Potential drug targets for asthma identified through Mendelian randomization analysis

Potential drug targets for asthma identified through Mendelian randomization 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 Potential drug targets for asthma identified through Mendelian randomization analysis Xingxuan Chen, Yu Shang, Danting Shen, Si Shi, Zhe wen, Lijuan Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4921839/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2025 Read the published version in Respiratory Research → Version 1 posted 8 You are reading this latest preprint version Abstract Background The emergence of new molecular targeted drugs marks a breakthrough in asthma treatment, particularly for severe cases. Yet, options for moderate-to-severe asthma treatment remain limited, highlighting the urgent need for novel therapeutic drug targets. In this study, we aimed to identify new treatment targets for asthma using the Mendelian randomization method and large-scale genome-wide association data (GWAS). Methods We utilized GWAS data from the UK Biobank (comprising 56,167 patients and 352,255 control subjects) and the FinnGen cohort (including 23,834 patients and 228,085 control subjects). Genetic instruments for 734 plasma proteins and 154 cerebrospinal fluid proteins were derived from recently published GWAS. Bidirectional Mendelian randomization analysis, Steiger filtering, colocalization, and phenotype scanning were employed for reverse causal inference detection, further substantiating the Mendelian randomization results. A protein-protein interaction network was also constructed to reveal potential associations between proteins and asthma medications. Results Under Bonferroni significance conditions, Mendelian randomization analysis revealed causal relationships between seven proteins and asthma. In plasma, we observed that an increase of one standard deviation in IL1R1[1.30 (95% CI, 1.20–1.42)], IL7R[1.07 (95% CI, 1.04–1.11)], ECM1[1.03 (95% CI, 1.02–1.05)], and CD200R1[1.18 (95% CI, 1.09–1.27)] were associated with an increased risk of asthma, while an increase in ADAM19 [0.87 (95% CI, 0.82–0.92)] was found to be protective. In the brain, each 10-fold increase in IL-6 sRa [1.29 (95% CI, 1.15–1.45)] was associated with an increased risk of asthma, while an increase in Layilin [0.61 (95% CI, 0.51–0.73)] was found to be protective. None of the seven proteins exhibited a reverse causal relationship. Colocalization analysis indicated that ECM1 (coloc.abf-PPH4 = 0.953), IL-6 sRa (coloc.abf-PPH4 = 0.966), and layilin (coloc.abf-PPH4 = 0.975) shared the same genetic variation as in asthma. Conclusion A causal relationship exists between genetically determined protein levels of IL1R1, IL7R, ECM1, CD200R1, ADAM19, IL-6 sRa, and Layilin (LAYN) and asthma. Moreover, the identified proteins may serve as attractive drug targets for asthma, especially ECM1 and Layilin (LAYN). However, further research is required to comprehensively understand the roles of these proteins in the occurrence and progression of asthma. Asthma Drug targets Mendelian randomization Therapeutic targets Cerebrospinal fluid proteins Plasma proteins Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Asthma is a chronic inflammatory respiratory disease characterised by airway inflammation, hypersensitivity, and hyperresponsiveness, leading to symptoms such as breathing difficulties, coughing, chest tightness, and wheezing 1 , 2 . Asthma exhibits significant heterogeneity in clinical symptoms, severity, and treatment outcomes 3 . With an in-depth understanding of the pathogenesis of asthma and continuous advancements in drug development technologies, an increasing number of novel drug targets have emerged and been applied in clinical treatment 4 , 5 . These new drugs act on key processes in the pathophysiology of asthma through various mechanisms, including anti-inflammatory effects, airway dilation, and modulation of immune responses 6 , bringing new hope and opportunities for asthma treatment. However, there are significant variations in the response to medication among different patients in clinical settings, posing a substantial therapeutic challenge for both healthcare teams and patients. In addition to daily management strategies, there is a growing need for novel therapeutic targets to address this challenge. Although large-scale randomised controlled trials (RCTs) are effective for evaluating drug treatment strategies, but they are both time-consuming and expensive 7 . Genome-wide Association Studies (GWAS) have identified numerous genetic loci associated with disease risk 8 , 9 , providing evidence for determining the molecular pathways involved in drug interventions for diseases. Moreover, evidence suggests that drug targets with human genetic support are more than twice as likely to be approved as those without 10 , 11 . Therefore, incorporating genetics into drug development is feasible. Mendelian randomization (MR) is a statistical analysis in genetics that can predict the efficacy of drugs by mimicking randomised controlled trials, and has been widely used in drug target development and drug repurposing 12 – 14 . Leveraging genome-wide association study (GWAS) data, MR utilizes single nucleotide polymorphisms (SNPs) closely associated with exposure as genetic tools to infer whether the association between exposure and outcome is causal. GWAS of plasma protein levels have identified genetic variations associated with proteins, commonly known as protein quantitative trait loci (pQTLs), which can guide the search for pathogenic genes and disease pathways, providing opportunities to use Mendelian randomization to explore drug targets 15 , 16 . Combining human genetics with high-throughput, population-scale proteomics helps to explore the relationship between the human genome and diseases. So far, few Mendelian randomization (MR) studies have explored drug targets for asthma using Protein Quantity Trait Loci (pQTL). In this study, we used the Mendelian randomization method and large-scale genome-wide association research data to identify new therapeutic targets for asthma. Figure 1 describes the design of this study. METHODS Data Cerebrospinal fluid pQTL (CSF pQTL) data were obtained from a study reporting 274 pQTLs for 184 CSF proteins 17 . Plasma pQTL data were retrieved from another study 18 that consolidated 3,606 pQTLs for 2,656 proteins using five previously published GWAS datasets 19 – 23 . Three MR assumptions influence the genetic instrumental variables used for MR analysis, so we only included pQTLs that met the following criteria: (1) showing genome-wide significant associations (P < 5×10 − 8 ); (2) located outside the major histocompatibility complex (MHC) region of humans (chr6, 26–34 Mb); (3) exhibiting independent associations [linkage disequilibrium (LD) r2 10 [F = (beta/se)2]. Finally, 154 cis-acting pQTLs for 154 proteins and 738 cis-acting SNPs for 734 proteins were included from the CSF and plasma proteins, respectively (Additional file 1:Table S1 ). Data checks were conducted on the original studies to ensure reliability. Additionally, the corresponding plasma pQTL data (4,907 plasma proteins measured in 35,559 subjects) were extracted from a study by Ferkingstad et al. 24 for external validation. Asthma data were derived from the GWAS data of the UK Biobank, comprising 56,167 patients and 352,255 control subjects 25 . Summary data for the GWAS analysis were sourced from the IEU Open GWAS project and can be downloaded from https://gwas.mrcieu.ac.uk/ . Asthma data for external validation were obtained from the FinnGen cohort 26 (23,834 patients and 228,085 control subjects; https://www.finngen.fi/en ). MR analysis The R package TwoSampleMR (version 4.3.1) ( https://github.com/MRCIEU/TwoSampleMR ) was used for MR analysis. Exposure and outcome data were imported and harmonised using built-in functions within the R package (harmonise_data). If only one pQTL was available for a given protein, the Wald ratio was used to compute the MR estimate for each SNP. When two or more instrumental variables were available, the inverse variance weighted (IVW) method was applied, followed by a heterogeneity analysis 27 . Odds ratios (OR) for increased asthma risk were presented as the increase in the standard deviations (SD) of plasma protein levels and a 10-fold increase in CSF protein levels. In the primary analysis, considering the false positives caused by multiple testing, Bonferroni correction was applied to determine the significance threshold after multiple testing, with a P-value < 5.63 × 10 − 5 (P = 0.05/888) defined as significant 28 . For external validation, MR analysis was performed only on the initially identified proteins, with the P-value threshold set at 0.05. The preliminary findings were validated by extracting the same SNPs used in the primary analysis from the database as genetic instruments and by using the most significant SNP for that protein across the genome in the same database as a genetic instrument. Colocalization analysis Colocalization analysis was used to identify whether the two phenotypes were driven by the same causal variant in a particular region, thereby strengthening the evidence of association between the two phenotypes. colocalization analysis for asthma risk is conducted using the R package "coloc" ( https://github.com/chr1swallace/coloc ). In a given region, the posterior probabilities obtained from the Colocalization analysis correspond to one of the following five hypotheses: PPH0, SNPs unrelated to either trait; PPH1, SNPs related to protein expression but not asthma risk; PPH2, SNPs related to asthma risk but not protein expression; PPH3, SNPs related to both asthma risk and protein expression but driven by different SNPs; and PPH4, SNPs related to both asthma risk and protein expression and driven by a common SNP. The significance threshold for colocalization was set at PPH4 > 0.80, where proteins colocalising with asthma risk can be considered potential drug targets 29 . Reverse causality detection Using asthma data from the primary analysis as exposure and the initially identified proteins as outcome(Additional file 1:Table S2 ), a bidirectional MR analysis was conducted to detect potential reverse causal relationships. The inverse variance-weighted method (IVW) 30 , weighted mode (WM), weighted median method (WME), simple mode (SM), and MR-Egger regression methods were employed for effect estimation. Statistical significance was set at P < 0.05 31 . Phenome-wide scan To further assess the pleiotropic effects of potential drug targets, we used the selected pQTLs as key terms to search the literature for associations with other traits. The PhenoScanner ( http://www.phenoscanner.medschl.cam.ac.uk/ ) was used to investigate whether SNPs were significantly associated with other traits and any known asthma risk factors, including metabolic traits, proteins, or clinical characteristics. Plasma and cerebrospinal fluid protein comparative analysis We hypothesised that due to the blood-brain barrier, there is a minimal correlation between pQTLs identified in the plasma and cerebrospinal fluid. Therefore, we investigated the correlation between pQTLs identified in the cerebrospinal fluid and the effect estimates of plasma proteins using MR analysis through Spearman correlation analysis. Different P-value thresholds were set to explore whether the correlation changed with increasing significance levels. Constructing protein-protein interaction networks Protein-protein interaction (PPI) networks