Linking single nucleotide polymorphisms to signaling blueprints in abdominal aortic aneurysms

ABSTRACT Abdominal aortic aneurysms (AAA) is a multifactorial complex disease with life-threatening consequences. While Genome-wide association studies (GWAS) have revealed several single

nucleotide polymorphisms (SNPs) located in the genome of individuals with AAA, the link between SNPs with the associated pathological signals, the influence of risk factors on their

distribution and their combined analysis is not fully understood. We integrated 86 AAA SNPs from GWAS and clinical cohorts from the literature to determine their phenotypical vulnerabilities

and association with AAA risk factors. The SNPs were annotated using snpXplorer AnnotateMe tool to identify their chromosomal position, minor allele frequency, CADD (Combined Annotation

Dependent Depletion), annotation-based pathogenicity score, variant consequence, and their associated gene. Gene enrichment analysis was performed using Gene Ontology and clustered using

REVIGO. The plug-in GeneMANIA in Cytoscape was applied to identify network integration with associated genes and functions. 15 SNPs affecting 20 genes with a CADD score above ten were

identified. AAA SNPs were predominantly located on chromosome 3 and 9. Stop-gained rs5516 SNP obtained high frequency in AAA and associated with proinflammatory and vascular remodeling

phenotypes. SNPs presence positively correlated with hypertension, dyslipidemia and smoking history. GO showed that AAA SNPs and their associated genes could regulate lipid metabolism,

extracellular matrix organization, smooth muscle cell proliferation, and oxidative stress, suggesting that part of these AAA traits could stem from genetic abnormalities. We show a library

of inborn SNPs and associated genes that manifest in AAA. We uncover their pathological signaling trajectories that likely fuel AAA development. SIMILAR CONTENT BEING VIEWED BY OTHERS THE

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CANDIDATE CAUSAL GENES FOR CALCIFIC AORTIC VALVE STENOSIS INVOLVING TISSUE-SPECIFIC REGULATION Article Open access 18 March 2024 INTRODUCTION Abdominal aortic aneurysms (AAA) are

characterized by localized weakening and dilation of the abdominal aorta1 that results from a cascade of mechanisms including transmural inflammation2, smooth muscle cell (SMC) apoptosis3

and extracellular matrix (ECM) degradation4 that collectively lead to the loss of aortic wall elasticity. AAA often coexists with other cardiovascular diseases5,6,7 and risk factors such as

aging and smoking8. However, the physiopathology of AAA is uniquely distinguished by signaling programs that result in the exaggerated degradation of the core constituents of the elements of

the ECM that lead to life-threatening aortic rupture. SNPs are single base-pair variations that occur in the DNA either within or outside the coding region of genes and have the potential

to interfere at different steps of gene expression depending on their genomic location9. For example, SNPs present in non-coding segments of the genome have been shown to modulate the

efficacy of gene transcription by impeding the accessibility of transcription factors in these response elements10. The presence of SNPs in coding region of genes can give rise to mRNA with

different bases at SNP site which could impact the proper translation of mRNA while the presence of SNPs within a coding sequence can lead to an amino-acid change and protein-misfolding9,11.

Recently, it has been shown that the identification and estimation of variance by all SNPs from GWAS of conventionally unrelated individuals might be a significant determinant of genetic

heritability to particular diseases12,13. Notably, previous genetic studies have reported that AAA occurrence can be heritable within families, reaching up to a 20% increase in AAA

susceptibility within first-degree relatives14,15,16. Notably, a population-based twin study estimated the heritability of AAA to be as strong as between 70 and 77%17. However, while the

inheritance of SNPs in AAA has not been directly studied in family cohorts, several gene polymorphisms such as _COL3A1_, _MYH11_, and _TGFBR2_ were also identified to be transmitted within a

familial lineage of AAA18. Despite this evidence pointing to the familial risk associated with AAA, there is a paucity of information linking these genetic signatures and their underlying

