Abstract
Abdominal aortic aneurysm has a high heritability and often co-occurs with other cardiometabolic disorders, suggesting shared genetic susceptibility. We investigate this commonality leveraging recent GWAS studies of abdominal aortic aneurysm and 32 cardiometabolic traits. We find significant genetic correlations between abdominal aortic aneurysm and 21 of the cardiometabolic traits investigated, including causal relationships with coronary artery disease, hypertension, lipid traits, and blood pressure. For each trait pair, we identify shared causal variants, genes, and pathways, revealing that cholesterol metabolism and inflammation are shared most prominently. Additionally, we show the tissue and cell type specificity in the shared signals, with strong enrichment across traits in the liver, arteries, adipose tissues, macrophages, adipocytes, and fibroblasts. Finally, we leverage drug-gene databases to identify several lipid-lowering drugs and antioxidants with high potential to treat abdominal aortic aneurysm with comorbidities. Our study provides insight into the shared genetic mechanism between abdominal aortic aneurysm and cardiometabolic traits, and identifies potential targets for pharmacological intervention.
Similar content being viewed by others
Introduction
Abdominal aortic aneurysm (AAA), defined as focal dilation of the abdominal aorta by 50% or reaching ≥ 30 mm in diameter, is a complex vascular disease with an estimated global prevalence of 0.92%1. It is asymptomatic in early disease stages, with most AAA discovered by incidental imaging or screening protocols. Once reaching 55 mm, the five-year cumulative rupture rate is 25-40%2. Among ruptured patients, a mortality rate as high as 80% was observed3, rendering AAA a leading cause of death.
AAA is characterized by remodeling and degradation of the extracellular matrix, apoptosis of smooth muscle cells, luminal thrombosis, and chronic inflammation4,5. Plaques consisting of lipids, blood cells and other plasma substances accumulate around the lesion sites, with abundant infiltration of innate and adaptive immune cells both in the thrombus and the arterial wall6. Meanwhile, metabolic homeostasis can be perturbed, resulting in enhanced glycolysis in the aortic wall7 and altered serum levels of amino acids and lipids8,9,10. Often, circulating total cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, and sulfur amino acids are elevated, whereas high-density lipoprotein cholesterol (HDL-C) and phosphatidylcholines are reduced. These changes resemble numerous other cardiovascular diseases (CVDs), such as coronary artery disease (CAD), myocardial infarction (MI), and peripheral arterial disease11. Indeed, atherosclerosis occurs in 25–55% AAA patients12, and known risk factors of AAA including male sex, age, smoking, hypercholesterolemia, hyperlipidemia, and hypertension13, are widely shared among CVDs.
AAA is highly heritable, with an estimated 70% heritability by family and twin studies14,15. In fact, high heritability is generally observed in cardiometabolic disorders16,17, rendering genetic studies a valuable tool to decipher the disease mechanisms18. Genome-wide association studies (GWAS), particularly those performed in recent years with large sample sizes, have uncovered single nucleotide variants (SNVs) associated with many complex diseases19. A recent meta-GWAS of AAA examined 39,221 cases and 1,086,107 controls, resulting in 141 susceptible loci20, a several-fold increase in disease loci compared to earlier studies21,22. Similarly, recent GWAS provided comprehensive variant profiles for dozens of cardiometabolic traits (CMTs), which have greatly enhanced our understanding of these diseases.
In this study, we leverage these large GWAS data to identify genetic factors shared by AAA and CMTs. We aim to identify shared SNVs and genes, as well as the enriched pathways, cell types, and tissues. Importantly, these results offer valuable information for prioritizing drugs that target shared genes for treating AAA with comorbid conditions.
Results
GWAS datasets
We obtained GWAS summary statistical data for 18 cardiometabolic diseases (CMDs) including AAA, 15 metabolic traits, and 6 immune cell traits (Fig. 1A). These traits are distributed over a broad spectrum of cardiac and metabolic functions, including heart functions, vascular circulation, glucose metabolism, lipid metabolism, and immunity. Most of the CMDs were studied in more than 10,000 case samples, whereas metabolic traits and immune cells were measured in a minimum of 560,000 individuals. Although European ancestry was dominant, many studies included various ancestral groups. Furthermore, the number of interrogated genotypes ranged between 4.5–52 million, and the significant SNVs (P < 5 × 10−8) were ample (Supplementary Data 1). Overall, these datasets present a state-of-the-art discovery power for common SNVs-based genetic susceptibility to cardiometabolic disorders. Around these datasets, we designed analysis modules to elucidate the shared genetic architecture of AAA and CMTs, including shared SNVs, genes, pathways, tissues, and cell types (Fig. 1B). Coherent signals from various analyses are found and presented below.
A Traits and diseases in this study include 18 cardiometabolic diseases, 15 metabolic traits, and 6 immune cell traits. This graph was created via https://www.biorender.com/. B Analysis modules included computing genome-wide genetic correlations, inferring causality between AAA and the traits by bidirectional Mendelian randomization, identifying shared causal variants, genes and pathways, discovering tissues and cell being impacted the most by the shared signals, and prioritizing drugs for treating AAA comorbidities. CMD: cardiometabolic diseases, MT: metabolic traits.
Genetic correlation
Genome-wide correlations computed by LDSC23 suggest positive correlations between AAA and 20 CMTs (Fig. 2A). The highest correlated traits are aortic aneurysms, followed by numerous diseases including MI, CAD, peripheral artery disease, subarachnoid hemorrhage, and heart failure (rg >= 0.3, P < 1 × 10−10). Compared to the disorders, the physiological traits display weaker correlations, with lipids, adiposity, blood pressure, and glucose traits in descending order. Only HDL-C presented a negative correlation with AAA (rg = −0.25, P = 7.61 × 10−32). Immune cell counts and percentages did not correlate with AAA, and thus were excluded from subsequent analyses. We also computed genetic correlation by functional elements. Repressors, enhancers and promoters tend to have the strongest correlations across traits (Supplementary Fig. 1), suggesting transcriptional regulation is genetically shared.
A The heatmap presents the genetic correlation rg calculated in LDSC, with the color scale indicating the strength of the correlation, and the rg value displayed next to the heatmap. The * marks the statistical significance: *: P < 0.05; **: P < 0.0016 (Bonferroni-corrected P value threshold). B Causal inference by two-sample Mendelian Randomization with five methods. Odds ratios are shown as dots, the color bars present +/− 95% confidence intervals, and P values are depicted above the bars. CMD: cardiometabolic diseases, MT: metabolic traits. IMC: immune cell traits. All reported P values are two-sided, unless stated otherwise. Source data are provided with this paper.
Causal Inference
Many cardiometabolic disorders share risk factors, rendering genetic correlation a result of complex pleiotropic effects. Mendelian Randomization (MR) overcomes the confounding factor issue and provides causal inference. We conducted bidirectional MR using several models and found a mutual causality between AAA and CAD (Fig. 2B). Furthermore, AAA was suggested as causal to MI. Reversely, 10 traits were inferred as causal to AAA, including hypertension (OR = 2.01, P = 3.36 × 10−4), lipid and adiposity traits (OR = 1.46-1.73, P < 1.24 × 10−12), CAD (OR = 1.23, P = 2 .34 × 10−5), and diastolic blood pressure (OR = 1.05, P = 1.13 × 10−11). Conversely, HDL-C (OR = 0.65, P = 2.28 × 10−21) and pulse pressure (OR = 0.97, P = 2.65 × 10−8) were causally protective against AAA. Note that no apparent horizontal pleiotropy was detected as the intercept of MR-Egger did not significantly deviate from zero (Supplementary Table 1).
