Polymorphisms in the airway epithelium related genes CDHR3 and EMSY are associated with asthma susceptibility

Polymorphisms in the airway epithelium related genes CDHR3 and EMSY are associated with asthma susceptibility | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Polymorphisms in the airway epithelium related genes CDHR3 and EMSY are associated with asthma susceptibility Miao miao Zhang, Guo Chen, Yu Wang, Shou quan Wu, Andrew J Sandford, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-28755/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Nov, 2020 Read the published version in BMC Pulmonary Medicine → Version 1 posted You are reading this latest preprint version Abstract Background : As a main line of defense of the respiratory tract, the airway epithelium plays an important role in the pathogenesis of asthma. CDHR3 and EMSY were reported to be expressed in the human airway epithelium. Although previous genome-wide association studies found that the two genes were associated with asthma susceptibility, similar observations have not been made in the Chinese Han population. Methods : A total of 300 asthma patients and 418 healthy controls who were unrelated Chinese Han individuals were enrolled. Tag-single nucleotide polymorphisms (Tag-SNPs) were genotyped and the associations between SNPs and asthma risk were analyzed by binary logistic regression analysis. Results : After adjusting for confounding factors, the A allele of rs3847076 in CDHR3 was associated with increased susceptibility to asthma (OR = 1.407, 95% CI: 1.030-1.923). For the EMSY gene, the T alleles of both rs2508746 and rs12278256 were related with decreased susceptibility to asthma (additive model: OR = 0.718, 95% CI: 0.536-0.961; OR = 0.558, 95% CI: 0.332-0.937, respectively). In addition, the GG genotype of rs1892953 showed an association with increased asthma risk under the recessive model (OR = 1.667, 95% CI: 1.104-2.518) and the GATCTGAGT haplotype in EMSY was associated with reduced asthma risk ( P = 0.037). Conclusions : This study identified novel associations of rs3847076 in CDHR3 , as well as rs1892953, rs2508746 and rs12278256 in EMSY with adult asthma susceptibility in the Chinese Han population. Our observations suggest that CDHR3 and EMSY may play important roles in the pathogenesis of asthma in Chinese individuals. Further study with larger sample size is needed. Trial Registration : Not applicable. Medical Genetics CDHR3 EMSY asthma polymorphism susceptibility Figures Figure 1 Figure 2 Introduction Asthma is a chronic airway inflammatory disease that affects populations throughout the world. A World Health Organization report [ 1 ] predicted that the number of asthma patients would increase to 400 million by 2025 and 250,000 patients may die from this disease each year. A recent survey indicated that the prevalence of asthma among individuals aged > 14 years was 1.24% and there are approximately 30 million asthmatic patients in China [ 2 ]. The pathogenesis of asthma is still incompletely understood but it is known that genetic factors play a significant part in asthma susceptibility. The heritability of asthma was estimated to be 60–70% in an Australian twin study [ 3 ]. Genetic factors contributed to 90% of the variance in the susceptibility to asthma in a 5-year-old twin pair study [ 4 ]. As the first barrier between the human body and the environment, the airway epithelium has an important role in regulating the inflammation, immunity and tissue repair in the pathogenesis of asthma [ 5 ]. One genome-wide association study (GWAS) of a Danish population identified Cadherin related family member 3 ( CDHR3 ), which is highly expressed in human airway epithelium, as a susceptibility locus for childhood asthma with severe exacerbations [ 6 ]. A GWAS in 2017 demonstrated that Chromosome 11 open reading frame 30 (C11orf30) , also called EMSY or BRCA2-interacting transcriptional repressor, another gene expressed in airway epithelium, was a risk locus for food allergy in a Canadian population [ 7 ] and this gene has been shown to be involved in the epigenetic regsulation of gene expression [ 8 ]. However, there have been few studies of these two genes in Chinese asthmatics. Therefore, this study aimed to investigate the association of common variants in CDHR3 and EMSY with adult asthma in the Chinese population. Materials And Methods Study population The asthmatic cases were diagnosed by at least three respiratory physicians from the West China Hospital according to the criteria of the Global Strategy for Asthma Management and Prevention [9]. The healthy controls were collected from the physical examination center in the same hospital. Subjects were excluded if any of the following conditions were present: chronic obstructive pulmonary disease, diabetes, tumors, any immune disease, and immune deficiency. Use of hormones or immunosuppressive drugs were also exclusion criteria. Cases and controls were unrelated Chinese Han individuals. After signing the informed consent, 3 ml of venous blood were drawn from every subject and stored in a -80°C freezer. All blood specimens were collected from September 2013 to September 2016. The study was approved by the ethical committee of the West China Hospital of Sichuan University (Protocol No. 23). SNP Selection and Genotyping Single nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) ≥ 0.05 and r 2 ≥ 0.64, located in the region 3000 base pairs upstream to 300 base pairs downstream of CDHR3 were downloaded from the Han Chinese in Beijing database of the Genome Variation Server 147 (http://gvs.gs.washington.edu/GVS/), which is an online resource based on dbSNP. The final selections were 23 tag-SNPs including rs3887998, rs12155008, rs41267, rs3892893, rs10270308, rs34426483, rs193795, rs2526978, rs381188, rs10241452, rs3847076, rs11981655, rs10808147, rs193806, rs2528883, rs41269, rs2526979, rs2526976, rs41262, rs41266, rs6967330, rs41270 and rs448024. The selection of SNPs in EMSY was based on the tag-SNP strategy and literature review [10-13]. The tag-SNP selection strategy was the same as above except for r 2 ≥ 0.80. The 17 SNPs selected were rs3753051, rs7125744, rs7926009, rs4945087, rs2508740, rs1939469, rs7115331, rs1044265, rs12278256, rs2513513, rs2508755, rs2155219, rs2513525, rs2508746, rs1892953, rs7130588 and rs10899234. Genomic DNA was extracted from the blood samples using a genomic DNA purification kit (Axygen Scientific Inc, Union City, CA, USA). SNPs were genotyped by Genesky Bio-Tech Co., Ltd ( http://geneskybiotech.com/index.html ) using the SNPscanTM multiplex SNP genotyping technique based on double ligation and multiplex fluorescence polymerase Chain Reaction (PCR) [14]. As a quality control measure, 5% of random samples were repeat genotyped with a concordance rate of 100%. Data Analyses Statistical tests were performed using the Statistical Package for the Social Sciences (SPSS, SPSS Inc., Chicago, IL, USA), version 21.0. A p value <0.05 was considered to be statistically significant. Continuous and categorical variables were analyzed by student’s t-test and chi-square test, respectively. Genotype distributions under additive, dominant and recessive models were calculated by binary logistic regression analysis. Hardy-Weinberg equilibrium (HWE) among the controls was tested using plink software. Haploview and SHEsis software ( http://analysis.bio-x.cn ) were combined to perform linkage disequilibrium (LD) and haplotype analysis. Statistically significant SNPs were predicted by the software RegulomeDB ( http://www.regulomedb.org/ ) and Haploreg v4 (http://compbio.mit.edu/HaploReg). Results Subject characteristics A total of 300 asthma patients and 418 healthy controls were enrolled. The average ages of asthma patients and controls were 43.6±13.48 and 44.09±13.75 years, respectively. No significant differences in sex, body mass index (BMI) and smoking history were observed between case and control groups (Table 1). Late-onset asthma (age of asthma onset ≥18 years) accounted for 74.3% in the case group. Association analyses between CDHR3 , EMSY SNPs and asthma susceptibility The characteristics of the selected SNPs are listed in Table S1 and S2. Rs10899234 in EMSY and rs6967330 in CDHR3 were excluded due to their deviation from HWE in the control subjects ( P <0.05). The genotyping assays failed for rs12155008, rs41270 and rs448024 in CDHR3 . After adjusting for confounding factors including age, sex, BMI and smoking history, four SNPs were found to be associated with asthma susceptibility (Table 2). The A allele of rs3847076 in CDHR3 was associated with increased susceptibility to asthma under the additive model ( P = 0.032, OR = 1.407, 95% CI: 1.030-1.923). For EMSY , both the TC/TT genotype and T allele of rs2508746 were associated with decreased risk of asthma (dominant model: P = 0.019, OR = 0.660, 95% CI: 0.465-0.935; additive model: P = 0.026, OR = 0.718, 95% CI: 0.536-0.961). The TG/TT genotype and T allele of rs12278256 were associated with reduced asthma risk (dominant model: P = 0.033, OR = 0.563, 95% CI: 0.332-1.953; additive model: P = 0.027, OR = 0.558, 95% CI: 0.332-0.937). Finally, the GG genotype of rs1892953 showed an association with increased asthma risk under the recessive model ( P = 0.015, OR = 1.667, 95% CI: 1.104-2.518). Stratified analysis results by gender, smoking status, BMI status and onset age of asthma were shown in Table 3. Allele A of rs3847076 was associated with increased susceptibility to asthma in male subgroup, smoking subgroup, non-overweight subgroup and late onset asthma subgroup ( P =0.023, OR=1.869; P =0.009, OR=2.168; P =0.005, OR=1.835 and P =0.023, OR=1.457, respectively). Similarly, rs2508746 