Genetic overlap between impaired lung function and cardiovascular diseases revealed by a large-scale genome-wide cross-trait analysis

Genetic overlap between impaired lung function and cardiovascular diseases revealed by a large-scale genome-wide cross-trait analysis | 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 Genetic overlap between impaired lung function and cardiovascular diseases revealed by a large-scale genome-wide cross-trait analysis Dongsheng Wu, Jian Zhou, Mengyuan Lyu, Quan Zheng, Tengyong Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4218165/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 Increasing evidence have highlighted the phenotypic association between impaired lung function and cardiovascular diseases (CVD), but underlying shared genetic basis contributing to this association remain largely unexplored. Methods Utilizing summary data from the large-scale genome-wide association studies, our investigation delved into several aspects: the global and local genetic correlations, pleiotropic loci, and causal association between lung function and three prevalent CVD, namely coronary artery disease (CAD), heart failure (HF), and stroke. Results Our findings revealed significantly negative genetic correlations between lung function and CAD (r g = -0.20 to -0.17), HF (r g = -0.18 to -0.17), and stroke (r g = -0.17 to -0.16). After separating the whole genome into 2,353 independent regions, we determined 13, 4, and 3 significant regions for CAD, HF, and stroke with lung function, respectively. Furthermore, the global and local shared genetic basis were confirmed by the identification of multiple pleiotropic loci and multiple shared gene–tissue pairs. The pleiotropic loci were largely enriched in brain-related tissues, while shared gene-tissue pairs exhibited significant enrichment across nervous, cardiovascular, digestive, endocrine/exocrine, and respiratory systems. Mendelian randomization analysis demonstrated a significant causal association of genetically predicted lung function with CAD [OR (odds ratio) = 0.69 to 0.72] and stroke (OR = 0.81 to 0.90) but not with HF. No evidence of reverse causality was found. Conclusions These findings comprehensively uncover a shared genetic architecture as well as a causal association between impaired lung function and CVD, emphasizing the opportunity to enhance the quality of existing intervention strategies. lung function cardiovascular diseases genetic correlation genome-wide cross-trait analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Cardiovascular diseases (CVD) persist as the leading cause of global mortality and a significant contributor to disability [ 1 ]. While common cardiovascular risk factors, such as smoking and adiposity [ 2 , 3 ], remain primary contributors to CVD, accumulating evidence suggests that impaired lung function might be considered as an independent risk factor for CVD. Epidemiological studies have highlighted that individuals with impaired lung function, especially those with chronic obstructive pulmonary disease (COPD) and asthma, exhibit an increased risk of CVD after accounting for confounding factors [ 4 – 7 ]. Moreover, shared genetic susceptibility loci between COPD and CVD on 11q22.3 (RP11-563P16.1), 16q23.1 (CFDP1), and 19q13.32 (RSPH6A) have been identified [ 8 ]. Mechanisms potentially linking impaired lung function to CVD include increased inflammatory responses, compromised vascular reactivity, and endothelial dysfunction [ 9 – 11 ]. These factors may lead to a mismatch in the ventilation/perfusion ratio or inefficient removal of cardiotoxic substances [ 12 ]. Despite this, the shared genetic basis underlying their comorbidity have not been fully elucidated. Genetic correlation analysis evaluates the correlation of genetic effects between two related traits, highlighting the shared etiology underlying such an association. Previous studies have suggested that both lung function and CVD are heritable traits, with estimated heritability of 8–13% [ 13 ] and 25–40% [ 13 – 15 ], respectively. Furthermore, large-scale genome-wide association studies (GWAS) have detected numerous genetic variants significantly associated with both traits [ 13 , 16 ]. Specifically, several genetic loci linked to lung function (e.g., BCAS3, SMAD3, KCNE2, and KIAA1462) have been associated with susceptibility to CVD [ 13 , 16 ]. These findings indicate a shared genetic basis between lung function and CVD, yet the precise extent and nature of this relationship remain unclear. In this context, several Mendelian randomization (MR) studies have been performed to explore the causality between these traits, and consistently found a negative association of genetically predicted impaired lung function on risk of CVD [ 17 – 19 ]. Nevertheless, these studies lack a systematic exploration of genetic correlations, comprehensive assessment of lung function traits, and some potential confounding factors that could influence the causal relationships. Altogether, understanding the intricate relationship between impaired pulmonary function and CVD development is paramount, not only in deciphering the mechanism of disease susceptibility but also in delineating precise strategies for prevention and risk stratification. Therefore, we utilized a methodology, namely genome-wide cross-trait analysis, to evaluate the shared genetic basis between impaired lung function and the risk of CVD. This approach encompasses assessments of genetic correlations, cross-trait meta-analysis and transcriptome-wide association studies (TWAS) for identifying shared loci, and bidirectional MR analysis to delve into causal association. This study involved three lung function traits [forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), and peak expiratory flow (PEF)], and three prevalent CVD [coronary artery disease (CAD), heart failure (HF), and stroke]. The study design is illustrated in Fig. 1 . Methods Summary-level genetic data In this study, genetic associations for lung function were evaluated utilizing data from the IEU Open GWAS project ( https://gwas.mrcieu.ac.uk/ ) [ 20 ]. Three lung function traits, including FVC (ID: ukb-b-7953), FEV1 (ID: ukb-b-19657), and PEF (ID: ukb-b-12019), involved a total of 421,986 participates from the UK Biobank. While the most extensive GWAS data for lung function to date was conducted by Shrine et al. [ 16 ], integrating data from the UK Biobank and SpiroMeta consortium, adjustments were made for covariates such as height and smoking, which are linked to both lung function and CVD. This adjustment introduces the potential for collider bias, as SNPs may be correlated with both these covariates and other risk factors [ 18 ]. Thus, only the GWAS data extracted from the UK Biobank was utilized in this study. The hitherto largest summary GWAS data for three prevalent CVD, namely CAD, HF, and stroke, were obtained. For CAD, summary GWAS data were obtained from a meta-analysis conducted by the CARDIoGRAMplusC4D consortium, incorporating 60,801 cases and 123,504 controls [ 13 ]. For HF, summary GWAS data were obtained from a meta-analysis by the HERMES, incorporating 47,309 cases and 930,014 controls [ 21 ]. For stroke, summary GWAS data were obtained from a meta-analysis by the ISGC, incorporating 40,585 cases and 406,111 controls [ 22 ]. This study included individuals exclusively of European ancestry. Statistical analysis Genetic correlation analysis We initially utilized the linkage disequilibrium (LD) score regression (LDSC) to estimate the global genetic correlation [ 23 ]. This method considers the effects of all other SNPs in LD with a specific SNP when estimating the GWAS effect size for that SNP. The genetic correlation estimates (r g ) range from − 1 to + 1, where negative values denote negative correlation and positive values denote positive correlation. For our analysis, we employed precomputed LD scores derived from approximately 1.2 million SNPs shared among individuals of European ancestry from the HapMap3 reference panel, renowned for its high-quality imputation. To ascertain statistical significance, we applied Bonferroni correction, setting a P-value threshold at P < 0.05/9. The global genetic correlation serves to quantify the overall impact of genome-wide variants. Despite the minimal genetic correlation between two traits, there might exist specific genomic regions influencing both traits. Consequently, we computed the pairwise local genetic correlation utilizing SUPERGNOVA [ 24 ]. This method facilitates a measurement of similarity between trait pairs attributed to genetic variations within specific genomic regions, utilizing around 2,353 LD-independent regions. To correct for multiple testing, we applied a Bonferroni correction, setting the significance threshold at P < 0.05/2,353. Cross-trait meta-analysis The presence of a significant genetic correlation often indicates potential horizontal pleiotropy (pleiotropy) or vertical pleiotropy (causal relationships). To explore this further, we conducted a cross-phenotypic association (CPASSOC) analysis to test the hypothesis that a single SNP can influence both traits [ 25 ]. Using GWAS summary data of related traits, CPASSOC can identify variants associated with at least one trait while accounting for population structure or hidden correlations. We performed pairwise SHet, a statistic particularly robust in the presence of heterogeneity, and identified independent loci through the clumping function in PLINK (1.9) with parameters (--clump-p1 5E-8, --clump-p2 1E-5, --clump-r2 0.2, --clump-kb 500) [ 26 ]. Pleiotropic SNPs were identified based on criteria: P CPASSOC < 5 × 10 − 8 and P single−trait < 1 × 10 − 5 observed in both traits. Additionally, a novel shared SNP was defined as follows: (i) not reaching genome-wide significance (5 × 10 − 8 < P single−trait < 1 × 10 − 5 ) in single-trait GWAS, and (ii) not in LD (r 2 < 0.2) with any previously reported SNPs in single-trait GWAS. To delve deeper into the biological implications of these shared SNPs, we annotated the linearly closest genes associated with the pleiotropic loci using the Ensemble Variant Effect Predictor (VEP) [ 27 ]. Fine mapping credible set analysis To explore the pleiotropic loci identified from CPASSOC, we employed the FM-summary based on Bayesian likelihood fine-mapping algorithm to identify a 99% credible set of causal SNPs [ 28 ]. For this analysis, variants within a 500 kb proximity of the index SNP were extracted. Using a flat prior and the steepest descent approximation, this algorithm focuses on mapping the primary signals to determine potential causal variants. Colocalization analysis To determine whether two GWAS signals originate from the same variant rather than distinct neighboring genetic variations, we conducted colocalization analysis employing the “Coloc” package, which operates within a Bayesian framework [ 29 ]. This analysis offers posterior probabilities for five hypotheses (H0-H4). For this purpose, variants within 500kb of each shared index SNP were extracted. We then calculated the posterior probability of hypothesis 4 (PPH4), which signifies the likelihood of a shared single causal variant between the two traits. A PPH4 value exceeding 0.5 indicates colocalized loci, suggesting a high probability of shared genetic influence through a single causal variant at that locus affecting both traits. Tissue enrichment analysis To gain biological insights into the shared genes from CPASSOC, we performed tissue enrichment analysis utilizing the FUMA GENE2FUNC pipeline, which integrates data from 54 distinct tissue types within GTEx (v.8) [ 30 ]. To obtain more shared genes for the enrichment analysis, criteria were relaxed to P CPASSOC < 5 × 10 − 8 and 5 × 10 − 8 < P single−trait < 1 × 10 − 3 . To correct for multiple testing, the Benjamin-Hochberg procedure was applied. Transcriptome wide association study (TWAS) Due to the crucial role of both tissue specificity and gene expression in elucidating shared biological mechanisms, we performed TWAS utilizing FUSION [ 31 ]. This