VEGF Gene Polymorphism And Study On Genetic Susceptibility Of Neuropsychiatric Lupus

VEGF Gene Polymorphism And Study On Genetic Susceptibility Of Neuropsychiatric Lupus | 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 VEGF Gene Polymorphism And Study On Genetic Susceptibility Of Neuropsychiatric Lupus 阳丽华 Yang, Min Jin, zhiang Liu, wei Zhou, Xi Li, Jian Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3998408/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: Vascular endothelial growth factor ( VEGF ) plays an important role in the pathogenesis of neuropsychiatric lupus ( NPSLE ), This study was designed for analysis of the relationship between single nucleotide polymorphisms of VEGF gene rs699947, rs10434, rs833070 and genetic susceptibility to NPSLE. Methods: A total of 82 patients diagnosed as NPSLE and without other autoimmune diseases were selected as the NPSLE group (positive group). Non-NPSLE group (positive control group) 166 patients with systemic lupus erythematosus without psychiatric symptoms. According to the principle of age matching with the positive control group, 150 healthy subjects were randomly selectedas the healthy control group (negative control group). The allele and genotype of three SNP loci and the correlation between NPSLE and non-NPSLE were analyzed. Results: 1. The genotype frequencies of GG, GA and AA genotypesat rs10434 locus were statistically significant in NPSLE group, non-NPSLE group and negative control group ( P 0.05). 3. rs10434 locus GG genotype and G gene as reference, A allele, mutant heterozygous model GA, dominant model (GA+AA ) were associated with therisk of NPSLE ( P < 0.05). Conclusion: We found that VEGF rs10434 A allele, mutant heterozygous model GA, dominant model GA+AA can increase the risk of NPSLE. A allele and dominant model GA+AA can increase the risk of non-NPSLE. There was no significant correlation between rs699947 and rs833070 polymorphism and the risk of NPSLE and non-NPSLE. VEGF Neuropsychiatric lupus Gene polymorphism Genetic susceptibility Figures Figure 1 1. Introduction Systemic lupus erythematosus (SLE) is an autoimmune disease involving a variety of immune disorders. Systemic multi-system and multi-organ involvement, repeated attacks and remission alternately, and the formation of a large number of autoantibodies and complements and immune complexes are the characteristics of SLE. Lesions mainly involve skin, joints, nervous system, kidney, lung, scrosal cavity and other parts [ 1 ]. SLE with neurological and/or psychiatric symptoms involving the nervous system, known as neuropsychiatric lupus (NPSLE) [ 2 ]. According to the literature data review showed that the prevalence of SLE with depression was 24% [ 3 ], the incidence of mental disorders at home and abroad were 20% -45% and 19% -38% [ 4 ]. Vascular endothelial growth factor (VEGF), also known as vascular permeability factor (VPF) or angiopoietin, is a kind of angiogenic mitogen and belongs to the family of vasoactive growth factors. It is a glycoprotein with high specificity that promotes the growth of vascular endothelial cells isolated from pituitary follicular cells,it can make extracellular matrix degeneration, promote angiogenesis and increase vascular permeability [ 5 ], can significantly induce and promote vascular endothelial cell division and migration, proliferation and promote the proliferation of lymphatic system [ 6 ], provide the basis for lymphatic metastasis [ 7 ]. There are at least 30 single nucleotide polymorphism sites (SNPs) in the VEGF gene. By changing some polymorphism sites, the transcription level of VEGF can be changed and the protein expression can be affected, resulting in differences in genetic susceptibility of individuals to diseases. We selected rs699947 (promoter), rs10434 and rs833070 (intron) as marker SNPs, which affected the normal brain morphology of healthy individuals [ 8 ]. This study aims to explore the relationship between the SNP of VEGF rs699947, rs10434 and rs833070 in NPSLE patients and neuropsychiatric manifestations related to lupus. 2. Materials and Methods Patients who met the diagnostic criteria of Classification Criteria for Systemic Lupus Erythematosus (1999 Edition) in the First Affiliated Hospital of Guangxi Medical University and the People's Hospital of Guangxi Zhuang Autonomous Region during the past 10 years from August 2020 to December 2020 were selected as the analysis objects. A total of 82 patients diagnosed as NPSLE and without other autoimmune diseases were selected as the NPSLE group (positive group). Non-NPSLE group (positive control group) 166 patients with systemic lupus erythematosus without psychiatric symptoms. According to the principle of age matching with the positive control group, 150 Healthy subjects from the physical examination center of Guangxi Zhuang Autonomous Region People's Hospital were selected as the healthy control group. Exclusion criteria: no previous cancer, heart, liver, spleen, lung and kidney, cachexia and other serious diseases. Women exclude special periods such as pregnancy and lactation. All experimental protocols were approved by the Ethics Committee of 923 Hospital Ethics Review and informed consent of all subjects was obtained. The age, medical history and other data of all patients was collected. The subjects were fasting venous blood 2 mL in EDTA-K 2 tube from 8:00 am to 9:00 am in the morning, fully mixed and stored in -80°C refrigerator. The sample DNA was extracted with AxyPrep blood genomic DNA kit (Ai Sijin Biotechnology (Hangzhou) Co., Ltd.). The qualified samples were DNA concentration greater than 10 ng / µL and OD260 / OD280 in the range of 1.7–2.0. The qualified samples were detected by multiple SNaPshot detection method to detect the SNP distribution of the three sites of VEGF gene rs699947, rs10434 and rs833070. Extension primers of the three sites are shown in Table 1 . The amplified products of rs699947, rs10434 and rs833070 of VEGF gene were 362 bp, 224 bp and 536 bp, respectively, and the electrophoretic result Marker of any 4–11 samples were shown in Fig. 1 . Table 1 SnaPshot PCR extension primers RS Primer name Sequence (5'to3') Product size (bp) RS Sequence primer direction Ntsadded rs699947 rs699947 TTTTTTTTTTTTTTTTTCGATCTGATTATCCACCCGAATC 40 R G/T rs10434 rs10434 TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTCGGGACATCTGCCGATGG 64 R C/T rs833070 rs833070 TTTTTTTTTTTTTTTTTTTTTTTTTTTTTACAGCACCCGAACATGATCAA 50 F A/G Note : F is forward primer, R is reverse primer PCR amplification program as follows: 95°C 15 min, 94°C 40s, 63°C 60s, 72°C 8 min, 15 cycles; 94°C 40 s, 56°C 40 s, 72°C 1.5min, 25cycles; 72°C for 8 min. 2.2 Statistical Analysis Hardy-Weinberg genetic balance test was used to detect the genetic balance of population genes. When P > 0.05, the genetic balance of population genes was indicated, and the samples were well representative. Statistical analysis using SPSS 23.0 software, P < 0.05 indicated that the difference was statistically significant. Enumeration data were tested by χ 2 test or Fisher exact probability method. Analysis of variance was used for age comparison among groups. The K-S normality test was performed on the data for each group, and the measurement data subject to normal distribution were expressed as (X̄± s). The ratio between groups was analyzed by one-way analysis of variance, and the LSD test was used for pairwise comparison between groups. Correlation between VEGF gene polymorphism and NPSLE risk was analyzed by binary Logistics regression, and OR value and 95% CI were calculated. 3. Results 3.1 Characteristics of the study population A total of 398 subjects underwent genetic analysis and were divided into three groups: 82 in the NPSLE group, 166 in the non-NPSLE group and 150 in the healthy control group. The characteristics of the three groups of cases and the control group are shown in Table 2 . The age distribution difference was statistically significant ( P < 0.05), indicating that the case group and the control group are comparable in the study population. Table 2 Results of one-way analysis of variance for age among groups Groups Gender / age NPSLE group N = 82(%) non-NPSLE group N = 166(%) negative control group N = 150(%) χ 2 /F P male 11(13.4) 24(14.5) 28(18.7) female 71(86.6) 142(85.5) 122(81.3) 1.932 0.381 Average age ( years ) 36.68 ± 14.41 35.62 ± 13.22 34.83 ± 7.45 244.16 0.000 3.2 Comparison of genotype and allele frequencies among three groups There were two alleles A and C at rs699947, and there were three genotypes: Wild homozygous AA, mutant heterozygous AC and mutant homozygous CC. Statistical analysis showed that there was no significant difference in genotype frequency (χ 2 = 8.453, P = 0.076), and there was no significant difference in allele frequencies of A and C (χ 2 = 0.315, P = 0.854), as shown in Table 3 . Hardy-Weinberg test results showed that the rs699947 and rs10434 loci did not conform to the HWE equilibrium analysis ( P 0.05). Table 3 Frequency distribution of genotype and allele at rs699947 locus in each group Groups NPSLE group N = 82(%) non-NPSLE group N = 166(%) negative control group N = 150(%) χ 2 P genotype frequency 8.453 0.076 AA 9(11.0) 8(4.8) 14(9.3) AC 53(64.6) 134(80.7) 110(73.4) CC 20(24.4) 24(14.5) 26(17.3) allele frequency 0.315 0.854 A 71(43.3) 150(45.2) 138(46.0) C 93(56.7) 182(54.8) 162(54.0) 3.2.1 Comparison of genotype and allele frequencies at rs10434 locus in each group There are G and A alleles in rs10434 locus, and there are three genotypes of wild homozygote GG, mutant heterozygote GA and mutant homozygote AA. After statistical analysis, the genotype frequency difference was statistically significant ( χ 2 = 31.476, P = 0.000 ), G and A allele frequency difference was not statistically significant ( χ 2 = 1.184, P = 0.553 ), see Table 4 . Table 4 Frequency distribution of genotype and allele at rs10434 locus in each group Groups NPSLE group N = 82(%) non-NPSLE group N = 166(%) negative control group N = 150(%) χ 2 P genotype frequency 31.476 0.000 GG 13(15.9) 48(28.9) 73(48.7) GA 66(80.5) 105(63.3) 71(47.3) AA 3(3.6) 13(7.8) 6(4.0) allele frequency 1.184 0.553 G 92(56.1) 201(60.5) 217(72.3) A 72(43.9) 131(39.5) 162(27.7) 3.2.2 Comparison of genotype and allele frequencies of VEGF gene rs833070 locus in each group There are two alleles G and A at rs833070. There are three genotypes GG, GA and AA. Statistical analysis showed that there was no significant difference in genotype frequency ( χ 2 = 7.264, P = 0.123 ), and there was no significant difference in allele frequencies of G and A ( χ 2 = 1.419, P = 0.492 ), as shown in Table 5 . Table 5 Frequency distribution of genotype and allele at rs833070 locus in each