TC+TT was related with decreased asthma susceptibility in the non-smoking subgroup, non-overweight subgroup, and late-onset asthma subgroup in dominant model ( P =0.014, OR=0.618; P =0.027, OR=0.612 and P =0.016, OR=0.637, respectively). Meanwhile, rs1892953 GG shown increased risk of asthma in the female subgroup, non-smoking subgroup, non-overweight subgroup, and late onset asthma subgroup in recessive model ( P =0.038, OR=1.738; P =0.04, OR=1.615; P =0.017, OR=1.910 and P =0.017, OR=1.680, respectively). Rs12278256 T was still associated with decreased asthma susceptibility in female subgroups, non-smoking subgroups, and non-overweight subgroups in additive model ( P =0.032, OR=0.465; P =0.02, OR=0.508 and P =0.028, OR=0.481, respectively). Haplotype and LD analysis The LD between SNPs of CDHR3 and EMSY was low and those SNPs were divided into eight haplotype blocks with Haploview software (Figures 1 and 2). Only the haplotype consisting of GATCTGAGT in block 1 of EMSY was associated with decreased risk of asthma ( P = 0.037, OR = 0.615, 95% CI: 0.388-0.975) (Table 4). Functional prediction results Four statistically significant SNPs were predicted using the software RegulomeDB and Haploreg v4 (Table S3). Rs144934374 is strongly linked to rs12278256 and its RegulomeDB scores is lower than that of rs12278256, suggesting that it may be the functional site represented by rs12278256. Acting as promoter histone marks or enhancer histone marks, or affecting DNAse is suggested to be associated with chromatin status, and binding proteins or altering regulatory motifs in ChIP-Seq suggest that transcription levels may be affected. It seems that these four SNPs may have certain effects on chromatin status and transcription level. Rs1892953 appears as an expression quantitative trait loci (eQTL) SNP in thyroid tissue [12]. Discussion In this group of Chinese Han adults, the relationship between two airway epithelial-related genes EMSY and CDHR3 and risk of asthma were investigated, and four polymorphisms related to asthma susceptibility were obtained, which were rs3847076 of CDHR3 and rs2508746, rs1892953 and rs12278256 of EMSY . A further subgroup analysis of these four variants revealed that their association with asthma was present in the subgroup. CDHR3 , located on chromosome 7, is specifically expressed in ciliated airway epithelial cells which are the targets of Rhinovirus C (RV-C) infection, and its expression was positively associated with RV-C binding, replication and entry into the host cells [15,16]. There are only a few studies describing the relationship between CDHR3 polymorphisms and asthma, and the results were inconsistent in different populations. The A allele of rs6967330 in CDHR3 increased the risk of wheezing illnesses and hospitalizations for childhood asthma in a Danish study [6]. Rs17152490, in LD with rs6967330 was reported to affect asthma risk through cis -regulation of its gene expression in cells from human bronchial epithelial biopsy [17] . However, rs6967330 was only related to early-onset asthma in a Japanese population [18] and no association between rs6967330 and asthma was found in Chinese children [19]. In the present study, rs6967330 was not in HWE and our data suggest that rs3847076 may increase the risk of asthma in adults, which were inconsistent with the previous studies. The potential reasons for this discrepancy are as follows: Firstly, the susceptibility to asthma may differ in different populations, and secondly, late-onset asthma patients accounted for the majority of the case group in this study, in contrast to the above Japanese study which reported the positive relationship between rs6967330 and early-onset asthma in children. A future study of different asthma phenotypes would be beneficial to the accurate prevention and treatment of asthma. EMSY , located on chromosome 11q13.5, is expressed in the human airway epithelium and encoded by the EMSY protein. GWAS studies showed that EMSY was involved in allergic diseases including atopic dermatitis and food allergy [20,21]. Several SNPs, rs7130588, rs10899234, rs6592657, as well as we studied SNPs rs2508746 and rs1892953 were associated with total serum immunoglobulin E (IgE) levels in non-Hispanic Caucasian asthmatic patients [10]. In an eQTL analysis, Li et al [17] reported that rs2508740, rs2513525, rs4300410 (in complete LD with rs7926009), rs10793169 (in complete LD with rs7926009), rs2513513 and rs4245443 were significantly correlated with mRNA expression levels of EMSY in human bronchial alveolar lavage. Another GWAS study reported that rs7130588 in EMSY was associated with asthma [22]. A meta-analysis demonstrated that rs2155219 in EMSY increased the risk of allergic sensitization [11]. In the present study, three SNPs (rs2508746, rs1892953 and rs12278256) were related to asthma susceptibility in the Chinese Han population, of which rs12278256 has not been reported in previous studies. As a variant located in the upstream region of EMSY, rs12278256 might affect the regulatory motifs and chromatin status of this gene and further study is needed to verify this hypothesis. Studies in the twin population have shown that susceptibility to asthma can be attributed to genetic factors [3,4]. Although current genome-wide association studies have identified numerous polymorphisms associated with asthma susceptibility, the odds ratio (OR) is around 1.2, and only a small percentage of asthma prevalence can be contributed to them. Some experts have proposed to study the interaction between genes and environment [23,24]. It is well known that environmental factors such as smoking and obesity are susceptibility factors for asthma, but the specific mechanism is not clear. A number of studies have shown that smoking is associated with increased risk of asthma, reduced efficacy of inhaled corticosteroids treatment, acute exacerbations, and airway remodeling in asthma [25-29]. Mechanisms of asthma in the obese may include mechanical factors and inflammatory immunity [30]. Studies have shown that the SNPs at 17q21.2 is associated with BMI levels in asthmatic patients [31]. In our study, further analysis of the interaction between rs3847076 and environmental factors (smoking, BMI) revealed negative (Table S4). Functional prediction suggests that rs3847076 may affect the motif TCF4, and further investigation is needed. Recently, genetic studies have detected a lot of susceptibility genes for asthma. This study was the first attempt to investigate the association between CDHR3 ， EMSY and adult asthma susceptibility in the Chinese Han population.We found rs3847076 in CDHR3 , rs2508746, rs1892953 and rs12278256 in EMSY were associated with the risk of adult asthma. However, there were some limitations to this study. Adjustment was not performed to correct the results for multiple testing, due to the weak effect of each single polymorphism on asthma susceptibility. In addition, the allergic phenotypes of the asthma patients were not clear and serum IgE levels were not analyzed in the study. Lastly, CDHR3 is a huge gene spanning over 159kb and the strategy of tag-SNPs selection with r 2 >0.64 in this study may have missed some SNPs associated with the disease. Conclusions In conclusion, this study is the first to identify that the airway epithelium related genes EMSY and CDHR3 were associated with adult asthma susceptibility in the Chinese Han population. The CDHR3 rs3847076 allele A and EMSY rs1892953 genotype GG may increase the risk of asthma. The EMSY rs2508746 and rs12278256 allele T may decrease asthma risk. A population with a larger sample size is needed for further exploration of the association. Abbreviations Tag-SNPs, tag-single nucleotide polymorphisms GWAS, genome-wide association study CDHR3 , cadherin related family member 3 C11orf30 ,chromosome 11 open reading frame 30 SNPs, single nucleotide polymorphisms MAF, minor allele frequency PCR, polymerase chain reaction SPSS, statistical package for the social sciences HWE, Hardy-Weinberg equilibrium LD, linkage disequilibrium BMI, body mass index RV-C, rhinovirus C IgE, immunoglobulin E eQTL, expression quantitative trait loci OR, odds ratio Declarations Ethics approval and consent to participate All protocols for this study were reviewed and approved by the Institutional Review Board of the West China Hospital of Sichuan University (Protocol No. 23). Written informed consent was obtained from all the study participants. Acknowledgements We thank everyone who provided blood samples and consent for genetic analysis. And we thank all of the clinicians, nurses and study coordinators for their contributions to the work. Funding This work was supported by the National Natural Science Foundation of China [Grant No. 81370121]; the Health and Family Planning Commission of Sichuan Province Project [Grant No. 16PJ413]; and the Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital Project [Grant No. 2017QN11]; and the Sichuan Provincial Cadre Health Research Project [Grant No. 2018-211]. Author Contributions Conceptualization, Jianqing He; Data curation, Yu Wang and Shou-Quan Wu; Formal analysis, Yu Wang and Shou-Quan Wu; Project administration, Miao-miao Zhang, Guo Chen and Jianqing He; Supervision, Jianqing He; Writing – original draft, Miao-miao Zhang and Guo Chen; Writing – review & editing, Andrew J Sandford. Competing interests The Authors declare that there is no conflict of interest. Consent for publication Not applicable. 