The relationship between genetic variation of genes in RLR-MAVS and cGAS-STING signaling pathway and other genes with HBV intrauterine transmission

The relationship between genetic variation of genes in RLR-MAVS and cGAS-STING signaling pathway and other genes with HBV intrauterine transmission | 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 The relationship between genetic variation of genes in RLR-MAVS and cGAS-STING signaling pathway and other genes with HBV intrauterine transmission Suihan Liu, Rufang Wu, Songxu Peng, Xukuan Zhang, Yukai Du This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1306080/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 Hepatitis B virus is a major public health problem worldwide. The genetic susceptibility of HBV intrauterine transmission has also attracted extensive attention from researchers. We explored the relationship between genetic variation of genes in RLR-MAVS and cGAS-STING signaling pathway and other genes with intrauterine transmission. Methods A case-control study was performed on HBsAg-positive pregnant women who delivered from January 2013 to August 2017 at Wuhan Medical Care Center for Women and Children, Wuhan, China. In total, 1356 infants born from those women were included, and there were 107 infants with HBV intrauterine infection. RLR-MAVS and cGAS-STING signaling pathway-related genes and IL10, IL12A, IL12B, TLR3, TLR9, TNFSF14 were selected as candidates. MassARRAY gene detection system was used for genotyping. Multivariate unconditional logistic regression, cumulative effect analysis, and multifactor dimensionality reduction were used for statistical analysis. Result The risk of HBV intrauterine transmission in infants with rs28413168 CC/CG genotype in INTS10 gene increased by 100% compared with those with GG genotype, and GG/AG genotype in MB21D1 gene was significantly lower than that in infants with AA genotype. Compared with the rs1800872 CC genotype in IL10 gene, the risk of HBV intrauterine transmission in infants with AA genotype was significantly increased. Compared with the rs3775291 CT/CC genotype in TLR3 gene, infants with TT genotype had significantly increased risk of HBV intrauterine transmission. The risk of HBV intrauterine transmission in infants with 5–8 risk alleles was 4.34 times higher than that in those with 0–2 risk alleles after adjustment for the age of delivery, parity, HBeAg, HBV DNA. Conclusions The genetic polymorphisms of INTS10 rs28413168, MB21D1 rs311678, IL10 rs1800872, TLR3 rs3775291 are closely related to the susceptibility of HBV intrauterine transmission. The more risk alleles an infant carries, the higher the risk of HBV intrauterine transmission. RLR-MAVS cGAS-STING genetic variation HBV intrauterine transmission Introduction About 350 million people worldwide are chronically infected with the hepatitis B virus (HBV)[ 1 ]. Controlling the further spread of HBV has become a major public health problem in the world. In highly endemic areas, about half of HBV infections originate from mother-to-child transmission (MTCT)[ 2 – 4 ]. Therefore, preventing MTCT of HBV has become an important way to eliminate HBV infection. In recent years, the development of next-generation sequencing technology has provided an important guarantee for a comprehensive and systematic study of the genetic susceptibility of diseases. Therefore, the genetic susceptibility of HBV intrauterine transmission has also attracted extensive attention from researchers. In clinical practice, it is often found that HBV pregnant women with similar general conditions have different risks of intrauterine transmission. The reasons behind this phenomenon have always been the focus of attention of researchers. In recent years, studies have reported the correlation between host gene polymorphism and the risk of HBV intrauterine transmission. Gao[ 5 ] reported that TLR3 (c.1377C/T) allele T and TLR9 (G2848A) allele A are protective factors for HBV intrauterine transmission. Another study[ 6 ] showed that the frequency of the G allele at the TNF-α-308 locus was significantly higher than that of the intrauterine uninfected group and the normal control group. Our previous research also found that genetic mutations in PDCD1 and LTA genes are associated with HBV intrauterine infection [ 7 ]. Wan[ 8 ] reported that CXCL13 rs355687 genotype CT has a lower risk of HBV intrauterine infection than genotype TT. This study also found that the neonatal HLA-C gene locus rs3130542 polymorphism is closely related to the risk of HBV intrauterine infection. But these studies are from the perspective of a single gene or a single site to analyze its relationship with HBV intrauterine transmission. At present, there is a lack of systematic and multi-level research on the genetic susceptibility of HBV intrauterine transmission. Retinoic acid-inducible gene I(RIG-I) is a pattern recognition receptor(PRR) discovered in recent years. HBV nucleic acid is recognized by RIG-I-like receptors to trigger signal transduction, induce the production of type I interferon(IFN-I) and pro-inflammatory cytokines, and then exert antiviral effects[ 9 ]. A study[ 10 ] found that RIG-I has a dual anti-HBV effect. On the one hand, RIG-I can recognize HBV pregenomic RNA (pgRNA), and further, induce the production of type III interferon to exert its anti-HBV effect. On the other hand, RIG-I can interfere with the interaction between HBV pgRNA and HBV polymerase to inhibit HBV replication. At present, there are limited studies on the association between the MAVS, IRF3, and IFNL1 gene polymorphisms of the key genes in the RIG-I signaling pathway and the intrauterine transmission of HBV. Cyclic GMP-AMP synthase(cGAS) is a nucleic acid transferase, which functions as a DNA sensor in mammals and recognizes DNA in the cytoplasm and produces cyclic GMP-AMP, activates stimulator of interferon genes(STING), regulates the secretion of downstream IFN-I and other cytokines, and initiates immune responses[ 11 , 12 ]. A German study[ 13 ] found that the silent expression of cGAS, STING, and TBK1 can lead to a large increase in HBV infection in HepG2-NTCP cells. After CRISPR/Cas9 gene knockout, HBV infection and replication increase. The study also found that overexpression of cGAS will lead to a reduction in HBV infection. He[ 14 ] reported that when the cGAS/STING pathway is activated by dsDNA or cGAMP, cultured cells and HBV replication in mice are significantly inhibited. In addition, knocking out cGAS in human peripheral blood mononuclear cells will lead to an increase in the level of intracellular HBV DNA. Therefore, the cGAS/STING pathway has played a role in the monitoring of HBV infection and may be used to develop new anti-HBV strategies. However, whether genetic polymorphisms in this pathway will affect the expression and function of the corresponding genes, which in turn affect the risk of intrauterine transmission in infants, remains unknown. Toll-like receptors (TLRs) are pattern recognition receptors that play an important role as a bridge between natural immunity and acquired immunity[ 15 , 16 ]. TLR3 and TLR9 are both important family members of TLRs. TLR3 can recognize double-stranded RNA viruses and dsRNA produced during virus replication. TLR9 can recognize viral DNA, induce specific immunity, and promote the body's elimination of viruses[ 17 – 20 ]. A study showed that TLR3 (c.1377C/T) allele T and TLR9 (G2848A) allele A are protective factors for HBV intrauterine transmission[ 5 ]. Research by Jiang found that compared with normal people, IL10 levels in HBV patients increased and IL12 levels decreased, indicating that IL10 and IL12 are closely related to hepatitis B infection[ 21 ]. Wang reported that in hemodialysis patients, IL12B 3'UTR gene polymorphism may be related to HBV susceptibility, and IL-10 gene polymorphism may be related to persistent HBV infection in China[ 22 ]. TNFSF14 is a gene encoding CD258, a member of the Tumor Necrosis Factor (TNF) superfamily, and its encoding protein CD258 is a ligand for the lymphotoxin β (LTβ) receptor. After T cells are activated, CD258 and LTB2LTA heterotrimers are expressed on the cell membrane, which binds and activates the LTβ receptor on liver cells[ 23 ]. The activated LTβ receptor can further increase the expression level of apolipoprotein B messenger RNA-editing enzyme catalytic subunit 3B gene in the nucleus, and then degrade HBV ccDNA in the nucleus, thereby exerting an anti-HBV effect [ 24 ]. To a certain extent, the above-mentioned genes are related to HBV clearance or persistent infection, but there is currently a lack of research on the association between these gene polymorphisms and HBV intrauterine transmission. Based on the genetic susceptibility of infants, this study selected key genes in the RLR-MAVS and cGAS-STING signaling pathways, including MAVS, IRF3, IFNL1, INTS10 in the RLR-MAVS pathway, MB21D1, TMEM173, IRF7, IFNB1 in the cGAS-STING pathway to explore the relationship between these candidate gene mutations and the risk of HBV intrauterine transmission. In addition, the influence of some candidate genes IL10, IL12A, IL12B, TLR3, TLR9, TNFSF14 gene polymorphisms on the intrauterine transmission of HBV was also discussed, and the susceptible genes and sites of HBV intrauterine transmission were identified. Materials And Methods Study subjects This study adopts a case-control study design. HBsAg-positive pregnant women and their infants were collected in Wuhan Maternity and Child Health Hospital from January 2013 to August 2017. A total of 1356 HBsAg-positive pregnant women and their infants were included. Methods Data and samples A self-designed questionnaire was used to collect the basic information of the research objects, including general demographic characteristics of pregnant women, basic conditions during pregnancy, and relevant information about infants. The samples in this study came from infants delivered by HBsAg-positive pregnant women. After obtaining the informed consent of the guardian, blood samples were collected. Screening of susceptibility sites for candidate genes This study screened out 4 key genes—MAVS, IRF3, IFNL1, INTS10 in the RLR-MAVS pathway, and 4 key genes—MB21D1, TMEM173, IRF7, IFNB1 in the cGAS-STING pathway, and 6 scattered genes—IL10, IL12A, IL12B, TLR3, TLR9, TNFSF14. Using bioinformatics analysis methods to select potential functional SNPs on candidate genes, the specific screening methods[25, 26] are as follows: (1) Use the F-SNP database (http://compbio.cs.queensu.ca/F-SNP) to screen candidate gene loci with potential functional effects in protein-coding, splicing regulation, transcription regulation or post-translational processes,(2) Filter the above loci, and the filter condition is that the minimum allele frequency (MAF) of the Chinese Han population (CHB) in Beijing, China reported in the genetic database of the Thousand Genome Project is greater than 5%, (3) On the SNAP Pairwise LD website (http://www.broadinstitute.org/mpg/snap/ldsearchpw.php), use 1000 Genomes Pilot1, CHBJPT, r2=0.8, distance limit=500kb as the benchmark to test whether there is a strong linkage disequilibrium between the above-mentioned sites. If it exists, choose one of the best. After screening, the final 22 SNP loci were eligible to be included for subsequent genotyping.2.3 Genomic DNA extraction and genotyping of infants. Genomic DNA was extracted from the blood samples using a TIANamp Blood DNA Kit DP318 (TIANGEN BIOTECH (BEIJING) CO., LTD, China) under the instruction of the manufacturer. DNA concentration and optical density were determined using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, Waltham, Massachusetts, USA). The DNA samples that passed the concentration and purity testing were sent to Beijing Bomiao Biological Company, and the company was entrusted to use the MassARRAY platform to perform genotyping testing on 23 SNP loci among the 15 candidates genes of the batch of samples. Estimation of power The power and sample size calculation v3.1.2 software was used to estimate the test power of this study. For genetic variants with MAF of 0.09, 0.28, and 0.47 at candidate loci, at the test level of α=0.05, with the current sample size, the confidence in detecting an association with an OR of 1.6 is 37.7%, 61.5%, and 64.2%, respectively. Statistical analysis Statistical analyses were performed by SPSS18. All P values were two-tailed with a statistically significant level set at 0.05. Comparison between groups was performed by two-tailed t test for continuous data, and Pearson Chi-Square (χ2) test in cross-tabulations for categorical data. Hardy-Weinberg balance test was used to estimate the reliability of group survey data. Multivariate unconditional logistic regression was used to analyze the association between each gene locus of infants and the risk of HBV intrauterine transmission under four different genetic models (codominant model, dominant model, recessive model, additive model). MDR software was used to analyze the interaction between genes and genes, and a model with good test sample accuracy, high cross-validation consistency, and significant replacement test results was selected as the best model for interaction. The cumulative effect of the positive sites found in the correlation analysis between SNP at a single site and intrauterine infection of HBV was performed. Multivariate unconditional Logistic regression analysis was used to calculate the risk of HBV intrauterine infection in infants with different numbers of alleles, expressed by OR value and its 95% CI. The linear trend test was used to analyze whether the risk of HBV intrauterine infection increased with the increase in the number of risk alleles. Results Comparison of basic characteristics Among the 1356 infants delivered by HBsAg-positive pregnant women, there were 107 