Cross-trait loci and causal variants
Through cross-trait meta-analysis by MTAG (Multi-Trait Analysis of GWAS)24 and CPASSOC (Cross-Phenotype Association Analysis)25, we identified 203 SNVs collectively shared by the 21 trait pairs (Supplementary Data 2). Overall, AAA shares the largest number of SNVs with CAD (N = 46), followed by lipid traits (about 20−40 SNVs) (Supplementary Fig. 2). Next, to derive shared causal SNVs, we first fine-mapped the SNVs with FM-summary26 for a 99% credible set, and then colocalized these SNVs across traits by Coloc27. As such, a total of 177 causal variants shared by two traits were derived (Supplementary Data 3). We also applied HyPrColoc28 and derived 47 causal variants shared by multiple traits (Fig. 3A). Among the 47 shared causal variants, only four had the smallest GWAS P values (Fig. 3B), reinforcing that local lead SNVs in GWAS may only tag the causal SNVs26.
A 47 causal variants are shared by multiple traits, as identified by HyPrColoc. B LocusZoom plots of four causal variants for AAA and multiple other CMTs. These variants are also the lead SNVs in the interrogated regions. P values from original GWAS studies are presented. C KEGG pathway enrichment of the shared genes between AAA and CMTs, categorized by biological mechanisms. Only the top 15 enriched pathways passing hypergeometric test P < 0.05 in each trait pair were included. Source data are provided in this paper.
We observed the shared SNVs, both causal and non-casual, clustered proximal to lipid-related genes (Supplementary Data 3). For example, LPA was the closest gene for 9 SNVs shared by AAA and 11 other traits, among which rs1045587229 was causal to 4 trait pairs, and rs14057088630, rs76735376, and rs6905073 were shared by at least 3 trait pairs. Similarly, CDKN2B-AS1 was annotated to 8 SNVs shared by 10 trait pairs, including rs153737131 which was causal to 3 trait pairs. We also rediscovered rs1274037432 on CELSR2 and rs1159114733 on PCSK9. Lastly, several shared causal SNVs were proximal to CETP, BUD13, TRIB1, LPL, and APOE, all of which encode lipid regulators and have been associated with CMDs34,35,36,37,38.
Shared genes and pathways
Annotating GWAS variants to genes solely by proximity is oversimplified and may not account for pleiotropy. We therefore adopted four approaches, TWAS-Fusion39, SMR40, MAGMA41, and GCTA-fastBAT42 to infer shared genes (Supplementary Fig. 3). Among these methods, the first two leverage expression quantitative trait loci (eQTL), and the latter two mainly utilize proximity for gene burden tests. We define disease genes as reported by all four methods and thus derived 405 genes (Supplementary Data 4), of which 109 genes were linked to minimally three AAA-trait pairs (Supplementary Fig. 4). Notably, CELSR2, PSRC1, LRP1, and NOC3L were each shared among 14 AAA-trait pairs or more. Such broad distribution suggests their essential roles in cardiac and metabolic functions. Interestingly, all four genes participate in lipid metabolism; furthermore, all but NOC3L have been reported in inflammation43,44.
Pooling genes from any of the four methods for an overview of biological pathways, we discovered that their functions were enriched in lipoprotein organization, cholesterol transport, and acylglycerol homeostasis (Supplementary Fig. 5A). Strikingly, cholesterol metabolism was the most enriched pathway across all 21 trait pairs (Supplementary Fig. 5B). When classifying by etiological mechanisms20, the most prominent enrichments appeared in cholesterol metabolism, PPAR pathway in lipid metabolism, TGF-β pathway in inflammation, and ECM-receptor interaction in extracellular matrix dysregulation (Fig. 3C).
Summarizing the shared SNVs and genes, we construct the comorbidity network for AAA, detailing the shared variants and genes for each trait pair (Fig. 4).
The inner circle presents independent variants shared between AAA-trait pairs, with 177 shared causal variants marked in asterisks (posterior probability of H4 [PP.H4] > 0.7). The outer circle presents the genes inferred by Annovar for the shared variants. Genes are highlighted by colors to indicate overlap with the four gene identification methods: GCTA-fastBAT, MAGMA, TWAS, and SMR, with gray color for those not identified by any method, black color for those identified by at least one method, and red color for those identified by all four methods. Source data are provided in this paper.
Tissue and cell-type specificity
The shared genes may function in certain tissues and cell types more specifically. We examined it from gene expression in GTEx45 and single-cell transcriptome, as well as heritability in tissue-specific genes and cell type-specific enhancers in CATLAS46. Combing both approaches, we discovered that liver, artery, and adipose tissue (Supplementary Fig. 6), and adipocytes, hepatocytes, fibroblasts, vascular smooth muscle cells, macrophages, and myeloid cells (Supplementary Fig. 7) were significantly enriched across many AAA-trait pairs, suggesting them as hubs for cardiac and metabolic functions (Fig. 5). Unique sharing is observed too. For example, muscle is only enriched by AAA and atrial fibrillation, the pituitary and brain are only enriched by AAA and BMI, and the pancreas is only enriched by AAA and HDL-C. While fibroblasts are broadly shared across traits, macrophages, and hepatocytes are more specific to AAA and lipid traits. Overall, these results align with the genes and pathways, highlighting lipid metabolism and immunity over and again.
A Enriched tissue types by the heritability or expression of the tissue-specific genes derived from GTEx. B Enriched cell types by the heritability of the cell type-specific enhancers derived from CATLAS, or expression of the cell type-specific genes in 11 single-cell transcriptome datasets. Source data are provided in this paper.
We additionally used SMR40 and TWAS39 to pinpoint gene-tissue effects for each CMT. Collectively, 116 genes were inferred for their directions of effect in tissues (Supplementary Fig. 8A-B). Here we highlight four most broadly shared genes: CELSR2, PSRC1, LRP1, and NOC3L. Both methods detected a negative relationship between CELSR2 expression in the liver with AAA and five other CMTs (Supplementary Fig. 8C). Negative relationships were found for NOC3L expression in the skeletal muscle, and PSRC1 expression in the liver, whole blood, and esophagus mucosa, with AAA and numerous other CMTs. Meanwhile, LRP1 expression in the tibial artery was suggested for a positive relationship with AAA but a negative relationship with CAD.
Drug for AAA with comorbid conditions
Collectively we identified 405 disease genes shared by AAA and various CMTs. As cardiometabolic disorders often coexist, we used these genes to identify drugs for treating AAA with comorbidities. As such, we utilized a pathway paring score approach developed in our earlier study47 to identify the best matching drugs and disease genes. Briefly, we computed the pathological pathways for each trait pair based on their shared genes, and the pharmacological pathways for each candidate drug based on their affected genes recorded in large drug-gene databases, e.g., DrugCentral48, DGIdb49, and PharmGKB50. The candidate drugs were mainly derived from screening cardiovascular compounds that targeted any of the 405 disease genes. We also supplemented the list with those compounds used in clinical practice or clinical trials for treating AAA. Collectively, 33 candidate drugs distributed in 6 functional classes were examined, namely antihypertension (11 drugs), lipid-lowering (8 drugs), glucose-lowering (3 drugs), antiarrhythmics (1 drug), antithrombosis (4 drugs), and antioxidant (6 drugs). Most of these drugs have been approved to treat various cardiovascular diseases (Supplementary Data 5).