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Tables Table 1 Characteristics of cases and controls Characteristic Control n(%) Case n(%) P value Gender Male 162 (38.76) 118 (39.33) 0.876 Female 256 (61.24) 182 (60.67) Age (mean±SD,years) 44.09 ± 13.75 43.6 ± 13.48 0.64 Smoking status Current and ex-smokers 55 (13.16) 49 (16.33) 0.179 Non-smoking 207 (49.52) 247 (82.33) Smoking status unclear 156(37.32) 4(1.33) BMI(mean±SD) 22.94 ± 3.34 23.11 ± 3.28 0.517 BMI<24 227（54.31) 197(65.67) BMI≥24 121(29.67) 103(34.33) Asthma onset time Early-onset asthma(<18 years old) 42 (14.00) Late-onset asthma(≥18 years old) 223 (74.33) Onset time unclear 35(11.67) Asthma with pulmonary function test FEV1% predicted (mean±SD) 83.61 ± 19.97 FEV1/FVC(mean±SD) 72.37 ± 13.63 Values are means ± standard deviation (SD) and absolute numbers (percentages). BMI, body mass index; Early-onset asthma, age of asthma onset <18years; Late-onset asthma, age of asthma onset ≥18years; FEV1, forced expiratory volume in one second; FVC, forced vital capacity. Table 2 The four SNPs associated with asthma Genes SNPs Genetic models Genotypes Control n(%) Case n(%) P* OR 95%CI * CDHR3 rs3847076 Dom CC 285(68.2) 185(61.7) 0.081 1.378(0.962-1.973) CA+AA 133(31.8) 115(38.3) Rec CC+CA 408(97.6) 285(95.0) 0.060 2.689(0.958-7.545) AA 10(2.4) 15(5.0) Add CC/CA/AA 0.032 * 1.407(1.030-1.923) * EMSY rs2508746 Dom CC 244(58.4) 197(65.7) 0.019 * 0.660(0.465-0.935) * TC+TT 174(41.6) 103(34.3) Rec CC+TC 396(94.7) 288(96.0) 0.445 0.733(0.331-1.626) TT 22(5.3) 12(4.0) Add CC/TC/TT 0.026 * 0.718(0.536-0.961) * EMSY rs1892953 Dom AA 115(27.5) 76(25.3) 0.647 1.094(0.745-1.605) GA+GG 303(72.5) 224(74.7) Rec AA+GA 319(76.3) 219(73.0) 0.015 * 1.667(1.104-2.518) * GG 99(23.7) 81(27.0) Add AA/GA/GG 0.081 1.240(0.974-1.579) EMSY rs12278256 Dom GG 357(85.4) 272(90.7) 0.033 * 0.563(0.332-0.953) * TG+TT 61(14.6) 28(9.3) Rec GG+TG 417(99.8) 300(100) 1 - TT 1(0.2) 0(0) Add GG/TG/TT 0.027 * 0.558(0.332-0.937) * * Adjusted for sex, age, body mass index and smoking history with logistic regression, P<0.05. Add: additive model; Dom: dominant model; Rec: recessive model. Table 3 Results of stratification analysis based on gender, smoking status, BMI status, and onset age of asthma SNPs Genetic models Stratified by gender Stratified by smoking status Stratified by BMI status Stratified by onset age of asthma P OR 95%CI P OR 95%CI P OR 95%CI P OR 95%CI rs3847076 Dom male 0.048* 1.834(1.005-3.347) smoking 0.018* 2.252(1.149-4.413) BMI＜24 0.004* 1.925(1.233-3.007) late onset asthma 0.063 1.428(0.981-2.077) Rec 0.115 5.656(0.654-48.882) 0.09 4.222(0.799-22.320) 0.12 2.835(0.761-10.559) 0.049* 2.861(1.006-8.134) Add 0.023* 1.869(1.091-3.202) 0.009* 2.168(1.212-3.872) 0.005* 1.835(1.234-2.726) 0.023* 1.457(1.054-2.013) rs2508746 Dom female - - non-smoking 0.014* 0.618(0.420-0.908) BMI＜24 0.027* 0.612(0.396-0.946) late onset asthma 0.016* 0.637(0.441-0.919) Rec - - 0.498 0.737(0.304-1.782) 0.862 0.920(0.361-2.347) 0.419 0.706(0.304-1.641) Add - - 0.022* 0.685(0.495-0.947) 0.06 0.710(0.496-1.015) 0.021* 0.696(0.511-0.948) rs1892953 Dom female 0.548 1.159(0.717-1.873) non-smoking 0.456 0.174(0.770-1.790) BMI＜24 0.702 1.097(0.682-1.766) late onset asthma 0.692 1.084(0.726-1.620) Rec 0.038* 1.738(1.031-2.927) 0.04* 1.615(1.021-2.553) 0.017* 1.910(1.123-3.250) 0.017* 1.680(1.095-2.578) Add 0.108 1.282(0.947-1.737) 0.091 1.259(0.964-1.644) 0.096 1.297(0.955-1.761) 0.094 1.241(0.964-1.599) rs12278256 Dom female 0.037* 0.468(0.229-0.955) non-smoking 0.023* 0.512(0.287-0.913) BMI＜24 0.033* 0.485(0.249-0.944) late onset asthma - - Rec 1 1 1 - - Add 0.032* 0.465(0.231-0.936) 0.02* 0.508(0.288-0.897) 0.028* 0.481(0.250-0.923) - - * Adjusted for sex, age, body mass index and smoking history with logistic regression, P<0.05. Add: additive model; Dom: dominant model; Rec: recessive model. Table 4 The association between EMSY haplotypes in block 1 and asthma susceptibility Haplotype Case N (%) Control N (%) Chi 2 Pearson's p OR (95% CI) AAGTTAAAT 207.00(0.345) 285.58(0.342) 0 0.999 1.000 (0.801-1.248) GAGCGGAGC 43.00(0.072) 57.59(0.069) 0.023 0.878 1.033 (0.685-1.556) GAGCTAAAT 44.00(0.073) 58.04(0.069) 0.054 0.815 1.050 (0.699-1.577) GAGCTGAGC 35.00(0.058) 52.18(0.062) 0.134 0.714 0.921 (0.592-1.432) GAGTTAGGT 178.00(0.297) 230.22(0.275) 0.589 0.443 1.095 (0.868-1.382) GATCTGAGT 28.00(0.047) 61.00(0.073) 4.346 0.037 * 0.615 (0.388-0.975) * GGGCTAAAT 60.00(0.100) 76.32(0.091) 0.246 0.62 1.094 (0.766-1.562) Global result 600 836 4.912565 0.555 For each haplotype, alleles were arranged in order of rs4945087, rs7125744, rs12278256, rs7926009, rs7115331, rs2508740, rs1939469, rs2508755 rs3753051. Supplementary Files SupplementaryMaterials.doc Cite Share Download PDF Status: Published Journal Publication published 18 Nov, 2020 Read the published version in BMC Pulmonary Medicine → 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-28755","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":575931,"identity":"792014b4-0a65-46e3-a96c-160edc8b9848","order_by":1,"name":"Miao miao Zhang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Miao","middleName":"miao","lastName":"Zhang","suffix":""},{"id":575932,"identity":"94b2ecb0-6e1f-47cd-a261-9a488e167586","order_by":2,"name":"Guo Chen","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guo","middleName":"","lastName":"Chen","suffix":""},{"id":575933,"identity":"bc9dca95-fc88-4449-b079-2ad8703956e1","order_by":3,"name":"Yu Wang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""},{"id":575934,"identity":"8d699502-dab9-495d-a328-09ce2a7a500a","order_by":4,"name":"Shou quan Wu","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shou","middleName":"quan","lastName":"Wu","suffix":""},{"id":575935,"identity":"0710c0c7-ee6e-4acc-9d22-aa119f17beae","order_by":5,"name":"Andrew J Sandford","email":"","orcid":"","institution":"The University of British Columbia and St.Paul's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"J","lastName":"Sandford","suffix":""},{"id":575936,"identity":"5816bdf6-14a4-449d-8747-da808a9321d0","order_by":6,"name":"Jian qing He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACxmYQaQDEEgyMDyBiCcRrYTYgSgsCSDCwSRClhbmd+dmDNwV37Bqkm49V/qg5zMDPnmPA8HMHPoexmRvOMXiW3CBzLO02z7HDDJI9bwwYe8/g9YuZNI/B4WQGiRyz24wNhxkMbuQYMDO24dPC/g2qJf9b4U+gFnvCWnjAttgBbWFj4AXZIkFYS5nkHIPDCQwSacbSPMfSeSTOPCs42ItHi2H/8W0Sb/4ctmeQSH748UeNtRx/e/LGBz/xaWkAEjwMDIn7D0AEeEDEAdwaGBjkocrs8SkaBaNgFIyCEQ4AOoxKiN9zRVYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-0557-3089","institution":"Sichuan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jian","middleName":"qing","lastName":"He","suffix":""}],"badges":[],"createdAt":"2020-05-14 06:14:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-28755/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-28755/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12890-020-01334-0","type":"published","date":"2020-11-19T02:38:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":1123507,"identity":"0771c26f-cdf7-4319-8bab-6ec4568bff0d","added_by":"auto","created_at":"2020-05-18 23:43:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2075124,"visible":true,"origin":"","legend":"Analysis of linkage disequilibrium of SNPs in EMSY\nNote: Each square represents the linkage disequilibrium of two corresponding SNPs, which is displayed as r2×100. The larger the darkness of the square, the larger the value of r2×100.\n","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-28755/v1/1.png"},{"id":1123509,"identity":"1bef14ff-db41-4ca1-9c79-05980a59f18b","added_by":"auto","created_at":"2020-05-18 23:43:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1389111,"visible":true,"origin":"","legend":"Analysis of linkage disequilibrium of tag-SNPs in CDHR3\nNote: Each square represents the linkage disequilibrium of two corresponding SNPs, which is displayed as r2×100. The larger the darkness of the square, the larger the value of r2×100.\n","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-28755/v1/2.png"},{"id":15666974,"identity":"309063cc-0f33-4bae-8ddf-642336af95ab","added_by":"auto","created_at":"2021-11-18 13:39:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2388141,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-28755/v1/3ff04805-4a97-4ad3-9234-a26d261ec8dd.pdf"},{"id":1123508,"identity":"4fa3d6d5-dc25-4f79-9ecf-3d4ead40c022","added_by":"auto","created_at":"2020-05-18 23:43:21","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":95232,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.doc","url":"https://assets-eu.researchsquare.com/files/rs-28755/v1/SupplementaryMaterials.doc"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePolymorphisms in the airway epithelium related genes\u003cem\u003e CDHR3\u003c/em\u003e and \u003cem\u003eEMSY \u003c/em\u003eare associated with asthma susceptibility\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eAsthma is a chronic airway inflammatory disease that affects populations throughout the world. A World Health Organization report [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] predicted that the number of asthma patients would increase to 400\u0026nbsp;million by 2025 and 250,000 patients may die from this disease each year. A recent survey indicated that the prevalence of asthma among individuals aged\u0026thinsp;\u0026gt;\u0026thinsp;14\u0026nbsp;years was 1.24% and there are approximately 30\u0026nbsp;million asthmatic patients in China [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The pathogenesis of asthma is still incompletely understood but it is known that genetic factors play a significant part in asthma susceptibility. The heritability of asthma was estimated to be 60\u0026ndash;70% in an Australian twin study [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Genetic factors contributed to 90% of the variance in the susceptibility to asthma in a 5-year-old twin pair study [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs the first barrier between the human body and the environment, the airway epithelium has an important role in regulating the inflammation, immunity and tissue repair in the pathogenesis of asthma [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. One genome-wide association study (GWAS) of a Danish population identified Cadherin related family member 3 (\u003cem\u003eCDHR3\u003c/em\u003e), which is highly expressed in human airway epithelium, as a susceptibility locus for childhood asthma with severe exacerbations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A GWAS in 2017 demonstrated that Chromosome 11 open reading frame 30 \u003cem\u003e(C11orf30)\u003c/em\u003e, also called \u003cem\u003eEMSY\u003c/em\u003e or BRCA2-interacting transcriptional repressor, another gene expressed in airway epithelium, was a risk locus for food allergy in a Canadian population [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and this gene has been shown to be involved in the epigenetic regsulation of gene expression [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, there have been few studies of these two genes in Chinese asthmatics. Therefore, this study aimed to investigate the association of common variants in \u003cem\u003eCDHR3\u003c/em\u003e and \u003cem\u003eEMSY\u003c/em\u003e with adult asthma in the Chinese population.