Multi-Trait Genetic Analysis of Asthma and Eosinophils Uncovers Novel Loci in East Asians

Multi-Trait Genetic Analysis of Asthma and Eosinophils Uncovers Novel Loci in East Asians | 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 Article Multi-Trait Genetic Analysis of Asthma and Eosinophils Uncovers Novel Loci in East Asians Xiao Chang, Lili Zhi, Yue Jiang, Lu Yu, Linzehao Li, Yingchao Song, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5425540/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 May, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Asthma is a prevalent respiratory condition with over 100 genetic loci identified through genome-wide association studies (GWAS). However, the genetic basis of asthma in East Asians remains underexplored. To address this, we performed a comprehensive analysis of shared genetic mechanisms between asthma and white blood cell (WBC) traits in East Asians, aiming to identify novel pleiotropic loci. Using linkage disequilibrium score regression (LDSC), we identified a significant genetic correlation between asthma and eosinophil count, further supported by Mendelian randomization (MR) analysis. A multi-trait analysis of GWAS (MTAG) uncovered 52 genome-wide significant loci, including 31 novel loci specific to East Asians. Notably, we discovered a missense variant (rs75326924) in the CD36 gene that exhibits increased expression in lymphocytes and ILC2-enriched cells in asthma patients, confirmed by flow cytometry. Proteomic profiling demonstrated downregulation of immune-related proteins such as Interleukin-7, Oncostatin M, and VEGFA in carriers of rs75326924, a variant previously associated with CD36 deficiency. Our findings provide insights into novel genetic loci and candidate genes underlying asthma in East Asians, offering potential targets for therapeutic interventions tailored to this population. Biological sciences/Genetics/Genetic association study/Genome-wide association studies Health sciences/Diseases/Respiratory tract diseases/Asthma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Asthma, a prevalent allergic respiratory disorder globally, is characterized by airway mucosal inflammation, wheezing, and shortness of breath. Its pathogenesis involves complex interactions between environmental triggers and genetic susceptibilities 1 . Recently, the Global Biobank Meta-analysis Initiative (GBMI) conducted the largest genome-wide association study (GWAS) on asthma 2 . This study collected data from asthma cohorts worldwide, encompassing over 150,000 patients with asthma and more than 1.6 million healthy controls. In total, 179 genetic loci associated with asthma susceptibility were identified, indicating a polygenic model of inheritance. However, the vast majority of genetic data in this study originated from European populations. Consequently, although there are several genetic loci shared between European and Asian populations, genetic underpins of asthma in East Asian populations remain to be unearthed. Recent advancements in statistical methodologies for GWAS analyses have facilitated joint analyses of traits with shared genetic architecture 3 – 5 . Multiple studies have focused on identifying shared genetic risk factors and pathways between asthma and coexisting conditions, including other allergic diseases and obesity 6 . However, only a few studies have examined if shared genetic mechanism exist between asthma and traits associated with white blood cells (WBC), which act as important indicators of immune function and play a critical role in allergic reactions. For instance, eosinophilic asthma is marked by a significant elevation of eosinophils 7 . Eosinophils are a type of WBC tending to accumulate at sites of allergic inflammation, which are involved in the development of asthma exacerbation 8 . Intriguingly, a recent study highlighted a shared genetic architecture between blood eosinophil counts and asthma risk in individuals of European ancestry, implicating a genetic link to the STAT6 signaling pathway in these two traits 9 . In this study, we first confirmed strong positive genetic correlations between asthma and WBC traits in East Asians. To further elucidate the genetic architecture of asthma in East Asians, we jointly analyzed the GWAS results of asthma and eosinophils using multi-trait analysis of GWAS (MTAG) and identified multiple pleiotropic loci shared by asthma and WBC traits in East Asians, specifically. Finally, we validated our findings in a Chinese cohort through comprehensive analysis of multi-omics data, affirming the importance of newly discovered loci and their associated genes in asthma susceptibility. RESULTS Study design An overview of the study design is shown in Fig. 1. We performed a genome-wide cross-trait analysis to quantify genetic correlation, identify pleiotropic loci, detect expression–trait associations, and infer causal relationships. The analysis included genetic correlation analysis, Mendelian randomization, and multi-trait GWAS, leading to the identification of significant loci, including a missense variant in the CD36 gene. These findings were validated through gene expression, flow cytometry, protein expression analysis, and further confirmed in a separate Chinese population GWAS. Shared heritability between asthma and hematological traits SNP-based heritability estimates were calculated by LDSC 10 to assess the proportion of phenotypic variance explained by the tested variants. Heritability estimates on the observed scale using GWAS summary statistics of East Asians were 2.30%, 9.30%, 11.37%, 11.51%, 8.83%, 9.19%, 12.55% and 16.55% for asthma, basophils, eosinophils, neutrophils, lymphocytes, monocytes, erythrocytes and platelets, respectively (Table S1 -2). We next investigated the genetic correlations of asthma and hematological traits using LDSC 10 . Significant genetic correlations were detected between asthma and certain WBC traits including eosinophils (R g = 0.50, P = \(\:2.38\times\:{10}^{-15}\) ), basophils (R g = 0.27, P = \(\:7.93\times\:{10}^{-5}\) ) and neutrophils (R g = 0.19, P = 0.0006) in East Asians (Fig. 2A and Table S3). We further investigated the pattern of SNP-heritability between asthma and various leukocyte traits across chromatin marks and nine cell types. Enrichment patterns further support the overlap in genetic influences on asthma and leukocyte counts, particularly in immune-related cell types. This was most prominently observed in annotations related to active regulatory elements, such as DNase I hypersensitive sites and histone modifications (H3K27ac and H3K4me3, Figure S1 ). These findings suggest a potential common genetic architecture underlying asthma susceptibility and leukocyte regulation, emphasizing the role of immune cell-specific genetic variation in the pathogenesis of asthma. We further used bi-directional Mendelian randomization (MR) instrumental analysis to investigate potential causality in the relationship between asthma and the correlated WBC traits (eosinophils, basophils and neutrophils) in East Asians. A strongly significant positive causal effect of eosinophils on asthma was observed, and vice versa (Fig. 2B). We also observed a significant positive causal effect of neutrophils on asthma. Local genetic correlations between asthma and hematological traits We next scanned the entire genome to identify distinct genomic loci associated with shared heritability among genetically correlated trait pairs. After accounting for multiple testing, six significantly correlated regions were pinpointed between asthma and eosinophils in East Asians (Figure S2 and Table S4). Conversely, only the leukocyte antigen (HLA) region exhibited correlation between asthma and neutrophils or asthma and monocytes in East Asians. Notably, no significant correlated region was observed between asthma and basophils. Multi-trait GWAS analysis between asthma and eosinophil counts Considering the strongest genetic correlation observed between asthma and eosinophils, we conducted MTAG analysis utilizing the East Asian asthma data from GBMI and eosinophils data from GWAS catalog for MTAG analysis. To maximize the discovery of potential loci, we also performed MTAG analysis using asthma data from BBJ and eosinophils data. Loci with MTAG P -values less than \(\:5\times\:{10}^{-8}\) in either analysis were considered significant pleiotropic loci. With increased statistical power, our analysis identified 52 genome-wide significant loci, including 31 loci not previously reported in the original asthma GWAS in East Asians (Fig. 3 and Table S5). Among these, nine newly identified loci have been previously documented in Europeans, suggesting a shared regulatory mechanism between Europeans and East Asians. Additionally, we observed a few signals in East Asians overlapping with those in Europeans within the same genomic regions, such as 8q24.21 (rs16902875) near MYC and 17q21.33 (rs2671655) near gene ZNF652 , indicating potential shared genetic susceptibility to asthma between the two populations. In contrast, nine loci were found to be specific to East Asian populations. Among them, the 18q21.1 locus (rs57631119) is located near the SMAD2 gene, which has been associated with airway remodeling in asthma 11 . Interestingly, multiple genes in the SMAD gene family, including SMAD3 , SMAD4 , and SMAD7 , have been significantly associated with asthma in GWASs of Europeans. Moreover, in vitro experiments have demonstrated that inhibiting the TGF-β/Smad signaling pathway can alleviate inflammation and allergic reactions, contributing to the amelioration of asthma symptoms 12 , 13 . Notably, we detected a signal at the 7p21.11 locus. The lead variant of the 7p21.11 locus (rs75326924) is a loss-of-function missense variant of the CD36 gene, found exclusively in East Asians. This variant is associated with a reduced risk of asthma, indicating a protective effect against the disease. According to the ClinVar database, rs75326924 is classified as pathogenic due to its role in causing platelet glycoprotein IV deficiency, thereby reinforcing the variant's functional significance and establishing CD36 as a causal gene. In further support of this, a prior study using a mouse model demonstrated that CD36 plays a crucial role in mediating asthma induced by house dust mites 14 . Collectively, these findings suggest that CD36 play an important role in asthma pathogenesis and may serve as a promising therapeutic target, particularly in East Asian populations. Replication of pleiotropic loci in the Chinese population We next conducted a GWA study in a Chinese cohort consisting of 1,040 asthmatic patients and 2,506 