mechanistic significance in the development of AAA. GWAS have facilitated the accessibility of disease-specific SNPs19. Identification of SNPs as predictive markers for disease risk has been

used for Alzheimer’s20, migraines21, and coronary artery disease22. In AAA, GWAS of the Million Veteran Program has examined the genetic associations of genetic variants in a subset of AAA

independent of family history23. As such, identification of AAA-specific SNPs would be beneficial to further characterize the pathogenic pathway associated with each SNP and refine our

understanding of the knowledge gap between genomic inheritance and the molecular trajectory that lead to the degradation of the aortic wall that occurs during AAA. In the current study, we

performed an integrative analysis combining the available GWAS catalogs in AAA along with non-GWAS gene association studies published in the literature. We identified 86 SNPs related to AAA

and its associated risk factors. We curated this result using multiple bioinformatic analyses to expose their potential phenotypical signals via which they could contribute to AAA

development. METHODS LITERATURE SEARCH Identification of studies was conducted through a literature search of PubMed, GWAS central, and GWAS registry. The search query used to retrieve

potentially eligible studies from PubMed was “abdominal aortic aneurysm AND genetics”, “abdominal aortic aneurysm AND SNPs”, and “abdominal aortic aneurysm AND GWAS”. In addition, we

searched for other possible SNPs outside of GWAS central and GWAS catalog by considering AAA candidate gene association studies with detailed clinical characteristics, reporting data on the

associations between AAA and different SNPs. We only included published full-text articles until April 2022. The flow diagram of the study inclusion is described in Fig. 1. SELECTION

CRITERIA AND QUALITY ASSESSMENT GWAS studies were included if they were listed in the GWAS directory with the trait label “abdominal aortic aneurysm” (EFO_0004214), with no other labels.

Non-GWAS studies were considered eligible if they evaluated the association between AAA and genetic polymorphism through effect measures of odds ratio (OR), with 95% confidence interval

(CI); if they reported allele and minor allele frequency (MAF) of population groups; and if controls were in Hardy–Weinberg equilibrium. We excluded the studies with data published only in

abstract form, studies where MAF in controls were lower than 1% (rare variants), with sample size fewer than 10 cases or controls, studies that were rebutted by others and studies that

combined data with other cardiovascular diseases. systematic reviews and animal in vivo studies were also not included in our analysis. We based our selection criteria on the principles

proposed by the Human Genome Epidemiology Network (HuGeNet) for the meta-analysis of molecular association studies20. The following clinical data were extracted from eligible AAA candidate

gene association studies when available: age, gender percentage, aortic diameter (mm), smoking history (past or current), hypertension, diabetes, coronary artery disease (CAD), peripheral

artery disease (PAD), and dyslipidemia (or hypercholesterolemia). SNPs frequency and genotype data of case and control groups were also extracted. SNPS-GENE ANNOTATION AND PATHWAY ENRICHMENT

ANALYSIS The SNPs used in Gene-set Enrichment Analysis were obtained from GWAS database with the trait label “abdominal aortic aneurysm” (EFO_0004214) and from other studies where allele

frequency in AAA was reported to be 3% more than in controls. The SNPs were annotated using snpXplorer AnnotateMe tool24 with the following settings: SNPs-gene annotation or Gene-set

Enrichment Analysis, GRCh38, GTEx tissue (Blood, Aorta, Coronary) and GO:BP. For each SNP input, SNPs-gene annotation provided the following information: chromosomal position; MAF;

CADD-annotation based pathogenicity score (CADD v1.6), variant consequence, affected gene; GTEx based eQTL (expression quantitative-trait-loci) and sQTL (splicing quantitative-trait-loci);

closest affected gene. Input SNPs approximate affected genes based on position within the genome, expression in GTEx tissue, or their direct coding gene. Gene enrichment analysis was

performed on AAA-related genes using Gene Ontology (GO) terms as the gene-set source. Clustering of the enriched GO terms was performed using REVIGO25, and annotated terms were selected

through a semantic similarity matrix and a dynamic cut tree algorithm for term-based clustering24. The plug-in GeneMANIA in Cytoscape was used as an analytical method to provide an

association network integration to predict gene function and gene–gene interaction of the SNPs-associated gene in this study26,27. GeneMANIA weighted each functional genomic dataset

according to its predictive value according to its gene query while suggesting more genes with similar domain structure or physical interaction27. Gene interaction was plotted via Cytoscape.