\u003c/p\u003e "},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe asthmatic cases were diagnosed by at least three respiratory physicians from the West China Hospital according to the criteria of the Global Strategy for Asthma Management and Prevention [9]. The healthy controls were collected from the physical examination center in the same hospital. Subjects were excluded if any of the following conditions were present: chronic obstructive pulmonary disease, diabetes, tumors, any immune disease, and immune deficiency. Use of hormones or immunosuppressive drugs were also exclusion criteria. Cases and controls were unrelated Chinese Han individuals. After signing the informed consent, 3 ml of venous blood were drawn from every subject and stored in a -80°C freezer. All blood specimens were collected from September 2013 to September 2016. The study was approved by the ethical committee of the West China Hospital of Sichuan University (Protocol No. 23).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNP Selection and Genotyping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) ≥ 0.05 and r\u003csup\u003e2 \u003c/sup\u003e≥ 0.64, located in the region 3000 base pairs upstream to 300 base pairs downstream of \u003cem\u003eCDHR3 \u003c/em\u003ewere downloaded from the Han Chinese in Beijing database of the Genome Variation Server 147 (http://gvs.gs.washington.edu/GVS/), which is an online resource based on dbSNP. The final selections were 23 tag-SNPs including rs3887998, rs12155008, rs41267, rs3892893, rs10270308, rs34426483, rs193795, rs2526978, rs381188, rs10241452, rs3847076, rs11981655, rs10808147, rs193806, rs2528883, rs41269, rs2526979, rs2526976, rs41262, rs41266, rs6967330, rs41270 and rs448024. The selection of SNPs in \u003cem\u003eEMSY\u003c/em\u003e was based on the tag-SNP strategy and literature review [10-13]. The tag-SNP selection strategy was the same as above except for r\u003csup\u003e2 \u003c/sup\u003e≥ 0.80. The 17 SNPs selected were rs3753051, rs7125744, rs7926009, rs4945087, rs2508740, rs1939469, rs7115331, rs1044265, rs12278256, rs2513513, rs2508755, rs2155219, rs2513525, rs2508746, rs1892953, rs7130588 and rs10899234.\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from the blood samples using a genomic DNA\u003c/p\u003e\n\u003cp\u003epurification kit (Axygen Scientific Inc, Union City, CA, USA). SNPs were genotyped by Genesky Bio-Tech Co., Ltd (\u003ca href=\"http://geneskybiotech.com/index.html\"\u003ehttp://geneskybiotech.com/index.html\u003c/a\u003e) using the SNPscanTM multiplex SNP genotyping technique based on double ligation and multiplex fluorescence polymerase Chain Reaction (PCR) [14]. As a quality control measure, 5% of random samples were repeat genotyped with a concordance rate of 100%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical tests were performed using the Statistical Package for the Social Sciences (SPSS, SPSS Inc., Chicago, IL, USA), version 21.0. A \u003cem\u003ep\u003c/em\u003e value \u0026lt;0.05 was considered to be statistically significant. Continuous and categorical variables were analyzed by student’s t-test and chi-square test, respectively. Genotype distributions under additive, dominant and recessive models were calculated by binary logistic regression analysis. Hardy-Weinberg equilibrium (HWE) among the controls was tested using plink software. Haploview and SHEsis software (\u003ca href=\"http://analysis.bio-x.cn/\"\u003ehttp://analysis.bio-x.cn\u003c/a\u003e) were combined to perform linkage disequilibrium (LD) and haplotype analysis. Statistically significant SNPs were predicted by the software RegulomeDB (\u003ca href=\"http://www.regulomedb.org/\"\u003ehttp://www.regulomedb.org/\u003c/a\u003e) and Haploreg v4 (http://compbio.mit.edu/HaploReg).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSubject characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 300 asthma patients and 418 healthy controls were enrolled. The average ages of asthma patients and controls were 43.6±13.48 and 44.09±13.75 years, respectively. No significant differences in sex, body mass index (BMI) and smoking history were observed between case and control groups (Table 1). Late-onset asthma (age of asthma onset ≥18 years) accounted for 74.3% in the case group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation analyses between \u003cem\u003eCDHR3\u003c/em\u003e, \u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eEMSY\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e SNPs and asthma susceptibility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe characteristics of the selected SNPs are listed in Table S1 and S2. Rs10899234 in \u003cem\u003eEMSY\u003c/em\u003e and rs6967330 in \u003cem\u003eCDHR3 \u003c/em\u003ewere excluded due to their deviation from HWE in the control subjects (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The genotyping assays failed for rs12155008, rs41270 and rs448024 in \u003cem\u003eCDHR3\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eAfter adjusting for confounding factors including age, sex, BMI and smoking history, four SNPs were found to be associated with asthma susceptibility (Table 2). The A allele of rs3847076 in \u003cem\u003eCDHR3 \u003c/em\u003ewas associated with increased susceptibility to asthma under the additive model (\u003cem\u003eP \u003c/em\u003e= 0.032, OR = 1.407, 95% CI: 1.030-1.923). For \u003cem\u003eEMSY\u003c/em\u003e, both the TC/TT genotype and T allele of rs2508746 were associated with decreased risk of asthma (dominant model: \u003cem\u003eP \u003c/em\u003e= 0.019, OR = 0.660, 95% CI: 0.465-0.935; additive model: \u003cem\u003eP \u003c/em\u003e= 0.026, OR = 0.718, 95% CI: 0.536-0.961). The TG/TT genotype and T allele of rs12278256 were associated with reduced asthma risk (dominant model: \u003cem\u003eP \u003c/em\u003e= 0.033, OR = 0.563, 95% CI: 0.332-1.953; additive model: \u003cem\u003eP \u003c/em\u003e= 0.027, OR = 0.558, 95% CI: 0.332-0.937). Finally, the GG genotype of rs1892953 showed an association with increased asthma risk under the recessive model (\u003cem\u003eP \u003c/em\u003e= 0.015, OR = 1.667, 95% CI: 1.104-2.518).\u003c/p\u003e\n\u003cp\u003eStratified analysis results by gender, smoking status, BMI status and onset age of asthma were shown in Table 3. Allele A of rs3847076 was associated with increased susceptibility to asthma in male subgroup, smoking subgroup, non-overweight subgroup and late onset asthma subgroup (\u003cem\u003eP\u003c/em\u003e=0.023, OR=1.869; \u003cem\u003eP\u003c/em\u003e=0.009, OR=2.168; \u003cem\u003eP\u003c/em\u003e=0.005, OR=1.835 and \u003cem\u003eP\u003c/em\u003e=0.023, OR=1.457, respectively). Similarly, rs2508746 TC+TT was related with decreased asthma susceptibility in the non-smoking subgroup, non-overweight subgroup, and late-onset asthma subgroup in dominant model (\u003cem\u003eP\u003c/em\u003e=0.014, OR=0.618; \u003cem\u003eP\u003c/em\u003e=0.027, OR=0.612 and \u003cem\u003eP\u003c/em\u003e=0.016, OR=0.637, respectively). Meanwhile, rs1892953 GG shown increased risk of asthma in the female subgroup, non-smoking subgroup, non-overweight subgroup, and late onset asthma subgroup in recessive model (\u003cem\u003eP\u003c/em\u003e=0.038, OR=1.738; \u003cem\u003eP\u003c/em\u003e=0.04, OR=1.615; \u003cem\u003eP\u003c/em\u003e=0.017, OR=1.910 and \u003cem\u003eP\u003c/em\u003e=0.017, OR=1.680, respectively). Rs12278256 T was still associated with decreased asthma susceptibility in female subgroups, non-smoking subgroups, and non-overweight subgroups in additive model (\u003cem\u003eP\u003c/em\u003e=0.032, OR=0.465; \u003cem\u003eP\u003c/em\u003e=0.02, OR=0.508 and \u003cem\u003eP\u003c/em\u003e=0.028, OR=0.481, respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHaplotype and LD analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LD between SNPs of \u003cem\u003eCDHR3\u003c/em\u003e and \u003cem\u003eEMSY\u003c/em\u003e was low and those SNPs were divided into eight haplotype blocks with Haploview software (Figures 1 and 2). Only the haplotype consisting of GATCTGAGT in block 1 of \u003cem\u003eEMSY\u003c/em\u003e was associated with decreased risk of asthma (\u003cem\u003eP \u003c/em\u003e= 0.037, OR = 0.615, 95% CI: 0.388-0.975) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional prediction results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour statistically significant SNPs were predicted using the software RegulomeDB and Haploreg v4 (Table S3). Rs144934374 is strongly linked to rs12278256 and its RegulomeDB scores is lower than that of rs12278256, suggesting that it may be the functional site represented by rs12278256. Acting as promoter histone marks or enhancer histone marks, or affecting DNAse is suggested to be associated with chromatin status, and binding proteins or altering regulatory motifs in ChIP-Seq suggest that transcription levels may be affected. It seems that these four SNPs may have certain effects on chromatin status and transcription level. Rs1892953 appears as an expression quantitative trait loci (eQTL) SNP in thyroid tissue [12].