group Groups NPSLE group N = 82(%) non-NPSLE group N = 166(%) negative control group N = 150(%) χ 2 P genotype frequency 7.264 0.123 GG 43(52.4) 90(54.2) 92(61.3) GA 31(37.8) 69(41.6) 45(30.0) AA 8(9.8) 7(4.2) 13(8.7) allele frequency 1.419 0.492 G 117(71.3) 249(75.0) 229(76.3) A 47(28.7) 83(25.0) 71(23.7) 3.3 Three-site gene mutations and risk assessment of NPSLE 3.3.1 Risk Assessment of the VEGF gene in NPSLE group and control group Logistic regression analysis was used to correct the age and gender factors, and the risk assessment of the gene models at three loci in the NPSLE group and the negative control group was analyzed. The results are shown in Table 6 . Table 6 Risk assessment of VEGF gene in NPSLE group and control group Site NPSLE group N = 82(%) negative control group N = 150(%) P OR OR(95%CI) rs699947 AA 9(11.0) 14(9.3) referene AC 53(64.6) 110(73.3) 0.637 0.800 0.315–2.027 CC 20(4.4) 26(17.4) 0.500 1.436 0.501–4.116 A allele 71(43.3) 138(46.0) reference C allele 93(56.7) 162(54.0) 0.418 1.174 0.796–1.732 dominant model AA 9(11.0) 14(9.3) reference AC + CC 73(89.0) 136(90.7) 0.858 0.920 0.370–2.290 implicit model AC + AA 62(95.6) 124(82.6) reference CC 20(4.4) 26(17.4) 0.111 1.743 0.880–3.452 rs10434 GG 13(15.9) 73(48.7) reference GA 66(80.4) 71(47.3) 0.000 5.069 2.565–10.020 AA 3(3.7) 6(4.0) 0.237 2.500 0.547–11.424 G allele 92(57.0) 217(72.3) reference A allele 72(44.0) 83(27.7) 0.001 1.990 1.331–2.975 dominant model GG 13(15.9) 73(48.7) reference GA + AA 69(84.1) 77(51.3) 0.000 4.867 2.474–9.573 implicit model GA + GG 79(96.3) 144(96.0) reference AA 3(3.7) 6(4.0) 0.770 0.808 0.194–3.369 rs833070 GG 43(52.4) 92(61.3) reference GA 31(37.8) 45(30.0) 0.297 1.371 0.758–2.483 AA 8(9.8) 13(8.7) 0.654 1.249 0.472–3.309 G allele 117(71.3) 229(76.3) reference A allele 47(28.7) 71(23.7) 0.349 1.232 0.796–1907 dominant model GG 43(52.4) 92(61.3) reference GA + AA 39(47.6) 58(38.7) 0.294 1.344 0.773–2.337 implicit model GA + GG 74(90.2) 137(91.3) reference AA 8(9.8) 13(8.7) 0.827 1.112 0.430–2.872 3.3.2 Risk assessment of gene models at three loci in non-NPSLE group and a negative control group After the age and gender factors were corrected by Logistic regression analysis, the risk assessment of gene models at three loci in non-NPSLE group and negative control group was analyzed, and the results are shown in Table 7 . Table 7 Risk assessment of VEGF gene in non-NPSLE group and control group Site NPSLE group N = 82(%) negative control group N = 150(%) P OR OR(95%CI) rs699947 AA 8(4.8) 14(9.3) reference AC 134(80.7) 110(73.3) 0.118 2.064 0.832–5.118 CC 24(14.5) 26(17.4) 0.352 1.637 0.581–4.615 A allele 150(45.2) 138(46.0) reference C allele 182(54.8) 162(54.0) 0.792 1.043 0.761–1.429 dominant model AA 8(4.8) 14(9.3) reference AC + CC 158(95.2) 136(90.7) 0.970 1.019 0.392–2.648 implicit model AC + AA 142(85.5) 124(82.6) reference CC 24(14.5) 26(17.4) 0.583 0.842 0.457–1.554 rs10434 GG 48(28.9) 73(48.7) reference GA 105(63.3) 71(47.3) 0.00 2.295 1.420–3.709 AA 13(7.8) 6(4.0) 0.023 3.365 1.179–9.603 G allele 201(60.5) 217(72.3) reference A allele 131(39.5) 83(27.7) 0.002 1.705 1.216–2.392 dominant model GG 48(28.9) 73(48.7) reference GA + AA 118(71.1) 77(51.3) 0.624 0.885 0.542–1.444 implicit model GA + GG 153(92.2) 144(96.0) reference AA 13(7.8) 6(4.0) 0.174 2.006 0.736–5.466 rs833070 GG 90(54.2) 92(61.3) reference GA 69(41.6) 45(30.0) 0.070 1.557 0.964–2.514 AA 7(4.2) 13(8.7) 0.255 0.570 0.217–1.499 G allele 249(75.0) 229(76.3) reference A allele dominant model 83(25.0) 71(23.7) 0.685 1.079 0.748–1.555 GG 90(54.2) 92(61.3) reference GA + AA 76(45.8) 58(38.7) 0.209 1.336 0.850–2.100 implicit model GA + GG 159(95.8) 137(91.3) reference AA 7(4.2) 13(8.7) 0.133 0.483 0.187–1.249 3.3.3 Risk assessment of gene models at three loci in NPSLE group and non-NPSLE group After the age and gender factors were corrected by Logistic regression analysis, the risk assessment of gene models at three loci in the NPSLE group and the non-NPSLE group was analyzed, and the results are shown in Table 8 . Table 8 Risk assessment of VEGF gene in NPSLE group and non-NPSLE group Site NPSLE group N = 82(%) non-NPSLE group N = 166(%) P OR OR(95%CI) rs699947 AA 9(11.0) 8(4.8) reference AC 53(64.6) 134(80.7) 0.045 0.356 0.130–0.977 CC 20(24.4) 24(14.5) 0.618 0.751 0.244–2.313 A allele 71(43.3) 150(45.2) reference C allele 93(56.7) 182(54.8) 0.680 1.083 0.742–1.579 Dominant model AA 9(11.0) 8(4.8) reference AC + CC 73(89.0) 158(95.2) 0.086 0.417 0.154–1.130 implicit model AC + AA 62(75.6) 142(85.5) reference CC 20(24.4) 24(14.5) 0.057 1.910 0.982–3.714 rs10434 GG 13(15.9) 48(28.9) reference GA 66(80.4) 105(63.3) 0.019 2.305 1.150–4.617 AA 3(3.7) 13(7.8) 0.794 0.828 0.201–3.416 G allele 92(56.1) 201(60.5) reference A allele 72(43.9) 131(39.5) 0.379 1.187 0.810–1.739 dominant model GG 13(15.9) 48(28.9) reference GA + AA 69(84.1) 118(71.1) 0.029 2.162 1.083–4.318 implicit model GA + GG 79(96.3) 153(92.2) reference AA 3(3.7) 13(7.8) 0.194 0.423 0.116–1.547 rs833070 GA 31(37.8) 69(41.6) 0.822 0.938 0.536–1.641 AA 8(9.8) 7(4.2) 0.115 2.383 0.809–7.019 G allele 117(71.3) 249(75.0) reference A allele 47(28.7) 83(25.0) 0.390 1.202 0.790–1.830 dominant model GG 43(52.4) 90(54.2) reference GA + AA 39(47.6) 76(45.8) 0.798 1.072 0.630–1.822 implicit model GA + GG 74(90.2) 159(95.8) reference AA 8(9.8) 7(4.2) 0.096 2.449 0.853–7.028 4. Discussion The etiology of NPSLE is still poorly understood. The pathogenesis of NPSLE is generally divided into genetic and environmental factors, neuroendocrine and immune factors, blood-brain barrier dysfunction factors, autoimmune-mediated neuronal damage and inflammatory mediators and vascular lesions. Genetic and environmental factors suggest that age, sex, race, region and environmental factors are the main pathogenesis of SLE. Families of siblings and twins showed a genetic strength of 40% [ 9 ][ 10 ]. At the same time, SLE has obvious racial susceptibility. In the United States, African Americans, Spanish and Asians have a higher proportion of SLE than Non-Spanish Caucasus [ 11 ]. Asians have a higher incidence of lupus nephritis, which is close to patients in the African Caribbean [ 12 ]. Neuroendocrine immune imbalance factors suggest that hormones and immunosuppressants commonly used in the treatment of SLE can better control the disease. But the use of these drugs often leads to blood glucose and metabolic disorders, resulting in full-moon face, centripetal obesity, femoral head necrosis and other complications. Pain, fatigue, rash and other factors affect the body neuroendocrine regulation, which is easy to cause anxiety, depression, fear and other negative emotions. Therefore, in clinical treatment, we should pay close attention to the emotional changes of patients, and give psychological intervention and treatment when necessary. In previous studies on SLE patients, it was found that the serum concentrations of interleukin-1 (IL-1), IL-6 and tumor necrosis factor-α (TNF-α) were significantly increased [ 13 ]. Inflammatory mediators (IMs) stimulate immune cells to release inflammatory factors through the blood-brain barrier, activate or aggravate the response of brain immune cells, or cause changes in the structure and function of neuroendocrine axis, neurotransmitters and emotion-regulated brain-related areas [ 14 ]. Such structural changes can be detected by magnetic resonance imaging (MRI). Multiple autoantibodies in SLE patients are the hallmarks of SLE. As early as 1997, anti-dsDNA antibodies have been included in one of the important classification criteria for the diagnosis of SLE by the American Rheumatism Association. Double-stranded DNA combined with its antibody formed immune complex group deposited in the capillaries and tissues and organs, damaged the heart, liver and nervous system of patients, and the higher the titer, the more serious the damage [ 15 ]. In addition, anti-cardiolipin antibody (ACA) in SLE can be bind to endothelial cells, trophoblasts and neuronal cells, and promote thrombosis by interacting with platelets, which is often considered to be associated with cognitive dysfunction [ 16 ]. There are studies that say [ 17 ] that ACA was closely related to the activity of NPSLE. Other studies have pointed out that anti-ribosomal P antibodies are also considered to be involved in the pathogenesis of NPSLE by binding to the surface of cell membrane, intracellular permeability, inhibiting protein synthesis, production of pro-inflammatory cytokines and apoptosis [ 18 ]. These are considered to be the main causes of autoimmune-mediated neuronal injury. In the theory of inflammatory mediators and vascular lesions, tissue and vascular endothelial cells are repeatedly stimulated by inflammatory mediators for a long time, resulting in organ and vascular inflammatory reactions, ischemic and thromboembolism cerebrovascular diseases by up-regulating soluble adhesion and participating in coagulation pathway activation. The imbalance between pro-inflammatory and anti-inflammatory cytokines causes small vascular lesions [ 19 ]. The pathogenesis of NPSLE may be related to autoantibodies, inflammatory mediators and / or vascular abnormalities [ 20 ]. VEGF is a part of the VEGF / platelet-derived growth factors (PDGF) gene family, also known as the cystine junction superfamily of growth factors [ 21 ]. It was initially found in a variety of cultured tumor cell lines, which can directly act on vascular endothelial cells, promote the proliferation of vascular endothelial cells and improve vascular permeability [ 22 ]. In 1989, Ferrara et al., a scientist from Genentech in the United States, completed the purification and isolation of human VEGF protein, which proved its key role in angiogenesis [ 23 ]. Human VEGF is located on chromosome 6p21.3, with a total length of 28 Kb and a coding gene length of 14Kb. Its molecular weight is between 34000 and 45000, and it is composed of eight exons separated by seven introns [ 24 ]. It is a functional glycoprotein highly conserved homodimers. The dimer is composed of single strands with molecular weight of 24kD and disulfide bonds, which contain cystine motifs. The positions of some disulfide bonds in the protein structure [ 25 ], and the hydrophobic groups composed of six polar amino acids at the N-terminal are its characteristics. VEGF monomer is not active and needs to bind and activate tyrosine kinase receptors on endothelial cells to play its unique molecular role. VEGF rs833070 was considered to be associated with laryngeal cancer [ 26 ], rheumatoid arthritis [ 27 ], Tunisia cervical cancer [ 28 ] and retinopathy in Japanese patients with type 1 diabetes [ 29 ]. In the analysis results of this study, there was no correlation between