Genome-Wide Pleiotropic Analyses Characterize the Genetic Etiology Shared between Cardiovascular and Gastrointestinal Tract Diseases

Genome-Wide Pleiotropic Analyses Characterize the Genetic Etiology Shared between Cardiovascular and Gastrointestinal Tract Diseases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-Wide Pleiotropic Analyses Characterize the Genetic Etiology Shared between Cardiovascular and Gastrointestinal Tract Diseases Zhengxi Qiu, Zhiyuan Li, Yirong Wu, Jinchen Chong, Chen Chen, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8960093/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study aimed to investigate the shared genetic etiology between gastrointestinal tract and cardiovascular diseases, and to identify shared genomic loci, genes, and pathways.​ Methods Using aggregated genome-wide association statistics from public data sources, a genome-wide pleiotropic association study was conducted. Multiple statistical genetic approaches were applied to sequentially explore pleiotropic associations across genome-wide single-nucleotide variations, genes, biological pathways, and gut microbiota. The potential shared genetic causes among 7 gastrointestinal tract diseases and 11 cardiovascular diseases were analyzed.​ Results A total of 350 pleiotropic loci were identified. Colocalization analysis detected 95 shared causal loci across disease pairs. Multiple gene pathways were found to be closely associated with these diseases, and key biological processes—including cell signal transduction and T-cell activation—exhibited significant correlations across the studied diseases. A robust pleiotropic association was observed between gastrointestinal tract and cardiovascular diseases, suggesting that these diseases may cross-influence each other in the context of different immune cells and tissues, while sharing common immune response mechanisms. Several immune cell populations, such as CD103⁺ CD11b⁺ dendritic cells, CD8⁺ splenic lymph node DCs, and γδ T cells, showed cross-disease correlations, highlighting the critical role of the immune system in the pathogenesis of both gastrointestinal tract and cardiovascular diseases. Multitrait colocalization analysis via HyPrColoc identified 6 pleiotropic loci harboring shared microbiota, underscoring the significant involvement of gut microbiota in disease pleiotropy.​ Conclusions This study establishes a genetic and mechanistic framework for the shared etiology of gastrointestinal tract and cardiovascular diseases, with pleiotropic variants, inflammatory pathways, and gut microbiota interactions as key drivers. These insights not only deepen our understanding of cardiovascular-gastrointestinal comorbidities but also lay the groundwork for precision medicine strategies targeting shared biological mechanisms. Genome-Wide Pleiotropic Analyses Gastrointestinal Tract Disease Cardiovascular Disease Inflammation and Immunity Microbiota Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Cardiovascular diseases (CVDs) and gastrointestinal tract diseases (GTDs) frequently cooccur, imposing greater health burdens than either condition does in isolation. Clinically, patients with GTDs such as inflammatory bowel disease (IBD) exhibit a significantly elevated risk of developing CVDs 1 . Individuals with CVDs face an increased risk of developing GTDs, including gastrointestinal ulcers, gastroesophageal reflux disease (GORD), and irritable bowel syndrome (IBS) 2 . These observations suggest a potential bidirectional relationship between CVDs and GTDs. Elucidating the high prevalence of CVDs in GTDs represents a critical research priority. Emerging evidence highlights inflammation and gut microbiota dysregulation as shared pathophysiological drivers of both cardiac and gastrointestinal disorders 3 . For example, intestinal inflammation in IBD patients, characterized by elevated levels of cytokines, C-reactive protein (CRP), and homocysteine, induces endothelial dysfunction, an early hallmark of atherosclerosis 4 . Moreover, lipid profile alterations in IBD closely mirror those observed in atherosclerotic patients 3 , 5 . Hypertension, atherosclerosis, and other CVDs, alongside GTDs such as IBD and IBS, are associated with alterations in the gut microbial composition 6 . Deciphering the shared mechanisms between these diseases could facilitate early diagnosis of co-occurring health events in patients and identify convergent therapeutic targets for novel interventions. Genome-wide association study (GWAS) involve diverse genetic variants, including single nucleotide variants (SNVs) 7 , 8 . Methods such as linkage disequilibrium (LD) score regression (LDSC) have been applied to investigate genetic correlations between psychiatric disorders, lung diseases, CVDs, liver diseases and many disease 9 10 11 . These studies, conducted by consortia such as the Inflammation Working Group of the CHARGE Consortium and the METASTROKE Consortium of the International Stroke Genetics Consortium, highlight the utility of genetic correlation analyses in uncovering shared biological pathways across diverse phenotypes. Increasing evidence supports a potential association between CVDs and GTDs clinically. However, systematic exploration of their common genetic etiology—specifically, identifying genome-wide significant genetic variants or loci underlying their shared genetic correlation—remains under explored. A shared genetic basis may reflect pleiotropy, a critical confounding factor in trait‒pair associations that can obscure causal inference 12 . Cross-trait analyses have been developed to identify pleiotropic genetic variants or loci across multiple traits by leveraging correlations in GWAS signals 13 , 14 , 15 . These analyses enable the identification of pleiotropic loci as intervention targets, potentially contributing to the prevention or treatment of these diseases. In this genome-wide pleiotropic analyses study, large-scale GWAS summary data were leveraged, and multiple statistical genetics approaches were employed to perform a comprehensive genome-wide pairwise pleiotropic analysis across CVDs and GTDs. CVDs included abdominal aortic aneurysm (ABAORTANEUR), atrial fibrillation and flutter (AF), aortic aneurysm (AORTANEUR), aortic dissection (AORTDIS), calcific aortic valvular stenosis (CAVS_OPERATED), major coronary heart disease (CHD), heart failure (HEARTFAIL), essential hypertension (HYPTENSESS), other peripheral vascular diseases (OTHPER), stroke (STR), and thoracic aortic aneurysm (THAORTANEUR). GTDs included common gastrointestinal tract diseases, such as inflammatory bowel disease (IBD), Crohn’s disease (CD), ulcerative colitis (UC), peptic ulcer disease (PUD), gastroesophageal reflux disease (GORD), and irritable bowel syndrome (IBS), and a combined phenotype of PUD, GORD, and related medications (PGM). This study aimed to systematically investigate the pleiotropic associations between CVDs and GTDs at the whole-genome, single-nucleotide variants (SNVs), gene, and biological pathway levels, with the goal of disentangling their shared genetic etiology. Specifically, LD score regression (LDSC) and hierarchical decomposition of linkage disequilibrium (HDL) were used to quantify genetic correlations, applied the PLACO method to identify pleiotropic loci and genes, conducted multitissue transcriptome-wide association studies (TWAS) and expression quantitative trait locus (eQTL) analyses to pinpoint common pathogenic genes and signaling pathways, utilized stratified LD score regression (SLDSC) and HyPrColoc to explore shared immune and gut microbiota mechanisms, and employed two-sample bidirectional Mendelian randomization (MR) to evaluate causal relationships between the disease classes. Methods 1. GWAS datasets A total of 11 CVDs and 7 GTDs were selected from publicly available GWAS summary statistics in European-ancestry populations. This selection was motivated by the limited availability of adequately powered GWAS datasets for other ancestries. To ensure sufficient statistical power, only GWASs with sample sizes exceeding 20,000 were included. Data were collected until August 25, 2021. The analysis included 286,935 CVD cases with 3,931,419 controls and 239,815 GTD cases with 1,730,685 controls. Detailed information on the total number of participants, cases, and controls is provided in Supplement Table S1. This genome-wide pleiotropic association study adhered to the Strengthening the Reporting of Genetic Association Studies (STREGA) guidelines. 2. Data quality control To ensure data reliability and consistency, rigorous quality control measures were implemented as follows: 1) Exclusion of nonbiallelic and ambiguous SNPs: Nonstandard SNPs, including multiallelic variants and those with indistinguishable alleles, were systematically excluded from the analysis. 2) Removal of SNPs without rs tags: SNPs lacking standard rsID tags were excluded to ensure that each variant had a definitive identifier. 3) Filtering of duplicate and mismatched SNPs: Duplicate SNPs, as well as those absent from the 1000 Genomes Project reference panel or with allele information inconsistent with reference data, were removed. 4) Exclusion of major histocompatibility complex(MHC) region SNPs: Due to the complex LD structure, SNPs within the MHC region on chromosome 6 (chr6: 28.5-33.5Mb) were excluded.5) Retention of SNPs with an minor allele frequency(MAF) > 0.01: SNPs with a MAF greater than 0.01 were retained to minimize statistical bias from low-frequency variants. Given that the GTDs and CVDs data originated from distinct research consortia, sample overlap was negligible, thereby reducing confounding effects from sample crossover. For each remaining SNPs, key statistics—including effect size, standard error, effect allele, and P value—were retained for subsequent analyses. 3. Genetic correlation analysis Genetic correlation analyses across traits were performed via two distinct methodologies: LDSC 16 (PMID: 25642630) and hierarchical decomposition of linkage disequilibrium (HDL) 17 (PMID: 32601477). These approaches aim to quantify genetic associations between trait pairs and characterize their shared polygenic architecture. For LDSC, European ancestry samples from the 1000 Genomes Project were used as a reference panel to calculate LD scores for each variant locus. LD scores quantify the extent to which genetic variation in one genomic region can be explained by neighboring regions, serving as a key metric for assessing polygenic trait structure. Genetic correlations were estimated while accounting for confounding factors such as population stratification and shared genetic variation between unrelated traits. To further mitigate biases, an unconstrained LDSC analysis was performed, allowing free estimation of the intercept to identify and adjust for residual confounding from a limited sample size, environmental factors, or population structure. This approach also facilitates the detection of potential sample overlap between GWAS datasets, as a significantly elevated intercept (> 1) may indicate overlapping samples and bias in genetic correlation estimates. For HDL, high-resolution likelihood estimation was employed to refine genetic correlation estimates, enhancing the precision of shared polygenic pattern detection across traits. 4. SNPs-level pleiotropic analysis under the composite null hypothesis (PLACO) SNPs-level PLACO is an innovative analytical framework for identifying pleiotropic loci associated with complex traits, relying solely on summary-level genotype‒phenotype association statistics 18 . This method utilizes the squared Z scores (Z²) of individual genetic variants to assess pleiotropic effects across multiple traits. First, Z² values were calculated for each variant, and SNPs with extreme Z² values (> 80) were excluded to mitigate analytical bias from strong association signals or data anomalies. Given the potential genetic correlation between gastrointestinal and cardiovascular diseases, a Z² correlation matrix was estimated for each variant. This matrix captures the correlation in effect directions and magnitudes across traits at each genomic locus, enabling the identification of shared genetic bases between the two disease categories. The intersection-union test (IUT) was subsequently applied to perform pleiotropy tests for each variant within a multiple hypothesis testing framework. The null hypothesis (H 0 ) assumes that the variant has no effect on either trait or influences only one trait, whereas the alternative hypothesis (H 1 ) posits simultaneous influence on both traits. The final IUT P value is defined as the maximum P value from all tests of H 0 and H 1 , controlling for false positive rates due to multiple testing. This approach balances power and specificity in detecting pleiotropic effects under stringent multiple-testing conditions. 5. Multi-marker Analysis of Genomic Annotation (MAGMA) Building upon the PLACO results, the identified loci were mapped to nearby genes to elucidate the shared biological mechanisms underlying pleiotropic sites. MAGMA was applied to genes at or overlapping with pleiotropic sites derived from the PLACO output and single-trait GWAS, aiming to identify candidate pleiotropic genes. The significance threshold for MAGMA was set at P < 0.05/ N _genes = 3×10⁻⁶. Functional mapping and annotation of genetic associations were used to characterize the biological functions of these loci 19 . Additionally, pathway enrichment analyses were performed via the Molecular Signatures Database (MSigDB) to functionally annotate the mapped genes. SNPs and risk loci within the MHC region were excluded from both the PLACO and MAGMA gene and gene-set analyses. 6. Colocalization analysis For pleiotropic loci annotated by FUMA, Bayesian colocalization analysis was performed via the R package Coloc 20 (PMID: 27866706) to evaluate the probability of shared causal variants between trait pairs. This approach assumes a single causal variant (SCV) within each gene region, suggesting that both traits are influenced by the same genetic variant. Colocalization analysis computes posterior probabilities (PPs) for five hypotheses: H0: Neither trait has a genetic association in the region; H1: Only trait 1 is associated; H2: Only trait 2 is associated; H3: Both traits are associated but via distinct causal variants; H4: Both traits are associated and share a common causal variant. The coloc.abf function was used with explicitly specified prior probabilities for (i) each trait having a causal variant and (ii) the two traits sharing a causal variant. This systematic assessment of pleiotropic loci across traits provides evidence for potential shared biological mechanisms. 7. Hypothesis prioritization for multitrait colocalization analysis (Hyprcoloc) HyPrColoc 21 (PMID:33536417), combined with multitrait colocalization analysis of immune GWAS data, has revealed the potential pivotal role of immune cells in the pathogenesis of CVDs and GTDs. This approach prioritizes gene loci with shared genetic variants across multiple traits, thereby uncovering genetic associations among diverse phenotypes. By applying HyPrColoc, GWAS data from various immune traits and target diseases (CVDs and GTDs) can be efficiently integrated to identify gene loci with significant colocalization signals. GWAS summary statistics for gut microbiota traits (as exposure data) were downloaded from the MiBioGen consortium ( http://www.mibiogen.org/ ) 22 . The gut microbiota was categorized into 211 taxonomic units, including 131 genera, 35 families, 20 orders, 16 classes, and 9 phyla. Microbiota data from 18,340 individuals across 24 cohorts were analyzed, comprising 5,717,754 SNPs derived from 16S fecal genotyping. Genotype imputation was performed via the HRC 1.0 or 1.1 reference panel. Subsequent association analyses employed Spearman correlation, adjusting for covariates, including age, sex, technical factors, and genetic principal components. 