analysis integrates summary-level GWAS data with expression weights obtained from GTEx (Genotype-Tissue Expression, version 8) across 49 distinct tissues [ 32 ]. Each analysis focused on a single tissue-trait pair. As an extension of TWAS, joint/conditional tests were performed to examine regions with multiple identified signals to obtain an independent set of gene-tissue pairs [ 33 ]. Shared gene-tissue pairs were determined by intersecting results among different traits. Bidirectional MR analysis To further explore vertical pleiotropy between the two traits, a bidirectional two-sample MR analysis was conducted. For lung function, independent instrumental variables (IVs) were selected by aggregating all variants achieving genome-wide significance (P < 5× 10 − 8 ) within a 10,000 kb range, employing a LD threshold of r 2 = 0.001. Similarly, independent IVs for CAD and HF were identified by aggregating variants meeting genome-wide significance criteria ( P < 5 × 10 − 8 ) within the same range and LD threshold. Additionally, variants reaching a relaxed selection threshold ( P < 5 × 10 − 6 ) for stroke were aggregated within the same range and LD threshold. The instrumental strength was assessed via the F-statistic, where a F-statistic < 10 indicates a weak IV [ 34 ]. Our primary analysis employed the inverse-variance weighted (IVW) method [ 35 ], supplemented by MR-Egger [ 36 ] and weighted median [ 37 ]. To validate the assumptions of the MR analysis, several sensitivity analyses were conducted. Initially, we removed palindromic IVs characterized by identical alleles on both forward and reverse strands. Additionally, we excluded pleiotropic IVs linked to potential confounding factors according to the GWAS catalog ( https://www.ebi.ac.uk/gwas/ ). Furthermore, we utilized MR-PRESSO to identify and adjust for potential outliers. Multivariable MR [ 38 ] was employed to adjust for significant confounding factors such as smoking initiation [ 39 ], alcohol consumption [ 39 ], body mass index (BMI) [ 40 ], physical activity [ 41 ], and sleep duration [ 42 ]. These confounders were individually and collectively integrated with lung function to ensure a comprehensive analysis. Finally, a reverse-direction MR was performed to explore the causal impact of genetic predisposition to CVD on lung function. Results Global genetic correlation As presented in Table 1 , significant global genetic correlations were observed between FVC (r g = -0.20, P = 1.83 × 10 − 16 ), FEV1 (r g = -0.17, P = 1.38 × 10 − 11 ) and CAD. For HF, significant global genetic correlations were identified with FVC (r g = -0.18, P = 6.39 × 10 − 9 ) and FEV1 (r g = -0.17, P = 2.59 × 10 − 9 ). For stroke, significant global genetic correlations were identified with FVC (r g = -0.17, P = 2.18 × 10 − 7 ) and FEV1 (r g = -0.16, P = 3.47 × 10 − 6 ), and suggestive global genetic correlation was identified with PEF (r g = -0.10, P = 1.60 × 10 − 2 ). Table 1 Genome-wide genetic correlation between lung function and cardiovascular diseases. Cardiovascular diseases Lung function r g 95% CI P value CAD FVC -0.20 (-0.25, -0.15) 1.83 × 10 − 16 FEV1 -0.17 (-0.22, -0.12) 1.38 × 10 − 11 PEF -0.05 (-0.11, 0.0004) 5.20 × 10 − 2 Heart failure FVC -0.18 (-0.24, -0.12) 6.39 × 10 − 9 FEV1 -0.17 (-0.23, -0.12) 2.59 × 10 − 9 PEF -0.06 (-0.12, 0.005) 0.07 Stroke FVC -0.17 (-0.24, -0.11) 2.18 × 10 − 7 FEV1 -0.16 (-0.22, -0.09) 3.47 × 10 − 6 PEF -0.10 (-0.18, -0.02) 1.60 × 10 − 2 Bold-face: P < 0.05/9. CAD, coronary artery disease; FVC, forced vital capacity; FEV1, forced expiratory volume in 1 second; PEF, peak expiratory flow. Local genetic correlation After dividing the entire genome into 2,353 LD-independent regions, our analysis revealed 13 genomic regions (FVC: 19p13.2, 2p24.1-p23.3, 2p13.3, 17q21.32-q21.33, 6q22.31-q22.33; FEV1: 19p13.2, 2p24.1-p23.3, 17q21.32-q21.33; PEF: 19p13.2, 16q23.1, 6q11.2, 2p21, 2p24.1-p23.3) suggesting a significant local genetic correlation between lung function and CAD (Fig. 2 A-C). Of note, 19:10,030,690 − 11,279,257 at 19p13.2 emerged as a significant region in three analyses, harboring BRG1, a previously reported locus for both impaired lung function (e.g., asthma) and CAD, and ILF3, a previously reported susceptible gene for CVD[ 43 – 45 ]. For HF, four regions were identified with FVC, one with FEV1, and three with PEF (Fig. 2 D-F). For stroke, three regions were identified with FVC, one with FEV1, and two with PEF (Fig. 2 G-I). Detailed information for each region is presented in Fig. 2 . Cross‑trait meta‑analysis Inspired by the notable genetic overlap between pulmonary function and CVD, our investigation extended to explore this genetic correlation at the level of individual variants. In total, 57 independent loci were identified from CPASSOC ( P CPASSOC < 5 × 10 − 8 and P lung function < 1 × 10 − 5 and P CVD < 1 × 10 − 5 ). Among these, 31 loci were shared between lung function and CAD, 10 loci were shared between lung function and HF, and 16 loci were shared between lung function and stroke (Fig. 3 , Table S2 ). Notably, the loci mapped to KIAA1462, ABO, BAG3, and CDK6 (specifically, index SNPs: rs2014144, rs2519093, rs17617337, rs42235) exhibited the most significant shared signals in each pairwise CPASSOC. After removing SNPs correlated (r 2 ≥ 0.2) with previously reported significant SNPs associated with single traits, our analysis revealed five novel SNPs shared between CVD and at least one pulmonary function trait, including three for FVC, one for FEV1, and one for PEF (Fig. 3 , Table 2 ). Notably, the most significant novel SNP rs3851738 ( P CPASSOC = 2.97 × 10 − 12 ) was located near CFDP1, a gene pivotal in cardiac development and function [ 46 ]. The second most significant novel SNP rs288184 ( P CPASSOC = 7.28 × 10 − 11 ) was mapped to FBXL17, known for its involvement in cardiovascular physiology through protein degradation [ 47 ]. The third most significant novel SNP rs3809114 ( P CPASSOC = 9.21 × 10 − 11 ) was located near to GIPR and SNRPD2. GIPR is implicated in regulating obesity and type 2 diabetes mellitus [ 48 ], while SNRPD2 operates as part of the components within the spliceosome-related activated pathway associated with small nuclear ribonucleoprotein polypeptides [ 49 ]. Table 2 Novel pleiotropic loci between lung function and cardiovascular diseases ( P CPASSOC < 5 × 10 − 8 and 5 × 10 − 8 < P single−trait < 1 × 10 − 5 , clumping r 2 = 0.2). SNP Chr BP A1/A2 Lung function Cardiovascular diseases P CPASSOC Gene a Subtype Beta P value Subtype Beta P value Lung function and CAD rs3172494 3 48731487 T/G FVC 0.01 2.80 × 10 − 6 CAD 0.06 9.71 × 10 − 6 1.73 × 10 − 9 IP6K2 rs288184 5 107347395 T/C FVC 0.01 1.20 × 10 − 7 CAD 0.06 9.56 × 10 − 6 7.28 × 10 − 11 FBXL17 rs1964272 19 46190268 A/G FVC 0.01 4.20 × 10 − 7 CAD 0.05 2.92 × 10 − 6 9.21 × 10 − 11 GIPR, SNRPD2 rs3851738 16 75387533 C/G FEV1 -0.01 6.40 × 10 − 7 CAD 0.05 1.88 × 10 − 6 2.97 × 10 − 12 CFDP1 rs10786712 10 104596396 T/C PEF 0.01 1.00 × 10 − 6 CAD 0.04 5.08 × 10 − 6 9.45 × 10 − 11 CYP17A1, CYP17A1-AS1, PFN1P11 Lung function and HF rs3820888 2 201180023 C/T FVC -0.04 4.71 × 10 − 6 HF -0.01 3.10 × 10 − 6 4.72 × 10 − 9 SPATS2L rs4657456 1 165591441 T/C FEV1 0.04 9.60 × 10 − 6 HF -0.01 3.50 × 10 − 6 1.89 × 10 − 9 RP11-306I1.2 Lung function and stroke rs2672587 10 124235355 C/G FVC 0.05 3.18 × 10 − 6 Stroke -0.01 2.10 × 10 − 7 3.26 × 10 − 12 HTRA1 rs149070156 10 124251048 T/C FVC 0.16 7.85 × 10 − 7 Stroke -0.02 1.30 × 10 − 6 5.01 × 10 − 12 HTRA1 rs2295786 10 105616482 T/A PEF 0.05 1.43 × 10 − 7 Stroke -0.01 6.80 × 10 − 6 4.10 × 10 − 12 SH3PXD2A a Gene symbol mapped by VEP. A1, effect allele; A2, alternative allele; CHR, chromosome; CAD, coronary artery disease; FVC, forced vital capacity; FEV1, forced expiratory volume in 1 second; PEF, peak expiratory flow. A total of two novel pleiotropic SNPs were shared between HF and lung function, with one associated with FVC (rs3820888) and the other linked to FEV1 (rs4657456). The former SNP is located near SPATS2L, recognized as a negative regulator impacting β2-adrenergic receptor levels [ 50 ], while the latter SNP was mapped to RP11-306I1.2, currently with little known biological function. Furthermore, three novel pleiotropic SNPs were identified between stroke and lung function, including two for FVC and one for PEF. Two out of the three shared SNPs (rs2672587 and rs149070156) were located near HTRA1, which is implicated in several severe pathologies such as arthritic diseases, age-related macular degeneration, and small cerebral vessel diseases [ 51 ]. The remaining SNP rs2295786 shared between stroke and PEF is mapped to SH3PXD2A, an adaptor protein involved in the extracellular matrix degradation and formation of podosomes and invadopodia [ 22 ]. Detailed annotations for each variant are provided in Table S3–S5. Identification of causal variants and colocalization Using FM-summary algorithm, we determined a 99% credible set of causal SNPs for each of the 57 shared SNPs, offering potential targets for subsequent experimental research (Table S6–8). In total, 1,421, 233, and 293 candidate SNPs were respectively identified as credible shared causal SNP sets between CAD, HF, stroke, and pulmonary function. To further determine whether genetic variants are responsible for the associations underlying distinct traits, a colocalization analysis was then conducted. This analysis revealed multiple loci that colocalized at the identical candidate SNPs that 13 out of 31 shared loci were identified between CAD and lung function, 10 out of 10 shared loci between HF and lung function, and 13 out of 16 shared loci between stroke and lung function (Table S9). Tissue enrichment analysis The shared genes associated with lung function (FVC and FEV1) and CAD were predominantly enriched in brain tissues, such as the amygdala, caudate basal ganglia, putamen basal ganglia, and substantia nigra (Fig. 4 A-B). Additionally, genes shared between PEF and stroke were notably enriched in the substantia nigra. No significant tissue enrichment was observed for shared genes associated with other lung function traits and CVD (Figures S1 -3). Transcriptome‑wide association study In total, 30 genes exhibited significant TWAS associations between CAD and at least one lung function trait: 20 genes with FVC, 11 with FEV1, and 13 with PEF. These genes exhibited significant enrichment in tissues across the nervous, cardiovascular, digestive, exocrine/endocrine, and respiratory systems (Table S10-12). Notably, six genes were situated at pleiotropic loci identified from CPASSOC, including IP6K2, ABO, ILF3, TEX41, INPP5B, and WDR12. Among these genes, five genes had previously been linked to lung function and/or cardiovascular disease (GWAS Catalog accessed by December 16, 2023), such as ILF3 associated with FEV1, and ABO, TEX41, INPP5B, and WDR12 associated with CAD. For HF, four genes showed significant TWAS associations with at least one lung function trait, with the ABO gene also identified in CPASSOC. For stroke, seven genes displayed significant TWAS associations with at least one lung function trait, with CDK6 and SH3PXD2A identified in CPASSOC. Bidirectional Mendelian randomization Inspired by the significant shared genetic basis, a bidirectional MR analysis was then conducted to investigate potential causal relationship. A total of 320, 261, and 137 SNPs were identified as IVs for FVC, FEV1, and PEF (Table S13-15), respectively. F-statistics indicated minimal evidence of weak instrument bias. As depicted in Fig. 4 A, using univariable MR, genetically predicted lung function (FVC: OR IVW = 0.69, 95% CI = 0.62 − 0.77, P = 3.80 × 10 − 11 ; FVE1: OR IVW = 0.72, 95% CI = 0.63 − 0.81, P = 2.37 × 10 − 7 ) presented an inverse association with the risk of CAD. Such a relationship remained consistent between lung function (FVC: OR IVW = 0.90, 95% CI = 0.82 − 0.99, P = 3.93 × 10 − 2 ; FEV1: OR IVW = 0.86, 95% CI = 0.77 − 0.96, P = 9.69 × 10 − 3 ; PEF: OR IVW = 0.81, 95% CI = 0.69 − 0.95, P = 1.08 × 10 − 2 ) and stroke. No significant causal relationship was found between