healthy controls. Our analysis successfully replicated six previously established loci and identified five novel pleiotropic loci using the MTAG approach in East Asian populations (Table S6; P < 0.05). Notably, this included the missense variant rs75326924 in CD36 , which exhibited a consistent effect direction with the MTAG findings (BETA = -1.08, P = 0.038). To further validate this association, we assessed rs75326924 in an independent GWAS of a Korean cohort [19], observing a similar effect direction (BETA = -0.54) with a P -value approaching significance ( P = 0.086). This suggests that an increased sample size could potentially lead to a statistically significant result. Differential gene expression and flow cytometry analysis of CD36 in asthma We analyzed RNA sequencing data of primary bronchial epithelial cells from 88 asthmatic patients and 42 healthy controls 15 , which were downloaded from the GEO database. Differential expression analysis reveals a significant up-regulation of CD36 gene expression in the asthmatic patient group compared to the healthy control group (Figure S3). We next conducted flow cytometric analysis on peripheral blood immune cells obtained from 22 asthmatic patients and 23 healthy non-allergic volunteers. (Fig. 4). Comparative analysis between asthmatic patients and healthy cohorts revealed no significant difference in the proportions of granulocytes or monocytes. However, there was a significant increase in the proportion of Type 2 Innate Lymphoid Cells (ILC2)-enriched cell populations (CD4-, CRTH2+) within the asthmatic population, aligning with prior research indicating the involvement of ILC2 in asthma progression (Fig. 4). Further examination of CD36 expression within each cell subset showed no significant difference in CD36 expression proportions among granulocyte or monocyte clusters between asthmatic patients and controls. However, an elevated proportion of CD36 expression was observed in lymphocytes and ILC2-enriched cell populations (Fig. 4). These findings corroborate our genomic and transcriptomic analyses, providing additional evidence that CD36 may serve as a potential therapeutic target in asthma treatment. Differential proteomic analysis of CD36 missense variant (rs75326924) To investigate potential downstream targets regulated by CD36 , we analyzed the proteomic data from 1,056 Chinese individuals who underwent Olink Explore inflammation proteomic profiling. Among them, five individuals carried the rs75326924 mutation at the T locus. Following Wilcoxon rank-sum test, we found significant expression alterations in 43 proteins ( P < 0.05) between carriers and non-carriers. Notably, all of the top eight proteins ( P < 0.01, Figure S4 and Table S7) were down-regulated. These proteins encompassed key players implicated in immune response and allergic reaction, including Interleukin-7 (IL7), Oncostatin M (OSM), and Vascular Endothelial Growth Factor A (VEGFA). Polygenic risk prediction We developed a PRS (PRS-32) for the Chinese population based on the count of risk alleles carried at 32 genome-wide significant loci identified in East Asians (Table S8). Each PRS was weighted by the overall effect sizes of the included alleles derived from the association analysis conducted in East Asians. PRS-32 exhibited a trend towards higher OR values in the second top quintiles (Fig. 5). In comparison, we constructed another PRS (PRS-63), which included 31 additional pleiotropic loci identified by MTAG for asthma and eosinophils. (Table S9). PRS-63 showed a steady rise in OR across the quintiles, with the highest risk observed in the top quintile group (Fig. 5). Consistent with this, the mean variance explained by the PRS (Liability R²) was 2.6% for PRS-32 and 2.9% for PRS-63, indicating the better predictive capability of PRS-63. DISSCUSSION In this study, we performed a genome-wide multi-trait analysis that systematically investigated the shared genetic architecture between asthma and WBC traits in East Asians. Leveraging the well-established genetic correlation between asthma and eosinophils, our analysis identified multiple novel East Asian-specific loci. Subsequent analysis using multi-omics approaches further validated these findings within the Chinese population. Our genetic correlation and MR analyses consistently identify eosinophils as the pivotal WBC, indicating the intricate relationship between immune cell profiles and asthma susceptibility in East Asians. This observation aligns with previous analyses conducted in European populations, reinforcing its robustness across different ethnic groups. Moreover, it is supported in epidemiological studies and in line with our current understanding of asthma pathogenesis 16 , 17 . The combined analysis of asthma and eosinophils has significantly enhanced our statistical power in detecting potential pleiotropic loci in East Asians. Our analysis has identified 31 novel significant loci in East Asians including nine loci previously reported in Europeans. In addition to this, three signals in East Asians overlap with those observed in Europeans within the same genomic regions, indicating shared genetic mechanisms between the two populations. Moreover, we have identified nine East Asian-specific loci, which exhibit a high frequency in East Asian populations compared to other ethnic groups. Intriguingly, we observed a significant association between rs75326924 and reduced risk of asthma. This missense variant results in a loss-of-function mutation in CD36 and is exclusively present in East Asians. Unlike most GWAS signals that are found in non-coding regions, making it challenging to pinpoint the causal variant, our findings strongly suggested rs75326924 as the protective variant in asthma. Additionally, studies have indicated that rs75326924 leads to decreased CD36 protein expression in platelets 18 , 19 , which in turn causes platelet glycoprotein IV deficiency disorder 20 . Despite limited research on CD36 and asthma, a previous study has shown that mice lacking CD36 exhibit impaired house dust mite uptake, reduced epithelial production of TSLP and IL-33, decreased frequencies of lung ILC2 populations, and suppressed allergic disease development 14 . In our pursuit of direct evidence linking CD36 to protection of asthma in humans, we conducted flow cytometric analysis on peripheral blood immune cells, comparing samples from asthmatic patients to those from healthy controls. Notably, our findings revealed a significant increase in CD36 expression within lymphocytes and ILC2-enriched cell populations among asthmatic individuals. This compelling observation suggest a significant role of CD36 as a key protective factor in the pathogenesis of asthma. Through the analysis of inflammation-related proteomic data from 1,056 Chinese individuals, we observed significant downregulation of inflammation-related proteins in samples carrying the rs75326924 variant in CD36 . Notably, many of these proteins are implicated in immune hypersensitivity, indicating potential pathways and targets influenced by CD36 dysregulation. For instance, IL7 promotes T and B cell proliferation and activation, while OSM influences immune cell activation and cytokine production. Dysregulation of IL7 and OSM can contribute to allergies and asthma by exacerbating immune responses 21 , 22 . We developed two distinct sets of PRSs for our Chinese cohort. These scores were constructed by utilizing results from MTAG outcomes and single-trait GWAS results. Through comparative analysis, we found that integrating GWAS data from asthma and eosinophils using MTAG improved the accuracy of predicting asthma susceptibility. This strategy offers a promising direction for asthma research, particularly valuable in non-European populations with limited sample sizes. Taken together, by elucidating the intricate genetic interplay between asthma and WBC traits in East Asians, our study not only enhances our understanding of the underlying mechanisms driving asthma susceptibility but also provides valuable insights into potential therapeutic targets and personalized treatment strategies tailored to this population. METHODS GWAS data of asthma and hematological traits GWAS summary statistics of asthma in East Asians were downloaded from GBMI study 2 , and summary statistics Japanese were obtained from studies of BioBank Japan through GWAS Catalog. The asthma GWAS in GBMI contains 18,549 cases and 322,655 controls, while the asthma GWAS in BioBank Japan includes 13,015 cases and 162,933 controls. Summary statistics of the largest GWAS for hematological traits 23 were obtained from GWAS Catalog 24 . Blood cell counts of basophils, eosinophils, neutrophils, lymphocytes, monocytes, erythrocytes and platelets were analyzed in this study. Details of each dataset were summarized in Table S1 . Estimation of genetic correlation and SNP-based heritability Genetic correlation r g between asthma and hematological traits was estimated by LD (Linkage Disequilibrium) score regression (LDSC) using GWAS summary statistics overlap with HapMap3 variants as recommended 10 . SNP based heritability of analyzed traits was also estimated by LDSC 10 . Pre-computed linkage disequilibrium scores for HapMap3 SNPs calculated based on East-Asian-ancestry or European-ancestry individuals from the 1000 Genomes Project were used in the analysis, and SNP markers with an imputation INFO score < 0.9 were excluded. More details about the method for calculating the genetic correlation were provide in the supplementary file. We corrected multiple testing for LDSC P -values by the Bonferroni method and a P -value of 0.00625 (0.05/8) was considered as the significance level for LDSC analysis. Cell-type-specific Enrichment of SNP Heritability Cell-type-specific SNP heritability enrichment was evaluated using stratified linkage disequilibrium score regression (S-LDSC) to identify functional categories or cell types that substantially contribute to the heritability of the traits investigated 25 . Annotation data from the Roadmap Epigenomics project, encompassing six chromatin marks (DHS, H3K27ac, H3K36me3, H3K4me1, H3K4me3, and H3K9ac) across 88 diverse cell types and tissues, were used to partition the SNP heritability of each trait. These cell-type annotations were organized into seven categories: central nervous system (CNS), digestive system, cardiovascular, musculoskeletal and connective tissue, immune and blood, pancreas, and others. Enrichment values for each annotation were scaled and visualized using hierarchical clustering, providing a comprehensive overview of cell-type-specific contributions to trait heritability. Mendelian