infants with the intrauterine transmission. The age of delivery of pregnant women in the HBV intrauterine transmission group (case group) was significantly higher than that of the control group (P = 0.006). Among pregnant women in the case group, the proportion of first delivery was significantly higher than that in the control group (P = 0.002). The positive rate of HBeAg during pregnancy and the positive rate of HBV DNA during the third trimester in the case group were significantly higher than the control group (P <0.001). The proportion of cesarean section in the control group was significantly higher than that case group (P <0.001). (Table 1) Table1 Comparison of general characteristics of pregnant women case control χ 2 /t P Maternal Characteristics N=107 N=1249 Age (years) 28.00 ± 3.98 29.14 ± 4.08 -2.774 0.006 Pre-pregnancy BMI(kg/m 2 ) 26.83 ± 3.09 27.19 ± 3.19 -1.106 0.269 Primiparous 9.147 0.002 Yes 91 (85.8) 899 (72.3) No 15 (14.2) 344 (27.7) HBeAg 60.980 <0.001 positive 49 (46.7) 193 (15.9) negative 56 (53.3) 1020 (84.1) HBV DNA 31.643 <0.001 positive 59 (61.5) 364 (32.9) negative 37 (38.5) 743 (67.1) Infant characteristics N=107 N=1249 Gender 0.031 0.861 Male 55 (51.4) 653 (52.3) Famale 52 (48.6) 596 (47.7) Pre-term birth(<37) 0.261 0.610 Yes 5 (4.7) 73 (5.9) No 102 (95.3) 1170 (94.1) Delivery 23.365 <0.001 Vaginal 63 (58.49) 440 (35.3) Cesarean 44 (41.1) 805 (64.7) SNPs selection 22 SNPs on 14 candidate genes were included in this study, as shown in Supplementary Table S1. The success rate of rs8126207 on the MAVS gene and rs311674 on the MB21D1 gene was less than 95%, and they were not included in the study. In addition, the Hardy-Weinberg balance test found that the distribution frequency of the genotypes of MAVS rs3746661, MB21D1 rs9359033, and TMEM173 rs1131769 in the control group did not meet the Hardy-Weinberg genetic balance (P > 0.05) and was also eliminated. A total of 17 gene loci were finally included in the subsequent analysis. The association between single gene locus polymorphism and intrauterine transmission After adjusting for maternal delivery age, parity, HBeAg, HBV DNA, and delivery, the results of multivariate logistic regression analysis are shown in Table 2. Only statistically significant data are listed here, and basic information about these SNPs is shown in Supplementary Table S2. In the RIG-I/MAVS signaling pathway, compared with the INTS10 rs28413168 GG genotype, the risk of intrauterine transmission of HBV in infants carrying the CC/CG genotype at this site increased by 100% (Dominant model: OR = 2.00, 95% CI: 1.26-3.19, P = 0.004), additive model showed that for each additional C allele, the risk of intrauterine transmission of HBV in infants increased by 57% (OR = 1.57, 95% CI: 1.09-2.27, P = 0.016). In the cGAS-STING pathway, there was a significant difference in the genotype distribution of MB21D1 rs311678 between the two groups of infants (P = 0.041), under the dominant model, the risk of intrauterine transmission of HBV in infants with GG/AG genotype was significantly lower than that of infants with AA genotype (OR=0.48, 95% CI: 0.30-0.78, P = 0.003), additive model showed that for every additional G allele in an infant, the risk of intrauterine transmission of HBV decreased by 45% (OR = 0.55, 95% CI: 0.36-0.82, P = 0.003). Compared with the IL10 rs1800872 CC genotype, infants with AA genotype had a significantly higher risk of intrauterine transmission of HBV (OR = 3.01, 95% CI: 1.02-9.84, P = 0.047). The SNP at rs3775291 locus of TLR3 in infants was also closely related to the intrauterine transmission of HBV. Compared with the TT genotype, infants with CC genotype had a significantly lower risk of intrauterine transmission of HBV (OR = 0.44, 95% CI: 0.21-0.91, P = 0.027), under the recessive model, infants with TT genotype have a significantly higher risk of HBV intrauterine transmission than those with CT/CC genotype (OR = 2.09, 95% CI: 1.07-4.12 , P = 0.032). Table 2 Association analysis of neonatal candidate gene locus polymorphisms and intrauterine transmission of HBV Gene Genotype control case P a OR (95% CI) # P b INTS10 rs28413168 N=1231 N=103 0.160 * CC 55 (4.5) 5 (4.9) Ref CG 402 (32.7) 43 (41.7) 0.78 (0.25-2.39) 0.659 GG 774 (62.9) 55 (53.4) 1.64 (0.53-5.11) 0.390 Dominant model 2.00 (1.26-3.19) 0.004 Implicit model 0.96 (0.32-2.89) 0.945 Additive model 1.57 (1.09-2.27) 0.016 MB21D1 rs311678 N=1218 N=106 0.041 GG 100 (8.2) 4 (3.8) Ref AG 492(40.4) 35 (33.0) 2.67 (0.90-7.91) 0.077 AA 626 (51.4) 67 (63.2) 1.33 (0.43-4.11) 0.616 Dominant model 0.48 (0.30-0.78) 0.003 Implicit model 0.49 (0.17-1.43) 0.192 Additive model 0.55 (0.36-0.82) 0.003 IL10 rs1800872 N=1218 N=104 0.733 CC 109 (8.9) 7 (6.7) Ref CA 525 (43.1) 45 (43.3) 2.44 (0.82-7.32) 0.111 AA 584 (47.9) 52 (50.0) 3.01 (1.02-9.84) 0.047 Dominant model 0.72 (0.45-1.13) 0.152 Implicit model 0.37 (0.13-1.07) 0.064 Additive model 0.69 (0.48-1.00) 0.051 TLR3 rs3775291 N=1215 N=98 0.007 TT 116 (9.5) 19 (19.4) Ref CT 549 (45.2) 36 (36.7) 0.52 (0.25-1.05) 0.069 CC 550 (45.3) 43 (43.9) 0.44 (0.21-0.91) 0.027 Dominant model 1.02 (0.64-1.63) 0.940 Implicit model 2.09 (1.07-4.12) 0.032 Additive model 1.21 (0.85-1.72) 0.303 * Fisher's exact test. a P-value obtained from chi-square test † OR and 95% CI obtained from the logistic regression model, adjusted for maternal age, parity, HBeAg, HBV DNA, and delivery b P-value obtained from the logistic regression model Multigene cumulative effect analysis The cumulative effect of the four positive sites found in the correlation analysis between a single SNP and HBV intrauterine transmission was analyzed. According to the results of single-site association analysis, INTS10 rs28413168 C allele, MB21D1 rs311678 A allele, IL10 rs1800872 A allele, and TLR3 rs3775291 T allele were considered as risk genes. The number of risk alleles carried by each infant was calculated, and this variable was included in the logistic regression model. The age of delivery, parity, HBeAg, HBV DNA, and after delivery found that the risk of intrauterine transmission of HBV in infants carrying 5-8 risk alleles is 4.34 times that of carrying 0-2 risk alleles (95%CI: 1.44-13.09, P = 0.009). The linear trend test results showed that the risk of HBV intrauterine transmission increased as the number of risk alleles carried by infants increased (P = 0.030). (Table 3). Table 3 Analysis of the cumulative effect of neonatal positive sites Number of risk genotypes case control OR (95%CI) P 0-2 10 (9.7) 190 (15.4) Ref 3-4 55 (53.4) 653 (56.4) 2.29 (0.78-6.76) 0.134 5-8 38 (36.9) 348 (28.2) 4.34 (1.44-13.09) 0.009 linear trend test 0.030 *Adjusted for maternal age, parity, HBeAg, HBV DNA, and delivery Gene interaction analysis Table 4 lists 3 different combinations of models. Taking the 1-site model MD21D1 rs311678 as an example, the cross-validation consistency is the largest (CVC value is 9/10), and the prediction error value is the smallest 45.1%. But after the 1000 permutation test, the model is not statistically significant. In addition to the two-site and three-site models, the P-value of the replacement test is also greater than 0.05, indicating that the 3 models are not statistically significant. Table 4 Analysis of 17 SNPs Interaction Number of SNPs Best model Training accuracy Test accuracy CVC 1 rs311678 0.568 0.549 9/10 2 rs3212227, rs7447927 0.612 0.509 5/10 3 rs3212227,rs352140, rs7447927 0.659 0.438 4/10 Discussion Polymorphism of key genes in RIG-I/MAVS signaling pathway In the RIG-I/MAVS signaling pathway, the INTS10 rs28413168 polymorphism was statistically associated with HBV intrauterine transmission. There is no research report on the association between the INTS10 rs28413168 gene polymorphism and intrauterine transmission of HBV or other diseases, but there have been related reports on the association study between the INTS10 gene polymorphisms at other loci and diseases. A South African study found that three polymorphisms (rs55830938, rs73599609, rs73667448) located between the INTS10/LPL gene are associated with systolic blood pressure[ 27 ]. The results of another American study suggest that the INTS10/LPL rs149352150 gene polymorphism is associated with male BMI[ 28 ]. INTS10 is a member of the integration factor complex family, which can affect the splicing of RNA precursors, which in turn affects the synthesis and expression of mRNA in the cell. A study found that INST10 has an effect of inhibiting virus infection that has never been discovered before[ 29 ]. The study found that the INTS10 rs7000921 locus polymorphism is closely related to the chronicity of HBV infection through the genome-wide association study. Subsequently, the team also found in vitro cell culture experiments that INTS10 was significantly related to HBV replication. In the RIG-I/MAVS pathway, overexpression of INST10 can increase the phosphorylation of IRF3, which in turn activates the activity of downstream IFN-stimulated response element (ISRE), increases the expression level of IFN-λ, and ultimately inhibits HBV replication. Since no functional study on INST10 rs28413168 has been found so far, we found that this site is located in the 5'non-transcribed region through the Ensembl database, suggesting that this site may be involved in the translation, transcription, and stability maintenance of INST10 mRNA. Through the website of rSNP and Fast SNP gene function prediction, our study found that INST10 rs28413168 gene polymorphism may directly participate in gene transcription regulation, and then affect the anti-HBV function of INST10 in the body. Polymorphism of key genes in cGAS-STING signaling pathway In the cGAS-STING pathway, there were significant differences in the genotype distribution of MB21D1 rs311678 infants between the case group and the control group. MB21D1, also known as cGAS, can be used as a cytoplasmic DNA sensor to participate in the recognition of different viruses. The combination of cGAS and DNAs can activate the interferon-stimulating gene STING, which in turn stimulates the production of IFN-I and antiviral ISGs. The lack of cGAS will lead to the reduction of the early antiviral inherent response to the body's viral infection[ 30 ]. Dansako[ 31 ] reported that cGAS and its adaptor protein (STING) recognize and bind dsDNA derived from the HBV genome to generate an innate immune response. In addition, the study also proved that HBV infection can induce the production of interferon-inducible gene 56 (ISG56) through the cGAS-STING signaling pathway, thereby inhibiting the assembly of HBV virus particles. A Japanese study found that daunorubicin can trigger an endogenous cGAS-dependent innate immune response to inhibit the production of HBV[ 32 ]. Therefore, the antiviral effect induced by the cGAS-STING signaling pathway plays an important role in identifying and controlling HBV infection. rs311678 is located in the 5th exon coding region of the cGAS gene. The mutation at this site is synonymous and will not cause any changes in the amino acids it encodes. However, Xiao[ 33 ] reported that the cGAS rs311678 locus polymorphism is associated with cervical precancerous lesions, and the cGAS rs311678 G allele reduces the risk of cervical precancerous lesions. Our research used the SNP function prediction website rSNP for function prediction, and the results suggested that this site is involved in post-transcriptional regulation and can bind to a variety of RNA-binding proteins, thereby affecting the expression level and function of cGAS. Polymorphism of IL-10 genes Previous studies have shown that IL-10 can regulate the immune response and inflammatory response by inhibiting the activity of antigen-presenting cells[ 34 ]. In addition, IL-10 can inhibit the maturation of various immune cells and the production of cytokines, reducing the immune response level[ 35 ]. The IL-10 promoter is highly polymorphic, but currently, only the rs1800871 (-819) and rs1800872 (-592) polymorphisms in the promoter region have been extensively studied. The main reason is that these two sites are respectively related to the Msll and Rsal restriction enzyme (re) sites, which may change the activity of the IL-10 gene[ 36 ]. It is reported that IL-10 promoter region polymorphism can affect IL-10 gene transcription and translation, leading to abnormal cell proliferation and cancer development[ 37 ]. Wu[ 38 ] reported that IL-10 rs1800872 (-592a/c) gene polymorphism is associated with spontaneous HBsAg seroconversion and good outcome of hepatitis B. Compared with hepatitis B carriers of CA and CC genotypes, patients with IL-10 rs1800872 AA genotype have higher serum IL-10 levels. In addition, Li[ 39 ] found that IL-10 levels in patients with chronic hepatitis B were significantly higher than those in the general population. These results were confirmed by another research[ 40 ]. However, a Japanese study showed that the IL-10rs1800872 genotype is not significantly associated with the risk of HBV infection[ 41 ]. Our study found that compared with infants with CC genotype, infants with AA genotype at this locus have a significantly higher risk of intrauterine transmission of HBV. We speculate that infants with the AA genotype may have higher levels of IL-10, which inhibits the maturation of various immune cells and the synthesis of cytokines. This will reduce the body's immune response level, leading to a significant increase in the risk of HBV intrauterine infection. Polymorphism of TLR3 genes TLR3 can recognize type I IFN, double-stranded RNA (dsRNA) viruses and dsRNA produced during virus replication[ 42 ], activate IRF3 and NF-κB, and also promote the expression of IFN-β and other pro-inflammatory factors[ 43 ]. TLR3 is mainly expressed in hepatocytes, macrophages, NK cells, and biliary epithelial cells, located in the plasma membrane or acidic endosome[ 44 ]. And macrophages