lung function and HF. Similar estimates in direction and magnitude were observed across MR-Egger regression, weighted median, and MR-PRESSO analyses. Sensitivity analyses, excluding pleiotropic or palindromic SNPs, yielded consistent findings, underscoring the robustness of the results. Furthermore, the bidirectional MR analysis did not reveal any genetic predisposition to CVD affecting lung function (Fig. 4 B, Tables S16-18). Detailed information of MR is provided in Fig. 4 . Considering potential mediating phenotypes or risk factors that may influence the correlation between lung function and CVD, we performed multivariable MR by incorporating each exposure with confounders (smoking, drinking, BMI, physical activity, and sleep duration). The effect of each lung function trait on CVD remained consistent in both direction and magnitude with univariable MR after adjusting for confounders (Figure S4). Discussion Exploring shared genetic effects that are minimally impacted by environmental factors offers deeper insights into the common biological basis linking associations underlying two different phenotypes. To our knowledge, this study represents the first large-scale genome-wide cross-trait analysis, delving into the shared genetic basis between pulmonary function and three prevalent CVD. These findings provide evidence of global and regional shared genetic basis, confirming an intrinsic correlation between these traits. Furthermore, the genetic overlap was dissected into pleiotropy and causal relationships, reflected by pleiotropic loci and genes revealed by CPASSOC or TWAS, and causal relationships demonstrated by MR analyses. Overall, statistically significant global genetic correlations were confirmed, with a shared genetic contribution of 16–20% between pulmonary function and CVD. Significant local signals detected in multiple specific genomic regions further validate the overall genetic correlation, emphasizing a crucial shared biological basis. The local regional analysis largely aligns with the global genetic analysis and MR analysis. Among the identified 24 shared local regions, 19 display a negative correlation between impaired lung function and CVD. The significantly global and regional genetic correlations could result from the pleiotropic effects of genetic elements shared between lung function and CVD or indicate a potential causal link between lung function and CVD. In our subsequent analysis, we identified 57 independent loci influencing susceptibility to both lung function and CVD, reflecting considerable genetic pleiotropy. It is notable that many of the observed cross-trait effects have previously been associated with inflammation (ILF3, NOS3, SKI) [ 52 – 54 ], stress responses (IP6K2, BAG3) [ 55 , 56 ], and the blood system (KIAA1462, ABO, CFDP1, WDR12) [ 46 , 57 – 59 ]. These findings shed light on potential mechanistic pathways linking lung function with CVD. Through colocalization analysis, multiple genes (ABO, BAG3, TEX41, KIAA1462, INPP5B, ZC3HC1) exhibited a shared causal mechanism (PPH4 > 0.5). In this study, we specifically focused on two interesting examples, KIAA1462 (PPH4 = 0.65) and BAG3 (PPH4 = 0.99), both shared between impaired lung function and CVD. KIAA1462 (also known as JCAD, a gene encoding a protein associated with CAD) has recently been identified as a new risk gene for CAD [ 13 ]. In CAD patients, the absence of KIAA1462 inhibits vascular inflammation, improves vascular activity, and suppresses atherosclerosis [ 57 ]. Additionally, its function in vascular development and tissue balance is associated with the occurrence of diseases related to impaired lung function [ 60 ]. Previous studies have shown decreased expression levels of KIAA1462 in COPD patients compared to non-COPD controls [ 60 ]. Furthermore, KIAA1462 has been identified as a gene associated with lung function [ 16 ]. BAG3 possesses multifaceted regulation abilities in major biological processes such as apoptosis, development, cellular cytoskeleton organization, and autophagy, thereby mediating adaptive responses of cells to stress stimuli [ 61 ]. In the heart, BAG3 inhibits cell apoptosis, promotes autophagy, and maintains myofibrillar structure [ 62 ]. Particularly, recent experimental studies suggest that BAG3-mediated sarcomere turnover forms the basis for maintaining myofilament function and is associated with the development of HF [ 63 , 64 ]. Moreover, BAG3 expression levels in COPD blood samples are higher than in normal blood samples, potentially influencing COPD development by regulating autophagy [ 65 ]. Further experimental research is needed to provide more detailed functional annotations for these identified genetic loci, especially those related to impaired lung function and CVD. Combined GWAS summary data with GTEx tissue expression data, the TWAS analysis elucidated putative shared mechanisms between impaired lung function and CVD at the gene-tissue level. Consistent with findings from CPASSOC, multiple genes associated with inflammation (ILF3), stress response (IP6K2), and the blood system (ABO, WDR12, SH3PXD2A) were shared between lung function and CVD. Furthermore, TWAS identified multiple genes in brain-related tissues (such as cortex, basal ganglia, hypothalamus), indicating a potential neurobiological mechanism. Consistently, GTEx tissue enrichment analysis corroborated these findings, demonstrating significant enrichment of shared genes in brain-related tissues. This confirms well-established knowledge that both impaired lung function and CVD are associated with multiple brain-related disorders [ 66 , 67 ]. Indeed, heart-brain interactions and brain-lung axis has been recently highlighted for complex communications in regulating multiple systemic diseases. For example, impaired lung function could potentially lead to an increased risk of brain disorders, such as depression and cognitive dysfunction [ 68 ], while individuals with these brain disorders exhibit a significantly elevated risk of CVD [ 66 ]. Additionally, we observed shared regulatory features in the digestive system, especially between lung function and CAD. Observational studies have reported comorbidities between respiratory diseases (i.e., COPD) and digestive diseases [i.e., gastroesophageal reflux disease and inflammatory bowel disease (IBD)], and related genetic overlap has been also reported in recent genetic research [ 69 – 71 ]. Moreover, the significance of the digestive system in the development of CVD is increasingly recognized [ 72 ]. For instance, the systemic inflammatory state in patients with IBD promotes atherosclerosis and mediates the occurrence and progression of CAD [ 72 ]. In summary, these shared biological pathways between impaired lung function and CVD offer therapeutic strategies for clinical practice of the coexisting conditions. Further research is warranted to comprehensively unravel these intricate mechanisms. Employing a comprehensive bidirectional MR analysis, this study reveals a significant causal association between impaired lung function and an elevated risk of CAD and stroke. These findings are largely consistent with observations from several prospective studies, where individuals with impaired lung function exhibited a higher risk of developing CVD [ 4 , 5 , 73 , 74 ]. Furthermore, our MR analysis not only corroborates previous findings [ 17 – 19 ], but also extends them in two critical aspects. Through the utilization of the multivariable MR, we minimized the impact of confounders. Additionally, we mitigated the influence of reverse causality by utilizing a bidirectional MR design. Our findings confirm the detrimental impact of impaired lung function on the risk of CVD and convey a crucial message that could provide insights for clinical and public health practices. Firstly, our work sheds light on the potential health risk in population with to impaired lung function. Secondly, this study provides evidence supporting personalized CVD screening that considers impaired lung function as a causal risk factor in the future for potentially enhancing preventive measures. Several limitations need to be acknowledged. Firstly, our findings were limited to population of European ancestry. Future studies involving other ancestry groups were warranted. Secondly, he two-sample MR analysis assumes a linear impact of exposure on the outcome [ 35 ]. However, recent study has suggested a potential U-shaped relationship between lung function and CVD risk, which could introduce complexities not captured in our analysis [ 73 ]. Thirdly, the genetic effects on lung function and CVD were predominantly derived from cross-sectional studies within the population, potentially lacking insights into longitudinal progression. Understanding alterations in lung function over time and their relationship with CVD progression requires further investigation. Lastly, our study utilized summary-level data due to inherent data limitations. While leveraging summary-level data offers the advantage of larger sample size, thereby enhancing statistical power in estimating causality, it is crucial to acknowledge its limitations. Unlike individual-level data, summary-level data lack the capacity to consider specific confounding factors unique to individual, such as local socioeconomic conditions, medical situations, and other individual-level factors. Future investigations should corroborate our findings using independent data. In conclusion, our study contributes significantly to the existing knowledge of the phenotypic association between impaired lung function and CVD by providing the evidence of genetic correlation, identifying pleiotropic loci, and revealing a potential causal association where impaired lung function may increase the risk of CVD. These findings shed light on possible biological mechanisms linking impaired lung function and CVD, offering valuable insights for future research endeavors aimed at reducing CVD risk. Abbreviations CVD cardiovascular diseases CAD coronary artery disease HF heart failure FVC forced vital capacity FEV1 forced expiratory volume in 1 second PEF peak expiratory flow COPD chronic obstructive pulmonary disease GWAS genome-wide association studies MR Mendelian randomization TWAS transcriptome-wide association studies LDSC linkage disequilibrium score regression CPASSOC cross-phenotypic association VEP Variant Effect Predictor PPH4 posterior probability of hypothesis 4 GTEx Genotype-Tissue Expression,BMI,body mass index. Declarations Ethics approval and consent to participate The ethical approval for each summary-level data can be found from the corresponding studies. Consent for publication Not applicable. Availability of data and materials This paper analyzes existing, publicly available data. Summary statistics for cardiovascular diseases are publicly available from https://www.ebi.ac.uk/gwas/. Summary statistics for lung function are publicly available from https://gwas.mrcieu.ac.uk/. The data for GTEx v8 multi-tissue expression are available from: http://gusevlab.org/projects/fusion/. Competing interests The authors declare that they have no competing interests. Finding This study was supported by National Natural Science Foundation of China (Grant number: 82102968 to Dr. Jian Zhou and Grant number: 82302624 to Dr. Mengyuan Lyu) and 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (Grant number: ZYGD18021 to Dr. Lunxu Liu). Authors' contributions Dongsheng Wu: formal analysis, validation, investigation, visualization, writing – original draft. Jian Zhou: formal analysis, funding acquisition, validation, visualization, investigation, writing – original draft. Mengyuan Lyu: formal analysis, validation, investigation, writing – original draft. Quan Zheng: investigation, validation, writing – original draft. Tengyong Wang: investigation, validation, writing – original draft. Yuchen Huang: methodology, writing – original draft. Lunxu Liu: conceptualization, funding acquisition, resources, supervision, project administration, writing – original draft, writing review & editing. All authors read and approved the final manuscript. Acknowledgments We express our gratitude for valuable public data resources provided by the researchers and databases. References Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982–3021. Oikonomou EK, Antoniades C. 