Association of TP53 polymorphic variants rs1042522 and rs1642785 with susceptibility and prognosis of acute lymphoblastic leukemia in a Brazilian Amazon population

Association of TP53 polymorphic variants rs1042522 and rs1642785 with susceptibility and prognosis of acute lymphoblastic leukemia in a Brazilian Amazon population | 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 Association of TP53 polymorphic variants rs1042522 and rs1642785 with susceptibility and prognosis of acute lymphoblastic leukemia in a Brazilian Amazon population Glenda Menezes Nogueira, Luca Gabriel Marques Gonçalves, Thaís Lohana Pereira-Ribeiro, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8679558/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2026 Read the published version in BMC Medical Genomics → Version 1 posted 12 You are reading this latest preprint version Abstract Background: Tumor suppressor genes play a central role in cancer development, and inherited genetic variation may influence both disease susceptibility and clinical outcomes. This study aimed to investigate the frequency and prognostic relevance of TP53 polymorphic variants in patients with acute lymphoblastic leukemia (ALL) from an admixed population in the Brazilian Amazon. Methods: A population-based case–control study was conducted including 193 patients diagnosed with ALL and 215 healthy controls. Germline TP53 polymorphisms rs1042522 and rs1642785 were genotyped, and allele and genotype frequencies were compared between groups. Associations with ALL susceptibility, relapse, and mortality were evaluated using multiple genetic models adjusted for age and sex. Combined genotype and haplotype analyses were performed, and overall survival was estimated using Kaplan–Meier curves and log-rank tests. Results: The CC genotype and C allele were more frequent among ALL cases than controls. The C allele of rs1042522 (p=0.021) and rs1642785 (p=0.022) were associated with increased susceptibility to ALL. In addition, the rs1042522- C was associated with a higher risk of relapse (p=0.016) and death (p=0.009). Protective effects against ALL were observed under the recessive model for rs1042522 (p=0.019) and the codominant model for rs1642785 (p=0.013). Both variants showed protective associations with mortality under the log-additive model. Combined genotype analysis revealed that rs1042522 CG and GG genotypes were associated with a reduced risk of relapse (p<0.001). Overall survival analysis revealed reduced survival associated with the CC genotype, whereas improved survival was observed for the heterozygous CG genotype. Haplotype analysis indicated that the GG haplotype conferred a reduced risk of ALL (p=0.007) and death (p=0.013). Conclusions: Our findings suggest that germline TP53 variants rs1042522 and rs1642785 modulate susceptibility and clinical outcomes in ALL, supporting their potential role as prognostic biomarkers. This study highlights the importance of population-based genomic investigations in underrepresented populations. TP53 genetic polymorphisms acute lymphoblastic leukemia Prognosis population genetics pediatric cancer Figures Figure 1 Figure 2 INTRODUCTION Acute lymphoblastic leukemia (ALL) is a lymphoproliferative hematological malignancy that predominantly affects children, with a peak incidence between 2 and 5 years of age (1,2) . In Brazil, approximately 11,540 cases of leukemia (acute and chronic) are estimated for each three-year period from 2023 to 2025. In the Northern region of the country, leukemia ranks as the sixth most frequent cancer among both men (4.53 per 100,000) and women (3.64 per 100,000), excluding non-melanoma skin cancers (3) . The etiology of ALL remains largely unknown; however, environmental factors such as ionizing radiation, as well as infections and genetic susceptibility, have been implicated in disease development. Although isolated genetic events do not fully explain leukemogenesis, the association of ALL with congenital syndromes, chromosomal translocations, and polymorphic genetic variants underscores the central role of genetic factors in its pathogenesis. Accordingly, genomic studies have increasingly focused on the impact of gene variations in pathways related to inflammation and tumor suppression (4,5) . A key gene involved in tumor suppression and activated in response to cellular stress signals, such as DNA damage, is tumor protein 53 ( TP53 ), which encodes the p53 phosphoprotein. TP53 plays a central role in safeguarding genomic integrity by regulating the cellular response to damage and preventing malignant transformation. Through its function as a transcription factor, TP53 controls the expression of multiple genes involved in critical cellular processes, including apoptosis, cell-cycle arrest, senescence, and DNA repair (6) . Given the indispensable role of TP53 , the presence of single nucleotide variants (SNVs), particularly rs1042522 , has been reported to partially impair specific p53 functions, such as apoptotic induction, potentially favoring sustained cellular proliferation (7) . In addition, rs1042522 has been described as a potential risk factor for acute myeloid leukemia (AML) (8) . In chronic lymphocytic leukemia (CLL), the TP53 variants rs1042522 , rs1642785 , and rs2909430 have been associated with an increased frequency of somatic TP53 mutations (9) . However, data