8. MR study All significant genetic loci independently associated with exposure were selected as instrumental variables ( P < 5×10⁻⁸) via the clumping function in PLINK software (version 1.9) 23 . The r² threshold for defining linkage disequilibrium between instrumental variables was set at 0.001, and the genomic window size was defined as 10,000kb. To ensure the robustness and strength of the selected instrumental variables, we further calculated the r² values and F statistics 24 for each variable. The formula for calculating the F value is as follows: \(F=(\frac{{n - 1 - k}}{k})(\frac{{{r^2}}}{{1 - {r^2}}})\) \(F=(\frac{{n - 1 - k}}{k})(\frac{{{r^2}}}{{1 - {r^2}}})\) In MR analyses, R 2 denotes the proportion of variance explained by instrumental variables (IVs), n represents the sample size, and k indicates the number of SNPs. The inverse variance weighted (IVW) method is the primary approach in MR, requiring IVs to satisfy three core assumptions: (1) the IV must be associated with the exposure; (2) the IV should not be correlated with confounding factors related to both the exposure and the outcome; and (3) the effect of the IV on the outcome must be entirely mediated through the exposure. The sensitivity analyses included the following. First, the Q test for IVW and MR‒Egger can detect potential violations of assumption 25 by assessing the heterogeneity of associations among individual instrumental variables (IVs). Second, MR‒Egger is employed to estimate horizontal pleiotropy through its intercept term, ensuring that genetic variants are independently associated with exposure and outcome as per assumption 26 . Additionally, supplementary analyses using alternative MR methods, such as the weighted median and weighted mode, which incorporate distinct modeling assumptions and advantages, further enhance the stability and robustness of the findings. Statistical analyses were conducted in R v3.5.3 via the Mendelian randomization package 27 9. Ethics This study was designed and conducted in accordance with ethical guidelines for human subject research and adheres to the principles of the Declaration of Helsinki. Data were obtained through an approved UK Biobank (UKB) project application (ID: 92675). UKB has received ethical approval from the National Health Service North West Centre for Research Ethics Committee (Ref: 21/NW/0157), and informed consent has been obtained from all participants. Results 1. Genetic correlations between CVDs and GTDs LDSC genetic correlation analysis revealed significant genetic correlations between CVDs and GTDs. Sensitivity analyses using HDL traits further validated the robustness of most findings. To account for multiple testing, the Bonferroni correction was applied ( P < 0.05/77 = 6.5×10⁻⁴), and numerous disease pairs retained statistically significant associations (Fig. 1 and Supplement Table S2). 2. Shared loci and genes between CVDs and GTDs Pleiotropy analysis by PLACO was conducted on disease pairs with significant correlations, resulting in the identification of a total of 350 pleiotropic loci. The identified pleiotropic loci and their corresponding basic information for each genomic risk locus are summarized in Supplement Table S3. The functional impacts of pleiotropic SNPs on genes are detailed in Supplement Table S4. Additionally, Manhattan and quantile‒quantile plots illustrating the relationships between diseases are provided in Supplement Figures S1-S12. Colocalization analysis revealed 95 shared causal loci (PP.H4 > 0.8) across the investigated diseases, as shown in Fig. 2 A and summarized in Table 1 . MAGMA gene set analysis for CVDs and GTDs revealed multiple associated gene pathways (Supplement Figure S13 and Table S5). Key findings include the following: 1) Biological processes linked to diseases, such as muscle contraction, cell signaling, and macro-molecule synthesis, are significantly associated. 2) Disease-specific pathways: GORD: Strong associations with muscle structure development, positive regulation of cholesterol storage, and transcriptional regulation ( P values: 10 − 9 -10 − 5 ). AF: Enriched pathways related to cardiac conduction, circulatory system processes, and vascular processes. IBD and HYPTENSESS: Close links to immune/inflammatory pathways (e.g., T-cell activation, response to oxidized compounds), implicating immune mechanisms in disease pathogenesis. Table 1 Overlapping pleiotropic loci and colocalization analysis of gastrointestinal diseases and different cardiovascular diseases Trait pairs Genomic Locus LeadSNPs P Nearby genes PP H4 CD & AF 10q11.23 rs4240499 9.74E-11 VSTM4 0.838 IBD & HYPTENSESS 10q21.3 rs7922082 2.01E-09 RP11-436D10.3 0.958 CD & AF 10q22.2 rs4691 3.46E-09 NDST2 0.834 PUD & HYPTENSESS 11p13 rs11604176 1.98E-08 DCDC1 0.966 PGM & HYPTENSESS 11p14.1 rs921880 2.55E-08 DCDC1 0.956 PGM & HYPTENSESS 11p15.4 rs11040804 6.59E-11 C11orf42 0.884 PUD & HYPTENSESS 11p15.4 rs10500661 9.04E-12 CNGA4, CCKBR 0.874 PUD & CHD 11p15.5 rs75613328 1.41E-08 TALDO1 0.874 PGM & HYPTENSESS 12p12.3 rs12297543 6.97E-10 PTPRO 0.860 PGM & AF 12q13.13 rs736825 1.13E-08 RP11-834C11.14 0.901 PGM & HEARTFAIL 12q13.13 rs12426399 5.58E-09 RP11-834C11.14 0.955 PGM & HYPTENSESS 12q13.13 rs11450619 3.03E-08 ACVRL1, ACVR1B 0.899 PGM & HYPTENSESS 12q13.13 rs2077177 6.44E-14 RP11-834C11.14 0.860 PGM & STR 12q13.13 rs4759318 8.88E-09 RP11-834C11.14 0.906 CD & CHD 12q15 rs10878972 3.82E-08 YEATS4 0.847 CD & HYPTENSESS 12q15 rs12366463 2.77E-08 RP11-1143G9.2, LYZ 0.946 PGM & AF 12q24.12 rs653178 2.59E-11 ATXN2 0.989 PGM & CHD 12q24.12 rs653178 1.72E-16 ATXN2 0.989 IBD & HYPTENSESS 12q24.13 rs233724 2.20E-12 RPH3A 0.938 CD & CHD 12q24.13 rs233724 6.80E-11 RPH3A 0.997 PGM & CHD 13q34 rs7333991 1.31E-09 COL4A2 0.809 CD & CHD 13q34 rs9521787 1.36E-08 COL4A2-AS2 0.918 IBD & HYPTENSESS 14q32.2 rs1461584 4.39E-08 RP11-61O1.1 0.931 IBD & HYPTENSESS 14q32.32 rs1136165 2.52E-12 CKB 0.930 CD & HYPTENSESS 14q32.32 rs7157631 6.12E-13 MARK3 0.912 CD & CHD 15q22.33 rs56062135 4.28E-19 SMAD3 0.997 CD & AF 15q26.1 rs4932373 1.35E-08 FES 0.837 CD & HEARTFAIL 16p11.2 rs34838 1.21E-13 CLN3, CLN3 0.912 PGM & HYPTENSESS 16q24.3 rs258319 4.82E-08 SPATA33 0.853 IBD & AF 17q12 rs12603332 6.52E-16 ORMDL3 0.855 GORD & HYPTENSESS 17q21.31 rs1053739 2.04E-08 NMT1 0.967 PGM & HYPTENSESS 17q21.31 rs1053739 2.78E-08 NMT1 0.953 GORD & HEARTFAIL 17q21.32 rs221606 1.24E-09 CDC27 0.901 GORD & HYPTENSESS 17q21.32 rs4968309 9.63E-09 MYL4 0.896 PGM & HYPTENSESS 17q21.32 rs6504743 4.54E-08 CDC27 0.852 IBD & HYPTENSESS 17q25.3 rs11658134 4.15E-08 CYTH1 0.888 CD & AF 18q21.1 rs79544247 6.41E-09 SMAD7 0.971 PGM & AF 19p13.11 rs34068189 2.40E-08 PGPEP1 0.839 IBD & HYPTENSESS 19q13.11 rs62126611 1.60E-12 SLC7A10, CTD-2540B15.12 0.845 IBD & AF 19q13.2 rs2115239 8.08E-09 ADCK4 0.930 CD & AF 19q13.2 rs2115239 1.55E-08 ADCK4 0.941 CD & CHD 1p13.2 rs2476601 3.79E-13 PTPN22 0.909 IBD & AF 1p32.2 rs72664358 8.23E-10 PPAP2B 0.980 PGM & AF 1p36.22 rs6660242 8.07E-09 CASZ1 0.922 IBD & HYPTENSESS 1q21.3 rs10305667 4.28E-08 ARNT 0.849 IBD & HYPTENSESS 1q22 rs760077 3.24E-09 MTX1 0.898 IBS & HYPTENSESS 1q25.2 rs2175177 3.98E-08 RP11-195C7.1 0.945 IBD & HYPTENSESS 20p12.2 rs6040076 2.58E-08 RP11-103J8.1 0.947 IBD & AF 20q12 rs17181845 6.41E-10 ZHX3 0.898 IBD & HYPTENSESS 20q12 rs17181845 1.42E-09 ZHX3 0.897 CD & AF 20q12 rs6065325 1.40E-08 ZHX3 0.958 CD & HYPTENSESS 20q12 rs6065325 2.62E-08 ZHX3 0.947 CD & AF 20q13.12 rs4810485 2.70E-08 CD40 0.807 IBD & HYPTENSESS 20q13.33 rs2427541 8.98E-10 AL158091.1, ABHD16B 0.985 CD & HYPTENSESS 20q13.33 rs2427539 4.93E-10 AL158091.1 0.989 IBD & HYPTENSESS 2p21 rs6747202 8.53E-10 THADA 0.973 CD & CHD 2p21 rs7589033 7.76E-10 THADA 0.936 CD & HYPTENSESS 2p21 rs12477432 1.96E-09 THADA 0.972 IBD & HYPTENSESS 2p23.2 rs1260326;rs11677002 4.12E-13 AC104695.3 0.948 CD & HYPTENSESS 2p23.2 rs1260326;rs4666067 9.65E-13 AC104695.3 0.958 IBS & HYPTENSESS 2p23.3 rs11608 6.79E-10 SLC5A6 0.931 IBD & HYPTENSESS 2p23.3 rs7578575 2.96E-13 DNMT3A 0.996 CD & HYPTENSESS 2p23.3 rs7578575 1.27E-11 DNMT3A 0.994 PGM & ABAORTANEUR 2p24.1 rs10193919 4.72E-12 AC012065.7, C2orf43 0.992 PGM & AORTANEUR 2p24.1 rs10193919 9.60E-12 AC012065.7, C2orf43 0.968 IBD & AF 2q32.1 rs2595391 1.74E-08 AC017101.10 0.816 IBD & AF 2q33.1 rs1217429 3.36E-08 C2orf47, SPATS2L 0.885 IBD & AF 3p14.1 rs6786528 4.46E-08 FRMD4B 0.824 GORD & HYPTENSESS 3p21.31 rs143171929 6.36E-09 RP11-694I15.7 0.967 PGM & HYPTENSESS 3p21.31 rs76367790 9.19E-09 RBM6 0.965 CD & AF 3p22.2 rs71323670 1.93E-08 SCN5A 0.893 CD & STR 3p25.3 rs62248311 3.32E-08 VGLL4 0.850 CD & AF 3q23 rs2871960 1.17E-09 ZBTB38 0.917 PGM & AF 4q24 rs2711894 6.86E-09 CENPE 0.908 CD & AF 4q24 rs4698861 1.09E-08 NFKB1, MANBA 0.947 IBS & AF 4q25 rs149054443 5.49E-10 RNU6-289P, RP11-255I10.1 0.933 PGM & ABAORTANEUR 5p15.33 rs61580655 9.39E-09 MIR4456, RP11-310P5.1 0.847 PGM & AORTANEUR 5p15.33 rs61384641 6.55E-09 MIR4456, RP11-310P5.1 0.852 IBD & AF 5q13.2 rs4703855 3.33E-08 RP11-389C8.1, YBX1P5 0.842 IBD & HYPTENSESS 5q31.1 rs3792894 4.14E-14 P4HA2 0.918 GORD & CHD 5q31.3 rs7356639 6.56E-09 RN7SKP246, CTC-342P15.1 0.953 PGM & CHD 5q31.3 rs7356639 7.23E-10 RN7SKP246, CTC-342P15.1 0.979 PUD & AF 5q35.2 rs75049939 7.64E-09 CPEB4 0.801 IBD & HYPTENSESS 6p22.2 rs198846 1.47E-10 HIST1H1T 0.825 CD & HYPTENSESS 6q22.32 rs10457481 4.75E-08 MIR588, RNU6-200P 0.911 IBD & HYPTENSESS 7p15.2 rs4722672 4.54E-10 RP1-170O19.14 0.994 PGM & HYPTENSESS 7p22.3 rs4721096 1.27E-12 MAD1L1 0.931 IBD & AF 7p22.3 rs798508 4.66E-10 GNA12 0.910 CD & HYPTENSESS 7q11.23 rs2529275;rs236660 4.02E-10 STAG3L2 0.810 IBD & AF 7q36.1 rs3918226 1.24E-08 NOS3 0.941 CD & STR 8p23.1 rs6601535 3.80E-08 SOX7, PINX1 0.814 CD & CHD 8q24.13 rs2980862 5.85E-09 RP11-136O12.2 0.910 PUD & AF 9q34.2 rs115478735 2.63E-11 ABO 0.925 PUD & HEARTFAIL 9q34.2 rs115478735 2.76E-10 ABO 0.955 Tissue-specific enrichment patterns across diseases and biomarkers are depicted in Fig. 2 B and Supplement Table S6, highlighting significant associations between multiple diseases and corresponding tissues. Notably, the small intestine terminal ileum and whole blood showed robust enrichment of numerous markers, suggesting that these tissues may play pivotal roles in the pathogenesis of related diseases, including IBD, AF, and HYPTENSESS. For example, IBD and HYPTENSESS exhibited substantial enrichment in these tissues, indicating their involvement in immune responses and metabolic dysregulation. Additionally, the spleen and whole blood were strongly enriched for markers associated with atrial fibrillation and diabetes, underscoring their critical roles in immune and cardiovascular health. Conversely, tissues such as Uterus, Esophagus muscularis, and others were enriched in digestive system diseases (e.g., peptic ulcers), highlighting their potential influence on gastrointestinal pathologies. 3. Prioritization of candidate pleiotropic genes and characterization of phenotype and tissue specificity MAGMA gene-based analysis of the PLACO results revealed 337 pleiotropic genes ( P < 0.05/18,645 = 2.682×10 − 6 ; Supplement Table S7). By integrating eQTL data from gastrointestinal, cardiovascular, and whole blood tissues to map pleiotropic genes, a total of 1,427 genes were identified. Figure 3 A illustrates the overlap of genes across methods, with details in Supplement Table S8. Figure 3 B shows genes identified by neighboring gene mapping, MAGMA, and eQTL analysis, along with their expression profiles across 54 tissues. Tissue-specific enrichment analysis revealed significant enrichment in heart, pancreas, and brain tissues (Fig. 3 C). 4. Protein‒protein interaction (PPI) network analysis Pathway enrichment analysis of pleiotropic genes is depicted in Fig. 4 and Supplement Table S9, while protein‒protein interaction (PPI) network analysis is shown in Supplement Figure S14. Positive regulation of interleukin-17(IL-17) production is the most important pathway in both CVDs and GTDs. 5. Synaptic and immune-related mechanisms shared between CVDs and GTDs Immunological abnormalities are a common pathogenic cause of various diseases. Meanwhile, PPI network analysis demonstrated the key role of immunological factor-IL17. This study explored the common role of immunity in CVDs and GTDs. Significant pleiotropic associations were identified between GTDs (e.g., GERD and IBD) and CVDs (e.g., HYPTENSESS and AF). These findings suggest bidirectional interactions between these diseases, potentially mediated by shared immune cell populations and tissues, with common immune response mechanisms likely playing a role. Specifically, immune cell subsets such as CD8⁺ T cells, T follicular helper (Tfh) cells, regulatory B cells, and MTS15⁺ Th17 cells were significantly enriched in both disease categories, highlighting their critical roles in the pathogenesis of gastrointestinal and cardiovascular conditions. Additionally, cross-disease correlations were observed for multiple cell populations, including CD103⁺ CD11b⁺ dendritic cells (DCs), CD8⁺ splenic lymph node DCs (DC.8⁺SLNs), and γδ T cells (Tgd.vgd⁺24ah⁺17. Th), underscoring the pleiotropic nature of immune system involvement. The results are visualized in Fig. 5 . 6. Multitrait colocalization analysis to pinpoint critical gut microbiota Multitrait colocalization analysis via HyPrColoc identified 6 pleiotropic loci harboring shared pathogenic microbiota, highlighting the significant role of the gut microbiota in disease pleiotropy (Supplement Table S10). For example, the Ruminococcus gauvreauii group was critically associated with CD/IBD and AF, whereas Firmicutes phylum members were significantly linked to PGM/PUD and HYPTENSESS. 7. MR and Associations between CVDs and GTDs Two-sample MR analysis revealed that significant causal associations were identified from CVDs to GTDs and vice versa (Supplement Figure S15). Discussion This study systematically identified extensive genome-wide genetic associations and significant genetic overlap between CVDs and GTDs. Comprehensive analyses revealed pleiotropic genetic variants, shared causal loci, and converging biological pathways, alongside gut microbiota signatures, that underpin the shared pathogenesis of these disease categories. These findings provide robust evidence for a common genetic etiology linking CVDs and GTDs, with implications for their comorbidity and joint therapeutic targeting. Using PLACO, 350 pleiotropic loci were identified, 95 of which were demonstrated to have shared causal variants (PP.H4 > 0.8) via colocalization analysis. These loci include genes such as ZHX3 (20q12), ATXN2 (12q24.12), and THADA (2p21), which are independently linked to both CVDs (e.g., AF) and GTDs (e.g., IBD). Two-sample Mendelian randomization (MR) analysis further revealed bidirectional causal associations between CVDs and GTDs, which is consistent with vertical pleiotropy, where shared variants influence both traits through common biological pathways. LDSC-based sample overlap detection and LHC-MR adjustments confirmed the robustness of these findings, ruling out major confounding factors from overlapping datasets. Central to the shared pathogenesis are inflammatory and immune pathways, particularly those involving IL-17 and Th17 cells 28 . MAGMA analysis identified IL-17 signaling as a key shared pathway, with Th17 cells serving as a critical immune cell subset. In GTDs, IL-17 exacerbates intestinal mucosal barrier dysfunction in ulcerative colitis and Crohn’s disease by increasing the levels of proinflammatory cytokines (e.g., IL-6 and CXCL1) and chemokines 29 31 . In CVDs, IL-17 promotes atherosclerotic plaque instability, endothelial dysfunction, and thrombosis, linking it to hypertension, myocardial infarction, and ischemic stroke 32 33 34–37 . Clinical studies have shown elevated plasma IL-17 levels and Th17 cell counts in patients with acute coronary syndrome compared with stable angina patients or healthy controls 48 , 49 , underscoring their role in disease progression. The plasticity of TH17 cells in diverse inflammatory micro-environments—regulated by transcription factors such as RORγt and cytokines such as IL-23—further bridges CVDs and GTDs 39 40–44 . For example, IL-17-induced secretion of granulocyte‒macrophage colony‒stimulating factor promotes both atherosclerotic lesion development and ulcerative colitis progression 45 , 46 , highlighting mechanistic convergence. The gut microbiota has emerged as a critical mediator of shared pathogenesis. HyPrColoc identified 6 pleiotropic loci involving taxa such as the Ruminococcus gauvreauii group, Collinsella, and Firmicutes phylum members, which were enriched in both CVDs and GTDs. Mechanistically, the microbiota influences cardiovascular health through pathways such as epithelial sodium/hydrogen exchanger activation and myocyte enhancer factor signaling 52 , whereas in the gut, taxa such as AKK modulate inflammation via FXR receptor activation 50 . The observed cross-disease correlations for the Ruminococcus gauvreauii and Lachnospiraceae FCS020 groups highlight their dual roles in gastrointestinal homeostasis and vascular dysfunction. The identification of shared genetic biomarkers (e.g., NMT1 and CDC27) and immune cell targets (e.g., TH17 and γδ T cells) offers opportunities for the early prediction of GTD-CVD comorbidities. For example, IL-17-targeted biologics, which are already used to treat inflammatory bowel disease, may have broader utility in preventing cardiovascular complications. Additionally, microbiota modulation—such as probiotic interventions targeting Ruminococcus or Collinsella—could represent a novel strategy to disrupt shared pathogenic pathways. While this study leveraged large-scale GWAS data, it is limited by the European-ancestry focus, which may restrict its generalizability to other populations. Future studies incorporating diverse ancestries and longitudinal data will be critical to validate these findings and dissect temporal relationships. Single-cell RNA sequencing and functional genomics experiments (e.g., CRISPR-Cas9) could further mechanistically validate the roles of pleiotropic genes such as THADA and ZHX3 in both tissue contexts. This study has several advantages. It is a large-scale sample focuses on the shared genetic characteristics between CVDs and GTDs. Through a systematic design, it comprehensively evaluates the genetic associations among various diseases, providing extensive data support for understanding the comorbidity mechanism of these two types of diseases. This study is the first to report a number of previously unrecognized pleiotropic variants or genes. These findings help elucidate the biological mechanisms of genetic factors in cardiovascular and gastrointestinal comorbidities, help promote an in-depth understanding of the genetic basis of disease co-occurrence in related fields, and provide a basis for further development of clinical therapies. This study also has several limitations that should be acknowledged. First, there was insufficient population representativeness. The study is limited to the European population. In the future, it is necessary to expand the sample size of the abovementioned population to enhance the ethnic/racial universality of the research conclusions. The existing analysis still has room for improvement. More refined statistical methods (such as local-level analysis) should be adopted to explore more potential genetic associations between CVDs and GTDs. At present, the identified pleiotropic genetic biomarkers have not been verified by in vivo or in vitro experiments, and their specific biological functions are still unclear. In the future, it will be necessary to analyze the mechanism of action of these biomarkers through cell models, animal experiments, etc. Although the discovered biomarkers are expected to become therapeutic targets and promote personalized medicine, their practical application value has not been verified in clinical settings. Subsequent studies should focus on their potential for clinical transformation, such as their diagnostic efficacy and ability to predict treatment response. In addition, there are various potential risks of bias, including insufficient statistical power in the analysis of genetic evidence for low-incidence diseases and weak instrument bias in Mendelian randomization analysis. Although multiple statistical methods have been used to correct for confounding factors, more rigorous study designs are still needed to further verify the robustness of the research results. Conclusion This study establishes a genetic and mechanistic framework for the shared etiology of CVDs and GTDs, highlighting pleiotropic variants, inflammatory pathways, and gut microbiota interactions as key drivers. These insights not only deepen our understanding of cardiovascular‒gastrointestinal comorbidities but also pave the way for precision medicine approaches targeting shared biological mechanisms. Abbreviations gastrointestinal tract diseases (GTDs), cardiovascular diseases (CVDs), inflammatory bowel disease (IBD), Crohn’s disease (CD), ulcerative colitis (UC), peptic ulcer disease (PUD), gastroesophageal reflux disease (GORD), irritable bowel syndrome (IBS), a combined phenotype of PUD, GORD, and related medications (PGM), abdominal aortic aneurysm (ABAORTANEUR), atrial fibrillation and flutter (AF), aortic aneurysm (AORTANEUR), aortic dissection (AORTDIS), calcific aortic valvular stenosis (CAVS_OPERATED), major coronary heart disease (CHD), heart failure (HEARTFAIL), essential hypertension (HYPTENSESS), other peripheral vascular diseases (OTHPER), stroke (STR), and thoracic aortic aneurysm (THAORTANEUR). Declarations Ethics approval and consent to participate This study was designed and conducted in accordance with ethical guidelines for human subject research and adheres to the principles of the Declaration of Helsinki. Data were obtained through an approved UK Biobank (UKB) project application (ID: 92675). UKB has received ethical approval from the National Health Service North West Centre for Research Ethics Committee (Ref: 21/NW/0157), and informed consent has been obtained from all participants. Consent for publication All authors have agreed to the publication of this article. Competing interests The authors declare that they have no conflicts of interest. All authors contributed to data analysis, drafting, or revision of the article; agreed on the journal to which the article is being submitted; provided final approval of the version to be published; and agreed to be accountable for all aspects of the work. Funding The present study was supported by the National Clinical Research Base of Traditional Chinese Medicine in Jiangsu Province (No. JD2023SZ04),Natural Science Foundation of Jiangsu Province,China(No.BK20251141), the Traditional Chinese and Western Medicine Colorectal Polyps Treatment Center Project of Jiangsu Provincial Hospital of Chinese Medicine, the Natural Science Foundation of China (82374449, 82341229 and 82174379), the Jiangsu Provincial Medical Key Laboratory (ZDXYS202208), and the Advantageous Disciplines of Jiangsu Province (035062005002–11). Author Contribution Yang Li, Zhengxi Qiu, and Zhiyuan Li drafted the manuscript and created all the tables. Yirong Wu, Jinchen Chong, and Jiaze Ma collected the data. Chen Chen, Jiepin Li, Yirong Yang, Zhihua Lu, Xiaomin Yuan, Yijia Zhu, and Tuo Chen analyzed the data and generated all the figures. Yang Li, Tingsheng Ling and Yugen Chen provided research funding and designed the study. All the authors reviewed the manuscript. Acknowledgements Not applicable. Data Availability The Program in Complex Trait Genomics (https://cnsgenomics.com/content/data), GWAS Catalog (https://www.ebi.ac.uk/gwas/), Regensburg GEM Platform (https://www.uni-regensburg.de/medizin/epidemiologie-praeventivmedizin/genetische-epidemiologie/software/index.html), and GWAS Atlas (https://atlas.ctglab.nl/traitDB) provided access to genome-wide association study data. We thank all research participants who provided DNA samples for these studies. References Schicho R, Marsche G, Storr M. Cardiovascular complications in inflammatory bowel disease. Curr Drug Targets. 2015;16(3):181–8. 10.2174/1389450116666150202161500 . Meda A, Fredrick F, Rathod U, Shah P, Jain R. Cardiovascular Manifestations in Inflammatory Bowel Disease. Curr Cardiol Rev. 2023;20(1):E241123223802. 10.2174/011573403X256094231031074753 . Sinha T, Zain Z, Bokhari SFH, et al. Navigating the Gut-Cardiac Axis: Understanding Cardiovascular Complications in Inflammatory Bowel Disease. Cureus. 2024;16(2):e55268. 10.7759/cureus.55268 . Sleutjes JAM, van Lennep JER, van der Woude CJ, de Vries AC. Thromboembolic and atherosclerotic cardiovascular events in inflammatory bowel disease: epidemiology, pathogenesis and clinical management. Ther Adv Gastroenterol. 2021;14:17562848211032126. 10.1177/17562848211032126 . Darweesh M, Haddaden M, Fatima Z, et al. The Incidence and Outcomes of Venous Thromboembolisms in Patients Admitted with Different Gastrointestinal Malignancies. Gastrointest Tumors. 2023;10(1):67–73. 10.1159/000538057 . Sleutjes JAM, van Roeters JE, de Vries AC. Spotlight on Cardiovascular Risk Assessment in Patients with Inflammatory Bowel Disease. Dig Dis Sci. 2022;67(9):4326–9. 10.1007/s10620-022-07449-0 . Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–9. 10.1038/s41586-018-0579-z . Wu Y, Murray GK, Byrne EM, Sidorenko J, Visscher PM, Wray NR. GWAS of peptic ulcer disease implicates Helicobacter pylori infection, other gastrointestinal disorders and depression. Nat Commun. 2021;12(1):1146. 10.1038/s41467-021-21280-7 . Tylee DS, Sun J, Hess JL, et al. Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data. Am J Med Genet Part B Neuropsychiatr Genet Off Publ Int Soc Psychiatr Genet. 2018;177(7):641–57. 10.1002/ajmg.b.32652 . Pouget JG, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Han B, et al. Cross-disorder analysis of schizophrenia and 19 immune-mediated diseases identifies shared genetic risk. Hum Mol Genet. 2019;28(20):3498–513. 10.1093/hmg/ddz145 . Duncan LE, Shen H, Ballon JS, Hardy KV, Noordsy DL, Levinson DF. Genetic Correlation Profile of Schizophrenia Mirrors Epidemiological Results and Suggests Link Between Polygenic and Rare Variant (22q11.2) Cases of Schizophrenia. Schizophr Bull. 2018;44(6):1350–61. 10.1093/schbul/sbx174 . Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8. 10.1038/s41588-018-0099-7 . Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW. Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet. 2013;14(7):483–95. 10.1038/nrg3461 . Hackinger S, Zeggini E. Statistical methods to detect pleiotropy in human complex traits. Open Biol. 2017;7(11):170125. 10.1098/rsob.170125 . Chung D, Yang C, Li C, Gelernter J, Zhao H. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet. 2014;10(11):e1004787. 10.1371/journal.pgen.1004787 . Bulik-Sullivan BK, Loh PR, Finucane HK, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291–5. 10.1038/ng.3211 . Ning Z, Pawitan Y, Shen X. High-definition likelihood inference of genetic correlations across human complex traits. Nat Genet. 2020;52(8):859–64. 10.1038/s41588-020-0653-y . Ray D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer. PLoS Genet. 2020;16(12):e1009218. 10.1371/journal.pgen.1009218 . Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50. 10.1073/pnas.0506580102 . Hormozdiari F, van de Bunt M, Segrè AV, et al. Colocalization of GWAS and eQTL Signals Detects Target Genes. Am J Hum Genet. 2016;99(6):1245–60. 10.1016/j.ajhg.2016.10.003 . Foley CN, Staley JR, Breen PG, et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. Nat Commun. 2021;12(1):764. 10.1038/s41467-020-20885-8 . Boer CG, Hatzikotoulas K, Southam L, et al. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell. 2021;184(24):6003–5. 10.1016/j.cell.2021.11.003 . Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75. 10.1086/519795 . Burgess S, Thompson SG, CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755–64. 10.1093/ije/dyr036 . Thompson SG, Sharp SJ. Explaining heterogeneity in meta-analysis: a comparison of methods. Stat Med. 1999;18(20):2693–708. 10.1002/(sici)1097-0258(19991030)18:20%3C2693::aid-sim235%3E3.0.co;2-v . Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR–Egger method. Eur J Epidemiol. 2017;32(5):377–89. 10.1007/s10654-017-0255-x . Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734–9. 10.1093/ije/dyx034 . Mills KHG. IL-17 and IL-17-producing cells in protection versus pathology. Nat Rev Immunol. 2023;23(1):38–54. 10.1038/s41577-022-00746-9 . Duerr RH, Taylor KD, Brant SR, et al. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science. 2006;314(5804):1461–3. 10.1126/science.1135245 . Qian Y, Liu C, Hartupee J, et al. The adaptor Act1 is required for interleukin 17-dependent signaling associated with autoimmune and inflammatory disease. Nat Immunol. 2007;8(3):247–56. 10.1038/ni1439 . Lee JS, Tato CM, Joyce-Shaikh B, et al. Interleukin-23-Independent IL-17 Production Regulates Intestinal Epithelial Permeability. Immunity. 2015;43(4):727–38. 10.1016/j.immuni.2015.09.003 . Wang Y, Li W, Zhao T, et al. Interleukin-17-Producing CD4 + T Cells Promote Inflammatory Response and Foster Disease Progression in Hyperlipidemic Patients and Atherosclerotic Mice. Front Cardiovasc Med. 2021;8:667768. 10.3389/fcvm.2021.667768 . Gil-Pulido J, Amézaga N, Jorgacevic I, et al. Interleukin-23 receptor expressing γδ T cells locally promote early atherosclerotic lesion formation and plaque necrosis in mice. Cardiovasc Res. 2022;118(14):2932–45. 10.1093/cvr/cvab359 . Dong X, Zhang X, Li C, et al. γδ T cells aggravate blood–brain-barrier injury via IL-17A in experimental ischemic stroke. Neurosci Lett. 2022;776:136563. 10.1016/j.neulet.2022.136563 . Tian J, Bai Y, You A, et al. Interleukin-17 receptor C gene polymorphism reduces treatment effect and promotes poor prognosis of ischemic stroke. Biosci Rep. 2019;39(10):BSR20190435. 10.1042/BSR20190435 . Lu L, Wang Y, Zhou L, et al. Vγ4 T-cell-derived IL-17A is essential for amplification of inflammatory cascades in ischemic brain tissue after stroke. Int Immunopharmacol. 2021;96:107678. 10.1016/j.intimp.2021.107678 . Robert M, Miossec P. Effects of Interleukin 17 on the cardiovascular system. Autoimmun Rev. 2017;16(9):984–91. 10.1016/j.autrev.2017.07.009 . Saigusa R, Winkels H, Ley K. T-cell subsets and functions in atherosclerosis. Nat Rev Cardiol. 2020;17(7):387–401. 10.1038/s41569-020-0352-5 . McGeachy MJ, Cua DJ, Gaffen SL. The IL-17 Family of Cytokines in Health and Disease. Immunity. 2019;50(4):892–906. 10.1016/j.immuni.2019.03.021 . Hirota K, Duarte JH, Veldhoen M, et al. Fate mapping of IL-17-producing T cells in inflammatory responses. Nat Immunol. 2011;12(3):255–63. 10.1038/ni.1993 . Marks BR, Nowyhed HN, Choi JY, et al. Thymic self-reactivity selects natural interleukin 17-producing T cells that can regulate peripheral inflammation. Nat Immunol. 2009;10(10):1125–32. 10.1038/ni.1783 . Zhou L, Ivanov II, Spolski R, et al. IL-6 programs T(H)-17 cell differentiation by promoting sequential engagement of the IL-21 and IL-23 pathways. Nat Immunol. 2007;8(9):967–74. 10.1038/ni1488 . Zielinski CE, Mele F, Aschenbrenner D, et al. Pathogen-induced human TH17 cells produce IFN-γ or IL-10 and are regulated by IL-1β. Nature. 2012;484(7395):514–8. 10.1038/nature10957 . Taleb S, Tedgui A, Mallat Z. IL-17 and Th17 cells in atherosclerosis: subtle and contextual roles. Arterioscler Thromb Vasc Biol. 2015;35(2):258–64. 10.1161/ATVBAHA.114.303567 . Ma Y, Zhang X, Xuan B, et al. Disruption of CerS6-mediated sphingolipid metabolism by FTO deficiency aggravates ulcerative colitis. Gut. 2024;73(2):268–81. 10.1136/gutjnl-2023-330009 . Dong G, Yang Y, Zhang H, et al. Protein Kinase CK2 Maintains Reciprocal Balance Between Th17 and Treg Cells in the Pathogenesis of UC. Inflamm Bowel Dis. 2022;28(6):830–42. 10.1093/ibd/izab312 . Fernandez DM, Rahman AH, Fernandez NF, et al. Single-cell immune landscape of human atherosclerotic plaques. Nat Med. 2019;25(10):1576–88. 10.1038/s41591-019-0590-4 . Hashmi S, Zeng QT. Role of interleukin-17 and interleukin-17-induced cytokines interleukin-6 and interleukin-8 in unstable coronary artery disease. Coron Artery Dis. 2006;17(8):699–706. 10.1097/01.mca.0000236288.94553.b4 . Cheng X, Yu X, Ding YJ, et al. The Th17/Treg imbalance in patients with acute coronary syndrome. Clin Immunol Orlando Fla. 2008;127(1):89–97. 10.1016/j.clim.2008.01.009 . Bu F, Ding Y, Chen T, et al. Total flavone of Abelmoschus Manihot improves colitis by promoting the growth of Akkermansia in mice. Sci Rep. 2021;11(1):20787. 10.1038/s41598-021-00070-7 . Xu J, Yang Y. Gut microbiome and its meta-omics perspectives: profound implications for cardiovascular diseases. Gut Microbes. 2021;13(1):1936379. 10.1080/19490976.2021.1936379 . Masenga SK, Kirabo A. Salt and Gut Microbiota in Heart Failure. Curr Hypertens Rep. 2023;25(8):173–84. 10.1007/s11906-023-01245-5 . Additional Declarations No competing interests reported. Supplementary Files FigureSupp.