rs833070 gene polymorphism and the risk of NPSLE and non-NPSLE. This is inconsistent with the results of the study that this locus may be a non-NPSLE susceptible gene in Han people in northern China [ 30 ]. On the one hand, it may be caused by the small sample size in this study; on the other hand, it may be caused by random sampling errors. Studies have shown that rs10434 A allele and AA genotype are more common in patients with B-cell chronic lymphocytic leukemia (B-CLL) than in the control group [ 31 ]. This study found that there were statistically significant differences in GG, GA and AA genotypes at rs10434 locus among groups (χ 2 = 31.476, P = 0.000). Therefore, we believe that different mutant genotypes have different genetic susceptibility to NPSLE and non-NPSLE. After adjusting for gender and age by Logistic regression analysis, it was found that rs10434 gene polymorphism(GA, A allele and dominant model (GA + AA)) was associated with the risk of NPSLE. The risk of NPSLE in the population carrying GA genotype was 5 times higher than that in the population carrying GG genotype, the risk of NPSLE in the population carrying A allele was 2 times higher than that in the population carrying G allele, and the risk of NPSLE in the population carrying GA + AA genotype was 4.9 times higher than that in the population carrying GG genotype. This is similar to Cairo [ 32 ] reported that rs10434 increased the sensitivity of non-NPSLE patients to neuropsychiatric symptoms, so we believe that GA allele and A allele may be risk factors for NPSLE. In addition, risk of GA genotype developing into non-NPSLE was 2.3 times higher than that of GG genotype. The risk of developing non-NPSLE with AA genotype was 3.4 times higher than that of developing non-NPSLE with GG genotype. The risk of developing non-NPSLE with A allele was 1.7 times higher than that of developing non-NPSLE with G allele. Therefore, we believe that rs10434 genotype GA, AA and allele A may be risk factors for developing non-NPSLE and NPSLE in the population. According to the racial susceptibility of SLE, we believe that rs10434 locus is also susceptible in Asian populations. In previous studies, especially in Asian population, rs699947 polymorphism was significantly associated with increased risk of bladder cancer and renal cell carcinoma [ 33 ]. In addition, bladder cancer [ 34 ], diabetic foot [ 35 ], multiple sclerosis [ 36 ], and cervical cancer [ 28 ] were mostly associated with rs699947: rs699947, as the promoter region site, was considered to be significantly correlated with the serum VEGF content in patients with cervical cancer. The CC type was significantly higher than that of AC and AA types, and the short-term efficacy of radiotherapy was lower than that of AC and AA types, but the incidence of adverse reactions was lower than that of AC type. Therefore, rs699947 mutation may affect the short-term efficacy and adverse reactions of radiotherapy in cervical cancer by affecting the expression of VEGF [ 37 ]. Nervous system involvement is currently the biggest clinical challenge in systemic lupus erythematosus. It can be directly from immune-mediated damage or secondary to treatment, infection, metabolic abnormalities or other SLE-independent diseases [38], covering central and peripheral nervous system symptoms [ 39 ], integrating neurological and psychiatric manifestations [ 40 ] [ 41 ]. Compared with the NPSLE group and the non-NPSLE group, the genotype AC of rs699947 was associated with the risk of NPSLE. The risk of NPSLE in people with AC genotype was 1/3 of that in people with AA genotype. Therefore, we believe that AC genotype may be a protective factor for NPSLE. The risk of NPSLE in the population with GA genotype at rs10434 was 2.3 times that of the population with GG genotype, and the risk of NPSLE in the population with GA + AA combined genotype was 2.2 times that of the population with GG genotype. Therefore, we speculated that GA allele might be a risk factor for NPSLE. In a prospective study on the long-term survival of newly diagnosed SLE patients in China [ 42 ], it was found that in the early univariate analysis, age growth was a predictor of mortality, but in the late multivariate model, it was found that age was replaced, and organ damage in the early stage of the disease was more important, which meant that the increase in mortality in patients with delayed SLE was more related to early organ damage than age itself [ 43 ]. Biogenetic analysis found that lupus nephritis in Asian patients had higher levels of autoantibodies, higher frequency of lupus nephritis and higher prevalence of active glomerulonephritis compared with white patients [ 44 ]. At the same time, it was found that the incidence of lupus nephritis in Asian patients was younger (the most common damage in children with SLE was kidney, neuropsychiatric system, optic nerve system and skeletal muscle [ 45 ]), system involvement was faster, organ damage was earlier, and neuropsychiatric damage score was more serious at 5 years. Although the sample size of our study is slightly smaller, there may be sampling errors, and are Asian, but for the early diagnosis and treatment of disease provides a scientific basis, early detection, control and remission of the disease, help to improve the survival rate of patients, increase the possibility of treatment benefit. Conclusion In conclusion, our study determined the relationships between rs699947, rs10434 and rs833070 gene polymorphisms and the risk of NPSLE, non-NPSLE risk in a southern Chinese population. Our data provide the first evidence that the VEGF rs10434 A allele, mutant heterozygous model GA, dominant model GA + AA can increase the risk of NPSLE. A allele and dominant model GA + AA can increase the risk of non-NPSLE. Declarations Ethics declarations All experimental protocols were approved by the Ethics Committee of 923 Hospital Ethics Review and informed consent of all subjects was obtained. This study was approval by Ethical Review of 923 Hospital (approval number 923LL-KY-2023LW-005-01). Accord with the Declaration of Helsinki. Acknowledgements We thank the patient for participating in the current study. Funding: This research was supported by Natural Science Foundation of Guangxi Zhuang Autonomous Region (2022GXNSFAA035637, 2022GXNSFBA035544), Scientific Research Projects of Health Commission of Guangxi Zhuang Autonomous Region (Z-A20220436, Z-20210591,Z20210295), and National Natural Science Foundation of China (81660275). Consent for publication The purpose of the study and its importance to patients and scientific community was explained to the affected individual by the clinicians. Informed consent was obtained from the patient to publish study finding and to publish the data of the patient. Conflicts of interest The author reports no conflicts of interest in this work. Authors’ contributions Conceived and designed the experiments: JW. Performed experiments: XL and WZ,analysed the data: ZAL and MJ, and LHY wrote the paper. All authors have read and agreed to the published version of the manuscript. References Mok, C. C., Chung, Y. K., Lee, C., Ho, L. Y., & To, C. H. (2023). Relationship between the EULAR/ACR classification criteria and organ damage in systemic lupus erythematosus. 32(3), 424–430. doi: 10.1177/09612033231153791 You Hanxiao, Li Mengtao, Zhang Fengchun, Zeng Xiaofeng, Hu Chaojun. 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(2023). Correspondence on ‘Performance of the 2019 EULAR/ACR classification criteria for systemic lupus erythematosus in early disease, across sexes and ethnicities’. 82(1), e15-e15. doi: 10.1136/annrheumdis-2020-219296%J Annals of the Rheumatic Diseases Ugarte-Gil, M. F., Dubey, J., Mcgwin, G., Vila, L., & Alarcon, G. S. (2020). THU0286 THE SYSTEMIC LUPUS INTERNATIONAL COLLABORATING CLINICS (SLICC) FRAILTY INDEX (SLICC-FI) PREDICTS DAMAGE ACCRUAL IN SYSTEMIC LUPUS ERYTHEMATOSUS (SLE) PATIENTS. DATA FROM A MULTI-ETHNIC, MULTI-CENTER US LUPUS COHORT. 79(Suppl 1), 370–370. doi: 10.1136/annrheumdis-2020-eular .5068%J Annals of the Rheumatic Diseases Demi, T., r, r., Fi, F., Akyurek, k., Abusoglu, S., Ali, A.,.. . Yilmaz, S. (2019). Association Between Disease Activity, Asymmetric dimethylarginine, Tumor Necrosis Factor-Alpha, Interleukin-10 And Neopterin with Echocardiography And Blood Pressure Monitoring Findings in Systemic Lupus Erythematosus Patients. 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(2022). Pathogenesis and treatment of neuropsychiatric systemic lupus erythematosus: A review. 10. doi: 10.3389/fcell.2022.998328 Lucic, A. T. (2022). Neuropsichiatric Manifestations of Systemic Lupus Erythematosus: Diagnosis and Treatment Approach %J Experimental and Applied Biomedical Research (EABR). 23(4), 361–367. doi:doi: 10.2478/sjecr-2017-0071 Moore, E., Huang, M. W., & Putterman, C. (2020). Advances in the diagnosis, pathogenesis and treatment of neuropsychiatric systemic lupus erythematosus. 32(2), 152–158. doi: 10.1097/bor.0000000000000682 Guzmán, A., Hernández-Coronado, C. G., Gutiérrez, C. G., & Rosales-Torres, A. M. (2023). The vascular endothelial growth factor (VEGF) system as a key regulator of ovarian follicle angiogenesis and growth. 90(4), 201–217. doi: https://doi.org/10.1002/mrd.23683 Shima DT,Gougos A,Miller JW,et al.Cloning and mRNA expression of vascular endothelial growth factor in ischemic retinas of Macaca fascicularis[J].Invest Ophthalmol Vis Sci,2018,37(7):1334. Kim, S.-H., Pei, Q.-M., Jiang, P., Liu, J., Sun, R.-F., Qian, X.-J., & Liu, J.-B. (2019). Upregulation of MUC5AC by VEGF in human primary bronchial epithelial cells: implications for asthma. Respiratory Research, 20(1), 282. doi: 10.1186/s12931-019-1245-1 Brogowska, K. K., Zajkowska, M., & Mroczko, B. (2023). Vascular Endothelial Growth Factor Ligands and Receptors in Breast Cancer. 12(6), 2412. Calvo, P. M., Hernández, R. G., de la Cruz, R. R., & Pastor, A. M. (2023). Role of vascular endothelial growth factor as a critical neurotrophic factor for the survival and physiology of motoneurons. 18(8), 1691–1696. doi: 10.4103/1673-5374.363194 Lamia Makni, Mouna Stayoussef, Ezeddine Ghazouani, et al. Distinct association of VEGF-A polymorphisms with laryngeal and nasopharyngeal cancer. 2016, 10:90–94. Yi Jin-Ping, Wu Yu-Zhang, Yu Nan, et al. VEGF Gene Polymorphisms Affect Serum Protein Levels and Alter Disease Activity and Synovial Lesions in Rheumatoid Arthritis.. 2016, 22:316–24. Zidi Sabrina, Stayoussef Mouna, Gazouani Ezzedine, et al. Relationship of common vascular endothelial growth factor polymorphisms and haplotypes with the risk of cervical cancer in Tunisians. 