Skip to main content
BMC is moving to Springer Nature Link. Visit this journal in its new home.
Download PDF

Exploring the genetic basis between inflammatory bowel disease and venous thromboembolism

Thrombosis Journal volume 23, Article number: 56 (2025) Cite this article

Abstract

Background

See More

The elevated prevalence of venous thromboembolism (VTE) among individuals diagnosed with inflammatory bowel disease (IBD) necessitates thorough investigation. Analyzing the genetic association mechanisms between these conditions is essential for comprehending their concurrent manifestation.

Methods

Using genome-wide association study (GWAS) datasets for IBD and VTE, we applied a comprehensive approach to explore the genetic connections between these two diseases. The analysis was conducted in four steps: first, we assessed the overall genetic correlation between IBD and VTE using linkage disequilibrium score regression and genetic covariance analysis; next, we analyzed specific chromosomal regions to understand the genetic characteristics in these areas; then, we used the conditional/conjunctional false discovery rate (cond/conjFDR) method to better identify and quantify the shared genetic loci that contribute to both diseases’ development.

Results

The genome-wide analysis revealed a strong genetic correlation between IBD, especially ulcerative colitis (UC), and VTE, while the correlation between Crohn’s disease (CD) and VTE was weaker. A detailed regional analysis identified specific chromosomal areas with genetic links to both diseases. Using the conjFDR method, we confirmed the shared genetic components between these conditions and identified key genetic variants that influence the development of both diseases.

Conclusion

This study provides genetic-level statistical evidence into the comorbidity mechanisms of IBD and VTE from a genetic standpoint, thereby enhancing the understanding of the underlying genetic basis contributing to their concurrent occurrence.

Introduction

As a multifaceted immunological disorder, inflammatory bowel disease (IBD) poses considerable medical challenges on a global scale. This condition is predominantly defined by recurrent inflammatory damage to the mucosal lining of the digestive tract and is categorized into two principal types: Crohn’s disease (CD) and ulcerative colitis (UC) [1]. Epidemiological investigations have highlighted particularly elevated incidence rates in North America and Europe, with certain developed regions documenting prevalence rates surpassing 0.3% [2, 3]. Current scientific findings have demonstrated that the pathogenesis of these diseases encompasses multiple dimensions, including genetic predisposition, host-microbiome interactions, and immune system dysregulation [4]. Notably, aside from inducing substantial damage to the digestive system, a subset of patients experience clinical manifestations affecting multiple organ systems, such as blood vessels, skin, bones, lungs, and the hepatobiliary system. These are collectively referred to as extra-intestinal manifestations [5]. Among these, venous thromboembolism (VTE), a prevalent thrombotic vascular disorder, constitutes one of the most frequently observed vascular complications in IBD patients [6]. Epidemiological evidence indicates that individuals diagnosed with IBD exhibit a 2- to 3-fold elevated risk of developing VTE compared to the general population, with this susceptibility increasing to 8- to 10-fold during active disease phases [7]. Nevertheless, conventional epidemiological studies are subject to various constraints, including challenges in mitigating confounding variables, biases in sample selection, and potential information attrition during follow-up, all of which may undermine the reliability of research outcomes [8]. To address these limitations, genome-wide association studies (GWAS) have emerged as a powerful approach for investigating the genetic architecture underlying complex traits. Previous research has utilized Mendelian randomization (MR) to assess the causal relationship between IBD and VTE, offering important insights into directional effects [9].

However, our study moves beyond causal inference by implementing a multi-layered analytical framework that characterizes genetic relationships between IBD and VTE across genome-wide, regional, and locus-specific levels. We selected a combination of complementary methods—Linkage disequilibrium score regression (LDSC) [10], genome-wide non-overlapping variant analysis (GNOVA) [11], local analysis of variant associations (LAVA) [12], and conditional/conjunctional false discovery rate (cond/conjFDR) [13]—each contributing uniquely to the analysis pipeline. LDSC and GNOVA were used to estimate genome-wide genetic correlations, with GNOVA offering increased robustness to sample overlap. LAVA enabled the identification of local genetic correlations across defined genomic regions, and conjFDR facilitated the detection of pleiotropic loci by leveraging cross-trait enrichment. Together, this integrative strategy provides a more comprehensive and fine-grained understanding of the shared genetic basis linking IBD and VTE, thereby advancing the field beyond previous MR-based studies.

Methods and materials

GWAS data

The selection of research data was conducted based on a comprehensive set of multidimensional evaluation criteria, encompassing study population size, genetic variant coverage, and the timeline of data publication. The GWAS data for IBD (including the CD and UC subtypes) was obtained from the large cohort study published by de Lange et al. [14]. The genomic association data for VTE came from the “I9_VTE” dataset in the FinnGen database R12 version (https://r12.finngen.fi/) [15] and the VTE data provided by Zhou et al. in the GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics) [16], with the latter VTE data serving as the validation set. The fundamental characteristics of these cohorts are summarized in Table 1.