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Genetic and observational associations of lung function with gastrointestinal tract diseases: pleiotropic and mendelian randomization analysis. Respir Res. 2023;24(1):315. Wang L, Cai Y, Garssen J, Henricks PAJ, Folkerts G, Braber S. The Bidirectional Gut-Lung Axis in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2023;207(9):1145–60. Budden KF, Gellatly SL, Wood DL, Cooper MA, Morrison M, Hugenholtz P, et al. Emerging pathogenic links between microbiota and the gut-lung axis. Nat Rev Microbiol. 2017;15(1):55–63. Gesualdo M, Scicchitano P, Carbonara S, Ricci G, Principi M, Ierardi E, et al. The association between cardiac and gastrointestinal disorders: causal or casual link? J Cardiovasc Med (Hagerstown). 2016;17(5):330–8. Cheng YJ, Chen ZG, Li ZY, Mei WY, Bi WT, Luo DL. Longitudinal change in lung function and subsequent risks of cardiovascular events: evidence from four prospective cohort studies. BMC Med. 2021;19(1):153. Hozawa A, Billings JL, Shahar E, Ohira T, Rosamond WD, Folsom AR. Lung function and ischemic stroke incidence: the Atherosclerosis Risk in Communities study. Chest. 2006;130(6):1642–9. Rodriguez M, Sabharwal B, Wei X, Krittanawong C, Verheyen E, Herzog E. The effect of cancer on outcomes of acute heart failure exacerbations: A 5-year nationwide analysis. J Am Coll Cardiol. 2018;71(11). Supplementary Files SupplemenatryFigure.docx SupplementaryTable.xlsx 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. 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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-4218165","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288322215,"identity":"35910f8d-206c-4c40-b3da-76ab8dbcb9c1","order_by":0,"name":"Dongsheng Wu","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Dongsheng","middleName":"","lastName":"Wu","suffix":""},{"id":288322216,"identity":"5464ce10-83f4-4714-9a43-db8efc8b254c","order_by":1,"name":"Jian Zhou","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhou","suffix":""},{"id":288322217,"identity":"b5ef399d-ff03-4971-94f2-c7ef3103d656","order_by":2,"name":"Mengyuan Lyu","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Mengyuan","middleName":"","lastName":"Lyu","suffix":""},{"id":288322218,"identity":"114b584b-888a-4f69-bc84-03e3e5162c94","order_by":3,"name":"Quan Zheng","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Quan","middleName":"","lastName":"Zheng","suffix":""},{"id":288322219,"identity":"8255b9f2-6cc7-4627-b42f-7090be91abcc","order_by":4,"name":"Tengyong Wang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Tengyong","middleName":"","lastName":"Wang","suffix":""},{"id":288322220,"identity":"09a6ee82-1005-4cbf-a34e-5215ca727485","order_by":5,"name":"Yuchen Huang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yuchen","middleName":"","lastName":"Huang","suffix":""},{"id":288322221,"identity":"8447a677-8b23-47c7-80b1-6df26b2082b2","order_by":6,"name":"Lunxu Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACPghlwcPA3sB4IIEYLWwQSoKHgecAA2lagCiB4QBRDmOTyD0mzcMgIWNw8/GDAw/bDucxsB8+ugG/lrxkY6AWHoPbaQYHEtsOFzPwpKXdwK8lx/AxREsCWEtigwSPGSEtBofBWm4e/0C0FqgtN3iItYXnjbHhHKAWyTM5BQcSzqUnthHyCz97jpnEGwYbe77jxzc+/FFmndjPfvgYXi1gwPgPxmCDxxTR4A+J6kfBKBgFo2BEAACtc0SSA+ga/QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3964-5378","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Lunxu","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-04-04 13:19:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4218165/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4218165/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54442361,"identity":"68d01be9-4ce0-4f96-b83e-d33014b22980","added_by":"auto","created_at":"2024-04-10 15:29:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":265474,"visible":true,"origin":"","legend":"\u003cp\u003eOverall study design of genome-wide cross-trait analysis. GWAS, genome-wide association study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4218165/v1/1714f7de110fcdcce568613b.png"},{"id":54442365,"identity":"36eec2c1-ae94-401f-9e26-3167c3d36cfb","added_by":"auto","created_at":"2024-04-10 15:29:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":462923,"visible":true,"origin":"","legend":"\u003cp\u003eLocal genetic correlation between lung function and CVD. In the QQ plots, red points represent genomic regions that contribute significant local genetic correlation as estimated by SUPERGNOVA (P \u0026lt; 0.05/2353).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4218165/v1/c3540577f71b6c45ed71b4fd.png"},{"id":54442366,"identity":"3ac200c7-8f33-4699-8c2e-b313361419d6","added_by":"auto","created_at":"2024-04-10 15:29:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":974258,"visible":true,"origin":"","legend":"\u003cp\u003ePleiotropic loci between lung function and CVD identified from cross-trait meta-analysis. (A) Pleiotropic loci identified between lung function and CAD. (B) Pleiotropic loci identified between lung function and HF. (C) Pleiotropic loci identified between lung function and stroke. In each circular Manhattan plot, the circle from periphery to center shows results of the cross-trait meta-analysis between each CVD and three lung function traits (dark blue: FVC, blue: FEV1, light blue: PEF). The red dots represent significant novel pleiotropic SNPs in cross-trait meta-analysis. The boxplot represents the number of known pleiotropic SNPs and novel pleiotropic SNPs between lung function and CVD. CVD, cardiovascular diseases; CAD, coronary artery disease; HF, heart failure; FVC, forced vital capacity; FEV1, forced expiratory volume in 1 second; PEF, peak expiratory flow.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4218165/v1/b5139562b3293feefae7f317.png"},{"id":54442367,"identity":"230aaa41-4625-4351-a5bf-cfdeca18a2c3","added_by":"auto","created_at":"2024-04-10 15:29:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1021703,"visible":true,"origin":"","legend":"\u003cp\u003eTissue enrichment analysis of associations between lung function and CVD. The genes were annotated for the significant variants identified from the Cross Phenotype Association analysis between (A) FVC and CAD, (B) FEV1 and CAD, (C) PEF and stroke. CV[75]D, cardiovascular diseases; CAD, coronary artery disease; FVC, forced vital capacity; FEV1, forced expiratory volume in 1 second; PEF, peak expiratory flow.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4218165/v1/8ec2d8ae0af2657d713281c7.png"},{"id":54442363,"identity":"56515ff8-2566-43de-b5e7-fbcc87ef4016","added_by":"auto","created_at":"2024-04-10 15:29:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":387143,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization analysis between lung function and CVD. The boxes represent the point estimate of the causal effects, and the error bars represent 95% confidence intervals. (A) Casual estimate from lung function to CVD, (B) Casual estimate from CVD to lung function. CVD, cardiovascular diseases; CAD, coronary artery disease; FVC, forced vital capacity; FEV1, forced expiratory volume in 1 second; PEF, peak expiratory flow.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4218165/v1/da774b1ebcabe8ea8e65ef8b.png"},{"id":56807634,"identity":"5fdaef56-e68c-4bee-abc1-803dd8361583","added_by":"auto","created_at":"2024-05-20 18:23:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3562884,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4218165/v1/3125a939-534e-469f-b6d7-486af0868617.pdf"},{"id":54442364,"identity":"7d2e6a98-d86d-4c5c-bd22-73e981d7a68c","added_by":"auto","created_at":"2024-04-10 15:29:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6553007,"visible":true,"origin":"","legend":"","description":"","filename":"SupplemenatryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-4218165/v1/0495d4a47b02060559a0726c.docx"},{"id":54442362,"identity":"8885f7b1-d388-4108-ab1c-ef4e4367f5f2","added_by":"auto","created_at":"2024-04-10 15:29:29","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":271468,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4218165/v1/21c23e99c17a8c341d3d115f.xlsx"}],"financialInterests":"","formattedTitle":"Genetic overlap between impaired lung function and cardiovascular diseases revealed by a large-scale genome-wide cross-trait analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eCardiovascular diseases (CVD) persist as the leading cause of global mortality and a significant contributor to disability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While common cardiovascular risk factors, such as smoking and adiposity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], remain primary contributors to CVD, accumulating evidence suggests that impaired lung function might be considered as an independent risk factor for CVD. Epidemiological studies have highlighted that individuals with impaired lung function, especially those with chronic obstructive pulmonary disease (COPD) and asthma, exhibit an increased risk of CVD after accounting for confounding factors [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, shared genetic susceptibility loci between COPD and CVD on 11q22.3 (RP11-563P16.1), 16q23.1 (CFDP1), and 19q13.32 (RSPH6A) have been identified [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Mechanisms potentially linking impaired lung function to CVD include increased inflammatory responses, compromised vascular reactivity, and endothelial dysfunction [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These factors may lead to a mismatch in the ventilation/perfusion ratio or inefficient removal of cardiotoxic substances [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Despite this, the shared genetic basis underlying their comorbidity have not been fully elucidated.\u003c/p\u003e \u003cp\u003eGenetic correlation analysis evaluates the correlation of genetic effects between two related traits, highlighting the shared etiology underlying such an association. Previous studies have suggested that both lung function and CVD are heritable traits, with estimated heritability of 8\u0026ndash;13% [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and 25\u0026ndash;40% [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], respectively. Furthermore, large-scale genome-wide association studies (GWAS) have detected numerous genetic variants significantly associated with both traits [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Specifically, several genetic loci linked to lung function (e.g., BCAS3, SMAD3, KCNE2, and KIAA1462) have been associated with susceptibility to CVD [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These findings indicate a shared genetic basis between lung function and CVD, yet the precise extent and nature of this relationship remain unclear. In this context, several Mendelian randomization (MR) studies have been performed to explore the causality between these traits, and consistently found a negative association of genetically predicted impaired lung function on risk of CVD [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nevertheless, these studies lack a systematic exploration of genetic correlations, comprehensive assessment of lung function traits, and some potential confounding factors that could influence the causal relationships.