consist of proteins that interact with each other, contributing to various biological processes such as signal transduction, gene expression regulation, energy and substance metabolism, and cell cycle control. By evaluating and analysing PPI networks, researchers can gain insights into how proteins interact within cells. In this study, we utilised the Search Tool for the Retrieval of Interacting Genes (STRING) database ( https://string-db.org/ ) with a confidence score of 0.4 32 to investigate the interactions among significant proteins. The PPI results were further visualised using Cytoscape (V3.9.1) 33 . Additionally, we summarised the existing therapeutic targets for asthma on the market and explored the corresponding drug targets based on the DrugBank database ( https://www.drugbank.ca ) associated with these asthma-related genes to predict potential therapeutic drugs 34 . RESULTS Screening for asthma-causing proteins in the proteome We conducted an MR analysis of 154 proteins in the cerebrospinal fluid and 734 proteins in the plasma. Under Bonferroni significance (P < 5.63×10 − 5), seven proteins were found to be associated with asthma risk (Table 1 and Fig. 2 A and B), including five plasma proteins: interleukin 1 receptor type 1 (IL1-R1), interleukin 7 receptor (IL-7R), extracellular matrix protein 1 (ECM1), CD200 receptor 1 (CD200R1), ADAM metallopeptidase domain 19 (ADAM19), and two cerebrospinal fluid proteins: IL-6 sRa (interleukin 6 receptor, IL6R) and Layilin (LAYN). Table 1 MR results for plasma and CSF proteins significantly associated with asthma after Tissue Protein UniProt ID SNPa Effect allele OR (95% CI)b P value PVE F statistics Author Plasma IL1R1 P14778 rs7588201 A 1.30 (1.20, 1.42) 1.63E-09 1.21% 39.3 Emilsson Plasma IL7R P16871 rs11957503 G 1.07 (1.04, 1.11) 7.48E-06 8.69% 94.74 Suhre Plasma ECM1 Q16610;A0A140VJI7 rs13294 A 1.03 (1.02, 1.05) 2.92E-05 36.97% 584.28 Suhre Plasma CD200R1 Q8TD46 rs6791672 A 1.18 (1.09, 1.27) 1.74E-05 1.45% 48.72 Sun Plasma ADAM19 Q9H013 rs7728609 C 0.87 (0.82, 0.92) 6.17E-07 2.72% 89.52 Emilsson CSF IL-6 sRa P08887 rs4129267 T 1.29 (1.15, 1.45) 1.93E-05 36.34% 476.66 Yang CSF Layilin Q6UX15 rs674230 G 0.61 (0.51, 0.73) 1.14E-07 14.18% 137.92 Yang Bonferroni correction PVE = proportion of variance explained. a All SNPs used were cis-acting. b Odds ratios for increased risk of asthma were expressed as per SD increase in plasma protein levels and per 10-fold increase in CSF protein levels. Specifically, an increase in IL1-R1 (OR = 1.30; 95%CI = 1.20–1.42, P = 1.63×10 − 9 ), IL-7R (OR = 1.07; 95%CI = 1.04–1.11, P = 7.48×10 − 6 ), ECM1 (OR = 1.03; 95%CI = 1.02–1.05, P = 2.92×10 − 5 ), CD200R1 (OR = 1.18; 95%CI = 1.09–1.27, P = 1.74×10 − 5 ), and IL-6 sRa (OR = 1.29; 95%CI = 1.15–1.45; P = 1.93×10 − 5 ) increased the risk of asthma. Conversely, an increase in ADAM19 (OR = 0.87; 95%CI = 0.82–0.92; P = 6.17 ×10 − 7 ) and Layilin (OR = 0.61; 95%CI = 0.51–0.73; P = 1.14×10 − 7 ) decreased the risk of asthma. The heterogeneity tests revealed no significant heterogeneity or outliers(Additional file 1:Table S3). Sensitivity analysis Due to the pleiotropic effects of instrumental variables (IVs) on MR, we conducted several sensitivity analyses. First, reverse MR analysis on the seven identified proteins and asthma did not reveal any causal effects of asthma on the levels of the seven identified proteins, and further directionality was ensured through Steiger filtering(Table 2 and Supplementary Fig. 1). Second, using the coloc package in R, we conducted colocalization analysis on genes within ± 1 Mb regions (upstream or downstream) of the seven identified pQTLs to further identify causative genetic variants associated with asthma, using PPH4 > 0.80 as a significant threshold. The results indicated that the plasma protein ECM1 shared causative variation with asthma (PP.H4 = 0.95), and the cerebrospinal fluid proteins IL-6 sRa (PP.H4 = 0.97) and Layilin (PP.H4 = 0.98) shared causative variations with asthma༈Table 2 , Supplementary Fig. 2 and Additional file 1:Table S7༉. Therefore, three potential druggable proteins were identified from the colocalization analysis, providing evidence of shared genetic effects between pQTLs and asthma risk. Table 2 Summary of reverse causality detection, Bayesian co-localization analysis and phenotype scanning on seven potential causal proteins Tissue Protein UniProt ID SNP Bidirectional MR (MR-IVW) a Steiger filtering coloc.abf. PPH4 Previously reported associations Plasma IL1R1 P14778 rs7588201 1.038 (0.985–1.093) Ture 1.201×10 − 8 0.016 Eczema b Allergic disease c Plasma IL7R P16871 rs11957503 0.973 (0.941–1.007) Ture 8.946×10 − 21 0.022 White blood cell b Plasma ECM1 Q16610; rs13294 0.980 (0.944–1.017) Ture 1.536×10 − 107 0.953 Monocyte c Blood platelet c Atopic dermatitis b Plasma CD200R1 Q8TD46 rs6791672 0.986 (0.953–1.021) Ture 5.767×10 − 11 0.181 Eczema c Allergic rhinitis b Plasma ADAM19 Q9H013 rs7728609 0.980 (0.948–1.013) Ture 3.876×10 − 19 0.0004 1 second forced expiratory volume c Peak expiratory flow c Age-related macular degeneration. b CSF IL-6 sRa P08887 rs4129267 1.016 (0.953–1.021) Ture 2.281×10 − 88 0.966 Red blood cell Distribution width c Coronary artery disease. b CSF Layilin Q6UX15 rs674230 0.954 (0.912–0.998) Ture 4.868×10 − 29 0.975 Allergic disease c MR-IVW = Mendelian randomization with inverse variance weighted method; PP = posterior probability; a Odds ratios per SD increase in plasma protein levels and per 10-fold increase in CSF protein levels as asthma risk increased. b SNP associated with traits directly. c SNP associated with traits mediated by its proxy. Third, by reviewing the literature and using PhenoScanner, we explored the potential confounding factors for the identified proteins. The results showed that IL1-R1 is associated with leukocytes and allergic diseases in humans, such as neutrophils and eosinophils; IL-7R is related to leukocytes in humans, including neutrophils, eosinophils, basophils, and lymphocytes; ECM1 is associated with monocytes, platelets, and atopic dermatitis; CD200R1 is related to eosinophils, neutrophils, basophils, hay fever, allergic rhinitis, or eczema in humans; ADAM19 is associated with forced expiratory volume in one second, peak expiratory flow rate, age-related macular degeneration; IL-6 sRa is related to leukocytes, haemoglobin concentration, mean corpuscular haemoglobin, monocytes, red cell distribution width, coronary artery disease in humans; Layilin is associated with allergic diseases. Asthma is an allergic disease, and we found that IL1R1 and Layilin are both associated with allergic diseases, whereas ECM1 is related to atopic dermatitis. Atopic dermatitis and asthma have been previously reported, indicating that these diseases may share a common aetiology. Furthermore, no significant confounding factors were identified(Table 2 and Additional file 1:Table S4). Comparison of proteins in plasma and cerebrospinal fluid At the protein level, a non-significant positive correlation (Spearman correlation coefficient = 0.044) was observed between the MR results of cerebrospinal fluid and plasma. Additionally, when using different P-value threshold restrictions to limit the number of proteins included in the analysis, the positive correlation persisted and remained non-significant (Supplementary Fig. 3). Next, the three potential drug target proteins identified through colocalization screening were loaded into the STRING database ( https://cn.string-db.org/ ) for network construction. The resulting file was imported into Cytoscape for visualisation of protein-protein interaction (PPI) networks, which displayed interactions between the three drug-target proteins and other proteins(Supplementary Figure S4). Furthermore, we constructed a PPI network with the additional four identified proteins and three potential drug target proteins, which revealed interactions between different proteins(Supplementary Figure S5). Notably, we observed a strong and reliable interaction between IL1-R1 and IL-7R. IL1-R1 is associated with anti-IL7R monoclonal antibodies (IL-7R), and IL-7R is the target of OSE-127 and GSK-2618960(Fig. 3 and Additional file 1:Table S5). Association between potential drug targets and current asthma medications We constructed a protein-protein interaction (PPI) network by associating three potential drug targets with four asthma drug targets (IgE, IL-5, IL-4, and TSLP). Seven proteins were loaded into the STRING database for network creation and the resulting file was imported into Cytoscape for PPI network visualisation(Supplementary Figure S6). We found that IL-6 sRa (IL-6R) is associated with IL-5 and IL-4. The latter are targets of the following asthma drugs: anti-IL-5/IL-5R monoclonal antibodies (Mepolizumab, Benralizumab, Reslizumab), and anti-IL-4Rα monoclonal antibody (Dupilumab) (Fig. 3 ). We also searched the DrugBank database for drugs targeting the identified potential pathogenic proteins (Additional file 1:Table S6). External validation of asthma Based on the primary analysis of pQTL data, we searched for the same variants in different datasets for external validation as well as significant variants unique to different datasets to enhance the scientific rigor of Mendelian randomization studies on drug target identification. Plasma pQTL data for exposure were extracted from the study by Ferkingstad et al., whereas asthma data for outcomes were obtained from the FinnGen database. The results revealed that IL1-R1 was also found to be associated with asthma in different databases, with an increased risk of asthma associated with elevated IL1-R1 levels (OR = 1.22; 95% CI = 1.09–1.37, p = 6.71×10 − 4 ). ADAM19 and IL-7R were also associated with asthma outcomes in the FinnGen database when the same variants identified in the primary analysis were used as exposure factors (Fig. 4 and Additional file 1:Table S8). DISCUSSION Research has demonstrated the efficacy of targeted drug therapy in improving asthma control; however, significant variations in drug responses exist among patients, underscoring the pressing need for more effective targeted therapies. Therefore, the quest for viable for asthma treatment targets is paramount. To date, Li et al. 35 have identified asthma genes through eQTL analysis of bronchial epithelial cells and bronchoalveolar lavage fluid; Zaid et al. 36 and Nieuwenhuis et al. 37 have identified a series of asthma drug targets based on GWAS and eQTL analysis; Wang et al. 38 have identified a series of asthma drug targets based on GWAS and pQTL analysis. In our study, using a larger cerebrospinal fluid protein database and plasma protein identification, we identified more comprehensive asthma-targeted protein sites. We identified seven proteins associated with asthma risk, three of which may serve as new asthma treatment targets. Furthermore, using protein-protein interaction networks and DrugBank, we identified drugs that may have therapeutic potential for patients with asthma. Our study complements previous related research by identifying seven proteins with a causal relationship with asthma risk. Through colocalization analysis of the seven initially identified proteins, three proteins were identified as potential drug targets for asthma, including ECM1, IL-6 sRa, and layilin. Additionally, using the same analysis method in the FinnGen database, we found that IL1-R1, ADAM19, and IL7R were associated with asthma risk, further demonstrating the stability of the results obtained in this study. Asthma is characterised by airway hyper-responsiveness and excessive