Table 1 Date sources
Full size table

To enhance the reliability of data analysis, a rigorous quality control framework was implemented. Genetic variants were incorporated into the analysis only when all three predefined criteria were simultaneously satisfied: [1] inclusion in the 1000 Genomes Project Phase 3 reference panel; [2] a minor allele frequency (MAF) exceeding 0.01 in European populations; and [3] restriction to bi-allelic variant types. Annotation of all selected variants was conducted based on the human reference genome (hg19/GRCh37), with variants lacking rsID information or containing duplicate rsID annotations being excluded. Considering the influence of genetic heterogeneity among populations, only data from individuals of European ancestry were utilized for this study. The P-value threshold for this study was set to 0.05, and the Benjamini-Hochberg (FDR) method was applied for multiple testing correction to reduce the risk of false-positive results. The detailed research workflow is depicted in Fig. 1.

Fig. 1
figure 1

Flowchart of the study. IBD, Inflammatory bowel disease; VTE, venous thromboembolism. This figure is created by biorender

Full size image

Global genetic correlation analyses

We employed LDSC to estimate genetic correlations between traits using GWAS summary statistics [10]. LDSC was chosen for its ability to distinguish true polygenic signal from confounding biases such as population stratification. All analyses were conducted using well-quality-controlled European-based GWAS data, with LD scores computed based on the 1000 Genomes Phase 3 reference panel. Genetic correlation was then derived by combining heritability and genetic covariance estimates, with standard errors calculated via the block jackknife method.

GNOVA was used to estimate genetic covariance between traits, with summary-level GWAS data subjected to standard quality control [11]. Unlike LDSC, GNOVA accounts for sample overlap and enables functional stratification using genomic annotations. We selected GNOVA for its strength in incorporating functional information (e.g., from GenoCanyon and GenoSkyline) to improve the resolution of genetic correlation analysis. This approach complements LDSC and provides a more detailed understanding of shared genetic architecture in annotated genomic regions.

Local genetic correlation (LGC) analyses

LAVA was applied to estimate LGCs across defined genomic regions, offering finer resolution than genome-wide approaches [12]. By partitioning the genome into approximately 2,500 independent LD blocks, LAVA quantifies localized genetic sharing between traits while controlling for sample overlap. It further supports conditional analyses to explore phenotype-specific effects. LAVA was selected in this study to complement global methods like LDSC and GNOVA, providing additional insight into the regional genetic architecture underlying complex trait correlations.

CondFDR/ConjFDR analysis

The condFDR and conjFDR methodologies are primarily employed to uncover genetic overlap between phenotypes using GWAS summary data [17]. The analytical process consists of several key steps: initially, condFDR analysis processes GWAS statistics across distinct phenotypes to compute the genetic association between each SNP and the primary phenotype while conditioning on the secondary phenotype. For this purpose, SNPs are ranked conditionally for each phenotype, and conditional quantile-quantile (Q-Q) plots are generated to detect significant genetic signals in relation to the secondary phenotype. Subsequently, these conditional Q-Q plots illustrate the manner in which SNP associations with the first phenotype are re-stratified when conditioned on the second phenotype. CondFDR values are derived based on conditional distributions to assess the false discovery rate of each SNP, thereby determining its true discovery rate. ConjFDR, an extension of condFDR, is designed to simultaneously identify SNPs exhibiting significant associations in both phenotypes. By alternating phenotype roles and reapplying condFDR computations, conjFDR is defined as the maximum condFDR value between the two phenotypes, providing a conservative estimate for detecting genomic regions shared between them. Through the maximization of condFDR, the conjFDR approach effectively eliminates single-phenotype effects and identifies shared genomic signals between phenotypes, thereby facilitating the discovery of common genetic mechanisms. These methodologies enhance GWAS discovery rates by identifying shared genetic loci and elucidating intricate genetic associations among phenotypes. In this study, the Functional Mapping and Annotation (FUMA) platform [18] was utilized to conduct genomic localization analysis of significantly associated genetic variants. The functional annotation of SNPs was performed using LD data from the 1000 Genomes Project (1KGP) European population. During the SNP selection process, independent significant SNPs were defined as those with condFDR < 0.05 and LD r² ≤ 0.6. SNPs with LD r² ≤ 0.1 were further classified as independent lead SNPs. For adjacent independent significant SNPs with a physical distance of less than 250 kb (defined by the most distal SNPs of each LD block), they were merged into a single genomic risk locus, with the SNP having the smallest P-value within the region designated as the lead SNP of the merged locus.The SNP2Gene module was employed to identify functional element characteristics and potential target genes corresponding to these variant loci.