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8960093","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596760434,"identity":"ec62e25c-22dc-4a57-9e7e-82dd57bcb290","order_by":0,"name":"Zhengxi Qiu","email":"","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhengxi","middleName":"","lastName":"Qiu","suffix":""},{"id":596760435,"identity":"8ea019a7-5d39-4f2b-91df-8155285f9305","order_by":1,"name":"Zhiyuan Li","email":"","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhiyuan","middleName":"","lastName":"Li","suffix":""},{"id":596760436,"identity":"18130aea-bcdf-4dc7-aa66-562a7e1c31c1","order_by":2,"name":"Yirong Wu","email":"","orcid":"","institution":"Affiliated Hangzhou First People’s Hospital, Westlake University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yirong","middleName":"","lastName":"Wu","suffix":""},{"id":596760437,"identity":"7bb9c409-7e8f-4b0a-92c0-1e5c07842229","order_by":3,"name":"Jinchen Chong","email":"","orcid":"","institution":"Jiangsu Province Key Laboratory of Tumor Systems Biology and Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jinchen","middleName":"","lastName":"Chong","suffix":""},{"id":596760438,"identity":"064e662b-d912-4ce5-b16b-856d5b3990c8","order_by":4,"name":"Chen Chen","email":"","orcid":"","institution":"Jiangsu Province Key Laboratory of Tumor Systems Biology and Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Chen","suffix":""},{"id":596760439,"identity":"346e139a-cf41-4e6c-8e8b-7b447081c78f","order_by":5,"name":"Jiepin Li","email":"","orcid":"","institution":"Jiangsu Province Key Laboratory of Tumor Systems Biology and Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiepin","middleName":"","lastName":"Li","suffix":""},{"id":596760440,"identity":"373d05db-b66a-4ec4-b41c-44534d279617","order_by":6,"name":"Yirong Yang","email":"","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yirong","middleName":"","lastName":"Yang","suffix":""},{"id":596760442,"identity":"fc95b58d-3ac8-4dc5-b690-9cffb6172652","order_by":7,"name":"Zhihua Lu","email":"","orcid":"","institution":"Jiangsu Province Key Laboratory of Tumor Systems Biology and Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhihua","middleName":"","lastName":"Lu","suffix":""},{"id":596760443,"identity":"7c7d0151-ce6e-436c-bdc7-0456f07d8a1b","order_by":8,"name":"Xiaomin Yuan","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaomin","middleName":"","lastName":"Yuan","suffix":""},{"id":596760444,"identity":"f2570c13-db34-4e00-95f0-bd0fac12f185","order_by":9,"name":"Yijia Zhu","email":"","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yijia","middleName":"","lastName":"Zhu","suffix":""},{"id":596760446,"identity":"301939f8-5dad-4b2a-8fde-68b95c2ea7fe","order_by":10,"name":"Tuo Chen","email":"","orcid":"","institution":"Affiliated Hospital of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Tuo","middleName":"","lastName":"Chen","suffix":""},{"id":596760447,"identity":"3579e152-4673-4484-b68c-61f1060c8da1","order_by":11,"name":"Jiaze Ma","email":"","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiaze","middleName":"","lastName":"Ma","suffix":""},{"id":596760449,"identity":"dad35bdc-83fc-47f5-8119-477ab078c6c2","order_by":12,"name":"Tingsheng Ling","email":"","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tingsheng","middleName":"","lastName":"Ling","suffix":""},{"id":596760450,"identity":"59a6870b-e22b-403f-92e7-8453b574cf67","order_by":13,"name":"Yugen Chen","email":"","orcid":"","institution":"Jiangsu Province Key Laboratory of Tumor Systems Biology and Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yugen","middleName":"","lastName":"Chen","suffix":""},{"id":596760451,"identity":"5747e815-c3c4-45e9-9575-08b850660766","order_by":14,"name":"Yang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYDAC5gMMBz78sGFgbIBwidDClsB4cGZPGmlamA9zsB2GW0pYh8ExHoPDDDzn85hnJD/dwFBhndjAfvYAYS0FFreLGWekmd1gOJOe2MCTl4Bfy/0eg8MzeG4nNs7IYbvB2HY4sUGCx4CwLTxs56Ba/hGv5QBUSwMRWiSPsRUAAzk5sbHnmdmNhGPpxm08Ofi18B1j3vzhww+7xI3tyc9ufKixlu1nP4Nfi8IBDogCwwYgkQDEbHjVA4F8A/sDCIOQylEwCkbBKBi5AACkpk6YNVrppAAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine, The Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-02-24 17:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8960093/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8960093/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103775258,"identity":"cf7c266f-749e-411d-8297-3d3b5b98cdf2","added_by":"auto","created_at":"2026-03-02 18:25:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168351,"visible":true,"origin":"","legend":"\u003cp\u003eLDSC genetic correlation analysis revealed significant genetic correlations between gastrointestinal tract diseases and cardiovascular diseases, and HDL analysis verified the stability of most results. Further multiple test correction using Bonferroni (P \u0026lt; 0.05/77 = 6.5E-4) showed that there were still significant associations between many disease pairs. The specific data are shown in Table S2.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8960093/v1/45c018ae2b853cc11153f32b.jpg"},{"id":103775296,"identity":"52367db6-fa9c-412a-aae2-f995928c6018","added_by":"auto","created_at":"2026-03-02 18:25:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":210077,"visible":true,"origin":"","legend":"\u003cp\u003eA. Co-localization analysis identified 95 shared causal loci (PP.H4 \u0026gt; 0.8) among disease pairs. B. The enrichment of different tissues in specific diseases or biomarkers reveals significant associations between multiple diseases and tissues, with detailed information provided in Supplementary Table S6.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8960093/v1/c9b2a2d4edc441213a2021e4.jpg"},{"id":103775221,"identity":"d0afc37e-8b53-43bd-9523-23401f5dff39","added_by":"auto","created_at":"2026-03-02 18:24:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":308442,"visible":true,"origin":"","legend":"\u003cp\u003eA. Gene overlap across different methods, with detailed information on these genes available in Supplementary Table S8; B. Expression profiles of pleiotropy-associated genes—identified via overlapping nearby genes, Magma-derived genes, and eQTL-related genes—in 54 distinct tissues; C. Tissue-specific enrichment analysis reveals that genes mapped to these loci are enriched in tissues including the heart, pancreas, and brain.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8960093/v1/42aa1e7ab7948b24dc073f84.jpg"},{"id":103775246,"identity":"088d4270-03eb-4708-af04-625a36e26f0c","added_by":"auto","created_at":"2026-03-02 18:24:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":246009,"visible":true,"origin":"","legend":"\u003cp\u003ePathway enrichment analysis of pleiotropic genes, with detailed information provided in Supplementary Table S9; the protein-protein interaction (PPI) network analysis of pleiotropic genes is shown in Supplementary Figure S14.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8960093/v1/64fdc58d6c19fb11c5f414a3.jpg"},{"id":103775242,"identity":"24ce4ce7-2161-4fd2-9c88-7bf2e0fede72","added_by":"auto","created_at":"2026-03-02 18:24:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":643776,"visible":true,"origin":"","legend":"\u003cp\u003eImmune cell atlas displaying co-localization signals across different phenotype pairs. Significant pleiotropic associations exist between gastrointestinal diseases (e.g., GORD, IBD) and cardiovascular diseases (e.g., PUD, CD8\u003csup\u003e+\u003c/sup\u003eAF), indicating that these diseases may interact reciprocally under the influence of distinct immune cells and tissues, sharing common immune response mechanisms.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8960093/v1/1517b257956e2132d1669aca.jpg"},{"id":104400135,"identity":"16fc34d1-bc57-4c16-ac02-46245ff9ac8d","added_by":"auto","created_at":"2026-03-11 12:08:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2966438,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8960093/v1/febec01e-9bcc-4701-aa6e-5fc9b4aac43c.pdf"},{"id":103775253,"identity":"da4cb9e8-0e3a-4552-ab1f-5ba698294363","added_by":"auto","created_at":"2026-03-02 18:24:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2214705,"visible":true,"origin":"","legend":"","description":"","filename":"FigureSupp.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8960093/v1/9e5b549139bd6622efa38a34.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-Wide Pleiotropic Analyses Characterize the Genetic Etiology Shared between Cardiovascular and Gastrointestinal Tract Diseases","fulltext":[{"header":"Background","content":"\u003cp\u003eCardiovascular diseases (CVDs) and gastrointestinal tract diseases (GTDs) frequently cooccur, imposing greater health burdens than either condition does in isolation. Clinically, patients with GTDs such as inflammatory bowel disease (IBD) exhibit a significantly elevated risk of developing CVDs\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Individuals with CVDs face an increased risk of developing GTDs, including gastrointestinal ulcers, gastroesophageal reflux disease (GORD), and irritable bowel syndrome (IBS)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These observations suggest a potential bidirectional relationship between CVDs and GTDs. Elucidating the high prevalence of CVDs in GTDs represents a critical research priority. Emerging evidence highlights inflammation and gut microbiota dysregulation as shared pathophysiological drivers of both cardiac and gastrointestinal disorders\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. For example, intestinal inflammation in IBD patients, characterized by elevated levels of cytokines, C-reactive protein (CRP), and homocysteine, induces endothelial dysfunction, an early hallmark of atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Moreover, lipid profile alterations in IBD closely mirror those observed in atherosclerotic patients\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Hypertension, atherosclerosis, and other CVDs, alongside GTDs such as IBD and IBS, are associated with alterations in the gut microbial composition\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Deciphering the shared mechanisms between these diseases could facilitate early diagnosis of co-occurring health events in patients and identify convergent therapeutic targets for novel interventions.\u003c/p\u003e \u003cp\u003eGenome-wide association study (GWAS) involve diverse genetic variants, including single nucleotide variants (SNVs)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Methods such as linkage disequilibrium (LD) score regression (LDSC) have been applied to investigate genetic correlations between psychiatric disorders, lung diseases, CVDs, liver diseases and many disease\u003csup\u003e9 10 11\u003c/sup\u003e. These studies, conducted by consortia such as the Inflammation Working Group of the CHARGE Consortium and the METASTROKE Consortium of the International Stroke Genetics Consortium, highlight the utility of genetic correlation analyses in uncovering shared biological pathways across diverse phenotypes. Increasing evidence supports a potential association between CVDs and GTDs clinically. However, systematic exploration of their common genetic etiology\u0026mdash;specifically, identifying genome-wide significant genetic variants or loci underlying their shared genetic correlation\u0026mdash;remains under explored. A shared genetic basis may reflect pleiotropy, a critical confounding factor in trait‒pair associations that can obscure causal inference\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Cross-trait analyses have been developed to identify pleiotropic genetic variants or loci across multiple traits by leveraging correlations in GWAS signals\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. These analyses enable the identification of pleiotropic loci as intervention targets, potentially contributing to the prevention or treatment of these diseases.\u003c/p\u003e \u003cp\u003eIn this genome-wide pleiotropic analyses study, large-scale GWAS summary data were leveraged, and multiple statistical genetics approaches were employed to perform a comprehensive genome-wide pairwise pleiotropic analysis across CVDs and GTDs. CVDs included abdominal aortic aneurysm (ABAORTANEUR), atrial fibrillation and flutter (AF), aortic aneurysm (AORTANEUR), aortic dissection (AORTDIS), calcific aortic valvular stenosis (CAVS_OPERATED), major coronary heart disease (CHD), heart failure (HEARTFAIL), essential hypertension (HYPTENSESS), other peripheral vascular diseases (OTHPER), stroke (STR), and thoracic aortic aneurysm (THAORTANEUR). GTDs included common gastrointestinal tract diseases, such as inflammatory bowel disease (IBD), Crohn\u0026rsquo;s disease (CD), ulcerative colitis (UC), peptic ulcer disease (PUD), gastroesophageal reflux disease (GORD), and irritable bowel syndrome (IBS), and a combined phenotype of PUD, GORD, and related medications (PGM). This study aimed to systematically investigate the pleiotropic associations between CVDs and GTDs at the whole-genome, single-nucleotide variants (SNVs), gene, and biological pathway levels, with the goal of disentangling their shared genetic etiology. Specifically, LD score regression (LDSC) and hierarchical decomposition of linkage disequilibrium (HDL) were used to quantify genetic correlations, applied the PLACO method to identify pleiotropic loci and genes, conducted multitissue transcriptome-wide association studies (TWAS) and expression quantitative trait locus (eQTL) analyses to pinpoint common pathogenic genes and signaling pathways, utilized stratified LD score regression (SLDSC) and HyPrColoc to explore shared immune and gut microbiota mechanisms, and employed two-sample bidirectional Mendelian randomization (MR) to evaluate causal relationships between the disease classes.\u003c/p\u003e "},{"header":"Methods","content":"\n\u003ch3\u003e1. GWAS datasets\u003c/h3\u003e\n\u003cp\u003eA total of 11 CVDs and 7 GTDs were selected from publicly available GWAS summary statistics in European-ancestry populations. This selection was motivated by the limited availability of adequately powered GWAS datasets for other ancestries. To ensure sufficient statistical power, only GWASs with sample sizes exceeding 20,000 were included. Data were collected until August 25, 2021. The analysis included 286,935 CVD cases with 3,931,419 controls and 239,815 GTD cases with 1,730,685 controls. Detailed information on the total number of participants, cases, and controls is provided in Supplement Table S1. This genome-wide pleiotropic association study adhered to the Strengthening the Reporting of Genetic Association Studies (STREGA) guidelines.\u003c/p\u003e\n\u003ch3\u003e2. Data quality control\u003c/h3\u003e\n\u003cp\u003eTo ensure data reliability and consistency, rigorous quality control measures were implemented as follows: 1) Exclusion of nonbiallelic and ambiguous SNPs: Nonstandard SNPs, including multiallelic variants and those with indistinguishable alleles, were systematically excluded from the analysis. 2) Removal of SNPs without rs tags: SNPs lacking standard rsID tags were excluded to ensure that each variant had a definitive identifier. 3) Filtering of duplicate and mismatched SNPs: Duplicate SNPs, as well as those absent from the 1000 Genomes Project reference panel or with allele information inconsistent with reference data, were removed. 4) Exclusion of major histocompatibility complex(MHC) region SNPs: Due to the complex LD structure, SNPs within the MHC region on chromosome 6 (chr6: 28.5-33.5Mb) were excluded.5) Retention of SNPs with an minor allele frequency(MAF)\u0026thinsp;\u0026gt;\u0026thinsp;0.01: SNPs with a MAF greater than 0.01 were retained to minimize statistical bias from low-frequency variants.\u003c/p\u003e \u003cp\u003eGiven that the GTDs and CVDs data originated from distinct research consortia, sample overlap was negligible, thereby reducing confounding effects from sample crossover. For each remaining SNPs, key statistics\u0026mdash;including effect size, standard error, effect allele, and \u003cem\u003eP\u003c/em\u003e value\u0026mdash;were retained for subsequent analyses.\u003c/p\u003e\n\u003ch3\u003e3. Genetic correlation analysis\u003c/h3\u003e\n\u003cp\u003eGenetic correlation analyses across traits were performed via two distinct methodologies: LDSC\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e (PMID: 25642630) and hierarchical decomposition of linkage disequilibrium (HDL)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e (PMID: 32601477). These approaches aim to quantify genetic associations between trait pairs and characterize their shared polygenic architecture. For LDSC, European ancestry samples from the 1000 Genomes Project were used as a reference panel to calculate LD scores for each variant locus. LD scores quantify the extent to which genetic variation in one genomic region can be explained by neighboring regions, serving as a key metric for assessing polygenic trait structure. Genetic correlations were estimated while accounting for confounding factors such as population stratification and shared genetic variation between unrelated traits. To further mitigate biases, an unconstrained LDSC analysis was performed, allowing free estimation of the intercept to identify and adjust for residual confounding from a limited sample size, environmental factors, or population structure. This approach also facilitates the detection of potential sample overlap between GWAS datasets, as a significantly elevated intercept (\u0026gt;\u0026thinsp;1) may indicate overlapping samples and bias in genetic correlation estimates. For HDL, high-resolution likelihood estimation was employed to refine genetic correlation estimates, enhancing the precision of shared polygenic pattern detection across traits.\u003c/p\u003e\n\u003ch3\u003e4. SNPs-level pleiotropic analysis under the composite null hypothesis (PLACO)\u003c/h3\u003e\n\u003cp\u003eSNPs-level PLACO is an innovative analytical framework for identifying pleiotropic loci associated with complex traits, relying solely on summary-level genotype‒phenotype association statistics\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This method utilizes the squared Z scores (Z\u0026sup2;) of individual genetic variants to assess pleiotropic effects across multiple traits. First, Z\u0026sup2; values were calculated for each variant, and SNPs with extreme Z\u0026sup2; values (\u0026gt;\u0026thinsp;80) were excluded to mitigate analytical bias from strong association signals or data anomalies. Given the potential genetic correlation between gastrointestinal and cardiovascular diseases, a Z\u0026sup2; correlation matrix was estimated for each variant. This matrix captures the correlation in effect directions and magnitudes across traits at each genomic locus, enabling the identification of shared genetic bases between the two disease categories. The intersection-union test (IUT) was subsequently applied to perform pleiotropy tests for each variant within a multiple hypothesis testing framework. The null hypothesis (H\u003csub\u003e0\u003c/sub\u003e) assumes that the variant has no effect on either trait or influences only one trait, whereas the alternative hypothesis (H\u003csub\u003e1\u003c/sub\u003e) posits simultaneous influence on both traits. The final IUT \u003cem\u003eP\u003c/em\u003e value is defined as the maximum \u003cem\u003eP\u003c/em\u003e value from all tests of H\u003csub\u003e0\u003c/sub\u003e and H\u003csub\u003e1\u003c/sub\u003e, controlling for false positive rates due to multiple testing. This approach balances power and specificity in detecting pleiotropic effects under stringent multiple-testing conditions.