novel regulator of the CCM pathway. TLNRD1 loss-of-function alters actin organization and barrier function in endothelial cells in vitro , and heart development in zebrafish in vivo . Conclusions: High-throughput functional analysis of 2,300 genes proximal to CAD GWAS loci prioritized pathways—such as angiogenesis and EC migration—that are regulated by multiple risk SNPs. Our study identifies new genes that likely influence risk for CAD, identifies convergence of CAD genes into certain pathways in endothelial cells, and prioritizes study of genes in the CCM-signaling pathway for contribution to CAD risk.

Abstract 14588: A Large-Scale Genome-Wide Association Study of Angiographically Determined Burden of Coronary Atherosclerosis in a Genetically Diverse Population

Introduction: Few large-scale genome wide association studies (GWAS) of angiographically derived burden of coronary atherosclerosis have been reported to date. The yield of this approach relative to case-control GWAS of coronary artery disease (CAD) remains unclear. Methods: We used the Veterans Affairs Clinical Assessment, Reporting, and Tracking Program, a national quality and safety organization for invasive cardiac procedures, to conduct the largest ever GWAS of burden of coronary atherosclerosis involving participants of the Million Veteran Program (MVP). Obstructive disease of a native major vessel was defined as the presence of at least one obstruction &gt;50% of the luminal diameter, or a prior revascularization procedure, involving the main artery and/or its major branches. Non-obstructive disease was defined as one or more vessels having one or more obstructions of &gt;20% but none more than 50%. We classified an individual’s extent of disease as normal, non-obstructive, one vessel, two vessel, or three vessel / left main CAD. GWAS was performed stratified by racial/ethnic group using linear regression in REGENIE, with age, sex, and genetic principal components as covariates. Stratified results were then combined through meta-analysis using GWAMA. Results: The extent of disease was quantified in 49,213 non-Hispanic White, 11,326 non-Hispanic Black, and 4,058 Hispanic MVP participants, a majority of which were subjects with clinical CAD. Forty-three loci reached genome wide significance including 27 among Whites, four among Blacks at SORT1 , LPL , COL4A2 , and LDLR , two among Hispanics at CDKN2B-AS1 and NRG4 , and an additional 13 through meta-analysis of all three strata. Two loci, HHEX and NRG4 , have not been previously established as susceptibility loci CAD. Conclusions: Large-scale GWAS of burden of coronary atherosclerosis complements discovery efforts of case-control GWAS of CAD. Despite substantially smaller sample sizes, this quantitative approach recapitulated many established loci for CAD, uncovered novel loci, and identified several known susceptibility loci in admixed Black and Hispanic populations. The latter required a remarkably larger sample size to achieve through GWAS of clinically defined events alone.

Abstract 14905: A Combination of Polygenic Risk Scores for Coronary Artery Disease and for Circulating Blood Lipids Predicts the Incidence of Myocardial Infarction and Coronary Revascularization

Introduction: Estimating the genetic risk of developing complex diseases, such as coronary artery disease (CAD), is now possible by aggregating data from genome-wide association studies (GWAS) into polygenic risk scores (PRS). This study aims to evaluate the performance of PRS derived from CAD (PRS CAD ) and blood lipids GWAS to predict severe CAD defined as myocardial infarction or coronary revascularization (MI-REVASC). Methods: Using nuclear magnetic resonance data from UK Biobank (UKB, n=90300), we conducted 29 GWAS for blood lipids (9 for fatty acids and 20 for lipoproteins). Summary statistics of these GWAS were then used to derive PRS in the rest of UKB (n=318422, median follow-up = 12.6 years). A PRS CAD was also calculated using the publicly available GWAS from the CARDIoGRAMplusC4D consortium. All PRS included &gt;1.1 million of genetic variants and were calculated using LDpred2. Hazard Ratio (HR) for incident MI-REVASC (n=10935) are reported with their 95% CIs, for 1-SD increase of each PRS. Selected independent PRS were combined into multivariate Cox regression models, for which predictive performance was evaluated. All models were adjusted for age, sex and genetic principal components. Results: Out of 30 PRS, 28 were significantly associated with incident MI-REVASC; PRS CAD showed the strongest association (HR=1.60 [1.57-1.63], p&lt;10e-300), whereas PRS for small LDL particles (sLDL-P) was the most associated blood lipids PRS (HR=1.14 [1.12-1.16], p=7.63e-42). The optimal model included PRS for CAD, sLDL-P, HDL-C and the PUFA/MUFA (poly-/mono- unsaturated fatty acids) ratio. Discriminative capacities for incident MI-REVASC were significantly increased in the model including PRS CAD (C-statistic (C) = 0.744 [0.739-0.748]) compared to the model without PRS (C= 0.710 [0.705-0.714]). No significant difference was observed when independent blood lipids PRS were added to the model (C= 0.745 [0.741 to 0.749]). Conclusions: Although independently associated with the incidence of severe CAD, blood lipids PRS provide little improvement in the predictive performance when added to PRS CAD .