Results

Global genetic correlation

Genome-wide association studies reveal a significant positive genetic correlation between IBD and VTE (FinnGen) (Rg = 0.1177, P = 0.005). Subtype analysis shows that UC exhibits a strong genetic correlation with VTE (FinnGen) (Rg = 0.1515, P = 0.0014), and the results from the validation set are consistent with these findings. The genetic correlation between CD and VTE is relatively weak, especially with no significant genetic overlap found in the VTE (FinnGen) dataset (Rg = 0.0583, P = 0.188). However, a slight correlation is observed in the VTE (Zhou) dataset, indicating a small genetic overlap between CD and VTE (Table 2). These observed genetic correlation patterns were further substantiated through cross-validation utilizing the GNOVA method (Table 2), offering novel insights into their shared genetic foundation.

Table 2 The genetic correlation and sample overlap correlation between IBD and VTE
Full size table

LGC

Chromosomal regional genetic association studies reveal diverse genetic interactions between IBD and its subtypes with VTE. Genomic analysis shows that the chromosomal regions associated with both IBD and VTE (FinnGen) are distributed on chromosomes 1, 2, 5, 12, and 18 (Fig. 2A, Supplementary Table 1). In the validation cohort, chromosomes 2, 5, and 12 were confirmed (Fig. 2B, Supplementary Table 2).

Fig. 2
figure 2

LAVA analysis of IBD and VTE. The dashed line indicates the expected line with a correction P of 0.05. (A) Local genetic correlation between IBD and VTE(FinnGen). (B) Local genetic correlation between IBD and VTE(Zhou). (C) Local genetic correlation between CD and VTE(FinnGen). (D) Local genetic correlation between CD and VTE(Zhou). (E) Local genetic correlation between UC and VTE(FinnGen). (F) Local genetic correlation between UC and VTE(Zhou). IBD, Inflammatory bowel disease; VTE, venous thromboembolism; CD, Crohn’s disease; UC, ulcerative colitis

Full size image

The local associations between CD and VTE (FinnGen) are primarily located on chromosomes 1, 5, 7, and 11 (Fig. 2C, Supplementary Table 3). Results from VTE (Zhou) also suggest localized associations on chromosomes 1, 5, and 11 (Fig. 2D, Supplementary Table 4).

Through two separate evaluations of local genetic interactions between UC and VTE (FinnGen, VTE (Zhou)), chromosomes 2, 5, and 6 were validated (Fig. 2E-F, Supplementary Tables 5–6).

This comprehensive and systematic chromosomal-level genetic association profile outlines the complex molecular interaction network between IBD and its subtypes with VTE, providing a solid genetic foundation for subsequent in-depth analysis of their shared mechanisms.

ConjFDR analysis identifies shared genomic loci between two traits

Q-Q plot analysis provides compelling evidence of notable genetic association characteristics between IBD and VTE (Fig. 3). A systematic leftward deviation in association statistics corresponding to one phenotype has been identified as the significance of the other phenotype intensifies. This pattern of deviation not only substantiates the genetic correlation between these two diseases but also implies the potential presence of shared pathogenic variant loci.

Fig. 3
figure 3

Conditional quantile-quantile plot. Conditional Q-Q plots illustrate nominal versus empirical -log10 P values in the primary phenotype, relative to the significance of SNP associations with the secondary phenotype at levels of P < 1.00, P < 0.1, P < 0.01, and P < 0.001, respectively. The dashed line indicates the expected line under the null hypothesis, and the deflection to the left indicates the degree of pleiotropic enrichment. (A) IBD-VTE(FinnGen). (B) VTE(FinnGen)-IBD. (C) IBD-VTE(Zhou). (D) VTE(Zhou)-IBD. (E) CD-VTE(FinnGen). (F) VTE(FinnGen)-CD. (G)CD-VTE(Zhou). (H) VTE(Zhou)-CD. (I) UC-VTE(FinnGen). (J) VTE(FinnGen)-UC. (K) UC-VTE(Zhou). (l) VTE(Zhou)-UC. IBD, Inflammatory bowel disease; VTE, venous thromboembolism; CD, Crohn’s disease; UC, ulcerative colitis

Full size image

The conjFDR method was applied to assess the genetic association between the two disease phenotypes. The Z-score statistical analysis results indicated that no statistically significant sample overlap was observed between VTE and IBD or its subtypes (all comparisons had P-values < 0.05) (see Table 2). At a significance level of conjFDR < 0.05, 23 shared genetic loci between IBD and VTE (FinnGen) were identified (Fig. 4A, Supplementary Table 7). In conjunction with the results from the validation set (14 loci) (Fig. 4B, Supplementary Table 8), 7 genes were confirmed, namely AL021068.1, ABO, FADS2, RP3-473L9.4, NFKBIA, CTC-539A10.7, and A4GALT.