2015, 74(1):108–12. Koji Nakanishi, Chizuru Watanabe. Single nucleotide polymorphisms of vascular endothelial growth factor gene intron 2 are markers for early progression of diabetic retinopathy in Japanese with type 1 diabetes. 2009, 402(1):171–175. Lu Haozhe, Lin Tao, Zhu Xiangyang, Zhang Jintao, Lu Jing. Association between single nucleotide polymorphisms of VEGF gene and SLE susceptibility in Chinese Han population [ J ].Cell and molecular immunology journal, 2010,26 (12): 1189–1192. Zhu L X et al. 3'-UTR polymorphism (rs10434) in the VEGF gene is associated with B-CLL in a Chinese population.[J]. Genetics and molecular research: GMR, 2015, 14(2) : 4085–9. Sherif Taha, Sherif Mohammed Gamal, Mohamed Nabil, et al. Vascular endothelial growth factor G1612A (rs10434) gene polymorphism and neuropsychiatric manifestations in systemic lupus erythematosus patients. 2017, 57(2):149–153. Yuxuan Song et al. Association between vascular endothelial growth factor rs699947 polymorphism and the risk of three major urologic neoplasms (bladder cancer, prostate cancer, and renal cell carcinoma): A meta-analysis involving 11,204 subjects[J]. Gene, 2018, Chih-Heng Chen, Chen-Hsun Ho, Steven Kuan-Hua Huang, et al. Association between VEGF gene promoter polymorphisms and bladder cancer: An updated meta-analysis. 2020, 131 Saja Talib Ahmed, Mufeed J. Ewadh, Zuhair Mohammed Ali Jeddoa. The association of vascular endothelial growth factor polymorphism (rs699947) with diabetic foot ulcer and oxidative status. 2020, 19 Saravani M., Rokni M., Mehrbani M., Amirkhosravi A., Faramarz S., Fatemi I., Esmaeili Tarzi M., Nematollahi M. H. The evaluation of VEGF and HIF-1alpha gene polymorphisms and multiple sclerosis susceptibility[J]. J Gene Med. 2019;21(12):e3132. Zhang Zegao, Yang Jie, Pilate Nuerbuk. Study on the correlation between rs699947 single nucleotide polymorphism in VEGF gene promoter region and short-term efficacy of radiotherapy and chemotherapy for cervical cancer [ J ]. Chinese Journal of Cancer Prevention and Treatment, 2015,22 (22): 1765–1768 + 1773. Wang, M.; Wang, Z.; Zhang, S.; Wu, Y.; Zhang, L.; Zhao, J.; Wang, Q.; Tian, X.; Li, M.; Zeng, X. Progress in the Pathogenesis and Treatment of Neuropsychiatric Systemic Lupus Erythematosus. J. Clin. Med. 2022, 11, 4955. https://doi.org/10.3390/jcm11174955 Yoshio, T. and Okamoto, H. (2015) Pathogenesis of Neuropsychiatric Syndromes of Systemic Lupus Erythematosus. Open Journal of Rheumatology and Autoimmune Diseases, 5, 46–56. doi: 10.4236/ojra.2015.52009 . Ichinose, K., Ohyama, K., Furukawa, K., Higuchi, O., Mukaino, A., Satoh, K.,.. . Kawakami, A. (2018). Novel anti-suprabasin antibodies may contribute to the pathogenesis of neuropsychiatric systemic lupus erythematosus. Clinical Immunology, 193, 123–130. doi: https://doi.org/10.1016/j.clim.2017.11.006 Mrak Daniel, Bonelli Michael*, Radner Helga, Neuropsychiatric Systemic Lupus Erythematosus: A Remaining Challenge, Current Pharmaceutical Design 2022; 28(11). https://dx.doi.org/10.2174/1381612828666220512102824 Mok, C. C., Ho, L. Y., Chan, K. L., Tse, S. M., & To, C. H. (2020). Trend of Survival of a Cohort of Chinese Patients With Systemic Lupus Erythematosus Over 25 Years. 7. doi: 10.3389/fmed.2020.00552 Co Rong Wang, Li-Min Ren, Ru Li, et al. Systemic lupus erythematosus (sle) 20 years of survival rate and prognostic factors analysis [J]. Chinese journal of medicine, 2019, 13 (3): 178–182. DOI: 10.3760 / cma. J.i SSN. 0376-2491.2019.03.005. Luo, W., Farinha, F., Isenberg, D. A., & Rahman, A. (2022). Survival analysis of mortality and development of lupus nephritis in patients with systemic lupus erythematosus up to 40 years of follow-up. Rheumatology, 62(1), 200–208. doi: 10.1093/rheumatology/keac218%J Rheumatology Martin K.R. Svačina, Man Mohan Mehndiratta, Christian A. Vedeler, Yogesh Sharma, Ilja Bobylev, Alina Sprenger, Gina Remke, Hauke Wüstenberg, Ines Klein, Abhijeet Joshi, Helmar C. Lehmann, Clinical management of chronic inflammatory demyelinating polyneuropathy (CIDP) in Europe and India: An exploratory study, Journal of the Neurological Sciences, 10.1016/j.jns.2021.117507 , 427, (117507), (2021). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3998408","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275777533,"identity":"bf503fff-fd24-49e4-b983-48398b6a267e","order_by":0,"name":"阳丽华 Yang","email":"","orcid":"","institution":"No.923 hospital of chinese people's libberation army","correspondingAuthor":false,"prefix":"","firstName":"阳丽华","middleName":"","lastName":"Yang","suffix":""},{"id":275777534,"identity":"71f6b6d9-80f0-486b-bb32-900de540663b","order_by":1,"name":"Min Jin","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Jin","suffix":""},{"id":275777535,"identity":"09f22ed8-eadc-48f5-b3ae-1c3d80b60652","order_by":2,"name":"zhiang Liu","email":"","orcid":"","institution":"No.923 hospital of chinese people's libberation army","correspondingAuthor":false,"prefix":"","firstName":"zhiang","middleName":"","lastName":"Liu","suffix":""},{"id":275777536,"identity":"341f353d-4d64-4558-b945-829014a347c7","order_by":3,"name":"wei Zhou","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"wei","middleName":"","lastName":"Zhou","suffix":""},{"id":275777537,"identity":"aa77f8cc-f5c8-4c55-b227-2bc51cb09490","order_by":4,"name":"Xi Li","email":"","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Li","suffix":""},{"id":275777538,"identity":"6cfe882c-4941-4876-8bbc-a78ce7d4dfaf","order_by":5,"name":"Jian Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYLACCQMGBjb59x8OfDCwsSNeCz9DguHBGQVpycTbJNmQYHyY58MhxgZCKg2Onz38wqLALs/gwIGEwzYGB5gZ2A8f3YBXy5m8NAsJg+Rig4MNBw7nGNzhY+BJS7uBT4vZgRwzAwkD5sQNhxkbgFqeMTNI8Jjh13L+DUhLfeKGY8wMhy0MgBoJarmRY/xAwuBw4sweNobDDMRosb/xxgwYyMcT+yV4GA72GKQlsxHyi2R/jvFniT/ViW0SPMwffvyxseNnP3wMrxYgYJOWQOESUA4CzB8/EKFqFIyCUTAKRjAAAKFxTtK9e6uGAAAAAElFTkSuQmCC","orcid":"","institution":"the First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-02-29 03:46:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3998408/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3998408/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52031450,"identity":"34e7c53a-afe7-4650-9734-cb9e7f851d64","added_by":"auto","created_at":"2024-03-05 16:25:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150672,"visible":true,"origin":"","legend":"\u003cp\u003eVEGF gene SNP locus rs699947、rs10434、rs833070 electrophoresis\u003c/p\u003e\n\u003cp\u003e4 to 11 are the numbers of randomly selected samples\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3998408/v1/ce37021e4b74a1f202c2422c.png"},{"id":53852342,"identity":"2a02bf1d-464e-4969-8544-16ad6accca93","added_by":"auto","created_at":"2024-04-01 10:21:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":689307,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3998408/v1/46bd2643-da2b-400e-970c-68fbff281ab8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"VEGF Gene Polymorphism And Study On Genetic Susceptibility Of Neuropsychiatric Lupus","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSystemic lupus erythematosus (SLE) is an autoimmune disease involving a variety of immune disorders. Systemic multi-system and multi-organ involvement, repeated attacks and remission alternately, and the formation of a large number of autoantibodies and complements and immune complexes are the characteristics of SLE. Lesions mainly involve skin, joints, nervous system, kidney, lung, scrosal cavity and other parts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. SLE with neurological and/or psychiatric symptoms involving the nervous system, known as neuropsychiatric lupus (NPSLE) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the literature data review showed that the prevalence of SLE with depression was 24% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the incidence of mental disorders at home and abroad were 20% -45% and 19% -38% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVascular endothelial growth factor (VEGF), also known as vascular permeability factor (VPF) or angiopoietin, is a kind of angiogenic mitogen and belongs to the family of vasoactive growth factors. It is a glycoprotein with high specificity that promotes the growth of vascular endothelial cells isolated from pituitary follicular cells,it can make extracellular matrix degeneration, promote angiogenesis and increase vascular permeability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], can significantly induce and promote vascular endothelial cell division and migration, proliferation and promote the proliferation of lymphatic system [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], provide the basis for lymphatic metastasis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. There are at least 30 single nucleotide polymorphism sites (SNPs) in the VEGF gene. By changing some polymorphism sites, the transcription level of VEGF can be changed and the protein expression can be affected, resulting in differences in genetic susceptibility of individuals to diseases. We selected rs699947 (promoter), rs10434 and rs833070 (intron) as marker SNPs, which affected the normal brain morphology of healthy individuals [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This study aims to explore the relationship between the SNP of VEGF rs699947, rs10434 and rs833070 in NPSLE patients and neuropsychiatric manifestations related to lupus.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003ePatients who met the diagnostic criteria of Classification Criteria for Systemic Lupus Erythematosus (1999 Edition) in the First Affiliated Hospital of Guangxi Medical University and the People\u0026apos;s Hospital of Guangxi Zhuang Autonomous Region during the past 10 years from August 2020 to December 2020 were selected as the analysis objects. A total of 82 patients diagnosed as NPSLE and without other autoimmune diseases were selected as the NPSLE group (positive group). Non-NPSLE group (positive control group) 166 patients with systemic lupus erythematosus without psychiatric symptoms. According to the principle of age matching with the positive control group, 150 Healthy subjects from the physical examination center of Guangxi Zhuang Autonomous Region People\u0026apos;s Hospital were selected as the healthy control group. Exclusion criteria: no previous cancer, heart, liver, spleen, lung and kidney, cachexia and other serious diseases. Women exclude special periods such as pregnancy and lactation. All experimental protocols were approved by the Ethics Committee of 923 Hospital Ethics Review and informed consent of all subjects was obtained.\u003c/p\u003e\n\u003cp\u003eThe age, medical history and other data of all patients was collected. The subjects were fasting venous blood 2 mL in EDTA-K\u003csub\u003e2\u003c/sub\u003e tube from 8:00 am to 9:00 am in the morning, fully mixed and stored in -80\u0026deg;C refrigerator.