Abstract 14675: High-Throughput CRISPR Screening Links CAD Loci to Endothelial Cell Programs and Causal Pathways

Introduction: Genome-wide association studies (GWAS) have discovered &gt;300 associations for CAD. Few loci are functionally characterized and may represent new mechanisms of disease. Hypothesis: Can a high-throughput, unbiased transcriptional screen for all candidate GWAS genes identify the causal genes and biological pathways at CAD GWAS loci in endothelial cells (ECs)? Methods: We applied CRISPRi-Perturb-seq to knock down the expression of all genes within 500 Kb of coronary artery disease GWAS loci (2,300 genes in total) and measure their effects on the transcriptome using single-cell RNA-seq. Results: We identified 60 programs of co-expressed genes, which represent core cellular pathways (i.e. ribosome biogenesis) and EC-specific pathways such as flow response and angiogenesis. The EC-specific programs show the greatest contribution to CAD heritability. 6 EC-specific programs have the greatest number of CAD GWAS candidate genes. One of the EC-specific programs had genes regulated by KLF -transcription factors and was enriched for shear-stress response genes. The novel CAD candidate genes in this program included multiple known mediators of the cerebral cavernous malformation (CCM)-signaling complex. 9 known members of the CCM-signaling cascade and several potentially novel mediators of CAD clustered together in the KLF Perturb-seq topic. These included genetic variants in the CCM2 , KLF4 , RAC1 , and HEG1 loci as well as genes will a similar transcriptional profile but no prior connection to the CCM-signaling complex. Conclusions: High-throughput functional analysis of 2,300 genes proximal to CAD GWAS loci prioritized pathways—such as angiogenesis and EC migration—that are regulated by multiple risk SNPs. Our study identifies new genes that likely influence risk for CAD, identifies convergence of CAD genes into certain pathways in endothelial cells, and demonstrates a generalizable strategy to connect disease variants to functions.

Combining polygenic risk scores to predict myocardial infarction in the Canadian longitudinal study on aging

Background and Aims : Estimating the risk of developing complex pathologies, such as coronary artery disease (CAD), is now possible by aggregating data from genome-wide association studies (GWAS) into polygenic risk scores (PRS). This study aims to evaluate the performance of PRS derived from CAD and blood lipids GWAS to predict myocardial infarction (MI).

Abstract 222: The Molecular Mechanisms Of Coronary Artery Disease Risk At The Pdgfd Locus

Background: Atherosclerotic cardiovascular diseases, including coronary artery disease (CAD), are the worldwide leading cause of death. Recent genome wide association studies (GWAS) have now identified over 250 genetic loci that are associated with CAD, and efforts in this lab and others are investigating the mechanisms of association at these loci. The CAD GWAS locus at 11q22.3 is of potential interest because it encodes the PDGFD gene, a member of the platelet-derived growth factor family whose expression is up-regulated in atherosclerosis. But whether disease allelic variation regulates PDGFD expression and how this growth factor might influence the risk of CAD has not been studied. Methods: The causal variant and gene were investigated with colocalization and in vitro transcription studies in cultured human coronary artery smooth muscle cells (SMC). Single-cell transcriptomic profiling (scRNAseq) and histological analyses were conducted in SMC-specific lineage tracing in an in vivo Pdgfd knockout mouse model of atherosclerosis. Results: The causal variant rs2019090 was linked to PDGFD expression in SMC. In silico analysis of transcription factor binding site prediction and in vitro haplotype-specific chromatin immunoprecipitation combined with qPCR analysis revealed another causal CAD factor forkhead box c1 (FOXC1) preferentially binds to the A-allele of variant rs2019090 and controls the expression level of PDGFD . scRNAseq data identified that loss of Pdgfd resulted in a marked increase in differentiated SMC but a decrease in fibromyocyte (FMC) and chondromyocyte (CMC) transition populations, as well as a significant decrease in plaque macrophages. Differentially expressed gene analysis in SMC lineage cells was highly enriched for CMC genes such as Col2a1 and Ibsp. Lesion histology analysis identified an increase in medial SMC, but a decrease in fibrous cap SMC, with decreased Cd68 and Col2a1 stained area. Conclusion: PDGFD is a causal CAD gene that increases disease risk by promoting SMC transition primarily to the CMC phenotype, and recruitment of monocyte-macrophage lineage cells to disease lesions. These findings support suppression of PDGFD as a possible therapy for human atherosclerotic disease.