The best-matching drugs were defined with pairing scores >= 0.5 (Fig. 6). Close to half drugs, which were distributed in 4 functional categories, were suggested to treat AAA with hypertension. Therein amlopidine has the highest pairing score, followed by several antioxidants. Lipid-lowering drugs obtained high pairing scores for various trait pairs. Particularly, simvastatin and lovastatin both achieved high scores for AAA comorbid with CMDs, such as hypertension, MI, subarachnoid hemorrhage, transient ischemic attack, venous thromboembolism, or peripheral artery disease. Interestingly, other lipid-lowering drugs are suggested for AAA with metabolic traits. For example, fenofibrate and gemfibrozil achieved high scores for AAA comorbid with LDL-C, nonHDL-C, triglycerides, or total cholesterol.
Matching scores greater than 0.5 are labeled.
Notably, several herb-based antioxidants achieved high scores for various trait pairs too, including resveratrol, a stilbenoid polyphenol naturally enriched in red grapes; tanshinone I, a terpenoid exacted from the dry root of Salvia miltiorrhiza (Danshen); and quercetin, a flavonol found in many plants. Most of these herb products are in phase 3 clinical trials (Supplementary Data 5) and have shown potential in preventing and treating CVDs, including AAA51,52,53,54. Our analysis supports their extended application in treating comorbid conditions in AAA.
Discussion
In this study, we discover extensive genetic associations between AAA and CMTs from GWAS summary statistics. Further analyses highlight the pleiotropic variants and genes, the biological pathways, and the types of cells and tissues that are shared by the trait pairs. All these findings help to elucidate the common genetic etiology between AAA and cardiometabolic disorders.
We discovered that among all CMDs outside of the aortic aneurysm family (i.e., AAA, TAA, and AA), CAD displays a consistently strong relationship with AAA. For example, it has the second highest genome-wide association (rg = 0.34) and is the only trait with mutual causality with AAA (ORAAA->CAD = 1.10, ORCAD->AAA = 1.23). Epidemiological studies reported many risk factors common to AAA and CAD55, and the two diseases tend to co-occur56,57. In our study, CAD shares the largest number of SNVs (N = 46), causal SNVs (N = 30), and disease genes (N = 50) with AAA. These shared signals were enriched for artery and liver tissues, reflecting their common malfunctions in artery and lipid metabolism. Artery-related diseases including peripheral artery disease (rg = 0.33) and subarachnoid hemorrhage (rg = 0.32), and cardiac-function-related diseases such as MI (rg = 0.38) and heart failure (rg = 0.30), also displayed top genome-wide associations with AAA, although no causal relationship was found, suggesting other risk factors may have confounded the associations.
Included in this study are the metabolic traits of lipids, adiposity, blood pressure, and glucose. By all levels of our inspection, lipid metabolism is most prominently shared. First, lipid traits rank as strong causal factors for AAA (OR = 1.46–1.73), next to hypertension, an established risk factor for AAA. Second, we observed clustering of the shared variants around lipid-related genes, including LPA, CDKN2B-AS1 and others. Third, the most broadly shared genes between AAA and CMTs, i.e., LRP1, PSRC1, CELSR2, and NOC3L, are all lipid related and their up or down regulation was associated with CMTs (Supplementary Fig. 8). Fourth, cholesterol metabolism appeared as the most significantly enriched biological pathway. Fifth, liver, adipose tissue, hepatocytes, and adipocytes are most broadly and significantly enriched among the AAA-CMT trait pairs. These tissues and cell types are important players in lipid metabolism and regulation. Lastly, lipid-reducing drugs were strong candidates to treat AAA with various comorbid CMTs. These results reinforce the notion that predisposition to lipid malfunction is a strong feature in CMTs58. Our study also supports a higher burden of inherited dyslipidaemia in patients of AAA, and lowering LDL-C serves as a therapeutic strategy for preventing and managing AAA59,60,61,62. In comparison, glucose traits demonstrate neither genetic correlation nor causality to AAA. The relationship between glucose and AAA has been paradoxical. While epidemiological studies have reported an inverse correlation between the risk and growth rate of AAA and diabetic traits (e.g., HbA1c level63, fasting glucose level64, and T2D diagnosis65), no genetic correlation has been reported thus far66, inviting further investigations. Lastly, among the blood pressure traits, only diastolic blood pressure displays a mild correlation (rg = 0.16) and a weak causality (ORDBP→AAA = 1.05).
Several genes appeared repetitively in our analyses. LPA encodes lipoprotein(a), which is pro-atherosclerotic, pro-inflammatory, pro-thrombotic, and anti-fibrinolytic. Substantial evidence suggest that elevated lipoprotein(a) promotes CAD, MI, atherosclerosis, and aortic valve stenosis67,68. CDK2B-AS1 encodes a long non-coding RNA that participates in inflammation as well as metabolism of lipids and carbohydrates, and has been linked to numerous CMDs and immune diseases69,70. LRP1 encodes LDL receptor-related protein and plays diverse roles in lipoprotein metabolism, endocytosis, cell growth, cell migration, inflammation, and apoptosis44. Furthermore, CELSR2 and PSRC1, together with SORT1, form a PRSC1-CELSR2-SORT1 axis which has been implicated in various CVDs43,71. SORT1 encodes sortilin 1 that functions in lipid metabolism and immune responses, such as V-LDL secretion, LDL-C metabolism, PCSK9 secretion, inflammation, and formation of foam cells72. Finally, NOC3L is involved in adipocyte differentiation and glucose metabolism, and its decreased expression is associated with islet dysfunction73.
We note that various disease genes in lipid metabolism are involved in immune responses too. Indeed, LPA is pro-inflammatory74; CDK2B-AS1 is not only associated with numerous CMDs but also with immune diseases, such as idiopathic pulmonary fibrosis and inflammatory bowel disease69,70,75. Interestingly, statins, other than lowering lipids, are found to inhibit inflammation in AAA76.
Indeed, there are abundant immune signals in our results. For example, IL-6 is an important cytokine in CVDs including AAA77. Enhanced IL-6 signaling will over-activate the JAK-STAT pathway, a critical pathway that affects many aspects of the mammalian immune system78. rs6734238 was reported to be associated with elevated circulating IL-679, whereas our analysis inferred this SNV as causal to AAA, LDL-C, total cholesterol, and triglycerides (Fig. 3B). We also identified two SNVs in the intronic regions of IL6R, rs4129267, and rs12126142, to be shared by AAA with atrial fibrillation and CAD, respectively. Furthermore, our pathway enrichment highlights the TGF-β signaling, which was shared by AAA and 12 CMTs (Fig. 3C). TGF-β regulates the differentiation and function of leukocytes and controls the type and scope of immune response80. Numerous studies have uncovered its importance in vascular smooth muscle cells (SMCs) and macrophages in the aneurysm development81,82. SMCs can transdifferentiate to foam cells, a crucial step in atherosclerosis83. In our analysis, both vascular SMCs and macrophages were enriched by several AAA-trait pairs. Indeed, various single-cell RNA-sequencing studies suggested them as essential cell types for AAA84,85. Our analysis reveals this close relationship also in genetic predisposition.