\u003c/p\u003e\n\u003cp\u003eThe sample DNA was extracted with AxyPrep blood genomic DNA kit (Ai Sijin Biotechnology (Hangzhou) Co., Ltd.). The qualified samples were DNA concentration greater than 10 ng / \u0026micro;L and OD260 / OD280 in the range of 1.7\u0026ndash;2.0. The qualified samples were detected by multiple SNaPshot detection method to detect the SNP distribution of the three sites of VEGF gene rs699947, rs10434 and rs833070. Extension primers of the three sites are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The amplified products of rs699947, rs10434 and rs833070 of VEGF gene were 362 bp, 224 bp and 536 bp, respectively, and the electrophoretic result Marker of any 4\u0026ndash;11 samples were shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSnaPshot PCR extension primers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimer name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSequence\u003c/p\u003e\n \u003cp\u003e(5\u0026apos;to3\u0026apos;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProduct size\u003c/p\u003e\n \u003cp\u003e(bp)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRS Sequence primer direction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNtsadded\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers699947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers699947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTTTTTTTTTTTTTTTTTCGATCTGATTATCCACCCGAATC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG/T\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTCGGGACATCTGCCGATGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC/T\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers833070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers833070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTTTTTTTTTTTTTTTTTTTTTTTTTTTTTACAGCACCCGAACATGATCAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote : F is forward primer, R is reverse primer\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003ePCR amplification program as follows: 95\u0026deg;C 15 min, 94\u0026deg;C 40s, 63\u0026deg;C 60s, 72\u0026deg;C 8 min, 15 cycles; 94\u0026deg;C 40 s, 56\u0026deg;C 40 s, 72\u0026deg;C 1.5min, 25cycles; 72\u0026deg;C for 8 min.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eHardy-Weinberg genetic balance test was used to detect the genetic balance of population genes. When \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, the genetic balance of population genes was indicated, and the samples were well representative.\u003c/p\u003e\n \u003cp\u003eStatistical analysis using SPSS 23.0 software, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated that the difference was statistically significant. Enumeration data were tested by \u0026chi;\u003csup\u003e2\u003c/sup\u003e test or Fisher exact probability method. Analysis of variance was used for age comparison among groups. The K-S normality test was performed on the data for each group, and the measurement data subject to normal distribution were expressed as (X̄\u0026plusmn;\u0026thinsp;s). The ratio between groups was analyzed by one-way analysis of variance, and the LSD test was used for pairwise comparison between groups.\u003c/p\u003e\n \u003cp\u003eCorrelation between VEGF gene polymorphism and NPSLE risk was analyzed by binary Logistics regression, and OR value and 95% CI were calculated.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of the study population\u003c/h2\u003e \u003cp\u003eA total of 398 subjects underwent genetic analysis and were divided into three groups: 82 in the NPSLE group, 166 in the non-NPSLE group and 150 in the healthy control group. The characteristics of the three groups of cases and the control group are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The age distribution difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that the case group and the control group are comparable in the study population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of one-way analysis of variance for age among groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003cp\u003eGender / age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPSLE\u003c/p\u003e \u003cp\u003egroup\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;82(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-NPSLE\u003c/p\u003e \u003cp\u003egroup\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;166(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enegative control group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;150(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/F\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11(13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28(18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71(86.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142(85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122(81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage age ( years )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.68\u0026thinsp;\u0026plusmn;\u0026thinsp;14.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.62\u0026thinsp;\u0026plusmn;\u0026thinsp;13.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.83\u0026thinsp;\u0026plusmn;\u0026thinsp;7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e244.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of genotype and allele frequencies among three groups\u003c/h2\u003e \u003cp\u003eThere were two alleles A and C at rs699947, and there were three genotypes: Wild homozygous AA, mutant heterozygous AC and mutant homozygous CC. Statistical analysis showed that there was no significant difference in genotype frequency (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;8.453, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.076), and there was no significant difference in allele frequencies of A and C (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.315, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.854), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Hardy-Weinberg test results showed that the rs699947 and rs10434 loci did not conform to the HWE equilibrium analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). It may be due to the fact that SLE in women of childbearing age and the small sample size in this study, rs833070 conformed to the HWE balance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency distribution of genotype and allele at rs699947 locus in each group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPSLE group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;82(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-NPSLE group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;166(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enegative control group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;150(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egenotype frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53(64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134(80.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110(73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eallele frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71(43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150(45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93(56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182(54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e162(54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Comparison of genotype and allele frequencies at rs10434 locus in each group\u003c/h2\u003e \u003cp\u003eThere are G and A alleles in rs10434 locus, and there are three genotypes of wild homozygote GG, mutant heterozygote GA and mutant homozygote AA. After statistical analysis, the genotype frequency difference was statistically significant ( χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;31.476, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000 ), G and A allele frequency difference was not statistically significant ( χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.184, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.553 ), see Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency distribution of genotype and allele at rs10434 locus in each group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPSLE group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;82(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-NPSLE group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;166(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enegative control group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;150(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egenotype frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48(28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73(48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66(80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105(63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71(47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eallele frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92(56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201(60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e217(72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72(43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131(39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e162(27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Comparison of genotype and allele frequencies of VEGF gene rs833070 locus in each group\u003c/h2\u003e \u003cp\u003eThere are two alleles G and A at rs833070. There are three genotypes GG, GA and AA. Statistical analysis showed that there was no significant difference in genotype frequency ( χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7.264, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.123 ), and there was no significant difference in allele frequencies of G and A ( χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.419, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.492 ), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency distribution of genotype and allele at rs833070 locus in each group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPSLE group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;82(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-NPSLE group N\u0026thinsp;=\u0026thinsp;166(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003enegative control group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;150(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egenotype frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43(52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90(54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92(61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69(41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45(30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eallele frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117(71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249(75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e229(76.