bronchoconstriction 39 . Brain-derived neurotrophic factors actively recruit eosinophils, stimulate their degranulation, and release major basic proteins, thereby enhancing parasympathetic nerve-mediated bronchoconstriction 40 . Simultaneously, neurogenic inflammation can trigger asthma attacks by releasing neuropeptides via local axon reflexes 41 . This further prompted us to conduct an MR analysis of cerebrospinal fluid proteins associated with asthma risk to explore the causal relationship between cerebrospinal fluid proteins and asthma and to identify potential drug targets. The cerebrospinal fluid proteins IL-6, sRa, and layilin have been identified as potential drug targets. In the PPI network analysis, lililin was found to be associated with three asthma drug targets (IgE, IL-5, and IL-4), further indicating that lililin may be a potential therapeutic target for asthma. IL1-R1 is a cytokine receptor that serves as the receptor for IL-1α, IL-1β, and IL-1RA. When bound to IL-1α and IL-1β, it activates intracellular signalling pathways 42 . A previous study indicated that during periods of psychological stress in patients with asthma, there is increased glucose metabolism in the amygdala, which is associated with increased IL-1 signalling in the airways, suggesting the existence of a brain immune pathway in asthma 43 . IL1-R1 exhibited a strong interaction with IL-33 in the PPI analysis. Studies have shown that IL-33 encodes a cytokine released during cellular damage, whereas IL1-RL1 encodes a part of the IL-33 receptor complex 44 . Recent advances in functional studies in human participants and mouse models of allergic airway disease suggest that IL-33 signalling plays a central role in driving TH2 inflammation, which is the core of eosinophilic allergic asthma 45 . Based on pharmacogenomic screening, IL-33 is a potential small-molecule therapeutic target. Currently, data from two Phase II clinical trials have shown that targeting IL-33 monoclonal antibodies or IL-33R monoclonal antibodies reduces acute asthma attacks compared to placebo 46 , 47 . Additionally, IL1-R1-targeting drugs were found in DrugBank, including Anakinra, SD118, OMS-103HP, and Foreskin fibroblasts (neonatal). As the concept of drug repositioning has been applied to drugs currently marketed or under development, this method can be used to investigate whether the aforementioned four drugs can also effectively treat asthma 48 . As the safety of these drugs has been established, this approach can enhance the efficiency of drug development, while reducing costs and time. IL-7R is also a cytokine receptor that binds to IL-7 or thymic stromal lymphopoietin (TSLP), activates JAK-STAT and other pathways and regulate type 2 inflammation 49 . In ovalbumin-induced allergic asthma mouse models, IL-7 signaling has been shown to be necessary for the survival of allergen-specific CD4 + T cells 50 . Additionally, IL1-R1 was found to interact with IL-7R in the PPI analysis. Currently, there are no studies on combined therapy targeting IL1-R1 and IL-7R, providing new insights for our research on targeted asthma medications. ECM1 was initially identified as an 85 kDa glycoprotein secreted by the mouse osteoblastic cell line MN7. The human homologue regulates endochondral bone formation, stimulates endothelial cell proliferation, and induces angiogenesis 51 . Li et al. confirmed that ECM1 was elevated and specifically expressed in Th2 cells, leading to exacerbated allergic airway inflammation 52 . Another study found that ECM1 inhibits the differentiation of Th17 cells in inflammatory diseases of the central nervous system; however, inhibiting Th17 cell differentiation can reduce the occurrence of asthma 53 . CD200R1 is an immunoregulatory receptor on the surface of myeloid cells. Upon binding to the cell surface glycoprotein CD200, it transmits immune inhibitory signals, resulting in the suppression of mast cell and eosinophil degranulation and modulation of macrophage function 54 . Lauzon-Joset et al. have demonstrated in animal models that CD200R1 activation eliminates airway hyperresponsiveness in experimental asthma 55 . Combined with previous research, our study found that ECM1 and CD200R1 are risk factors for asthma, indicating that we can systematically obtain more experimental data, including GWAS and basic research, to elucidate this further. Additionally, the colocalization analysis of plasma ECM1 and asthma-shared causal variant sites suggests a higher likelihood of it becoming a potential therapeutic target. Our study has some limitations. First, we tested the effects of proteins from different studies, and inconsistencies in the measurements between different studies may lead to biased results. Additionally, patients with different types of asthma may exhibit different genetic variations. Second, most proteins have only one cis-acting SNP that is significantly associated with the whole genome (P < 5 × 10 − 8 ), lacking trans-acting pQTLs, which limits the application of analysis, including alternative MR algorithms, heterogeneity testing, and pleiotropy testing. However, our investigation of the main discovered SNPs suggested that most SNPs had F-statistics > 10. Furthermore, the effect allele frequencies of the plasma pQTLs retrieved from matched human genome constructs for ADAM19 were close to 0.5, indicating low reliability in the direction of its effect. Therefore, the effects of ADAM19 should be interpreted with caution. Third, our analysis was conducted on populations of European ancestry, making it difficult to generalise the results to other races. Further research in non-European populations is required to translate these findings to clinical applications. Finally, although we found some interactions between the pathogenic proteins of current asthma medications and drug targets, the results of the PPI analysis were suggestive rather than conclusive, and more research, such as studies using cell lines, animal models, and clinical samples, is needed to validate these findings. CONCLUSION Our study demonstrated a causal relationship between genetic determinants, including IL1R1, IL7R, ECM1, CD200R1, ADAM19, IL-6 sRa, and Layilin (LAYN) protein levels, and asthma. Additionally, the identified proteins may serve as attractive drug targets for asthma, particularly ECM1 and Layilin (LAYN). However, further research is required to fully understand the roles of these proteins in the onset and progression of asthma. Our findings provide important insights into the discovery of novel therapeutic targets for asthma. Through the integration of Mendelian randomization, drug prediction, phenotype scanning, gene colocalization analysis, protein-protein interaction network construction, and external validation, our study offers valuable guidance for developing more effective and targeted treatment approaches. Abbreviations CSF Cerebrospinal Fluid GWAS Genome-Wide Association Studies MR Mendelian Randomization SNP Single Nucleotide Polymorphism pQTL Protein Quantitative Trait Loci OR Odds Ratio IVW Inverse Variance Weighted WM Weighted Mode WME Weighted Median Method SM Simple Mode PPI Protein-Protein Interaction IL1 R1-Interleukin 1 Receptor Type 1 IL 7R-Interleukin 7 Receptor ECM1 Extracellular Matrix Protein 1 CD200R1 CD200 Receptor 1 ADAM19 ADAM Metallopeptidase Domain 19 IL 6 sRa-Interleukin 6 soluble Receptor alpha LAYN Layilin Declarations Ethics approval and consent to participate This research utilized published studies and consortia that have made their summary statistics publicly available. All original studies included in this research have obtained approval from their respective ethical review boards, and participants have provided informed consent. It is important to note that no individual-level data was utilized in this study. As a result, no new ethical review board approval was necessary for this research. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Contributor Information Lijuan Li,Email: [email protected] Hong Chen,Email: [email protected] Funding This research was supported in part by the Key Research and Development Program of Heilongjiang (JD22C008), (GZ20210158), (GA21C012). Author Contribution HC and LL designed the study and supervised the project. XC and YS performed all the Mendelian randomization analyses described here. SS, ZW, and DS searched the literature. XC, YS, and DS wrote and edited the manuscript. All authors contributed to the article and approved the submitted version. Acknowledgments Special thanks to the IEU open GWAS project developed by The MRC Integrative Epidemiology Unit (IEU) at the University of Bristol and FinnGen studies. 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Emilsson V, et al. Co-regulatory networks of human serum proteins link genetics to disease. Science. 2018;361:769–73. 10.1126/science.aaq1327 . Folkersen L, et al. Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease. PLoS Genet. 2017;13:e1006706. 10.1371/journal.pgen.1006706 . Ferkingstad E, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53:1712–21. 10.1038/s41588-021-00978-w . Valette K, Li Z, Bon-Baret V, et al. Prioritization of candidate causal genes for asthma in susceptibility loci derived from UK Biobank. Commun Biol. 2021;4(1):700. 10.1038/s42003-021-02227-6 . Published 2021 Jun 8. FinnGen. FinnGen Documentation of R10 release. 2023. https://www.finngen.fi/en Deng YT, et al. Identifying causal genes for depression via integration of the proteome and transcriptome from brain and blood. Mol Psychiatry. 2022;27:2849–57. 10.1038/s41380-022-01507-9 . Curtin F, Schulz P. 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Cytoscape Automation: empowering workflow-based network analysis. Genome Biol. 2019;20:185. 10.1186/s13059-019-1758-4 . Wishart DS, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46:D1074–82. 10.1093/nar/gkx1037 . Li X, et al. eQTL of bronchial epithelial cells and bronchial alveolar lavage deciphers GWAS-identified asthma genes. Allergy. 2015;70:1309–18. 10.1111/all.12683 . El-Husseini ZW, Gosens R, Dekker F, Koppelman GH. The genetics of asthma and the promise of genomics-guided drug target discovery. Lancet Respir Med. 2020;8:1045–56. 10.1016/S2213-2600(20)30363-5 . Nieuwenhuis MA, et al. Combining genomewide association study and lung eQTL analysis provides evidence for novel genes associated with asthma. Allergy. 2016;71:1712–20. 10.1111/all.12990 . Wang Y, Wang J, Yan Z, Liu S, Xu W. Potential drug targets for asthma identified in the plasma and brain through Mendelian randomization analysis. Front Immunol. 2023;14:1240517. 10.3389/fimmu.2023.1240517 . Sockrider M, Fussner L, What Is Asthma?. Am J Respir Crit Care Med. 2020;202:P25–6. 10.1164/rccm.2029P25 . Drake MG, et al. Eosinophil and airway nerve interactions in asthma. J Leukoc Biol. 2018;104:61–7. 10.1002/JLB.3MR1117-426R . Strek ME. Difficult asthma. Proc Am Thorac Soc. 2006;3:116–23. 10.1513/pats.200510-115JH . Muller M, Herrath J, Malmstrom V. IL-1R1 is expressed on both Helios(+) and Helios(-) FoxP3(+) CD4(+) T cells in the rheumatic joint. Clin Exp Immunol. 2015;182:90–100. 10.1111/cei.12668 . Rosenkranz MA, et al. Role of amygdala in stress-induced upregulation of airway IL-1 signaling in asthma. Biol Psychol. 2022;167:108226. 10.1016/j.biopsycho.2021.108226 . Grotenboer NS, Ketelaar ME, Koppelman GH, Nawijn MC. Decoding asthma: translating genetic variation in IL33 and IL1RL1 into disease pathophysiology. J Allergy Clin Immunol. 2013;131:856–65. 10.1016/j.jaci.2012.11.028 . Travers J, et al. Chromatin regulates IL-33 release and extracellular cytokine activity. Nat Commun. 