A Mann–Whitney U test was performed using R software28 on patient clinical data. Comparisons of age, aortic diameter, male percentage, smoking history (past and current), hypertension,

dyslipidemia, diabetes, CAD, and PAD were performed if data was available. Data were plotted using R studio (v.1.3.1093). RESULTS DISTRIBUTION OF AAA SNPS IN THE GENOME AND THEIR GENE

REGULATION The literature search identified 86 SNPs related to AAA, 48 of which originated from the recorded GWAS databases, and 38 were from AAA gene association studies in which SNPs

frequency in AAA patients was at least 3% higher than in the control group. The variant-gene mapping procedure performed with SNPs snpXplorer AnnotateMe tool showed that most of the

identified SNPs were annotated based on their chromosomal position (n = 49), followed by their eQTL GTEx tissue expression (n = 28) and direct gene coding region (n = 9) (Fig. 2A,B),

implying the possible effect of the SNPs of interest in AAA. The specificity of each SNPs was observed since the majority of the SNPs (_N_ = 57 variants) are mapped and annotated for one

gene (Fig. 2C). We observed a random distribution of SNPs across different chromosomes. Chromosome 3 and 9 predominantly harbored most of the AAA SNPs (Fig. 2D). No SNPs were found on

chromosome 17, X, and Y, suggesting that AAA SNPs identified in this study are not sex-linked (Fig. 2D). On average, each chromosome contains 4 SNPs, with chromosomes 3 and 9 in contain the

most SNPs (Fig. 2E). In the chromosome 3, we found 8 SNPs that affect a single gene (_TGFBR2)_ and 1 SNP that affects 9 different genes (_ITIH4, DNAH1, SFMBT1, GNL3, GLYCTK, NT5DC2, STIMATE,

GLYCTK-AS1, MUSTN1_). In the chromosome 9, we found 3 SNPs that affect a single gene (TGFBR1). The complete SNP chromosome mapping in a 1:250,000 scale of pixel to base pair can be seen in

Supplementary Fig. 1. Single SNP can affect multiple genes depending on their genomic location. Most of the SNPs associated with AAA reside in the intronic, regulatory, upstream, downstream,

or intergenic region of their respective gene (Fig. 2F). In particular, SNPs with high CADD pathogenicity scores (> 10) result in non-synonymous, noncoding change, and stop gained

mutations. A high CADD pathogenicity score is indicative of the deleterious effect of the SNP, as compared to other possible mutations within the human genome29. SNPS AND THE ASSOCIATED

GENES ARE LINKED WITH HIGH PATHOGENICITY IN AAA We identified 15 SNPs affecting 20 genes with a CADD (Combined Annotation Dependent Depletion) pathogenicity score above 10 (Table 1). CADD

scores correlate with pathogenicity, disease severity, regulatory effects, and complex trait associations. A score greater than 10 indicates that the nucleotide substitution is predicted to

be the 10% most deleterious substitution within the human genome, a score of 20 or greater indicates the 1% most deleterious, a score of 30 or greater indicates the 0.1% most deleterious and

so on. We found 1 SNP with a CADD score above 30 (rs5516, _KLK1_), 1 SNP with a CADD score above 20 (rs1801133, _MTHFR_), and 13 SNPs with a CADD score above 10. We confirmed the

association between SNPs with high CADD pathogenicity scores with their frequency on the retrieved 38 AAA-gene association studies (Table 2). We identified four SNPs and associated genes

consisting of the SNP rs5516 (_KLK1_), rs1800795 (_IL-6_), rs2230806 (_ABCA1_), and rs243865 (_MMP-2_) have both higher CADD scores and frequency in AAA patients. However, we observed that a

high CADD score does not necessarily correlate with frequency in AAA. BIOLOGICAL TRAITS ASSOCIATED WITH AAA SNPS Using SnpXplorer AnnotateMe platform, gene enrichment analysis from the