and NK cells play a vital role in the process of clearing HBV[ 45 , 46 ]. In addition, during HBV infection, TLR3 can activate hepatic non-parenchymal cells, produce interferon β, and inhibit virus replication[ 47 ]. This study found that the SNP at rs3775291 of neonatal TLR3 is closely related to the intrauterine transmission of HBV, which is similar to the results of previous studies. Fischer[ 48 ] found that compared with HBeAg-positive patients, the CC genotype of TLR3 rs3775291 appeared more frequently in patients with HBeAg seroclearance, while the TT genotype was more common in HBeAg-positive patients. This further supports that the rs3775291 mutation is related to the innate immune response during HBV infection. In addition, Rong's[ 49 ] research also showed that compared with the CC genotype, subjects carrying CT genotype and TT genotype at this locus had an increased risk of chronic HBV infection by 1.42 folds and 2.31 folds, respectively. The specific mechanism of this phenomenon is not yet clear. It may be that mutations at this site cause changes in protein or gene expression, affecting the function and efficiency of signal transduction and thereby altering the immune response. SNPs rs3775291 is a non-synonymous mutant gene locus located in the fourth exon region of TLR3. The mutation at this locus reduces the location of the soluble outer domain and the dimerization of TLR3 on the membrane, resulting in a decrease in its ability to bind to dsRNA and the signal transduction efficiency. Svensson A[ 50 ] detected the expression level of TLR3 mRNA in peripheral blood mononuclear cells (PBMC) of multiple patients infected with human herpesvirus type 2 (HSV-2). It was found that the appearance of the T allele increased the expression of TLR3 mRNA in PBMC relative to the allele C. However, a study showed that the polymorphism of TLR3 rs3775291 locus does not affect the expression of TLR3 in the cell and the localization in the cell, but it will affect the activity of TLR3 and the expression of the receptor on the cell surface[ 51 ]. Multiple genes and HBV intrauterine transmission According to the theory of common diseases-common mutations, common complex diseases are affected by multiple common mutations, and the effect of each common mutation is weak. As a complex disease, the MTCT of HBV is often affected by multiple genes or multiple sites during its occurrence and development. These genes or loci may interact with each other. This study explored the cumulative effect of multiple positive gene loci on the risk of HBV intrauterine transmission. It was found that infants carrying the INTS10 rs28413168 C allele, MB21D1 rs311678 A allele, IL10 rs1800872 A allele, and TLR3 rs3775291 T allele at the same time significantly increased the risk of intrauterine transmission of HBV. This suggested that these four sites may have an additive effect on the risk of HBV intrauterine transmission, but the specific biological mechanism depends on the elucidation of the function of each gene and in-depth functional experimental research. Conclusion Infants carrying the INTS10 rs28413168 C allele, MB21D1 rs311678 A allele, IL10 rs1800872 A allele, and TLR3 rs3775291 T allele will significantly increase the risk of HBV intrauterine infection. The above four sites may have an additive effect on the risk of HBV intrauterine transmission, but no gene-gene interaction between these genes has been found. Declarations Ethics approval and consent to participate The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Tongji Hospital, Tongji Medical College, HUST. Written informed consent was obtained from individual or guardian participants. Consent for publication This article was published with the consent of all the authors. Availability of data and material All data generated or analysed during this study are included in this published article. Competing interests The authors declare that they have no competing interests. Funding Supported by: This work was funded by the National Natural Science Foundation of China, grant number (81872632 and 82103861). Authors' contributions Suihan Liu and Rufang Wu wrote the paper, Songxu Peng and Xukuan Zhang revised the paper, Yukai Du conceived of and revised the paper. *These authors contributed equally this article. Acknowledgements Not applicable. Authors' information Suihan Liu, Xukuan Zhang, Yukai Du, Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, China Rufang Wu, Department of Obstetrics and Gynecology, the Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China Songxu Peng, Department of Maternal and Child Health, Xiangya School of Public Health, Central South University, No. 110, Xiangya Road, Changsha, 410008, China Corresponding author: Yukai Du, PhD, MD, Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, China [email protected] References Trépo C, Chan HLY, Lok A: Hepatitis B virus infection . Lancet (London, England) 2014, 384 (9959):2053–2063. Thio CL, Guo N, Xie C, Nelson KE, Ehrhardt S: Global elimination of mother-to-child transmission of hepatitis B: revisiting the current strategy . Lancet Infect Dis 2015, 15 (8):981–985. Ren Y, Guo Y, Feng L, Li T, Du Y: Controversy and Strategies Exploration in Blocking Mother-to-Child Transmission of Hepatitis B . Int Rev Immunol 2016, 35 (3):249–259. Xu Y, Liu H, Wang Y, Hao R, Li Z, Song H: The next step in controlling HBV in China . BMJ (Clinical research ed) 2013, 347 :f4503. Gao Y, Guo J, Zhang F, Guo Z, Zhang LR, Wang T, Wang B, Feng SY, Wang SP: Evaluation of neonatal Toll-like receptors 3 (c.1377C/T) and 9 (G2848A) gene polymorphisms in HBV intrauterine transmission susceptibility . Epidemiol Infect 2015, 143 (9):1868–1875. Chatzidaki V, Choumerianou D, Dimitriou H, Kouroumalis E, Galanakis E: Genetic variants associated with susceptibility to mother-to-child transmission of hepatitis B virus . Eur J Gastroenterol Hepatol 2012, 24 (10):1185–1190. Liu T, Wan Z, Peng S, Wang Y, Chen H, Li X, Du Y: Genetic variations in LTA gene and PDCD1 gene and intrauterine infection of hepatitis B virus: a case-control study in China . Amino Acids 2018, 50 (7):877–883. Wan Z, Lin X, Li T, Zhou A, Yang M, Hu D, Feng L, Peng S, Fan L, Tu S et al : Genetic variant in CXCL13 gene is associated with susceptibility to intrauterine infection of hepatitis B virus . Sci Rep 2016, 6 :26465. Xu X-X, Wan H, Nie L, Shao T, Xiang L-X, Shao J-Z: RIG-I: a multifunctional protein beyond a pattern recognition receptor . Protein Cell 2018, 9 (3):246–253. Sato S, Li K, Kameyama T, Hayashi T, Ishida Y, Murakami S, Watanabe T, Iijima S, Sakurai Y, Watashi K et al : The RNA sensor RIG-I dually functions as an innate sensor and direct antiviral factor for hepatitis B virus . Immunity 2015, 42 (1):123–132. Hall J, Ralph EC, Shanker S, Wang H, Byrnes LJ, Horst R, Wong J, Brault A, Dumlao D, Smith JF et al : The catalytic mechanism of cyclic GMP-AMP synthase (cGAS) and implications for innate immunity and inhibition . Protein Sci 2017, 26 (12):2367–2380. Kato K, Omura H, Ishitani R, Nureki O: Cyclic GMP-AMP as an Endogenous Second Messenger in Innate Immune Signaling by Cytosolic DNA . Annu Rev Biochem 2017, 86 :541–566. Verrier ER, Yim S-A, Heydmann L, El Saghire H, Bach C, Turon-Lagot V, Mailly L, Durand SC, Lucifora J, Durantel D et al : Hepatitis B Virus Evasion From Cyclic Guanosine Monophosphate-Adenosine Monophosphate Synthase Sensing in Human Hepatocytes . Hepatology 2018, 68 (5):1695–1709. He J, Hao R, Liu D, Liu X, Wu S, Guo S, Wang Y, Tien P, Guo D: Inhibition of hepatitis B virus replication by activation of the cGAS-STING pathway . J Gen Virol 2016, 97 (12):3368–3378. Kawai T, Akira S: The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors . Nat Immunol 2010, 11 (5):373–384. Zhang E, Lu M: Toll-like receptor (TLR)-mediated innate immune responses in the control of hepatitis B virus (HBV) infection . Med Microbiol Immunol 2015, 204 (1):11–20. Shahrakyvahed A, Sanchooli J, Sanadgol N, Arababadi MK, Kennedy D: TLR9: an important molecule in the fight against hepatitis B virus . Postgrad Med J 2014, 90 (1065):396–401. Chihab H, Zaidane I, Elhabazi A, Jadid F-Z, El Fihri R, Elmessaoudi-Idrissi M, Chair M, Badre W, Tahiri M, Pineau P et al : Toll-like receptor 9 polymorphisms and Hepatitis B virus clearance in Moroccan chronic carriers . Gene 2019, 687 :212–218. EzzEl-Din AM, Galal SH, Ahmed AO, Kamel SI, Kamal DT: Toll Like Receptor 3 Polymorphisms in Hepatitis B Virus Infection . Egypt J Immunol 2017, 24 (2):173–185. Zhang B, Liu Y, Wang X, Li J, Xu X, Guo L, Ho W-Z: TLR3 Activation of Hepatic Stellate Cell Line Suppresses HBV Replication in HepG2 Cells . Front Immunol 2018, 9 :2921. Jiang H, Cao F, Cao H, Rao Q, Yang Y: Associations of human leukocyte antigen and interleukin-18 gene polymorphisms with viral load in patients with hepatitis B infection . Medicine (Baltimore) 2018, 97 (30):e11249. Wang C, Zhang X, Zhu B, Hu D, Wu J, Yu R, Zhao W: Relationships between tumour necrosis factor-α, interleukin-12B and interleukin-10 gene polymorphisms and hepatitis B in Chinese Han haemodialysis patients . Nephrology (Carlton) 2012, 17 (2):167–174. Bertoletti A, Ferrari C: Adaptive immunity in HBV infection . J Hepatol 2016, 64 (1 Suppl):S71-S83. Lucifora J, Xia Y, Reisinger F, Zhang K, Stadler D, Cheng X, Sprinzl MF, Koppensteiner H, Makowska Z, Volz T et al : Specific and nonhepatotoxic degradation of nuclear hepatitis B virus cccDNA . Science 2014, 343 (6176):1221–1228. Kong R, Shao S, Wang J, Zhang X, Guo S, Zou L, Zhong R, Lou J, Zhou J, Zhang J et al : Genetic variant in DIP2A gene is associated with developmental dyslexia in Chinese population . Am J Med Genet B Neuropsychiatr Genet 2016, 171B (2):203–208. Shao S, Kong R, Zou L, Zhong R, Lou J, Zhou J, Guo S, Wang J, Zhang X, Zhang J et al : The Roles of Genes in the Neuronal Migration and Neurite Outgrowth Network in Developmental Dyslexia: Single- and Multiple-Risk Genetic Variants . Mol Neurobiol 2016, 53 (6):3967–3975. Hendry LM, Sahibdeen V, Choudhury A, Norris SA, Ramsay M, Lombard Z: Insights into the genetics of blood pressure in black South African individuals: the Birth to Twenty cohort . BMC Med Genomics 2018, 11 (1):2. Ng MCY, Graff M, Lu Y, Justice AE, Mudgal P, Liu C-T, Young K, Yanek LR, Feitosa MF, Wojczynski MK et al : Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium . PLoS Genet 2017, 13 (4):e1006719. Li Y, Si L, Zhai Y, Hu Y, Hu Z, Bei J-X, Xie B, Ren Q, Cao P, Yang F et al : Genome-wide association study identifies 8p21.3 associated with persistent hepatitis B virus infection among Chinese . Nat Commun 2016, 7 :11664. Anghelina D, Lam E, Falck-Pedersen E: Diminished Innate Antiviral Response to Adenovirus Vectors in cGAS/STING-Deficient Mice Minimally Impacts Adaptive Immunity . J Virol 2016, 90 (13):5915–5927. Dansako H, Ueda Y, Okumura N, Satoh S, Sugiyama M, Mizokami M, Ikeda M, Kato N: The cyclic GMP-AMP synthetase-STING signaling pathway is required for both the innate immune response against HBV and the suppression of HBV assembly . FEBS J 2016, 283 (1):144–156. Imai H, Dansako H, Ueda Y, Satoh S, Kato N: Daunorubicin, a topoisomerase II poison, suppresses viral production of hepatitis B virus by inducing cGAS-dependent innate immune response . Biochem Biophys Res Commun 2018, 504 (4):672–678. Xiao D, Huang W, Ou M, Guo C, Ye X, Liu Y, Wang M, Zhang B, Zhang N, Huang S et al : Interaction between susceptibility loci in cGAS-STING pathway, MHC gene and HPV infection on the risk of cervical precancerous lesions in Chinese population . Oncotarget 2016, 7 (51):84228–84238. Moore KW, de Waal Malefyt R, Coffman RL, O'Garra A: Interleukin-10 and the interleukin-10 receptor . Annu Rev Immunol 2001, 19 :683–765. Donnelly RP, Dickensheets H, Finbloom DS: The interleukin-10 signal transduction pathway and regulation of gene expression in mononuclear phagocytes . J Interferon Cytokine Res 1999, 19 (6):563–573. Moghimi M, Ahrar H, Karimi-Zarchi M, Aghili K, Salari M, Zare-Shehneh M, Neamatzadeh H: Association of IL-10 rs1800871 and rs1800872 Polymorphisms with Breast Cancer Risk: A Systematic Review and Meta-Analysis . Asian Pac J Cancer Prev 2018, 19 (12):3353–3359. Niu Y-M, Du X-Y, Cai H-X, Zhang C, Yuan R-X, Zeng X-T, Luo J: Increased risks between Interleukin - 10 gene polymorphisms and haplotype and head and neck cancer : a meta - analysis . Sci Rep 2015, 5 :17149. Wu J-F, Hsu H-Y, Chiu Y-C, Chen H-L, Ni Y-H, Chang M-H: The effects of cytokines on spontaneous hepatitis B surface antigen seroconversion in chronic hepatitis B virus infection . J Immunol 2015, 194 (2):690–696. Li M-H, Lu Y, Zhang L, Wang X-Y, Ran C-P, Hao H-X, Zhang D, Qu X-J, Shen G, Wu S-L et al : Association of Cytokines with Alanine Aminotransferase, Hepatitis B Virus Surface Antigen and Hepatitis B Envelope Antigen Levels in Chronic Hepatitis B . Chin Med J (Engl) 2018, 131 (15):1813–1818. Özgüler M, Akbulut HH, Akbulut A: Evaluation of Interleukin-10 Levels in Patients Diagnosed with Chronic Hepatitis . West Indian Med J 2015, 64 (2):71–75. Miyazoe S, Hamasaki K, Nakata K, Kajiya Y, Kitajima K, Nakao K, Daikoku M, Yatsuhashi H, Koga M, Yano M et al : Influence of interleukin-10 gene promoter polymorphisms on disease progression in patients chronically infected with hepatitis B virus . Am J Gastroenterol 2002, 97 (8):2086–2092. Karimi-Googheri M, Arababadi MK: TLR3 plays significant roles against hepatitis B virus . Mol Biol Rep 2014, 41 (5):3279–3286. Oshiumi H, Matsumoto M, Funami K, Akazawa T, Seya T: TICAM-1, an adaptor molecule that participates in Toll-like receptor 3-mediated interferon-beta induction . Nat Immunol 2003, 4 (2):161–167. Ma Z, Zhang E, Yang D, Lu M: Contribution of Toll-like receptors to the control of hepatitis B virus infection by initiating antiviral innate responses and promoting specific adaptive immune responses . Cellular & molecular immunology 2015, 12 (3):273–282. Knolle PA, Gerken G: Local control of the immune response in the liver . Immunol Rev 2000, 174 :21–34. Kimura K, Kakimi K, Wieland S, Guidotti LG, Chisari FV: Activated intrahepatic antigen-presenting cells inhibit hepatitis B virus replication in the liver of transgenic mice . J Immunol 2002, 169 (9):5188–5195. Wu J, Lu M, Meng Z, Trippler M, Broering R, Szczeponek A, Krux F, Dittmer U, Roggendorf M, Gerken G et al : Toll-like receptor-mediated control of HBV replication by nonparenchymal liver cells in mice . Hepatology 2007, 46 (6):1769–1778. Fischer J, Koukoulioti E, Schott E, Fülöp B, Heyne R, Berg T, van Bömmel F: Polymorphisms in the Toll-like receptor 3 (TLR3) gene are associated with the natural course of hepatitis B virus infection in Caucasian population . Sci Rep 2018, 8 (1):12737. Rong Y, Song H, You S, Zhu B, Zang H, Zhao Y, Li Y, Wan Z, Liu H, Zhang A et al : Association of Toll-like receptor 3 polymorphisms with chronic hepatitis B and hepatitis B-related acute-on-chronic liver failure . Inflammation 2013, 36 (2):413–418. Svensson A, Tunbäck P, Nordström I, Padyukov L, Eriksson K: Polymorphisms in Toll-like receptor 3 confer natural resistance to human herpes simplex virus type 2 infection . J Gen Virol 2012, 93 (Pt 8):1717–1724. Ranjith-Kumar CT, Miller W, Sun J, Xiong J, Santos J, Yarbrough I, Lamb RJ, Mills J, Duffy KE, Hoose S et al : Effects of single nucleotide polymorphisms on Toll-like receptor 3 activity and expression in cultured cells . The Journal of biological chemistry 2007, 282 (24):17696–17705. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1306080","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":92311869,"identity":"b95de26f-5092-4826-b454-e27716a2e656","order_by":0,"name":"Suihan Liu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Suihan","middleName":"","lastName":"Liu","suffix":""},{"id":92311870,"identity":"f588f7ac-ca16-4b87-84c5-89aa8383a153","order_by":1,"name":"Rufang Wu","email":"","orcid":"","institution":"the Central Hospital of Wuhan, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Rufang","middleName":"","lastName":"Wu","suffix":""},{"id":92311871,"identity":"4a56ce3a-191f-4107-ac41-df55f567e23c","order_by":2,"name":"Songxu Peng","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Songxu","middleName":"","lastName":"Peng","suffix":""},{"id":92311872,"identity":"da702bc5-6f23-4f5f-9f7a-5c5f1a3ffda4","order_by":3,"name":"Xukuan Zhang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xukuan","middleName":"","lastName":"Zhang","suffix":""},{"id":92311873,"identity":"07396498-a874-4b2b-8db2-e1d9a2c24ea2","order_by":4,"name":"Yukai Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDCCAwwMjA0MEnJszIyNDxIqaojXYszP3nzY4MGZY0RrYUic2XMsTfJhCzNhHXw3cswkZ9RYMG4AMioSG9gY+Nu7E/BqkQRp2XBMgtkAyLiRuEOGQeLM2Q14tYBUSj5gk2CDaDnDxmAgkUuMln8SPCBGQWIbM5FaNrZJSEgCvc9AlBbJM8+KLWf2SRiAAlki4cwxHoJ+4TuevPFmz7e6+jZgVH78UVEjx9/ei18LAwOHAQqXh4ByEGB/QISiUTAKRsEoGNEAAAweTdkqa+1qAAAAAElFTkSuQmCC","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yukai","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2022-01-28 11:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1306080/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1306080/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19788483,"identity":"5c00f815-00c1-49c8-b465-d2bdd1b06514","added_by":"auto","created_at":"2022-03-30 19:11:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1478403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1306080/v1/07a62b16-0a15-4b5a-9ad1-b6c0cb40226d.pdf"},{"id":19788482,"identity":"8b50a308-5f32-489a-b60a-dcd57ca70f0d","added_by":"auto","created_at":"2022-03-30 19:11:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1478403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1306080/v1/da8df534-f03a-4d4c-81f6-aaae0b3717ff.pdf"},{"id":19788481,"identity":"744b90ec-23dd-435e-98d8-679e9f0c1fa1","added_by":"auto","created_at":"2022-03-30 19:11:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":38899,"visible":true,"origin":"","legend":"","description":"","filename":"supplementtable.docx","url":"https://assets-eu.researchsquare.com/files/rs-1306080/v1/e4cd0ea68ae42abf59cc048c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The relationship between genetic variation of genes in RLR-MAVS and cGAS-STING signaling pathway and other genes with HBV intrauterine transmission","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAbout 350\u0026nbsp;million people worldwide are chronically infected with the hepatitis B virus (HBV)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Controlling the further spread of HBV has become a major public health problem in the world. In highly endemic areas, about half of HBV infections originate from mother-to-child transmission (MTCT)[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, preventing MTCT of HBV has become an important way to eliminate HBV infection. In recent years, the development of next-generation sequencing technology has provided an important guarantee for a comprehensive and systematic study of the genetic susceptibility of diseases. Therefore, the genetic susceptibility of HBV intrauterine transmission has also attracted extensive attention from researchers.\u003c/p\u003e \u003cp\u003eIn clinical practice, it is often found that HBV pregnant women with similar general conditions have different risks of intrauterine transmission. The reasons behind this phenomenon have always been the focus of attention of researchers. In recent years, studies have reported the correlation between host gene polymorphism and the risk of HBV intrauterine transmission. Gao[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] reported that TLR3 (c.1377C/T) allele T and TLR9 (G2848A) allele A are protective factors for HBV intrauterine transmission. Another study[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] showed that the frequency of the G allele at the TNF-α-308 locus was significantly higher than that of the intrauterine uninfected group and the normal control group. Our previous research also found that genetic mutations in PDCD1 and LTA genes are associated with HBV intrauterine infection [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Wan[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported that CXCL13 rs355687 genotype CT has a lower risk of HBV intrauterine infection than genotype TT. This study also found that the neonatal HLA-C gene locus rs3130542 polymorphism is closely related to the risk of HBV intrauterine infection. But these studies are from the perspective of a single gene or a single site to analyze its relationship with HBV intrauterine transmission. At present, there is a lack of systematic and multi-level research on the genetic susceptibility of HBV intrauterine transmission.\u003c/p\u003e \u003cp\u003eRetinoic acid-inducible gene I(RIG-I) is a pattern recognition receptor(PRR) discovered in recent years. HBV nucleic acid is recognized by RIG-I-like receptors to trigger signal transduction, induce the production of type I interferon(IFN-I) and pro-inflammatory cytokines, and then exert antiviral effects[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A study[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] found that RIG-I has a dual anti-HBV effect. On the one hand, RIG-I can recognize HBV pregenomic RNA (pgRNA), and further, induce the production of type III interferon to exert its anti-HBV effect. On the other hand, RIG-I can interfere with the interaction between HBV pgRNA and HBV polymerase to inhibit HBV replication. At present, there are limited studies on the association between the MAVS, IRF3, and IFNL1 gene polymorphisms of the key genes in the RIG-I signaling pathway and the intrauterine transmission of HBV.\u003c/p\u003e \u003cp\u003eCyclic GMP-AMP synthase(cGAS) is a nucleic acid transferase, which functions as a DNA sensor in mammals and recognizes DNA in the cytoplasm and produces cyclic GMP-AMP, activates stimulator of interferon genes(STING), regulates the secretion of downstream IFN-I and other cytokines, and initiates immune responses[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A German study[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] found that the silent expression of cGAS, STING, and TBK1 can lead to a large increase in HBV infection in HepG2-NTCP cells. After CRISPR/Cas9 gene knockout, HBV infection and replication increase. The study also found that overexpression of cGAS will lead to a reduction in HBV infection. He[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] reported that when the cGAS/STING pathway is activated by dsDNA or cGAMP, cultured cells and HBV replication in mice are significantly inhibited. In addition, knocking out cGAS in human peripheral blood mononuclear cells will lead to an increase in the level of intracellular HBV DNA. Therefore, the cGAS/STING pathway has played a role in the monitoring of HBV infection and may be used to develop new anti-HBV strategies. However, whether genetic polymorphisms in this pathway will affect the expression and function of the corresponding genes, which in turn affect the risk of intrauterine transmission in infants, remains unknown.\u003c/p\u003e \u003cp\u003eToll-like receptors (TLRs) are pattern recognition receptors that play an important role as a bridge between natural immunity and acquired immunity[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. TLR3 and TLR9 are both important family members of TLRs. TLR3 can recognize double-stranded RNA viruses and dsRNA produced during virus replication. TLR9 can recognize viral DNA, induce specific immunity, and promote the body's elimination of viruses[\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A study showed that TLR3 (c.1377C/T) allele T and TLR9 (G2848A) allele A are protective factors for HBV intrauterine transmission[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Research by Jiang found that compared with normal people, IL10 levels in HBV patients increased and IL12 levels decreased, indicating that IL10 and IL12 are closely related to hepatitis B infection[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Wang reported that in hemodialysis patients, IL12B 3'UTR gene polymorphism may be related to HBV susceptibility, and IL-10 gene polymorphism may be related to persistent HBV infection in China[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. TNFSF14 is a gene encoding CD258, a member of the Tumor Necrosis Factor (TNF) superfamily, and its encoding protein CD258 is a ligand for the lymphotoxin β (LTβ) receptor. After T cells are activated, CD258 and LTB2LTA heterotrimers are expressed on the cell membrane, which binds and activates the LTβ receptor on liver cells[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The activated LTβ receptor can further increase the expression level of apolipoprotein B messenger RNA-editing enzyme catalytic subunit 3B gene in the nucleus, and then degrade HBV ccDNA in the nucleus, thereby exerting an anti-HBV effect [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To a certain extent, the above-mentioned genes are related to HBV clearance or persistent infection, but there is currently a lack of research on the association between these gene polymorphisms and HBV intrauterine transmission.\u003c/p\u003e \u003cp\u003eBased on the genetic susceptibility of infants, this study selected key genes in the RLR-MAVS and cGAS-STING signaling pathways, including MAVS, IRF3, IFNL1, INTS10 in the RLR-MAVS pathway, MB21D1, TMEM173, IRF7, IFNB1 in the cGAS-STING pathway to explore the relationship between these candidate gene mutations and the risk of HBV intrauterine transmission. In addition, the influence of some candidate genes IL10, IL12A, IL12B, TLR3, TLR9, TNFSF14 gene polymorphisms on the intrauterine transmission of HBV was also discussed, and the susceptible genes and sites of HBV intrauterine transmission were identified.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopts a case-control study design. HBsAg-positive pregnant women and their infants were collected in Wuhan Maternity and Child Health Hospital from January 2013 to August 2017. A total of 1356 HBsAg-positive pregnant women and their infants were included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA self-designed questionnaire was used to collect the basic information of the research objects, including general demographic characteristics of pregnant women, basic conditions during pregnancy, and relevant information about infants. The samples in this study came from infants delivered by HBsAg-positive pregnant women. After obtaining the informed consent of the guardian, blood samples were collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening of susceptibility sites for candidate genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study screened out 4 key genes\u0026mdash;MAVS, IRF3, IFNL1, INTS10 in the RLR-MAVS pathway, and 4 key genes\u0026mdash;MB21D1, TMEM173, IRF7, IFNB1 in the cGAS-STING pathway, and 6 scattered genes\u0026mdash;IL10, IL12A, IL12B, TLR3, TLR9, TNFSF14. Using bioinformatics analysis methods to select potential functional SNPs on candidate genes, the specific screening methods[25, 26] are as follows: (1) Use the F-SNP database (http://compbio.cs.queensu.ca/F-SNP) to screen candidate gene loci with potential functional effects in protein-coding, splicing regulation, transcription regulation or post-translational processes,(2) Filter the above loci, and the filter condition is that the minimum allele frequency (MAF) of the Chinese Han population (CHB) in Beijing, China reported in the genetic database of the Thousand Genome Project is greater than 5%, (3) On the SNAP Pairwise LD website (http://www.broadinstitute.org/mpg/snap/ldsearchpw.php), use 1000 Genomes Pilot1, CHBJPT, r2=0.8, distance limit=500kb as the benchmark to test whether there is a strong linkage disequilibrium between the above-mentioned sites. If it exists, choose one of the best. After screening, the final 22 SNP loci were eligible to be included for subsequent genotyping.2.3 Genomic DNA extraction and genotyping of infants.