\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this group of Chinese Han adults, the relationship between two airway epithelial-related genes \u003cem\u003eEMSY\u003c/em\u003e and \u003cem\u003eCDHR3\u003c/em\u003e and risk of asthma were investigated, and four polymorphisms related to asthma susceptibility were obtained, which were rs3847076 of \u003cem\u003eCDHR3 \u003c/em\u003eand rs2508746, rs1892953 and rs12278256 of \u003cem\u003eEMSY\u003c/em\u003e. A further subgroup analysis of these four variants revealed that their association with asthma was present in the subgroup.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCDHR3\u003c/em\u003e, located on chromosome 7, is specifically expressed in ciliated airway epithelial cells which are the targets of Rhinovirus C (RV-C) infection, and its expression was positively associated with RV-C binding, replication and entry into the host cells [15,16]. There are only a few studies describing the relationship between \u003cem\u003eCDHR3\u003c/em\u003e polymorphisms and asthma, and the results were inconsistent in different populations. The A allele of rs6967330 in \u003cem\u003eCDHR3\u003c/em\u003e increased the risk of wheezing illnesses and hospitalizations for childhood asthma in a Danish study [6]. Rs17152490, in LD with rs6967330 was reported to affect asthma risk through \u003cem\u003ecis\u003c/em\u003e-regulation of its gene expression in cells from human bronchial epithelial biopsy [17] . However, rs6967330 was only related to early-onset asthma in a Japanese population [18] and no association between rs6967330 and asthma was found in Chinese children [19]. In the present study, rs6967330 was not in HWE and our data suggest that rs3847076 may increase the risk of asthma in adults, which were inconsistent with the previous studies. The potential reasons for this discrepancy are as follows: Firstly, the susceptibility to asthma may differ in different populations, and secondly, late-onset asthma patients accounted for the majority of the case group in this study, in contrast to the above Japanese study which reported the positive relationship between rs6967330 and early-onset asthma in children. A future study of different asthma phenotypes would be beneficial to the accurate prevention and treatment of asthma.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u003cem\u003eEMSY\u003c/em\u003e, located on chromosome 11q13.5, is expressed in the human airway epithelium and encoded by the EMSY protein. GWAS studies showed that \u003cem\u003eEMSY\u003c/em\u003e was involved in allergic diseases including atopic dermatitis and food allergy [20,21]. Several SNPs, rs7130588, rs10899234, rs6592657, as well as we studied SNPs rs2508746 and rs1892953 were associated with total serum immunoglobulin E (IgE) levels in non-Hispanic Caucasian asthmatic patients [10]. In an eQTL analysis, Li et al [17] reported that rs2508740, rs2513525, rs4300410 (in complete LD with rs7926009), rs10793169 (in complete LD with rs7926009), rs2513513 and rs4245443 were significantly correlated with mRNA expression levels of \u003cem\u003eEMSY\u003c/em\u003e in human bronchial alveolar lavage. Another GWAS study reported that rs7130588 in \u003cem\u003eEMSY \u003c/em\u003ewas associated with asthma [22]. A meta-analysis demonstrated that rs2155219 in \u003cem\u003eEMSY \u003c/em\u003eincreased the risk of allergic sensitization [11]. In the present study, three SNPs (rs2508746, rs1892953 and rs12278256) were related to asthma susceptibility in the Chinese Han population, of which rs12278256 has not been reported in previous studies. As a variant located in the upstream region of \u003cem\u003eEMSY, \u003c/em\u003ers12278256 might affect the regulatory motifs and chromatin status of this gene and further study is needed to verify this hypothesis.\u003c/p\u003e\n\u003cp\u003eStudies in the twin population have shown that susceptibility to asthma can be attributed to genetic factors [3,4]. Although current genome-wide association studies have identified numerous polymorphisms associated with asthma susceptibility, the odds ratio (OR) is around 1.2, and only a small percentage of asthma prevalence can be contributed to them. Some experts have proposed to study the interaction between genes and environment [23,24]. It is well known that environmental factors such as smoking and obesity are susceptibility factors for asthma, but the specific mechanism is not clear. A number of studies have shown that smoking is associated with increased risk of asthma, reduced efficacy of inhaled corticosteroids treatment, acute exacerbations, and airway remodeling in asthma [25-29]. Mechanisms of asthma in the obese may include mechanical factors and inflammatory immunity [30]. Studies have shown that the SNPs at 17q21.2 is associated with BMI levels in asthmatic patients [31]. In our study, further analysis of the interaction between rs3847076 and environmental factors (smoking, BMI) revealed negative (Table S4). Functional prediction suggests that rs3847076 may affect the motif TCF4, and further investigation is needed.\u003c/p\u003e\n\u003cp\u003eRecently, genetic studies have detected a lot of susceptibility genes for asthma. This study was the first attempt to investigate the association between \u003cem\u003eCDHR3\u003c/em\u003e，\u003cem\u003eEMSY\u003c/em\u003e and adult asthma susceptibility in the Chinese Han population.We found rs3847076 in \u003cem\u003eCDHR3\u003c/em\u003e, rs2508746, rs1892953 and rs12278256 in \u003cem\u003eEMSY\u003c/em\u003e were associated with the risk of adult asthma. However, there were some limitations to this study. Adjustment was not performed to correct the results for multiple testing, due to the weak effect of each single polymorphism on asthma susceptibility. In addition, the allergic phenotypes of the asthma patients were not clear and serum IgE levels were not analyzed in the study. Lastly, \u003cem\u003eCDHR3\u003c/em\u003e is a huge gene spanning over 159kb and the strategy of tag-SNPs selection with r\u003csup\u003e2\u003c/sup\u003e\u0026gt;0.64 in this study may have missed some SNPs associated with the disease.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study is the first to identify that the airway epithelium related genes\u003cem\u003e EMSY\u003c/em\u003e and \u003cem\u003eCDHR3\u003c/em\u003e were associated with adult asthma susceptibility in the Chinese Han population. The \u003cem\u003eCDHR3\u003c/em\u003e rs3847076 allele A and \u003cem\u003eEMSY\u003c/em\u003e rs1892953 genotype GG may increase the risk of asthma. The \u003cem\u003eEMSY\u003c/em\u003e rs2508746 and rs12278256 allele T may decrease asthma risk. A population with a larger sample size is needed for further exploration of the association.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTag-SNPs, tag-single nucleotide polymorphisms\u003c/p\u003e\n\u003cp\u003eGWAS, genome-wide association study\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCDHR3\u003c/em\u003e, cadherin related family member 3\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eC11orf30\u003c/em\u003e,chromosome 11 open reading frame 30\u003c/p\u003e\n\u003cp\u003eSNPs, single nucleotide polymorphisms\u003c/p\u003e\n\u003cp\u003eMAF, minor allele frequency\u003c/p\u003e\n\u003cp\u003ePCR, polymerase chain reaction \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSPSS, statistical package for the social sciences\u003c/p\u003e\n\u003cp\u003eHWE, Hardy-Weinberg equilibrium\u003c/p\u003e\n\u003cp\u003eLD, linkage disequilibrium\u003c/p\u003e\n\u003cp\u003eBMI, body mass index\u003c/p\u003e\n\u003cp\u003eRV-C, rhinovirus C\u003c/p\u003e\n\u003cp\u003eIgE, immunoglobulin E\u003c/p\u003e\n\u003cp\u003eeQTL, expression quantitative trait loci\u003c/p\u003e\n\u003cp\u003eOR, odds ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll protocols for this study were reviewed and approved by the Institutional Review Board of the West China Hospital of Sichuan University (Protocol No. 23). Written informed consent was obtained from all the study participants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe thank everyone who provided blood samples and consent for genetic analysis. And we thank all of the clinicians, nurses and study coordinators for their contributions to the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [Grant No. 81370121]; the Health and Family Planning Commission of Sichuan Province Project [Grant No. 16PJ413]; and the Sichuan Academy of Medical Sciences \u0026amp; Sichuan Provincial People's Hospital Project [Grant No. 2017QN11]; and the Sichuan Provincial Cadre Health Research Project [Grant No. 2018-211].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Jianqing He; Data curation, Yu Wang and Shou-Quan Wu; Formal analysis, Yu Wang and Shou-Quan Wu; Project administration, Miao-miao Zhang, Guo Chen and Jianqing He; Supervision, Jianqing He; Writing – original draft, Miao-miao Zhang and Guo Chen; Writing – review \u0026amp; editing, Andrew J Sandford.