GWAS-catalog associations of the AAA SNPs showed strong correlations with various lipid metabolism pathways such as LDL-cholesterol measurement (52%), total cholesterol measurement (34%),

triglyceride measurement (26%), HDL-cholesterol measurement (23%), and CRP measurement (14%) (Fig. 3A). Only 49% of all tested SNPs were found to be directly labeled with the AAA trait, this

may suggest that the remaining 51% have an indirect association with AAA occurrence through various lipid metabolism pathways and genes. As for associations with other cardiovascular

diseases, AAA and CAD share 25% of the same SNPs, while AAA and MI only share 10%. In addition to SNPs, we present the GWAS-catalog associations of the genes associated with AAA SNPs.

AAA-SNPs associated genes have correlations with various diseases such as coronary artery disease (21%), myocardial infarction (12%), and type II diabetes (10%). AAA SNPs-associated genes

were also found to be associated with genes relating to total cholesterol measurement (17%), LDL-cholesterol measurement (15%), CRP measurement (12%), HDL-cholesterol measurement (10%), and

triglyceride measurement (9%) (Fig. 3B). GENE ONTOLOGY ANALYSIS OF AAA SNPS Gene-set enrichment analysis was done with the genes associated with AAA SNPs, using Gene Ontology (GO) as the

gene-set source. The gene-set enrichment was then visualized using REVIGO25. Annotated GO terms were selected using a 2-step process as described by Tesi et al. (2021) in the snpXplorer web

server24. The GO term clustering and annotation in Fig. 4 depict the most prominent and significant biological processes and associated genes in AAA based on the snpXplorer annotation

algorithm. The AAA-associated genes were found to be prominently involved in cell population proliferation, followed by regulation of cell population proliferation (_p_ = 3.19 × 10−8),

muscle cell proliferation (_p_ = 3.08 × 10−6), regulation of smooth muscle cell proliferation (_p_ = 4.97 × 10−6), regulation of plasma lipoprotein particle levels (_p_ = 1.58 × 10−5),

regulation of protein kinase activity (_p_ = 2.15 × 10−5), regulation of phosphorus metabolic process (_p_ = 2.77 × 10−5), and blood circulation (_p_ = 4.63 × 10−5). Images processed using

SNPsXplorer showing that these annotated terms were found to be most significant according to the semantic similarity (Supplementary Fig. 2) and a dynamic cut tree algorithm for term-based

clustering and p values (Supplementary Fig. 3). The most significant gene ontology terms associated with the SNPs associated genes are summarized in Table 3. SIGNIFICANT SIGNALING NETWORKS

ASSOCIATED WITH AAA SNPS To assess the interaction between SNPs associated genes related to AAA, gene–gene interactomes were constructed and plotted using GeneMANIA plugins in Cytoscape

(Fig. 5). The most significant Genes related to top 25 GO annotated functions (Table 4) were included to further grouped and visualized on the network module to see their network association

according to their shared pathways, co-localization, co-expression, and physical interaction (Fig. 5A). As a hallmark of pathological remodeling in AAA, we observed the strongest

interaction between _TGFB1_ with the _TGFB1_ receptor family and notably strong co-localization with _MMP2_ and _MMP9_, suggesting the role of _TGFB1_ pathway as one of the reminiscent

factors that could link the presence of SNPs to the development of AAA. This analysis also includes several additional genes predicted to convey a strong interaction with some of our gene

candidates. 14 genes were associated with lipid metabolism pathways; regulation of plasma lipoprotein level, regulation of lipid localization, regulation of lipid transport, and regulation

of cholesterol transport (Fig. 5B). 10 genes were associated with extracellular matrix (ECM) organization (Fig. 5C). 11 genes were associated with smooth muscle cell proliferation pathways