\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted from the blood samples using a TIANamp Blood DNA Kit DP318 (TIANGEN BIOTECH (BEIJING) CO., LTD, China) under the instruction of the manufacturer. DNA concentration and optical density were determined using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, Waltham, Massachusetts, USA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe DNA samples that passed the concentration and purity testing were sent to Beijing Bomiao Biological Company, and the company was entrusted to use the MassARRAY platform to perform genotyping testing on 23 SNP loci among the 15 candidates genes of the batch of samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimation of power\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe power and sample size calculation v3.1.2 software was used to estimate the test power of this study. For genetic variants with MAF of 0.09, 0.28, and 0.47 at candidate loci, at the test level of \u0026alpha;=0.05, with the current sample size, the confidence in detecting an association with an OR of 1.6 is 37.7%, 61.5%, and 64.2%, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed by SPSS18.\u0026nbsp;All \u003cem\u003eP\u003c/em\u003e values were two-tailed with a statistically significant level set at 0.05. Comparison between groups was performed by two-tailed t test for continuous data, and Pearson Chi-Square (\u0026chi;2) test in cross-tabulations for categorical data. Hardy-Weinberg balance test was used to estimate the reliability of group survey data. Multivariate unconditional logistic regression was used to analyze the association between each gene locus of infants and the risk of HBV intrauterine transmission under four different genetic models (codominant model, dominant model, recessive model, additive model). MDR software was used to analyze the interaction between genes and genes, and a model with good test sample accuracy, high cross-validation consistency, and significant replacement test results was selected as the best model for interaction. The cumulative effect of the positive sites found in the correlation analysis between SNP at a single site and intrauterine infection of HBV was performed. Multivariate unconditional Logistic regression analysis was used to calculate the risk of HBV intrauterine infection in infants with different numbers of alleles, expressed by OR value and its 95% CI. The linear trend test was used to analyze whether the risk of HBV intrauterine infection increased with the increase in the number of risk alleles.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eComparison of basic characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 1356 infants delivered by HBsAg-positive pregnant women, there were 107 infants with the intrauterine transmission.\u0026nbsp;The age of delivery of pregnant women in the HBV intrauterine transmission group (case group) was significantly higher than that of the control group (P = 0.006). Among pregnant women in the case group, the proportion of first delivery was significantly higher than that in the control group (P = 0.002). The positive rate of HBeAg during pregnancy and the positive rate of HBV DNA during the third trimester in the case group were significantly higher than the control group (P \u0026lt;0.001). The proportion of cesarean section in the control group was significantly higher than that case group (P \u0026lt;0.001). (Table 1)\u003c/p\u003e\n\u003cp\u003eTable1 Comparison of general characteristics of pregnant women\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003ecase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003cem\u003e/t\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eMaternal Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003eN=107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003eN=1249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e28.00 ± 3.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e29.14 ± 4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003cp\u003e-2.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003ePre-pregnancy BMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e26.83 ± 3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e27.19 ± 3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003cp\u003e-1.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003ePrimiparous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003cp\u003e9.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e91 (85.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e899 (72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e15 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e344 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eHBeAg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003cp\u003e60.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e49 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e193 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e56 (53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e1020 (84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eHBV DNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003cp\u003e31.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e59 (61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e364 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003enegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e37 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e743 (67.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eInfant characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003eN=107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003eN=1249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e55 (51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e653 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eFamale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e52 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e596 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003ePre-term birth(\u0026lt;37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e5 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e73 (5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e102 (95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e1170 (94.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eDelivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003cp\u003e23.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eVaginal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e63 (58.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e440 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"31.304347826086957%\"\u003e\n \u003cp\u003eCesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e44 (41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"22.956521739130434%\"\u003e\n \u003cp\u003e805 (64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.478260869565217%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.304347826086957%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSNPs selection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e22 SNPs on 14 candidate genes were included in this study, as shown in Supplementary Table S1. The success rate of rs8126207 on the MAVS gene and rs311674 on the MB21D1 gene was less than 95%, and they were not included in the study. In addition, the Hardy-Weinberg balance test found that the distribution frequency of the genotypes of MAVS rs3746661, MB21D1 rs9359033, and TMEM173 rs1131769 in the control group did not meet the Hardy-Weinberg genetic balance (P \u0026gt; 0.05) and was also eliminated. A total of 17 gene loci were finally included in the subsequent analysis.\u003c/p\u003e\n\u003cp\u003eThe association between single gene locus polymorphism and intrauterine transmission\u003c/p\u003e\n\u003cp\u003eAfter adjusting for maternal delivery age, parity, HBeAg, HBV DNA, and delivery, the results of multivariate logistic regression analysis are shown in Table 2. Only statistically significant data are listed here, and basic information about these SNPs is shown in Supplementary Table S2.\u003c/p\u003e\n\u003cp\u003eIn the RIG-I/MAVS signaling pathway, compared with the INTS10 rs28413168 GG genotype, the risk of intrauterine transmission of HBV in infants carrying the CC/CG genotype at this site increased by 100% (Dominant model: OR = 2.00, 95% CI: 1.26-3.19, P = 0.004), additive model showed that for each additional C allele, the risk of intrauterine transmission of HBV in infants increased by 57% (OR = 1.57, 95% CI: 1.09-2.27, P = 0.016). In the cGAS-STING pathway, there was a significant difference in the genotype distribution of MB21D1 rs311678 between the two groups of infants (P = 0.041), under the dominant model, the risk of intrauterine transmission of HBV in infants with GG/AG genotype was significantly lower than that of infants with AA genotype (OR=0.48, 95% CI: 0.30-0.78, P = 0.003), additive model showed that for every additional G allele in an infant, the risk of intrauterine transmission of HBV decreased by 45% (OR = 0.55, 95% CI: 0.36-0.82, P = 0.003).\u003c/p\u003e\n\u003cp\u003eCompared with the IL10 rs1800872 CC genotype, infants with AA genotype had a significantly higher risk of intrauterine transmission of HBV\u0026nbsp;(OR = 3.01, 95% CI: 1.02-9.84, P = 0.047). The SNP at rs3775291 locus of TLR3 in infants was also closely related to the intrauterine transmission of HBV. Compared with the TT genotype, infants with CC genotype had a significantly lower risk of intrauterine transmission of HBV (OR = 0.44, 95% CI: 0.21-0.91, P = 0.027), under the recessive model, infants with TT genotype have a significantly higher risk of HBV intrauterine transmission than those with CT/CC genotype (OR = 2.09, 95% CI: 1.07-4.12 , P = 0.032).\u003c/p\u003e\n\u003cp\u003eTable 2 Association analysis of neonatal candidate gene locus polymorphisms and intrauterine transmission of HBV\u003c/p\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003ecase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003eOR (95% CI)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003cp\u003eINTS10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003ers28413168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003eN=1231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003eN=103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003cp\u003e0.160\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003e\u0026nbsp; CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003e55 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003e5 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003e\u0026nbsp; CG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003e402 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003e43 (41.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e0.78 (0.25-2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003e\u0026nbsp; GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003e774 (62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003e55 (53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e1.64 (0.53-5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eDominant model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.00 (1.26-3.19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eImplicit model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e0.96 (0.32-2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eAdditive model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.57 (1.09-2.27)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003cp\u003eMB21D1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003ers311678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003eN=1218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003eN=106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003e\u0026nbsp; GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003e100 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003e4 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003e\u0026nbsp; AG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003e492(40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003e35 (33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e2.67 (0.90-7.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003e\u0026nbsp; AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003e626 (51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003e67 (63.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e1.33 (0.43-4.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eDominant model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.48 (0.30-0.78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eImplicit model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e0.49 (0.17-1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eAdditive model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.55 (0.36-0.82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003cp\u003eIL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003ers1800872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003eN=1218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003eN=104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003e\u0026nbsp; CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003e109 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003e7 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003e\u0026nbsp; CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003e525 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003e45 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e2.44 (0.82-7.