Fig. 4
figure 4

Manhattan plot displaying common genetic variants jointly associated with IBD and VTE. The black dotted horizontal line represents the threshold for significant shared associations at conjFDR < 0.05. Independent lead SNPs are highlighted and encircled in black. (A) ConjFDR Manhattan plot of IBD and VTE(FinnGen). (B) ConjFDR Manhattan plot of IBD and VTE(Zhou). (C) ConjFDR Manhattan plot of CD and VTE(FinnGen). (D) ConjFDR Manhattan plot of CD and VTE(Zhou). (E) ConjFDR Manhattan plot of UC and VTE(FinnGen). (F) ConjFDR Manhattan plot of UC and VTE(Zhou).The shared risk loci between VTE and IBD, CD and UC were marked. The statistically significant causality is defined to be conjFDR < 0.05. IBD, Inflammatory bowel disease; VTE, venous thromboembolism; CD, Crohn’s disease; UC, ulcerative colitis

Full size image

Subtype analysis further revealed 21 overlapping genes between VTE (FinnGen) and CD (Fig. 4C, Supplementary Table 9) and 12 overlapping genes with VTE (Zhou) (Fig. 4D, Supplementary Table 10), with TMEM258 and SYNGR1 being common genes.

UC shared 5 and 4 genetic loci with VTE (FinnGen) and VTE (Zhou), respectively (Fig. 4E-F, Supplementary Tables 11–12), with BRAP and GGT7 identified as common genes. These systematic findings provide empirical evidence for understanding the genetic mechanisms underlying the associations between these two disease categories.

Discussion

In this study, we integrated multiple complementary genetic analysis methods, including LDSC, GNOVA, LAVA, and conjFDR, to systematically evaluate the genetic associations between IBD, its subtypes, and VTE. We also utilized two independent VTE datasets (FinnGen and Zhou) for validation, and the results were largely consistent across both analyses, supporting the robustness and reproducibility of our findings. Building upon previous Mendelian randomization studies that suggested a potential causal relationship between UC and VTE [9], our study further expands the understanding of this association by uncovering genetic correlations at both genome-wide and regional levels. Through this multi-level analytical framework, we identified several shared genetic signals across the genome and highlighted candidate loci and genes that may contribute to VTE risk in patients with IBD.

The research results indicate a significant overall positive genetic association between VTE and IBD, UC, while the overall correlation with CD appears to be relatively weak. This disparity carries substantial clinical relevance and biological implications. The relatively stronger genetic correlation between UC and VTE, compared to CD, may help explain certain epidemiological observations. Although a relationship cannot be established, several population-based studies have suggested that patients with UC may have a slightly higher risk of developing VTE than those with CD. For instance, a large cohort study reported a 1.5-fold increased risk in UC (hazard ratio = 1.53, 95% CI: 1.24–1.88), and a higher incidence of VTE was observed among patients with severe UC compared to severe CD (7.5% vs. 3.9%) [19, 20]. Consistent with these findings, a meta-analysis involving over 36,000 individuals indicated that UC patients experienced a greater reduction in VTE risk from prophylactic anticoagulation [21]. Differences in disease course may also contribute to this variation. A long-term follow-up study showed that VTE risk in UC remained elevated throughout the disease course, whereas in CD it was more transient and primarily associated with periods of active disease [22]. These clinical patterns may reflect underlying biological differences. Notably, the lower heritability of CD relative to UC may partly account for the weaker genetic correlation observed with VTE. Despite this, our conjFDR analysis identified 21 pleiotropic loci shared between CD and VTE, indicating that both UC and CD share a substantial degree of genetic overlap with thrombotic risk. These results highlight the importance of cautious interpretation when comparing their genetic associations with VTE. From a mechanistic perspective, UC and CD differ in their inflammatory profiles and anatomical involvement. UC is restricted to the mucosal and submucosal layers of the colon, whereas CD affects the full thickness of the gastrointestinal tract [23]. The superficial inflammation seen in UC may contribute to heightened systemic coagulation responses. Supporting this, previous studies have reported significantly higher levels of plasma D-dimer and fibrinogen in UC patients compared to those with CD [24]. Furthermore, UC-specific genetic variants identified through genome-wide association studies include genes implicated in vascular endothelial function and thrombogenesis [25], while CD-specific variants are more commonly involved in autophagy and microbial processing pathways [26]. Additionally, UC shares greater genetic similarity with autoimmune diseases such as type 1 diabetes and systemic lupus erythematosus, both of which are associated with increased thrombotic risk [27]. Taken together, these findings support a potentially stronger genetic link between UC and VTE; however, further mechanistic and functional studies are needed to clarify the biological relevance and clinical implications of these associations. Additionally, it should be noted that the discrepancy between genetic correlation and the number of genetic loci in the results of this study may be due to the different assumptions and approaches of the analysis methods used. The global genetic correlation analysis primarily focuses on average correlations across the entire genome, whereas the ConjFDR analysis targets shared genetic loci between multiple traits, enabling it to capture local genetic relationships that may be overlooked in the global analysis.