2018;9:3244. 10.1038/s41467-018-05485-x . Wechsler ME, et al. Efficacy and Safety of Itepekimab in Patients with Moderate-to-Severe Asthma. N Engl J Med. 2021;385:1656–68. 10.1056/NEJMoa2024257 . Kelsen SG, et al. Astegolimab (anti-ST2) efficacy and safety in adults with severe asthma: A randomized clinical trial. J Allergy Clin Immunol. 2021;148:790–8. 10.1016/j.jaci.2021.03.044 . Jarada TN, Rokne JG, Alhajj R. A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Cheminform. 2020;12:46. 10.1186/s13321-020-00450-7 . Barata JT, et al. Activation of PI3K is indispensable for interleukin 7-mediated viability, proliferation, glucose use, and growth of T cell acute lymphoblastic leukemia cells. J Exp Med. 2004;200:659–69. 10.1084/jem.20040789 . Mai HL, et al. IL-7 receptor blockade following T cell depletion promotes long-term allograft survival. J Clin Invest. 2014;124:1723–33. 10.1172/JCI66287 . Mongiat M, et al. Perlecan protein core interacts with extracellular matrix protein 1 (ECM1), a glycoprotein involved in bone formation and angiogenesis. J Biol Chem. 2003;278:17491–9. 10.1074/jbc.M210529200 . Li Z, et al. ECM1 controls T(H)2 cell egress from lymph nodes through re-expression of S1P(1). Nat Immunol. 2011;12:178–85. 10.1038/ni.1983 . Su P, et al. Novel Function of Extracellular Matrix Protein 1 in Suppressing Th17 Cell Development in Experimental Autoimmune Encephalomyelitis. J Immunol. 2016;197:1054–64. 10.4049/jimmunol.1502457 . Cherwinski HM, et al. The CD200 receptor is a novel and potent regulator of murine and human mast cell function. J Immunol. 2005;174:1348–56. 10.4049/jimmunol.174.3.1348 . Lauzon-Joset JF, et al. Lung CD200 Receptor Activation Abrogates Airway Hyperresponsiveness in Experimental Asthma. Am J Respir Cell Mol Biol. 2015;53:276–84. 10.1165/rcmb.2014-0229OC . Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additional fle 1: Supplementary tables. Table S1.Genetic instruments of plasma and brain proteins for MR analysis. Table S2.Genetic instruments of asthma for bidirectional MR.Table S3. Heterogeneity analysis on proteins with two or more instruments.Table S4.Previously-reported genome-wide significant association of SNPs as genetic instruments of seven potential causal proteins.Table S5. Medications for asthma and their corresponding drug targets.Table S6. Current medications targeting seven potential causal proteins and interacted proteins.Table S7. Colocalization analysis of seven potential causal proteins and asthma.Table S8. Genetic instruments of seven potential causal proteins for external validation. Additionalfile2.docx Additional file 2: Supplementary figures. Figure S1. Bidirectional MR analysis for asthma on levels of seven potential causal proteins.Figure S2. Bayesian colocalization analysis of seven potential causal proteins and asthma.Figure S3. Comparison analysis of MR estimates between plasma proteome and CSF proteome.Figure S4. Potential drug target protein-protein interaction network among the suggestive causal proteins (P < 0.05). Figure S5. Seven identified protein-protein interaction network among the suggestive causal proteins (P < 0.05).Figure S6. Four asthma drug targets protein-protein interaction network among the suggestive causal proteins (P < 0.05). 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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-4921839","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351031650,"identity":"dfcf6002-9fdf-451e-a296-0f71c6a6fdfa","order_by":0,"name":"Xingxuan Chen","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xingxuan","middleName":"","lastName":"Chen","suffix":""},{"id":351031651,"identity":"0bcf6579-af20-413f-b638-9571b23bace5","order_by":1,"name":"Yu Shang","email":"","orcid":"","institution":"The Second Hospital of Heilongjiang Province","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Shang","suffix":""},{"id":351031652,"identity":"1b5e4c38-aaa9-438d-9cf2-dcfd940a714d","order_by":2,"name":"Danting Shen","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Danting","middleName":"","lastName":"Shen","suffix":""},{"id":351031653,"identity":"64558834-e976-4a68-8f4b-643c871ce4af","order_by":3,"name":"Si Shi","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Si","middleName":"","lastName":"Shi","suffix":""},{"id":351031654,"identity":"32140b81-1053-43b0-90bb-d08647ddc4cb","order_by":4,"name":"Zhe wen","email":"","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"wen","suffix":""},{"id":351031656,"identity":"bf63aa49-6fef-419d-ab4c-04e373cf3784","order_by":5,"name":"Lijuan Li","email":"","orcid":"","institution":"China-Japan Friendship Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Li","suffix":""},{"id":351031658,"identity":"fb30c550-eb75-4bd7-ad43-5d1d12a41a4b","order_by":6,"name":"Hong Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYDACCQYGZhBtf+BA+ocPP2wY+JiJ1cJw8MAzxpk9aQxsxGs5fPAZMwfbYQY2Qjr4Zzcfe1xQcceuse1w2mMGnvPybOw8ZtIFDHZyug04LLlzLN14xplnyc08QEaBxW3DNmaglhkMycZmB7BrMZDIMZPmbTuczCZxJkF6Bs9tRrAWHoYDidtwasn/BtbCI//+gzQP2zl7IrTksIG02EkwHEgDajmQSFCLxI00oIIzhxMMGA4kG87sSU5uY2YrtuYxwO0X/hnJz6R5Kg7bA7UkPvjww862n//wxts8FXZyuLTAQGIDgs1hAHQwfuUgYI/EZn9AWP0oGAWjYBSMJAAAPmxYuLWcEQwAAAAASUVORK5CYII=","orcid":"","institution":"The Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-08-16 01:51:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4921839/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4921839/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12931-024-03086-5","type":"published","date":"2025-01-13T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66555776,"identity":"da2fad5c-f1fb-4650-9185-d230d685e897","added_by":"auto","created_at":"2024-10-14 09:27:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147315,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design for the identification of plasma and brain proteins causally associated with asthma.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4921839/v1/c9c34d9b42df7df21e660db2.png"},{"id":66555780,"identity":"cebe619a-bf45-44ba-a0c5-2a8614727663","added_by":"auto","created_at":"2024-10-14 09:27:48","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218202,"visible":true,"origin":"","legend":"\u003cp\u003eMR results for plasma and CSF proteins and the risk of astnma.Volcano plots of the MR results for (A) 734 plasma and (B) 154 CSF proteins on the risk of asthma.\u0026nbsp;A\u0026nbsp;and\u0026nbsp;B\u0026nbsp;show MR analysis with Wald ratio or inverse variance weighted method on plasma and CSF proteins on the risk of asthma, respectively. OR for increased risk of asthma were expressed as per SD increase in plasma protein levels and per 10-fold increase in CSF protein levels. Dashed horizontal black line corresponded to\u0026nbsp;P = 5.63×10\u003csup\u003e−5\u003c/sup\u003e\u0026nbsp;(0.05/888). ln = natural logarithm; PVE = proportion of variance explained.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4921839/v1/a7fcfd6cd45b902fb7db8aca.jpeg"},{"id":66555778,"identity":"6fdc167d-5e46-4960-bcef-401870735da1","added_by":"auto","created_at":"2024-10-14 09:27:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32502,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction between current asthma medications targets and identified potential drug targets.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4921839/v1/18cc36a64c60c0c717a06751.png"},{"id":66555779,"identity":"128d6ca1-9072-4ae6-ad08-c38c6835252b","added_by":"auto","created_at":"2024-10-14 09:27:48","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":169129,"visible":true,"origin":"","legend":"\u003cp\u003eExternal validation of the causal relationship between seven potential causal proteins and asthma MR analysis on the causal relationship of six potential causal proteins on asthma using data from the FinnGen cohort. OR for increased risk of MS were expressed as per SD increase in plasma protein levels and per 10-fold increase in CSF protein levels.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4921839/v1/420b5923dec5c4168040232c.jpeg"},{"id":74284556,"identity":"cf10e1b8-d5ea-4795-94d5-f0498d8086f4","added_by":"auto","created_at":"2025-01-20 16:08:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1621165,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4921839/v1/8abaeb34-ae15-47fa-9a1f-46aa7e6068ea.pdf"},{"id":66555775,"identity":"ac1c2af4-273b-4a66-9806-156224fff5ff","added_by":"auto","created_at":"2024-10-14 09:27:47","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":158153,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional fle 1: Supplementary tables. \u0026nbsp;Table S1.\u003c/strong\u003eGenetic instruments of plasma and brain proteins for MR analysis.\u003cstrong\u003e Table S2.\u003c/strong\u003eGenetic instruments of asthma for bidirectional MR.\u003cstrong\u003eTable S3. \u003c/strong\u003eHeterogeneity analysis on proteins with two or more instruments.\u003cstrong\u003eTable S4.\u003c/strong\u003ePreviously-reported genome-wide significant association of SNPs as genetic instruments of seven potential causal proteins.\u003cstrong\u003eTable S5.\u003c/strong\u003e Medications for asthma and their corresponding drug targets.\u003cstrong\u003eTable S6.\u003c/strong\u003e Current medications targeting seven potential causal proteins and interacted proteins.\u003cstrong\u003eTable S7. \u003c/strong\u003eColocalization analysis of seven potential causal proteins and asthma.\u003cstrong\u003eTable S8.\u003c/strong\u003e Genetic instruments of seven potential causal proteins for external validation.\u003c/p\u003e","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4921839/v1/3ca0e4b8c761d8d6891c2513.xlsx"},{"id":66555781,"identity":"0e10bbd0-4263-4699-bf43-26087aaab68a","added_by":"auto","created_at":"2024-10-14 09:27:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9209444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2: Supplementary figures. Figure S1. \u003c/strong\u003eBidirectional MR analysis for asthma on levels of seven potential causal proteins.\u003cstrong\u003eFigure S2. \u003c/strong\u003eBayesian colocalization analysis of seven potential causal proteins and asthma.\u003cstrong\u003eFigure S3. \u003c/strong\u003eComparison analysis of MR estimates between plasma proteome and CSF proteome.\u003cstrong\u003eFigure S4.\u003c/strong\u003e Potential drug target protein-protein interaction network among the suggestive causal proteins (P \u0026lt; 0.05). \u003cstrong\u003eFigure S5. \u003c/strong\u003eSeven identified protein-protein interaction network among the suggestive causal proteins (P \u0026lt; 0.05).\u003cstrong\u003eFigure S6.\u003c/strong\u003e Four asthma drug targets protein-protein interaction network among the suggestive causal proteins (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4921839/v1/efe74fd4fc7a1550a201aca2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Potential drug targets for asthma identified through Mendelian randomization analysis","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eAsthma is a chronic inflammatory respiratory disease characterised by airway inflammation, hypersensitivity, and hyperresponsiveness, leading to symptoms such as breathing difficulties, coughing, chest tightness, and wheezing\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Asthma exhibits significant heterogeneity in clinical symptoms, severity, and treatment outcomes\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. With an in-depth understanding of the pathogenesis of asthma and continuous advancements in drug development technologies, an increasing number of novel drug targets have emerged and been applied in clinical treatment\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These new drugs act on key processes in the pathophysiology of asthma through various mechanisms, including anti-inflammatory effects, airway dilation, and modulation of immune responses\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, bringing new hope and opportunities for asthma treatment. However, there are significant variations in the response to medication among different patients in clinical settings, posing a substantial therapeutic challenge for both healthcare teams and patients. In addition to daily management strategies, there is a growing need for novel therapeutic targets to address this challenge.