\u003c/p\u003e\n\u003ch3\u003e5. Multi-marker Analysis of Genomic Annotation (MAGMA)\u003c/h3\u003e\n\u003cp\u003eBuilding upon the PLACO results, the identified loci were mapped to nearby genes to elucidate the shared biological mechanisms underlying pleiotropic sites. MAGMA was applied to genes at or overlapping with pleiotropic sites derived from the PLACO output and single-trait GWAS, aiming to identify candidate pleiotropic genes. The significance threshold for MAGMA was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05/\u003cem\u003eN\u003c/em\u003e_genes\u0026thinsp;=\u0026thinsp;3\u0026times;10⁻⁶. Functional mapping and annotation of genetic associations were used to characterize the biological functions of these loci\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Additionally, pathway enrichment analyses were performed via the Molecular Signatures Database (MSigDB) to functionally annotate the mapped genes. SNPs and risk loci within the MHC region were excluded from both the PLACO and MAGMA gene and gene-set analyses.\u003c/p\u003e\n\u003ch3\u003e6. Colocalization analysis\u003c/h3\u003e\n\u003cp\u003eFor pleiotropic loci annotated by FUMA, Bayesian colocalization analysis was performed via the R package Coloc\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e (PMID: 27866706) to evaluate the probability of shared causal variants between trait pairs. This approach assumes a single causal variant (SCV) within each gene region, suggesting that both traits are influenced by the same genetic variant. Colocalization analysis computes posterior probabilities (PPs) for five hypotheses: H0: Neither trait has a genetic association in the region; H1: Only trait 1 is associated; H2: Only trait 2 is associated; H3: Both traits are associated but via distinct causal variants; H4: Both traits are associated and share a common causal variant. The coloc.abf function was used with explicitly specified prior probabilities for (i) each trait having a causal variant and (ii) the two traits sharing a causal variant. This systematic assessment of pleiotropic loci across traits provides evidence for potential shared biological mechanisms.\u003c/p\u003e\n\u003ch3\u003e7. Hypothesis prioritization for multitrait colocalization analysis (Hyprcoloc)\u003c/h3\u003e\n\u003cp\u003eHyPrColoc\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e (PMID:33536417), combined with multitrait colocalization analysis of immune GWAS data, has revealed the potential pivotal role of immune cells in the pathogenesis of CVDs and GTDs. This approach prioritizes gene loci with shared genetic variants across multiple traits, thereby uncovering genetic associations among diverse phenotypes. By applying HyPrColoc, GWAS data from various immune traits and target diseases (CVDs and GTDs) can be efficiently integrated to identify gene loci with significant colocalization signals.\u003c/p\u003e \u003cp\u003eGWAS summary statistics for gut microbiota traits (as exposure data) were downloaded from the MiBioGen consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mibiogen.org/\u003c/span\u003e\u003cspan address=\"http://www.mibiogen.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e22\u003c/sup\u003e. The gut microbiota was categorized into 211 taxonomic units, including 131 genera, 35 families, 20 orders, 16 classes, and 9 phyla. Microbiota data from 18,340 individuals across 24 cohorts were analyzed, comprising 5,717,754 SNPs derived from 16S fecal genotyping. Genotype imputation was performed via the HRC 1.0 or 1.1 reference panel. Subsequent association analyses employed Spearman correlation, adjusting for covariates, including age, sex, technical factors, and genetic principal components.\u003c/p\u003e\n\u003ch3\u003e8. MR study\u003c/h3\u003e\n\u003cp\u003eAll significant genetic loci independently associated with exposure were selected as instrumental variables (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10⁻⁸) via the clumping function in PLINK software (version 1.9)\u003csup\u003e23\u003c/sup\u003e. The r\u0026sup2; threshold for defining linkage disequilibrium between instrumental variables was set at 0.001, and the genomic window size was defined as 10,000kb. To ensure the robustness and strength of the selected instrumental variables, we further calculated the r\u0026sup2; values and F statistics\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e for each variable. The formula for calculating the F value is as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(F=(\\frac{{n - 1 - k}}{k})(\\frac{{{r^2}}}{{1 - {r^2}}})\\)\u003c/span\u003e \u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(F=(\\frac{{n - 1 - k}}{k})(\\frac{{{r^2}}}{{1 - {r^2}}})\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eIn MR analyses, R\u003csup\u003e2\u003c/sup\u003e denotes the proportion of variance explained by instrumental variables (IVs), n represents the sample size, and k indicates the number of SNPs. The inverse variance weighted (IVW) method is the primary approach in MR, requiring IVs to satisfy three core assumptions: (1) the IV must be associated with the exposure; (2) the IV should not be correlated with confounding factors related to both the exposure and the outcome; and (3) the effect of the IV on the outcome must be entirely mediated through the exposure. The sensitivity analyses included the following. First, the Q test for IVW and MR‒Egger can detect potential violations of assumption\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e by assessing the heterogeneity of associations among individual instrumental variables (IVs). Second, MR‒Egger is employed to estimate horizontal pleiotropy through its intercept term, ensuring that genetic variants are independently associated with exposure and outcome as per assumption\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Additionally, supplementary analyses using alternative MR methods, such as the weighted median and weighted mode, which incorporate distinct modeling assumptions and advantages, further enhance the stability and robustness of the findings. Statistical analyses were conducted in R v3.5.3 via the Mendelian randomization package\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003e9. Ethics\u003c/h3\u003e\n\u003cp\u003eThis study was designed and conducted in accordance with ethical guidelines for human subject research and adheres to the principles of the Declaration of Helsinki. Data were obtained through an approved UK Biobank (UKB) project application (ID: 92675). UKB has received ethical approval from the National Health Service North West Centre for Research Ethics Committee (Ref: 21/NW/0157), and informed consent has been obtained from all participants.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003e1. Genetic correlations between CVDs and GTDs\u003c/h3\u003e\n\u003cp\u003eLDSC genetic correlation analysis revealed significant genetic correlations between CVDs and GTDs. Sensitivity analyses using HDL traits further validated the robustness of most findings. To account for multiple testing, the Bonferroni correction was applied (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05/77\u0026thinsp;=\u0026thinsp;6.5\u0026times;10⁻⁴), and numerous disease pairs retained statistically significant associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplement Table S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e2. Shared loci and genes between CVDs and GTDs\u003c/h3\u003e\n\u003cp\u003ePleiotropy analysis by PLACO was conducted on disease pairs with significant correlations, resulting in the identification of a total of 350 pleiotropic loci. The identified pleiotropic loci and their corresponding basic information for each genomic risk locus are summarized in Supplement Table S3. The functional impacts of pleiotropic SNPs on genes are detailed in Supplement Table S4. Additionally, Manhattan and quantile‒quantile plots illustrating the relationships between diseases are provided in Supplement Figures S1-S12.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eColocalization analysis revealed 95 shared causal loci (PP.H4\u0026thinsp;\u0026gt;\u0026thinsp;0.8) across the investigated diseases, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. MAGMA gene set analysis for CVDs and GTDs revealed multiple associated gene pathways (Supplement Figure S13 and Table S5). Key findings include the following: 1) Biological processes linked to diseases, such as muscle contraction, cell signaling, and macro-molecule synthesis, are significantly associated. 2) Disease-specific pathways: GORD: Strong associations with muscle structure development, positive regulation of cholesterol storage, and transcriptional regulation (\u003cem\u003eP\u003c/em\u003e values: 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e-10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). AF: Enriched pathways related to cardiac conduction, circulatory system processes, and vascular processes. IBD and HYPTENSESS: Close links to immune/inflammatory pathways (e.g., T-cell activation, response to oxidized compounds), implicating immune mechanisms in disease pathogenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverlapping pleiotropic loci and colocalization analysis of gastrointestinal diseases and different cardiovascular diseases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait pairs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenomic\u003c/p\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeadSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNearby genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePP H4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10q11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4240499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.74E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVSTM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10q21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7922082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.01E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-436D10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10q22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.46E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNDST2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11p13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers11604176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.98E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDCDC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11p14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers921880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.55E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDCDC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11p15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers11040804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.59E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC11orf42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11p15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10500661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.04E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCNGA4, CCKBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUD \u0026amp; CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11p15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers75613328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTALDO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12p12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers12297543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.97E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePTPRO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12q13.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers736825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-834C11.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; HEARTFAIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12q13.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers12426399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.58E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-834C11.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12q13.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers11450619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.03E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACVRL1, ACVR1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12q13.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2077177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.44E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-834C11.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; STR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12q13.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4759318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.88E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-834C11.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12q15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10878972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.82E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYEATS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12q15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers12366463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.77E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-1143G9.2, LYZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12q24.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers653178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eATXN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12q24.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers653178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eATXN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12q24.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers233724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRPH3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12q24.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers233724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.80E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRPH3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13q34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7333991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOL4A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13q34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers9521787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOL4A2-AS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14q32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1461584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.39E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-61O1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14q32.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1136165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.52E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14q32.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7157631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.12E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMARK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15q22.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers56062135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.28E-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSMAD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15q26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4932373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; HEARTFAIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16p11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers34838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCLN3, CLN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16q24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers258319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.82E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSPATA33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17q12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers12603332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.52E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eORMDL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGORD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17q21.