Some comorbid genes have been identified in this study. The polymorphism of the ABO gene significantly influences the risk of VTE in patients with IBD. Research indicates that the ABO gene participates in the pathogenesis of VTE by regulating the expression levels and activity of blood coagulation factors [28]. IBD patients carrying non-O alleles, particularly the A type, exhibit a higher risk of VTE, primarily due to the glycosyltransferases encoded by the ABO gene, which directly affect the plasma concentrations and half-lives of von Willebrand factor (vWF) and factor VIII [29]. During the active phase of IBD, inflammatory factors released in the intestinal inflammatory microenvironment promote endothelial cell damage and platelet activation, while ABO genotype-related glycosylation modifications further regulate the molecular characteristics of endothelial cell surfaces, influencing platelet adhesion and aggregation [30]. The FADS2 gene, a key regulatory enzyme in polyunsaturated fatty acid (PUFAs) metabolism, promotes the biosynthesis of arachidonic acid (AA) and eicosapentaenoic acid (EPA), which directly participate in inflammation and coagulation balance regulation [31]. Clinical studies show that FADS2 gene polymorphisms, particularly at rs174537 and rs174616 loci, are significantly associated with plasma pre-inflammatory mediator levels and VTE risk in IBD patients [32, 33]. IBD patients carrying high-activity variants of the FADS2 gene display higher arachidonic acid/linoleic acid ratios and a procoagulant state, which may increase VTE risk through multiple mechanisms, including enhancing platelet aggregation, promoting tissue factor expression, and inhibiting the fibrinolytic system [34]. The NFKBIA gene encodes the IκBα protein, a key inhibitor of the nuclear factor κB (NF-κB) signaling pathway, and its genetic variations are significantly associated with the severity of IBD and VTE risk [35, 36]. In the intestinal inflammatory environment, functional polymorphisms in the NFKBIA gene lead to abnormal expression or activity of IκBα, disrupting the normal inhibitory mechanism of the NF-κB pathway. This promotes the overexpression of inflammatory mediators such as TNF-α, IL-6, and IL-1β. These pro-inflammatory cytokines not only exacerbate the intestinal inflammation in IBD but also induce endothelial cells to express tissue factor, inhibit the protein C and antithrombin III systems, and promote platelet activation, collectively contributing to a procoagulant state [37]. In CD patients, TMEM258 mutations lead to protein dysfunction, which not only exacerbates the intestinal inflammatory response but also alters the activity and half-life of coagulation proteins through changes in their glycosylation modifications [38]. A clinical translational study has shown that incorporating TMEM258 variant information into the VTE risk assessment model for CD patients improves predictive accuracy [39]. In CD patients, carriers of the high-risk SYNGR1 allele have been reported to exhibit significant coagulation abnormalities and an increased incidence of VTE [25]. In UC patients with an inflammatory state, abnormal BRAP expression has been associated with overactivation of the MAPK/ERK signaling pathway, promoting the release of pro-inflammatory cytokines (IL-6, TNF-α), which may contribute to endothelial dysfunction and coagulation cascade initiation [40]. While SYNGR1 has been classified as a member of the γ-glutamyltransferase family and may be involved in glutathione metabolism and redox homeostasis, its specific role in intestinal inflammation and vascular function remains unclear and requires further validation [41]. Similarly, although GGT7 dysfunction has been linked to oxidative stress and endothelial damage—potentially promoting coagulation through increased expression of tissue factor and adhesion molecules—direct evidence in the context of IBD is currently limited [42]. Taken together, genes such as ABO, FADS2, NFKBIA, and BRAP have mechanistic support in the literature, whereas others, including SYNGR1 and GGT7, should be interpreted with caution and require further functional investigation. Beyond these examples, the remaining identified loci merit attention but will need additional validation in future studies.

In this study, a multi-level genetic analysis was conducted to explore the genetic association between IBD and VTE. By utilizing GWAS and SNP site identification, significant genetic interactions between these conditions were established, offering novel insights into the underlying disease mechanisms. Nevertheless, three primary limitations were recognized in this research. Firstly, LD may introduce biases affecting the precision of association estimates. Secondly, potential sample overlap among datasets could influence the robustness of the findings. Lastly, as the study population was confined to individuals of European ancestry, the generalizability of the conclusions remains constrained. Future validation in diverse ethnic populations is required to enhance the applicability of these findings.

Conclusion

Through a systematic genomic analysis, we identified significant overlapping genetic characteristics between IBD and VTE, providing speculative genetic evidence based on large-scale statistical analysis of European population GWAS to help elucidate the potential clinical correlation between these two conditions. Multiple shared genetic variant loci were detected, which may serve as potential targets for disease prevention, diagnostic, or therapeutic intervention. These findings not only improve our understanding of the common pathogenic mechanisms underlying IBD and VTE but also establish a scientific basis for the precise identification of high-risk individuals and the development of personalized treatment strategies. The potential use of these genes as diagnostic, predictive, or intervention targets could have significant implications for clinical practice, making the research more actionable in a clinical setting.