\u003c/p\u003e \u003cp\u003eAlthough large-scale randomised controlled trials (RCTs) are effective for evaluating drug treatment strategies, but they are both time-consuming and expensive\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Genome-wide Association Studies (GWAS) have identified numerous genetic loci associated with disease risk\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, providing evidence for determining the molecular pathways involved in drug interventions for diseases. Moreover, evidence suggests that drug targets with human genetic support are more than twice as likely to be approved as those without\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Therefore, incorporating genetics into drug development is feasible.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a statistical analysis in genetics that can predict the efficacy of drugs by mimicking randomised controlled trials, and has been widely used in drug target development and drug repurposing\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Leveraging genome-wide association study (GWAS) data, MR utilizes single nucleotide polymorphisms (SNPs) closely associated with exposure as genetic tools to infer whether the association between exposure and outcome is causal. GWAS of plasma protein levels have identified genetic variations associated with proteins, commonly known as protein quantitative trait loci (pQTLs), which can guide the search for pathogenic genes and disease pathways, providing opportunities to use Mendelian randomization to explore drug targets\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Combining human genetics with high-throughput, population-scale proteomics helps to explore the relationship between the human genome and diseases. So far, few Mendelian randomization (MR) studies have explored drug targets for asthma using Protein Quantity Trait Loci (pQTL). In this study, we used the Mendelian randomization method and large-scale genome-wide association research data to identify new therapeutic targets for asthma. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the design of this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eCerebrospinal fluid pQTL (CSF pQTL) data were obtained from a study reporting 274 pQTLs for 184 CSF proteins\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Plasma pQTL data were retrieved from another study\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e that consolidated 3,606 pQTLs for 2,656 proteins using five previously published GWAS datasets\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Three MR assumptions influence the genetic instrumental variables used for MR analysis, so we only included pQTLs that met the following criteria: (1) showing genome-wide significant associations (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e); (2) located outside the major histocompatibility complex (MHC) region of humans (chr6, 26\u0026ndash;34 Mb); (3) exhibiting independent associations [linkage disequilibrium (LD) r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001]; (4) being cis-acting pQTL; and (5) having an F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 [F = (beta/se)2]. Finally, 154 cis-acting pQTLs for 154 proteins and 738 cis-acting SNPs for 734 proteins were included from the CSF and plasma proteins, respectively (Additional file 1:Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Data checks were conducted on the original studies to ensure reliability. Additionally, the corresponding plasma pQTL data (4,907 plasma proteins measured in 35,559 subjects) were extracted from a study by Ferkingstad et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e for external validation.\u003c/p\u003e \u003cp\u003eAsthma data were derived from the GWAS data of the UK Biobank, comprising 56,167 patients and 352,255 control subjects\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Summary data for the GWAS analysis were sourced from the IEU Open GWAS project and can be downloaded from \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. Asthma data for external validation were obtained from the FinnGen cohort\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e (23,834 patients and 228,085 control subjects; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMR analysis\u003c/h2\u003e \u003cp\u003eThe R package TwoSampleMR (version 4.3.1) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MRCIEU/TwoSampleMR\u003c/span\u003e\u003cspan address=\"https://github.com/MRCIEU/TwoSampleMR\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for MR analysis. Exposure and outcome data were imported and harmonised using built-in functions within the R package (harmonise_data). If only one pQTL was available for a given protein, the Wald ratio was used to compute the MR estimate for each SNP. When two or more instrumental variables were available, the inverse variance weighted (IVW) method was applied, followed by a heterogeneity analysis\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Odds ratios (OR) for increased asthma risk were presented as the increase in the standard deviations (SD) of plasma protein levels and a 10-fold increase in CSF protein levels.\u003c/p\u003e \u003cp\u003eIn the primary analysis, considering the false positives caused by multiple testing, Bonferroni correction was applied to determine the significance threshold after multiple testing, with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;5.63 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e (P\u0026thinsp;=\u0026thinsp;0.05/888) defined as significant\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. For external validation, MR analysis was performed only on the initially identified proteins, with the P-value threshold set at 0.05. The preliminary findings were validated by extracting the same SNPs used in the primary analysis from the database as genetic instruments and by using the most significant SNP for that protein across the genome in the same database as a genetic instrument.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eColocalization analysis\u003c/h2\u003e \u003cp\u003eColocalization analysis was used to identify whether the two phenotypes were driven by the same causal variant in a particular region, thereby strengthening the evidence of association between the two phenotypes. colocalization analysis for asthma risk is conducted using the R package \"coloc\" (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/chr1swallace/coloc\u003c/span\u003e\u003cspan address=\"https://github.com/chr1swallace/coloc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In a given region, the posterior probabilities obtained from the Colocalization analysis correspond to one of the following five hypotheses: PPH0, SNPs unrelated to either trait; PPH1, SNPs related to protein expression but not asthma risk; PPH2, SNPs related to asthma risk but not protein expression; PPH3, SNPs related to both asthma risk and protein expression but driven by different SNPs; and PPH4, SNPs related to both asthma risk and protein expression and driven by a common SNP. The significance threshold for colocalization was set at PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.80, where proteins colocalising with asthma risk can be considered potential drug targets\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eReverse causality detection\u003c/h2\u003e \u003cp\u003eUsing asthma data from the primary analysis as exposure and the initially identified proteins as outcome(Additional file 1:Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), a bidirectional MR analysis was conducted to detect potential reverse causal relationships. The inverse variance-weighted method (IVW)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, weighted mode (WM), weighted median method (WME), simple mode (SM), and MR-Egger regression methods were employed for effect estimation. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePhenome-wide scan\u003c/h2\u003e \u003cp\u003eTo further assess the pleiotropic effects of potential drug targets, we used the selected pQTLs as key terms to search the literature for associations with other traits. The PhenoScanner (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to investigate whether SNPs were significantly associated with other traits and any known asthma risk factors, including metabolic traits, proteins, or clinical characteristics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePlasma and cerebrospinal fluid protein comparative analysis\u003c/h2\u003e \u003cp\u003eWe hypothesised that due to the blood-brain barrier, there is a minimal correlation between pQTLs identified in the plasma and cerebrospinal fluid. Therefore, we investigated the correlation between pQTLs identified in the cerebrospinal fluid and the effect estimates of plasma proteins using MR analysis through Spearman correlation analysis. Different P-value thresholds were set to explore whether the correlation changed with increasing significance levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eConstructing protein-protein interaction networks\u003c/h2\u003e \u003cp\u003eProtein-protein interaction (PPI) networks consist of proteins that interact with each other, contributing to various biological processes such as signal transduction, gene expression regulation, energy and substance metabolism, and cell cycle control. By evaluating and analysing PPI networks, researchers can gain insights into how proteins interact within cells.\u003c/p\u003e \u003cp\u003eIn this study, we utilised the Search Tool for the Retrieval of Interacting Genes (STRING) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a confidence score of 0.4\u003csup\u003e32\u003c/sup\u003e to investigate the interactions among significant proteins. The PPI results were further visualised using Cytoscape (V3.9.1)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Additionally, we summarised the existing therapeutic targets for asthma on the market and explored the corresponding drug targets based on the DrugBank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.drugbank.ca\u003c/span\u003e\u003cspan address=\"https://www.drugbank.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) associated with these asthma-related genes to predict potential therapeutic drugs\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eScreening for asthma-causing proteins in the proteome\u003c/h2\u003e \u003cp\u003eWe conducted an MR analysis of 154 proteins in the cerebrospinal fluid and 734 proteins in the plasma. Under Bonferroni significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5.63\u0026times;10\u003csup\u003e\u0026minus;\u003c/sup\u003e5), seven proteins were found to be associated with asthma risk (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B), including five plasma proteins: interleukin 1 receptor type 1 (IL1-R1), interleukin 7 receptor (IL-7R), extracellular matrix protein 1 (ECM1), CD200 receptor 1 (CD200R1), ADAM metallopeptidase domain 19 (ADAM19), and two cerebrospinal fluid proteins: IL-6 sRa (interleukin 6 receptor, IL6R) and Layilin (LAYN).