regarding hematological malignancies such as ALL—especially concerning TP53 polymorphic variants—remain limited in Brazil, with a marked scarcity of information from the Amazon region. To address this gap, the present study investigated the frequency and prognostic relevance of the TP53 variants rs1042522 and rs1642785 in patients with ALL from the Brazilian Amazon, supporting their potential role as prognostic biomarkers in this population. MATERIALS AND METHODS Study design and study population This case–control study included 193 patients diagnosed with acute lymphoblastic leukemia (ALL) who were treated at the Fundação Hospitalar de Hematologia e Hemoterapia do Amazonas (HEMOAM). Patients of all age groups, both sexes and unrelated individuals, were eligible for inclusion. The control group comprised 215 healthy individuals who underwent blood donation or routine screening at HEMOAM. All controls were screened by serological testing for HIV, hepatitis C virus (HCV), hepatitis B virus (HBV), human T-lymphotropic virus types 1 and 2 (HTLV-1/2), syphilis, and Chagas disease to ensure their healthy status. In addition, participants were interviewed to assess the presence of comorbidities and other risk factors, in accordance with technical guidelines established by the Brazilian Ministry of Health. Individuals with familial relationships were excluded from the study. Patients who underwent bone marrow transplantation during the follow-up period and those diagnosed with other hematological malignancies were also excluded. Ethical issues This study was approved by the Research Ethics Committee of HEMOAM under approval number 3,335,123/2019 (CAAE: 12615918.9.0000.0009), in accordance with the principles of the Declaration of Helsinki and Resolution No. 466/12 of the Brazilian National Health Council, which governs research involving human subjects. Biological sample collection and data acquisition Approximately 2 mL of peripheral blood was collected by venipuncture into vacuum tubes containing EDTA from participants in both groups. Samples were obtained from the Biorepository of Cellular and Molecular Biomarkers in Acute Lymphoblastic Leukemia (BCM-ALL) at HEMOAM. Sociodemographic data (age, sex, race/ethnicity, and place of origin), clinical data (treatment protocol, risk group, post-induction relapse, and death), and laboratory data (immunophenotype) were retrieved from physical medical records archived in the Medical and Statistical Care System (SAME) and from electronic medical records (iDoctor and Softlab systems) at HEMOAM. Race/ethnicity was defined by self-reported skin color or race, or by parental/legal guardian report for minors, according to the classification of the Brazilian Institute of Geography and Statistics (IBGE): White, Black, Brown (Mixed-race), Yellow (Asian), and Indigenous. In addition, relapse was defined as treatment failure occurring after completion of remission induction therapy (day 35), according to the treatment protocol. Death was assessed over a minimum follow-up period of five years after diagnosis. DNA extraction and assessment of nucleic acid purity Genomic DNA was extracted using a commercial column-based method with the PureLink™ Genomic DNA Mini Kit (Invitrogen™), according to the manufacturer’s instructions. DNA concentration and purity were assessed by measuring absorbance ratios at 260/280 nm and 260/230 nm using a NanoDrop™ 2000/2000c spectrophotometer (Thermo Scientific™). SNV selection criteria and genotyping by quantitative PCR Polymorphic variants were selected based on information retrieved from the Cancer Genome Anatomy Project and SNP500 databases. The selected variants included the TP53 missense variant rs1042522 , corresponding to a c.215C>G substitution in the coding sequence that results in an amino acid change (p.Pro72Arg), as well as two intronic variants, TP53 rs1642785 (C>G) and rs2909430 (C>T), located in exon 4 and introns 2 and 4, respectively. The criteria for SNV selection included their predicted functional effects, minor allele frequency (MAF ≥ 3%), and previously reported associations with hematological malignancies, including leukemia. Genotyping of the selected variants was performed using quantitative real-time PCR (qPCR) with allele-specific TaqMan probes (Applied Biosystems) ( Supplementary Table 1 ), following the protocol described by Alves et al. (2021) (10) . Amplification was carried out using the StepOnePlus™ Real-Time PCR System, and genotype assignment was performed with StepOne Software v2.3, which allowed genotype discrimination based on the amplification curves generated. Data analysis and statistical methods Descriptive and statistical analyses were performed using GraphPad Prism version 10 (San Diego, CA, USA). Comparisons between groups were conducted using Fisher’s exact test, with results expressed as odds ratios and 95% confidence intervals (95% CI). Overall survival (OS) was defined as the time from diagnosis to death from any cause and was estimated using the Kaplan–Meier method, with differences between groups assessed by the log-rank test. Hardy–Weinberg equilibrium (HWE) for all SNVs was evaluated using R software version 4.4.1 (www.r-project.org) with the SNPassoc package. The same package was used to assess associations between allele and genotype frequencies and susceptibility to ALL, relapse, and death under