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47(28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71(23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Three-site gene mutations and risk assessment of NPSLE\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e3.3.1\u003c/b\u003e Risk Assessment of the VEGF gene in NPSLE group and control group\u003c/h2\u003e \u003cp\u003eLogistic regression analysis was used to correct the age and gender factors, and the risk assessment of the gene models at three loci in the NPSLE group and the negative control group was analyzed. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk assessment of VEGF gene in NPSLE group and control group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPSLE group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;82(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative control group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;150(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers699947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereferene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53(64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110(73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.315\u0026ndash;2.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26(17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.501\u0026ndash;4.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71(43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93(56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162(54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.796\u0026ndash;1.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edominant model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73(89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136(90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.370\u0026ndash;2.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimplicit model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62(95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124(82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26(17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.880\u0026ndash;3.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73(48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66(80.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71(47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.565\u0026ndash;10.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.547\u0026ndash;11.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92(57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e217(72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72(44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83(27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.331\u0026ndash;2.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edominant model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73(48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69(84.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77(51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.474\u0026ndash;9.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimplicit model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79(96.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144(96.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.194\u0026ndash;3.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers833070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43(52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92(61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45(30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.758\u0026ndash;2.483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.472\u0026ndash;3.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117(71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229(76.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47(28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71(23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.796\u0026ndash;1907\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edominant model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43(52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92(61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39(47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58(38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.773\u0026ndash;2.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimplicit model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74(90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137(91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.430\u0026ndash;2.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3.2 Risk assessment of gene models at three loci in non-NPSLE group and a negative control group\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter the age and gender factors were corrected by Logistic regression analysis, the risk assessment of gene models at three loci in non-NPSLE group and negative control group was analyzed, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk assessment of VEGF gene in non-NPSLE group and control group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPSLE group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;82(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enegative control group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;150(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers699947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e134(80.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110(73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.832\u0026ndash;5.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26(17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.581\u0026ndash;4.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150(45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e182(54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162(54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.761\u0026ndash;1.429\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edominant model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158(95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136(90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.392\u0026ndash;2.648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimplicit model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142(85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124(82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26(17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.457\u0026ndash;1.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48(28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73(48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105(63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71(47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.420\u0026ndash;3.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.179\u0026ndash;9.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e201(60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e217(72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131(39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83(27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.216\u0026ndash;2.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edominant model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48(28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73(48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118(71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77(51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.542\u0026ndash;1.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimplicit model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153(92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144(96.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.736\u0026ndash;5.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers833070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90(54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92(61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69(41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45(30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.964\u0026ndash;2.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.217\u0026ndash;1.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249(75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229(76.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA allele\u003c/p\u003e \u003cp\u003edominant model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71(23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.748\u0026ndash;1.555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90(54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92(61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76(45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58(38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.850\u0026ndash;2.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimplicit model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159(95.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137(91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.187\u0026ndash;1.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Risk assessment of gene models at three loci in NPSLE group and non-NPSLE group\u003c/h2\u003e \u003cp\u003eAfter the age and gender factors were corrected by Logistic regression analysis, the risk assessment of gene models at three loci in the NPSLE group and the non-NPSLE group was analyzed, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk assessment of VEGF gene in NPSLE group and non-NPSLE group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPSLE group N\u0026thinsp;=\u0026thinsp;82(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-NPSLE group N\u0026thinsp;=\u0026thinsp;166(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers699947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53(64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134(80.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.130\u0026ndash;0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.244\u0026ndash;2.