\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\u003eMR results for plasma and CSF proteins significantly associated with asthma after\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniProt ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSNPa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003cp\u003eallele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003cp\u003e(95% CI)b\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 \u003cp\u003ePVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF\u003c/p\u003e \u003cp\u003estatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL1R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP14778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers7588201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.30 (1.20, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.63E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEmilsson\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL7R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP16871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers11957503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.07 (1.04, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.48E-06\u003c/p\u003e \u003c/td\u003e 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\u003cp\u003e2.92E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e36.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e584.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSuhre\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD200R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ8TD46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers6791672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18 (1.09, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.74E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e48.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSun\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADAM19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9H013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers7728609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.87 (0.82, 0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.17E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e89.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEmilsson\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL-6 sRa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP08887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers4129267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.29 (1.15, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.93E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e36.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e476.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYang\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLayilin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ6UX15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers674230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61 (0.51, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e137.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYang\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eBonferroni correction\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003ePVE\u0026thinsp;=\u0026thinsp;proportion of variance explained.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003eAll SNPs used were cis-acting.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003eb\u003c/sup\u003eOdds ratios for increased risk of asthma were expressed as per SD increase in plasma protein levels and per 10-fold increase in CSF protein levels.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpecifically, an increase in IL1-R1 (OR\u0026thinsp;=\u0026thinsp;1.30; 95%CI\u0026thinsp;=\u0026thinsp;1.20\u0026ndash;1.42, P\u0026thinsp;=\u0026thinsp;1.63\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e), IL-7R (OR\u0026thinsp;=\u0026thinsp;1.07; 95%CI\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;1.11, P\u0026thinsp;=\u0026thinsp;7.48\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), ECM1 (OR\u0026thinsp;=\u0026thinsp;1.03; 95%CI\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;1.05, P\u0026thinsp;=\u0026thinsp;2.92\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), CD200R1 (OR\u0026thinsp;=\u0026thinsp;1.18; 95%CI\u0026thinsp;=\u0026thinsp;1.09\u0026ndash;1.27, P\u0026thinsp;=\u0026thinsp;1.74\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), and IL-6 sRa (OR\u0026thinsp;=\u0026thinsp;1.29; 95%CI\u0026thinsp;=\u0026thinsp;1.15\u0026ndash;1.45; P\u0026thinsp;=\u0026thinsp;1.93\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) increased the risk of asthma. Conversely, an increase in ADAM19 (OR\u0026thinsp;=\u0026thinsp;0.87; 95%CI\u0026thinsp;=\u0026thinsp;0.82\u0026ndash;0.92; P\u0026thinsp;=\u0026thinsp;6.17 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e) and Layilin (OR\u0026thinsp;=\u0026thinsp;0.61; 95%CI\u0026thinsp;=\u0026thinsp;0.51\u0026ndash;0.73; P\u0026thinsp;=\u0026thinsp;1.14\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e) decreased the risk of asthma. The heterogeneity tests revealed no significant heterogeneity or outliers(Additional file 1:Table S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eDue to the pleiotropic effects of instrumental variables (IVs) on MR, we conducted several sensitivity analyses. First, reverse MR analysis on the seven identified proteins and asthma did not reveal any causal effects of asthma on the levels of the seven identified proteins, and further directionality was ensured through Steiger filtering(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Fig.\u0026nbsp;1). Second, using the coloc package in R, we conducted colocalization analysis on genes within \u0026plusmn;\u0026thinsp;1 Mb regions (upstream or downstream) of the seven identified pQTLs to further identify causative genetic variants associated with asthma, using PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.80 as a significant threshold. The results indicated that the plasma protein ECM1 shared causative variation with asthma (PP.H4\u0026thinsp;=\u0026thinsp;0.95), and the cerebrospinal fluid proteins IL-6 sRa (PP.H4\u0026thinsp;=\u0026thinsp;0.97) and Layilin (PP.H4\u0026thinsp;=\u0026thinsp;0.98) shared causative variations with asthma༈Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Fig.\u0026nbsp;2 and Additional file 1:Table S7༉. Therefore, three potential druggable proteins were identified from the colocalization analysis, providing evidence of shared genetic effects between pQTLs and asthma risk.\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\u003eSummary of reverse causality detection, Bayesian co-localization analysis and phenotype scanning on seven potential causal proteins\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniProt ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBidirectional MR (MR-IVW)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSteiger filtering\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecoloc.abf.\u003c/p\u003e \u003cp\u003ePPH4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePreviously reported associations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL1R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP14778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers7588201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.038 (0.985\u0026ndash;1.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTure\u003c/p\u003e \u003cp\u003e1.201\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEczema \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAllergic disease \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL7R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP16871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers11957503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.973 (0.941\u0026ndash;1.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTure\u003c/p\u003e \u003cp\u003e8.946\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;21\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWhite blood cell \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eECM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ16610;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers13294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.980 (0.944\u0026ndash;1.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTure\u003c/p\u003e \u003cp\u003e1.536\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;107\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMonocyte \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBlood platelet \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAtopic dermatitis \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD200R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ8TD46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers6791672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.986 (0.953\u0026ndash;1.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTure\u003c/p\u003e \u003cp\u003e5.767\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEczema \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAllergic rhinitis \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADAM19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ9H013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers7728609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.980 (0.948\u0026ndash;1.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTure\u003c/p\u003e \u003cp\u003e3.876\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;19\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 second forced expiratory volume \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePeak expiratory flow \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAge-related macular degeneration. \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL-6 sRa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP08887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers4129267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.016 (0.953\u0026ndash;1.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTure\u003c/p\u003e \u003cp\u003e2.281\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;88\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRed blood cell Distribution width \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCoronary artery disease. \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLayilin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ6UX15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ers674230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.954 (0.912\u0026ndash;0.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTure\u003c/p\u003e \u003cp\u003e4.868\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;29\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAllergic disease \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eMR-IVW\u0026thinsp;=\u0026thinsp;Mendelian randomization with inverse variance weighted method; PP\u0026thinsp;=\u0026thinsp;posterior probability;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e Odds ratios per SD increase in plasma protein levels and per 10-fold increase in CSF protein levels as asthma risk increased.