(Fig. 5D). 10 genes were associated with reactive oxygen species metabolism (Fig. 5E). IL-6, TGFB1, and TGFB1 receptor family shared three modules, the most out of all genes. IL-6 is

involved in lipid metabolism, ECM organization, and smooth muscle cell proliferation pathways. While TGFB1 and the TGFB1 receptor family are involved in ECM organization, smooth muscle cell

proliferation, and ROS metabolism pathways. CLINICAL CHARACTERISTICS OF AAA PATIENTS Out of the AAA candidate gene association studies in the literature, we found 15 studies with complete

clinical data, totaling a sample size of 10.956 (5676 control & 5280 case). The available clinical data were age (mean), men (n), aortic diameter (mm, mean), smoking (current &

past), hypertension, diabetes, CAD (coronary artery disease), PAD (Peripheral Artery Disease), and dyslipidemia. Data variabilities between each variables are different depending on the

availability. A detailed summary of each study can be found in Supplementary Table 1. The non-disease control and AAA case groups shared a similar average age (69.1 and 70.9 years). Several

clinical characteristics, such as a history of smoking (past or current, _p_ = 0.037), hypertension (_p_ = 0.013), and dyslipidemia (_p_ = 0.042), were positively associated with AAA.

Conversely, there were no significant differences in the presence of diabetes, CAD, and PAD between the two populations. This association is summarized in Fig. 6. DISCUSSION As a complex

multifactorial disease, there is little evidence pinpointing the specific genetic predisposition that could contribute to the development of AAA. Previous studies have identified several

SNPs associated with AAA, but little is known about the underlying mechanisms and biological significance of the identified SNPs to AAA pathobiology. In this integrative in silico analysis,

we used snpXplorer AnnotateMe platform to merge the SNPs candidate from the GWAS catalog and previously published AAA clinical cohorts. We further identify several SNPs with possible

pathological signaling pathways associated with AAA as well as their correlative risk factors in these cohorts. In the present study, we calculated the frequency of the top 20 SNPs in AAA

compared to non-disease control and measured the CADD pathogenicity score in order to observe their association with the risk of developing AAA. Here, we identified SNP rs5516, a stop-gained

mutation for _KLK1_ (Kallikrein 1), with the highest pathogenicity score and a significantly high frequency in AAA (17.8%). Indeed, previous SNPs studies performed in both Australian and

Asian cohorts have shown the association of rs5516 with AAA30,43. Our analysis identified other interesting polymorphism profiles associated with significant genes that correlated with AAA.

We identified a high frequency of rs1800629 for _TNF-α_, and rs1800795 for _IL-6_, inflammatory cytokines well-known to mediate the inflammatory response and smooth muscle cell proliferation

during AAA development44,45,46. Indeed, Jablonska et al_._ reported that the mutation detected in both of the alleles increased the risk of AAA formation among heterozygous carriers31.

However, our result show that CADD score did not necessarily correlate with the frequency of AAA in the observed studies. The SNP rs1801133, located in the coding region of _MTHFR_

(Methylenetetrahydrofolate Reductase), was one of the highest-scoring SNPs, yet it’s CT/CC allele was only 3% more expressed in Greek AAA patients than in controls34. However, in this study,

the analysis of the SNPs in the UK cohort revealed that the frequency of SNP rs1801133 was significantly more frequent. This is most likely due to the population, socio-economical,

environmental and genome differences in these two cohorts living in two different geographic areas. Indeed, the meta-analysis performed for SNPs on _MTHFR_ showed a discrepancy regarding the

protective or pathogenic role of either SNP rs1801133 or _MTHFR_47,48,49. Therefore, this result contextualizes the plasticity of population-specific SNPs and its phenotypic effect to the

development of AAA. Several other SNPs with high frequency in AAA such as rs2230806 (_ABCA1_), rs3775290 (_TLR3_), and rs10757278 (_CDKN2B_) were also strongly associated with significant