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71(43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150(45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93(56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182(54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.742\u0026ndash;1.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominant model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u0026thinsp;+\u0026thinsp;CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73(89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158(95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.154\u0026ndash;1.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimplicit model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62(75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142(85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20(24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24(14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.982\u0026ndash;3.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers10434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48(28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66(80.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105(63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.150\u0026ndash;4.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.201\u0026ndash;3.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92(56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201(60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72(43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131(39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.810\u0026ndash;1.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edominant model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48(28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69(84.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118(71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.083\u0026ndash;4.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimplicit model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79(96.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153(92.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.116\u0026ndash;1.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers833070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69(41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.536\u0026ndash;1.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.809\u0026ndash;7.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117(71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249(75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA allele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47(28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.790\u0026ndash;1.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edominant model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43(52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90(54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39(47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76(45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.630\u0026ndash;1.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eimplicit model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;GG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74(90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159(95.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.853\u0026ndash;7.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe etiology of NPSLE is still poorly understood. The pathogenesis of NPSLE is generally divided into genetic and environmental factors, neuroendocrine and immune factors, blood-brain barrier dysfunction factors, autoimmune-mediated neuronal damage and inflammatory mediators and vascular lesions.\u003c/p\u003e \u003cp\u003eGenetic and environmental factors suggest that age, sex, race, region and environmental factors are the main pathogenesis of SLE. Families of siblings and twins showed a genetic strength of 40% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. At the same time, SLE has obvious racial susceptibility. In the United States, African Americans, Spanish and Asians have a higher proportion of SLE than Non-Spanish Caucasus [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Asians have a higher incidence of lupus nephritis, which is close to patients in the African Caribbean [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNeuroendocrine immune imbalance factors suggest that hormones and immunosuppressants commonly used in the treatment of SLE can better control the disease. But the use of these drugs often leads to blood glucose and metabolic disorders, resulting in full-moon face, centripetal obesity, femoral head necrosis and other complications. Pain, fatigue, rash and other factors affect the body neuroendocrine regulation, which is easy to cause anxiety, depression, fear and other negative emotions. Therefore, in clinical treatment, we should pay close attention to the emotional changes of patients, and give psychological intervention and treatment when necessary.\u003c/p\u003e \u003cp\u003eIn previous studies on SLE patients, it was found that the serum concentrations of interleukin-1 (IL-1), IL-6 and tumor necrosis factor-α (TNF-α) were significantly increased [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Inflammatory mediators (IMs) stimulate immune cells to release inflammatory factors through the blood-brain barrier, activate or aggravate the response of brain immune cells, or cause changes in the structure and function of neuroendocrine axis, neurotransmitters and emotion-regulated brain-related areas [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Such structural changes can be detected by magnetic resonance imaging (MRI).\u003c/p\u003e \u003cp\u003eMultiple autoantibodies in SLE patients are the hallmarks of SLE. As early as 1997, anti-dsDNA antibodies have been included in one of the important classification criteria for the diagnosis of SLE by the American Rheumatism Association. Double-stranded DNA combined with its antibody formed immune complex group deposited in the capillaries and tissues and organs, damaged the heart, liver and nervous system of patients, and the higher the titer, the more serious the damage [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, anti-cardiolipin antibody (ACA) in SLE can be bind to endothelial cells, trophoblasts and neuronal cells, and promote thrombosis by interacting with platelets, which is often considered to be associated with cognitive dysfunction [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. There are studies that say [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] that ACA was closely related to the activity of NPSLE. Other studies have pointed out that anti-ribosomal P antibodies are also considered to be involved in the pathogenesis of NPSLE by binding to the surface of cell membrane, intracellular permeability, inhibiting protein synthesis, production of pro-inflammatory cytokines and apoptosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These are considered to be the main causes of autoimmune-mediated neuronal injury.\u003c/p\u003e \u003cp\u003eIn the theory of inflammatory mediators and vascular lesions, tissue and vascular endothelial cells are repeatedly stimulated by inflammatory mediators for a long time, resulting in organ and vascular inflammatory reactions, ischemic and thromboembolism cerebrovascular diseases by up-regulating soluble adhesion and participating in coagulation pathway activation. The imbalance between pro-inflammatory and anti-inflammatory cytokines causes small vascular lesions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The pathogenesis of NPSLE may be related to autoantibodies, inflammatory mediators and / or vascular abnormalities [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVEGF is a part of the VEGF / platelet-derived growth factors (PDGF) gene family, also known as the cystine junction superfamily of growth factors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It was initially found in a variety of cultured tumor cell lines, which can directly act on vascular endothelial cells, promote the proliferation of vascular endothelial cells and improve vascular permeability [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In 1989, Ferrara et al., a scientist from Genentech in the United States, completed the purification and isolation of human VEGF protein, which proved its key role in angiogenesis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHuman VEGF is located on chromosome 6p21.3, with a total length of 28 Kb and a coding gene length of 14Kb. Its molecular weight is between 34000 and 45000, and it is composed of eight exons separated by seven introns [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It is a functional glycoprotein highly conserved homodimers. The dimer is composed of single strands with molecular weight of 24kD and disulfide bonds, which contain cystine motifs. The positions of some disulfide bonds in the protein structure [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and the hydrophobic groups composed of six polar amino acids at the N-terminal are its characteristics. VEGF monomer is not active and needs to bind and activate tyrosine kinase receptors on endothelial cells to play its unique molecular role.\u003c/p\u003e \u003cp\u003eVEGF rs833070 was considered to be associated with laryngeal cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], rheumatoid arthritis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], Tunisia cervical cancer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and retinopathy in Japanese patients with type 1 diabetes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In the analysis results of this study, there was no correlation between rs833070 gene polymorphism and the risk of NPSLE and non-NPSLE. This is inconsistent with the results of the study that this locus may be a non-NPSLE susceptible gene in Han people in northern China [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. On the one hand, it may be caused by the small sample size in this study; on the other hand, it may be caused by random sampling errors.