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e SNP associated with traits directly.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ec\u003c/sup\u003e SNP associated with traits mediated by its proxy.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThird, by reviewing the literature and using PhenoScanner, we explored the potential confounding factors for the identified proteins. The results showed that IL1-R1 is associated with leukocytes and allergic diseases in humans, such as neutrophils and eosinophils; IL-7R is related to leukocytes in humans, including neutrophils, eosinophils, basophils, and lymphocytes; ECM1 is associated with monocytes, platelets, and atopic dermatitis; CD200R1 is related to eosinophils, neutrophils, basophils, hay fever, allergic rhinitis, or eczema in humans; ADAM19 is associated with forced expiratory volume in one second, peak expiratory flow rate, age-related macular degeneration; IL-6 sRa is related to leukocytes, haemoglobin concentration, mean corpuscular haemoglobin, monocytes, red cell distribution width, coronary artery disease in humans; Layilin is associated with allergic diseases. Asthma is an allergic disease, and we found that IL1R1 and Layilin are both associated with allergic diseases, whereas ECM1 is related to atopic dermatitis. Atopic dermatitis and asthma have been previously reported, indicating that these diseases may share a common aetiology. Furthermore, no significant confounding factors were identified(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Additional file 1:Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eComparison of proteins in plasma and cerebrospinal fluid\u003c/h2\u003e \u003cp\u003eAt the protein level, a non-significant positive correlation (Spearman correlation coefficient\u0026thinsp;=\u0026thinsp;0.044) was observed between the MR results of cerebrospinal fluid and plasma. Additionally, when using different P-value threshold restrictions to limit the number of proteins included in the analysis, the positive correlation persisted and remained non-significant (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eNext, the three potential drug target proteins identified through colocalization screening were loaded into the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for network construction. The resulting file was imported into Cytoscape for visualisation of protein-protein interaction (PPI) networks, which displayed interactions between the three drug-target proteins and other proteins(Supplementary Figure S4).\u003c/p\u003e \u003cp\u003eFurthermore, we constructed a PPI network with the additional four identified proteins and three potential drug target proteins, which revealed interactions between different proteins(Supplementary Figure S5). Notably, we observed a strong and reliable interaction between IL1-R1 and IL-7R. IL1-R1 is associated with anti-IL7R monoclonal antibodies (IL-7R), and IL-7R is the target of OSE-127 and GSK-2618960(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Additional file 1:Table S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAssociation between potential drug targets and current asthma medications\u003c/p\u003e \u003cp\u003eWe constructed a protein-protein interaction (PPI) network by associating three potential drug targets with four asthma drug targets (IgE, IL-5, IL-4, and TSLP). Seven proteins were loaded into the STRING database for network creation and the resulting file was imported into Cytoscape for PPI network visualisation(Supplementary Figure S6). We found that IL-6 sRa (IL-6R) is associated with IL-5 and IL-4. The latter are targets of the following asthma drugs: anti-IL-5/IL-5R monoclonal antibodies (Mepolizumab, Benralizumab, Reslizumab), and anti-IL-4Rα monoclonal antibody (Dupilumab) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We also searched the DrugBank database for drugs targeting the identified potential pathogenic proteins (Additional file 1:Table S6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eExternal validation of asthma\u003c/h2\u003e \u003cp\u003eBased on the primary analysis of pQTL data, we searched for the same variants in different datasets for external validation as well as significant variants unique to different datasets to enhance the scientific rigor of Mendelian randomization studies on drug target identification. Plasma pQTL data for exposure were extracted from the study by Ferkingstad et al., whereas asthma data for outcomes were obtained from the FinnGen database. The results revealed that IL1-R1 was also found to be associated with asthma in different databases, with an increased risk of asthma associated with elevated IL1-R1 levels (OR\u0026thinsp;=\u0026thinsp;1.22; 95% CI\u0026thinsp;=\u0026thinsp;1.09\u0026ndash;1.37, p\u0026thinsp;=\u0026thinsp;6.71\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). ADAM19 and IL-7R were also associated with asthma outcomes in the FinnGen database when the same variants identified in the primary analysis were used as exposure factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Additional file 1:Table S8).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eResearch has demonstrated the efficacy of targeted drug therapy in improving asthma control; however, significant variations in drug responses exist among patients, underscoring the pressing need for more effective targeted therapies. Therefore, the quest for viable for asthma treatment targets is paramount. To date, Li et al.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e have identified asthma genes through eQTL analysis of bronchial epithelial cells and bronchoalveolar lavage fluid; Zaid et al.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and Nieuwenhuis et al.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e have identified a series of asthma drug targets based on GWAS and eQTL analysis; Wang et al.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e have identified a series of asthma drug targets based on GWAS and pQTL analysis. In our study, using a larger cerebrospinal fluid protein database and plasma protein identification, we identified more comprehensive asthma-targeted protein sites. We identified seven proteins associated with asthma risk, three of which may serve as new asthma treatment targets. Furthermore, using protein-protein interaction networks and DrugBank, we identified drugs that may have therapeutic potential for patients with asthma. Our study complements previous related research by identifying seven proteins with a causal relationship with asthma risk. Through colocalization analysis of the seven initially identified proteins, three proteins were identified as potential drug targets for asthma, including ECM1, IL-6 sRa, and layilin. Additionally, using the same analysis method in the FinnGen database, we found that IL1-R1, ADAM19, and IL7R were associated with asthma risk, further demonstrating the stability of the results obtained in this study.\u003c/p\u003e \u003cp\u003eAsthma is characterised by airway hyper-responsiveness and excessive bronchoconstriction\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Brain-derived neurotrophic factors actively recruit eosinophils, stimulate their degranulation, and release major basic proteins, thereby enhancing parasympathetic nerve-mediated bronchoconstriction\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Simultaneously, neurogenic inflammation can trigger asthma attacks by releasing neuropeptides via local axon reflexes\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. This further prompted us to conduct an MR analysis of cerebrospinal fluid proteins associated with asthma risk to explore the causal relationship between cerebrospinal fluid proteins and asthma and to identify potential drug targets. The cerebrospinal fluid proteins IL-6, sRa, and layilin have been identified as potential drug targets. In the PPI network analysis, lililin was found to be associated with three asthma drug targets (IgE, IL-5, and IL-4), further indicating that lililin may be a potential therapeutic target for asthma.\u003c/p\u003e \u003cp\u003eIL1-R1 is a cytokine receptor that serves as the receptor for IL-1α, IL-1β, and IL-1RA. When bound to IL-1α and IL-1β, it activates intracellular signalling pathways\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. A previous study indicated that during periods of psychological stress in patients with asthma, there is increased glucose metabolism in the amygdala, which is associated with increased IL-1 signalling in the airways, suggesting the existence of a brain immune pathway in asthma\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. IL1-R1 exhibited a strong interaction with IL-33 in the PPI analysis. Studies have shown that IL-33 encodes a cytokine released during cellular damage, whereas IL1-RL1 encodes a part of the IL-33 receptor complex\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Recent advances in functional studies in human participants and mouse models of allergic airway disease suggest that IL-33 signalling plays a central role in driving TH2 inflammation, which is the core of eosinophilic allergic asthma\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Based on pharmacogenomic screening, IL-33 is a potential small-molecule therapeutic target. Currently, data from two Phase II clinical trials have shown that targeting IL-33 monoclonal antibodies or IL-33R monoclonal antibodies reduces acute asthma attacks compared to placebo\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Additionally, IL1-R1-targeting drugs were found in DrugBank, including Anakinra, SD118, OMS-103HP, and Foreskin fibroblasts (neonatal). As the concept of drug repositioning has been applied to drugs currently marketed or under development, this method can be used to investigate whether the aforementioned four drugs can also effectively treat asthma\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. As the safety of these drugs has been established, this approach can enhance the efficiency of drug development, while reducing costs and time. IL-7R is also a cytokine receptor that binds to IL-7 or thymic stromal lymphopoietin (TSLP), activates JAK-STAT and other pathways and regulate type 2 inflammation\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. In ovalbumin-induced allergic asthma mouse models, IL-7 signaling has been shown to be necessary for the survival of allergen-specific CD4\u0026thinsp;+\u0026thinsp;T cells\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Additionally, IL1-R1 was found to interact with IL-7R in the PPI analysis. Currently, there are no studies on combined therapy targeting IL1-R1 and IL-7R, providing new insights for our research on targeted asthma medications.