Replication and Functional Prediction of Two GWAS-Reported SNPs Located on RAD50 Gene Associated with Asthma in Pakistani Children

Replication and Functional Prediction of Two GWAS-Reported SNPs Located on RAD50 Gene Associated with Asthma in Pakistani Children | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Replication and Functional Prediction of Two GWAS-Reported SNPs Located on RAD50 Gene Associated with Asthma in Pakistani Children Muhammad Usman Ghani, Muhammad Farooq Sabar, Iqbal Bano, Qurat-ul ain, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7456490/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Genome-wide association studies (GWAS) have indicated that several single nucleotide variants (SNVs) of the RAD50 gene are significantly associated with childhood-onset asthma. However, the biological role of RAD50 , and its genomic variants that predispose individuals to asthma, remains unclear. This case-control study aimed to investigate the association of two Single nucleotide polymorphisms (SNPs) rs2244012, and rs6871536 of RAD50 with asthma susceptibility using experimental and computational tools . Methods: The case-control study involved 355 participants: “176 asthma cases [mean age (sd) = 8.91 ±3.05] and 179 healthy controls [mean age (sd) = 11.10 ±8.86] from local Punjabi population of Pakistan. The SNPs were analyzed using a modified single base extension method. The allelic association with asthma and linkage disequilibrium (LD) between the two main SNPs were performed using the SHEsis tool. SNPStats was used to assess the association of SNPs under genotypic models and interaction with non-genetic factors. The LD calculator of ENSEMBL employed for the identification of proxy SNPs in high LD (r^2 > 0.97) to main SNPs. Additionally, HaploReg(v4.1) was utilized to gauge the impact of SNPs on genomic regulations. Results: In current study, both SNPs were found to have a significant association (p-value <0.05) with childhood-onset asthma development under allelic and genotypic models. The alternative “G” allele of rs2244012 is shown to modify two regulatory motifs: Nrf-2 and Zbtb12, while the alternative "C" allele of rs6871536 is predicted to alter the OSF-2 motif. Moreover, 10 SNVs proximal to rs2244012 and 21 SNVs near rs6871536 are in high LD in the Punjabi population of Lahore, Pakistan (PJL). These proxy/high-LD SNVs also displayed the potential to change DNA regulatory motifs. Conclusion: the rs2244012, and rs6871536 variants of RAD50 gene are significantly association with childhood asthma in Pakistan. Despite being intronic variants, it is our inference that these two SNPs have the potential to either independently or synergistically regulate inflammatory responses via nearby SNVs. Asthma Genetics Genome-Wide Association Study Inflammation Pakistan SNP Figures Figure 1 Background Asthma is a prevalent inflammatory disorder of the airways, governed by T-lymphocytes. This disorder arises from a complex interplay of risk factors, encompassing both genetic and environmental elements. The condition manifests due to excessive mucus production, inflammation, and airway wall remodeling, culminating in bronchial hyper-reactivity and airway obstruction [ 1 – 4 ]. Approximately a quatar of a billion people worldwide suffer from asthma, and the global burden continues to increase, particularly in low and middle-income countries [ 5 – 7 ]. Several genes within the cytokine gene cluster on chromosome 5, specifically IL3, IL4, CSF2, TSLP, IL5, RAD50, and IL13 , are recognized as potential asthma susceptibility genes due to their roles in inflammatory regulation among sensitized asthma patients. RAD50 , located in the 5q31 genomic region of this gene cluster between the IL5 and IL13 genes, encodes a protein responsible for DNA double-strand break repair. Even though the expression of this protein is relatively low in most tissues, making its direct function related to asthma unclear, the MRN complex (comprising MRE11, RAD50, and NBS1 ) plays a role in the somatic hypermutation and gene conversion of the immunoglobulin region [ 8 – 10 ]. Despite limited evidence regarding the gene expression of RAD50 in airways and its direct association with asthma, genome wide association studies (GWAS) and candidate gene studies have identified genomic variants of this gene as being significantly linked to the disease. Consequently, further exploration is required to understand the functional relationship of this gene with asthma [ 11 , 12 ]. At the 3' end of the RAD50 gene lies a locus control region (LCR) that contains numerous conserved non-coding sequences and enhancer elements. These elements regulate the neighboring IL4 and IL13 genes. SNPs located within this LCR have been linked to asthma and variations in total serum IgE levels [ 13 ]. Li et al. conducted a GWAS on asthma-related traits in 473 asthma patients from the TENOR study and 1,892 general population controls and identified the RAD50-IL13 region as having the most robust association, with multiple SNPs significantly linked to asthma susceptibility. Notably, the SNP rs2244012 in the RAD50 gene, in conjunction with the rs1063355 SNP in the HLA-DQB1 region, exhibited a strong association with asthma. Additionally, imputation pinpointed several other SNPs in the RAD50-IL13 region on chromosome 5q31 that were related to the disease [ 8 ]. Bønnelykke et al. conducted a GWAS on patients with the asthma phenotype, characterized by recurrent, severe exacerbations between 2 and 6 years of age. The study included a total of 1,173 cases and 2,522 controls, identifying five loci ( GSDMB, IL33, RAD50, IL1RL1, and CDHR3 ). Within RAD50 , rs6871536 was identified as potentially associated with asthma (OR = 1.44, P = 1.7 × 10 − 9) [ 14 ]. We carried out a case-control study to assess the role of the asthma susceptibility SNP variants (rs2244012 and rs6871536) of the RAD50 gene, as predicted by GWAS, in relation to asthma pathogenesis within a scarcely studied Pakistani population. We also employed bioinformatics tools to investigate the potential effects of these intronic variants on the regulation of inflammatory and immunogenic pathways. Methods Our case-control study involved 355 participants: 176 asthmatic children (cases) and 179 healthy children (controls) from Pakistan. Patients with asthma, as diagnosed by physicians, were recruited from the asthma unit of The Children’s Hospital and Institute of Child Health (CH & ICH), Lahore. For the purposes of this retrospective investigation, only pediatrically healthy children were enrolled. We obtained written informed consent from the guardians of both the cases and controls. The study received ethical approval from the institutional review board of CH & ICH, Lahore, and the bioethical committee of The University of the Punjab, Lahore. Whole blood samples from both groups were subjected to genomic DNA extraction using the phenol-chloroform method [ 15 ]. We determined the alleles of the target SNPs (rs2244012 and rs6871536) using a modified single base extension (SBE)/SNaPshot genotyping method, as described in our previously published work [ 16 ]. Both SNPs were amplified as two amplicons in a multiplex PCR, and the amplified amplicons were used as templates in a modified SBE/SNaPshot reaction for SNP identification, which was performed on ABI-3130XL genetic analyzer. Statistical analysis in case-control study: Linkage disequilibrium (LD) between SNPs and their association with the disease in the allelic model (single site analysis) was determined through SHEsis Pluse [ 17 ]. It is an online platform for multi-allelic association test. The input data was prepared according the given format and default parameters were used for analysis. The allelic model tests the association between each allele of the target SNP and disease to evaluate if a specific allele is statistically more frequent in one of the groups. In SHEsis Plus, the effect allele will be considered as the allele with the lowest frequency in the study population. Another online tool, SNPStats [ 18 ], was used to determine the association of these SNPs in various inheritance (genotypic) models. The