Overall, many of our results recapitulate the relationships of AAA with its risk factors and known disease markers, indicating our results captured the main components of AAA genetics. To confirm this, we analyzed the AAA single-trait GWAS loci. Many of the shared lipid-related genes are reproduced, and genes in various pathological mechanisms are connected (Supplementary Fig. 9–10). The most significantly enriched terms are lipid processes and cholesterol metabolism. The most enriched tissues are liver and blood vessels, and the most enriched cell types are fibroblasts, with a few others showing marginal enrichment, including endothelial cells, stromal cells, mesenchymal stem cells, macrophages, neutrophils, and monocytes. Therefore, the shared signals are the main signals in AAA genetics.
Finally, the shared disease genes are transformed into treatment proposals for treating AAA with comorbid conditions. Most drug candidates we discovered have been used to treat CVDs, and some are in clinical trials for repurposing to treat AAA, including atorvastatin, curcumin, metformin, and five other drugs (Supplementary Data 6). Note that numerous drugs with high matching scores in our analysis are not in clinical tests, inviting future studies for their therapeutic potential.
There are several limitations of this study. First, the GWAS data type enables analysis of common SNVs, but omits other variant types such as rare variants, the short insertions and deletions, and structural variants. Indeed, our previous whole-genome study identified a list of rare variants with strong predictability to AAA18. Second, we focus on genetics in this study as CMTs harbor high heritability in general, however, other factors such as epigenetics can also play important roles. As an example, smoking, as an established risk factor of AAA, is known to cause vast epigenetic changes86. Third, CMTs cover a plethora of diseases and physiological traits, and those included in our analysis are only representative. Fourth, our inference of molecular and cellular mechanisms may be limited by the reference knowledgebases and databases. For example, we only deciphered the directions of effects for a quarter of the disease genes, due to the lack of variant-gene expression models in GTEx. With future improvements in the data types and references, we will gain further power to interpret results and infer the genetic mechanisms of AAA and other CMTs.
Methods
Study populations
We obtained summary statistics for AAA from a multi-ancestry meta-GWAS study (39,221 cases and 1,086,107 controls)20. Summary statistics for the other 32 CMTs were derived from UK Biobank (https://www.ukbiobank.ac.uk/), FinnGen (https://www.finngen.fi/en) or numerous large consortia87,88,89. Summary statistics for the counts and percentages of six immune cell traits were derived from the Blood Cell Consortium (BCX)90, including white blood cell (WBC), basophil (BASO), eosinophil (EO), lymphocyte (LYMPH), monocyte (MONO), and neutrophil (NEUT), obtained from 563,085 participants of European ancestry. Information of these GWAS studies is provided in Supplementary Data 1.
Genome-wide genetic correlation
We computed genome-wide genetic correlation between traits using linkage disequilibrium (LD) score regression (LDSC)23. Briefly, it quantifies the separate contributions of polygenic effects by examining the relationship between LD scores and test statistics of SNVs from GWAS summary results, producing a genetic correlation based on the deviation of chi-square statistics from the null hypothesis. LDSC also applies a self-estimated intercept during the analysis to account for shared subjects between studies. The derived estimates range from –1 to 1, with –1 indicating a perfect negative genetic correlation and 1 indicating a perfect positive genetic correlation. We used pre-computed LD scores obtained from ~1.2 million common SNVs in the well-imputed HapMap3 European ancestry panel. A Bonferroni-corrected P value threshold of 0.0015 (0.05/32) was used to define statistical significance.
Genetic correlation by functional categories
We used LDSC to estimate genetic correlations between traits in 24 functional categories91, e.g., transcribed regions, repressed regions, conserved regions, coding regions, promotors, enhancers, super-enhancers, introns, transcription factor binding sites (TFBS), DNaseI digital genomic footprinting (DGF) regions, DNase I hypersensitivity sites (DHSs), fetal DHS, untranslated regions (UTR), and histone marks (H3K4me1, H3K4me3, H3K9ac, and H3K27ac) from the Roadmap Epigenomics Project91,92. For each functional category, SNVs from the panel of HapMap3 European ancestry were assigned and LD scores were calculated, generating the “baseline model” (https://github.com/bulik/ldsc/wiki/Partitioned-Heritability). We downloaded them as the ldscore reference file to compute heritability enrichment and genetic correlation for the 24 functional categories.
Mendelian randomization (MR) analysis
We used five MR methods to infer causal relationships between AAA and CMTs: inverse variance weighting (IVW)93, MR-Egger94, weighted median95, MR-APSS96, and CAUSE97.
These methods utilize different assumptions about horizontal pleiotropy. Briefly, IVW assumes mean zero if uncorrelated pleiotropy is present, and such pleiotropy only adds noise to the regression of the meta-analyzed SNV effects with multiplicative random effects93. MR-Egger further allows for the presence of directional (i.e., non-zero mean) uncorrelated pleiotropy and adds an intercept to the IVW regression to exclude such confounding effect94. The weighted median approach provides a robust estimate of causal effects even when up to 50% of genetic variants are invalid95. The recently published MR-APSS accounts for pleiotropy and sample structure, simultaneously96. Specifically, for decomposing the observed SNV effects, a foreground-background model is employed, in which the background model accounts for confounding factors (including correlated pleiotropy and sample structure) hidden in the GWAS summary statistics, and the foreground model performs causal inference while accounting for uncorrelated pleiotropy. CAUSE is a Bayesian MR method accounting for both correlated and uncorrelated pleiotropy97. Compared to the other MR methods, CAUSE further corrects correlated pleiotropy by evaluating the joint distribution of effect sizes from instrumental SNVs, assuming that the ‘true’ causal effect can influence all instrumental SNVs while correlated pleiotropy only influences a subset of them. CAUSE improves the power of MR analysis by including a larger number of LD-pruned SNVs with P < = 1 × 10−3 and provides a model comparison approach to distinguish causality from horizontal pleiotropy.
For selecting instruments, we used the genome-wide significance threshold P = 5 × 10−8 for IVW, MR Egger, and Weighted-median, the default threshold P = 1 × 10−3 for CAUSE, and the default threshold P = 5 × 10−5 for MR-APSS. We only selected independent SNVs (LD clumping r2 < 0.001 within 1000 Kb using PLINK v1.998) based on the European ancestry panel in the 1000 Genomes Project. In each LD block, we chose the variant with the smallest association P value with the exposure. Further, we used PhenoScanner (http://www.phenoscanner.medschl.cam.ac.uk/) and GWAS Catalog (https://www.ebi.ac.uk/gwas/) to exclude SNVs associated with the outcome and its risk factors. IVW was used as the primary method, and the other four methods were used as sensitive analysis. A causal estimate was considered significant if it passed the P value threshold in the primary analysis, i.e., IVW, and displayed a consistent direction of effect in all five MR methods. We used Cochran’s Q-test to check for heterogeneity and MR-Egger intercept for horizontal pleiotropy. These MR analyses were performed in the R packages TwoSampleMR99, MRAPSS96 and CAUSE97.