Data availability

All the GWAS data and statistical software used in this study were publicly available (which can be accessed through the following URLs), and all the generated results in this study were provided in the main text and supplemental data. IEU database: https://gwas.mrcieu.ac.uk. LDSC: https://github.com/bulik/ldsc. conjFDR: https://github.com/precimed/pleiofdr. FUMA: https://fuma.ctglab.nl.

References

  1. Zhang Y-Z, Li Y-Y. Inflammatory bowel disease: pathogenesis. World J Gastroenterol. 2014;20:91–9. https://doi.org/10.3748/wjg.v20.i1.91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. GBD 2017 Inflammatory Bowel Disease Collaborators. The global, regional, and National burden of inflammatory bowel disease in 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet Gastroenterol Hepatol. 2020;5:17–30. https://doi.org/10.1016/S2468-1253(19)30333-4.

    Article  Google Scholar 

  3. Jairath V, Feagan BG. Global burden of inflammatory bowel disease. Lancet Gastroenterol Hepatol. 2020;5:2–3. https://doi.org/10.1016/S2468-1253(19)30358-9.

    Article  PubMed  Google Scholar 

  4. Fiocchi C. Inflammatory bowel disease pathogenesis: where are we? J Gastroenterol Hepatol. 2015;30:12–8. https://doi.org/10.1111/jgh.12751.

    Article  PubMed  Google Scholar 

  5. Rogler G, Singh A, Kavanaugh A, Rubin DT. Extraintestinal manifestations of inflammatory bowel disease: current concepts, treatment, and implications for disease management. Gastroenterology. 2021;161:1118–32. https://doi.org/10.1053/j.gastro.2021.07.042.

    Article  CAS  PubMed  Google Scholar 

  6. Murthy SK, Nguyen GC. Venous thromboembolism in inflammatory bowel disease: an epidemiological review. Am J Gastroenterol. 2011;106:713–8. https://doi.org/10.1038/ajg.2011.53.

    Article  PubMed  Google Scholar 

  7. Grainge MJ, West J, Card TR. Venous thromboembolism during active disease and remission in inflammatory bowel disease: A cohort study. Lancet (lond Engl). 2010;375:657–63. https://doi.org/10.1016/S0140-6736(09)61963-2.

    Article  Google Scholar 

  8. Grimes DA, Schulz KF. Bias and causal associations in observational research. Lancet (lond Engl). 2002;359:248–52. https://doi.org/10.1016/S0140-6736(02)07451-2.

    Article  Google Scholar 

  9. Lv X, Gao X, Liu J, Deng Y, Nie Q, Fan X, Ye Z, Liu P, Wen J. Immune-mediated inflammatory diseases and risk of venous thromboembolism: A Mendelian randomization study. Front Immunol. 2022;13. https://doi.org/10.3389/fimmu.2022.1042751.

  10. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh P-R, ReproGen Consortium PG, Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Duncan L, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47:1236–41. https://doi.org/10.1038/ng.3406.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lu Q, Li B, Ou D, Erlendsdottir M, Powles RL, Jiang T, Hu Y, Chang D, Jin C, Dai W, et al. A powerful approach to estimating Annotation-Stratified genetic covariance via GWAS summary statistics. Am J Hum Genet. 2017;101:939–64. https://doi.org/10.1016/j.ajhg.2017.11.001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Werme J, van der Sluis S, Posthuma D, de Leeuw CA. An integrated framework for local genetic correlation analysis. Nat Genet. 2022;54:274–82. https://doi.org/10.1038/s41588-022-01017-y.

    Article  CAS  PubMed  Google Scholar 

  13. Wang S, Dan Y-L, Yang Y, Tian Y. The shared genetic etiology of antisocial behavior and psychiatric disorders: insights from Pleiotropy and causality analysis. J Affect Disord. 2024;365:534–41. https://doi.org/10.1016/j.jad.2024.08.149.

    Article  PubMed  Google Scholar 

  14. de Lange KM, Moutsianas L, Lee JC, Lamb CA, Luo Y, Kennedy NA, Jostins L, Rice DL, Gutierrez-Achury J, Ji S-G, et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat Genet. 2017;49:256–61. https://doi.org/10.1038/ng.3760.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613:508–18. https://doi.org/10.1038/s41586-022-05473-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhou W, Kanai M, Wu K-HH, Rasheed H, Tsuo K, Hirbo JB, Wang Y, Bhattacharya A, Zhao H, Namba S, et al. Global biobank meta-analysis initiative: powering genetic discovery across human disease. Cell Genomics. 2022;2:100192. https://doi.org/10.1016/j.xgen.2022.100192.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Smeland OB, Frei O, Shadrin A, O’Connell K, Fan C-C, Bahrami S, Holland D, Djurovic S, Thompson WK, Dale AM, et al. Discovery of shared genomic loci using the conditional false discovery rate approach. Hum Genet. 2020;139:85–94. https://doi.org/10.1007/s00439-019-02060-2.