randomization Instrumental variables (IVs) were selected from exposure GWAS data through LD clumping (r 2 threshold: 0.01, P -value threshold: \(\:5\times\:{10}^{-8}\) , window size: 10 mb). Data corresponding to these IVs were then extracted from both exposure and outcome datasets and harmonized. Bidirectional Mendelian randomization analysis was conducted using the Inverse Variance Weighted (IVW) method implemented in the R package TwoSampleMR 26 , 27 , which combines effects across multiple SNPs to estimate causal effects. Local genetic correlation analysis Given that genetic correlation, as estimated by LDSC, integrates data from all genetic variants across the genome, we proceeded to assess the pairwise local genetic correlation using ρ-HESS (heritability estimation from summary statistics) 28 . ρ-HESS is designed to quantify the local genetic correlation between pairs of traits within each of the 1703 pre-specified LD-independent segments, with an average length of 1.6 Mb. We considered statistical significance with a Bonferroni correction, setting the threshold at P < 0.05/1703. Multi-trait GWAS analysis Multi-trait GWAS meta-analysis for asthma and different hematological traits was performed by MTAG. MTAG method can increase the power to detect loci from correlated traits by analyzing GWAS summary statistics jointly. The first step of MTAG is to filter variants by removing non common SNPs, duplicated SNPs, or SNPs with strand ambiguity. MTAG then estimates the pairwise genetic correlation between asthma and hematological traits using LDSC 10 and uses these estimates to calibrate the variance-covariance matrix of the random effect component. MTAG next performs a random-effect meta-analysis to generate the SNP-level summary statistics. Loci are considered as significant with the trait of interest if the P -value is less than \(\:5\times\:{10}^{-8}\) in the MTAG analysis and the P -value is less than 0.01 in the original GWAS. GWAS analysis of Chinese individuals A total of 1,100 asthmatic patients were recruited from The First Affiliated Hospital of Shandong First Medical University. Patients diagnosed with asthma were identified based on the criteria outlined in the 2023 GINA Report (Global Strategy for Asthma Management and Prevention) 29 . Additionally, asthmatic patients from the CAS cohort who are currently undergoing asthma treatment were included based on healthcare and lifestyle questionnaires. A total of 2,506 control subjects were selected from the CAS cohort based on questionnaire information indicating no history of allergies or respiratory diseases. The CAS cohort is a prospective multi-omics cohort comprising 3,197 employees (49.0%) from various institutes or offices of the Chinese Academy of Sciences in Beijing, China 30 – 33 . This study was approved by the Institutional Review Boards of The First Affiliated Hospital of Shandong First Medical University, Beijing Institute of Genomics (Chinese Academy of Sciences) and Beijing Zhongguancun Hospital. Genotyping was conducted using the Infinium Asian Screening Array. Individuals with low genotype call rate (< 95%, n = 31), gender mismatch (n = 0), possible contamination (n = 9) or departure from Chinese Han population (n = 13) were removed before association test. SNPs were excluded if they were not on autosomal chromosomes, had a missing call rate ≥ 5%, had a minor allele frequency ≤ 1%, or had a Hardy-Weinberg equilibrium P value ≤ \(\:1\times\:{10}^{-5}\) . After quality control, a total of 3,546 samples is left for further analyses. Imputation was done by Minimac3 using 1000 Genomes Project Phase 3 version 5 genotype data as reference. Multivariable logistic regression was employed to examine the association between genetic variants and the status of asthma diagnosis, utilizing PLINK 1.9 34 . The covariates included in the regression model were sex and the first five principal components (PCs). Flow cytometry analysis Two milliliters (ml) of human peripheral blood were collected using an anticoagulant tube and diluted with phosphate-buffered saline (PBS) at a 1:1 ratio. Ficoll-Paque™ PLUS (Cytiva, 17144002) was added into a 15 ml centrifuge tube. Diluted peripheral blood was carefully layered onto the Ficoll along the tube wall, and after centrifugation, the middle white membrane layer containing Peripheral Blood Mononuclear Cells (PBMCs) was collected. PBMCs were washed and resuspended in PBS. Cells were then stained with monoclonal antibodies specific to CD4 (Biolegend, clone: OKT4, 317407), CD36 (Biolegend, clone: 5-271, 336207), and CRTH2 (Biolegend, clone: BM16, 350129). Staining was performed according to the manufacturer's instructions. Subsequently, flow cytometry analysis was performed utilizing the BD-FACSAria™ Fusion platform. Olink proteomics analysis In order to identify inflammatory signatures associated with the CD36 missense variant (rs75326924), plasma proteins were measured in the CAS cohort 1K multi-omics subgroup (n = 1,056) using the Olink Explore 384 Inflammation panel. The concentrations of proteins were quantified as Normalized Protein Expression (NPX), which represents Olink’s normalized relative unit on a log2 scale. Three protein assays (BCL2L11, BID, and MGLL) were excluded due to Olink QC warnings, leaving a total of 365 proteins for subsequent analysis. Differential protein expression was assessed using a two-sided t-test between CC and CT groups based on the rs75326924 genotype. Polygenic risk analysis Two sets of polygenic risk score (PRS) for the Chinese cohort were constructed using the PLINK software. The first set, PRS-32, utilized a total of 32 genome-wide association study (GWAS) significant loci previously identified in East Asians to construct the PRS. The second set, PRS-64, utilized a total of 64 significant pleiotropic loci identified in this study for PRS construction. For each PRS set, the PLINK score command was employed to calculate the PRS. This command integrates the weighted sum of risk alleles across the specified loci for each individual in the study cohort. Subsequently, the PRS values were standardized based on the mean and standard deviation of the PRS distribution in the population to facilitate comparison and interpretation. In the evaluation phase, Nagelkerke’s R 2 was computed to assess the predictive power of the model. This was accomplished by comparing the full model incorporating PRS and five principal components (PCs) against a null model devoid of PRS. Subsequently, Nagelkerke’s R 2 was transformed from the observed scale to the liability scale, with consideration of a population prevalence of 5%. References Zhu, Z. et al. A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases. Nat Genet 50 , 857-864 (2018). Tsuo, K. et al. Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity. Cell Genom 2 , 100212 (2022). Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet 50 , 229-237 (2018). Zhu, Z., Anttila, V., Smoller, J.W. & Lee, P.H. Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies. PLoS One 13 , e0193256 (2018). Song, Y. et al. Multitrait Genetic Analysis Identifies Novel Pleiotropic Loci for Depression and Schizophrenia in East Asians. Schizophr Bull (2024). Zhu, Z. et al. Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank. J Allergy Clin Immunol 145 , 537-549 (2020). Berry, M. et al. Pathological features and inhaled corticosteroid response of eosinophilic and non-eosinophilic asthma. Thorax 62 , 1043-9 (2007). Bel, E.H. et al. Oral glucocorticoid-sparing effect of mepolizumab in eosinophilic asthma. N Engl J Med 371 , 1189-97 (2014). Li, B. et al. Shared genetic architecture of blood eosinophil counts and asthma in UK Biobank. ERJ Open Res 9 (2023). Bulik-Sullivan, B.K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47 , 291-5 (2015). Sagara, H. et al. Activation of TGF-beta/Smad2 signaling is associated with airway remodeling in asthma. J Allergy Clin Immunol 110 , 249-54 (2002). Li, M. et al. Scutellarin Alleviates Ovalbumin-Induced Airway Remodeling in Mice and TGF-beta-Induced Pro-fibrotic Phenotype in Human Bronchial Epithelial Cells via MAPK and Smad2/3 Signaling Pathways. Inflammation (2024). Xuan, A., Yang, M., Xia, Q. & Sun, Q. Downregulation of NOX4 improves airway remodeling and inflammation by the TGF-beta1-Smad2/3 pathway in asthma. Cell Mol Biol (Noisy-le-grand) 69 , 201-206 (2023). Patel, P.S. & Kearney, J.F. CD36 and Platelet-Activating Factor Receptor Promote House Dust Mite Allergy Development. J Immunol 199 , 1184-1195 (2017). Magnaye, K.M. et al. DNA methylation signatures in airway cells from adult children of asthmatic mothers reflect subtypes of severe asthma. Proc Natl Acad Sci U S A 119 , e2116467119 (2022). Mallah, N., Rodriguez-Segade, S., Gonzalez-Barcala, F.J. & Takkouche, B. Blood eosinophil count as predictor of asthma exacerbation. A meta-analysis. Pediatr Allergy Immunol 32 , 465-478 (2021). Kerkhof, M. et al. Association between blood eosinophil count and risk of readmission for patients with asthma: Historical cohort study. PLoS One 13 , e0201143 (2018). Xu, X. et al. Variants of CD36 gene and their association with CD36 protein expression in platelets. Blood Transfus 12 , 557-64 (2014). Masuda, Y. et al. Diverse CD36 expression among Japanese population: defective CD36 mutations cause platelet and monocyte CD36 reductions in not only deficient but also normal phenotype subjects. Thromb Res 135 , 951-7 (2015). Kashiwagi, H. et al. Analyses of genetic abnormalities in type I CD36 deficiency in Japan: identification and cell biological characterization of two novel mutations that cause CD36 deficiency in man. Hum Genet 108 , 459-66 (2001). Kelly, E.A. et al. Potential contribution of IL-7 to allergen-induced eosinophilic airway inflammation in asthma. J Immunol 182 , 1404-10 (2009). Pothoven, K.L. et al. Oncostatin M promotes mucosal epithelial barrier dysfunction, and its expression is increased in patients with eosinophilic mucosal disease. J Allergy Clin Immunol 136 , 737-746 e4 (2015). Chen, M.H. et al. Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. Cell 182 , 1198-1213 e14 (2020). Sollis, E. et al. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res 51 , D977-D985 (2023). Finucane, H.K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat Genet 47 , 1228-35 (2015). Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 7 (2018). Hemani, G., Tilling, K. & Davey Smith, G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet 13 , e1007081 (2017). Shi, H., Mancuso, N., Spendlove, S. & Pasaniuc, B. Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits. Am J Hum Genet 101 , 737-751 (2017). Venkatesan, P. 2023 GINA report for asthma. Lancet Respir Med 11 , 589 (2023). Zheng, Z. et al. DNA methylation clocks for estimating biological age in Chinese cohorts. Protein Cell (2024). Zhang, Q.X. et al. Searching across-cohort relatives in 54,092 GWAS samples via encrypted genotype regression. PLoS Genet 20 , e1011037 (2024). Peng, Q. et al. Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits. Nat Genet (2024). Du, Z. et al. Whole Genome Analyses of Chinese Population and De Novo Assembly of A Northern Han Genome. Genomics Proteomics Bioinformatics 17 , 229-247 (2019). Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81 , 559-75 (2007). Additional Declarations There is NO Competing Interest. Supplementary Files AsthmaCD36NCSuppl.docx Cite Share Download PDF Status: Published Journal Publication published 31 May, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5425540","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":381567039,"identity":"8670a86c-b2c6-4bcd-a104-2caff1fba68d","order_by":0,"name":"Xiao Chang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCTBpA8SMjQcYGA4gCeLXkgbS0kCSlsNgkjgt/LObnz382nZeTrf9MNCWmjty5gzMB2/z4LPkzjFzY5kzt43NziQCtRx7ZmzZwJZsjU+LgUSCmbRExe3EbQeAWhgbDiduOMBjJo1fS/o3aQmDc/Xbzj+EaeH/RkBLjpnkh4oDCWY3ELaw4dUicSOnTJrhTLLhthtAWxKOHTa2bGYztpyDRwv/jPRtkj/b7OTNzqc/fPCh5rCcOXvzwxtv8GgBAWa4MxJATmUmoBwEGH8g8wyI0DEKRsEoGAUjCwAACfRUTfkATq4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0230-0416","institution":"The Children's Hospital of Philadelphia","correspondingAuthor":true,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Chang","suffix":""},{"id":381567040,"identity":"a85b5527-cf76-4085-9ea2-75e20afd89cf","order_by":1,"name":"Lili Zhi","email":"","orcid":"","institution":"The First Affiliated Hospital of Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Zhi","suffix":""},{"id":381567041,"identity":"157c8e3c-bb7f-4c9f-90d2-ca01c544b51c","order_by":2,"name":"Yue Jiang","email":"","orcid":"","institution":"Shandong First Medical University \u0026 Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Jiang","suffix":""},{"id":381567042,"identity":"31eac386-2458-416f-82c9-0fd7b32e37b5","order_by":3,"name":"Lu Yu","email":"","orcid":"","institution":"Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Yu","suffix":""},{"id":381567043,"identity":"a7e39be8-06ac-40c7-bf7e-c6f9d8afb26b","order_by":4,"name":"Linzehao Li","email":"","orcid":"","institution":"Shandong First Medical University \u0026 Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Linzehao","middleName":"","lastName":"Li","suffix":""},{"id":381567044,"identity":"eb805f65-4a0b-4d7b-80e7-57b4e621488f","order_by":5,"name":"Yingchao Song","email":"","orcid":"","institution":"Shandong First Medical University \u0026 Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yingchao","middleName":"","lastName":"Song","suffix":""},{"id":381567045,"identity":"c4292e38-8569-4725-9a5e-26d82c83c0eb","order_by":6,"name":"Bichen Peng","email":"","orcid":"","institution":"Shandong First Medical University \u0026 Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bichen","middleName":"","lastName":"Peng","suffix":""},{"id":381567046,"identity":"f4a0e493-a80a-49b0-8d4d-8b0366ec0add","order_by":7,"name":"Chumeng Zhang","email":"","orcid":"","institution":"Shandong First Medical University \u0026 Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chumeng","middleName":"","lastName":"Zhang","suffix":""},{"id":381567047,"identity":"ce75050f-7e68-43aa-808d-a03c4911277d","order_by":8,"name":"Hengxuan Jiang","email":"","orcid":"","institution":"Shandong First Medical University \u0026 Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hengxuan","middleName":"","lastName":"Jiang","suffix":""},{"id":381567048,"identity":"82cdd586-55f2-4255-a87e-84a90688fce8","order_by":9,"name":"Ren Li","email":"","orcid":"","institution":"School of Basic Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ren","middleName":"","lastName":"Li","suffix":""},{"id":381567049,"identity":"177ddeb9-d981-49bc-b375-702f41d15c12","order_by":10,"name":"Frank Mentch","email":"","orcid":"","institution":"Children's Hospital of Philadelphia","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"","lastName":"Mentch","suffix":""},{"id":381567050,"identity":"b7e0f5c7-c32b-4481-a42e-d4bf04b8092e","order_by":11,"name":"Joseph Glessner","email":"","orcid":"https://orcid.org/0000-0001-5131-2811","institution":"The Children's Hospital of Philadelphia","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Glessner","suffix":""},{"id":381567051,"identity":"864654b0-278a-47cf-ba2e-f8a4242713c2","order_by":12,"name":"Peilin Jia","email":"","orcid":"","institution":"CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, University of Chinese Academy of Sciences, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Peilin","middleName":"","lastName":"Jia","suffix":""},{"id":381567052,"identity":"83089095-de64-45df-8271-a21a64f8025a","order_by":13,"name":"Qiwen Zheng","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qiwen","middleName":"","lastName":"Zheng","suffix":""},{"id":381567053,"identity":"a45dec6f-22ff-49a3-ac12-d5454b5c2a27","order_by":14,"name":"Hua Tang","email":"","orcid":"","institution":"Shandong First Medical University \u0026 Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Tang","suffix":""},{"id":381567054,"identity":"29de7a52-d27b-41db-a4aa-e0cc7ad8351f","order_by":15,"name":"Hakon Hakonarson","email":"","orcid":"https://orcid.org/0000-0003-2814-7461","institution":"Center for Applied Genomics, Children's Hospital of Philadelphia, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvana","correspondingAuthor":false,"prefix":"","firstName":"Hakon","middleName":"","lastName":"Hakonarson","suffix":""}],"badges":[],"createdAt":"2024-11-10 11:00:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5425540/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5425540/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-60405-0","type":"published","date":"2025-05-31T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69832866,"identity":"79cdff80-9a93-4cda-ab90-f5d91edb0cdc","added_by":"auto","created_at":"2024-11-25 15:47:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":242451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of study design and analysis workflow.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1zipped.png","url":"https://assets-eu.researchsquare.com/files/rs-5425540/v1/814523a17fafe8e2cfd48483.png"},{"id":69831937,"identity":"c9279ec7-9b49-4527-b7c7-7a4ac7aeffdb","added_by":"auto","created_at":"2024-11-25 15:39:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107676,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic correlation and bi-directional causal relationship between Asthma and hematological traits.\u003c/strong\u003e (A) Global genetic correlations among Asthma and hematological traits. The colors represent different cell types: erythrocytes (red), leukocytes (blue), and platelets (orange). The error bars indicate the 95% confidence intervals, and asterisks denote statistically significant correlations after Bonferroni correction. (B) Bi-directional causal relationship Asthma and hematological traits. The left panel represents the exposure of asthma on hematological traits, while the right panel represents the outcome of asthma influenced by these traits.\u003c/p\u003e","description":"","filename":"Figure2zipped.png","url":"https://assets-eu.researchsquare.com/files/rs-5425540/v1/3c71fedeef369e79b7453580.png"},{"id":69831941,"identity":"f9b1a10b-c9d0-431b-90e7-032542dd0013","added_by":"auto","created_at":"2024-11-25 15:39:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2395637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircular Manhattan plot of significant loci associated with asthma in East Asian populations.\u003c/strong\u003e The outermost ring represents human chromosomes (chr1 to chr22), while the subsequent inner rings display significant associations from MTAG-GBMI, MTAG-BBJ, Asthma-GBMI, Asthma-BBJ, and eosinophils. Newly identified loci are highlighted in red, and previously known loci are shown in black.\u003c/p\u003e","description":"","filename":"Figure3zipped.png","url":"https://assets-eu.researchsquare.com/files/rs-5425540/v1/cd0b200091bd361e2be6d546.png"},{"id":69831942,"identity":"ca7738f0-f023-49fb-8668-c23aee2ffc26","added_by":"auto","created_at":"2024-11-25 15:39:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3895235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eElevated CD36 Expression in the Peripheral Blood of Asthma Patients.\u003c/strong\u003e (A) Flow cytometry was used to detect the proportion of granulocytes, monocytes, and lymphocytes, as well as the expression of CD36 and CRTH2 in the peripheral blood of normal and asthmatic patients. (B) Statistics of the proportion of granulocytes, monocytes, and ILC2 (CD4- CRTH2+) enriched cells as a percentage of total cells or lymphocytes, respectively, showing no significant differences except for ILC2 enriched cells (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; ns: not significant). (C) Statistics of the expression of CD36 in granulocytes, monocytes, and lymphocytes, and the mean fluorescence intensity (MFI) of CD36 in ILC2 enriched cells, indicating significantly higher expression in asthmatic patients (**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; ns: not significant; Student’s unpaired t-test).\u003c/p\u003e","description":"","filename":"Figure4zipped.png","url":"https://assets-eu.researchsquare.com/files/rs-5425540/v1/4e1b04da61a617b4995fc671.png"},{"id":69831939,"identity":"3c4f88d1-7c41-4cc5-98f6-591f2f680e63","added_by":"auto","created_at":"2024-11-25 15:39:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":23265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOdds ratios for asthma by PRS percentiles\u003c/strong\u003e. Two different PRS models, PRS-32 and PRS-63, are presented. The x-axis represents the PRS quintiles, and the y-axis shows the odds ratio with a 95% confidence interval.\u003c/p\u003e","description":"","filename":"Figure5zipped.png","url":"https://assets-eu.researchsquare.com/files/rs-5425540/v1/09f11e87b9f56fcced2102d7.png"},{"id":83723260,"identity":"89802368-20fe-4f5a-b50b-d89a801b6443","added_by":"auto","created_at":"2025-06-01 07:08:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7508550,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5425540/v1/e7c884c7-3bdd-4a66-80a9-69854a491c09.pdf"},{"id":69831940,"identity":"b0cceb64-0c56-4580-90d7-4a52250094f6","added_by":"auto","created_at":"2024-11-25 15:39:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1616701,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"AsthmaCD36NCSuppl.docx","url":"https://assets-eu.researchsquare.com/files/rs-5425540/v1/c1bdd7056a6831352f1c38cd.