Cross-trait meta-analysis
To identify pleiotropic loci shared between two traits, we performed a cross-trait meta-analysis of GWAS summary statistics using MTAG24. MTAG applies generalized inverse-variance-weighted meta-analysis for multiple traits; in addition, it accommodates potential sample overlap between GWAS. Its key assumption is that all SNVs share the same variance-covariance matrix of effect sizes among traits. As initially described24, MTAG is a consistent estimator whose effect estimates have a lower genome-wide mean squared error than the corresponding single-trait GWAS estimates. In addition, association statistics from MTAG also yield stronger statistical power and little inflation of the FDR for each analyzed trait with high correlation24.
As the assumptions in MTAG, i.e., equal SNV heritability for each trait and the same genetic covariance between traits, could be violated, we performed cross-phenotype association analysis (CPASSOC)25 across traits as a sensitivity analysis. CPASSOC integrates GWAS summary statistics from multiple traits to detect shared variants while controlling population structure and cryptic relatedness25. It provides two test statistics, SHom and SHet. SHom is based on the fixed-effect meta-analysis and can be viewed as the maximum of weighted sum of trait-specific genetic effects. It is less powerful under the presence of between-study heterogeneity, which is common when meta-analyzing multiple traits. SHet is an extension to SHom with improved power that allows for heterogeneous effects of a trait from different study designs, environmental factors, or populations, as well as heterogeneous effects for different phenotypes, which is more common in practice. SHet was thus adopted in our analysis. We applied PLINK clumping to obtain the independent SNVs (parameters: --clump-p1 5 × 10−8 --clump-p2 1 × 10−5 ---clump-r2 0.1 --clump-kb 1000). Significant pleiotropic SNVs were defined as variants with P values in both GWAS studies and P value in the meta-analysis (i.e., PMTAG & PCPASSOC) < 5 × 10−8. We used ANNOVAR for functional annotation of the variants identified by MTAG and CPASSOC. The shared SNVs are visualized in a circular dendrogram using the R package ggraph.
Fine-mapping credible set analysis
We identified a 99% credible set of causal variants by FM-summary (https://github.com/hailianghuang/FM-summary)26, a Bayesian fine-mapping method. For each shared SNV identified in the cross-trait meta-analysis, we extracted variants within 500 Kb around the index SNV as input for FM-summary. FM-summary set a flat prior and produced a posterior inclusion probability (PIP) of a true association between a phenotype and a variant using the steepest descent approximation. A 99% credible set is equivalent to ranking the SNVs from largest to smallest PIPs and taking the cumulative sum of PIPs until it is at least 99%.
Colocalization analysis
We used the R package coloc27 to determine whether the association signals for AAA and CMTs co-localize. For each of the 203 shared SNVs between traits, we extracted the variants within 500 Kb of the index SNV and calculated the probability that the two traits share one common causal variant (H4). Loci with a probability greater than 0.7 were considered to colocalize. We estimated the posterior probability (PP) of multiple traits sharing the same SNV using a Bayesian divisive clustering algorithm implemented by HyPrColoc28 (v.1.0.0) in R v.4.2.3.
Gene-based association analysis
We used TWAS-fusion39, SMR40, MAGMA41, and GCTA-fastBAT42 to identify genes shared by AAA trait pairs. Input files for all four gene-level analyzes were the complete GWAS summary statistics from MTAG in the meta-analysis. In each method, the P value threshold was adjusted by Bonferroni correction.
TWAS identifies tissue-specific gene-trait associations by integrating GWAS with cis-SNVs based gene expression model39,100. We conducted TWAS using the FUSION software39 based on 43 post-mortem tissue expression profiles in GTEx (version 6, with pre-computed models)45.
Summary-data-based Mendelian Randomization (SMR) analysis integrates GWAS and eQTL studies to identify genes whose expression levels are associated with a complex trait due to pleiotropy or causality40. A significant SMR association could be explained by a causal effect (i.e., the causal variant influences disease risk via changes in gene expression), pleiotropy (i.e., the causal variant has pleiotropic effects on gene expression and disease risk), or linkage (i.e., different causal variants exist for gene expression and disease). SMR implements the HEIDI-outlier test to distinguish pleiotropy from linkage. We implemented SMR using cis-eQTL summary data for whole blood from eQTLGen101, a meta-analysis of 31,684 blood samples, and from GTEx V8 for 9 relevant tissues, including artery aorta, adipose subcutaneous, artery coronary, artery tibial, heart atrial appendage, heart left ventricle, kidney cortex, liver, and whole blood. Genes associated with AAA-trait pairs were defined as PSMR passing the Bonferroni-corrected thresholds and PHEIDI > 0.05.
MAGMA41 (Multi-marker Analysis of GenoMic Annotation) is a fast and flexible method that uses a multiple regression approach to properly incorporate LD between markers and detect multi-marker effects. We ran MAGMA with default parameters, with the European ancestry panel in the 1000 Genomes Project (Phase 3) as the LD reference.
We applied a fourth approach, GCTA-fastBAT42, a fast set-based association analysis. In brief, it calculates the association P value for a set of SNVs from an approximated distribution of the sum of χ2-statistics over all SNVs using GWAS summary data and LD correlations from a reference sample set with individual-level genotypes42. We used the European ancestry panel in the 1000 Genomes Project (Phase 3) as the LD reference.
Stratified LD score regression for tissue and cell type specificity
We used LD score regression applied to specifically expressed genes (LDSC-SEG)102 for tissues or cell types to test for heritability enrichment. For tissues, pre-computed LD scores from GTEx103, which contained gene expression data for 53 tissues, were provided by LDSC-SEG and used in our analysis. We also obtained the activity profile of candidate cis-regulatory elements (cCREs) in 222 cell types from CATLAS46. We mapped the genotypes of European ancestry in the 1000 Genomes Project to the cell type-specific cCREs and calculated the cell type-specific ldscore. We applied FDR correction for each dataset respectively to account for multiple testing and considered FDR corrected P < 0.05 as significant.
GTEx Tissue Specific Expression Analysis (TSEA)
We conducted tissue-specific expression analysis (TSEA)104 on the shared genes against the RNA-seq data in GTEx, which contained gene expression profiles of 1839 samples from 45 different tissues derived from 189 post-mortem subjects. We merged the shared genes for each trait pair from the four gene-based analyses to derive a collection of shared genes. Hypergeometric tests are used to determine if tissue-specific genes are enriched in the input genes. We used Benjamini–Hochberg correction to account for multiple testing (FDR < 0.05).
Cell-type-specific enrichment analysis (CSEA)
We performed cell-type specific enrichment (CSEA) on the shared genes using WebCSEA105. WebCSEA provides a gene set query against tissue-cell-type (TCs) expression signatures of 11 single-cell gene expression datasets106,107,108,109,110,111,112. Specifically, Dai et al.105 collected more than 5.5 million cells from 111 tissues and 1355 TCs, filtered out the low expression genes, and used an in-house t-statistic-based method “deTS” to train the tissue-cell-type signature genes. Genes with the top 5% t-statistic scores in focal cell type are defined as cell type-specific genes. We conducted Fisher’s exact test to assess whether the shared genes for each trait pair are overrepresented with the cell type-specific genes.
Over-representation enrichment analysis
We used the “clusterProfiler” package113 to perform GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis on the genes. Benjamini–Hochberg procedure was used to account for multiple tests (FDR < 0.05).