    Article  CAS  PubMed  Google Scholar 

  18. Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8:1826. https://doi.org/10.1038/s41467-017-01261-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Scoville EA, Konijeti GG, Nguyen DD, Sauk J, Yajnik V, Ananthakrishnan AN. Venous thromboembolism in patients with inflammatory bowel diseases: A case-control study of risk factors. Inflamm Bowel Dis. 2014;20:631–6. https://doi.org/10.1097/MIB.0000000000000007.

    Article  PubMed  Google Scholar 

  20. Nguyen GC, Sam J. Rising prevalence of venous thromboembolism and its impact on mortality among hospitalized inflammatory bowel disease patients. Am J Gastroenterol. 2008;103:2272–80. https://doi.org/10.1111/j.1572-0241.2008.02052.x.

    Article  PubMed  Google Scholar 

  21. Ananthakrishnan AN, Cagan A, Gainer VS, Cheng S-C, Cai T, Scoville E, Konijeti GG, Szolovits P, Shaw SY, Churchill S, et al. Thromboprophylaxis is associated with reduced post-hospitalization venous thromboembolic events in patients with inflammatory bowel diseases. Clin Gastroenterol Hepatol: Off Clin Pract J Am Gastroenterol Assoc. 2014;12:1905–10. https://doi.org/10.1016/j.cgh.2014.02.034.

    Article  Google Scholar 

  22. Bernstein CN, Nabalamba A. Hospitalization, surgery, and readmission rates of IBD in canada: A population-based study. Am J Gastroenterol. 2006;101:110–8. https://doi.org/10.1111/j.1572-0241.2006.00330.x.

    Article  PubMed  Google Scholar 

  23. Ordás I, Eckmann L, Talamini M, Baumgart DC, Sandborn WJ. Ulcerative colitis. Lancet (lond Engl). 2012;380:1606–19. https://doi.org/10.1016/S0140-6736(12)60150-0.

    Article  Google Scholar 

  24. Solem CA, Loftus EV, Tremaine WJ, Sandborn WJ. Venous thromboembolism in inflammatory bowel disease. Am J Gastroenterol. 2004;99:97–101. https://doi.org/10.1046/j.1572-0241.2003.04026.x.

    Article  PubMed  Google Scholar 

  25. Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, Lee JC, Schumm LP, Sharma Y, Anderson CA, et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature. 2012;491:119–24. https://doi.org/10.1038/nature11582.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Liu JZ, van Sommeren S, Huang H, Ng SC, Alberts R, Takahashi A, Ripke S, Lee JC, Jostins L, Shah T, et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat Genet. 2015;47:979–86. https://doi.org/10.1038/ng.3359.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tsoi LC, Spain SL, Knight J, Ellinghaus E, Stuart PE, Capon F, Ding J, Li Y, Tejasvi T, Gudjonsson JE, et al. Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity. Nat Genet. 2012;44:1341–8. https://doi.org/10.1038/ng.2467.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Song J, Chen F, Campos M, Bolgiano D, Houck K, Chambless LE, Wu KK, Folsom AR, Couper D, Boerwinkle E, et al. Quantitative influence of ABO blood groups on factor VIII and its ratio to von Willebrand factor, novel observations from an ARIC study of 11,673 subjects. PLoS ONE. 2015;10:e0132626. https://doi.org/10.1371/journal.pone.0132626.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wu O, Bayoumi N, Vickers MA, Clark P. ABO(H) blood groups and vascular disease: A systematic review and meta-analysis. J Thromb Haemost: JTH. 2008;6:62–9. https://doi.org/10.1111/j.1538-7836.2007.02818.x.

    Article  CAS  PubMed  Google Scholar 

  30. Johansson PI, Sørensen AM, Perner A, Welling KL, Wanscher M, Larsen CF, Ostrowski SR. Disseminated intravascular coagulation or acute coagulopathy of trauma shock early after trauma? An observational study. Crit Care (lond Engl). 2011;15:R272. https://doi.org/10.1186/cc10553.

    Article  Google Scholar 

  31. Mathias RA, Pani V, Chilton FH. Genetic variants in the FADS gene: implications for dietary recommendations for fatty acid intake. Curr Nutr Rep. 2014;3:139–48. https://doi.org/10.1007/s13668-014-0079-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Costea I, Mack DR, Lemaitre RN, Israel D, Marcil V, Ahmad A, Amre DK. Interactions between the dietary polyunsaturated fatty acid ratio and genetic factors determine susceptibility to pediatric crohn’s disease. Gastroenterology. 2014;146:929–31. https://doi.org/10.1053/j.gastro.2013.12.034.

    Article  CAS  PubMed  Google Scholar 

  33. Martinelli N, Girelli D, Malerba G, Guarini P, Illig T, Trabetti E, Sandri M, Friso S, Pizzolo F, Schaeffer L, et al. FADS genotypes and desaturase activity estimated by the ratio of arachidonic acid to Linoleic acid are associated with inflammation and coronary artery disease. Am J Clin Nutr. 2008;88:941–9. https://doi.org/10.1093/ajcn/88.4.941.