\u003c/p\u003e \u003cp\u003eStudies have shown that rs10434 A allele and AA genotype are more common in patients with B-cell chronic lymphocytic leukemia (B-CLL) than in the control group [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This study found that there were statistically significant differences in GG, GA and AA genotypes at rs10434 locus among groups (χ\u003csup\u003e2\u003c/sup\u003e = 31.476, \u003cem\u003eP\u003c/em\u003e = 0.000). Therefore, we believe that different mutant genotypes have different genetic susceptibility to NPSLE and non-NPSLE. After adjusting for gender and age by Logistic regression analysis, it was found that rs10434 gene polymorphism(GA, A allele and dominant model (GA + AA)) was associated with the risk of NPSLE. The risk of NPSLE in the population carrying GA genotype was 5 times higher than that in the population carrying GG genotype, the risk of NPSLE in the population carrying A allele was 2 times higher than that in the population carrying G allele, and the risk of NPSLE in the population carrying GA + AA genotype was 4.9 times higher than that in the population carrying GG genotype. This is similar to Cairo [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] reported that rs10434 increased the sensitivity of non-NPSLE patients to neuropsychiatric symptoms, so we believe that GA allele and A allele may be risk factors for NPSLE. In addition, risk of GA genotype developing into non-NPSLE was 2.3 times higher than that of GG genotype. The risk of developing non-NPSLE with AA genotype was 3.4 times higher than that of developing non-NPSLE with GG genotype. The risk of developing non-NPSLE with A allele was 1.7 times higher than that of developing non-NPSLE with G allele. Therefore, we believe that rs10434 genotype GA, AA and allele A may be risk factors for developing non-NPSLE and NPSLE in the population. According to the racial susceptibility of SLE, we believe that rs10434 locus is also susceptible in Asian populations.\u003c/p\u003e \u003cp\u003eIn previous studies, especially in Asian population, rs699947 polymorphism was significantly associated with increased risk of bladder cancer and renal cell carcinoma [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition, bladder cancer [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], diabetic foot [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], multiple sclerosis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and cervical cancer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] were mostly associated with rs699947: rs699947, as the promoter region site, was considered to be significantly correlated with the serum VEGF content in patients with cervical cancer. The CC type was significantly higher than that of AC and AA types, and the short-term efficacy of radiotherapy was lower than that of AC and AA types, but the incidence of adverse reactions was lower than that of AC type. Therefore, rs699947 mutation may affect the short-term efficacy and adverse reactions of radiotherapy in cervical cancer by affecting the expression of VEGF [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNervous system involvement is currently the biggest clinical challenge in systemic lupus erythematosus. It can be directly from immune-mediated damage or secondary to treatment, infection, metabolic abnormalities or other SLE-independent diseases [38], covering central and peripheral nervous system symptoms [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e39\u003c/span\u003e], integrating neurological and psychiatric manifestations [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Compared with the NPSLE group and the non-NPSLE group, the genotype AC of rs699947 was associated with the risk of NPSLE. The risk of NPSLE in people with AC genotype was 1/3 of that in people with AA genotype. Therefore, we believe that AC genotype may be a protective factor for NPSLE. The risk of NPSLE in the population with GA genotype at rs10434 was 2.3 times that of the population with GG genotype, and the risk of NPSLE in the population with GA + AA combined genotype was 2.2 times that of the population with GG genotype. Therefore, we speculated that GA allele might be a risk factor for NPSLE.\u003c/p\u003e \u003cp\u003eIn a prospective study on the long-term survival of newly diagnosed SLE patients in China [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], it was found that in the early univariate analysis, age growth was a predictor of mortality, but in the late multivariate model, it was found that age was replaced, and organ damage in the early stage of the disease was more important, which meant that the increase in mortality in patients with delayed SLE was more related to early organ damage than age itself [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Biogenetic analysis found that lupus nephritis in Asian patients had higher levels of autoantibodies, higher frequency of lupus nephritis and higher prevalence of active glomerulonephritis compared with white patients [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. At the same time, it was found that the incidence of lupus nephritis in Asian patients was younger (the most common damage in children with SLE was kidney, neuropsychiatric system, optic nerve system and skeletal muscle [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]), system involvement was faster, organ damage was earlier, and neuropsychiatric damage score was more serious at 5 years. Although the sample size of our study is slightly smaller, there may be sampling errors, and are Asian, but for the early diagnosis and treatment of disease provides a scientific basis, early detection, control and remission of the disease, help to improve the survival rate of patients, increase the possibility of treatment benefit.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study determined the relationships between rs699947, rs10434 and rs833070 gene polymorphisms and the risk of NPSLE, non-NPSLE risk in a southern Chinese population. Our data provide the first evidence that the VEGF rs10434 A allele, mutant heterozygous model GA, dominant model GA + AA can increase the risk of NPSLE. A allele and dominant model GA + AA can increase the risk of non-NPSLE.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental protocols were approved by the Ethics Committee of 923 Hospital Ethics Review and informed consent of all subjects was obtained.\u003c/p\u003e\n\u003cp\u003eThis study was approval by Ethical Review of 923 Hospital (approval number 923LL-KY-2023LW-005-01). Accord with the Declaration of Helsinki.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the patient for participating in the current study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Natural Science Foundation of Guangxi Zhuang Autonomous Region (2022GXNSFAA035637, 2022GXNSFBA035544), Scientific Research Projects of Health Commission of Guangxi Zhuang Autonomous Region (Z-A20220436, Z-20210591,Z20210295), and National Natural Science Foundation of China (81660275).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of the study and its importance to patients and scientific community was explained to the affected individual by the clinicians. Informed consent was obtained from the patient to publish study finding and to publish the data of the patient.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author reports no conflicts of interest in this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceived and designed the experiments: JW. Performed experiments: XL and WZ,analysed the data: ZAL and MJ, and LHY wrote the paper. All authors have read and agreed to the published version of the manuscript.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMok, C. C., Chung, Y. K., Lee, C., Ho, L. Y., \u0026amp; To, C. H. (2023). 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Lehmann, Clinical management of chronic inflammatory demyelinating polyneuropathy (CIDP) in Europe and India: An exploratory study, Journal of the Neurological Sciences, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jns.2021.117507\u003c/span\u003e\u003cspan address=\"10.1016/j.jns.2021.117507\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, 427, (117507), (2021).\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":"VEGF, Neuropsychiatric lupus, Gene polymorphism, Genetic susceptibility","lastPublishedDoi":"10.21203/rs.3.rs-3998408/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3998408/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eVascular endothelial growth factor ( VEGF ) plays an important role in the pathogenesis of neuropsychiatric lupus ( NPSLE ), This study was designed for \u0026nbsp;analysis of the relationship between single nucleotide polymorphisms of VEGF gene rs699947, rs10434, rs833070 and genetic susceptibility to NPSLE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 82 patients diagnosed as NPSLE and without other autoimmune diseases were selected as the NPSLE group (positive group). Non-NPSLE group (positive control group) 166 patients with systemic lupus erythematosus without psychiatric symptoms. According to the principle of age matching with the positive control group, 150 healthy subjects were randomly selectedas the healthy control group (negative control group). The allele and genotype of three SNP loci and the correlation between NPSLE and non-NPSLE were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e 1. The genotype frequencies of GG, GA and AA genotypesat rs10434 locus were statistically significant in NPSLE group, non-NPSLE group and negative control group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). 2. The genotype and allele frequencies of rs699947 and rs833070 loci werenot statistically significant in the NPSLE group, non-NPSLE group and negative control group (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). 3. rs10434 locus GG genotype and G gene as reference, A allele, mutant heterozygous model GA, dominant model (GA+AA ) were associated with therisk of NPSLE (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eWe found that VEGF rs10434 A allele, mutant heterozygous model GA, dominant model GA+AA can increase the risk of NPSLE. A allele and dominant model GA+AA can increase the risk of non-NPSLE. There was no significant correlation between rs699947 and rs833070 polymorphism and the risk of NPSLE and non-NPSLE.\u003c/p\u003e","manuscriptTitle":"VEGF Gene Polymorphism And Study On Genetic Susceptibility Of Neuropsychiatric Lupus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 16:25:18","doi":"10.21203/rs.3.rs-3998408/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":"ef036cd1-d23c-4250-ae88-5b55d70c1b9a","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-01T10:20:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-05 16:25:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3998408","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3998408","identity":"rs-3998408","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}