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome or all data, models, or code generated or used during the study are available from the corresponding author by request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. 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Identification of IL6R and chromosome 11q13.5 as risk loci for asthma. \u003cem\u003eLancet \u003c/em\u003e\u003cstrong\u003e2011\u003c/strong\u003e, \u003cem\u003e378\u003c/em\u003e, 1006-1014, doi:10.1016/S0140-6736(11)60874-X.\u003c/li\u003e\n\u003cli\u003eMoffatt, M.F.; Gut, I.G.; Demenais, F.; Strachan, D.P.; Bouzigon, E.; Heath, S.; von Mutius, E.; Farrall, M.; Lathrop, M.; Cookson, W., et al. A large-scale, consortium-based genomewide association study of asthma. \u003cem\u003eN Engl J Med \u003c/em\u003e\u003cstrong\u003e2010\u003c/strong\u003e, \u003cem\u003e363\u003c/em\u003e, 1211-1221, doi:10.1056/NEJMoa0906312.\u003c/li\u003e\n\u003cli\u003eOber, C. Asthma Genetics in the Post-GWAS Era. \u003cem\u003eAnn Am Thorac Soc \u003c/em\u003e\u003cstrong\u003e2016\u003c/strong\u003e, \u003cem\u003e13 Suppl 1\u003c/em\u003e, S85-90, doi:10.1513/AnnalsATS.201507-459MG.\u003c/li\u003e\n\u003cli\u003eNakamura, K.; Nagata, C.; Fujii, K.; Kawachi, T.; Takatsuka, N.; Oba, S.; Shimizu, H. Cigarette smoking and the adult onset of bronchial asthma in Japanese men and women. \u003cem\u003eAnn Allergy Asthma Immunol \u003c/em\u003e\u003cstrong\u003e2009\u003c/strong\u003e, \u003cem\u003e102\u003c/em\u003e, 288-293, doi:10.1016/S1081-1206(10)60333-X.\u003c/li\u003e\n\u003cli\u003eShimoda, T.; Obase, Y.; Kishikawa, R.; Iwanaga, T. Influence of cigarette smoking on airway inflammation and inhaled corticosteroid treatment in patients with asthma. \u003cem\u003eAllergy Asthma Proc \u003c/em\u003e\u003cstrong\u003e2016\u003c/strong\u003e, \u003cem\u003e37\u003c/em\u003e, 50-58, doi:10.2500/aap.2016.37.3944.\u003c/li\u003e\n\u003cli\u003eHeijink, I.; van Oosterhout, A.; Kliphuis, N.; Jonker, M.; Hoffmann, R.; Telenga, E.; Klooster, K.; Slebos, D.J.; ten Hacken, N.; Postma, D., et al. Oxidant-induced corticosteroid unresponsiveness in human bronchial epithelial cells. \u003cem\u003eThorax \u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e69\u003c/em\u003e, 5-13, doi:10.1136/thoraxjnl-2013-203520.\u003c/li\u003e\n\u003cli\u003eSilverman, R.A.; Boudreaux, E.D.; Woodruff, P.G.; Clark, S.; Camargo, C.A., Jr. Cigarette smoking among asthmatic adults presenting to 64 emergency departments. \u003cem\u003eChest \u003c/em\u003e\u003cstrong\u003e2003\u003c/strong\u003e, \u003cem\u003e123\u003c/em\u003e, 1472-1479, doi:10.1378/chest.123.5.1472.\u003c/li\u003e\n\u003cli\u003eFattahi, F.; Hylkema, M.N.; Melgert, B.N.; Timens, W.; Postma, D.S.; ten Hacken, N.H. Smoking and nonsmoking asthma: differences in clinical outcome and pathogenesis. \u003cem\u003eExpert Rev Respir Med \u003c/em\u003e\u003cstrong\u003e2011\u003c/strong\u003e, \u003cem\u003e5\u003c/em\u003e, 93-105, doi:10.1586/ers.10.85.\u003c/li\u003e\n\u003cli\u003eDixon, A.E.; Holguin, F.; Sood, A.; Salome, C.M.; Pratley, R.E.; Beuther, D.A.; Celedon, J.C.; Shore, S.A.; American Thoracic Society Ad Hoc Subcommittee on, O.; Lung, D. An official American Thoracic Society Workshop report: obesity and asthma. \u003cem\u003eProc Am Thorac Soc \u003c/em\u003e\u003cstrong\u003e2010\u003c/strong\u003e, \u003cem\u003e7\u003c/em\u003e, 325-335, doi:10.1513/pats.200903-013ST.\u003c/li\u003e\n\u003cli\u003eWang, L.; Murk, W.; DeWan, A.T. Genome-Wide Gene by Environment Interaction Analysis Identifies Common SNPs at 17q21.2 that Are Associated with Increased Body Mass Index Only among Asthmatics. \u003cem\u003ePLoS One \u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e10\u003c/em\u003e, e0144114, doi:10.1371/journal.pone.0144114.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Characteristics of cases and controls\u003c/p\u003e\n\u003ctable width=\"580\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eControl n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003eCase n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eGender\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e162 (38.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e118 (39.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"64\"\u003e\n\u003cp\u003e0.876\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e256 (61.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e182 (60.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eAge (mean±SD,years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e44.09 ± 13.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e43.6 ± 13.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eSmoking status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eCurrent and ex-smokers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e55 (13.16)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e49 (16.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"64\"\u003e\n\u003cp\u003e0.179\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eNon-smoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e207 (49.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e247 (82.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eSmoking status unclear\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e156(37.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e4(1.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eBMI(mean±SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e22.94 ± 3.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e23.11 ± 3.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e0.517\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eBMI\u0026lt;24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e227（54.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e197(65.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eBMI≥24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e121(29.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e103(34.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eAsthma onset time\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eEarly-onset asthma(\u0026lt;18 years old)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e42 (14.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eLate-onset asthma(≥18 years old)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e223 (74.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003e\u0026nbsp;Onset time unclear\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e35(11.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eAsthma with pulmonary function test\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eFEV1% predicted (mean±SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e\u0026nbsp;83.61 ± 19.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"306\"\u003e\n\u003cp\u003eFEV1/FVC(mean±SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e72.37 ± 13.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"64\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are means ± standard deviation (SD) and absolute numbers (percentages). BMI, body mass index; Early-onset asthma, age of asthma onset \u0026lt;18years; Late-onset asthma, age of asthma onset ≥18years; FEV1, forced expiratory volume in one second; FVC, forced vital capacity.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e The four SNPs associated with asthma\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eGenes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003eSNPs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eGenetic models\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eGenotypes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003eControl n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eCase n(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"197\"\u003e\n\u003cp\u003e\u003cem\u003eP*\u003c/em\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; OR 95%CI\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cem\u003eCDHR3\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003ers3847076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eDom\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e285(68.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e185(61.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.378(0.962-1.973)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eCA+AA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e133(31.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e115(38.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eRec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eCC+CA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e408(97.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e285(95.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e2.689(0.958-7.545)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e10(2.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e15(5.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eAdd\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eCC/CA/AA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.032\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.407(1.030-1.923)\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cem\u003eEMSY\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003ers2508746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eDom\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e244(58.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e197(65.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.019\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e0.660(0.465-0.935)\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eTC+TT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e174(41.