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Multi-Trait Genetic Analysis of Asthma and Eosinophils Uncovers Novel Loci in East Asians","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAsthma, a prevalent allergic respiratory disorder globally, is characterized by airway mucosal inflammation, wheezing, and shortness of breath. Its pathogenesis involves complex interactions between environmental triggers and genetic susceptibilities \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Recently, the Global Biobank Meta-analysis Initiative (GBMI) conducted the largest genome-wide association study (GWAS) on asthma \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This study collected data from asthma cohorts worldwide, encompassing over 150,000 patients with asthma and more than 1.6\u0026nbsp;million healthy controls. In total, 179 genetic loci associated with asthma susceptibility were identified, indicating a polygenic model of inheritance. However, the vast majority of genetic data in this study originated from European populations. Consequently, although there are several genetic loci shared between European and Asian populations, genetic underpins of asthma in East Asian populations remain to be unearthed.\u003c/p\u003e \u003cp\u003eRecent advancements in statistical methodologies for GWAS analyses have facilitated joint analyses of traits with shared genetic architecture \u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Multiple studies have focused on identifying shared genetic risk factors and pathways between asthma and coexisting conditions, including other allergic diseases and obesity \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, only a few studies have examined if shared genetic mechanism exist between asthma and traits associated with white blood cells (WBC), which act as important indicators of immune function and play a critical role in allergic reactions. For instance, eosinophilic asthma is marked by a significant elevation of eosinophils \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Eosinophils are a type of WBC tending to accumulate at sites of allergic inflammation, which are involved in the development of asthma exacerbation \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Intriguingly, a recent study highlighted a shared genetic architecture between blood eosinophil counts and asthma risk in individuals of European ancestry, implicating a genetic link to the \u003cem\u003eSTAT6\u003c/em\u003e signaling pathway in these two traits \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we first confirmed strong positive genetic correlations between asthma and WBC traits in East Asians. To further elucidate the genetic architecture of asthma in East Asians, we jointly analyzed the GWAS results of asthma and eosinophils using multi-trait analysis of GWAS (MTAG) and identified multiple pleiotropic loci shared by asthma and WBC traits in East Asians, specifically. Finally, we validated our findings in a Chinese cohort through comprehensive analysis of multi-omics data, affirming the importance of newly discovered loci and their associated genes in asthma susceptibility.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eStudy design\u003c/p\u003e \u003cp\u003eAn overview of the study design is shown in Fig.\u0026nbsp;1. We performed a genome-wide cross-trait analysis to quantify genetic correlation, identify pleiotropic loci, detect expression\u0026ndash;trait associations, and infer causal relationships. The analysis included genetic correlation analysis, Mendelian randomization, and multi-trait GWAS, leading to the identification of significant loci, including a missense variant in the CD36 gene. These findings were validated through gene expression, flow cytometry, protein expression analysis, and further confirmed in a separate Chinese population GWAS.\u003c/p\u003e \u003cp\u003eShared heritability between asthma and hematological traits\u003c/p\u003e \u003cp\u003eSNP-based heritability estimates were calculated by LDSC \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e to assess the proportion of phenotypic variance explained by the tested variants. Heritability estimates on the observed scale using GWAS summary statistics of East Asians were 2.30%, 9.30%, 11.37%, 11.51%, 8.83%, 9.19%, 12.55% and 16.55% for asthma, basophils, eosinophils, neutrophils, lymphocytes, monocytes, erythrocytes and platelets, respectively (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-2). We next investigated the genetic correlations of asthma and hematological traits using LDSC \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Significant genetic correlations were detected between asthma and certain WBC traits including eosinophils (R\u003csub\u003eg\u003c/sub\u003e = 0.50, \u003cem\u003eP\u003c/em\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:2.38\\times\\:{10}^{-15}\\)\u003c/span\u003e\u003c/span\u003e), basophils (R\u003csub\u003eg\u003c/sub\u003e = 0.27, \u003cem\u003eP\u003c/em\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:7.93\\times\\:{10}^{-5}\\)\u003c/span\u003e\u003c/span\u003e) and neutrophils (R\u003csub\u003eg\u003c/sub\u003e = 0.19, \u003cem\u003eP\u003c/em\u003e = 0.0006) in East Asians (Fig.\u0026nbsp;2A and Table S3). We further investigated the pattern of SNP-heritability between asthma and various leukocyte traits across chromatin marks and nine cell types. Enrichment patterns further support the overlap in genetic influences on asthma and leukocyte counts, particularly in immune-related cell types. This was most prominently observed in annotations related to active regulatory elements, such as DNase I hypersensitive sites and histone modifications (H3K27ac and H3K4me3, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These findings suggest a potential common genetic architecture underlying asthma susceptibility and leukocyte regulation, emphasizing the role of immune cell-specific genetic variation in the pathogenesis of asthma.\u003c/p\u003e \u003cp\u003eWe further used bi-directional Mendelian randomization (MR) instrumental analysis to investigate potential causality in the relationship between asthma and the correlated WBC traits (eosinophils, basophils and neutrophils) in East Asians. A strongly significant positive causal effect of eosinophils on asthma was observed, and vice versa (Fig.\u0026nbsp;2B). We also observed a significant positive causal effect of neutrophils on asthma.\u003c/p\u003e \u003cp\u003eLocal genetic correlations between asthma and hematological traits\u003c/p\u003e \u003cp\u003eWe next scanned the entire genome to identify distinct genomic loci associated with shared heritability among genetically correlated trait pairs. After accounting for multiple testing, six significantly correlated regions were pinpointed between asthma and eosinophils in East Asians (Figure S2 and Table S4). Conversely, only the leukocyte antigen (HLA) region exhibited correlation between asthma and neutrophils or asthma and monocytes in East Asians. Notably, no significant correlated region was observed between asthma and basophils.\u003c/p\u003e \u003cp\u003eMulti-trait GWAS analysis between asthma and eosinophil counts\u003c/p\u003e \u003cp\u003eConsidering the strongest genetic correlation observed between asthma and eosinophils, we conducted MTAG analysis utilizing the East Asian asthma data from GBMI and eosinophils data from GWAS catalog for MTAG analysis. To maximize the discovery of potential loci, we also performed MTAG analysis using asthma data from BBJ and eosinophils data. Loci with MTAG \u003cem\u003eP\u003c/em\u003e-values less than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:5\\times\\:{10}^{-8}\\)\u003c/span\u003e\u003c/span\u003e in either analysis were considered significant pleiotropic loci. With increased statistical power, our analysis identified 52 genome-wide significant loci, including 31 loci not previously reported in the original asthma GWAS in East Asians (Fig.\u0026nbsp;3 and Table S5). Among these, nine newly identified loci have been previously documented in Europeans, suggesting a shared regulatory mechanism between Europeans and East Asians. Additionally, we observed a few signals in East Asians overlapping with those in Europeans within the same genomic regions, such as 8q24.21 (rs16902875) near \u003cem\u003eMYC\u003c/em\u003e and 17q21.33 (rs2671655) near gene \u003cem\u003eZNF652\u003c/em\u003e, indicating potential shared genetic susceptibility to asthma between the two populations. In contrast, nine loci were found to be specific to East Asian populations. Among them, the 18q21.1 locus (rs57631119) is located near the \u003cem\u003eSMAD2\u003c/em\u003e gene, which has been associated with airway remodeling in asthma \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Interestingly, multiple genes in the SMAD gene family, including \u003cem\u003eSMAD3\u003c/em\u003e, \u003cem\u003eSMAD4\u003c/em\u003e, and \u003cem\u003eSMAD7\u003c/em\u003e, have been significantly associated with asthma in GWASs of Europeans. Moreover, in vitro experiments have demonstrated that inhibiting the TGF-β/Smad signaling pathway can alleviate inflammation and allergic reactions, contributing to the amelioration of asthma symptoms\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Notably, we detected a signal at the 7p21.11 locus. The lead variant of the 7p21.11 locus (rs75326924) is a loss-of-function missense variant of the \u003cem\u003eCD36\u003c/em\u003e gene, found exclusively in East Asians. This variant is associated with a reduced risk of asthma, indicating a protective effect against the disease. According to the ClinVar database, rs75326924 is classified as pathogenic due to its role in causing platelet glycoprotein IV deficiency, thereby reinforcing the variant's functional significance and establishing \u003cem\u003eCD36\u003c/em\u003e as a causal gene. In further support of this, a prior study using a mouse model demonstrated that \u003cem\u003eCD36\u003c/em\u003e plays a crucial role in mediating asthma induced by house dust mites \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Collectively, these findings suggest that CD36 play an important role in asthma pathogenesis and may serve as a promising therapeutic target, particularly in East Asian populations.