selection of the statistical model that best fit the data in this study was based on the Akaike information Criteria (AIC) value. The genotypic model evaluates the effect of the different genotypes of a single genetic variant (i.e., the combination of alleles present at a specific locus for a specific individual) in relation to a phenotype. The data was arranged in an excel file for both softwares according to data guidelines provided on the website. No paramerters were changed and the analysis was adjusted by sex as a covariate. SNPStats also helped to analyze the interaction of SNP variants with various non-genetic factors (Gender, Onset Age, Residential Area, Consanguineous Marriage, Pre-natal Smoke Exposure). This was done under the statistical model that observed most suitable for genotypic association analysis, with a p-value < 0.05 considered indicative of a significant association. Bioinformatics Analysis: Both SNPs of the RAD50 gene were further investigated using Linkage Disequilibrium Calculator hosted by ENSEMBL( http://useast.ensembl.org/Homo_sapiens/Tools/LD ). Our goal was to identify SNVs in strong LD (r^2 > 0.97) with rs2244012 and rs6871536, located within 9kb upstream and downstream of their genomic position (GRCh38.p13). We narrowed our search to SNVs specific to the Punjabi population of Lahore, Pakistan (1000GENOMES:phase_3:PJL). SNVs found to be in high LD with our target SNPs (rs2244012 and rs6871536) were subsequently examined in HaploReg v4.1 to assess their potential impacts on the target SNPs of the RAD50 gene. Results The case group studied in this research included 67.92% male participant out of which 50% belonged to urban residential areas. Cousin marriage among parents of cases was 63.29% and 52.38% had mother’s smoking exposure during gestation. Similarly, controls include 52.1% males, 69.23% urban residential area, 45.13% cousin marriage, and 38.19% mother’s smoking exposure during gestation. The demographic characteristics of study participants also given in (Table 1 ). Table 1 Descriptive characteristics of case-control study participants. Variables Asthmatics Healthy Controls Study Participants 176 179 Mean Age (Years ± SD) 8.91 ± 3.05 11.10 ± 8.86 Sex (Male %) 108 (67.92%) 88 (52.1%) Residential Area (Urban %) 79 (50.0%) 108 (69.23%) Parents Inter-relation (Cousin-Marriage) 50 (63.29%) 65 (45.13%) Mother’s Smoke Exposure during gestation (Yes %) 77 (52.38%) 55 (38.19%) The statistical analysis of targeted genomic variants predicted both SNPs (rs2244012, rs6871536) to be significantly associated with asthma disease in both allelic (rs2244012 p-value 0.0014, rs6871536 p-value 0.020) and genotypic models (rs2244012 p-value 0.0039, rs6871536 p-value 0.038) as mentioned in Table 2 , Table 3 , and Table 4 . Table 2 Association of SNPs in Allelic Model SNP Effect allele MAF OR P-value FDR_BH rs2244012 G 0.296 1.80838(1.273 ~ 2.57) 9.22e-04 0.001 rs6871536 C 0.293 1.57504(1.11 ~ 2.25) 0.012 0.012 MAF: Minor allele frequency Table 3 Association of rs2244012 in Multiple Inheritance Models rs2244012 association with response group (adjusted by sex) Model Genotype Control Case OR [95% CI] P-value AIC Codominant A/A 66.5% 51.6% 1.00 0.0085 438.6 A/G 28.1% 38.2% 1.78 (1.09–2.89) G/G 5.4% 10.2% 2.96 (1.22–7.21) Dominant A/A 66.5% 51.6% 1.00 0.0039 437.8 A/G-G/G 33.5% 48.4% 1.95 (1.23–3.08) Recessive A/A-A/G 94.6% 89.8% 1.00 0.044 442 G/G 5.4% 10.2% 2.40 (1.00-5.72) Overdominant A/A-G/G 71.9% 61.8% 1.00 0.06 442.6 A/G 28.1% 38.2% 1.57 (0.98–2.52) P-value < 0.05 was considered cut off value for level of significance and lowest Akaike information criterion ( AIC ) test value represents best fit model according to the guidelines of the tool used. Odds ratio is referenced to the reference allele Table 4 Association of rs6871536 in Multiple Inheritance Models rs6871536 association with response group (adjusted by sex) Model Genotype Control Case OR (95% CI) P-value AIC Codominant T/T 61.7% 52.9% 1.00 0.06 408.8 C/T 32.9% 36.6% 1.34 (0.81–2.20) C/C 5.4% 10.5% 2.85 (1.12–7.22) Dominant T/T 61.7% 52.9% 1.00 0.076 409.3 C/T-C/C 38.3% 47.1% 1.53 (0.95–2.45) Recessive T/T-C/T 94.6% 89.5% 1.00 0.038 408.1 C/C 5.4% 10.5% 2.55 (1.03–6.32) Overdominant T/T-C/C 67.1% 63.4% 1.00 0.5 412 C/T 32.9% 36.6% 1.18 (0.73–1.92) P-value < 0.05 was considered cut off value for level of significance and lowest Akaike information criterion ( AIC ) test value represents best fit model according to the guidelines of tool used. Genotypic analysis of rs2244012, adjusting for sex, revealed significant associations under dominant (P = 0.0039), co-dominant (P = 0.009), and recessive (P = 0.044) inheritance models in our sample population (Table 3 ). The dominant model was predicted to best fit model based on lowest AIC value. The dominant model suggests that both “G/G” homozygous and “A/G” heterozygous genotypes of rs2244012 may enhance childhood asthma onset susceptibility in studied population. When adjusting for sex, genotypic analysis of rs6871536 revealed significant associations only under the recessive model (p-value = 0.038) (Table 4 ). The recessive model, with the lowest AIC value of 408.1, was optimal, indicating that only the “C/C” homozygous genotype of rs6871536 heightens asthma risk. The association of target SNPs with covariates is given in Table 5 . Table 5 Association of SNPs with covariates SNP ID Best Fit Inheritance Model Sex Onset Age Residential Area Consanguineous Marriage Pre-natal Smoke Exposure p-Value Trend p-Value Trend p-Value Trend p-Value Trend p-Value Trend rs2244012 dominant 0.84 N/A 0.66 N/A 0.053 R & SU 0.23 N/A 0.97 N/A rs6871536 recessive 0.82 N/A 0.90 N/A 0.55 N/A 0.59 N/A 0.41 N/A SU = Sub urban, R = rural The main target SNPs of RAD-50 gene are in high LD (r 2 ≥ 0.97) in “Punjabi in Lahore-Pakistan (1000GENOMES:phase_3: PJL )” and their alternative alleles are known to alter the DNA regulatory motifs as described in Table 6 . Table 6 LD between RAD-50 gene’s SNPs and HaploReg analysis Sr.no. Chr. Position dbSNP ID Sequence Consequence Motifs Changed TF Binding Affinity Score LD in “PJL” Function of Transcription Factor (TF) 1. 5:132565533 rs2244012 intron variant Nrf-2, Zbtb12 A = 11.2, G = 12.5 A = 9.9 G = 3.1 0.974420 Nrf-2 [anti-inflammatory response] 1 , Zbtb12 [DNA methylation, Inflamation] 2 2. 5:132634182 rs6871536 intron variant Osf2 T = 0.5 C = 12.5 0.974420 Osf2 [Asthma and allergy] 3 LD in “PJL”: Linkage Disequilibrium among main SNPs (rs2244012, rs6871536) in “Punjabi in Lahore-Pakistan (1000GENOMES:phase_3: PJL )” population. 1 Ahmed, S. M., Luo, L., Namani, A., Wang, X. J. & Tang, X. Nrf2 signaling pathway: Pivotal roles in inflammation. Biochim Biophys Acta Mol Basis Dis 1863 , 585–597, doi: 10.1016/j.bbadis.2016.11.005 (2017). 2 Noro, F. et al. ZBTB12 DNA methylation is associated with coagulation-and inflammation-related blood cell parameters: findings from the Moli-family cohort. Clinical epigenetics 11 , 74 (2019). 