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1053739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.04E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17q21.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1053739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.78E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGORD \u0026amp; HEARTFAIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17q21.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers221606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCDC27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGORD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17q21.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4968309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.63E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMYL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17q21.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers6504743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.54E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCDC27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17q25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers11658134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.15E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCYTH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18q21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers79544247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.41E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSMAD7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19p13.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers34068189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePGPEP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19q13.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers62126611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.60E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSLC7A10, CTD-2540B15.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19q13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2115239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.08E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADCK4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19q13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2115239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADCK4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1p13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2476601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.79E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePTPN22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1p32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers72664358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.23E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPAP2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1p36.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers6660242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.07E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCASZ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1q21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10305667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.28E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eARNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1q22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers760077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.24E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMTX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBS \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1q25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2175177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.98E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-195C7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20p12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers6040076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.58E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-103J8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20q12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers17181845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.41E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZHX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20q12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers17181845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZHX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20q12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers6065325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZHX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20q12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers6065325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.62E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZHX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20q13.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4810485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.70E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCD40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20q13.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2427541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.98E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAL158091.1, ABHD16B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20q13.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2427539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.93E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAL158091.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2p21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers6747202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.53E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTHADA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2p21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7589033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.76E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTHADA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2p21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers12477432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTHADA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2p23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1260326;rs11677002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.12E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAC104695.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2p23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1260326;rs4666067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.65E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAC104695.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBS \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2p23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers11608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.79E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSLC5A6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2p23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7578575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.96E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNMT3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2p23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7578575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDNMT3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; ABAORTANEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2p24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10193919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.72E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAC012065.7, C2orf43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; AORTANEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2p24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10193919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.60E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAC012065.7, C2orf43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2q32.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2595391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAC017101.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2q33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1217429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.36E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC2orf47, SPATS2L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3p14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers6786528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.46E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFRMD4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGORD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3p21.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers143171929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.36E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-694I15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3p21.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers76367790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.19E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRBM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3p22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers71323670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.93E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSCN5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; STR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3p25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers62248311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.32E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVGLL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3q23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2871960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZBTB38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4q24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2711894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.86E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCENPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4q24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4698861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNFKB1, MANBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBS \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4q25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers149054443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.49E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRNU6-289P, RP11-255I10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; ABAORTANEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5p15.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers61580655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.39E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMIR4456, RP11-310P5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; AORTANEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5p15.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers61384641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.55E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMIR4456, RP11-310P5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5q13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4703855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.33E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-389C8.1, YBX1P5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5q31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers3792894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.14E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP4HA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGORD \u0026amp; CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5q31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7356639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.56E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRN7SKP246, CTC-342P15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5q31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7356639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.23E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRN7SKP246, CTC-342P15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5q35.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers75049939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.64E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCPEB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6p22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers198846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHIST1H1T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6q22.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers10457481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.75E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMIR588, RNU6-200P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7p15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4722672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.54E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP1-170O19.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGM \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7p22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4721096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27E-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMAD1L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7p22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers798508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.66E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGNA12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; HYPTENSESS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7q11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2529275;rs236660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.02E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTAG3L2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7q36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers3918226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNOS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; STR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8p23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers6601535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.80E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSOX7, PINX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD \u0026amp; CHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8q24.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2980862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.85E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-136O12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUD \u0026amp; AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9q34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers115478735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.63E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eABO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePUD \u0026amp; HEARTFAIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9q34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers115478735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.76E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eABO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTissue-specific enrichment patterns across diseases and biomarkers are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and Supplement Table S6, highlighting significant associations between multiple diseases and corresponding tissues. Notably, the small intestine terminal ileum and whole blood showed robust enrichment of numerous markers, suggesting that these tissues may play pivotal roles in the pathogenesis of related diseases, including IBD, AF, and HYPTENSESS. For example, IBD and HYPTENSESS exhibited substantial enrichment in these tissues, indicating their involvement in immune responses and metabolic dysregulation. Additionally, the spleen and whole blood were strongly enriched for markers associated with atrial fibrillation and diabetes, underscoring their critical roles in immune and cardiovascular health. Conversely, tissues such as Uterus, Esophagus muscularis, and others were enriched in digestive system diseases (e.g., peptic ulcers), highlighting their potential influence on gastrointestinal pathologies.