four genetic inheritance models: codominant, dominant, recessive, and overdominant. The best-fitting genetic model was selected based on the Akaike Information Criterion (AIC). P-values were adjusted for multiple comparisons using the Bonferroni correction, and statistical significance was defined as p <0.025. Combined analyses (gene–gene interactions) of the SNVs were also performed using the SNPassoc package, considering p < 0.05 as statistically significant. Linkage disequilibrium (LD) was assessed using the haplo.stats package. LD was evaluated using D′ to estimate the strength of disequilibrium and pairwise r² to indicate the degree of correlation between allele frequencies, with r² values > 0.8 indicating strong LD, values < 0.8 indicating weak LD, and values < 0.1 indicating negative LD. Only haplotypes with a frequency greater than 5% were included in comparative analyses. RESULTS Sociodemographic, clinical, and epidemiological profile of the study population A total of 215 healthy individuals were included in the control group, with a median age of 29 years. The case group comprised 193 patients diagnosed with acute lymphoblastic leukemia (ALL), with a median age of 9 years. In both groups, males were predominant, accounting for 67% of the control group and 59% of the ALL group. Regarding racial distribution, 82% of patients with ALL self-identified as Brown (Mixed-race). All individuals in the control group (100%) resided in Manaus, while 71% of patients in the ALL group also reported residence in the capital city. Among 70 patients with available information, 56% reported no family history of cancer ( Table 1 ). With respect to immunophenotype, 91% of patients with ALL presented with B-cell ALL (B-ALL), whereas 9% had T-cell ALL (T-ALL). Additionally, 68% of patients were classified into the high-risk group. Most patients were treated according to the Berlin–Frankfurt–Münster (BFM) European Group protocol (50%), while 32% received treatment following the Brazilian Childhood Leukemia Treatment Group (GBTLI) protocol. During treatment, 53% of patients did not relapse after remission induction, and 71% were alive at the end of follow-up ( Table 1 ). Table 1. Sociodemographic, laboratory and clinical characteristics of the study population. Variables (completeness) Controls 215 (100) ALL 191 (98) Age , years, median [IQR] a 29 [4-65] 9 [0-69] Sex 215 (100) 193 (100) Male, n (%) 145 (67) 114 (59) Female, n (%) 70 (33) 79 (41) Ethnicity 120 (62) White, n (%) - 12 (10) Admixed, n (%) - 99 (82) Other, n (%) - 9 (8) Immunophenotype 193 (100) B-ALL, n (%) - 176 (91) T-ALL, n (%) - 17 (9) Residence 191 (98) Manaus, n (%) 215 (100) 136 (71) Interior of Amazonas, n (%) - 53 (28) Other state, n (%) - 2 (1) Risk Group 179 (92) Low Risk, n (%) - 58 (32) High Risk, n (%) - 121 (68) Treatment protocol 179 (92) GBTLI, n (%) - 57 (32) BFM, n (%) - 90 (50) GMALL, n (%) - 22 (12) HyperCVAD, n (%) - 3 (2) GMALL + HyperCVAD, n (%) - 7 (4) Relapse 145 (75) Yes, n (%) - 68 (47) No, n (%) - 77 (53) Death 185 (96) Yes, n (%) - 54 (29) No, n (%) - 131 (71) Family history of cancer 70 (36) Yes, n (%) - 31 (44) No, n (%) - 39 (56) IQR = Interquartile Range. Other: black 0 (0%), indigenous 8 (6.67%) and yellow 1 (0.83%). Other states: Pará 1 (0.52%) and Roraima 1 (0.52%). European Group Berlin-Frankfurt-Münster (BFM). Brazilian Group for the Treatment of Leukemia in Childhood (GBTLI). Group for Adult Acute Lymphoblastic Leukemia (GMALL). Ethnicity was defined by self-declaration of color or race or declaration by parents/guardians for minors, following the classification used by the Brazilian Institute of Geography and Statistics (IBGE): white, black, brown, yellow, and indigenous. Allele frequencies and their association with prognosis in patients with acute lymphoblastic leukemia Allele frequency analysis revealed a higher prevalence of the C allele in both study groups. However, comparative analysis showed that the C allele was more frequent in the case group than in the control group, whereas a higher proportion of the G allele was observed among healthy individuals ( Supplementary Figures 1A and 1B ). Allelic association analysis demonstrated that the C allele of rs1042522 was associated with an increased risk of developing ALL compared with the G allele (OR: 1.44; 95% CI: 1.06–1.96; p = 0.021) ( Table 2 ). In addition, the rs1042522 -C was associated with a higher risk of relapse (OR: 1.97; 95% CI: 1.14–3.32; p= 0.016) ( Table 3 ) and death (OR: 2.15; 95% CI: 1.21–2.87; p= 0.009) ( Table 4 ). Table 2. Multivariate analysis adjusted for sex and age for the association of single nucleotide variants (SNVs) with the risk of acute lymphoblastic leukemia. Genetic models Controls n= 215 (%) ALL n= 193 (%) OR (95% CI) p value AIC OR (95% CI) adj p value adj AIC rs1042522 C>G (HWE:1.0000) Codominant C/C 96 (44.7) 106 (54.9) C/G 95 (44.2) 75 (38.9) 0.71 (0.47 – 1.08) 0.056 564.7 0.75 (0.46 – 1.23) 0.033 443.7 G/G 24 (11.2) 12 (6.2) 0.45 (0.21 – 0.95) 0.34 (0.14 – 0.79) Dominant C/C 96 (44.7) 106 (54.9) C/G-G/G 119 (55.3) 87 (45.1) 0.66 (0.45 – 0.98) 0.038 564.1 0.65 (0.41 – 1.03) 0.067 445.1 Recessive C/C-C/G 191 (88.8) 181 (93.8) G/G 24 (11.2) 12 (6.2) 0.53 (0.26 – 1.09) 0.075 565.3 0.39 (0.17 – 0.88) 0.019 443.0 Overdominant C/C-G/G 120 (55.8) 118 (61.1) C/G 95 (44.2) 75 (38.9) 0.80 (0.54 – 1.19) 0.275 567.2 0.89 (0.56 – 