Drug target analysis
We defined the genes identified by all four gene-based analyses as the disease genes for each AAA-trait pair and thus obtained 405 genes collectively. For deriving drugs that match the best with the genes, we leveraged the biological pathway analysis. First, we applied clusterProfiler to the shared genes to compute the pathological pathways enriched for each AAA trait pair. Next, we carried multiple steps to derive candidate drugs for scrutinization: (1) queried three large drug-gene databases, DrugCentral48, DGIdb49, and PharmGKB50, for drugs that target any of the 405 candidate genes. This initial screening led to about 1200 compounds, most of which were initially designed for treating cancer; (2) limited the compounds to those already in use by standard clinical practices for treating cardiovascular diseases, which drastically shrank the list to 21 drugs; and (3) supplemented the list with 12 drugs used in clinics or proposed by clinical trials for treating AAA, such as amlodipoine, pitavastatin, and vitamin E. Collectively, 33 candidate drugs were derived. Then, for each drug, we queried the three drug-gene databases again for all their affected genes and computed their enriched pharmacological pathways in clusterProfiler. Finally, we calculated the pairing scores between the pathological pathways of the cross-trait and the pharmacological pathways of the drug47.
Inquiry of clinical trial information
We queried the ChEMBL database (https://www.ebi.ac.uk/chembl) for information on action targets, action types, indications, and clinical development activities associated with the 33 candidate drugs. We also searched the https://www.ClinicalTrials.gov website for additional information on clinical trials of drugs.
Software packages used
Publicly available software was used to perform the analyses. Analysis software includes LDSC, PLINK, LAVA, TwoSampleMR (https://mrcieu.github.io/TwoSampleMR), CAUSE, MR-APSS, MR-BMA (https://github.com/verena-zuber/demo_AMD), MTAG: (https://github.com/JonJala/mtag), CPASSOC (http://hal.case.edu/~xxz10/zhu-web), Coloc (https://github.com/chr1swallace/coloc), HyPrColoc (https://github.com/cnfoley/hyprcoloc), FM-summary (https://github.com/hailianghuang/FM-summary), FUSION, MAGMA, SMR (https://cnsgenomics.com/software/smr/#Overview), GCTA-fastBAT (https://yanglab.westlake.edu.cn/software/gcta/#fastBAT), TSEA, FUMA, and WebCSEA (https://bioinfo.uth.edu/webcsea).
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability
GWAS summary statistics for this study were listed in Supplementary Data 1. Whole blood-based cis-eQTL summary statistics (the dataset “Full cis-eQTL summary statistics”) were downloaded from eQTLGen (https://molgenis26.gcc.rug.nl/downloads/eqtlgen/cis-eqtl/2019-12-11-cis-eQTLsFDR-ProbeLevel-CohortInfoRemoved-BonferroniAdded.txt.gz). eQTL summary data of multiple tissues (the dataset “GTEx_Analysis_v8_eQTL.tar”) were downloaded from GTEx (https://gtexportal.org/home/downloads/adult-gtex/qtl), specifically, https://storage.googleapis.com/adult-gtex/bulk-qtl/v8/single-tissue-cis-qtl/GTEx_Analysis_v8_eQTL.tar). Source data are provided with this paper.
References
Song, P. et al. The global and regional prevalence of abdominal aortic aneurysms: a systematic review and modeling analysis. Ann. Surg. 277, 912–919 (2023).
Aggarwal, S., Qamar, A., Sharma, V. & Sharma, A. Abdominal aortic aneurysm: a comprehensive review. Exp. Clin. Cardiol. 16, 11–15 (2011).
Reimerink, J. J., van der Laan, M. J., Koelemay, M. J., Balm, R. & Legemate, D. A. Systematic review and meta-analysis of population-based mortality from ruptured abdominal aortic aneurysm. Br. J. Surg. 100, 1405–1413 (2013).
Golledge, J., Thanigaimani, S., Powell, J. T. & Tsao, P. S. Pathogenesis and management of abdominal aortic aneurysm. Eur. heart J. 44, 2682–2697 (2023).
Qian, G., Adeyanju, O., Olajuyin, A. & Guo, X. Abdominal aortic aneurysm formation with a focus on vascular smooth muscle cells. Life (Basel, Switz.) 12, 191 (2022).
Golledge, J. Abdominal aortic aneurysm: update on pathogenesis and medical treatments. Nat. Rev. Cardiol. 16, 225–242 (2019).
Tsuruda, T. et al. Inhibition of development of abdominal aortic aneurysm by glycolysis restriction. Arterioscler Thromb. Vasc. Biol. 32, 1410–1417 (2012).
Lieberg, J. et al. Metabolomic profile of abdominal aortic aneurysm. Metabolites 11, 555 (2021).
Hou, Y. et al. Advanced research of abdominal aortic aneurysms on metabolism. Front Cardiovasc Med 8, 630269 (2021).
Takagi, H., Manabe, H., Kawai, N., Goto, S. N. & Umemoto, T. Serum high-density and low-density lipoprotein cholesterol is associated with abdominal aortic aneurysm presence: a systematic review and meta-analysis. Int Angiol. 29, 371–375 (2010).
Frostegård, J. Immunity, atherosclerosis and cardiovascular disease. BMC Med 11, 117 (2013).
Toghill, B. J., Saratzis, A. & Bown, M. J. Abdominal aortic aneurysm-an independent disease to atherosclerosis? Cardiovasc Pathol. 27, 71–75 (2017).
Forsdahl, S. H., Singh, K., Solberg, S. & Jacobsen, B. K. Risk factors for abdominal aortic aneurysms: a 7-year prospective study: the Tromsø Study, 1994-2001. Circulation 119, 2202–2208 (2009).
Powell, J. T. & Greenhalgh, R. M. Multifactorial inheritance of abdominal aortic aneurysm. Eur. J. Vasc. Surg. 1, 29–31 (1987).
Wahlgren, C. M., Larsson, E., Magnusson, P. K., Hultgren, R. & Swedenborg, J. Genetic and environmental contributions to abdominal aortic aneurysm development in a twin population. J. Vasc. Surg. 51, 3–7 (2010).
O’Sullivan, J. W. et al. Polygenic risk scores for cardiovascular disease: a scientific statement from the american heart association. Circulation 146, e93–e118 (2022).
Hong, X. et al. Cardiometabolic traits in adult twins: heritability and bmi impact with age. Nutrients 15, 164 (2022).
Li, J. et al. Decoding the genomics of abdominal aortic aneurysm. Cell 174, 1361–1372.e1310 (2018).
Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179–189 (2020).
Roychowdhury, T. et al. Genome-wide association meta-analysis identifies risk loci for abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target. Nat. Genet 55, 1831–184 (2023).
Klarin, D. et al. Genetic architecture of abdominal aortic aneurysm in the million veteran program. Circulation 142, 1633–1646 (2020).
Jones, G. T. et al. Meta-analysis of genome-wide association studies for abdominal aortic aneurysm identifies four new disease-specific risk loci. Circ. Res 120, 341–353 (2017).
Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet 47, 1236–1241 (2015).
Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet 50, 229–237 (2018).
Zhu, X. et al. Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. Am. J. Hum. Genet 96, 21–36 (2015).