\u003c/p\u003e \u003cp\u003eAltogether, understanding the intricate relationship between impaired pulmonary function and CVD development is paramount, not only in deciphering the mechanism of disease susceptibility but also in delineating precise strategies for prevention and risk stratification. Therefore, we utilized a methodology, namely genome-wide cross-trait analysis, to evaluate the shared genetic basis between impaired lung function and the risk of CVD. This approach encompasses assessments of genetic correlations, cross-trait meta-analysis and transcriptome-wide association studies (TWAS) for identifying shared loci, and bidirectional MR analysis to delve into causal association. This study involved three lung function traits [forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), and peak expiratory flow (PEF)], and three prevalent CVD [coronary artery disease (CAD), heart failure (HF), and stroke]. The study design is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSummary-level genetic data\u003c/h2\u003e \u003cp\u003eIn this study, genetic associations for lung function were evaluated utilizing data from the IEU Open GWAS project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Three lung function traits, including FVC (ID: ukb-b-7953), FEV1 (ID: ukb-b-19657), and PEF (ID: ukb-b-12019), involved a total of 421,986 participates from the UK Biobank. While the most extensive GWAS data for lung function to date was conducted by Shrine et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], integrating data from the UK Biobank and SpiroMeta consortium, adjustments were made for covariates such as height and smoking, which are linked to both lung function and CVD. This adjustment introduces the potential for collider bias, as SNPs may be correlated with both these covariates and other risk factors [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Thus, only the GWAS data extracted from the UK Biobank was utilized in this study.\u003c/p\u003e \u003cp\u003eThe hitherto largest summary GWAS data for three prevalent CVD, namely CAD, HF, and stroke, were obtained. For CAD, summary GWAS data were obtained from a meta-analysis conducted by the CARDIoGRAMplusC4D consortium, incorporating 60,801 cases and 123,504 controls [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For HF, summary GWAS data were obtained from a meta-analysis by the HERMES, incorporating 47,309 cases and 930,014 controls [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For stroke, summary GWAS data were obtained from a meta-analysis by the ISGC, incorporating 40,585 cases and 406,111 controls [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This study included individuals exclusively of European ancestry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eGenetic correlation analysis\u003c/h2\u003e \u003cp\u003eWe initially utilized the linkage disequilibrium (LD) score regression (LDSC) to estimate the global genetic correlation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This method considers the effects of all other SNPs in LD with a specific SNP when estimating the GWAS effect size for that SNP. The genetic correlation estimates (r\u003csub\u003eg\u003c/sub\u003e) range from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1, where negative values denote negative correlation and positive values denote positive correlation. For our analysis, we employed precomputed LD scores derived from approximately 1.2\u0026nbsp;million SNPs shared among individuals of European ancestry from the HapMap3 reference panel, renowned for its high-quality imputation. To ascertain statistical significance, we applied Bonferroni correction, setting a P-value threshold at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05/9.\u003c/p\u003e \u003cp\u003eThe global genetic correlation serves to quantify the overall impact of genome-wide variants. Despite the minimal genetic correlation between two traits, there might exist specific genomic regions influencing both traits. Consequently, we computed the pairwise local genetic correlation utilizing SUPERGNOVA [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This method facilitates a measurement of similarity between trait pairs attributed to genetic variations within specific genomic regions, utilizing around 2,353 LD-independent regions. To correct for multiple testing, we applied a Bonferroni correction, setting the significance threshold at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05/2,353.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCross-trait meta-analysis\u003c/h2\u003e \u003cp\u003eThe presence of a significant genetic correlation often indicates potential horizontal pleiotropy (pleiotropy) or vertical pleiotropy (causal relationships). To explore this further, we conducted a cross-phenotypic association (CPASSOC) analysis to test the hypothesis that a single SNP can influence both traits [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Using GWAS summary data of related traits, CPASSOC can identify variants associated with at least one trait while accounting for population structure or hidden correlations. We performed pairwise SHet, a statistic particularly robust in the presence of heterogeneity, and identified independent loci through the clumping function in PLINK (1.9) with parameters (--clump-p1 5E-8, --clump-p2 1E-5, --clump-r2 0.2, --clump-kb 500) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePleiotropic SNPs were identified based on criteria: \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eCPASSOC\u003c/em\u003e\u003c/sub\u003e \u0026lt; 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003esingle\u0026minus;trait\u003c/em\u003e\u003c/sub\u003e \u0026lt; 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e observed in both traits. Additionally, a novel shared SNP was defined as follows: (i) not reaching genome-wide significance (5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e \u0026lt; \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003esingle\u0026minus;trait\u003c/em\u003e\u003c/sub\u003e \u0026lt; 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) in single-trait GWAS, and (ii) not in LD (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.2) with any previously reported SNPs in single-trait GWAS. To delve deeper into the biological implications of these shared SNPs, we annotated the linearly closest genes associated with the pleiotropic loci using the Ensemble Variant Effect Predictor (VEP) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFine mapping credible set analysis\u003c/h2\u003e \u003cp\u003eTo explore the pleiotropic loci identified from CPASSOC, we employed the FM-summary based on Bayesian likelihood fine-mapping algorithm to identify a 99% credible set of causal SNPs [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For this analysis, variants within a 500 kb proximity of the index SNP were extracted. Using a flat prior and the steepest descent approximation, this algorithm focuses on mapping the primary signals to determine potential causal variants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eColocalization analysis\u003c/h2\u003e \u003cp\u003eTo determine whether two GWAS signals originate from the same variant rather than distinct neighboring genetic variations, we conducted colocalization analysis employing the \u0026ldquo;Coloc\u0026rdquo; package, which operates within a Bayesian framework [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This analysis offers posterior probabilities for five hypotheses (H0-H4). For this purpose, variants within 500kb of each shared index SNP were extracted. We then calculated the posterior probability of hypothesis 4 (PPH4), which signifies the likelihood of a shared single causal variant between the two traits. A PPH4 value exceeding 0.5 indicates colocalized loci, suggesting a high probability of shared genetic influence through a single causal variant at that locus affecting both traits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTissue enrichment analysis\u003c/h2\u003e \u003cp\u003eTo gain biological insights into the shared genes from CPASSOC, we performed tissue enrichment analysis utilizing the FUMA GENE2FUNC pipeline, which integrates data from 54 distinct tissue types within GTEx (v.8) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To obtain more shared genes for the enrichment analysis, criteria were relaxed to \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eCPASSOC\u003c/em\u003e\u003c/sub\u003e \u0026lt; 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e \u0026lt; \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003esingle\u0026minus;trait\u003c/em\u003e\u003c/sub\u003e \u0026lt; 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. To correct for multiple testing, the Benjamin-Hochberg procedure was applied.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eTranscriptome wide association study (TWAS)\u003c/h2\u003e \u003cp\u003eDue to the crucial role of both tissue specificity and gene expression in elucidating shared biological mechanisms, we performed TWAS utilizing FUSION [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This analysis integrates summary-level GWAS data with expression weights obtained from GTEx (Genotype-Tissue Expression, version 8) across 49 distinct tissues [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Each analysis focused on a single tissue-trait pair. As an extension of TWAS, joint/conditional tests were performed to examine regions with multiple identified signals to obtain an independent set of gene-tissue pairs [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Shared gene-tissue pairs were determined by intersecting results among different traits.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBidirectional MR analysis\u003c/h2\u003e \u003cp\u003eTo further explore vertical pleiotropy between the two traits, a bidirectional two-sample MR analysis was conducted. For lung function, independent instrumental variables (IVs) were selected by aggregating all variants achieving genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) within a 10,000 kb range, employing a LD threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001. Similarly, independent IVs for CAD and HF were identified by aggregating variants meeting genome-wide significance criteria (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) within the same range and LD threshold. Additionally, variants reaching a relaxed selection threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) for stroke were aggregated within the same range and LD threshold. The instrumental strength was assessed via the F-statistic, where a F-statistic\u0026thinsp;\u0026lt;\u0026thinsp;10 indicates a weak IV [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur primary analysis employed the inverse-variance weighted (IVW) method [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], supplemented by MR-Egger [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and weighted median [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. To validate the assumptions of the MR analysis, several sensitivity analyses were conducted. Initially, we removed palindromic IVs characterized by identical alleles on both forward and reverse strands. Additionally, we excluded pleiotropic IVs linked to potential confounding factors according to the GWAS catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Furthermore, we utilized MR-PRESSO to identify and adjust for potential outliers. Multivariable MR [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] was employed to adjust for significant confounding factors such as smoking initiation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], alcohol consumption [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], body mass index (BMI) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], physical activity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and sleep duration [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These confounders were individually and collectively integrated with lung function to ensure a comprehensive analysis. Finally, a reverse-direction MR was performed to explore the causal impact of genetic predisposition to CVD on lung function.