1.42) 0.637 448.3 Alleles C 287 (66.74) 287 (74.35) 1.44 (1.06 – 1.96) 0.021 G 143 (33.26) 99 (25.65) Log-Additive 0,1,2 215 (52.7) 193 (47.3) 0.69 (0.51 – 0.94) 0.017 562.7 0.65 (0.45 – 0.92) 0.014 442.6 rs1642785 C>G (HWE: 0.5363) Codominant C/C 101 (47.0) 111 (57.5) C/G 95 (44.2) 73 (37.8) 0.70 (0.47 – 1.05) 0.054 564.6 0.65 (0.40 – 1.06) 0.013 441.9 G/G 19 (8.8) 9 (4.7) 0.43 (0.19 – 1.00) 0.28 (0.11 – 0.73) Dominant C/C 101 (47.0) 111 (57.5) C/G-G/G 114 (53.0) 82 (42.5) 0.65 (0.44 – 0.97) 0.033 563.9 0.58 (0.36 – 0.92) 0.018 443.0 Recessive C/C-C/G 196 (91.2) 184 (95.3) G/G 19 (8.8) 9 (4.7) 0.50 (0.22 –1.14) 0.091 565.6 0.34 (0.14 – 0.86) 0.018 442.9 Overdominant C/C-G/G 120 (55.8) 120 (62.2) 0.77 (0.52 – 1.14) 0.191 566.7 0.77 (0.48 – 1.22) 0.259 447.2 C/G 95 (44.2) 73 (37.8) Alleles C 270 (69.07) 295 (76.42) 1.45 (1.06 – 1.97) 0.022 G 116 (30.93) 91 (23.58) Log-Additive 0,1,2 215 (52.7) 193 (47.3) 0.68 (0.49 – 0.93) 0.016 562.7 0.59 (0.40 – 0.85) 0.004 440.4 Adjusted for sex and age (p-value adj, OR adj); OR: Odds Ratio; p-value: < 0.025; 95% confidence interval; AIC: Akaike Criterion Value; HWE: Hardy-Weinberg equilibrium. Similarly, the rs1642785 -C was associated with increased susceptibility to ALL compared with the G allele (OR: 1.45; 95% CI: 1.06–1.97; p= 0.022). However, no statistically significant associations were observed between rs1642785 and relapse (OR: 1.57; 95% CI: 0.91–2.73; p= 0.063) or death (OR: 1.78; 95% CI: 0.98–3.32; p= 0.058). Assessment of Hardy–Weinberg equilibrium indicated that rs2909430 was not in equilibrium and was monomorphic in the association analyses. Consequently, case–control comparative analysis for this SNV was not performed. Nevertheless, allele frequency comparison across populations revealed similarity with the low frequency reported in East Asian populations ( Supplementary Figure 2 ). Furthermore, genotype frequencies of rs1042522 and r s1642785 in the study population were comparable to those observed in admixed American populations ( Supplementary Figure 3 ). Genotype frequencies and their association with prognosis in patients with acute lymphoblastic leukemia The distribution of genotype frequencies for the polymorphic variants showed a similar pattern for both SNVs ( rs1042522 and rs1642785 ), with the CC, CG, and GG genotypes observed, respectively. Despite this shared distribution pattern, the CC genotype was more prevalent in cases than in controls, whereas the GG genotype was more frequent among controls for both rs1042522 and rs1642785 ( Supplementary Figures 1C and 1D ). Adjusted analyses revealed that the TP53 SNVs rs1042522 ( adj OR: 0.39; 95% CI: 0.17–0.88; p= 0.019) and rs1642785 ( adj OR: 0.65; 95% CI: 0.40–1.06; p= 0.013) were associated with a protective effect against the development of ALL after adjustment for age and sex ( Table 2 ). Table 3 presents the association analyses between genotypes and relapse following remission induction. In this cohort, rs1042522 was significantly associated with protection against relapse (adjusted OR: 0.51; 95% CI: 0.29–0.92; adjusted p= 0.022), whereas rs1642785 showed no statistically significant association (adjusted OR: 0.52; 95% CI: 0.26–1.05; adjusted p= 0.067). Table 3. Association analysis of SNVs in the TP53 gene with relapse in patients with acute lymphoblastic leukemia. Genetic models No n=77 (%) Yes n=68 (%) OR (95% CI) adj p value adj AIC rs1042522 C>G (HWE:1.0000) Codominant C/C 33 (43.4) 43 (63.2) C/G 36 (47.4) 23 (33.8) 0.52 (0.25 – 1.05) 0.073 196.5 G/G 7 (9.2) 2 (2.9) 0.26 (0.05 – 1.36) Dominant C/C 33 (43.4) 43 (63.2) C/G-G/G 43 (56.6) 25 (36.8) 0.48 (0.24 – 0.95) 0.033 195.2 Recessive C/C-C/G 69 (90.8) 66 (97.1) G/G 7 (9.2) 2 (2.9) 0.35 (0.07 – 1.78) 0.173 197.9 Overdominant C/C-G/G 40 (52.6) 45 (66.2) C/G 36 (47.4) 23 (33.8) 0.59 (0.30 – 1.18) 0.133 197.5 Alleles C 102 (67.11) 109 (80.15) 1.97 (1.14– 3.32) 0.016 G 50 (32.89) 27 (19.85) Log-Additive 0,1,2 76 (52.8) 68 (47.2) 0.51 (0.29 – 0.92) 0.022 194.5 rs1642785 C>G (HWE:0.5553) Codominant C/C 37 (48.7) 45 (66.2) 0.180 C/G 35 (46.1) 20 (29.4) 0.51 (0.25 – 1.05) 198.3 G/G 4 (5.3) 3 (4.4) 0.80 (0.16 – 3.94) Dominant C/C 37 (48.7) 45 (66.2) 0.54 (0.27 – 1.07) 0.076 196.6 C/G-G/G 39 (51.3) 23 (33.8) Recessive C/C-C/G 72 (94.7) 65 (95.6) G/G 4 (5.3) 3 (4.4) 1.07 (0.22 – 5.11) 0.935 199.7 Overdominant C/C-G/G 41 (53.9) 48 (70.6) C/G 35 (46.1) 20 (29.4) 0.52 (0.26 – 1.05) 0.067 196.4 Alleles C 109 (71.71) 104 (80) 1.57 (0.91 – 2.73) 0.063 G 43 (28.29) 26 (20) Log-Additive 0,1,2 76 (52.8) 68 (47.2) 0.64 (0.35 – 1.17) 0.141 197.6 Adjusted for sex and age (p-value adj, OR adj); OR: Odds Ratio; p-value: < 0.025; 95% confidence interval; AIC: Akaike Criterion Value; HWE: Hardy-Weinberg equilibrium. Furthermore, both rs1042522 (adjusted OR: 0.45; 95% CI: 0.25–0.81; adjusted p= 0.005) and rs1642785 (adjusted OR: 0.52; 95% CI: 0.29–0.94; adjusted p= 0.024) were associated with a protective effect against death ( Table 4 ). Table 4. Association analysis of SNVs in the TP53 gene with death in patients with acute lymphoblastic leukemia. Genetic models No n=131 (%) Yes n=54 (%) OR (95% CI) adj p value adj AIC rs1042522 C>G (HWE:1.0000) Codominant C/C 63 (48.1) 37 (68.5) C/G 57 (43.5) 16 (29.6) 0.48 (0.24 – 0.95) 0.018 221.5 G/G 11 (8.4) 1 (1.9) 0.15 (0.02 – 1.25) Dominant C/C 63 (48.1) 37 (68.5) C/G-G/G 68 (51.9) 17 (31.5) 0.43 (0.22 – 0.83) 