3 Zielinska-Blizniewska, H. et al. Association of the − 33C/G OSF-2 and the 140A/G LF gene polymorphisms with the risk of chronic rhinosinusitis with nasal polyps in a Polish population. Mol Biol Rep 39 , 5449–5457, doi: 10.1007/s11033-011-1345-6 (2012). The LD analysis of rs2244012 in ENSEMBL predicted that 10 SNVs are in strong LD (r 2 > 0.97) to rs2244012 (including rs10079653, rs62383710, rs34776903, rs2706345, rs2243677, rs2706347, rs2706348, rs2706349, rs56668723, rs62383714) with high LD (r^2 > 0.97) in “Punjabi in Lahore-Pakistan (1000GENOMES:phase_3: PJL )” population. The HaploReg analysis of these SNVs is presented in Supplementary Table 1. HaploReg pointed out the effect of these SNVs on DNA regulatory motifs. The LD analysis of rs6871536 in ENSEMBL predicted that 21 SNVs are in strong LD (r 2 > 0.97) to RAD-50.rs6871536 (like rs6873732, rs6874184, rs62383757, rs62383758, rs3798135, rs3798134, rs56183820, rs6873897, rs12332204, rs6596087, rs6866095, rs10052993, rs12653750, rs2040703, rs2040704, rs11420290, rs377716981, rs6872131, rs2074369, rs7737470, rs11308531) in “Punjabi in Lahore-Pakistan (1000GENOMES:phase_3: PJL )” Population. The HaploReg analysis of these SNVs is presented in Supplementary Table 2. Discussion: Asthma is a heterogeneous disorder which affects the lungs and have a different onset age. Usually it is categorized as childhood and adult onset asthma with various cutoff points such as 12, 16, 18, or more recently identified 40y and onward referred as late-onset phenotype [ 19 ]. RAD50 is a plausible candidate gene implicated in asthma pathogenesis. Variants within RAD50 add to the complexity of factors in the cytokine gene cluster on chromosome 5q31, underscoring the necessity for thorough research [ 20 , 21 ]. The two SNPs from the RAD50 gene, rs2244012 and rs6871536, which are featured in this study, are situated in two introns of RAD50 . They have been previously identified as asthma susceptibility variants in prior GWAS [ 8 , 14 ]. In this study, the alternative allele “G” of RAD50 's rs2244012 showed a significant association with asthma in the allelic model (P = 0.0014). The odds ratio for this allele was higher in asthma patients, with an OR of 1.768 (1.245 ~ 2.510) as determined by the SHEsis analysis (Table 2 ). The dominant model was predicted to be the best fit in the genotypic analysis, indicating that both the G/G (homozygous) and A/G (heterozygous) genotypes enhance the risk of childhood-onset asthma (Table 3 ). A single copy of the altered allele “G” increases the risk of the disease, and having two copies of the altered allele (G/G genotype) may increase the risk even more. Similarly, the risk allele “C” of the rs6871536 SNP variant was more common in asthma patients compared to controls in the studied population. This allele was significantly associated with asthma in the allelic model (p-value = 0.020) with an OR of 1.522 (1.066 ~ 2.174) (Table 2 ), suggesting that this variants may independently associated with the risk of childhood-onset asthma in Pakistan. The recessive model was found to be the best fit and statistically significant in genotyping analysis, predicting that the "C/C" homozygous genotype of rs6871536 poses a risk for childhood-onset asthma (Table 4 ). Both SNP variants, rs2244012 and rs6871536, demonstrated significant associations with childhood-onset asthma susceptibility in the allelic and genotypic models in our studied population. Notably, a similar association of these variants with asthma has been reported in other populations, including German, Danish, and American groups [ 14 , 20 , 22 ]. Analysis using HaploReg revealed that the “G” allele of the rs2244012 variant alters two regulatory motifs: Nrf-2 and Zbtb12. The altered “G” allele slightly increases the binding affinity of the Nrf-2 transcription factor (12.5) relative to the “A” (11.2) reference allele. In contrast, the alternative allele significantly decreases the binding affinity of the transcription factor in the case of Zbtb12 (Table 6 ). The Nrf-2 transcription factor plays a pivotal role in the regulation of inflammatory genes. It aids in the anti-inflammatory process by mobilizing inflammatory cells. Moreover, Nrf-2 modulates gene expression via the anti-oxidant response element (ARE). Notably, the Nrf2/ARE signaling pathway is critically involved in suppressing inflammatory progression and orchestrating the regulation of anti-inflammatory genes [ 23 ]. The Zbtb12 is a recognized transcription factor, and methylation of Zbtb12 Factor 2 (including CpG units 8, 9–10, 16, 21) is positively associated with TNF-ɑ stimulated procoagulant activity, a measure of procoagulant and inflammatory potential of blood cells [ 24 ]. The presence of the “G” allele in the rs2244012 variant may lead to alterations in regulatory motifs, potentially disrupting the regulation of the RAD50 gene. Furthermore, HaploReg analysis indicated that the “C” allele (alternative) of the rs6871536 variant affects the OSF-2 motif, the alternative “C” allele significantly enhances the binding affinity (12.5) of the OSF-2 transcription factor compared to the reference “T” allele binding affinity (0.5) for OSF-2 (Table 6 ). OSF-2, also known as Periostin, primarily plays a role in osteoblasts and is responsible for bone formation. While Periostin's expression is minimal in most adult tissues, it is markedly elevated at sites of inflammation, tumors, and injury [ 25 ]. Notably, increased levels of serum Periostin are consistently observed in conditions like asthma and other allergic diseases [ 26 ]. The alteration of the OSF-2/Periostin motif by the “C” allele of the rs6871536 variant might influence the regulation of the RAD50 gene. LD analysis for the rs2244012 SNP variant identified 10 single nucleotide variants within the Punjabi population of Lahore, Pakistan (Supplementary Table 1). These high LD SNPs could potentially affect RAD50 functionality synergistically. On the other hand, the rs6871536 SNP is in high LD with 21 single nucleotide variants within the same population (Supplementary Table 2). Functional analyses of these high LD variants indicate that they can modify regulatory motifs associated with the regulation of pro/anti-inflammatory genes. These genes either directly or indirectly contribute to asthma's pathogenesis, as elaborated in Supplementary Tables 1 and 2. Considering the roles of the target SNPs (rs2244012 and rs6871536) of RAD50 and their associated high LD/proxy SNPs in regulating the RAD50 gene through changes in regulatory motifs, it's plausible to infer that even as intronic variants, rs2244012 and rs6871536 may influence RAD50 gene expression. This could be either independently or in synergy with neighboring single nucleotide variants co-inheriting with these primary SNPs. Understanding the role of these variants as eQTL would be interesting to know further for confirmation if they are really involved in the expression of RAD50 gene. Nonetheless, the limited sample size may influence the statistical robustness of the results. Hence, investigations involving larger cohorts and diverse ethnic populations are recommended to further assess the role of these variants in asthma. Conclusion As indicated by GWASs these variants (rs2244012, rs6871536) are significantly linked to asthma susceptibility, our study also identifies a strong association within our study population. Although rs2244012 and rs6871536 are intronic SNP variants, they possess the potential to regulate inflammatory/immunogenic pathways. This regulation can occur independently or in synergy with nearby DNA sequence variants. Declarations Ethics approval and consent to participate The study received ethical approval from the institutional review board of CH & ICH, Lahore (Ref. No. 1/158/16 dated 20 June 2016), and the bioethical committee of The University of the Punjab, Lahore (dated 18 February 2019), and the written informed consent obtained from the guardians of both the cases and controls. Competing interests The authors declare no competing interests. Authors’contributions M.U.G.: Study plan, Data correction, analysis and writing the text. M.F.S.: Supervise, data evaluation. I.B.: Sampling and interpretation of data M.S. & Q.U.A: Manuscript writing and data evaluation. Z.M. and M.U.K: Sampling, data records, statistical analysis. All authors reviewed the manuscript. Funding No fnancial support was received Author Contribution M.U.G.: Study plan, Data correction, analysis and writing the text.M.F.S.: Supervise, data evaluation.I.B.: Sampling and interpretation of dataM.S. & Q.U.A: Manuscript writing and data evaluation.Z.M. and M.U.K: Sampling, data records, statistical analysis. All authors reviewed the manuscript. Data Availability All data generated or analysed during this study are included in this published article [and its supplementary information files] References Lambrecht, B.N. and H. Hammad, The airway epithelium in asthma . 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Respiratory research, 2015. 16(1): p. 1–10. Corren, J., et al., Lebrikizumab treatment in adults with asthma . New England Journal of Medicine, 2011. 365(12): p. 1088–1098. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7456490","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513991010,"identity":"e4209224-0ab4-4e31-9647-e2806aa15d0b","order_by":0,"name":"Muhammad Usman Ghani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFADCeYDIIqxgYGBmVgtbAkka+ExIE6LwfnTiZ8LGOzkdWf3fHvMw2Aju+EA72EDvFpu5G6WnsGQbLjtztntxjwMacYbDvAlJ+DXwrtBmofhAOO2G7nbgIzDiRsO8BgfwO+ws5t/A7XYb7uR8wyo5T8RWg6ADT+QCNTCBmaAtOB1mCTQPdY8BsnJ2+4cMzecY5BsPPMwXzJe7/MBHXabp8LOdtvt5mcP3lTYyfYd7z0sgU8L1Hlgkg3CYOYhrAEG2KA0CVpGwSgYBaNgRAAAfUNKbmpkHYEAAAAASUVORK5CYII=","orcid":"","institution":"University of the Punjab","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Usman","lastName":"Ghani","suffix":""},{"id":513991011,"identity":"0e922a5f-3218-464d-863c-9e5f0c052cf5","order_by":1,"name":"Muhammad Farooq Sabar","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Farooq","lastName":"Sabar","suffix":""},{"id":513991013,"identity":"197b9825-39f8-4652-a3c6-516140bf6212","order_by":2,"name":"Iqbal Bano","email":"","orcid":"","institution":"The Children’s Hospital, The Institute of Child Health","correspondingAuthor":false,"prefix":"","firstName":"Iqbal","middleName":"","lastName":"Bano","suffix":""},{"id":513991015,"identity":"215f5f1d-f31c-4a85-b492-7ea9da11ab3d","order_by":3,"name":"Qurat-ul ain","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Qurat-ul","middleName":"","lastName":"ain","suffix":""},{"id":513991017,"identity":"3593738e-1be7-47c2-8ef1-c4cb93107d46","order_by":4,"name":"Mariam Shahid","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Mariam","middleName":"","lastName":"Shahid","suffix":""},{"id":513991020,"identity":"692cbddb-b99e-4508-b09e-673a50b4bcb8","order_by":5,"name":"Zohair Mehdi","email":"","orcid":"","institution":"University of the Punjab","correspondingAuthor":false,"prefix":"","firstName":"Zohair","middleName":"","lastName":"Mehdi","suffix":""},{"id":513991024,"identity":"fa04e6af-1970-4132-aa3c-923307b5ed91","order_by":6,"name":"Muhammad Umer Khan","email":"","orcid":"","institution":"The University of Lahore","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Umer","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2025-08-25 19:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7456490/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7456490/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91817157,"identity":"9079dc25-8f31-4836-95de-e83ac840c9cd","added_by":"auto","created_at":"2025-09-22 06:53:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73915,"visible":true,"origin":"","legend":"","description":"","filename":"250827ManuscriptRAD50.docx","url":"https://assets-eu.researchsquare.com/files/rs-7456490/v1/4cd86f11f406508aa056c49f.docx"},{"id":91816644,"identity":"374029f4-c810-4c8d-8c6d-4261f8bd13a4","added_by":"auto","created_at":"2025-09-22 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1","display":"","copyAsset":false,"role":"figure","size":67195,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1abstract.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7456490/v1/c97b999218c7780008ef8ecf.jpeg"},{"id":92307736,"identity":"09064ab6-fee6-41d6-9bf4-0a4c081cb0f6","added_by":"auto","created_at":"2025-09-27 08:31:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":919222,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7456490/v1/1a71dd3d-0d6c-4fba-a528-6f5547a53651.pdf"},{"id":91486821,"identity":"d2ef97bb-c5a4-49d1-b1c0-44ca101a1152","added_by":"auto","created_at":"2025-09-17 04:58:48","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":53061,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7456490/v1/938e908d3a8e37084b002dc0.docx"},{"id":91486829,"identity":"d1907e42-c591-4627-8e21-b0648e3d3c60","added_by":"auto","created_at":"2025-09-17 04:58:48","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":60862,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7456490/v1/31a8f4d826cfb7a8fe16a380.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Replication and Functional Prediction of Two GWAS-Reported SNPs Located on RAD50 Gene Associated with Asthma in Pakistani Children","fulltext":[{"header":"Background","content":"\u003cp\u003eAsthma is a prevalent inflammatory disorder of the airways, governed by T-lymphocytes. This disorder arises from a complex interplay of risk factors, encompassing both genetic and environmental elements. The condition manifests due to excessive mucus production, inflammation, and airway wall remodeling, culminating in bronchial hyper-reactivity and airway obstruction [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Approximately a quatar of a billion people worldwide suffer from asthma, and the global burden continues to increase, particularly in low and middle-income countries [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral genes within the cytokine gene cluster on chromosome 5, specifically \u003cem\u003eIL3, IL4, CSF2, TSLP, IL5, RAD50, and IL13\u003c/em\u003e, are recognized as potential asthma susceptibility genes due to their roles in inflammatory regulation among sensitized asthma patients. \u003cem\u003eRAD50\u003c/em\u003e, located in the 5q31 genomic region of this gene cluster between the \u003cem\u003eIL5\u003c/em\u003e and \u003cem\u003eIL13\u003c/em\u003e genes, encodes a protein responsible for DNA double-strand break repair. Even though the expression of this protein is relatively low in most tissues, making its direct function related to asthma unclear, the MRN complex (comprising \u003cem\u003eMRE11, RAD50, and NBS1\u003c/em\u003e) plays a role in the somatic hypermutation and gene conversion of the immunoglobulin region [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite limited evidence regarding the gene expression of \u003cem\u003eRAD50\u003c/em\u003e in airways and its direct association with asthma, genome wide association studies (GWAS) and candidate gene studies have identified genomic variants of this gene as being significantly linked to the disease. Consequently, further exploration is required to understand the functional relationship of this gene with asthma [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. At the 3' end of the \u003cem\u003eRAD50\u003c/em\u003e gene lies a locus control region (LCR) that contains numerous conserved non-coding sequences and enhancer elements. These elements regulate the neighboring \u003cem\u003eIL4\u003c/em\u003e and \u003cem\u003eIL13\u003c/em\u003e genes. SNPs located within this LCR have been linked to asthma and variations in total serum IgE levels [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Li et al. conducted a GWAS on asthma-related traits in 473 asthma patients from the TENOR study and 1,892 general population controls and identified the \u003cem\u003eRAD50-IL13\u003c/em\u003e region as having the most robust association, with multiple SNPs significantly linked to asthma susceptibility. Notably, the SNP rs2244012 in the \u003cem\u003eRAD50\u003c/em\u003e gene, in conjunction with the rs1063355 SNP in the \u003cem\u003eHLA-DQB1\u003c/em\u003e region, exhibited a strong association with asthma. Additionally, imputation pinpointed several other SNPs in the \u003cem\u003eRAD50-IL13\u003c/em\u003e region on chromosome 5q31 that were related to the disease [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. B\u0026oslash;nnelykke et al. conducted a GWAS on patients with the asthma phenotype, characterized by recurrent, severe exacerbations between 2 and 6 years of age. The study included a total of 1,173 cases and 2,522 controls, identifying five loci (\u003cem\u003eGSDMB, IL33, RAD50, IL1RL1, and CDHR3\u003c/em\u003e). Within \u003cem\u003eRAD50\u003c/em\u003e, rs6871536 was identified as potentially associated with asthma (OR\u0026thinsp;=\u0026thinsp;1.44, P\u0026thinsp;=\u0026thinsp;1.7 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;9) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe carried out a case-control study to assess the role of the asthma susceptibility SNP variants (rs2244012 and rs6871536) of the \u003cem\u003eRAD50\u003c/em\u003e gene, as predicted by GWAS, in relation to asthma pathogenesis within a scarcely studied Pakistani population. We also employed bioinformatics tools to investigate the potential effects of these intronic variants on the regulation of inflammatory and immunogenic pathways.