\u003c/p\u003e \u003cp\u003eReplication of pleiotropic loci in the Chinese population\u003c/p\u003e \u003cp\u003eWe next conducted a GWA study in a Chinese cohort consisting of 1,040 asthmatic patients and 2,506 healthy controls. Our analysis successfully replicated six previously established loci and identified five novel pleiotropic loci using the MTAG approach in East Asian populations (Table S6; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, this included the missense variant rs75326924 in \u003cem\u003eCD36\u003c/em\u003e, which exhibited a consistent effect direction with the MTAG findings (BETA = -1.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038). To further validate this association, we assessed rs75326924 in an independent GWAS of a Korean cohort [19], observing a similar effect direction (BETA = -0.54) with a \u003cem\u003eP\u003c/em\u003e-value approaching significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.086). This suggests that an increased sample size could potentially lead to a statistically significant result.\u003c/p\u003e \u003cp\u003eDifferential gene expression and flow cytometry analysis of CD36 in asthma\u003c/p\u003e \u003cp\u003eWe analyzed RNA sequencing data of primary bronchial epithelial cells from 88 asthmatic patients and 42 healthy controls \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, which were downloaded from the GEO database. Differential expression analysis reveals a significant up-regulation of \u003cem\u003eCD36\u003c/em\u003e gene expression in the asthmatic patient group compared to the healthy control group (Figure S3).\u003c/p\u003e \u003cp\u003eWe next conducted flow cytometric analysis on peripheral blood immune cells obtained from 22 asthmatic patients and 23 healthy non-allergic volunteers. (Fig.\u0026nbsp;4). Comparative analysis between asthmatic patients and healthy cohorts revealed no significant difference in the proportions of granulocytes or monocytes. However, there was a significant increase in the proportion of Type 2 Innate Lymphoid Cells (ILC2)-enriched cell populations (CD4-, CRTH2+) within the asthmatic population, aligning with prior research indicating the involvement of ILC2 in asthma progression (Fig.\u0026nbsp;4). Further examination of CD36 expression within each cell subset showed no significant difference in CD36 expression proportions among granulocyte or monocyte clusters between asthmatic patients and controls. However, an elevated proportion of CD36 expression was observed in lymphocytes and ILC2-enriched cell populations (Fig.\u0026nbsp;4). These findings corroborate our genomic and transcriptomic analyses, providing additional evidence that CD36 may serve as a potential therapeutic target in asthma treatment.\u003c/p\u003e \u003cp\u003eDifferential proteomic analysis of CD36 missense variant (rs75326924)\u003c/p\u003e \u003cp\u003eTo investigate potential downstream targets regulated by \u003cem\u003eCD36\u003c/em\u003e, we analyzed the proteomic data from 1,056 Chinese individuals who underwent Olink Explore inflammation proteomic profiling. Among them, five individuals carried the rs75326924 mutation at the T locus. Following Wilcoxon rank-sum test, we found significant expression alterations in 43 proteins (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between carriers and non-carriers. Notably, all of the top eight proteins (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Figure S4 and Table S7) were down-regulated. These proteins encompassed key players implicated in immune response and allergic reaction, including Interleukin-7 (IL7), Oncostatin M (OSM), and Vascular Endothelial Growth Factor A (VEGFA).\u003c/p\u003e \u003cp\u003ePolygenic risk prediction\u003c/p\u003e \u003cp\u003eWe developed a PRS (PRS-32) for the Chinese population based on the count of risk alleles carried at 32 genome-wide significant loci identified in East Asians (Table S8). Each PRS was weighted by the overall effect sizes of the included alleles derived from the association analysis conducted in East Asians. PRS-32 exhibited a trend towards higher OR values in the second top quintiles (Fig.\u0026nbsp;5). In comparison, we constructed another PRS (PRS-63), which included 31 additional pleiotropic loci identified by MTAG for asthma and eosinophils. (Table S9). PRS-63 showed a steady rise in OR across the quintiles, with the highest risk observed in the top quintile group (Fig.\u0026nbsp;5). Consistent with this, the mean variance explained by the PRS (Liability R\u0026sup2;) was 2.6% for PRS-32 and 2.9% for PRS-63, indicating the better predictive capability of PRS-63.\u003c/p\u003e"},{"header":"DISSCUSSION","content":"\u003cp\u003eIn this study, we performed a genome-wide multi-trait analysis that systematically investigated the shared genetic architecture between asthma and WBC traits in East Asians. Leveraging the well-established genetic correlation between asthma and eosinophils, our analysis identified multiple novel East Asian-specific loci. Subsequent analysis using multi-omics approaches further validated these findings within the Chinese population.\u003c/p\u003e \u003cp\u003eOur genetic correlation and MR analyses consistently identify eosinophils as the pivotal WBC, indicating the intricate relationship between immune cell profiles and asthma susceptibility in East Asians. This observation aligns with previous analyses conducted in European populations, reinforcing its robustness across different ethnic groups. Moreover, it is supported in epidemiological studies and in line with our current understanding of asthma pathogenesis \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe combined analysis of asthma and eosinophils has significantly enhanced our statistical power in detecting potential pleiotropic loci in East Asians. Our analysis has identified 31 novel significant loci in East Asians including nine loci previously reported in Europeans. In addition to this, three signals in East Asians overlap with those observed in Europeans within the same genomic regions, indicating shared genetic mechanisms between the two populations. Moreover, we have identified nine East Asian-specific loci, which exhibit a high frequency in East Asian populations compared to other ethnic groups. Intriguingly, we observed a significant association between rs75326924 and reduced risk of asthma. This missense variant results in a loss-of-function mutation in \u003cem\u003eCD36\u003c/em\u003e and is exclusively present in East Asians. Unlike most GWAS signals that are found in non-coding regions, making it challenging to pinpoint the causal variant, our findings strongly suggested rs75326924 as the protective variant in asthma. Additionally, studies have indicated that rs75326924 leads to decreased CD36 protein expression in platelets \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, which in turn causes platelet glycoprotein IV deficiency disorder \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Despite limited research on \u003cem\u003eCD36\u003c/em\u003e and asthma, a previous study has shown that mice lacking \u003cem\u003eCD36\u003c/em\u003e exhibit impaired house dust mite uptake, reduced epithelial production of TSLP and IL-33, decreased frequencies of lung ILC2 populations, and suppressed allergic disease development \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In our pursuit of direct evidence linking CD36 to protection of asthma in humans, we conducted flow cytometric analysis on peripheral blood immune cells, comparing samples from asthmatic patients to those from healthy controls. Notably, our findings revealed a significant increase in CD36 expression within lymphocytes and ILC2-enriched cell populations among asthmatic individuals. This compelling observation suggest a significant role of CD36 as a key protective factor in the pathogenesis of asthma.\u003c/p\u003e \u003cp\u003eThrough the analysis of inflammation-related proteomic data from 1,056 Chinese individuals, we observed significant downregulation of inflammation-related proteins in samples carrying the rs75326924 variant in \u003cem\u003eCD36\u003c/em\u003e. Notably, many of these proteins are implicated in immune hypersensitivity, indicating potential pathways and targets influenced by CD36 dysregulation. For instance, \u003cem\u003eIL7\u003c/em\u003e promotes T and B cell proliferation and activation, while \u003cem\u003eOSM\u003c/em\u003e influences immune cell activation and cytokine production. Dysregulation of \u003cem\u003eIL7\u003c/em\u003e and \u003cem\u003eOSM\u003c/em\u003e can contribute to allergies and asthma by exacerbating immune responses \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe developed two distinct sets of PRSs for our Chinese cohort. These scores were constructed by utilizing results from MTAG outcomes and single-trait GWAS results. Through comparative analysis, we found that integrating GWAS data from asthma and eosinophils using MTAG improved the accuracy of predicting asthma susceptibility. This strategy offers a promising direction for asthma research, particularly valuable in non-European populations with limited sample sizes.\u003c/p\u003e \u003cp\u003eTaken together, by elucidating the intricate genetic interplay between asthma and WBC traits in East Asians, our study not only enhances our understanding of the underlying mechanisms driving asthma susceptibility but also provides valuable insights into potential therapeutic targets and personalized treatment strategies tailored to this population.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eGWAS data of asthma and hematological traits\u003c/p\u003e\n\u003cp\u003eGWAS summary statistics of asthma in East Asians were downloaded from GBMI study \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, and summary statistics Japanese were obtained from studies of BioBank Japan through GWAS Catalog. The asthma GWAS in GBMI contains 18,549 cases and 322,655 controls, while the asthma GWAS in BioBank Japan includes 13,015 cases and 162,933 controls. Summary statistics of the largest GWAS for hematological traits \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e were obtained from GWAS Catalog \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Blood cell counts of basophils, eosinophils, neutrophils, lymphocytes, monocytes, erythrocytes and platelets were analyzed in this study. Details of each dataset were summarized in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eEstimation of genetic correlation and SNP-based heritability\u003c/p\u003e\n\u003cp\u003eGenetic correlation r\u003csub\u003eg\u003c/sub\u003e between asthma and hematological traits was estimated by LD (Linkage Disequilibrium) score regression (LDSC) using GWAS summary statistics overlap with HapMap3 variants as recommended \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. SNP based heritability of analyzed traits was also estimated by LDSC \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Pre-computed linkage disequilibrium scores for HapMap3 SNPs calculated based on East-Asian-ancestry or European-ancestry individuals from the 1000 Genomes Project were used in the analysis, and SNP markers with an imputation INFO score\u0026thinsp;\u0026lt;\u0026thinsp;0.9 were excluded. More details about the method for calculating the genetic correlation were provide in the supplementary file. We corrected multiple testing for LDSC \u003cem\u003eP\u003c/em\u003e-values by the Bonferroni method and a \u003cem\u003eP\u003c/em\u003e-value of 0.00625 (0.05/8) was considered as the significance level for LDSC analysis.