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003e\u0026nbsp; AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003e584 (47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003e52 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e3.01 (1.02-9.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eDominant model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e0.72 (0.45-1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eImplicit model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e0.37 (0.13-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eAdditive model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e0.69 (0.48-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003cp\u003eTLR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003ers3775291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003eN=1215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003eN=98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003e\u0026nbsp; TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003e116 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003e19 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003e\u0026nbsp; CT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003e549 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003e36 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e0.52 (0.25-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003e\u0026nbsp; CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003cp\u003e550 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003cp\u003e43 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.44 (0.21-0.91)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eDominant model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e1.02 (0.64-1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eImplicit model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.09 (1.07-4.12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"8.150851581508515%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.0316301703163%\"\u003e\n \u003cp\u003eAdditive model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.909975669099758%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.503649635036496%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.975669099756692%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.425790754257907%\"\u003e\n \u003cp\u003e1.21 (0.85-1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.002433090024331%\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;* Fisher's exact test.\u003c/p\u003e\n\u003cp\u003ea P-value obtained from chi-square test\u003c/p\u003e\n\u003cp\u003e† OR and 95% CI obtained from the logistic regression model, adjusted for maternal age, parity, HBeAg, HBV DNA, and delivery\u003c/p\u003e\n\u003cp\u003eb P-value obtained from the logistic regression model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultigene cumulative effect analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cumulative effect of the four positive sites found in the correlation analysis between a single SNP and HBV intrauterine transmission was analyzed. According to the results of single-site association analysis, INTS10 rs28413168 C allele, MB21D1 rs311678 A allele, IL10 rs1800872 A allele, and TLR3 rs3775291 T allele were considered as risk genes. The number of risk alleles carried by each infant was calculated, and this variable was included in the logistic regression model. The age of delivery, parity, HBeAg, HBV DNA, and after delivery found that the risk of intrauterine transmission of HBV in infants carrying 5-8 risk alleles is 4.34 times that of carrying 0-2 risk alleles (95%CI: 1.44-13.09, P = 0.009). The linear trend test results showed that the risk of HBV intrauterine transmission increased as the number of risk alleles carried by infants increased (P = 0.030). (Table 3).\u003c/p\u003e\n\u003cp\u003eTable 3 Analysis of the cumulative effect of neonatal positive sites\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.010544815465728%\"\u003e\n \u003cp\u003eNumber of risk genotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.87170474516696%\"\u003e\n \u003cp\u003ecase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.87170474516696%\"\u003e\n \u003cp\u003econtrol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.1195079086116%\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.126537785588752%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.010544815465728%\"\u003e\n \u003cp\u003e0-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.87170474516696%\"\u003e\n \u003cp\u003e10 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.87170474516696%\"\u003e\n \u003cp\u003e190 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.1195079086116%\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.126537785588752%\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.010544815465728%\"\u003e\n \u003cp\u003e3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.87170474516696%\"\u003e\n \u003cp\u003e55 (53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.87170474516696%\"\u003e\n \u003cp\u003e653 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.1195079086116%\"\u003e\n \u003cp\u003e2.29 (0.78-6.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.126537785588752%\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.010544815465728%\"\u003e\n \u003cp\u003e5-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.87170474516696%\"\u003e\n \u003cp\u003e38 (36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.87170474516696%\"\u003e\n \u003cp\u003e348 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.1195079086116%\"\u003e\n \u003cp\u003e4.34 (1.44-13.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.126537785588752%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"26.010544815465728%\"\u003e\n \u003cp\u003elinear trend test\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.87170474516696%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.87170474516696%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.1195079086116%\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.126537785588752%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003e*Adjusted for maternal age, parity, HBeAg, HBV DNA, and delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eGene interaction analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 lists 3 different combinations of models. Taking the 1-site model MD21D1 rs311678 as an example, the cross-validation consistency is the largest (CVC value is 9/10), and the prediction error value is the smallest 45.1%. But after the 1000 permutation test, the model is not statistically significant. In addition to the two-site and three-site models, the P-value of the replacement test is also greater than 0.05, indicating that the 3 models are not statistically significant.\u003c/p\u003e\n\u003cp\u003eTable 4 Analysis of 17 SNPs Interaction\u003c/p\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.250883392226148%\"\u003e\n \u003cp\u003eNumber of SNPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"40.10600706713781%\"\u003e\n \u003cp\u003eBest model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.784452296819786%\"\u003e\n \u003cp\u003eTraining accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.021201413427562%\"\u003e\n \u003cp\u003eTest accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.837455830388693%\"\u003e\n \u003cp\u003eCVC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.250883392226148%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"40.10600706713781%\"\u003e\n \u003cp\u003ers311678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.784452296819786%\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.021201413427562%\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.837455830388693%\"\u003e\n \u003cp\u003e9/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.250883392226148%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"40.10600706713781%\"\u003e\n \u003cp\u003ers3212227, rs7447927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.784452296819786%\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.021201413427562%\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.837455830388693%\"\u003e\n \u003cp\u003e5/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.250883392226148%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"40.10600706713781%\"\u003e\n \u003cp\u003ers3212227,rs352140, rs7447927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.784452296819786%\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.021201413427562%\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.837455830388693%\"\u003e\n \u003cp\u003e4/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePolymorphism of key genes in RIG-I/MAVS signaling pathway\u003c/p\u003e \u003cp\u003eIn the RIG-I/MAVS signaling pathway, the INTS10 rs28413168 polymorphism was statistically associated with HBV intrauterine transmission. There is no research report on the association between the INTS10 rs28413168 gene polymorphism and intrauterine transmission of HBV or other diseases, but there have been related reports on the association study between the INTS10 gene polymorphisms at other loci and diseases. A South African study found that three polymorphisms (rs55830938, rs73599609, rs73667448) located between the INTS10/LPL gene are associated with systolic blood pressure[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The results of another American study suggest that the INTS10/LPL rs149352150 gene polymorphism is associated with male BMI[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eINTS10 is a member of the integration factor complex family, which can affect the splicing of RNA precursors, which in turn affects the synthesis and expression of mRNA in the cell. A study found that INST10 has an effect of inhibiting virus infection that has never been discovered before[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The study found that the INTS10 rs7000921 locus polymorphism is closely related to the chronicity of HBV infection through the genome-wide association study. Subsequently, the team also found in vitro cell culture experiments that INTS10 was significantly related to HBV replication. In the RIG-I/MAVS pathway, overexpression of INST10 can increase the phosphorylation of IRF3, which in turn activates the activity of downstream IFN-stimulated response element (ISRE), increases the expression level of IFN-λ, and ultimately inhibits HBV replication. Since no functional study on INST10 rs28413168 has been found so far, we found that this site is located in the 5'non-transcribed region through the Ensembl database, suggesting that this site may be involved in the translation, transcription, and stability maintenance of INST10 mRNA. Through the website of rSNP and Fast SNP gene function prediction, our study found that INST10 rs28413168 gene polymorphism may directly participate in gene transcription regulation, and then affect the anti-HBV function of INST10 in the body.\u003c/p\u003e \u003cp\u003ePolymorphism of key genes in cGAS-STING signaling pathway\u003c/p\u003e \u003cp\u003eIn the cGAS-STING pathway, there were significant differences in the genotype distribution of MB21D1 rs311678 infants between the case group and the control group. MB21D1, also known as cGAS, can be used as a cytoplasmic DNA sensor to participate in the recognition of different viruses. The combination of cGAS and DNAs can activate the interferon-stimulating gene STING, which in turn stimulates the production of IFN-I and antiviral ISGs. The lack of cGAS will lead to the reduction of the early antiviral inherent response to the body's viral infection[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Dansako[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] reported that cGAS and its adaptor protein (STING) recognize and bind dsDNA derived from the HBV genome to generate an innate immune response. In addition, the study also proved that HBV infection can induce the production of interferon-inducible gene 56 (ISG56) through the cGAS-STING signaling pathway, thereby inhibiting the assembly of HBV virus particles. A Japanese study found that daunorubicin can trigger an endogenous cGAS-dependent innate immune response to inhibit the production of HBV[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, the antiviral effect induced by the cGAS-STING signaling pathway plays an important role in identifying and controlling HBV infection.\u003c/p\u003e \u003cp\u003ers311678 is located in the 5th exon coding region of the cGAS gene. The mutation at this site is synonymous and will not cause any changes in the amino acids it encodes. However, Xiao[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] reported that the cGAS rs311678 locus polymorphism is associated with cervical precancerous lesions, and the cGAS rs311678 G allele reduces the risk of cervical precancerous lesions. Our research used the SNP function prediction website rSNP for function prediction, and the results suggested that this site is involved in post-transcriptional regulation and can bind to a variety of RNA-binding proteins, thereby affecting the expression level and function of cGAS.