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e Our case-control study involved 355 participants: 176 asthmatic children (cases) and 179 healthy children (controls) from Pakistan. Patients with asthma, as diagnosed by physicians, were recruited from the asthma unit of The Children\u0026rsquo;s Hospital and Institute of Child Health (CH \u0026amp; ICH), Lahore. For the purposes of this retrospective investigation, only pediatrically healthy children were enrolled.\u003c/p\u003e\u003cp\u003eWe obtained written informed consent from the guardians of both the cases and controls. The study received ethical approval from the institutional review board of CH \u0026amp; ICH, Lahore, and the bioethical committee of The University of the Punjab, Lahore.\u003c/p\u003e\u003cp\u003eWhole blood samples from both groups were subjected to genomic DNA extraction using the phenol-chloroform method [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We determined the alleles of the target SNPs (rs2244012 and rs6871536) using a modified single base extension (SBE)/SNaPshot genotyping method, as described in our previously published work [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Both SNPs were amplified as two amplicons in a multiplex PCR, and the amplified amplicons were used as templates in a modified SBE/SNaPshot reaction for SNP identification, which was performed on ABI-3130XL genetic analyzer.\u003c/p\u003e\u003cp\u003eStatistical analysis in case-control study: Linkage disequilibrium (LD) between SNPs and their association with the disease in the allelic model (single site analysis) was determined through SHEsis Pluse [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It is an online platform for multi-allelic association test. The input data was prepared according the given format and default parameters were used for analysis. The allelic model tests the association between each allele of the target SNP and disease to evaluate if a specific allele is statistically more frequent in one of the groups. In SHEsis Plus, the effect allele will be considered as the allele with the lowest frequency in the study population. Another online tool, SNPStats [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], was used to determine the association of these SNPs in various inheritance (genotypic) models. The selection of the statistical model that best fit the data in this study was based on the Akaike information Criteria (AIC) value. The genotypic model evaluates the effect of the different genotypes of a single genetic variant (i.e., the combination of alleles present at a specific locus for a specific individual) in relation to a phenotype. The data was arranged in an excel file for both softwares according to data guidelines provided on the website. No paramerters were changed and the analysis was adjusted by sex as a covariate. SNPStats also helped to analyze the interaction of SNP variants with various non-genetic factors (Gender, Onset Age, Residential Area, Consanguineous Marriage, Pre-natal Smoke Exposure). This was done under the statistical model that observed most suitable for genotypic association analysis, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered indicative of a significant association.\u003c/p\u003e\u003cp\u003eBioinformatics Analysis:\u003c/p\u003e\u003cp\u003eBoth SNPs of the \u003cem\u003eRAD50\u003c/em\u003e gene were further investigated using Linkage Disequilibrium Calculator hosted by ENSEMBL(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://useast.ensembl.org/Homo_sapiens/Tools/LD\u003c/span\u003e\u003cspan address=\"http://useast.ensembl.org/Homo_sapiens/Tools/LD\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Our goal was to identify SNVs in strong LD (r^2\u0026thinsp;\u0026gt;\u0026thinsp;0.97) with rs2244012 and rs6871536, located within 9kb upstream and downstream of their genomic position (GRCh38.p13). We narrowed our search to SNVs specific to the Punjabi population of Lahore, Pakistan (1000GENOMES:phase_3:PJL). SNVs found to be in high LD with our target SNPs (rs2244012 and rs6871536) were subsequently examined in HaploReg v4.1 to assess their potential impacts on the target SNPs of the \u003cem\u003eRAD50\u003c/em\u003e gene.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe case group studied in this research included 67.92% male participant out of which 50% belonged to urban residential areas. Cousin marriage among parents of cases was 63.29% and 52.38% had mother\u0026rsquo;s smoking exposure during gestation. Similarly, controls include 52.1% males, 69.23% urban residential area, 45.13% cousin marriage, and 38.19% mother\u0026rsquo;s smoking exposure during gestation. The demographic characteristics of study participants also given in (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive characteristics of case-control study participants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsthmatics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHealthy Controls\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudy Participants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e179\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Age (Years\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.10\u0026thinsp;\u0026plusmn;\u0026thinsp;8.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (Male %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (67.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88 (52.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidential Area (Urban %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108 (69.23%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParents Inter-relation (Cousin-Marriage)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (63.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (45.13%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMother\u0026rsquo;s Smoke Exposure during gestation (Yes %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77 (52.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (38.19%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe statistical analysis of targeted genomic variants predicted both SNPs (rs2244012, rs6871536) to be significantly associated with asthma disease in both allelic (rs2244012 p-value 0.0014, rs6871536 p-value 0.020) and genotypic models (rs2244012 p-value 0.0039, rs6871536 p-value 0.038) as mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of SNPs in Allelic Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEffect allele\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMAF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFDR_BH\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ers2244012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.80838(1.273\u0026thinsp;~\u0026thinsp;2.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.22e-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ers6871536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.57504(1.11\u0026thinsp;~\u0026thinsp;2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eMAF: Minor allele frequency\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of rs2244012 in Multiple Inheritance Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003ers2244012 association with response group (adjusted by sex)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eCodominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.0085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e438.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.78 (1.09\u0026ndash;2.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.96 (1.22\u0026ndash;7.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.0039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e437.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA/G-G/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.95 (1.23\u0026ndash;3.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRecessive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA/A-A/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e442\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.40 (1.00-5.