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e103(34.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eRec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eCC+TC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e396(94.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e288(96.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.445\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e0.733(0.331-1.626)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e22(5.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e12(4.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eAdd\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eCC/TC/TT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.026\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e0.718(0.536-0.961)\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cem\u003eEMSY\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003ers1892953\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eDom\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e115(27.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e76(25.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.647\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.094(0.745-1.605)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eGA+GG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e303(72.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e224(74.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eRec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eAA+GA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e319(76.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e219(73.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.015\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.667(1.104-2.518)\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e99(23.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e81(27.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eAdd\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eAA/GA/GG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e1.240(0.974-1.579)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u003cem\u003eEMSY\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003ers12278256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eDom\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eGG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e357(85.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e272(90.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.033\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e0.563(0.332-0.953)\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eTG+TT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e61(14.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e28(9.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eRec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eGG+TG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e417(99.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e300(100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eTT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e1(0.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0(0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"75\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eAdd\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003eGG/TG/TT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e0.027\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e0.558(0.332-0.937)\u003cem\u003e*\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Adjusted for sex, age, body mass index and smoking history with logistic regression, P\u0026lt;0.05. Add: additive model; Dom: dominant model; Rec: recessive model.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Results of stratification analysis based on gender, smoking status, BMI status, and onset age of asthma\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"60\"\u003e\n\u003cp\u003eSNPs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"54\"\u003e\n\u003cp\u003eGenetic models\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"42\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"142\"\u003e\n\u003cp\u003eStratified by gender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"62\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"142\"\u003e\n\u003cp\u003eStratified by smoking status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"49\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"142\"\u003e\n\u003cp\u003eStratified by BMI status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"52\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"137\"\u003e\n\u003cp\u003eStratified by onset age of asthma\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eOR 95%CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eOR 95%CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003eOR 95%CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003eOR 95%CI\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\n\u003cp\u003ers3847076\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eDom\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"42\"\u003e\n\u003cp\u003emale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.048*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.834(1.005-3.347)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"62\"\u003e\n\u003cp\u003esmoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.018*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.252(1.149-4.413)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"49\"\u003e\n\u003cp\u003eBMI＜24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.004*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.925(1.233-3.007)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"52\"\u003e\n\u003cp\u003elate onset asthma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.428(0.981-2.077)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eRec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e5.656(0.654-48.882)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e4.222(0.799-22.320)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e2.835(0.761-10.559)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.049*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.861(1.006-8.134)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eAdd\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.023*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.869(1.091-3.202)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.009*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e2.168(1.212-3.872)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.005*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.835(1.234-2.726)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.023*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.457(1.054-2.013)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\n\u003cp\u003ers2508746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eDom\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"42\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"62\"\u003e\n\u003cp\u003enon-smoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.014*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.618(0.420-0.908)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"49\"\u003e\n\u003cp\u003eBMI＜24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.027*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.612(0.396-0.946)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"52\"\u003e\n\u003cp\u003elate onset asthma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.016*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.637(0.441-0.919)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eRec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.498\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e0.737(0.304-1.782)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.862\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e0.920(0.361-2.347)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.419\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e0.706(0.304-1.641)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eAdd\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.022*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.685(0.495-0.947)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e0.710(0.496-1.015)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.021*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.696(0.511-0.948)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\n\u003cp\u003ers1892953\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eDom\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"42\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.548\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1.159(0.717-1.873)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"62\"\u003e\n\u003cp\u003enon-smoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.456\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e0.174(0.770-1.790)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"49\"\u003e\n\u003cp\u003eBMI＜24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.702\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1.097(0.682-1.766)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"52\"\u003e\n\u003cp\u003elate onset