Interpreting coronary artery disease GWAS results: A functional genomics approach assessing biological significance.

Genome-wide association studies (GWAS) have implicated 58 loci in coronary artery disease (CAD). However, the biological basis for these associations, the relevant genes, and causative variants often remain uncertain. Since the vast majority of GWAS loci reside outside coding regions, most exert regulatory functions. Here we explore the complexity of each of these loci, using tissue specific RNA sequencing data from GTEx to identify genes that exhibit altered expression patterns in the context of GWAS-significant loci, expanding the list of candidate genes from the 75 currently annotated by GWAS to 245, with almost half of these transcripts being non-coding. Tissue specific allelic expression imbalance data, also from GTEx, allows us to uncover GWAS variants that mark functional variation in a locus, e.g., rs7528419 residing in the SORT1 locus, in liver specifically, and rs72689147 in the GUYC1A1 locus, across a variety of tissues. We consider the GWAS variant rs1412444 in the LIPA locus in more detail as an example, probing tissue and transcript specific effects of genetic variation in the region. By evaluating linkage disequilibrium (LD) between tissue specific eQTLs, we reveal evidence for multiple functional variants within loci. We identify 3 variants (rs1412444, rs1051338, rs2250781) that when considered together, each improve the ability to account for LIPA gene expression, suggesting multiple interacting factors. These results refine the assignment of 58 GWAS loci to likely causative variants in a handful of cases and for the remainder help to re-prioritize associated genes and RNA isoforms, suggesting that ncRNAs maybe a relevant transcript in almost half of CAD GWAS results. Our findings support a multi-factorial system where a single variant can influence multiple genes and each genes is regulated by multiple variants.

A Genome-Wide Association Study on Abdominal Adiposity-Related Traits in Adult Korean Men

Introduction: Although previous genome-wide association studies (GWASs) have identified genetic susceptibility loci for abdominal adiposity, GWASs on Asian samples remain scarce. Therefore, we performed a GWAS for abdominal adipose tissue depots in a Korean population. Methods: A total of 1,937 Korean men were included in the study. Areas of abdominal fat were quantified by computed tomography. We performed a GWAS analysis under an additive model, and a replication study was conducted on 480 additional Korean adult men. Results: In the discovery step, we identified a total of 10 single-nucleotide polymorphisms (SNPs) associated with adiposity indicators (p < 1 × 10⁻⁵). The top SNP, rs1028014, for visceral adipose tissue (VAT) was located in the ZMAT4 gene and remained significant after adjustment for body mass index (BMI). Three additional SNPs were also associated with VAT-adj-BMI and located within the SLC26A10, FAM155A, and COL4A1-COL4A2 genes, respectively. In addition, we identified a SNP (rs4668224) of the MYO3B gene for visceral-to-subcutaneous fat ratio. For subcutaneous adipose tissue and total adipose tissue, two (rs6585735 and rs363527) and three SNPs (rs1487892, rs9357565, and rs1985358) were found, respectively. Overall, eight SNPs were used in the replication study; however, none of the SNPs reached our level of significance for replication (p < 0.0063). Nevertheless, rs4773144 of COL4A1-COL4A2 for VAT-adj-BMI was the most interesting SNP identified in previous GWASs for coronary artery disease (based on the same risk allele “G”), along with functional effects. Conclusion: This study suggests for the first time that an SNP (rs4773144) of COL4A1-COL4A2 may contribute to the increase in VAT level, especially in adult Korean men.

Integrative Prioritization of Causal Genes for Coronary Artery Disease.

Hundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets. We aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task). We identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks. We identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.