\u003c/p\u003e \u003cp\u003ePolymorphism of IL-10 genes\u003c/p\u003e \u003cp\u003ePrevious studies have shown that IL-10 can regulate the immune response and inflammatory response by inhibiting the activity of antigen-presenting cells[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In addition, IL-10 can inhibit the maturation of various immune cells and the production of cytokines, reducing the immune response level[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The IL-10 promoter is highly polymorphic, but currently, only the rs1800871 (-819) and rs1800872 (-592) polymorphisms in the promoter region have been extensively studied. The main reason is that these two sites are respectively related to the Msll and Rsal restriction enzyme (re) sites, which may change the activity of the IL-10 gene[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. It is reported that IL-10 promoter region polymorphism can affect IL-10 gene transcription and translation, leading to abnormal cell proliferation and cancer development[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Wu[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] reported that IL-10 rs1800872 (-592a/c) gene polymorphism is associated with spontaneous HBsAg seroconversion and good outcome of hepatitis B. Compared with hepatitis B carriers of CA and CC genotypes, patients with IL-10 rs1800872 AA genotype have higher serum IL-10 levels. In addition, Li[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] found that IL-10 levels in patients with chronic hepatitis B were significantly higher than those in the general population. These results were confirmed by another research[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, a Japanese study showed that the IL-10rs1800872 genotype is not significantly associated with the risk of HBV infection[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Our study found that compared with infants with CC genotype, infants with AA genotype at this locus have a significantly higher risk of intrauterine transmission of HBV. We speculate that infants with the AA genotype may have higher levels of IL-10, which inhibits the maturation of various immune cells and the synthesis of cytokines. This will reduce the body's immune response level, leading to a significant increase in the risk of HBV intrauterine infection.\u003c/p\u003e \u003cp\u003ePolymorphism of TLR3 genes\u003c/p\u003e \u003cp\u003eTLR3 can recognize type I IFN, double-stranded RNA (dsRNA) viruses and dsRNA produced during virus replication[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], activate IRF3 and NF-κB, and also promote the expression of IFN-β and other pro-inflammatory factors[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. TLR3 is mainly expressed in hepatocytes, macrophages, NK cells, and biliary epithelial cells, located in the plasma membrane or acidic endosome[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. And macrophages and NK cells play a vital role in the process of clearing HBV[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In addition, during HBV infection, TLR3 can activate hepatic non-parenchymal cells, produce interferon β, and inhibit virus replication[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study found that the SNP at rs3775291 of neonatal TLR3 is closely related to the intrauterine transmission of HBV, which is similar to the results of previous studies. Fischer[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] found that compared with HBeAg-positive patients, the CC genotype of TLR3 rs3775291 appeared more frequently in patients with HBeAg seroclearance, while the TT genotype was more common in HBeAg-positive patients. This further supports that the rs3775291 mutation is related to the innate immune response during HBV infection. In addition, Rong's[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] research also showed that compared with the CC genotype, subjects carrying CT genotype and TT genotype at this locus had an increased risk of chronic HBV infection by 1.42 folds and 2.31 folds, respectively. The specific mechanism of this phenomenon is not yet clear. It may be that mutations at this site cause changes in protein or gene expression, affecting the function and efficiency of signal transduction and thereby altering the immune response.\u003c/p\u003e \u003cp\u003eSNPs rs3775291 is a non-synonymous mutant gene locus located in the fourth exon region of TLR3. The mutation at this locus reduces the location of the soluble outer domain and the dimerization of TLR3 on the membrane, resulting in a decrease in its ability to bind to dsRNA and the signal transduction efficiency. Svensson A[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] detected the expression level of TLR3 mRNA in peripheral blood mononuclear cells (PBMC) of multiple patients infected with human herpesvirus type 2 (HSV-2). It was found that the appearance of the T allele increased the expression of TLR3 mRNA in PBMC relative to the allele C. However, a study showed that the polymorphism of TLR3 rs3775291 locus does not affect the expression of TLR3 in the cell and the localization in the cell, but it will affect the activity of TLR3 and the expression of the receptor on the cell surface[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultiple genes and HBV intrauterine transmission\u003c/p\u003e \u003cp\u003eAccording to the theory of common diseases-common mutations, common complex diseases are affected by multiple common mutations, and the effect of each common mutation is weak. As a complex disease, the MTCT of HBV is often affected by multiple genes or multiple sites during its occurrence and development. These genes or loci may interact with each other. This study explored the cumulative effect of multiple positive gene loci on the risk of HBV intrauterine transmission. It was found that infants carrying the INTS10 rs28413168 C allele, MB21D1 rs311678 A allele, IL10 rs1800872 A allele, and TLR3 rs3775291 T allele at the same time significantly increased the risk of intrauterine transmission of HBV. This suggested that these four sites may have an additive effect on the risk of HBV intrauterine transmission, but the specific biological mechanism depends on the elucidation of the function of each gene and in-depth functional experimental research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eInfants carrying the INTS10 rs28413168 C allele, MB21D1 rs311678 A allele, IL10 rs1800872 A allele, and TLR3 rs3775291 T allele will significantly increase the risk of HBV intrauterine infection. The above four sites may have an additive effect on the risk of HBV intrauterine transmission, but no gene-gene interaction between these genes has been found.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Tongji Hospital, Tongji Medical College, HUST. Written informed consent was obtained from individual or guardian participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article was published with the consent of all the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupported by: This work was funded by the National Natural Science Foundation of China, grant number (81872632 and 82103861).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSuihan Liu and Rufang Wu wrote the paper, Songxu Peng and Xukuan Zhang revised the paper, Yukai Du conceived of and revised the paper.\u003c/p\u003e\n\u003cp\u003e*These authors contributed equally this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSuihan Liu, Xukuan Zhang, Yukai Du, Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, China\u003c/p\u003e\n\u003cp\u003eRufang Wu, Department of Obstetrics and Gynecology, the Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China\u003c/p\u003e\n\u003cp\u003eSongxu Peng, Department of Maternal and Child Health, Xiangya School of Public Health, Central South University, No. 110, Xiangya Road, Changsha, 410008, China\u003c/p\u003e\n\u003cp\u003eCorresponding author: Yukai Du, PhD, MD, Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan 430030, China\u003c/p\u003e\n\u003cp\[email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTr\u0026eacute;po C, Chan HLY, Lok A: \u003cb\u003eHepatitis B virus infection\u003c/b\u003e. Lancet (London, England) 2014, \u003cb\u003e384\u003c/b\u003e(9959):2053\u0026ndash;2063.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThio CL, Guo N, Xie C, Nelson KE, Ehrhardt S: \u003cb\u003eGlobal elimination of mother-to-child transmission of hepatitis B: revisiting the current strategy\u003c/b\u003e. Lancet Infect Dis 2015, \u003cb\u003e15\u003c/b\u003e(8):981\u0026ndash;985.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen Y, Guo Y, Feng L, Li T, Du Y: \u003cb\u003eControversy and Strategies Exploration in Blocking Mother-to-Child Transmission of Hepatitis B\u003c/b\u003e. Int Rev Immunol 2016, \u003cb\u003e35\u003c/b\u003e(3):249\u0026ndash;259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Liu H, Wang Y, Hao R, Li Z, Song H: \u003cb\u003eThe next step in controlling HBV in China\u003c/b\u003e. BMJ (Clinical research ed) 2013, \u003cb\u003e347\u003c/b\u003e:f4503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Y, Guo J, Zhang F, Guo Z, Zhang LR, Wang T, Wang B, Feng SY, Wang SP: \u003cb\u003eEvaluation of neonatal Toll-like receptors 3 (c.1377C/T) and 9 (G2848A) gene polymorphisms in HBV intrauterine transmission susceptibility\u003c/b\u003e. Epidemiol Infect 2015, \u003cb\u003e143\u003c/b\u003e(9):1868\u0026ndash;1875.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatzidaki V, Choumerianou D, Dimitriou H, Kouroumalis E, Galanakis E: \u003cb\u003eGenetic variants associated with susceptibility to mother-to-child transmission of hepatitis B virus\u003c/b\u003e. Eur J Gastroenterol Hepatol 2012, \u003cb\u003e24\u003c/b\u003e(10):1185\u0026ndash;1190.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu T, Wan Z, Peng S, Wang Y, Chen H, Li X, Du Y: \u003cb\u003eGenetic variations in LTA gene and PDCD1 gene and intrauterine infection of hepatitis B virus: a case-control study in China\u003c/b\u003e. Amino Acids 2018, \u003cb\u003e50\u003c/b\u003e(7):877\u0026ndash;883.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan Z, Lin X, Li T, Zhou A, Yang M, Hu D, Feng L, Peng S, Fan L, Tu S \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eGenetic variant in CXCL13 gene is associated with susceptibility to intrauterine infection of hepatitis B virus\u003c/b\u003e. Sci Rep 2016, \u003cb\u003e6\u003c/b\u003e:26465.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu X-X, Wan H, Nie L, Shao T, Xiang L-X, Shao J-Z: \u003cb\u003eRIG-I: a multifunctional protein beyond a pattern recognition receptor\u003c/b\u003e. Protein Cell 2018, \u003cb\u003e9\u003c/b\u003e(3):246\u0026ndash;253.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSato S, Li K, Kameyama T, Hayashi T, Ishida Y, Murakami S, Watanabe T, Iijima S, Sakurai Y, Watashi K \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eThe RNA sensor RIG-I dually functions as an innate sensor and direct antiviral factor for hepatitis B virus\u003c/b\u003e. Immunity 2015, \u003cb\u003e42\u003c/b\u003e(1):123\u0026ndash;132.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall J, Ralph EC, Shanker S, Wang H, Byrnes LJ, Horst R, Wong J, Brault A, Dumlao D, Smith JF \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eThe catalytic mechanism of cyclic GMP-AMP synthase (cGAS) and implications for innate immunity and inhibition\u003c/b\u003e. Protein Sci 2017, \u003cb\u003e26\u003c/b\u003e(12):2367\u0026ndash;2380.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato K, Omura H, Ishitani R, Nureki O: \u003cb\u003eCyclic GMP-AMP as an Endogenous Second Messenger in Innate Immune Signaling by Cytosolic DNA\u003c/b\u003e. Annu Rev Biochem 2017, \u003cb\u003e86\u003c/b\u003e:541\u0026ndash;566.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerrier ER, Yim S-A, Heydmann L, El Saghire H, Bach C, Turon-Lagot V, Mailly L, Durand SC, Lucifora J, Durantel D \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eHepatitis B Virus Evasion From Cyclic Guanosine Monophosphate-Adenosine Monophosphate Synthase Sensing in Human Hepatocytes\u003c/b\u003e. Hepatology 2018, \u003cb\u003e68\u003c/b\u003e(5):1695\u0026ndash;1709.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe J, Hao R, Liu D, Liu X, Wu S, Guo S, Wang Y, Tien P, Guo D: \u003cb\u003eInhibition of hepatitis B virus replication by activation of the cGAS-STING pathway\u003c/b\u003e. J Gen Virol 2016, \u003cb\u003e97\u003c/b\u003e(12):3368\u0026ndash;3378.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawai T, Akira S: \u003cb\u003eThe role of pattern-recognition receptors in innate immunity: update on Toll-like receptors\u003c/b\u003e. Nat Immunol 2010, \u003cb\u003e11\u003c/b\u003e(5):373\u0026ndash;384.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang E, Lu M: \u003cb\u003eToll-like receptor (TLR)-mediated innate immune responses in the control of hepatitis B virus (HBV) infection\u003c/b\u003e. Med Microbiol Immunol 2015, \u003cb\u003e204\u003c/b\u003e(1):11\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahrakyvahed A, Sanchooli J, Sanadgol N, Arababadi MK, Kennedy D: \u003cb\u003eTLR9: an important molecule in the fight against hepatitis B virus\u003c/b\u003e. Postgrad Med J 2014, \u003cb\u003e90\u003c/b\u003e(1065):396\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChihab H, Zaidane I, Elhabazi A, Jadid F-Z, El Fihri R, Elmessaoudi-Idrissi M, Chair M, Badre W, Tahiri M, Pineau P \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eToll-like receptor 9 polymorphisms and Hepatitis B virus clearance in Moroccan chronic carriers\u003c/b\u003e. Gene 2019, \u003cb\u003e687\u003c/b\u003e:212\u0026ndash;218.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEzzEl-Din AM, Galal SH, Ahmed AO, Kamel SI, Kamal DT: \u003cb\u003eToll Like Receptor 3 Polymorphisms in Hepatitis B Virus Infection\u003c/b\u003e. Egypt J Immunol 2017, \u003cb\u003e24\u003c/b\u003e(2):173\u0026ndash;185.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang B, Liu Y, Wang X, Li J, Xu X, Guo L, Ho W-Z: \u003cb\u003eTLR3 Activation of Hepatic Stellate Cell Line Suppresses HBV Replication in HepG2 Cells\u003c/b\u003e. Front Immunol 2018, \u003cb\u003e9\u003c/b\u003e:2921.