Farh, K. K. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).
Wallace, C. Statistical testing of shared genetic control for potentially related traits. Genet Epidemiol. 37, 802–813 (2013).
Foley, C. N. et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. Nat. Commun. 12, 764 (2021).
Clarke, R. et al. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. N. Engl. J. Med 361, 2518–2528 (2009).
Mack, S. et al. A genome-wide association meta-analysis on lipoprotein (a) concentrations adjusted for apolipoprotein (a) isoforms. J. Lipid Res 58, 1834–1844 (2017).
Jiang, D. et al. Post-GWAS functional analysis identifies CUX1 as a regulator of p16(INK4a) and cellular senescence. Nat. Aging 2, 140–154 (2022).
Small, A. M. et al. Multiancestry genome-wide association study of aortic stenosis identifies multiple novel loci in the million veteran program. Circulation 147, 942–955 (2023).
Rao, A. S. et al. Large-scale phenome-wide association study of Pcsk9 variants demonstrates protection against ischemic stroke. Circ. Genom. Precis Med 11, e002162 (2018).
Kitamoto, A. et al. Association of polymorphisms in GCKR and TRIB1 with nonalcoholic fatty liver disease and metabolic syndrome traits. Endocr. J. 61, 683–689 (2014).
Yeh, K. H. et al. Genetic variants at the APOE locus predict cardiometabolic traits and metabolic syndrome: a Taiwan biobank study. Genes (Basel) 13, 1366 (2022).
Khera, A. V. et al. Association of rare and common variation in the lipoprotein lipase gene with coronary artery disease. Jama 317, 937–946 (2017).
Zhang, L. et al. Association of BUD13 polymorphisms with metabolic syndrome in Chinese population: a case-control study. Lipids Health Dis. 16, 127 (2017).
Schmidt, A. F. et al. Cholesteryl ester transfer protein (CETP) as a drug target for cardiovascular disease. Nat. Commun. 12, 5640 (2021).
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet 48, 245–252 (2016).
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet 48, 481–487 (2016).
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 11, e1004219 (2015).
Bakshi, A. et al. Fast set-based association analysis using summary data from GWAS identifies novel gene loci for human complex traits. Sci. Rep. 6, 32894 (2016).
Castillo-Avila, R. G. et al. Association between genetic variants of CELSR2-PSRC1-SORT1 and cardiovascular diseases: a systematic review and meta-analysis. J. Cardiovasc Dev. Dis. 10, 91 (2023).
Sizova, O., John, L. S., Ma, Q. & Molldrem, J. J. Multi-faceted role of LRP1 in the immune system. Front Immunol. 14, 1166189 (2023).
The Genotype-Tissue Expression (GTEx) project. Nat Genet 45, 580-585 (2013).
Zhang, K. et al. A single-cell atlas of chromatin accessibility in the human genome. Cell 184, 5985–6001 (2021).
Gao, H. et al. Combined therapy of prednisone and mTOR inhibitor sirolimus for treating retroperitoneal fibrosis. Ann. Rheum. Dis. 82, 688–697 (2023).
Ursu, O. et al. DrugCentral 2018: an update. Nucleic Acids Res 47, D963–d970 (2019).
Cotto, K. C. et al. DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Res 46, D1068–d1073 (2018).
Barbarino, J. M., Whirl-Carrillo, M., Altman, R. B. & Klein, T. E. PharmGKB: A worldwide resource for pharmacogenomic information. Wiley Interdiscip. Rev. Syst. Biol. Med 10, e1417 (2018).
Hosseini, A. et al. Potential benefits of phytochemicals for abdominal aortic aneurysm. Curr. Med Chem. 28, 8595–8607 (2021).
Kaneko, H. et al. Resveratrol prevents the development of abdominal aortic aneurysm through attenuation of inflammation, oxidative stress, and neovascularization. Atherosclerosis 217, 350–357 (2011).
Shang, T. et al. Inhibition of experimental abdominal aortic aneurysm in a rat model by way of tanshinone IIA. J. Surg. Res 178, 1029–1037 (2012).
Wang, L. et al. Quercetin downregulates cyclooxygenase-2 expression and HIF-1α/VEGF signaling-related angiogenesis in a mouse model of abdominal aortic aneurysm. Biomed. Res Int 2020, 9485398 (2020).
Xiao, J. et al. Comparisons of risk factors for abdominal aortic aneurysm and coronary heart disease: a prospective cohort study. Angiology 72, 24–31 (2021).
Hernesniemi, J. A., Vänni, V. & Hakala, T. The prevalence of abdominal aortic aneurysm is consistently high among patients with coronary artery disease. J. Vasc. Surg. 62, 232–240.e233 (2015).
Durieux, R. et al. High prevalence of abdominal aortic aneurysm in patients with three-vessel coronary artery disease. Eur. J. Vasc. Endovasc. Surg.: Off. J. Eur. Soc. Vasc. Surg. 47, 273–278 (2014).
Atanasovska, B., Kumar, V., Fu, J., Wijmenga, C. & Hofker, M. H. GWAS as a Driver of Gene Discovery in Cardiometabolic Diseases. Trends Endocrinol. Metab. 26, 722–732 (2015).
Harrison, S. C. et al. Genetic association of lipids and lipid drug targets with abdominal aortic aneurysm: a meta-analysis. JAMA Cardiol. 3, 26–33 (2018).
Wemmelund, H. et al. Statin use and rupture of abdominal aortic aneurysm. Br. J. Surg. 101, 966–975 (2014).
Salata, K. et al. Statins reduce abdominal aortic aneurysm growth, rupture, and perioperative mortality: a systematic review and meta-analysis. J. Am. Heart Assoc. 7, e008657 (2018).
Twine, C. P. & Williams, I. M. Systematic review and meta-analysis of the effects of statin therapy on abdominal aortic aneurysms. Br. J. Surg. 98, 346–353 (2011).
Kristensen, K. L., Dahl, M., Rasmussen, L. M. & Lindholt, J. S. Glycated hemoglobin is associated with the growth rate of abdominal aortic aneurysms: a substudy from the viva (viborg vascular) randomized screening trial. Arteriosclerosis, thrombosis, Vasc. Biol. 37, 730–736 (2017).
Kubota, Y., Folsom, A. R., Pankow, J. S., Wagenknecht, L. E. & Tang, W. Diabetes-related factors and abdominal aortic aneurysm events: the Atherosclerotic Risk in Communities Study. Ann. Epidemiol. 28, 102–106 (2018).
Raffort, J. et al. Diabetes and aortic aneurysm: current state of the art. Cardiovascular Res. 114, 1702–1713 (2018).
Lareyre, F., Hassen-Khodja, R. & Raffort, J. Translational applications of glucose metabolism in abdominal aortic aneurysm. J. Vasc. Surg. 70, 2093–2097 (2019).
Nordestgaard, B. G. & Langsted, A. Lipoprotein (a) as a cause of cardiovascular disease: insights from epidemiology, genetics, and biology. J. Lipid Res 57, 1953–1975 (2016).
Page, M. M. et al. Coronary artery disease and the risk-associated LPA variants, rs3798220 and rs10455872, in patients with suspected familial hypercholesterolaemia. Clin. Chim. Acta 510, 211–215 (2020).