0.010 220.9 Recessive C/C-C/G 120 (91.6) 53 (98.1) G/G 11 (8.4) 1 (1.9) 0.21 (0.03 – 1.63) 0.067 224.1 Overdominant C/C-G/G 72 (56.5) 38 (70.4) 0.55 (0.28 – 1.08) 0.075 224.3 C/G 57 (43.5) 16 (29.6) Alleles C 183 (69.85) 90 (83.33) 2.15 (1.21 – 3.87) 0.009 G 79 (30.15) 18 (16.67) Log-Additive 0,1,2 131 (70.8) 54 (29.2) 0.45 (0.25 – 0.81) 0.005 219.6 rs1642785 C>G (HWE:0.5553) Codominant C/C 68 (51.9) 37 (68.5) C/G 55 (42.0) 16 (29.6) 0.53 (0.27 – 1.06) 0.078 224.3 G/G 8 (6.1) 1 (1.9) 0.23 (0.03 – 1.91) Dominant C/C 68 (51.9) 37 (68.5) 0.50 (0.25 – 0.97) 0.036 223.0 C/G-G/G 63 (48.1) 17 (31.5) Recessive C/C-C/G 123 (93.9) 53 (98.1) G/G 8 (6.1) 1 (1.9) 0.29 (0.04 – 2.38) 0.182 225.6 Overdominant C/C-G/G 76 (58.0) 38 (70.4) 0.58 (0.29 – 1.15) 0.112 224.9 C/G 55 (42.0) 16 (29.6) Alleles C 191 (73.75) 90 (83.33) 1.78 (0.98 – 3.22) 0.058 G 68 (26.25) 18 (16.67) Log-Additive 0,1,2 131 (70.8) 54 (29.2) 0.52 (0.29 – 0.94) 0.024 222.4 Adjusted for sex and age (p-value adj, OR adj); OR: Odds Ratio; p-value: < 0.025; 95% confidence interval; AIC: Akaike Criterion Value; HWE: Hardy-Weinberg equilibrium. Interaction Analysis of rs1042522 and rs1642785 on Clinical Outcomes To evaluate interactions between the SNVs, a combined analysis of rs1042522 and rs1642785 was performed using a logistic regression model adjusted for age and sex. The results showed that, among the analyzed combinations and interactions, individuals heterozygous for rs1042522 (CG) exhibited a protective effect against relapse compared with CC homozygotes (adjusted OR: 0.82; 95% CI: 0.25–0.98; p < 0.001). No statistically significant associations were observed between genotype combinations and susceptibility to ALL or death. However, age emerged as a significant covariate influencing clinical outcomes. Increasing age was associated with an 8% reduction in the risk of ALL susceptibility per year (adjusted OR: 0.92; 95% CI: 0.90–0.93; p < 0.001), while it was also associated with a 4% increase in the risk of relapse following remission induction therapy (adjusted OR: 1.04; 95% CI: 1.00–1.07; p= 0.026), as shown in Supplementary Table 2 . Impact of TP53 rs1042522 and rs1642785 on Overall Survival Overall survival (OS) analysis according to TP53 SNV genotypes revealed that specific TP53 genotypes significantly influenced survival outcomes in patients with ALL. For rs1042522 , individuals with the CC genotype were associated with a 2.08-fold higher mortality risk compared with patients harboring the CG genotype (p= 0.030; Figure 1A ), with a median survival of only 22 months ( Table 5 ). In contrast, heterozygous CG individuals showed significantly prolonged overall survival, with a median of 75 months ( Table 5 ). Similar findings were observed for TP53 rs1642785 . Patients homozygous for the C allele (CC vs. CG) demonstrated a strong association with poorer survival (HR: 2.36; 95% CI: 1.24–4.50; p= 0.012), with a median survival of only 20 months ( Table 5 ). Conversely, individuals with the heterozygous CG genotype of rs1642785 exhibited a protective advantage ( Figure 1B ). Table 5. Overall survival analysis according to genotypes of the evaluated single nucleotide variants. Genetic models ALL Deaths n= 44 (%) Median Survival (months) HR (95% CI) Log-rank p value rs1042522 C>G C/C 30 (68.2) 22.00 2.08 (1.09 – 3.96) 0.030 C/G 13 (29.5) 75.00 G/G 1 (2.3) - - - rs1642785 C>G C/C 31 (70.5) 20.00 2.36 (1.24 – 4.50) 0.012 C/G 13 (29.5) 75.00 G/G 0 (0) - - - HR: Hazard Ratio (log-rank); p value log-rank (Mantel-Cox) test: <0.05; CI of Ratio 95%: confidence interval. Haplotype analysis of TP53 v ariants The polymorphic variants under investigation were found to be in strong linkage disequilibrium (LD), with a D′ value of 0.893, indicating that they are frequently inherited together. The pairwise correlation coefficient (r²= 0.716) reflected a strong correlation between allele frequencies in the analysis of ALL susceptibility. Similar LD patterns were observed in analyses involving relapse and death, with D′ = 0.891 and r² = 0.711. Four haplotypes were identified, of which two exhibited frequencies greater than 5%. The CC haplotype was the most prevalent in the studied population ( Figure 2A ). Both common haplotypes showed significant associations with ALL. Individuals carrying the GG haplotype had a 42% lower risk of developing ALL compared with those carrying the CC haplotype (adjusted OR: 0.58; 95% CI: 0.40–0.86; p = 0.007), as well as a reduced risk of death (adjusted OR: 0.44; 95% CI: 0.23–0.85; p = 0.0138). Age was identified as a significant covariate, with the risk of ALL decreasing by 8% per additional year of age (adjusted OR: 0.92; 95% CI: 0.90–0.93; p < 0.001), whereas the risk of relapse increased by 4% per year (adjusted OR: 1.04; 95% CI: 1.00–1.07; p = 0.0275). The remaining two haplotypes showed frequencies below the predefined threshold and were therefore classified as rare haplotypes. These haplotypes did not demonstrate statistically significant associations with ALL susceptibility or clinical outcomes (adjusted OR: 0.76; 95% CI: 0.42–1.38; p = 0.368) ( Figure 2B ). DISCUSSION The TP53 gene plays a central role in maintaining genomic stability by regulating the expression of target genes involved in cell-cycle control mechanisms, thereby ensuring cellular homeostasis. However, TP53 alterations are frequently observed across multiple human