asthma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.692\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.084(0.726-1.620)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eRec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.038*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.738(1.031-2.927)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.04*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.615(1.021-2.553)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.017*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.910(1.123-3.250)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.017*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e\u003cstrong\u003e1.680(1.095-2.578)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eAdd\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1.282(0.947-1.737)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1.259(0.964-1.644)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.096\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e1.297(0.955-1.761)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e0.094\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e1.241(0.964-1.599)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\n\u003cp\u003ers12278256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eDom\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"42\"\u003e\n\u003cp\u003efemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.037*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.468(0.229-0.955)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"62\"\u003e\n\u003cp\u003enon-smoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.023*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.512(0.287-0.913)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"49\"\u003e\n\u003cp\u003eBMI＜24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.033*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.485(0.249-0.944)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" width=\"52\"\u003e\n\u003cp\u003elate onset asthma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eRec\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"60\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"54\"\u003e\n\u003cp\u003eAdd\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.032*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.465(0.231-0.936)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.02*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.508(0.288-0.897)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.028*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"90\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.481(0.250-0.923)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"52\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Adjusted for sex, age, body mass index and smoking history with logistic regression, P\u0026lt;0.05. Add: additive model; Dom: dominant model; Rec: recessive model.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e The association between \u003cem\u003eEMSY\u003c/em\u003e haplotypes in block 1 and asthma susceptibility\u003c/p\u003e\n\u003ctable\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003eHaplotype\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003eCase N (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003eControl N (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003eChi\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003ePearson's p\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003eOR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003eAAGTTAAAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e207.00(0.345)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e285.58(0.342)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e1.000 (0.801-1.248)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003eGAGCGGAGC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e43.00(0.072)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e57.59(0.069)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.878\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e1.033 (0.685-1.556)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003eGAGCTAAAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e44.00(0.073)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e58.04(0.069)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.815\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e1.050 (0.699-1.577)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003eGAGCTGAGC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e35.00(0.058)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e52.18(0.062)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.714\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e0.921 (0.592-1.432)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003eGAGTTAGGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e178.00(0.297)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e230.22(0.275)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.443\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e1.095 (0.868-1.382)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003eGATCTGAGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e28.00(0.047)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e61.00(0.073)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e4.346\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.037\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e0.615 (0.388-0.975)\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003eGGGCTAAAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e60.00(0.100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e76.32(0.091)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e0.246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e1.094 (0.766-1.562)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"115\"\u003e\n\u003cp\u003eGlobal result\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"94\"\u003e\n\u003cp\u003e600\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e836\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"74\"\u003e\n\u003cp\u003e4.912565\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"85\"\u003e\n\u003cp\u003e0.555\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFor each haplotype, alleles were arranged in order of rs4945087, rs7125744, rs12278256, rs7926009, rs7115331, rs2508740, rs1939469, rs2508755 rs3753051.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CDHR3, EMSY, asthma, polymorphism, susceptibility","lastPublishedDoi":"10.21203/rs.3.rs-28755/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-28755/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: As a main line of defense of the respiratory tract, the airway epithelium plays an important role in the pathogenesis of asthma. \u003cem\u003eCDHR3 \u003c/em\u003eand \u003cem\u003eEMSY\u003c/em\u003e were reported to be expressed in the human airway epithelium. Although previous genome-wide association studies found that the two genes were associated with asthma susceptibility, similar observations have not been made in the Chinese Han population.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A total of 300 asthma patients and 418 healthy controls who were unrelated Chinese Han individuals were enrolled. Tag-single nucleotide polymorphisms (Tag-SNPs) were genotyped and the associations between SNPs and asthma risk were analyzed by binary logistic regression analysis.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: After adjusting for confounding factors, the A allele of rs3847076 in \u003cem\u003eCDHR3\u003c/em\u003e was associated with increased susceptibility to asthma (OR = 1.407, 95% CI: 1.030-1.923). For the \u003cem\u003eEMSY\u003c/em\u003e gene, the T alleles of both rs2508746 and rs12278256 were related with decreased susceptibility to asthma (additive model: OR = 0.718, 95% CI: 0.536-0.961; OR = 0.558, 95% CI: 0.332-0.937, respectively). In addition, the GG genotype of rs1892953 showed an association with increased asthma risk under the recessive model (OR\u003cem\u003e \u003c/em\u003e= 1.667, 95% CI: 1.104-2.518) and the GATCTGAGT haplotype in \u003cem\u003eEMSY \u003c/em\u003ewas associated with reduced asthma risk (\u003cem\u003eP \u003c/em\u003e= 0.037).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: This study identified novel associations of rs3847076 in\u003cem\u003e CDHR3\u003c/em\u003e, as well as rs1892953, rs2508746 and rs12278256 in \u003cem\u003eEMSY\u003c/em\u003e with adult asthma susceptibility in the Chinese Han population. Our observations suggest that \u003cem\u003eCDHR3\u003c/em\u003e and \u003cem\u003eEMSY\u003c/em\u003e may play important roles in the pathogenesis of asthma in Chinese individuals. Further study with larger sample size is needed.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTrial Registration\u003c/strong\u003e: Not applicable.\u003c/p\u003e","manuscriptTitle":"Polymorphisms in the airway epithelium related genes CDHR3 and EMSY are associated with asthma susceptibility","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-05-18 23:43:21","doi":"10.21203/rs.3.rs-28755/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f18a2035-9c33-437b-bb31-f50fd16d4401","owner":[],"postedDate":"May 18th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":102497,"name":"Medical Genetics"}],"tags":[],"updatedAt":"2021-07-22T02:38:37+00:00","versionOfRecord":{"articleIdentity":"rs-28755","link":"https://doi.org/10.1186/s12890-020-01334-0","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2020-11-19 02:38:37","publishedOnDateReadable":"November 19th, 2020"},"versionCreatedAt":"2020-05-18 23:43:21","video":"","vorDoi":"10.1186/s12890-020-01334-0","vorDoiUrl":"https://doi.org/10.1186/s12890-020-01334-0","workflowStages":[]},"version":"v1","identity":"rs-28755","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-28755","identity":"rs-28755","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}