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGlobal genetic correlation\u003c/h2\u003e \u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, significant global genetic correlations were observed between FVC (r\u003csub\u003eg\u003c/sub\u003e = -0.20, P\u0026thinsp;=\u0026thinsp;1.83 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e), FEV1 (r\u003csub\u003eg\u003c/sub\u003e = -0.17, P\u0026thinsp;=\u0026thinsp;1.38 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e) and CAD. For HF, significant global genetic correlations were identified with FVC (r\u003csub\u003eg\u003c/sub\u003e = -0.18, P\u0026thinsp;=\u0026thinsp;6.39 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e) and FEV1 (r\u003csub\u003eg\u003c/sub\u003e = -0.17, P\u0026thinsp;=\u0026thinsp;2.59 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e). For stroke, significant global genetic correlations were identified with FVC (r\u003csub\u003eg\u003c/sub\u003e = -0.17, P\u0026thinsp;=\u0026thinsp;2.18 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e) and FEV1 (r\u003csub\u003eg\u003c/sub\u003e = -0.16, P\u0026thinsp;=\u0026thinsp;3.47 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), and suggestive global genetic correlation was identified with PEF (r\u003csub\u003eg\u003c/sub\u003e = -0.10, P\u0026thinsp;=\u0026thinsp;1.60 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\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\u003eGenome-wide genetic correlation between lung function and cardiovascular diseases.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular diseases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003er\u003csub\u003eg\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-0.25, -0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.83 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;16\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-0.22, -0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.38 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;11\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-0.11, 0.0004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.20 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-0.24, -0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.39 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;9\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-0.23, -0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.59 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;9\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-0.12, 0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-0.24, -0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.18 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;7\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFEV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-0.22, -0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.47 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;6\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e(-0.18, -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60 \u003cb\u003e\u0026times;\u003c/b\u003e 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBold-face: P\u0026thinsp;\u0026lt;\u0026thinsp;0.05/9. CAD, coronary artery disease; FVC, forced vital capacity; FEV1, forced expiratory volume in 1 second; PEF, peak expiratory flow.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLocal genetic correlation\u003c/h2\u003e \u003cp\u003eAfter dividing the entire genome into 2,353 LD-independent regions, our analysis revealed 13 genomic regions (FVC: 19p13.2, 2p24.1-p23.3, 2p13.3, 17q21.32-q21.33, 6q22.31-q22.33; FEV1: 19p13.2, 2p24.1-p23.3, 17q21.32-q21.33; PEF: 19p13.2, 16q23.1, 6q11.2, 2p21, 2p24.1-p23.3) suggesting a significant local genetic correlation between lung function and CAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). Of note, 19:10,030,690\u0026thinsp;\u0026minus;\u0026thinsp;11,279,257 at 19p13.2 emerged as a significant region in three analyses, harboring BRG1, a previously reported locus for both impaired lung function (e.g., asthma) and CAD, and ILF3, a previously reported susceptible gene for CVD[\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. For HF, four regions were identified with FVC, one with FEV1, and three with PEF (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F). For stroke, three regions were identified with FVC, one with FEV1, and two with PEF (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-I). Detailed information for each region is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCross‑trait meta‑analysis\u003c/h2\u003e \u003cp\u003eInspired by the notable genetic overlap between pulmonary function and CVD, our investigation extended to explore this genetic correlation at the level of individual variants. In total, 57 independent loci were identified from CPASSOC (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eCPASSOC\u003c/em\u003e\u003c/sub\u003e \u0026lt; 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003elung function\u003c/em\u003e\u003c/sub\u003e \u0026lt; 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eCVD\u003c/em\u003e\u003c/sub\u003e \u0026lt; 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). Among these, 31 loci were shared between lung function and CAD, 10 loci were shared between lung function and HF, and 16 loci were shared between lung function and stroke (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Notably, the loci mapped to KIAA1462, ABO, BAG3, and CDK6 (specifically, index SNPs: rs2014144, rs2519093, rs17617337, rs42235) exhibited the most significant shared signals in each pairwise CPASSOC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter removing SNPs correlated (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.2) with previously reported significant SNPs associated with single traits, our analysis revealed five novel SNPs shared between CVD and at least one pulmonary function trait, including three for FVC, one for FEV1, and one for PEF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the most significant novel SNP rs3851738 (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eCPASSOC\u003c/em\u003e\u003c/sub\u003e = 2.97 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e) was located near CFDP1, a gene pivotal in cardiac development and function [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The second most significant novel SNP rs288184 (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eCPASSOC\u003c/em\u003e\u003c/sub\u003e = 7.28 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e) was mapped to FBXL17, known for its involvement in cardiovascular physiology through protein degradation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The third most significant novel SNP rs3809114 (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eCPASSOC\u003c/em\u003e\u003c/sub\u003e = 9.21 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e) was located near to GIPR and SNRPD2. GIPR is implicated in regulating obesity and type 2 diabetes mellitus [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], while SNRPD2 operates as part of the components within the spliceosome-related activated pathway associated with small nuclear ribonucleoprotein polypeptides [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNovel pleiotropic loci between lung function and cardiovascular diseases (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eCPASSOC\u003c/em\u003e\u003c/sub\u003e \u0026lt; 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e \u0026lt; \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003esingle\u0026minus;trait\u003c/em\u003e\u003c/sub\u003e \u0026lt; 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, clumping r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eA1/A2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eLung function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eCardiovascular diseases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eCPASSOC\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGene \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSubtype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLung function and CAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3172494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48731487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e2.80 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c11\"\u003e \u003cp\u003e9.71 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.73 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eIP6K2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers288184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107347395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.20 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c11\"\u003e \u003cp\u003e9.56 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7.28 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eFBXL17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1964272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46190268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e4.20 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c11\"\u003e \u003cp\u003e2.92 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.21 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eGIPR, SNRPD2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3851738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75387533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFEV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e6.40 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c11\"\u003e \u003cp\u003e1.88 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.97 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eCFDP1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10786712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104596396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c11\"\u003e \u003cp\u003e5.08 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.45 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eCYP17A1, CYP17A1-AS1, PFN1P11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLung function and HF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3820888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201180023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e4.71 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c11\"\u003e \u003cp\u003e3.10 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.72 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSPATS2L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4657456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165591441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFEV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e9.60 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c11\"\u003e \u003cp\u003e3.50 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.89 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eRP11-306I1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLung function and stroke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2672587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124235355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e3.18 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c11\"\u003e \u003cp\u003e2.10 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.26 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eHTRA1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers149070156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124251048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e7.85 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c11\"\u003e \u003cp\u003e1.30 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.01 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eHTRA1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2295786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105616482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePEF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.43 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c11\"\u003e \u003cp\u003e6.80 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.10 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSH3PXD2A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e\u003csup\u003ea\u003c/sup\u003e Gene symbol mapped by VEP. A1, effect allele; A2, alternative allele; CHR, chromosome; CAD, coronary artery disease; FVC, forced vital capacity; FEV1, forced expiratory volume in 1 second; PEF, peak expiratory flow.