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOverdominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA/A-G/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e442.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA/G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.57 (0.98\u0026ndash;2.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered cut off value for level of significance and lowest Akaike information criterion (\u003cb\u003eAIC\u003c/b\u003e) test value represents best fit model according to the guidelines of the tool used. Odds ratio is referenced to the reference allele\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of rs6871536 in Multiple Inheritance Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003ers6871536 association with response group (adjusted by sex)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eCodominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e408.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.34 (0.81\u0026ndash;2.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.85 (1.12\u0026ndash;7.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e409.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC/T-C/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.53 (0.95\u0026ndash;2.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRecessive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT/T-C/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e408.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.55 (1.03\u0026ndash;6.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOverdominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT/T-C/C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC/T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e36.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.18 (0.73\u0026ndash;1.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eP-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered cut off value for level of significance and lowest Akaike information criterion (\u003cb\u003eAIC\u003c/b\u003e) test value represents best fit model according to the guidelines of tool used.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGenotypic analysis of rs2244012, adjusting for sex, revealed significant associations under dominant (P\u0026thinsp;=\u0026thinsp;0.0039), co-dominant (P\u0026thinsp;=\u0026thinsp;0.009), and recessive (P\u0026thinsp;=\u0026thinsp;0.044) inheritance models in our sample population (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The dominant model was predicted to best fit model based on lowest AIC value. The dominant model suggests that both \u0026ldquo;G/G\u0026rdquo; homozygous and \u0026ldquo;A/G\u0026rdquo; heterozygous genotypes of rs2244012 may enhance childhood asthma onset susceptibility in studied population. When adjusting for sex, genotypic analysis of rs6871536 revealed significant associations only under the recessive model (p-value\u0026thinsp;=\u0026thinsp;0.038) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The recessive model, with the lowest AIC value of 408.1, was optimal, indicating that only the \u0026ldquo;C/C\u0026rdquo; homozygous genotype of rs6871536 heightens asthma risk. The association of target SNPs with covariates is given in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of SNPs with covariates\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSNP ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBest Fit Inheritance Model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eOnset Age\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eResidential Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eConsanguineous Marriage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u003cp\u003ePre-natal Smoke Exposure\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTrend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTrend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTrend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTrend\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eTrend\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ers2244012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eR \u0026amp; SU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ers6871536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erecessive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eSU\u0026thinsp;=\u0026thinsp;Sub urban, R\u0026thinsp;=\u0026thinsp;rural\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe main target SNPs of \u003cem\u003eRAD-50\u003c/em\u003e gene are in high LD (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.97) in \u0026ldquo;Punjabi in Lahore-Pakistan (1000GENOMES:phase_3:\u003cb\u003ePJL\u003c/b\u003e)\u0026rdquo; and their alternative alleles are known to alter the DNA regulatory motifs as described in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLD between RAD-50 gene\u0026rsquo;s SNPs and HaploReg analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSr.no.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChr. Position\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edbSNP ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSequence Consequence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMotifs Changed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTF Binding Affinity Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLD in\u003c/p\u003e\u003cp\u003e\u0026ldquo;PJL\u0026rdquo;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFunction of Transcription Factor (TF)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5:132565533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers2244012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eintron variant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNrf-2,\u003c/p\u003e\u003cp\u003eZbtb12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eA\u0026thinsp;=\u0026thinsp;11.2, G\u0026thinsp;=\u0026thinsp;12.5\u003c/p\u003e\u003cp\u003eA\u0026thinsp;=\u0026thinsp;9.9\u003c/p\u003e\u003cp\u003eG\u0026thinsp;=\u0026thinsp;3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.974420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eNrf-2\u003c/b\u003e [anti-inflammatory response] \u003csup\u003e1\u003c/sup\u003e,\u003c/p\u003e\u003cp\u003e\u003cb\u003eZbtb12\u003c/b\u003e [DNA methylation, Inflamation] \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5:132634182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers6871536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eintron variant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOsf2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eT\u0026thinsp;=\u0026thinsp;0.5\u003c/p\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.974420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eOsf2\u003c/b\u003e [Asthma and allergy] \u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eLD in \u0026ldquo;PJL\u0026rdquo;: Linkage Disequilibrium among main SNPs (rs2244012, rs6871536) in \u0026ldquo;Punjabi in Lahore-Pakistan (1000GENOMES:phase_3:\u003cb\u003ePJL\u003c/b\u003e)\u0026rdquo; population.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e1 Ahmed, S. M., Luo, L., Namani, A., Wang, X. J. \u0026amp; Tang, X. Nrf2 signaling pathway: Pivotal roles in inflammation. \u003cem\u003eBiochim Biophys Acta Mol Basis Dis\u003c/em\u003e \u003cb\u003e1863\u003c/b\u003e, 585\u0026ndash;597, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbadis.2016.11.005\u003c/span\u003e\u003cspan address=\"10.1016/j.bbadis.2016.11.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e2 Noro, F. \u003cem\u003eet al.\u003c/em\u003e ZBTB12 DNA methylation is associated with coagulation-and inflammation-related blood cell parameters: findings from the Moli-family cohort. \u003cem\u003eClinical epigenetics\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 74 (2019).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e3 Zielinska-Blizniewska, H. \u003cem\u003eet al.