\u003c/p\u003e\n\u003ch3\u003e3. Prioritization of candidate pleiotropic genes and characterization of phenotype and tissue specificity\u003c/h3\u003e\n\u003cp\u003eMAGMA gene-based analysis of the PLACO results revealed 337 pleiotropic genes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05/18,645\u0026thinsp;=\u0026thinsp;2.682\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e; Supplement Table S7). By integrating eQTL data from gastrointestinal, cardiovascular, and whole blood tissues to map pleiotropic genes, a total of 1,427 genes were identified. Figure\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e3\u003c/span\u003eA illustrates the overlap of genes across methods, with details in Supplement Table S8. Figure\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e3\u003c/span\u003eB shows genes identified by neighboring gene mapping, MAGMA, and eQTL analysis, along with their expression profiles across 54 tissues. Tissue-specific enrichment analysis revealed significant enrichment in heart, pancreas, and brain tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e4. Protein‒protein interaction (PPI) network analysis\u003c/h3\u003e\n\u003cp\u003ePathway enrichment analysis of pleiotropic genes is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplement Table S9, while protein‒protein interaction (PPI) network analysis is shown in Supplement Figure S14. Positive regulation of interleukin-17(IL-17) production is the most important pathway in both CVDs and GTDs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e5. Synaptic and immune-related mechanisms shared between CVDs and GTDs\u003c/h3\u003e\n\u003cp\u003eImmunological abnormalities are a common pathogenic cause of various diseases. Meanwhile, PPI network analysis demonstrated the key role of immunological factor-IL17. This study explored the common role of immunity in CVDs and GTDs. Significant pleiotropic associations were identified between GTDs (e.g., GERD and IBD) and CVDs (e.g., HYPTENSESS and AF). These findings suggest bidirectional interactions between these diseases, potentially mediated by shared immune cell populations and tissues, with common immune response mechanisms likely playing a role. Specifically, immune cell subsets such as CD8⁺ T cells, T follicular helper (Tfh) cells, regulatory B cells, and MTS15⁺ Th17 cells were significantly enriched in both disease categories, highlighting their critical roles in the pathogenesis of gastrointestinal and cardiovascular conditions. Additionally, cross-disease correlations were observed for multiple cell populations, including CD103⁺ CD11b⁺ dendritic cells (DCs), CD8⁺ splenic lymph node DCs (DC.8⁺SLNs), and γδ T cells (Tgd.vgd⁺24ah⁺17. Th), underscoring the pleiotropic nature of immune system involvement. The results are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig19\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e6. Multitrait colocalization analysis to pinpoint critical gut microbiota\u003c/h3\u003e\n\u003cp\u003eMultitrait colocalization analysis via HyPrColoc identified 6 pleiotropic loci harboring shared pathogenic microbiota, highlighting the significant role of the gut microbiota in disease pleiotropy (Supplement Table S10). For example, the Ruminococcus gauvreauii group was critically associated with CD/IBD and AF, whereas Firmicutes phylum members were significantly linked to PGM/PUD and HYPTENSESS.\u003c/p\u003e\n\u003ch3\u003e7. MR and Associations between CVDs and GTDs\u003c/h3\u003e\n\u003cp\u003eTwo-sample MR analysis revealed that significant causal associations were identified from CVDs to GTDs and vice versa (Supplement Figure S15).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically identified extensive genome-wide genetic associations and significant genetic overlap between CVDs and GTDs. Comprehensive analyses revealed pleiotropic genetic variants, shared causal loci, and converging biological pathways, alongside gut microbiota signatures, that underpin the shared pathogenesis of these disease categories. These findings provide robust evidence for a common genetic etiology linking CVDs and GTDs, with implications for their comorbidity and joint therapeutic targeting.\u003c/p\u003e \u003cp\u003eUsing PLACO, 350 pleiotropic loci were identified, 95 of which were demonstrated to have shared causal variants (PP.H4\u0026thinsp;\u0026gt;\u0026thinsp;0.8) via colocalization analysis. These loci include genes such as ZHX3 (20q12), ATXN2 (12q24.12), and THADA (2p21), which are independently linked to both CVDs (e.g., AF) and GTDs (e.g., IBD). Two-sample Mendelian randomization (MR) analysis further revealed bidirectional causal associations between CVDs and GTDs, which is consistent with vertical pleiotropy, where shared variants influence both traits through common biological pathways. LDSC-based sample overlap detection and LHC-MR adjustments confirmed the robustness of these findings, ruling out major confounding factors from overlapping datasets.\u003c/p\u003e \u003cp\u003eCentral to the shared pathogenesis are inflammatory and immune pathways, particularly those involving IL-17 and Th17 cells\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. MAGMA analysis identified IL-17 signaling as a key shared pathway, with Th17 cells serving as a critical immune cell subset. In GTDs, IL-17 exacerbates intestinal mucosal barrier dysfunction in ulcerative colitis and Crohn\u0026rsquo;s disease by increasing the levels of proinflammatory cytokines (e.g., IL-6 and CXCL1) and chemokines\u003csup\u003e29 31\u003c/sup\u003e. In CVDs, IL-17 promotes atherosclerotic plaque instability, endothelial dysfunction, and thrombosis, linking it to hypertension, myocardial infarction, and ischemic stroke\u003csup\u003e32 33 34\u0026ndash;37\u003c/sup\u003e. Clinical studies have shown elevated plasma IL-17 levels and Th17 cell counts in patients with acute coronary syndrome compared with stable angina patients or healthy controls\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, underscoring their role in disease progression. The plasticity of TH17 cells in diverse inflammatory micro-environments\u0026mdash;regulated by transcription factors such as RORγt and cytokines such as IL-23\u0026mdash;further bridges CVDs and GTDs\u003csup\u003e39 40\u0026ndash;44\u003c/sup\u003e. For example, IL-17-induced secretion of granulocyte‒macrophage colony‒stimulating factor promotes both atherosclerotic lesion development and ulcerative colitis progression\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, highlighting mechanistic convergence.\u003c/p\u003e \u003cp\u003eThe gut microbiota has emerged as a critical mediator of shared pathogenesis. HyPrColoc identified 6 pleiotropic loci involving taxa such as the Ruminococcus gauvreauii group, Collinsella, and Firmicutes phylum members, which were enriched in both CVDs and GTDs. Mechanistically, the microbiota influences cardiovascular health through pathways such as epithelial sodium/hydrogen exchanger activation and myocyte enhancer factor signaling\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, whereas in the gut, taxa such as AKK modulate inflammation via FXR receptor activation\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The observed cross-disease correlations for the Ruminococcus gauvreauii and Lachnospiraceae FCS020 groups highlight their dual roles in gastrointestinal homeostasis and vascular dysfunction.\u003c/p\u003e \u003cp\u003eThe identification of shared genetic biomarkers (e.g., NMT1 and CDC27) and immune cell targets (e.g., TH17 and γδ T cells) offers opportunities for the early prediction of GTD-CVD comorbidities. For example, IL-17-targeted biologics, which are already used to treat inflammatory bowel disease, may have broader utility in preventing cardiovascular complications. Additionally, microbiota modulation\u0026mdash;such as probiotic interventions targeting Ruminococcus or Collinsella\u0026mdash;could represent a novel strategy to disrupt shared pathogenic pathways. While this study leveraged large-scale GWAS data, it is limited by the European-ancestry focus, which may restrict its generalizability to other populations. Future studies incorporating diverse ancestries and longitudinal data will be critical to validate these findings and dissect temporal relationships. Single-cell RNA sequencing and functional genomics experiments (e.g., CRISPR-Cas9) could further mechanistically validate the roles of pleiotropic genes such as THADA and ZHX3 in both tissue contexts.\u003c/p\u003e \u003cp\u003eThis study has several advantages. It is a large-scale sample focuses on the shared genetic characteristics between CVDs and GTDs. Through a systematic design, it comprehensively evaluates the genetic associations among various diseases, providing extensive data support for understanding the comorbidity mechanism of these two types of diseases. This study is the first to report a number of previously unrecognized pleiotropic variants or genes. These findings help elucidate the biological mechanisms of genetic factors in cardiovascular and gastrointestinal comorbidities, help promote an in-depth understanding of the genetic basis of disease co-occurrence in related fields, and provide a basis for further development of clinical therapies.\u003c/p\u003e \u003cp\u003eThis study also has several limitations that should be acknowledged. First, there was insufficient population representativeness. The study is limited to the European population. In the future, it is necessary to expand the sample size of the abovementioned population to enhance the ethnic/racial universality of the research conclusions. The existing analysis still has room for improvement. More refined statistical methods (such as local-level analysis) should be adopted to explore more potential genetic associations between CVDs and GTDs. At present, the identified pleiotropic genetic biomarkers have not been verified by in vivo or in vitro experiments, and their specific biological functions are still unclear. In the future, it will be necessary to analyze the mechanism of action of these biomarkers through cell models, animal experiments, etc. Although the discovered biomarkers are expected to become therapeutic targets and promote personalized medicine, their practical application value has not been verified in clinical settings. Subsequent studies should focus on their potential for clinical transformation, such as their diagnostic efficacy and ability to predict treatment response. In addition, there are various potential risks of bias, including insufficient statistical power in the analysis of genetic evidence for low-incidence diseases and weak instrument bias in Mendelian randomization analysis. Although multiple statistical methods have been used to correct for confounding factors, more rigorous study designs are still needed to further verify the robustness of the research results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study establishes a genetic and mechanistic framework for the shared etiology of CVDs and GTDs, highlighting pleiotropic variants, inflammatory pathways, and gut microbiota interactions as key drivers. These insights not only deepen our understanding of cardiovascular‒gastrointestinal comorbidities but also pave the way for precision medicine approaches targeting shared biological mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003egastrointestinal tract diseases (GTDs), cardiovascular diseases (CVDs), inflammatory bowel disease (IBD), Crohn\u0026rsquo;s disease (CD), ulcerative colitis (UC), peptic ulcer disease (PUD), gastroesophageal reflux disease (GORD), irritable bowel syndrome (IBS), a combined phenotype of PUD, GORD, and related medications (PGM), abdominal aortic aneurysm (ABAORTANEUR), atrial fibrillation and flutter (AF), aortic aneurysm (AORTANEUR), aortic dissection (AORTDIS), calcific aortic valvular stenosis (CAVS_OPERATED), major coronary heart disease (CHD), heart failure (HEARTFAIL), essential hypertension (HYPTENSESS), other peripheral vascular diseases (OTHPER), stroke (STR), and thoracic aortic aneurysm (THAORTANEUR).\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eThis study was designed and conducted in accordance with ethical guidelines for human subject research and adheres to the principles of the Declaration of Helsinki. Data were obtained through an approved UK Biobank (UKB) project application (ID: 92675). UKB has received ethical approval from the National Health Service North West Centre for Research Ethics Committee (Ref: 21/NW/0157), and informed consent has been obtained from all participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eAll authors have agreed to the publication of this article.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest. All authors contributed to data analysis, drafting, or revision of the article; agreed on the journal to which the article is being submitted; provided final approval of the version to be published; and agreed to be accountable for all aspects of the work.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe present study was supported by the National Clinical Research Base of Traditional Chinese Medicine in Jiangsu Province (No. JD2023SZ04),Natural Science Foundation of Jiangsu Province,China(No.BK20251141), the Traditional Chinese and Western Medicine Colorectal Polyps Treatment Center Project of Jiangsu Provincial Hospital of Chinese Medicine, the Natural Science Foundation of China (82374449, 82341229 and 82174379), the Jiangsu Provincial Medical Key Laboratory (ZDXYS202208), and the Advantageous Disciplines of Jiangsu Province (035062005002\u0026ndash;11).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYang Li, Zhengxi Qiu, and Zhiyuan Li drafted the manuscript and created all the tables. Yirong Wu, Jinchen Chong, and Jiaze Ma collected the data. Chen Chen, Jiepin Li, Yirong Yang, Zhihua Lu, Xiaomin Yuan, Yijia Zhu, and Tuo Chen analyzed the data and generated all the figures. Yang Li, Tingsheng Ling and Yugen Chen provided research funding and designed the study. All the authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe Program in Complex Trait Genomics (https://cnsgenomics.com/content/data), GWAS Catalog (https://www.ebi.ac.uk/gwas/), Regensburg GEM Platform (https://www.uni-regensburg.de/medizin/epidemiologie-praeventivmedizin/genetische-epidemiologie/software/index.html), and GWAS Atlas (https://atlas.ctglab.nl/traitDB) provided access to genome-wide association study data. We thank all research participants who provided DNA samples for these studies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchicho R, Marsche G, Storr M. Cardiovascular complications in inflammatory bowel disease. Curr Drug Targets. 2015;16(3):181\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/1389450116666150202161500\u003c/span\u003e\u003cspan address=\"10.2174/1389450116666150202161500\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeda A, Fredrick F, Rathod U, Shah P, Jain R. Cardiovascular Manifestations in Inflammatory Bowel Disease. Curr Cardiol Rev. 2023;20(1):E241123223802. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/011573403X256094231031074753\u003c/span\u003e\u003cspan address=\"10.2174/011573403X256094231031074753\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinha T, Zain Z, Bokhari SFH, et al. Navigating the Gut-Cardiac Axis: Understanding Cardiovascular Complications in Inflammatory Bowel Disease. Cureus. 2024;16(2):e55268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7759/cureus.55268\u003c/span\u003e\u003cspan address=\"10.7759/cureus.55268\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSleutjes JAM, van Lennep JER, van der Woude CJ, de Vries AC. Thromboembolic and atherosclerotic cardiovascular events in inflammatory bowel disease: epidemiology, pathogenesis and clinical management. Ther Adv Gastroenterol. 2021;14:17562848211032126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/17562848211032126\u003c/span\u003e\u003cspan address=\"10.1177/17562848211032126\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarweesh M, Haddaden M, Fatima Z, et al. The Incidence and Outcomes of Venous Thromboembolisms in Patients Admitted with Different Gastrointestinal Malignancies. Gastrointest Tumors. 2023;10(1):67\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000538057\u003c/span\u003e\u003cspan address=\"10.1159/000538057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSleutjes JAM, van Roeters JE, de Vries AC. Spotlight on Cardiovascular Risk Assessment in Patients with Inflammatory Bowel Disease. Dig Dis Sci. 2022;67(9):4326\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10620-022-07449-0\u003c/span\u003e\u003cspan address=\"10.1007/s10620-022-07449-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-018-0579-z\u003c/span\u003e\u003cspan address=\"10.1038/s41586-018-0579-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Murray GK, Byrne EM, Sidorenko J, Visscher PM, Wray NR. GWAS of peptic ulcer disease implicates \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection, other gastrointestinal disorders and depression. Nat Commun. 