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA total of two novel pleiotropic SNPs were shared between HF and lung function, with one associated with FVC (rs3820888) and the other linked to FEV1 (rs4657456). The former SNP is located near SPATS2L, recognized as a negative regulator impacting β2-adrenergic receptor levels [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], while the latter SNP was mapped to RP11-306I1.2, currently with little known biological function. Furthermore, three novel pleiotropic SNPs were identified between stroke and lung function, including two for FVC and one for PEF. Two out of the three shared SNPs (rs2672587 and rs149070156) were located near HTRA1, which is implicated in several severe pathologies such as arthritic diseases, age-related macular degeneration, and small cerebral vessel diseases [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The remaining SNP rs2295786 shared between stroke and PEF is mapped to SH3PXD2A, an adaptor protein involved in the extracellular matrix degradation and formation of podosomes and invadopodia [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDetailed annotations for each variant are provided in Table S3\u0026ndash;S5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of causal variants and colocalization\u003c/h2\u003e \u003cp\u003eUsing FM-summary algorithm, we determined a 99% credible set of causal SNPs for each of the 57 shared SNPs, offering potential targets for subsequent experimental research (Table S6\u0026ndash;8). In total, 1,421, 233, and 293 candidate SNPs were respectively identified as credible shared causal SNP sets between CAD, HF, stroke, and pulmonary function.\u003c/p\u003e \u003cp\u003eTo further determine whether genetic variants are responsible for the associations underlying distinct traits, a colocalization analysis was then conducted. This analysis revealed multiple loci that colocalized at the identical candidate SNPs that 13 out of 31 shared loci were identified between CAD and lung function, 10 out of 10 shared loci between HF and lung function, and 13 out of 16 shared loci between stroke and lung function (Table S9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTissue enrichment analysis\u003c/h2\u003e \u003cp\u003eThe shared genes associated with lung function (FVC and FEV1) and CAD were predominantly enriched in brain tissues, such as the amygdala, caudate basal ganglia, putamen basal ganglia, and substantia nigra (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). Additionally, genes shared between PEF and stroke were notably enriched in the substantia nigra. No significant tissue enrichment was observed for shared genes associated with other lung function traits and CVD (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome‑wide association study\u003c/h2\u003e \u003cp\u003eIn total, 30 genes exhibited significant TWAS associations between CAD and at least one lung function trait: 20 genes with FVC, 11 with FEV1, and 13 with PEF. These genes exhibited significant enrichment in tissues across the nervous, cardiovascular, digestive, exocrine/endocrine, and respiratory systems (Table S10-12). Notably, six genes were situated at pleiotropic loci identified from CPASSOC, including IP6K2, ABO, ILF3, TEX41, INPP5B, and WDR12. Among these genes, five genes had previously been linked to lung function and/or cardiovascular disease (GWAS Catalog accessed by December 16, 2023), such as ILF3 associated with FEV1, and ABO, TEX41, INPP5B, and WDR12 associated with CAD. For HF, four genes showed significant TWAS associations with at least one lung function trait, with the ABO gene also identified in CPASSOC. For stroke, seven genes displayed significant TWAS associations with at least one lung function trait, with CDK6 and SH3PXD2A identified in CPASSOC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eBidirectional Mendelian randomization\u003c/h2\u003e \u003cp\u003eInspired by the significant shared genetic basis, a bidirectional MR analysis was then conducted to investigate potential causal relationship. A total of 320, 261, and 137 SNPs were identified as IVs for FVC, FEV1, and PEF (Table S13-15), respectively. F-statistics indicated minimal evidence of weak instrument bias. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, using univariable MR, genetically predicted lung function (FVC: OR\u003csub\u003eIVW\u003c/sub\u003e = 0.69, 95% CI\u0026thinsp;=\u0026thinsp;0.62\u0026thinsp;\u0026minus;\u0026thinsp;0.77, P\u0026thinsp;=\u0026thinsp;3.80 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e; FVE1: OR\u003csub\u003eIVW\u003c/sub\u003e = 0.72, 95% CI\u0026thinsp;=\u0026thinsp;0.63\u0026thinsp;\u0026minus;\u0026thinsp;0.81, P\u0026thinsp;=\u0026thinsp;2.37 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e) presented an inverse association with the risk of CAD. Such a relationship remained consistent between lung function (FVC: OR\u003csub\u003eIVW\u003c/sub\u003e = 0.90, 95% CI\u0026thinsp;=\u0026thinsp;0.82\u0026thinsp;\u0026minus;\u0026thinsp;0.99, P\u0026thinsp;=\u0026thinsp;3.93 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e; FEV1: OR\u003csub\u003eIVW\u003c/sub\u003e = 0.86, 95% CI\u0026thinsp;=\u0026thinsp;0.77\u0026thinsp;\u0026minus;\u0026thinsp;0.96, P\u0026thinsp;=\u0026thinsp;9.69 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e; PEF: OR\u003csub\u003eIVW\u003c/sub\u003e = 0.81, 95% CI\u0026thinsp;=\u0026thinsp;0.69\u0026thinsp;\u0026minus;\u0026thinsp;0.95, P\u0026thinsp;=\u0026thinsp;1.08 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and stroke. No significant causal relationship was found between lung function and HF. Similar estimates in direction and magnitude were observed across MR-Egger regression, weighted median, and MR-PRESSO analyses. Sensitivity analyses, excluding pleiotropic or palindromic SNPs, yielded consistent findings, underscoring the robustness of the results. Furthermore, the bidirectional MR analysis did not reveal any genetic predisposition to CVD affecting lung function (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Tables S16-18). Detailed information of MR is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eConsidering potential mediating phenotypes or risk factors that may influence the correlation between lung function and CVD, we performed multivariable MR by incorporating each exposure with confounders (smoking, drinking, BMI, physical activity, and sleep duration). The effect of each lung function trait on CVD remained consistent in both direction and magnitude with univariable MR after adjusting for confounders (Figure S4).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eExploring shared genetic effects that are minimally impacted by environmental factors offers deeper insights into the common biological basis linking associations underlying two different phenotypes. To our knowledge, this study represents the first large-scale genome-wide cross-trait analysis, delving into the shared genetic basis between pulmonary function and three prevalent CVD. These findings provide evidence of global and regional shared genetic basis, confirming an intrinsic correlation between these traits. Furthermore, the genetic overlap was dissected into pleiotropy and causal relationships, reflected by pleiotropic loci and genes revealed by CPASSOC or TWAS, and causal relationships demonstrated by MR analyses.\u003c/p\u003e \u003cp\u003eOverall, statistically significant global genetic correlations were confirmed, with a shared genetic contribution of 16\u0026ndash;20% between pulmonary function and CVD. Significant local signals detected in multiple specific genomic regions further validate the overall genetic correlation, emphasizing a crucial shared biological basis. The local regional analysis largely aligns with the global genetic analysis and MR analysis. Among the identified 24 shared local regions, 19 display a negative correlation between impaired lung function and CVD.\u003c/p\u003e \u003cp\u003eThe significantly global and regional genetic correlations could result from the pleiotropic effects of genetic elements shared between lung function and CVD or indicate a potential causal link between lung function and CVD. In our subsequent analysis, we identified 57 independent loci influencing susceptibility to both lung function and CVD, reflecting considerable genetic pleiotropy. It is notable that many of the observed cross-trait effects have previously been associated with inflammation (ILF3, NOS3, SKI) [\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], stress responses (IP6K2, BAG3) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], and the blood system (KIAA1462, ABO, CFDP1, WDR12) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. These findings shed light on potential mechanistic pathways linking lung function with CVD. Through colocalization analysis, multiple genes (ABO, BAG3, TEX41, KIAA1462, INPP5B, ZC3HC1) exhibited a shared causal mechanism (PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.5). In this study, we specifically focused on two interesting examples, KIAA1462 (PPH4\u0026thinsp;=\u0026thinsp;0.65) and BAG3 (PPH4\u0026thinsp;=\u0026thinsp;0.99), both shared between impaired lung function and CVD.\u003c/p\u003e \u003cp\u003eKIAA1462 (also known as JCAD, a gene encoding a protein associated with CAD) has recently been identified as a new risk gene for CAD [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In CAD patients, the absence of KIAA1462 inhibits vascular inflammation, improves vascular activity, and suppresses atherosclerosis [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Additionally, its function in vascular development and tissue balance is associated with the occurrence of diseases related to impaired lung function [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Previous studies have shown decreased expression levels of KIAA1462 in COPD patients compared to non-COPD controls [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Furthermore, KIAA1462 has been identified as a gene associated with lung function [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. BAG3 possesses multifaceted regulation abilities in major biological processes such as apoptosis, development, cellular cytoskeleton organization, and autophagy, thereby mediating adaptive responses of cells to stress stimuli [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In the heart, BAG3 inhibits cell apoptosis, promotes autophagy, and maintains myofibrillar structure [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Particularly, recent experimental studies suggest that BAG3-mediated sarcomere turnover forms the basis for maintaining myofilament function and is associated with the development of HF [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Moreover, BAG3 expression levels in COPD blood samples are higher than in normal blood samples, potentially influencing COPD development by regulating autophagy [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Further experimental research is needed to provide more detailed functional annotations for these identified genetic loci, especially those related to impaired lung function and CVD.