AAA pathways50,51,52. The loss of _CDKN2B_ promotes p53-dependent smooth muscle cell apoptosis and aneurysm formation. In parallel, our analysis revealed high pathogenicity of regulatory

_CDKN2B_ SNPs (CADD score: 10.42), where this SNP was observed in Chinese and European cohorts39,53 and mark the possible genetic-environment interplay in AAA development. However, we were

unable to validate its shared associated network in AAA-related pathways along with other gene candidates listed in this study. Therefore, a deeper understanding of the role of _CDKN2B_ in

AAA is warranted. The profile of SNPs found in the DNA between genes can act as an essential biological marker with a strong association with the disease. Interestingly, our descriptive

analysis conducted from the gene association clinical studies described significant traits related to the well-described risk factors of AAA such as CAD and dyslipidemia pointing a

possibility that specific SNPs profile might factor on the progression of these risk factors, and eventually fuel the expansion of AAA. For example, our analysis showed rs429358 polymorphism

in _APOE_, a canonical transporter of cholesterol particles54, had a significant pathogenicity score in AAA patients. However, this SNP was found to be unassociated with AAA in an

Australian cohort55. This discrepancy could rely on the characteristics of the cohorts and the type of analysis performed in each study. Genotyping of specific _APOE_ alleles was performed

in the Australian AAA cohort in 640 samples compared to GWAS population study from 7600 AAA cases using DNA sequencing cross referenced with DNA variants library from European-descent

veterans across USA23. The sensitivity of the meta-analysis, difference in geographic cohorts studied and the power of the sample size were likely more amenable to capture associations of

AAA with rs429358 _APOE_ SNPs polymorphism. This further emphasizes the need to perform large-scale worldwide multi-centered studies including populations from disparate groups to uncover

the full spectrum of drivers of AAA genetic vulnerability. Notably, a previous multi-ethnic cohort study has described that the polymorphism of APOE had a significant association with

dyslipidemia in Asian ethnic groups56. The _APOE_ gene is polymorphic and co-exists as APOE-ε2, -ε3 and -ε4 alleles57. APOE-ε4 has been shown to associate with increased in LDL-cholesterol

levels and higher cardiovascular risk58. The highest frequency of APOE-ε4 carrier was observed in the Malay population and was associated with high LDL-C levels. These observations suggest

that the inherent geographical genetic background of certain ethnic groups could present a diverse portfolio of SNPs associated with AAA, which warrants further studies. The main strength of

this study is the stringency of our method to include the most recent GWAS catalogs in AAA and combine the data with other available cohorts in the literature to comprehensively identify

SNPs with a strong association with AAA. We have summarized and visualized the genomic distribution and frequency of each SNP using the most recent bioinformatic software (snpXplorer)24 to

identify their associated molecular pathways and variant consequences. We have integrated this result into pathway enrichment analysis to focus on the biological interaction of the

associated genes in the AAA pathogenesis. We acknowledge several limitations in our study. Considering the limited genomic studies performed in AAA, the number of samples included in our

analysis is lower than other similar in silico SNPs analysis in other diseases. This limitation hindered us from performing a meta-analysis on each SNPs candidate to validate its consistency

between each geographical origin and to confirm the region-specific susceptibility of AAA59. Indeed, the small number of cohorts is also a major limitation in performing and assessing the

predictive potential of these SNPs candidates in AAA. Moreover, the technological advances in the development of sequencing such as whole-exome sequencing could facilitate the identification

of SNPs in the human genomic data. However, this would require significant financial resources and might not be applicable in each clinical setting, thus limiting the potential to use SNPs

profile as a predictive marker to identify AAA. In conclusion, we have identified a significant profile of polymorphism associated with important risk factors such as dyslipidemia and the

main pathobiological pathways of AAA development. Further investigation in large population studies will be necessary to confirm this finding and to finally reveal the specific genetic

heritage of individuals carrying a risk of developing AAA. DATA AVAILABILITY GWAS datasets analyzed for this study are available in GWAS catalog with the trait label “abdominal aortic

aneurysm” (EFO_0004214). The datasets were derived from the following public domain resources: https://www.ebi.ac.uk/gwas/. Non-GWAS studies are openly available at locations cited in the