\u003c/em\u003e Association of the \u0026minus;\u0026thinsp;33C/G OSF-2 and the 140A/G LF gene polymorphisms with the risk of chronic rhinosinusitis with nasal polyps in a Polish population. \u003cem\u003eMol Biol Rep\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e, 5449\u0026ndash;5457, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11033-011-1345-6\u003c/span\u003e\u003cspan address=\"10.1007/s11033-011-1345-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe LD analysis of rs2244012 in ENSEMBL predicted that 10 SNVs are in strong LD (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.97) to rs2244012 (including rs10079653, rs62383710, rs34776903, rs2706345, rs2243677, rs2706347, rs2706348, rs2706349, rs56668723, rs62383714) with high LD (r^2\u0026thinsp;\u0026gt;\u0026thinsp;0.97) in \u0026ldquo;Punjabi in Lahore-Pakistan (1000GENOMES:phase_3:\u003cb\u003ePJL\u003c/b\u003e)\u0026rdquo; population. The HaploReg analysis of these SNVs is presented in Supplementary Table\u0026nbsp;1. HaploReg pointed out the effect of these SNVs on DNA regulatory motifs.\u003c/p\u003e\u003cp\u003eThe LD analysis of rs6871536 in ENSEMBL predicted that 21 SNVs are in strong LD (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.97) to RAD-50.rs6871536 (like rs6873732, rs6874184, rs62383757, rs62383758, rs3798135, rs3798134, rs56183820, rs6873897, rs12332204, rs6596087, rs6866095, rs10052993, rs12653750, rs2040703, rs2040704, rs11420290, rs377716981, rs6872131, rs2074369, rs7737470, rs11308531) in \u0026ldquo;Punjabi in Lahore-Pakistan (1000GENOMES:phase_3:\u003cb\u003ePJL\u003c/b\u003e)\u0026rdquo; Population. The HaploReg analysis of these SNVs is presented in Supplementary Table\u0026nbsp;2.\u003c/p\u003e"},{"header":"Discussion:","content":"\u003cp\u003eAsthma is a heterogeneous disorder which affects the lungs and have a different onset age. Usually it is categorized as childhood and adult onset asthma with various cutoff points such as 12, 16, 18, or more recently identified 40y and onward referred as late-onset phenotype [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. \u003cem\u003eRAD50\u003c/em\u003e is a plausible candidate gene implicated in asthma pathogenesis. Variants within \u003cem\u003eRAD50\u003c/em\u003e add to the complexity of factors in the cytokine gene cluster on chromosome 5q31, underscoring the necessity for thorough research [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The two SNPs from the \u003cem\u003eRAD50\u003c/em\u003e gene, rs2244012 and rs6871536, which are featured in this study, are situated in two introns of \u003cem\u003eRAD50\u003c/em\u003e. They have been previously identified as asthma susceptibility variants in prior GWAS [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In this study, the alternative allele \u0026ldquo;G\u0026rdquo; of \u003cem\u003eRAD50\u003c/em\u003e's rs2244012 showed a significant association with asthma in the allelic model (P\u0026thinsp;=\u0026thinsp;0.0014). The odds ratio for this allele was higher in asthma patients, with an OR of 1.768 (1.245\u0026thinsp;~\u0026thinsp;2.510) as determined by the SHEsis analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The dominant model was predicted to be the best fit in the genotypic analysis, indicating that both the G/G (homozygous) and A/G (heterozygous) genotypes enhance the risk of childhood-onset asthma (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A single copy of the altered allele \u0026ldquo;G\u0026rdquo; increases the risk of the disease, and having two copies of the altered allele (G/G genotype) may increase the risk even more.\u003c/p\u003e\u003cp\u003eSimilarly, the risk allele \u0026ldquo;C\u0026rdquo; of the rs6871536 SNP variant was more common in asthma patients compared to controls in the studied population. This allele was significantly associated with asthma in the allelic model (p-value\u0026thinsp;=\u0026thinsp;0.020) with an OR of 1.522 (1.066\u0026thinsp;~\u0026thinsp;2.174) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting that this variants may independently associated with the risk of childhood-onset asthma in Pakistan. The recessive model was found to be the best fit and statistically significant in genotyping analysis, predicting that the \"C/C\" homozygous genotype of rs6871536 poses a risk for childhood-onset asthma (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBoth SNP variants, rs2244012 and rs6871536, demonstrated significant associations with childhood-onset asthma susceptibility in the allelic and genotypic models in our studied population. Notably, a similar association of these variants with asthma has been reported in other populations, including German, Danish, and American groups [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnalysis using HaploReg revealed that the \u0026ldquo;G\u0026rdquo; allele of the rs2244012 variant alters two regulatory motifs: Nrf-2 and Zbtb12. The altered \u0026ldquo;G\u0026rdquo; allele slightly increases the binding affinity of the Nrf-2 transcription factor (12.5) relative to the \u0026ldquo;A\u0026rdquo; (11.2) reference allele. In contrast, the alternative allele significantly decreases the binding affinity of the transcription factor in the case of Zbtb12 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The Nrf-2 transcription factor plays a pivotal role in the regulation of inflammatory genes. It aids in the anti-inflammatory process by mobilizing inflammatory cells. Moreover, Nrf-2 modulates gene expression via the anti-oxidant response element (ARE). Notably, the Nrf2/ARE signaling pathway is critically involved in suppressing inflammatory progression and orchestrating the regulation of anti-inflammatory genes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The Zbtb12 is a recognized transcription factor, and methylation of Zbtb12 Factor 2 (including CpG units 8, 9\u0026ndash;10, 16, 21) is positively associated with TNF-ɑ stimulated procoagulant activity, a measure of procoagulant and inflammatory potential of blood cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The presence of the \u0026ldquo;G\u0026rdquo; allele in the rs2244012 variant may lead to alterations in regulatory motifs, potentially disrupting the regulation of the RAD50 gene.\u003c/p\u003e\u003cp\u003eFurthermore, HaploReg analysis indicated that the \u0026ldquo;C\u0026rdquo; allele (alternative) of the rs6871536 variant affects the OSF-2 motif, the alternative \u0026ldquo;C\u0026rdquo; allele significantly enhances the binding affinity (12.5) of the OSF-2 transcription factor compared to the reference \u0026ldquo;T\u0026rdquo; allele binding affinity (0.5) for OSF-2 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). OSF-2, also known as Periostin, primarily plays a role in osteoblasts and is responsible for bone formation. While Periostin's expression is minimal in most adult tissues, it is markedly elevated at sites of inflammation, tumors, and injury [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Notably, increased levels of serum Periostin are consistently observed in conditions like asthma and other allergic diseases [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The alteration of the OSF-2/Periostin motif by the \u0026ldquo;C\u0026rdquo; allele of the rs6871536 variant might influence the regulation of the \u003cem\u003eRAD50\u003c/em\u003e gene.\u003c/p\u003e\u003cp\u003eLD analysis for the rs2244012 SNP variant identified 10 single nucleotide variants within the Punjabi population of Lahore, Pakistan (Supplementary Table\u0026nbsp;1). These high LD SNPs could potentially affect \u003cem\u003eRAD50\u003c/em\u003e functionality synergistically.\u003c/p\u003e\u003cp\u003eOn the other hand, the rs6871536 SNP is in high LD with 21 single nucleotide variants within the same population (Supplementary Table\u0026nbsp;2). Functional analyses of these high LD variants indicate that they can modify regulatory motifs associated with the regulation of pro/anti-inflammatory genes. These genes either directly or indirectly contribute to asthma's pathogenesis, as elaborated in Supplementary Tables\u0026nbsp;1 and 2.