cancers (6) . Disruption of TP53 may compromise the wild-type function of the p53 protein, contributing to tumor promotion and disease progression (7) . In acute lymphoblastic leukemia (ALL), alterations in the TP53/RB1 tumor suppressor pathway are commonly reported, particularly in high-risk patients (11) . Increasing evidence suggests that polymorphic variants in TP53 are associated with susceptibility to ALL and may provide important insights into genetic factors influencing disease incidence and heterogeneity (5,12) . In the present study, the clinical and sociodemographic characteristics of the study population were consistent with those reported in previous epidemiological investigations conducted in the same region (13) . Our findings demonstrated that the TP53 rs1042522 -GG genotype exerted a protective effect against ALL. These results are in agreement with the study by Skhoun et al. (2022), which reported that the CG and GG genotypes of rs1042522 were associated with a protective effect against ALL in Moroccan children (14) . Nevertheless, the role of rs1042522 appears to be context-dependent, with studies reporting heterogeneous findings ranging from no association to either protective or risk effects across different cancer types (15) . In Brazil, a single study conducted in a predominantly Caucasian population from the Southeast region reported an association between the rs1042522 -C and increased susceptibility to ALL (16) . A meta-analysis reported that the rs1042522 -CC is correlated with an increased risk of ALL (OR = 1.73; 95% CI = 1.07–2.81) (17) . Although the specific effect of the rs1042522 -CC was not explicitly modeled as an isolated variable in our analysis, with findings suggest that this genotype is associated with increased susceptibility to ALL when compared with the CG and GG genotypes, given its higher prevalence in the case group. Similarly, Basabaeen et al. (2019) reported up to a tenfold increase in the risk of B-cell chronic lymphocytic leukemia (B-CLL) among individuals from the Sudanese population carrying the CC genotype compared with those harboring the GG genotype (18) . The rs1642785 SNV also demonstrated a protective effect against ALL in our cohort. In patients with colorectal cancer diagnosed before the age of 57 years, the rs1642785- CG has been reported to confer protection against disease development (19) . In contrast, the CC genotype has been associated with an increased risk of CLL (OR = 1.73; 95% CI = 1.01–2.95; p = 0.048) and with a higher frequency of somatic TP53 mutations (9) . The rs1642785- CG has also been identified in sporadic and familial cases of AML and ALL, exhibiting a similar degree of homozygosity to rs1042522 in most cases (20) . To our knowledge, this is the first study to investigate the impact of rs1642785 on ALL. Considering that ALL predominantly affects pediatric populations, findings from studies on CLL and AML may reflect genetic risk patterns primarily relevant to adult disease. Furthermore, combined analysis of the SNVs adjusted for age and sex demonstrated that increasing age was strongly associated with protection against ALL susceptibility, reinforcing the well-established epidemiological observation that ALL is more prevalent in children and has a lower incidence in adults (2,13) . In the allele-based analysis, we observed that the C allele of both variants was associated with an increased risk of developing ALL. In patients with chronic lymphocytic leukemia (CLL), the C allele of rs1042522 and rs1642785 has been associated with higher expression of lipoprotein lipase (LPL) in leukemic B cells. Increased LPL expression may promote neoplastic progression by supplying metabolic substrates and creating a microenvironment favorable to cell survival and proliferation. In this context, Abramenko et al. (2021) suggested that these SNVs may act as genetic modifiers, influencing the development of CLL (21) . It has also been reported that the rs1642785 -C affects mRNA stability, leading to reduced levels of p53 pre-mRNA and total TP53 transcripts (22) . Reduced TP53 transcription or impaired activation under conditions of cellular stress may compromise tumor suppressor function, thereby facilitating tumor development and progression (23) . Conversely, the rs1642785-G has been described as significantly more frequent in individuals with glioma than in control populations, suggesting that this variant may also confer increased risk in other oncological contexts (24) . To assess genetic variability, we compared allele frequencies across different populations. The frequencies of both SNVs in our cohort were similar to those reported in admixed American populations, reflecting the high degree of genetic admixture in the Brazilian population, which is characterized by ancestral contributions from Indigenous peoples, Europeans, and Africans. In Northern Brazil, the focus of the present study, Indigenous South American ancestry predominates (25) . This genetic background may partially explain discrepancies among studies in literature and the allele frequency patterns observed here in relation to ALL susceptibility. Indeed, admixed children, particularly those with Indigenous ancestry, have been reported to exhibit increased susceptibility to ALL due to the presence of ancestral-specific genetic variants (10) . In addition, some authors