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differences between human populations. _Genes (Basel)_ 13, 1472 (2022). Article  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS B.R lab is supported by the National Institute of

Health (R01 HL146627 and R01HL149927). M.S is funded by the AHA postdoctoral award (907602). L.M. is supported by the German Center for Cardiovascular Research (DZHK), the German Research

Council (DFG) sponsored TRR267, the National Institute of Health (NIH; 1R011HL150359-01), the Bavarian State Ministry of Health and Care through the research project DigiMed Bayern, as well

as the Swedish Heart-Lung-Foundation (20210450) and Swedish Research Council (Vetenkapsrådet, 2019-01577). P.T. is supported by the California Tobacco Related Disease Research Program of the

University of California (T29IR0636) the VA Office of Research and Development (BX-003362-01). KG is supported by the National Institute of Health (R01 HL15674). AUTHOR INFORMATION Author

notes * These authors contributed equally: Chrysania Lim and Muhammad Yogi Pratama. AUTHORS AND AFFILIATIONS * Division of Vascular and Endovascular Surgery, Department of Surgery, New York

University Langone Medical Center, New York, USA Chrysania Lim, Muhammad Yogi Pratama, Cristobal Rivera, Michele Silvestro, Thomas Maldonado & Bhama Ramkhelawon * Department of

Biomedicine, Indonesia International Institute for Life-Sciences (i3L), Jakarta, Indonesia Chrysania Lim & Muhammad Yogi Pratama * Department of Cell Biology, New York University Langone

Medical Center, New York, USA Muhammad Yogi Pratama, Cristobal Rivera, Michele Silvestro & Bhama Ramkhelawon * VA Palo Alto Health Care System, Palo Alto, CA, USA Philip S. Tsao *

Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA Philip S. Tsao * Department of Vascular and Endovascular Surgery, Technical University Munich, Munich,

Germany Lars Maegdefessel * German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Berlin, Germany Lars Maegdefessel * Department of Medicine, Karolinska

Institute, Stockholm, Sweden Lars Maegdefessel * Department of Surgery, University of Michigan, Ann Arbor, MI, USA Katherine A. Gallagher Authors * Chrysania Lim View author publications You

can also search for this author inPubMed Google Scholar * Muhammad Yogi Pratama View author publications You can also search for this author inPubMed Google Scholar * Cristobal Rivera View

author publications You can also search for this author inPubMed Google Scholar * Michele Silvestro View author publications You can also search for this author inPubMed Google Scholar *

Philip S. Tsao View author publications You can also search for this author inPubMed Google Scholar * Lars Maegdefessel View author publications You can also search for this author inPubMed 

Google Scholar * Katherine A. Gallagher View author publications You can also search for this author inPubMed Google Scholar * Thomas Maldonado View author publications You can also search

for this author inPubMed Google Scholar * Bhama Ramkhelawon View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS B.R. conceptually developed and

supervised the project. C.L. collected, performed formal analysis, wrote the original draft. M.Y.P. curated, visualized and validated the data, wrote the original draft. C.R. curated and

visualized the data. P.T. performed formal analyses. T.M., K.G., P.T., L.M, M.S, C.R. provided feedback and discussion. CORRESPONDING AUTHOR Correspondence to Bhama Ramkhelawon. ETHICS

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visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Lim, C., Pratama, M.Y., Rivera, C. _et al._ Linking single nucleotide

polymorphisms to signaling blueprints in abdominal aortic aneurysms. _Sci Rep_ 12, 20990 (2022). https://doi.org/10.1038/s41598-022-25144-y Download citation * Received: 12 September 2022 *

Accepted: 25 November 2022 * Published: 05 December 2022 * DOI: https://doi.org/10.1038/s41598-022-25144-y SHARE THIS ARTICLE Anyone you share the following link with will be able to read

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