\u003c/p\u003e \u003cp\u003eECM1 was initially identified as an 85 kDa glycoprotein secreted by the mouse osteoblastic cell line MN7. The human homologue regulates endochondral bone formation, stimulates endothelial cell proliferation, and induces angiogenesis\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Li et al. confirmed that ECM1 was elevated and specifically expressed in Th2 cells, leading to exacerbated allergic airway inflammation\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Another study found that ECM1 inhibits the differentiation of Th17 cells in inflammatory diseases of the central nervous system; however, inhibiting Th17 cell differentiation can reduce the occurrence of asthma\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. CD200R1 is an immunoregulatory receptor on the surface of myeloid cells. Upon binding to the cell surface glycoprotein CD200, it transmits immune inhibitory signals, resulting in the suppression of mast cell and eosinophil degranulation and modulation of macrophage function\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Lauzon-Joset et al. have demonstrated in animal models that CD200R1 activation eliminates airway hyperresponsiveness in experimental asthma\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Combined with previous research, our study found that ECM1 and CD200R1 are risk factors for asthma, indicating that we can systematically obtain more experimental data, including GWAS and basic research, to elucidate this further. Additionally, the colocalization analysis of plasma ECM1 and asthma-shared causal variant sites suggests a higher likelihood of it becoming a potential therapeutic target.\u003c/p\u003e \u003cp\u003eOur study has some limitations. First, we tested the effects of proteins from different studies, and inconsistencies in the measurements between different studies may lead to biased results. Additionally, patients with different types of asthma may exhibit different genetic variations. Second, most proteins have only one cis-acting SNP that is significantly associated with the whole genome (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), lacking trans-acting pQTLs, which limits the application of analysis, including alternative MR algorithms, heterogeneity testing, and pleiotropy testing. However, our investigation of the main discovered SNPs suggested that most SNPs had F-statistics\u0026thinsp;\u0026gt;\u0026thinsp;10. Furthermore, the effect allele frequencies of the plasma pQTLs retrieved from matched human genome constructs for ADAM19 were close to 0.5, indicating low reliability in the direction of its effect. Therefore, the effects of ADAM19 should be interpreted with caution. Third, our analysis was conducted on populations of European ancestry, making it difficult to generalise the results to other races. Further research in non-European populations is required to translate these findings to clinical applications. Finally, although we found some interactions between the pathogenic proteins of current asthma medications and drug targets, the results of the PPI analysis were suggestive rather than conclusive, and more research, such as studies using cell lines, animal models, and clinical samples, is needed to validate these findings.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur study demonstrated a causal relationship between genetic determinants, including IL1R1, IL7R, ECM1, CD200R1, ADAM19, IL-6 sRa, and Layilin (LAYN) protein levels, and asthma. Additionally, the identified proteins may serve as attractive drug targets for asthma, particularly ECM1 and Layilin (LAYN). However, further research is required to fully understand the roles of these proteins in the onset and progression of asthma. Our findings provide important insights into the discovery of novel therapeutic targets for asthma. Through the integration of Mendelian randomization, drug prediction, phenotype scanning, gene colocalization analysis, protein-protein interaction network construction, and external validation, our study offers valuable guidance for developing more effective and targeted treatment approaches.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCerebrospinal Fluid\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\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle Nucleotide Polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epQTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein Quantitative Trait Loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInverse Variance Weighted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeighted Mode\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeighted Median Method\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSimple Mode\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein-Protein Interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eR1-Interleukin 1 Receptor Type 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e7R-Interleukin 7 Receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eECM1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtracellular Matrix Protein 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCD200R1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCD200 Receptor 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADAM19\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eADAM Metallopeptidase Domain 19\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e6 sRa-Interleukin 6 soluble Receptor alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAYN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLayilin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003ch2\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis research utilized published studies and consortia that have made their summary statistics publicly available. All original studies included in this research have obtained approval from their respective ethical review boards, and participants have provided informed consent. It is important to note that no individual-level data was utilized in this study. As a result, no new ethical review board approval was necessary for this research.\u003c/p\u003e \u003ch2\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003ch2\u003eContributor Information\u003c/h2\u003e \u003cp\u003eLijuan Li,Email:[email protected]\u003c/p\u003e \u003cp\u003eHong Chen,Email: [email protected]\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported in part by the Key Research and Development Program of Heilongjiang (JD22C008), (GZ20210158), (GA21C012).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHC and LL designed the study and supervised the project. XC and YS performed all the Mendelian randomization analyses described here. SS, ZW, and DS searched the literature. XC, YS, and DS wrote and edited the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eSpecial thanks to the IEU open GWAS project developed by The MRC Integrative Epidemiology Unit (IEU) at the University of Bristol and FinnGen studies.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Additional Material.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eReddel HK, et al. Global Initiative for Asthma Strategy 2021: executive summary and rationale for key changes. 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Lung CD200 Receptor Activation Abrogates Airway Hyperresponsiveness in Experimental Asthma. Am J Respir Cell Mol Biol. 2015;53:276\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1165/rcmb.2014-0229OC\u003c/span\u003e\u003cspan address=\"10.1165/rcmb.2014-0229OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Asthma, Drug targets, Mendelian randomization, Therapeutic targets, Cerebrospinal fluid proteins, Plasma proteins","lastPublishedDoi":"10.21203/rs.3.rs-4921839/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4921839/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe emergence of new molecular targeted drugs marks a breakthrough in asthma treatment, particularly for severe cases. Yet, options for moderate-to-severe asthma treatment remain limited, highlighting the urgent need for novel therapeutic drug targets. In this study, we aimed to identify new treatment targets for asthma using the Mendelian randomization method and large-scale genome-wide association data (GWAS).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe utilized GWAS data from the UK Biobank (comprising 56,167 patients and 352,255 control subjects) and the FinnGen cohort (including 23,834 patients and 228,085 control subjects). Genetic instruments for 734 plasma proteins and 154 cerebrospinal fluid proteins were derived from recently published GWAS. Bidirectional Mendelian randomization analysis, Steiger filtering, colocalization, and phenotype scanning were employed for reverse causal inference detection, further substantiating the Mendelian randomization results. A protein-protein interaction network was also constructed to reveal potential associations between proteins and asthma medications.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUnder Bonferroni significance conditions, Mendelian randomization analysis revealed causal relationships between seven proteins and asthma. In plasma, we observed that an increase of one standard deviation in IL1R1[1.30 (95% CI, 1.20\u0026ndash;1.42)], IL7R[1.07 (95% CI, 1.04\u0026ndash;1.11)], ECM1[1.03 (95% CI, 1.02\u0026ndash;1.05)], and CD200R1[1.18 (95% CI, 1.09\u0026ndash;1.27)] were associated with an increased risk of asthma, while an increase in ADAM19 [0.87 (95% CI, 0.82\u0026ndash;0.92)] was found to be protective. In the brain, each 10-fold increase in IL-6 sRa [1.29 (95% CI, 1.15\u0026ndash;1.45)] was associated with an increased risk of asthma, while an increase in Layilin [0.61 (95% CI, 0.51\u0026ndash;0.73)] was found to be protective. None of the seven proteins exhibited a reverse causal relationship. Colocalization analysis indicated that ECM1 (coloc.abf-PPH4\u0026thinsp;=\u0026thinsp;0.953), IL-6 sRa (coloc.abf-PPH4\u0026thinsp;=\u0026thinsp;0.966), and layilin (coloc.abf-PPH4\u0026thinsp;=\u0026thinsp;0.975) shared the same genetic variation as in asthma.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA causal relationship exists between genetically determined protein levels of IL1R1, IL7R, ECM1, CD200R1, ADAM19, IL-6 sRa, and Layilin (LAYN) and asthma. Moreover, the identified proteins may serve as attractive drug targets for asthma, especially ECM1 and Layilin (LAYN). However, further research is required to comprehensively understand the roles of these proteins in the occurrence and progression of asthma.\u003c/p\u003e","manuscriptTitle":"Potential drug targets for asthma identified through Mendelian randomization analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 09:27:41","doi":"10.21203/rs.3.rs-4921839/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-08T16:29:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-06T15:24:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184775499943902242964341150241391234278","date":"2024-08-28T22:20:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203300929029205419807403777722756311313","date":"2024-08-28T20:08:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-26T14:27:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-17T12:12:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-17T09:52:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Respiratory Research","date":"2024-08-16T01:50:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee2b586d-9228-42b5-917b-050b0c7f4246","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:00:36+00:00","versionOfRecord":{"articleIdentity":"rs-4921839","link":"https://doi.org/10.1186/s12931-024-03086-5","journal":{"identity":"respiratory-research","isVorOnly":false,"title":"Respiratory Research"},"publishedOn":"2025-01-13 15:57:13","publishedOnDateReadable":"January 13th, 2025"},"versionCreatedAt":"2024-10-14 09:27:41","video":"","vorDoi":"10.1186/s12931-024-03086-5","vorDoiUrl":"https://doi.org/10.1186/s12931-024-03086-5","workflowStages":[]},"version":"v1","identity":"rs-4921839","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4921839","identity":"rs-4921839","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}