    Article  CAS  PubMed  Google Scholar 

  34. Huang M-C, Chang W-T, Chang H-Y, Chung H-F, Chen F-P, Huang Y-F, Hsu C-C, Hwang S-J. FADS gene polymorphisms, fatty acid desaturase activities, and HDL-C in type 2 diabetes. Int J Environ Res Public Health. 2017;14:572. https://doi.org/10.3390/ijerph14060572.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Klein W, Tromm A, Folwaczny C, Hagedorn M, Duerig N, Epplen JT, Schmiegel WH, Griga T. A polymorphism of the NFKBIA gene is associated with crohn’s disease patients lacking a predisposing allele of the CARD15 gene. Int J Colorectal Dis. 2004;19:153–6. https://doi.org/10.1007/s00384-003-0531-y.

    Article  PubMed  Google Scholar 

  36. Zhang G-L, Zou Y-F, Feng X-L, Shi H-J, Du X-F, Shao M-H, Gu Y, Zhou Q. Association of the NFKBIA gene polymorphisms with susceptibility to autoimmune and inflammatory diseases: A meta-analysis. Inflamm Res: Off J Eur Histamine Res Soc [et Al ]. 2011;60:11–8. https://doi.org/10.1007/s00011-010-0216-2.

    Article  CAS  Google Scholar 

  37. Vallance TM, Zeuner M-T, Williams HF, Widera D, Vaiyapuri S. Toll-like receptor 4 signalling and its impact on platelet function, thrombosis, and haemostasis. Mediators Inflamm. 2017;2017:9605894. https://doi.org/10.1155/2017/9605894.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Mohanan V, Nakata T, Desch AN, Lévesque C, Boroughs A, Guzman G, Cao Z, Creasey E, Yao J, Boucher G, et al. C1orf106 is a colitis risk gene that regulates stability of epithelial adherens junctions. Sci (N Y NY). 2018;359:1161–6. https://doi.org/10.1126/science.aan0814.

    Article  CAS  Google Scholar 

  39. Ananthakrishnan AN, Bernstein CN, Iliopoulos D, Macpherson A, Neurath MF, Ali RAR, Vavricka SR, Fiocchi C. Environmental triggers in IBD: A review of progress and evidence. Nat Rev Gastroenterol Hepatol. 2018;15:39–49. https://doi.org/10.1038/nrgastro.2017.136.

    Article  PubMed  Google Scholar 

  40. Liao Y-C, Wang Y-S, Guo Y-C, Ozaki K, Tanaka T, Lin H-F, Chang M-H, Chen K-C, Yu M-L, Sheu S-H, et al. BRAP activates inflammatory cascades and increases the risk for carotid atherosclerosis. Mol Med (camb Mass). 2011;17:1065–74. https://doi.org/10.2119/molmed.2011.00043.

    Article  CAS  PubMed  Google Scholar 

  41. Kurano M, Hasegawa K, Kunimi M, Hara M, Yatomi Y, Teramoto T, Tsukamoto K. Sitosterol prevents obesity-related chronic inflammation. Biochim Biophys Acta Mol Cell Biol Lipids. 2018;1863:191–8. https://doi.org/10.1016/j.bbalip.2017.12.004.

    Article  CAS  PubMed  Google Scholar 

  42. Iba T, Levy JH. Inflammation and thrombosis: roles of neutrophils, platelets and endothelial cells and their interactions in thrombus formation during sepsis. J Thromb Haemost: JTH. 2018;16:231–41. https://doi.org/10.1111/jth.13911.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript, and also thanks Biorender (biorender.com) for providing the graphical tools used in this study.

Funding

This study was supported by the 2022 Zhenjiang Social Development Guiding Science and Technology Program (Grant No. FZ2022023).

Author information

Authors and Affiliations

  1. Department of Anorectal Surgery, Danyang Hospital of Traditional Chinese Medicine, Zhenjiang, China

    Yongli Wu

  2. Department of General Surgery, The Affiliated Jiangning Hospital with Nanjing Medical University, Nanjing, China

    Chao Shang

Authors
  1. Yongli Wu
    View author publications

    Search author on:PubMed Google Scholar

  2. Chao Shang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Yongli Wu: Conceptualization, methodology, formal analysis, data curation, writing-original draft preparation; Chao Shang: Supervision, writing-review and editing. All authors contributed to the article and approved the final version of the manuscript.

Corresponding author

Correspondence to Chao Shang.

Ethics declarations

Ethics approval and consent to participate

Not applicable. The data used for analysis were obtained from published studies and public databases. The GWAS database is a database of publicly available datasets, where each study has been approved by local institutional review boards and ethics committees.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Y., Shang, C. Exploring the genetic basis between inflammatory bowel disease and venous thromboembolism. Thrombosis J 23, 56 (2025). https://doi.org/10.1186/s12959-025-00745-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12959-025-00745-5

Thrombosis Journal

ISSN: 1477-9560

Contact us