Song, C., Qi, Y., Zhang, J., Guo, C. & Yuan, C. CDKN2B-AS1: an indispensable Long non-coding RNA in multiple diseases. Curr. Pharm. Des. 26, 5335–5346 (2020).
Hannou, S. A., Wouters, K., Paumelle, R. & Staels, B. Functional genomics of the CDKN2A/B locus in cardiovascular and metabolic disease: what have we learned from GWASs? Trends Endocrinol. Metab. 26, 176–184 (2015).
Zhou, Y. J. et al. Association of variants in CELSR2-PSRC1-SORT1 with risk of serum lipid traits, coronary artery disease and ischemic stroke. Int J. Clin. Exp. Pathol. 8, 9543–9551 (2015).
Goettsch, C., Kjolby, M. & Aikawa, E. Sortilin and Its Multiple Roles in Cardiovascular and Metabolic Diseases. Arterioscler Thromb. Vasc. Biol. 38, 19–25 (2018).
Jin, H. et al. Genome-Wide Association Study on Longitudinal Change in Fasting Plasma Glucose in Korean Population. Diabetes Metab. J. 47, 255–266 (2023).
Ugovšek, S. & Šebeštjen, M. Lipoprotein(a)-The Crossroads of Atherosclerosis, Atherothrombosis and Inflammation. Biomolecules 12, 26 (2021).
AbdulAzeez, S. et al. Intronic Polymorphisms in the CDKN2B-AS1 Gene Are Strongly Associated with the Risk of Myocardial Infarction and Coronary Artery Disease in the Saudi Population. Int J. Mol. Sci. 17, 395 (2016).
Yoshimura, K. et al. Inhibitory effect of statins on inflammation-related pathways in human abdominal aortic aneurysm tissue. Int J. Mol. Sci. 16, 11213–11228 (2015).
Xiao, J. et al. Experimental abdominal aortic aneurysm growth is inhibited by blocking the JAK2/STAT3 pathway. Int J. Cardiol. 312, 100–106 (2020).
Villarino, A. V., Kanno, Y. & O’Shea, J. J. Mechanisms and consequences of Jak-STAT signaling in the immune system. Nat. Immunol. 18, 374–384 (2017).
Ahluwalia, T. S. et al. Genome-wide association study of circulating interleukin 6 levels identifies novel loci. Hum. Mol. Genet 30, 393–409 (2021).
Sanjabi, S., Oh, S. A. & Li, M. O. Regulation of the Immune Response by TGF-β: From Conception to Autoimmunity and Infection. Cold Spring Harb. Perspect. Biol. 9, a022236 (2017).
Liu, X. et al. Single-cell RNA sequencing identifies an Il1rn(+)/Trem1(+) macrophage subpopulation as a cellular target for mitigating the progression of thoracic aortic aneurysm and dissection. Cell Discov. 8, 11 (2022).
Angelov, S. N. et al. TGF-β (Transforming Growth Factor-β) signaling protects the thoracic and abdominal aorta from angiotensin ii-induced pathology by distinct mechanisms. Arterioscler Thromb. Vasc. Biol. 37, 2102–2113 (2017).
Lacolley, P., Regnault, V., Nicoletti, A., Li, Z. & Michel, J. B. The vascular smooth muscle cell in arterial pathology: a cell that can take on multiple roles. Cardiovasc Res 95, 194–204 (2012).
Yang, H., Zhou, T., Stranz, A., DeRoo, E. & Liu, B. Single-cell rna sequencing reveals heterogeneity of vascular cells in early stage murine abdominal aortic aneurysm-brief report. Arterioscler Thromb. Vasc. Biol. 41, 1158–1166 (2021).
Cheng, S. et al. Identification of key monocytes/macrophages related gene set of the early-stage abdominal aortic aneurysm by integrated bioinformatics analysis and experimental validation. Front Cardiovasc Med 9, 950961 (2022).
Aune, D., Schlesinger, S., Norat, T. & Riboli, E. Tobacco smoking and the risk of abdominal aortic aneurysm: a systematic review and meta-analysis of prospective studies. Sci. Rep. 8, 14786 (2018).
Roychowdhury, T. et al. Regulatory variants in TCF7L2 are associated with thoracic aortic aneurysm. Am. J. Hum. Genet 108, 1578–1589 (2021).
Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat. Genet 54, 1803–1815 (2022).
Malik, R. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet 50, 524–537 (2018).
Vuckovic, D. et al. The polygenic and monogenic basis of blood traits and diseases. Cell 182, 1214–1231 (2020).
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet 47, 1228–1235 (2015).
Bernstein, B. E. et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28, 1045–1048 (2010).
Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 37, 658–665 (2013).
Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 32, 377–389 (2017).
Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 40, 304–314 (2016).
Hu, X. et al. Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics. Proc. Natl Acad. Sci. USA 119, e2106858119 (2022).
Morrison, J., Knoblauch, N., Marcus, J. H., Stephens, M. & He, X. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat. Genet 52, 740–747 (2020).
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet 81, 559–575 (2007).
Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 7, e34408 (2018).
Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet 47, 1091–1098 (2015).
Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet 53, 1300–1310 (2021).
Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet 50, 621–629 (2018).
GTEx Consortium. Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
Dougherty, J. D., Schmidt, E. F., Nakajima, M. & Heintz, N. Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells. Nucleic Acids Res 38, 4218–4230 (2010).
Dai, Y. et al. WebCSEA: web-based cell-type-specific enrichment analysis of genes. Nucleic Acids Res 50, W782–w790 (2022).
Voigt, A. P. et al. Single-cell transcriptomics of the human retinal pigment epithelium and choroid in health and macular degeneration. Proc. Natl Acad. Sci. USA 116, 24100–24107 (2019).
MacParland, S. A. et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat. Commun. 9, 4383 (2018).
Madissoon, E. et al. scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation. Genome Biol. 21, 1 (2019).
Jones, R. C. et al. The tabula sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, eabl4896 (2022).
Vieira Braga, F. A. et al. A cellular census of human lungs identifies novel cell states in health and in asthma. Nat. Med 25, 1153–1163 (2019).
Segerstolpe, Å. et al. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 24, 593–607 (2016).
Enge, M. et al. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell 171, 321–330.e314 (2017).
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).
Acknowledgements
S.Z. is a Ph.D. student at School of Life Sciences, Fudan University. This study was supported by the research grants awarded to C.P. from National Natural Science Foundation of China (No. 32270626) and Greater Bay Area Institute of Precision Medicine (Guangzhou) (I0005 and R2001). We thank members of the Laboratory of Intelligent Computing in Biomedicine in the Greater Bay Area Institute of Precision Medicine (Guangzhou) for insightful discussions and suggestions.
Author information
Authors and Affiliations
Contributions
C.P., S.Z., and P.S.T. collectively designed the study. S.Z. and C.P. performed bioinformatic and statistical analyses and generated the figures and tables. P.S.T. and C.P. provided critical biological insight in interpreting the results. C.P. and S.Z. drafted the manuscript. All authors critically reviewed the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Zheng, S., Tsao, P.S. & Pan, C. Abdominal aortic aneurysm and cardiometabolic traits share strong genetic susceptibility to lipid metabolism and inflammation. Nat Commun 15, 5652 (2024). https://doi.org/10.1038/s41467-024-49921-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-024-49921-7
- Springer Nature Limited