\u003c/p\u003e \u003cp\u003eCombined GWAS summary data with GTEx tissue expression data, the TWAS analysis elucidated putative shared mechanisms between impaired lung function and CVD at the gene-tissue level. Consistent with findings from CPASSOC, multiple genes associated with inflammation (ILF3), stress response (IP6K2), and the blood system (ABO, WDR12, SH3PXD2A) were shared between lung function and CVD. Furthermore, TWAS identified multiple genes in brain-related tissues (such as cortex, basal ganglia, hypothalamus), indicating a potential neurobiological mechanism. Consistently, GTEx tissue enrichment analysis corroborated these findings, demonstrating significant enrichment of shared genes in brain-related tissues. This confirms well-established knowledge that both impaired lung function and CVD are associated with multiple brain-related disorders [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Indeed, heart-brain interactions and brain-lung axis has been recently highlighted for complex communications in regulating multiple systemic diseases. For example, impaired lung function could potentially lead to an increased risk of brain disorders, such as depression and cognitive dysfunction [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], while individuals with these brain disorders exhibit a significantly elevated risk of CVD [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Additionally, we observed shared regulatory features in the digestive system, especially between lung function and CAD. Observational studies have reported comorbidities between respiratory diseases (i.e., COPD) and digestive diseases [i.e., gastroesophageal reflux disease and inflammatory bowel disease (IBD)], and related genetic overlap has been also reported in recent genetic research [\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Moreover, the significance of the digestive system in the development of CVD is increasingly recognized [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. For instance, the systemic inflammatory state in patients with IBD promotes atherosclerosis and mediates the occurrence and progression of CAD [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. In summary, these shared biological pathways between impaired lung function and CVD offer therapeutic strategies for clinical practice of the coexisting conditions. Further research is warranted to comprehensively unravel these intricate mechanisms.\u003c/p\u003e \u003cp\u003eEmploying a comprehensive bidirectional MR analysis, this study reveals a significant causal association between impaired lung function and an elevated risk of CAD and stroke. These findings are largely consistent with observations from several prospective studies, where individuals with impaired lung function exhibited a higher risk of developing CVD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Furthermore, our MR analysis not only corroborates previous findings [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], but also extends them in two critical aspects. Through the utilization of the multivariable MR, we minimized the impact of confounders. Additionally, we mitigated the influence of reverse causality by utilizing a bidirectional MR design. Our findings confirm the detrimental impact of impaired lung function on the risk of CVD and convey a crucial message that could provide insights for clinical and public health practices. Firstly, our work sheds light on the potential health risk in population with to impaired lung function. Secondly, this study provides evidence supporting personalized CVD screening that considers impaired lung function as a causal risk factor in the future for potentially enhancing preventive measures.\u003c/p\u003e \u003cp\u003eSeveral limitations need to be acknowledged. Firstly, our findings were limited to population of European ancestry. Future studies involving other ancestry groups were warranted. Secondly, he two-sample MR analysis assumes a linear impact of exposure on the outcome [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, recent study has suggested a potential U-shaped relationship between lung function and CVD risk, which could introduce complexities not captured in our analysis [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Thirdly, the genetic effects on lung function and CVD were predominantly derived from cross-sectional studies within the population, potentially lacking insights into longitudinal progression. Understanding alterations in lung function over time and their relationship with CVD progression requires further investigation. Lastly, our study utilized summary-level data due to inherent data limitations. While leveraging summary-level data offers the advantage of larger sample size, thereby enhancing statistical power in estimating causality, it is crucial to acknowledge its limitations. Unlike individual-level data, summary-level data lack the capacity to consider specific confounding factors unique to individual, such as local socioeconomic conditions, medical situations, and other individual-level factors. Future investigations should corroborate our findings using independent data.\u003c/p\u003e \u003cp\u003eIn conclusion, our study contributes significantly to the existing knowledge of the phenotypic association between impaired lung function and CVD by providing the evidence of genetic correlation, identifying pleiotropic loci, and revealing a potential causal association where impaired lung function may increase the risk of CVD. These findings shed light on possible biological mechanisms linking impaired lung function and CVD, offering valuable insights for future research endeavors aimed at reducing CVD risk.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiovascular diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecoronary artery disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eheart failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFVC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eforced vital capacity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFEV1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eforced expiratory volume in 1 second\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epeak expiratory flow\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egenome-wide association studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etranscriptome-wide association studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elinkage disequilibrium score regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPASSOC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecross-phenotypic association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVEP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariant Effect Predictor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPH4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eposterior probability of hypothesis 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGTEx\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenotype-Tissue Expression,BMI,body mass index.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical approval for each summary-level data can be found from the corresponding studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper analyzes existing, publicly available data. Summary statistics for cardiovascular diseases are publicly available from https://www.ebi.ac.uk/gwas/. Summary statistics for lung function are publicly available from https://gwas.mrcieu.ac.uk/. The data for GTEx v8 multi-tissue expression are available from: http://gusevlab.org/projects/fusion/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National Natural Science Foundation of China (Grant number: 82102968 to Dr. Jian Zhou and Grant number: 82302624 to Dr. Mengyuan Lyu) and 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (Grant number: ZYGD18021 to Dr. Lunxu Liu).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDongsheng Wu:\u003c/strong\u003e formal analysis, validation, investigation, visualization, writing \u0026ndash; original draft. \u003cstrong\u003eJian Zhou:\u0026nbsp;\u003c/strong\u003eformal analysis, funding acquisition, validation, visualization, investigation, writing \u0026ndash; original draft.\u003cstrong\u003e\u0026nbsp;Mengyuan Lyu:\u0026nbsp;\u003c/strong\u003eformal analysis, validation, investigation, writing \u0026ndash; original draft. \u003cstrong\u003eQuan Zheng:\u003c/strong\u003e investigation,\u0026nbsp;validation, writing \u0026ndash; original draft. \u003cstrong\u003eTengyong Wang:\u003c/strong\u003e investigation, validation, writing \u0026ndash; original draft. \u003cstrong\u003eYuchen Huang:\u003c/strong\u003e methodology, writing \u0026ndash; original draft. \u003cstrong\u003eLunxu Liu:\u003c/strong\u003e conceptualization, funding acquisition, resources, supervision, project administration, writing \u0026ndash; original draft, writing review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude for valuable public data resources provided by the researchers and databases.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u0026ndash;2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982\u0026ndash;3021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOikonomou EK, Antoniades C. The role of adipose tissue in cardiovascular health and disease. Nat Rev Cardiol. 2019;16(2):83\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKondo T, Nakano Y, Adachi S, Murohara T. Effects of Tobacco Smoking on Cardiovascular Disease. 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J Am Coll Cardiol. 2018;71(11).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"lung function, cardiovascular diseases, genetic correlation, genome-wide cross-trait analysis","lastPublishedDoi":"10.21203/rs.3.rs-4218165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4218165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIncreasing evidence have highlighted the phenotypic association between impaired lung function and cardiovascular diseases (CVD), but underlying shared genetic basis contributing to this association remain largely unexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUtilizing summary data from the large-scale genome-wide association studies, our investigation delved into several aspects: the global and local genetic correlations, pleiotropic loci, and causal association between lung function and three prevalent CVD, namely coronary artery disease (CAD), heart failure (HF), and stroke.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur findings revealed significantly negative genetic correlations between lung function and CAD (r\u003csub\u003eg\u003c/sub\u003e = -0.20 to -0.17), HF (r\u003csub\u003eg\u003c/sub\u003e = -0.18 to -0.17), and stroke (r\u003csub\u003eg\u003c/sub\u003e = -0.17 to -0.16). After separating the whole genome into 2,353 independent regions, we determined 13, 4, and 3 significant regions for CAD, HF, and stroke with lung function, respectively. Furthermore, the global and local shared genetic basis were confirmed by the identification of multiple pleiotropic loci and multiple shared gene\u0026ndash;tissue pairs. The pleiotropic loci were largely enriched in brain-related tissues, while shared gene-tissue pairs exhibited significant enrichment across nervous, cardiovascular, digestive, endocrine/exocrine, and respiratory systems. Mendelian randomization analysis demonstrated a significant causal association of genetically predicted lung function with CAD [OR (odds ratio)\u0026thinsp;=\u0026thinsp;0.69 to 0.72] and stroke (OR\u0026thinsp;=\u0026thinsp;0.81 to 0.90) but not with HF. No evidence of reverse causality was found.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings comprehensively uncover a shared genetic architecture as well as a causal association between impaired lung function and CVD, emphasizing the opportunity to enhance the quality of existing intervention strategies.\u003c/p\u003e","manuscriptTitle":"Genetic overlap between impaired lung function and cardiovascular diseases revealed by a large-scale genome-wide cross-trait analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 15:29:24","doi":"10.21203/rs.3.rs-4218165/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":"77939735-f14d-45f1-8ce9-b1390ef89433","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-20T18:18:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 15:29:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4218165","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4218165","identity":"rs-4218165","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}