have hypothesized that the frequency of the rs1042522 variant may vary according to altitude, suggesting that Andean populations may have undergone genetic adaptation to environmental factors such as hypoxia, ultraviolet radiation, and extreme climatic conditions. Such adaptations may involve selective combinations of alleles and genotypes within the TP53 pathway, further reinforcing the importance of considering genetic associations within a population- and region-specific context (26) . To further investigate the prognostic impact of the studied variants in ALL, we evaluated their association with relapses, which remains a major therapeutic challenge. Combined SNV analysis revealed that individuals heterozygous for rs1042522 (CG) had a lower risk of relapse compared with those carrying the CC genotype. In high-grade osteosarcoma, rs1042522 has been associated with an increased risk of relapse (27) , whereas data reported by Popek-Marciniec et al. (2023) showed no association between rs1042522 and relapse risk in patients with multiple myeloma (28) . These findings highlight the context-dependent role of this variant across different malignancies. Allele frequency analysis further demonstrated that the C allele was associated with an increased risk of relapse. It is well established that the C allele (Pro72) has a reduced ability to induce apoptosis in damaged cells, whereas the G allele (Arg72) is more effective due to its greater mitochondrial localization and enhanced transcription of several TP53 -regulated pro-apoptotic genes. In contrast, the C allele is more efficient in promoting cell-cycle arrest, favoring DNA damage repair (29) . This functional divergence may help explain our findings, as individuals carrying the C allele may exhibit increased resistance to chemotherapy in this context. Notably, inactivation of the rs1042522 -C has been reported to influence the progression of BCR::ABL1-positive ALL. In approximately 12% of relapse cases, loss of heterozygosity of this allele was observed in leukemic cells, suggesting functional inactivation that may contribute to treatment resistance (30) . The rs1642785 SNV exhibited a protective effect with respect to relapse. When evaluating the outcome of death, the log-additive model showed that both SNVs exerted a significant protective effect. However, the C allele of rs1042522 was associated with an increased risk for this outcome. In multiple myeloma, rs1042522 has been reported to have no significant impact on mortality (28) . Similarly, a large cohort study reported no significant association between rs1042522 and cancer-related mortality (31) . Overall, data regarding the specific roles of these SNVs in relapse and death remain limited, particularly in hematological malignancies. To our knowledge, this is the first study to associate rs1642785 with these clinical outcomes in ALL. Overall survival analysis of both SNVs revealed that the CG genotype was associated with longer survival than the CC genotype in patients with ALL. Our combined analyses further reinforced the protective role of the heterozygous CG genotype, corroborating the findings observed across different analytical approaches. Skhoun et al. (2022) also suggested that the heterozygous genotype confers a survival advantage in patients with ALL (14) . In addition, the CG and GG genotypes have been associated with improved OS in glioblastoma (32) . Regarding rs1642785 , a study in osteosarcoma similarly reported a protective effect, with associations to improved event-free survival and OS (27) . OS analysis also associated the CC genotype of both SNVs with poorer survival compared with the CG genotype. Indeed, rs1042522 has previously been associated with reduced OS (p = 0.004), with a mean survival of only 12 months reported for patients with plasma cell myeloma carrying the CC genotype (33) . In acute myeloid leukemia (AML), genotype distributions did not show a significant impact on OS; however, rs1042522 has been reported to reduce apoptotic potential, thereby increasing leukemia risk and being associated with poorer OS and higher relapse rates (8) . In addition, rs1642785 has been shown to influence survival outcomes in patients with osteosarcoma (34) . Given the close genomic proximity of the studied SNVs, linkage disequilibrium analysis enabled haplotype-based evaluation, revealing that inherited allele combinations, particularly the GG haplotype, were associated with protection against ALL and death when compared with the CC haplotype. Preliminary evidence has suggested co-segregation of rs1042522 and rs1642785 , supporting the presence of linkage disequilibrium between these variants (20) . Age also emerged as an important variable in haplotype analyses, acting as an increasing risk factor for relapse with each additional year. In this regard, the literature consistently reports that older individuals with ALL tend to respond less favorably to therapy due to differences in tumor biology when compared with younger patients (35) . Despite the strengths of this study, some limitations should be acknowledged. The relatively modest sample size compared with large-scale polymorphism studies limited statistical power and resulted in the absence of events for certain genotype combinations. For overall survival analyses, complete information on the date of death was available for only 