\u003c/p\u003e\n\u003cp\u003eCell-type-specific Enrichment of SNP Heritability\u003c/p\u003e\n\u003cp\u003eCell-type-specific SNP heritability enrichment was evaluated using stratified linkage disequilibrium score regression (S-LDSC) to identify functional categories or cell types that substantially contribute to the heritability of the traits investigated \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Annotation data from the Roadmap Epigenomics project, encompassing six chromatin marks (DHS, H3K27ac, H3K36me3, H3K4me1, H3K4me3, and H3K9ac) across 88 diverse cell types and tissues, were used to partition the SNP heritability of each trait. These cell-type annotations were organized into seven categories: central nervous system (CNS), digestive system, cardiovascular, musculoskeletal and connective tissue, immune and blood, pancreas, and others. Enrichment values for each annotation were scaled and visualized using hierarchical clustering, providing a comprehensive overview of cell-type-specific contributions to trait heritability.\u003c/p\u003e\n\u003cp\u003eMendelian randomization\u003c/p\u003e\n\u003cp\u003eInstrumental variables (IVs) were selected from exposure GWAS data through LD clumping (r\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e threshold: 0.01, \u003cem\u003eP\u003c/em\u003e-value threshold: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:5\\times\\:{10}^{-8}\\)\u003c/span\u003e\u003c/span\u003e, window size: 10 mb). Data corresponding to these IVs were then extracted from both exposure and outcome datasets and harmonized. Bidirectional Mendelian randomization analysis was conducted using the Inverse Variance Weighted (IVW) method implemented in the R package TwoSampleMR \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, which combines effects across multiple SNPs to estimate causal effects.\u003c/p\u003e\n\u003cp\u003eLocal genetic correlation analysis\u003c/p\u003e\n\u003cp\u003eGiven that genetic correlation, as estimated by LDSC, integrates data from all genetic variants across the genome, we proceeded to assess the pairwise local genetic correlation using \u0026rho;-HESS (heritability estimation from summary statistics) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. \u0026rho;-HESS is designed to quantify the local genetic correlation between pairs of traits within each of the 1703 pre-specified LD-independent segments, with an average length of 1.6 Mb. We considered statistical significance with a Bonferroni correction, setting the threshold at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05/1703.\u003c/p\u003e\n\u003cp\u003eMulti-trait GWAS analysis\u003c/p\u003e\n\u003cp\u003eMulti-trait GWAS meta-analysis for asthma and different hematological traits was performed by MTAG. MTAG method can increase the power to detect loci from correlated traits by analyzing GWAS summary statistics jointly. The first step of MTAG is to filter variants by removing non common SNPs, duplicated SNPs, or SNPs with strand ambiguity. MTAG then estimates the pairwise genetic correlation between asthma and hematological traits using LDSC \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e and uses these estimates to calibrate the variance-covariance matrix of the random effect component. MTAG next performs a random-effect meta-analysis to generate the SNP-level summary statistics. Loci are considered as significant with the trait of interest if the \u003cem\u003eP\u003c/em\u003e-value is less than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:5\\times\\:{10}^{-8}\\)\u003c/span\u003e\u003c/span\u003e in the MTAG analysis and the \u003cem\u003eP\u003c/em\u003e-value is less than 0.01 in the original GWAS.\u003c/p\u003e\n\u003cp\u003eGWAS analysis of Chinese individuals\u003c/p\u003e\n\u003cp\u003eA total of 1,100 asthmatic patients were recruited from The First Affiliated Hospital of Shandong First Medical University. Patients diagnosed with asthma were identified based on the criteria outlined in the 2023 GINA Report (Global Strategy for Asthma Management and Prevention) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Additionally, asthmatic patients from the CAS cohort who are currently undergoing asthma treatment were included based on healthcare and lifestyle questionnaires. A total of 2,506 control subjects were selected from the CAS cohort based on questionnaire information indicating no history of allergies or respiratory diseases. The CAS cohort is a prospective multi-omics cohort comprising 3,197 employees (49.0%) from various institutes or offices of the Chinese Academy of Sciences in Beijing, China \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This study was approved by the Institutional Review Boards of The First Affiliated Hospital of Shandong First Medical University, Beijing Institute of Genomics (Chinese Academy of Sciences) and Beijing Zhongguancun Hospital.\u003c/p\u003e\n\u003cp\u003eGenotyping was conducted using the Infinium Asian Screening Array. Individuals with low genotype call rate (\u0026lt;\u0026thinsp;95%, n\u0026thinsp;=\u0026thinsp;31), gender mismatch (n\u0026thinsp;=\u0026thinsp;0), possible contamination (n\u0026thinsp;=\u0026thinsp;9) or departure from Chinese Han population (n\u0026thinsp;=\u0026thinsp;13) were removed before association test. SNPs were excluded if they were not on autosomal chromosomes, had a missing call rate\u0026thinsp;\u0026ge;\u0026thinsp;5%, had a minor allele frequency\u0026thinsp;\u0026le;\u0026thinsp;1%, or had a Hardy-Weinberg equilibrium \u003cem\u003eP\u003c/em\u003e value \u0026le; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1\\times\\:{10}^{-5}\\)\u003c/span\u003e\u003c/span\u003e. After quality control, a total of 3,546 samples is left for further analyses. Imputation was done by Minimac3 using 1000 Genomes Project Phase 3 version 5 genotype data as reference. Multivariable logistic regression was employed to examine the association between genetic variants and the status of asthma diagnosis, utilizing PLINK 1.9 \u003csup\u003e34\u003c/sup\u003e. The covariates included in the regression model were sex and the first five principal components (PCs).\u003c/p\u003e\n\u003cp\u003eFlow cytometry analysis\u003c/p\u003e\n\u003cp\u003eTwo milliliters (ml) of human peripheral blood were collected using an anticoagulant tube and diluted with phosphate-buffered saline (PBS) at a 1:1 ratio. Ficoll-Paque\u0026trade; PLUS (Cytiva, 17144002) was added into a 15 ml centrifuge tube. Diluted peripheral blood was carefully layered onto the Ficoll along the tube wall, and after centrifugation, the middle white membrane layer containing Peripheral Blood Mononuclear Cells (PBMCs) was collected. PBMCs were washed and resuspended in PBS. Cells were then stained with monoclonal antibodies specific to CD4 (Biolegend, clone: OKT4, 317407), CD36 (Biolegend, clone: 5-271, 336207), and CRTH2 (Biolegend, clone: BM16, 350129). Staining was performed according to the manufacturer\u0026apos;s instructions. Subsequently, flow cytometry analysis was performed utilizing the BD-FACSAria\u0026trade; Fusion platform.\u003c/p\u003e\n\u003cp\u003eOlink proteomics analysis\u003c/p\u003e\n\u003cp\u003eIn order to identify inflammatory signatures associated with the CD36 missense variant (rs75326924), plasma proteins were measured in the CAS cohort 1K multi-omics subgroup (n\u0026thinsp;=\u0026thinsp;1,056) using the Olink Explore 384 Inflammation panel. The concentrations of proteins were quantified as Normalized Protein Expression (NPX), which represents Olink\u0026rsquo;s normalized relative unit on a log2 scale. Three protein assays (BCL2L11, BID, and MGLL) were excluded due to Olink QC warnings, leaving a total of 365 proteins for subsequent analysis. Differential protein expression was assessed using a two-sided t-test between CC and CT groups based on the rs75326924 genotype.\u003c/p\u003e\n\u003cp\u003ePolygenic risk analysis\u003c/p\u003e\n\u003cp\u003eTwo sets of polygenic risk score (PRS) for the Chinese cohort were constructed using the PLINK software. The first set, PRS-32, utilized a total of 32 genome-wide association study (GWAS) significant loci previously identified in East Asians to construct the PRS. The second set, PRS-64, utilized a total of 64 significant pleiotropic loci identified in this study for PRS construction. For each PRS set, the PLINK score command was employed to calculate the PRS. This command integrates the weighted sum of risk alleles across the specified loci for each individual in the study cohort. Subsequently, the PRS values were standardized based on the mean and standard deviation of the PRS distribution in the population to facilitate comparison and interpretation. In the evaluation phase, Nagelkerke\u0026rsquo;s R\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e was computed to assess the predictive power of the model. This was accomplished by comparing the full model incorporating PRS and five principal components (PCs) against a null model devoid of PRS. Subsequently, Nagelkerke\u0026rsquo;s R\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e was transformed from the observed scale to the liability scale, with consideration of a population prevalence of 5%.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZhu, Z.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 857-864 (2018).\u003c/li\u003e\n \u003cli\u003eTsuo, K.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Multi-ancestry meta-analysis of asthma identifies novel associations and highlights the value of increased power and diversity. \u003cem\u003eCell Genom\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 100212 (2022).\u003c/li\u003e\n \u003cli\u003eTurley, P.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Multi-trait analysis of genome-wide association summary statistics using MTAG. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 229-237 (2018).\u003c/li\u003e\n \u003cli\u003eZhu, Z., Anttila, V., Smoller, J.W. \u0026amp; Lee, P.H. Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, e0193256 (2018).\u003c/li\u003e\n \u003cli\u003eSong, Y.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Multitrait Genetic Analysis Identifies Novel Pleiotropic Loci for Depression and Schizophrenia in East Asians. \u003cem\u003eSchizophr Bull\u003c/em\u003e (2024).\u003c/li\u003e\n \u003cli\u003eZhu, Z.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank. \u003cem\u003eJ Allergy Clin Immunol\u003c/em\u003e \u003cstrong\u003e145\u003c/strong\u003e, 537-549 (2020).\u003c/li\u003e\n \u003cli\u003eBerry, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Pathological features and inhaled corticosteroid response of eosinophilic and non-eosinophilic asthma. \u003cem\u003eThorax\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 1043-9 (2007).\u003c/li\u003e\n \u003cli\u003eBel, E.H.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Oral glucocorticoid-sparing effect of mepolizumab in eosinophilic asthma. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e371\u003c/strong\u003e, 1189-97 (2014).\u003c/li\u003e\n \u003cli\u003eLi, B.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Shared genetic architecture of blood eosinophil counts and asthma in UK Biobank. \u003cem\u003eERJ Open Res\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e(2023).\u003c/li\u003e\n \u003cli\u003eBulik-Sullivan, B.K.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 291-5 (2015).\u003c/li\u003e\n \u003cli\u003eSagara, H.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Activation of TGF-beta/Smad2 signaling is associated with airway remodeling in asthma. \u003cem\u003eJ Allergy Clin Immunol\u003c/em\u003e \u003cstrong\u003e110\u003c/strong\u003e, 249-54 (2002).\u003c/li\u003e\n \u003cli\u003eLi, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Scutellarin Alleviates Ovalbumin-Induced Airway Remodeling in Mice and TGF-beta-Induced Pro-fibrotic Phenotype in Human Bronchial Epithelial Cells via MAPK and Smad2/3 Signaling Pathways. \u003cem\u003eInflammation\u003c/em\u003e (2024).\u003c/li\u003e\n \u003cli\u003eXuan, A., Yang, M., Xia, Q. \u0026amp; Sun, Q. Downregulation of NOX4 improves airway remodeling and inflammation by the TGF-beta1-Smad2/3 pathway in asthma. \u003cem\u003eCell Mol Biol (Noisy-le-grand)\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e, 201-206 (2023).\u003c/li\u003e\n \u003cli\u003ePatel, P.S. \u0026amp; Kearney, J.F. CD36 and Platelet-Activating Factor Receptor Promote House Dust Mite Allergy Development. \u003cem\u003eJ Immunol\u003c/em\u003e \u003cstrong\u003e199\u003c/strong\u003e, 1184-1195 (2017).\u003c/li\u003e\n \u003cli\u003eMagnaye, K.M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e DNA methylation signatures in airway cells from adult children of asthmatic mothers reflect subtypes of severe asthma. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e119\u003c/strong\u003e, e2116467119 (2022).\u003c/li\u003e\n \u003cli\u003eMallah, N., Rodriguez-Segade, S., Gonzalez-Barcala, F.J. \u0026amp; Takkouche, B. Blood eosinophil count as predictor of asthma exacerbation. A meta-analysis. \u003cem\u003ePediatr Allergy Immunol\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 465-478 (2021).\u003c/li\u003e\n \u003cli\u003eKerkhof, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Association between blood eosinophil count and risk of readmission for patients with asthma: Historical cohort study. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, e0201143 (2018).\u003c/li\u003e\n \u003cli\u003eXu, X.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Variants of CD36 gene and their association with CD36 protein expression in platelets. \u003cem\u003eBlood Transfus\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 557-64 (2014).\u003c/li\u003e\n \u003cli\u003eMasuda, Y.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Diverse CD36 expression among Japanese population: defective CD36 mutations cause platelet and monocyte CD36 reductions in not only deficient but also normal phenotype subjects. \u003cem\u003eThromb Res\u003c/em\u003e \u003cstrong\u003e135\u003c/strong\u003e, 951-7 (2015).\u003c/li\u003e\n \u003cli\u003eKashiwagi, H.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Analyses of genetic abnormalities in type I CD36 deficiency in Japan: identification and cell biological characterization of two novel mutations that cause CD36 deficiency in man. \u003cem\u003eHum Genet\u003c/em\u003e \u003cstrong\u003e108\u003c/strong\u003e, 459-66 (2001).\u003c/li\u003e\n \u003cli\u003eKelly, E.A.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Potential contribution of IL-7 to allergen-induced eosinophilic airway inflammation in asthma. \u003cem\u003eJ Immunol\u003c/em\u003e \u003cstrong\u003e182\u003c/strong\u003e, 1404-10 (2009).\u003c/li\u003e\n \u003cli\u003ePothoven, K.L.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Oncostatin M promotes mucosal epithelial barrier dysfunction, and its expression is increased in patients with eosinophilic mucosal disease. \u003cem\u003eJ Allergy Clin Immunol\u003c/em\u003e \u003cstrong\u003e136\u003c/strong\u003e, 737-746 e4 (2015).\u003c/li\u003e\n \u003cli\u003eChen, M.H.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e182\u003c/strong\u003e, 1198-1213 e14 (2020).\u003c/li\u003e\n \u003cli\u003eSollis, E.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, D977-D985 (2023).\u003c/li\u003e\n \u003cli\u003eFinucane, H.K.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Partitioning heritability by functional annotation using genome-wide association summary statistics. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 1228-35 (2015).\u003c/li\u003e\n \u003cli\u003eHemani, G.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e The MR-Base platform supports systematic causal inference across the human phenome. \u003cem\u003eElife\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e(2018).\u003c/li\u003e\n \u003cli\u003eHemani, G., Tilling, K. \u0026amp; Davey Smith, G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. \u003cem\u003ePLoS Genet\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, e1007081 (2017).\u003c/li\u003e\n \u003cli\u003eShi, H., Mancuso, N., Spendlove, S. \u0026amp; Pasaniuc, B. Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits. \u003cem\u003eAm J Hum Genet\u003c/em\u003e \u003cstrong\u003e101\u003c/strong\u003e, 737-751 (2017).\u003c/li\u003e\n \u003cli\u003eVenkatesan, P. 2023 GINA report for asthma. \u003cem\u003eLancet Respir Med\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 589 (2023).\u003c/li\u003e\n \u003cli\u003eZheng, Z.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e DNA methylation clocks for estimating biological age in Chinese cohorts. \u003cem\u003eProtein Cell\u003c/em\u003e (2024).\u003c/li\u003e\n \u003cli\u003eZhang, Q.X.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Searching across-cohort relatives in 54,092 GWAS samples via encrypted genotype regression. \u003cem\u003ePLoS Genet\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, e1011037 (2024).\u003c/li\u003e\n \u003cli\u003ePeng, Q.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits. \u003cem\u003eNat Genet\u003c/em\u003e (2024).\u003c/li\u003e\n \u003cli\u003eDu, Z.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Whole Genome Analyses of Chinese Population and De Novo Assembly of A Northern Han Genome. \u003cem\u003eGenomics Proteomics Bioinformatics\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 229-247 (2019).\u003c/li\u003e\n \u003cli\u003ePurcell, S.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e PLINK: a tool set for whole-genome association and population-based linkage analyses. \u003cem\u003eAm J Hum Genet\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e, 559-75 (2007).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5425540/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5425540/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAsthma is a prevalent respiratory condition with over 100 genetic loci identified through genome-wide association studies (GWAS). However, the genetic basis of asthma in East Asians remains underexplored. To address this, we performed a comprehensive analysis of shared genetic mechanisms between asthma and white blood cell (WBC) traits in East Asians, aiming to identify novel pleiotropic loci. Using linkage disequilibrium score regression (LDSC), we identified a significant genetic correlation between asthma and eosinophil count, further supported by Mendelian randomization (MR) analysis. A multi-trait analysis of GWAS (MTAG) uncovered 52 genome-wide significant loci, including 31 novel loci specific to East Asians. Notably, we discovered a missense variant (rs75326924) in the \u003cem\u003eCD36\u003c/em\u003e gene that exhibits increased expression in lymphocytes and ILC2-enriched cells in asthma patients, confirmed by flow cytometry. Proteomic profiling demonstrated downregulation of immune-related proteins such as Interleukin-7, Oncostatin M, and VEGFA in carriers of rs75326924, a variant previously associated with CD36 deficiency. Our findings provide insights into novel genetic loci and candidate genes underlying asthma in East Asians, offering potential targets for therapeutic interventions tailored to this population.\u003c/p\u003e","manuscriptTitle":"Multi-Trait Genetic Analysis of Asthma and Eosinophils Uncovers Novel Loci in East Asians","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 15:39:49","doi":"10.21203/rs.3.rs-5425540/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e55b12ce-dfe5-4a8e-b194-03c5aff0c3f9","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40645160,"name":"Biological sciences/Genetics/Genetic association study/Genome-wide association studies"},{"id":40645161,"name":"Health sciences/Diseases/Respiratory tract diseases/Asthma"}],"tags":[],"updatedAt":"2025-06-01T07:07:47+00:00","versionOfRecord":{"articleIdentity":"rs-5425540","link":"https://doi.org/10.1038/s41467-025-60405-0","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-05-31 04:00:00","publishedOnDateReadable":"May 31st, 2025"},"versionCreatedAt":"2024-11-25 15:39:49","video":"","vorDoi":"10.1038/s41467-025-60405-0","vorDoiUrl":"https://doi.org/10.1038/s41467-025-60405-0","workflowStages":[]},"version":"v1","identity":"rs-5425540","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5425540","identity":"rs-5425540","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}