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang H, Cao F, Cao H, Rao Q, Yang Y: \u003cb\u003eAssociations of human leukocyte antigen and interleukin-18 gene polymorphisms with viral load in patients with hepatitis B infection\u003c/b\u003e. Medicine (Baltimore) 2018, \u003cb\u003e97\u003c/b\u003e(30):e11249.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Zhang X, Zhu B, Hu D, Wu J, Yu R, Zhao W: \u003cb\u003eRelationships between tumour necrosis factor-α, interleukin-12B and interleukin-10 gene polymorphisms and hepatitis B in Chinese Han haemodialysis patients\u003c/b\u003e. Nephrology (Carlton) 2012, \u003cb\u003e17\u003c/b\u003e(2):167\u0026ndash;174.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertoletti A, Ferrari C: \u003cb\u003eAdaptive immunity in HBV infection\u003c/b\u003e. J Hepatol 2016, \u003cb\u003e64\u003c/b\u003e(1 Suppl):S71-S83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLucifora J, Xia Y, Reisinger F, Zhang K, Stadler D, Cheng X, Sprinzl MF, Koppensteiner H, Makowska Z, Volz T \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eSpecific and nonhepatotoxic degradation of nuclear hepatitis B virus cccDNA\u003c/b\u003e. Science 2014, \u003cb\u003e343\u003c/b\u003e(6176):1221\u0026ndash;1228.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong R, Shao S, Wang J, Zhang X, Guo S, Zou L, Zhong R, Lou J, Zhou J, Zhang J \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eGenetic variant in DIP2A gene is associated with developmental dyslexia in Chinese population\u003c/b\u003e. Am J Med Genet B Neuropsychiatr Genet 2016, \u003cb\u003e171B\u003c/b\u003e(2):203\u0026ndash;208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao S, Kong R, Zou L, Zhong R, Lou J, Zhou J, Guo S, Wang J, Zhang X, Zhang J \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eThe Roles of Genes in the Neuronal Migration and Neurite Outgrowth Network in Developmental Dyslexia: Single- and Multiple-Risk Genetic Variants\u003c/b\u003e. Mol Neurobiol 2016, \u003cb\u003e53\u003c/b\u003e(6):3967\u0026ndash;3975.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHendry LM, Sahibdeen V, Choudhury A, Norris SA, Ramsay M, Lombard Z: \u003cb\u003eInsights into the genetics of blood pressure in black South African individuals: the Birth to Twenty cohort\u003c/b\u003e. BMC Med Genomics 2018, \u003cb\u003e11\u003c/b\u003e(1):2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg MCY, Graff M, Lu Y, Justice AE, Mudgal P, Liu C-T, Young K, Yanek LR, Feitosa MF, Wojczynski MK \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eDiscovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium\u003c/b\u003e. PLoS Genet 2017, \u003cb\u003e13\u003c/b\u003e(4):e1006719.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Si L, Zhai Y, Hu Y, Hu Z, Bei J-X, Xie B, Ren Q, Cao P, Yang F \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eGenome-wide association study identifies 8p21.3 associated with persistent hepatitis B virus infection among Chinese\u003c/b\u003e. Nat Commun 2016, \u003cb\u003e7\u003c/b\u003e:11664.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnghelina D, Lam E, Falck-Pedersen E: \u003cb\u003eDiminished Innate Antiviral Response to Adenovirus Vectors in cGAS/STING-Deficient Mice Minimally Impacts Adaptive Immunity\u003c/b\u003e. J Virol 2016, \u003cb\u003e90\u003c/b\u003e(13):5915\u0026ndash;5927.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDansako H, Ueda Y, Okumura N, Satoh S, Sugiyama M, Mizokami M, Ikeda M, Kato N: \u003cb\u003eThe cyclic GMP-AMP synthetase-STING signaling pathway is required for both the innate immune response against HBV and the suppression of HBV assembly\u003c/b\u003e. FEBS J 2016, \u003cb\u003e283\u003c/b\u003e(1):144\u0026ndash;156.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImai H, Dansako H, Ueda Y, Satoh S, Kato N: \u003cb\u003eDaunorubicin, a topoisomerase II poison, suppresses viral production of hepatitis B virus by inducing cGAS-dependent innate immune response\u003c/b\u003e. Biochem Biophys Res Commun 2018, \u003cb\u003e504\u003c/b\u003e(4):672\u0026ndash;678.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao D, Huang W, Ou M, Guo C, Ye X, Liu Y, Wang M, Zhang B, Zhang N, Huang S \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eInteraction between susceptibility loci in cGAS-STING pathway, MHC gene and HPV infection on the risk of cervical precancerous lesions in Chinese population\u003c/b\u003e. Oncotarget 2016, \u003cb\u003e7\u003c/b\u003e(51):84228\u0026ndash;84238.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore KW, de Waal Malefyt R, Coffman RL, O'Garra A: \u003cb\u003eInterleukin-10 and the interleukin-10 receptor\u003c/b\u003e. Annu Rev Immunol 2001, \u003cb\u003e19\u003c/b\u003e:683\u0026ndash;765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonnelly RP, Dickensheets H, Finbloom DS: \u003cb\u003eThe interleukin-10 signal transduction pathway and regulation of gene expression in mononuclear phagocytes\u003c/b\u003e. J Interferon Cytokine Res 1999, \u003cb\u003e19\u003c/b\u003e(6):563\u0026ndash;573.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoghimi M, Ahrar H, Karimi-Zarchi M, Aghili K, Salari M, Zare-Shehneh M, Neamatzadeh H: \u003cb\u003eAssociation of IL-10 rs1800871 and rs1800872 Polymorphisms with Breast Cancer Risk: A Systematic Review and Meta-Analysis\u003c/b\u003e. Asian Pac J Cancer Prev 2018, \u003cb\u003e19\u003c/b\u003e(12):3353\u0026ndash;3359.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu Y-M, Du X-Y, Cai H-X, Zhang C, Yuan R-X, Zeng X-T, Luo J: \u003cb\u003eIncreased risks between Interleukin\u003c/b\u003e-\u003cb\u003e10 gene polymorphisms and haplotype and head and neck cancer\u003c/b\u003e: \u003cb\u003ea meta\u003c/b\u003e-\u003cb\u003eanalysis\u003c/b\u003e. \u003cem\u003eSci Rep\u003c/em\u003e 2015, \u003cb\u003e5\u003c/b\u003e:17149.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J-F, Hsu H-Y, Chiu Y-C, Chen H-L, Ni Y-H, Chang M-H: \u003cb\u003eThe effects of cytokines on spontaneous hepatitis B surface antigen seroconversion in chronic hepatitis B virus infection\u003c/b\u003e. J Immunol 2015, \u003cb\u003e194\u003c/b\u003e(2):690\u0026ndash;696.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M-H, Lu Y, Zhang L, Wang X-Y, Ran C-P, Hao H-X, Zhang D, Qu X-J, Shen G, Wu S-L \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eAssociation of Cytokines with Alanine Aminotransferase, Hepatitis B Virus Surface Antigen and Hepatitis B Envelope Antigen Levels in Chronic Hepatitis B\u003c/b\u003e. Chin Med J (Engl) 2018, \u003cb\u003e131\u003c/b\u003e(15):1813\u0026ndash;1818.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;zg\u0026uuml;ler M, Akbulut HH, Akbulut A: \u003cb\u003eEvaluation of Interleukin-10 Levels in Patients Diagnosed with Chronic Hepatitis\u003c/b\u003e. West Indian Med J 2015, \u003cb\u003e64\u003c/b\u003e(2):71\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiyazoe S, Hamasaki K, Nakata K, Kajiya Y, Kitajima K, Nakao K, Daikoku M, Yatsuhashi H, Koga M, Yano M \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eInfluence of interleukin-10 gene promoter polymorphisms on disease progression in patients chronically infected with hepatitis B virus\u003c/b\u003e. Am J Gastroenterol 2002, \u003cb\u003e97\u003c/b\u003e(8):2086\u0026ndash;2092.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarimi-Googheri M, Arababadi MK: \u003cb\u003eTLR3 plays significant roles against hepatitis B virus\u003c/b\u003e. Mol Biol Rep 2014, \u003cb\u003e41\u003c/b\u003e(5):3279\u0026ndash;3286.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOshiumi H, Matsumoto M, Funami K, Akazawa T, Seya T: \u003cb\u003eTICAM-1, an adaptor molecule that participates in Toll-like receptor 3-mediated interferon-beta induction\u003c/b\u003e. Nat Immunol 2003, \u003cb\u003e4\u003c/b\u003e(2):161\u0026ndash;167.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa Z, Zhang E, Yang D, Lu M: \u003cb\u003eContribution of Toll-like receptors to the control of hepatitis B virus infection by initiating antiviral innate responses and promoting specific adaptive immune responses\u003c/b\u003e. Cellular \u0026amp; molecular immunology 2015, \u003cb\u003e12\u003c/b\u003e(3):273\u0026ndash;282.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnolle PA, Gerken G: \u003cb\u003eLocal control of the immune response in the liver\u003c/b\u003e. Immunol Rev 2000, \u003cb\u003e174\u003c/b\u003e:21\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimura K, Kakimi K, Wieland S, Guidotti LG, Chisari FV: \u003cb\u003eActivated intrahepatic antigen-presenting cells inhibit hepatitis B virus replication in the liver of transgenic mice\u003c/b\u003e. J Immunol 2002, \u003cb\u003e169\u003c/b\u003e(9):5188\u0026ndash;5195.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Lu M, Meng Z, Trippler M, Broering R, Szczeponek A, Krux F, Dittmer U, Roggendorf M, Gerken G \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eToll-like receptor-mediated control of HBV replication by nonparenchymal liver cells in mice\u003c/b\u003e. Hepatology 2007, \u003cb\u003e46\u003c/b\u003e(6):1769\u0026ndash;1778.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFischer J, Koukoulioti E, Schott E, F\u0026uuml;l\u0026ouml;p B, Heyne R, Berg T, van B\u0026ouml;mmel F: \u003cb\u003ePolymorphisms in the Toll-like receptor 3 (TLR3) gene are associated with the natural course of hepatitis B virus infection in Caucasian population\u003c/b\u003e. Sci Rep 2018, \u003cb\u003e8\u003c/b\u003e(1):12737.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRong Y, Song H, You S, Zhu B, Zang H, Zhao Y, Li Y, Wan Z, Liu H, Zhang A \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eAssociation of Toll-like receptor 3 polymorphisms with chronic hepatitis B and hepatitis B-related acute-on-chronic liver failure\u003c/b\u003e. Inflammation 2013, \u003cb\u003e36\u003c/b\u003e(2):413\u0026ndash;418.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSvensson A, Tunb\u0026auml;ck P, Nordstr\u0026ouml;m I, Padyukov L, Eriksson K: \u003cb\u003ePolymorphisms in Toll-like receptor 3 confer natural resistance to human herpes simplex virus type 2 infection\u003c/b\u003e. J Gen Virol 2012, \u003cb\u003e93\u003c/b\u003e(Pt 8):1717\u0026ndash;1724.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRanjith-Kumar CT, Miller W, Sun J, Xiong J, Santos J, Yarbrough I, Lamb RJ, Mills J, Duffy KE, Hoose S \u003cem\u003eet al\u003c/em\u003e: \u003cb\u003eEffects of single nucleotide polymorphisms on Toll-like receptor 3 activity and expression in cultured cells\u003c/b\u003e. The Journal of biological chemistry 2007, \u003cb\u003e282\u003c/b\u003e(24):17696\u0026ndash;17705.\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":"RLR-MAVS, cGAS-STING, genetic variation, HBV, intrauterine transmission","lastPublishedDoi":"10.21203/rs.3.rs-1306080/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1306080/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHepatitis B virus is a major public health problem worldwide. The genetic susceptibility of HBV intrauterine transmission has also attracted extensive attention from researchers. We explored the relationship between genetic variation of genes in RLR-MAVS and cGAS-STING signaling pathway and other genes with intrauterine transmission.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A case-control study was performed on HBsAg-positive pregnant women who delivered from January 2013 to August 2017 at Wuhan Medical Care Center for Women and Children, Wuhan, China. In total, 1356 infants born from those women were included, and there were 107 infants with HBV intrauterine infection. RLR-MAVS and cGAS-STING signaling pathway-related genes and IL10, IL12A, IL12B, TLR3, TLR9, TNFSF14 were selected as candidates. MassARRAY gene detection system was used for genotyping. Multivariate unconditional logistic regression, cumulative effect analysis, and multifactor dimensionality reduction were used for statistical analysis.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThe risk of HBV intrauterine transmission in infants with rs28413168 CC/CG genotype in INTS10 gene increased by 100% compared with those with GG genotype, and GG/AG genotype in MB21D1 gene was significantly lower than that in infants with AA genotype. Compared with the rs1800872 CC genotype in IL10 gene, the risk of HBV intrauterine transmission in infants with AA genotype was significantly increased. Compared with the rs3775291 CT/CC genotype in TLR3 gene, infants with TT genotype had significantly increased risk of HBV intrauterine transmission. The risk of HBV intrauterine transmission in infants with 5\u0026ndash;8 risk alleles was 4.34 times higher than that in those with 0\u0026ndash;2 risk alleles after adjustment for the age of delivery, parity, HBeAg, HBV DNA.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe genetic polymorphisms of INTS10 rs28413168, MB21D1 rs311678, IL10 rs1800872, TLR3 rs3775291 are closely related to the susceptibility of HBV intrauterine transmission. The more risk alleles an infant carries, the higher the risk of HBV intrauterine transmission.\u003c/p\u003e","manuscriptTitle":"The relationship between genetic variation of genes in RLR-MAVS and cGAS-STING signaling pathway and other genes with HBV intrauterine transmission","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-03-30 19:11:01","doi":"10.21203/rs.3.rs-1306080/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":"ed1f2b76-a018-4bb2-9b02-ae58ca5f32e0","owner":[],"postedDate":"March 30th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-03-30T19:11:03+00:00","versionOfRecord":[],"versionCreatedAt":"2022-03-30 19:11:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1306080","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1306080","identity":"rs-1306080","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}