\u003c/p\u003e\u003cp\u003eConsidering the roles of the target SNPs (rs2244012 and rs6871536) of \u003cem\u003eRAD50\u003c/em\u003e and their associated high LD/proxy SNPs in regulating the \u003cem\u003eRAD50\u003c/em\u003e gene through changes in regulatory motifs, it's plausible to infer that even as intronic variants, rs2244012 and rs6871536 may influence \u003cem\u003eRAD50\u003c/em\u003e gene expression. This could be either independently or in synergy with neighboring single nucleotide variants co-inheriting with these primary SNPs. Understanding the role of these variants as eQTL would be interesting to know further for confirmation if they are really involved in the expression of \u003cem\u003eRAD50\u003c/em\u003e gene.\u003c/p\u003e\u003cp\u003eNonetheless, the limited sample size may influence the statistical robustness of the results. Hence, investigations involving larger cohorts and diverse ethnic populations are recommended to further assess the role of these variants in asthma.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAs indicated by GWASs these variants (rs2244012, rs6871536) are significantly linked to asthma susceptibility, our study also identifies a strong association within our study population. Although rs2244012 and rs6871536 are intronic SNP variants, they possess the potential to regulate inflammatory/immunogenic pathways. This regulation can occur independently or in synergy with nearby DNA sequence variants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eThe study received ethical approval from the institutional review board of CH \u0026amp; ICH, Lahore (Ref. No. 1/158/16 dated 20 June 2016), and the bioethical committee of The University of the Punjab, Lahore (dated 18 February 2019), and the written informed consent obtained from the guardians of both the cases and controls.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eAuthors\u0026rsquo;contributions\u003c/h2\u003e\u003cp\u003eM.U.G.: Study plan, Data correction, analysis and writing the text.\u003c/p\u003e\u003cp\u003eM.F.S.: Supervise, data evaluation.\u003c/p\u003e\u003cp\u003eI.B.: Sampling and interpretation of data\u003c/p\u003e\u003cp\u003eM.S. \u0026amp; Q.U.A: Manuscript writing and data evaluation.\u003c/p\u003e\u003cp\u003eZ.M. and M.U.K: Sampling, data records, statistical analysis.\u003c/p\u003e\u003cp\u003e All authors reviewed the manuscript.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo fnancial support was received\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.U.G.: Study plan, Data correction, analysis and writing the text.M.F.S.: Supervise, data evaluation.I.B.: Sampling and interpretation of dataM.S. \u0026amp; Q.U.A: Manuscript writing and data evaluation.Z.M. and M.U.K: Sampling, data records, statistical analysis. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLambrecht, B.N. and H. Hammad, \u003cem\u003eThe airway epithelium in asthma\u003c/em\u003e. Nature Medicine, 2012. 18(5): p. 684\u0026ndash;692.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhani, M.U., et al., \u003cem\u003eEvaluation of ADAM33 gene's single nucleotide polymorphism variants against asthma and the unique pattern of inheritance in Northern and Central Punjab, Pakistan\u003c/em\u003e. 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Kuwait Journal of Science, 2021. 48(2): p. 1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShen, J., et al., \u003cem\u003eSHEsisPlus, a toolset for genetic studies on polyploid species\u003c/em\u003e. Sci Rep, 2016. 6: p. 24095.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSol\u0026eacute;, X., et al., \u003cem\u003eSNPStats: a web tool for the analysis of association studies\u003c/em\u003e. Bioinformatics, 2006. 22(15): p. 1928\u0026ndash;1929.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaan, E.J., et al., \u003cem\u003eCharacterization of asthma by age of onset: a multi-database cohort study\u003c/em\u003e. The Journal of Allergy and Clinical Immunology: In Practice, 2022. 10(7): p. 1825\u0026ndash;1834. e8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeidinger, S., et al., \u003cem\u003eGenome-wide scan on total serum IgE levels identifies FCER1A as novel susceptibility locus\u003c/em\u003e. PLoS Genet, 2008. 4(8): p. e1000166.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSabar, M.F., et al., \u003cem\u003ers12603332 is associated with male asthma patients specifically in urban areas of Lahore, Pakistan\u003c/em\u003e. J Asthma, 2017. 54(9): p. 887\u0026ndash;892.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurk, W., et al., \u003cem\u003eAttempted replication of 50 reported asthma risk genes identifies a SNP in RAD50 as associated with childhood atopic asthma\u003c/em\u003e. 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Respiratory research, 2015. 16(1): p. 1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorren, J., et al., \u003cem\u003eLebrikizumab treatment in adults with asthma\u003c/em\u003e. New England Journal of Medicine, 2011. 365(12): p. 1088\u0026ndash;1098.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Asthma, Genetics, Genome-Wide Association Study, Inflammation, Pakistan, SNP","lastPublishedDoi":"10.21203/rs.3.rs-7456490/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7456490/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eGenome-wide association studies (GWAS) have indicated that several single nucleotide variants (SNVs) of the \u003cem\u003eRAD50\u003c/em\u003e gene are significantly associated with childhood-onset asthma. However, the biological role of \u003cem\u003eRAD50\u003c/em\u003e, and its genomic variants that predispose individuals to asthma, remains unclear. This case-control study aimed to investigate the association of two Single nucleotide polymorphisms (SNPs) rs2244012, and rs6871536 of \u003cem\u003eRAD50 \u003c/em\u003ewith asthma susceptibility using experimental and computational tools\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The case-control study involved 355 participants: “176 asthma cases [mean age (sd) = 8.91 ±3.05] and 179 healthy controls [mean age (sd) = 11.10 ±8.86] from local Punjabi population of Pakistan. The SNPs were analyzed using a modified single base extension method. The allelic association with asthma and linkage disequilibrium (LD) between the two main SNPs were performed using the SHEsis tool. SNPStats was used to assess the association of SNPs under genotypic models and interaction with non-genetic factors. The LD calculator of ENSEMBL employed for the identification of proxy SNPs in high LD \u0026nbsp;(r^2 \u0026gt; 0.97) to main SNPs. Additionally, HaploReg(v4.1) was utilized to gauge the impact of SNPs on genomic regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In current study, both SNPs were found to have a significant association (p-value \u0026lt;0.05) with childhood-onset asthma development under allelic and genotypic models. The alternative “G” allele of rs2244012 is shown to modify two regulatory motifs: Nrf-2 and Zbtb12, while the alternative \"C\" allele of rs6871536 is predicted to alter the OSF-2 motif. Moreover, 10 SNVs proximal to rs2244012 and 21 SNVs near rs6871536 are in high LD in the Punjabi population of Lahore, Pakistan (PJL). These proxy/high-LD SNVs also displayed the potential to change DNA regulatory motifs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e the rs2244012, and rs6871536 variants of \u003cem\u003eRAD50 \u003c/em\u003egene are significantly association with childhood asthma in Pakistan. Despite being intronic variants, it is our inference that these two SNPs have the potential to either independently or synergistically regulate inflammatory responses via nearby SNVs.\u003c/p\u003e","manuscriptTitle":"Replication and Functional Prediction of Two GWAS-Reported SNPs Located on RAD50 Gene Associated with Asthma in Pakistani Children","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 04:58:43","doi":"10.21203/rs.3.rs-7456490/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":"294260d1-e137-4263-ba65-15ec504fee29","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-27T08:23:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 04:58:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7456490","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7456490","identity":"rs-7456490","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}