Abstract MP35: Single-cell Analysis Uncovers Multiple Adventitial Fibroblast Populations With Expression Of CAD GWAS Genes In Murine Atherosclerosis

The arterial adventitia is a layer of cells outside the external lamina of blood vessels that contribute to the progression of coronary artery disease (CAD) like atherosclerosis. Adventitial fibroblasts are known regulators of vascular remodeling through their deposition of collagen fibrils around vessels during atherosclerotic lesion development. However, the molecular identity and function of the participating fibroblasts have not been well studied. We used single cell RNA sequencing (scRNA-seq) to characterize the transcriptome of adventitial fibroblasts and other cell types at multiple timepoints during atherogenesis in hyperlipidemic Ldlr knockout mice. Specifically, scRNA-seq was applied to Ldlr KO aortic adventitia after 0, 9, and 16 weeks of western diet (WD) feeding. Unbiased clustering analysis uncovered 9 different cell clusters, including 5 different fibroblast populations (Fibro1-5), and a single cluster each of macrophages, T cells, SMCs, and ECs. We observed that, as atherosclerosis progressed from 9 to 16 weeks of feedings, the proportion of total cells that were Fibro1, 3, and 4 decreased (33, 40, and 13%), while populations of Fibro2 and 5 slightly increased (13 and 18%). All of non-fibroblast populations (Macrophage, T cells, SMCs and ECs) increased from 9-16wks of diet. Next, we asked whether genes associated with coronary artery disease by human genome-wide association studies (GWAS) are expressed in different adventitial fibroblast clusters. We determined the expression of 266 genes implicated in CAD by GWAS and found 201 genes (76%) were expressed in adventitial cells at either/both 9 or 16wks. 34 genes displayed large changes in expression from 9 to 16wks. We identified five genes that were highly expressed in the adventitial Fibro5 cluster specifically, and additional other CAD GWAS genes specifically expressed in adventitial macrophages, SMCs, and ECs. In summary, we present here a cell atlas defined by scRNA-seq that reveals the heterogeneity amongst the fibroblasts in adventitial cells during atherogenesis. Additionally, we find that many CAD GWAS genes are expressed in adventitial cell populations, raising the intriguing possibility that CAD GWAS are identifying adventitial mechanisms that contribute to CAD.

Genetics of coronary artery disease in the post-GWAS era.

During the past decade, genome-wide association studies (GWAS) have transformed our understanding of many heritable traits. Three recent large-scale GWAS meta-analyses now further markedly expand the knowledge on coronary artery disease (CAD) genetics in doubling the number of loci with genome-wide significant signals. Here, we review the unprecedented discoveries of CAD GWAS on low-frequency variants, underrepresented populations, sex differences and integrated polygenic risk. We present the milestones of CAD GWAS and post-GWAS studies from 2007 to 2021, and the trend in identification of variants with smaller odds ratio by year due to the increasing sample size. We compile the 321 CAD loci discovered thus far and classify candidate genes as well as distinct functional pathways on the road to indepth biological investigation and identification of novel treatment targets. We draw attention to systems genetics in integrating these loci into gene regulatory networks within and across tissues. We review the traits, biomarkers and diseases scrutinized by Mendelian randomization studies for CAD. Finally, we discuss the potentials and concerns of polygenic scores in predicting CAD risk in patient care as well as future directions of GWAS and post-GWAS studies in the field of precision medicine.

Effects of selenium on coronary artery disease, type 2 diabetes and their risk factors: a Mendelian randomization study.

The impact of selenium on coronary artery disease (CAD) and type 2 diabetes (T2D) remains unclear with inconsistent results from observational studies and randomized controlled trials. We used Mendelian randomization to obtain unconfounded estimates of the effect of selenium on CAD, T2D, lipids and glycemic traits. We applied genetic variants strongly (P < 5 × 10-8) associated with blood and toenail selenium to publicly available summary statistics from large consortia genome-wide association studies of CAD (76,014 cases and 264,785 non-cases), T2D (74,124 cases and 824,006 controls), lipids and glycemic traits. Variant specific Wald estimates were combined using inverse variance weighting, with several sensitivity analyses. Genetically predicted selenium was associated with higher T2D (OR 1.27, 95% CI 1.07-1.50, P = 0.006). There was little evidence of an association with CAD. Genetically predicted selenium was associated with lower low-density lipoprotein (LDL) cholesterol, lower high-density lipoprotein (HDL) cholesterol, higher fasting insulin and higher homeostasis model assessment of insulin resistance. These results were not robust to all sensitivity analyses. No associations with triglycerides, fasting glucose or homeostasis model assessment of β-cell function were evident. Our study suggests selenium may increase the risk of T2D, possibly through insulin resistance rather than pancreatic beta cell function, but may reduce lipids. We found little evidence of an association with CAD, although an inverse association cannot be definitively excluded. The effect of selenium on these outcomes warrants further investigation.