2021;12(1):1146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-021-21280-7\u003c/span\u003e\u003cspan address=\"10.1038/s41467-021-21280-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTylee DS, Sun J, Hess JL, et al. Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data. Am J Med Genet Part B Neuropsychiatr Genet Off Publ Int Soc Psychiatr Genet. 2018;177(7):641\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ajmg.b.32652\u003c/span\u003e\u003cspan address=\"10.1002/ajmg.b.32652\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePouget JG, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Han B, et al. Cross-disorder analysis of schizophrenia and 19 immune-mediated diseases identifies shared genetic risk. Hum Mol Genet. 2019;28(20):3498\u0026ndash;513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/hmg/ddz145\u003c/span\u003e\u003cspan address=\"10.1093/hmg/ddz145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuncan LE, Shen H, Ballon JS, Hardy KV, Noordsy DL, Levinson DF. Genetic Correlation Profile of Schizophrenia Mirrors Epidemiological Results and Suggests Link Between Polygenic and Rare Variant (22q11.2) Cases of Schizophrenia. Schizophr Bull. 2018;44(6):1350\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/schbul/sbx174\u003c/span\u003e\u003cspan address=\"10.1093/schbul/sbx174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-018-0099-7\u003c/span\u003e\u003cspan address=\"10.1038/s41588-018-0099-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW. Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet. 2013;14(7):483\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrg3461\u003c/span\u003e\u003cspan address=\"10.1038/nrg3461\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHackinger S, Zeggini E. Statistical methods to detect pleiotropy in human complex traits. Open Biol. 2017;7(11):170125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rsob.170125\u003c/span\u003e\u003cspan address=\"10.1098/rsob.170125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChung D, Yang C, Li C, Gelernter J, Zhao H. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet. 2014;10(11):e1004787. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pgen.1004787\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgen.1004787\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulik-Sullivan BK, Loh PR, Finucane HK, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ng.3211\u003c/span\u003e\u003cspan address=\"10.1038/ng.3211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNing Z, Pawitan Y, Shen X. High-definition likelihood inference of genetic correlations across human complex traits. Nat Genet. 2020;52(8):859\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-020-0653-y\u003c/span\u003e\u003cspan address=\"10.1038/s41588-020-0653-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRay D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer. PLoS Genet. 2020;16(12):e1009218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pgen.1009218\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgen.1009218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.0506580102\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0506580102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHormozdiari F, van de Bunt M, Segr\u0026egrave; AV, et al. Colocalization of GWAS and eQTL Signals Detects Target Genes. Am J Hum Genet. 2016;99(6):1245\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ajhg.2016.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ajhg.2016.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoley CN, Staley JR, Breen PG, et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. Nat Commun. 2021;12(1):764. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-020-20885-8\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-20885-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoer CG, Hatzikotoulas K, Southam L, et al. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell. 2021;184(24):6003\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2021.11.003\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2021.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePurcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1086/519795\u003c/span\u003e\u003cspan address=\"10.1086/519795\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Thompson SG, CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyr036\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyr036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson SG, Sharp SJ. Explaining heterogeneity in meta-analysis: a comparison of methods. Stat Med. 1999;18(20):2693\u0026ndash;708. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/(sici)1097-0258(19991030)18:20%3C2693::aid-sim235%3E3.0.co;2-v\u003c/span\u003e\u003cspan address=\"10.1002/(sici)1097-0258(19991030)18:20%3C2693::aid-sim235%3E3.0.co;2-v\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR\u0026ndash;Egger method. Eur J Epidemiol. 2017;32(5):377\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10654-017-0255-x\u003c/span\u003e\u003cspan address=\"10.1007/s10654-017-0255-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyx034\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyx034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMills KHG. IL-17 and IL-17-producing cells in protection versus pathology. Nat Rev Immunol. 2023;23(1):38\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41577-022-00746-9\u003c/span\u003e\u003cspan address=\"10.1038/s41577-022-00746-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuerr RH, Taylor KD, Brant SR, et al. A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science. 2006;314(5804):1461\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.1135245\u003c/span\u003e\u003cspan address=\"10.1126/science.1135245\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQian Y, Liu C, Hartupee J, et al. The adaptor Act1 is required for interleukin 17-dependent signaling associated with autoimmune and inflammatory disease. Nat Immunol. 2007;8(3):247\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ni1439\u003c/span\u003e\u003cspan address=\"10.1038/ni1439\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JS, Tato CM, Joyce-Shaikh B, et al. Interleukin-23-Independent IL-17 Production Regulates Intestinal Epithelial Permeability. Immunity. 2015;43(4):727\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.immuni.2015.09.003\u003c/span\u003e\u003cspan address=\"10.1016/j.immuni.2015.09.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Li W, Zhao T, et al. Interleukin-17-Producing CD4\u0026thinsp;+\u0026thinsp;T Cells Promote Inflammatory Response and Foster Disease Progression in Hyperlipidemic Patients and Atherosclerotic Mice. Front Cardiovasc Med. 2021;8:667768. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2021.667768\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2021.667768\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGil-Pulido J, Am\u0026eacute;zaga N, Jorgacevic I, et al. Interleukin-23 receptor expressing γδ T cells locally promote early atherosclerotic lesion formation and plaque necrosis in mice. Cardiovasc Res. 2022;118(14):2932\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cvr/cvab359\u003c/span\u003e\u003cspan address=\"10.1093/cvr/cvab359\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong X, Zhang X, Li C, et al. γδ T cells aggravate blood\u0026ndash;brain-barrier injury via IL-17A in experimental ischemic stroke. Neurosci Lett. 2022;776:136563. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neulet.2022.136563\u003c/span\u003e\u003cspan address=\"10.1016/j.neulet.2022.136563\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian J, Bai Y, You A, et al. Interleukin-17 receptor C gene polymorphism reduces treatment effect and promotes poor prognosis of ischemic stroke. Biosci Rep. 2019;39(10):BSR20190435. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1042/BSR20190435\u003c/span\u003e\u003cspan address=\"10.1042/BSR20190435\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu L, Wang Y, Zhou L, et al. Vγ4 T-cell-derived IL-17A is essential for amplification of inflammatory cascades in ischemic brain tissue after stroke. Int Immunopharmacol. 2021;96:107678. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.intimp.2021.107678\u003c/span\u003e\u003cspan address=\"10.1016/j.intimp.2021.107678\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobert M, Miossec P. Effects of Interleukin 17 on the cardiovascular system. Autoimmun Rev. 2017;16(9):984\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.autrev.2017.07.009\u003c/span\u003e\u003cspan address=\"10.1016/j.autrev.2017.07.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaigusa R, Winkels H, Ley K. T-cell subsets and functions in atherosclerosis. Nat Rev Cardiol. 2020;17(7):387\u0026ndash;401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41569-020-0352-5\u003c/span\u003e\u003cspan address=\"10.1038/s41569-020-0352-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGeachy MJ, Cua DJ, Gaffen SL. The IL-17 Family of Cytokines in Health and Disease. Immunity. 2019;50(4):892\u0026ndash;906. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.immuni.2019.03.021\u003c/span\u003e\u003cspan address=\"10.1016/j.immuni.2019.03.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirota K, Duarte JH, Veldhoen M, et al. Fate mapping of IL-17-producing T cells in inflammatory responses. Nat Immunol. 2011;12(3):255\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ni.1993\u003c/span\u003e\u003cspan address=\"10.1038/ni.1993\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarks BR, Nowyhed HN, Choi JY, et al. Thymic self-reactivity selects natural interleukin 17-producing T cells that can regulate peripheral inflammation. Nat Immunol. 2009;10(10):1125\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ni.1783\u003c/span\u003e\u003cspan address=\"10.1038/ni.1783\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou L, Ivanov II, Spolski R, et al. IL-6 programs T(H)-17 cell differentiation by promoting sequential engagement of the IL-21 and IL-23 pathways. Nat Immunol. 2007;8(9):967\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ni1488\u003c/span\u003e\u003cspan address=\"10.1038/ni1488\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZielinski CE, Mele F, Aschenbrenner D, et al. Pathogen-induced human TH17 cells produce IFN-γ or IL-10 and are regulated by IL-1β. Nature. 2012;484(7395):514\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature10957\u003c/span\u003e\u003cspan address=\"10.1038/nature10957\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaleb S, Tedgui A, Mallat Z. IL-17 and Th17 cells in atherosclerosis: subtle and contextual roles. Arterioscler Thromb Vasc Biol. 2015;35(2):258\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/ATVBAHA.114.303567\u003c/span\u003e\u003cspan address=\"10.1161/ATVBAHA.114.303567\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa Y, Zhang X, Xuan B, et al. Disruption of CerS6-mediated sphingolipid metabolism by FTO deficiency aggravates ulcerative colitis. Gut. 2024;73(2):268\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gutjnl-2023-330009\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2023-330009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong G, Yang Y, Zhang H, et al. Protein Kinase CK2 Maintains Reciprocal Balance Between Th17 and Treg Cells in the Pathogenesis of UC. Inflamm Bowel Dis. 2022;28(6):830\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ibd/izab312\u003c/span\u003e\u003cspan address=\"10.1093/ibd/izab312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandez DM, Rahman AH, Fernandez NF, et al. Single-cell immune landscape of human atherosclerotic plaques. Nat Med. 2019;25(10):1576\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-019-0590-4\u003c/span\u003e\u003cspan address=\"10.1038/s41591-019-0590-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashmi S, Zeng QT. Role of interleukin-17 and interleukin-17-induced cytokines interleukin-6 and interleukin-8 in unstable coronary artery disease. Coron Artery Dis. 2006;17(8):699\u0026ndash;706. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/01.mca.0000236288.94553.b4\u003c/span\u003e\u003cspan address=\"10.1097/01.mca.0000236288.94553.b4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng X, Yu X, Ding YJ, et al. The Th17/Treg imbalance in patients with acute coronary syndrome. Clin Immunol Orlando Fla. 2008;127(1):89\u0026ndash;97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clim.2008.01.009\u003c/span\u003e\u003cspan address=\"10.1016/j.clim.2008.01.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBu F, Ding Y, Chen T, et al. Total flavone of Abelmoschus Manihot improves colitis by promoting the growth of Akkermansia in mice. Sci Rep. 2021;11(1):20787. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-00070-7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-00070-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu J, Yang Y. Gut microbiome and its meta-omics perspectives: profound implications for cardiovascular diseases. Gut Microbes. 2021;13(1):1936379. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/19490976.2021.1936379\u003c/span\u003e\u003cspan address=\"10.1080/19490976.2021.1936379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasenga SK, Kirabo A. Salt and Gut Microbiota in Heart Failure. Curr Hypertens Rep. 2023;25(8):173\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11906-023-01245-5\u003c/span\u003e\u003cspan address=\"10.1007/s11906-023-01245-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Genome-Wide Pleiotropic Analyses, Gastrointestinal Tract Disease, Cardiovascular Disease, Inflammation and Immunity, Microbiota","lastPublishedDoi":"10.21203/rs.3.rs-8960093/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8960093/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the shared genetic etiology between gastrointestinal tract and cardiovascular diseases, and to identify shared genomic loci, genes, and pathways.​\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing aggregated genome-wide association statistics from public data sources, a genome-wide pleiotropic association study was conducted. Multiple statistical genetic approaches were applied to sequentially explore pleiotropic associations across genome-wide single-nucleotide variations, genes, biological pathways, and gut microbiota. The potential shared genetic causes among 7 gastrointestinal tract diseases and 11 cardiovascular diseases were analyzed.​\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 350 pleiotropic loci were identified. Colocalization analysis detected 95 shared causal loci across disease pairs. Multiple gene pathways were found to be closely associated with these diseases, and key biological processes\u0026mdash;including cell signal transduction and T-cell activation\u0026mdash;exhibited significant correlations across the studied diseases. A robust pleiotropic association was observed between gastrointestinal tract and cardiovascular diseases, suggesting that these diseases may cross-influence each other in the context of different immune cells and tissues, while sharing common immune response mechanisms. Several immune cell populations, such as CD103⁺ CD11b⁺ dendritic cells, CD8⁺ splenic lymph node DCs, and γδ T cells, showed cross-disease correlations, highlighting the critical role of the immune system in the pathogenesis of both gastrointestinal tract and cardiovascular diseases. Multitrait colocalization analysis via HyPrColoc identified 6 pleiotropic loci harboring shared microbiota, underscoring the significant involvement of gut microbiota in disease pleiotropy.​\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study establishes a genetic and mechanistic framework for the shared etiology of gastrointestinal tract and cardiovascular diseases, with pleiotropic variants, inflammatory pathways, and gut microbiota interactions as key drivers. These insights not only deepen our understanding of cardiovascular-gastrointestinal comorbidities but also lay the groundwork for precision medicine strategies targeting shared biological mechanisms.\u003c/p\u003e","manuscriptTitle":"Genome-Wide Pleiotropic Analyses Characterize the Genetic Etiology Shared between Cardiovascular and Gastrointestinal Tract Diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 18:23:11","doi":"10.21203/rs.3.rs-8960093/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14435f0c-db20-4d38-a08b-f15d62adb9a0","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T18:23:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 18:23:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8960093","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8960093","identity":"rs-8960093","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}