44 patients, limiting the number of evaluable events. The highly unbalanced genotypic distribution, including the very low frequency of the GG genotype (Table 5), reduced the statistical power to detect genotype-specific survival differences. In addition, the analyzed variants may be co-inherited as haplotypes, potentially contributing to the overlapping survival patterns observed. Larger cohorts will be necessary to independently assess the impact of these variants on overall survival. In addition, functional and gene expression data were not available, which limits direct inference regarding the biological mechanisms underlying the observed associations. Finally, differences in genetic ancestry and population structure may contribute to variability across studies, reinforcing the importance of regional and population-specific investigations. Future studies integrating larger cohorts and functional approaches will be essential to validate and expand upon these findings. CONCLUSION In conclusion, the present study demonstrates that the TP53 variants rs1042522 and rs1642785 are significantly associated with susceptibility and clinical outcomes in patients with ALL from the Brazilian Amazon. Our findings highlight that these variants modulate the risk of disease development, relapses, and overall survival, with a notable protective effect conferred by specific heterozygous genotypes and the GG haplotype. These results support the role of germline TP53 variation as a relevant prognostic biomarker in ALL, especially in populations with unique genetic backgrounds. Thus, our data emphasizes the importance of regional genetic studies and warrant further investigation in larger cohorts and functional assays to explore the clinical utility of these variants in risk stratification and personalized therapy. Declarations AUTHOR CONTRIBUTIONS Conceptualization: Allyson Guimarães Costa, Glenda Menezes Nogueira, and Fabíola Silva Alves-Hanna. Data curation: Glenda Menezes Nogueira, Thaís Lohana Pereira Ribeiro, Fabíola Silva Alves-Hanna, Fábio Magalhães Gama, and Nilberto Dias Araújo. Formal analysis: Glenda Menezes Nogueira, Fabíola Silva Alves-Hanna, and Allyson Guimarães Costa. Funding acquisition: Adriana Malheiro, Andréa Monteiro Tarragô and Allyson Guimarães Costa. Investigation: Glenda Menezes Nogueira, Luca Gabriel Marques Gonçalves, Larissa Silva Santos, Thaís Lohana Pereira Ribeiro, Fábio Magalhães Gama, Nilberto Dias Araújo and Fabíola Silva Alves-Hanna. Methodology: Glenda Menezes Nogueira, Luca Gabriel Marques Gonçalves, Larissa Silva Santos, Thaís Lohana Pereira Ribeiro and Fabíola Silva Alves-Hanna. Project administration: Adriana Malheiro, Andréa Monteiro Tarragô and Allyson Guimarães Costa. Writing – original draft: Glenda Menezes Nogueira, Fabíola Silva Alves-Hanna, and Allyson Guimarães Costa. Writing – review & editing: Glenda Menezes Nogueira, Fábio Magalhães Gama, Nilberto Dias Araújo Adriana Malheiro, Andréa Monteiro Tarragô, Fabíola Silva Alves-Hanna, and Allyson Guimarães Costa. ETHICS APPROVAL AND CONSENT TO PARTICIPATE All study protocols and informed consent procedures were approved by the Research Ethics Committee of HEMOAM (CEP/HEMOAM; approval number 3,335,123/2019). All patients were treated in accordance with the guidelines and recommendations of the Brazilian Ministry of Health. CONFLICT OF INTEREST The authors declare that they have no commercial or financial relationships that could be construed as a potential conflict of interest. DECLARATION OF INTEREST STATEMENT The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s. Due to ethical restrictions regarding patient privacy, data are available upon request. Data are available upon request from the Ethics Committee of HEMOAM (CEP-HEMOAM), [email protected] , for researchers who meet the criteria for access to confidential data. Additional requests for the data may be sent to the corresponding author or coauthors Allyson G. Costa ( [email protected] ) and Fabíola S. Alves-Hanna ( [email protected] ). ACKNOWLEDGMENTS The authors thank the Genomics Laboratory of the Fundação Hospitalar de Hematologia e Hemoterapia do Amazonas (HEMOAM) for technical support and for providing the facilities necessary to conduct the experiments. FUNDING This work was supported by a grant from the Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM) (PRÓ-ESTADO Program - #002/2008, #007/2018, #005/2019 and POSGRAD Program - #002/2025) and Genomic Surveillance Network in Health of the State of Amazonas (REGESAM), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (INCT-Sangue – Process #405918/2022-4) e Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (PDPG-CONSOLIDACAO-3-4 Program – Process #88887.707248/2022-00). GMN, LGMG, TLPR, LSS, and LSP received undergraduate and master’s scholarships from FAPEAM. FSH was supported by a CNPq Junior Postdoctoral Fellowship (PDJ). AM is level 1B research fellow of the CNPq. AGC is a level 2 research fellow of CNPq. The funding agencies had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. References Fujita TC, Sousa-Pereira N, Amarante MK, Watanabe MAE. Acute lymphoid leukemia etiopathogenesis. Mol Biol Rep . 2021;48(1):817–22. Terwilliger T, Abdul-Hay M. Acute lymphoblastic leukemia: a comprehensive review and 2017 update. Blood Cancer J . 2017;7(6):e577. Instituto Nacional de Câncer José Alencar Gomes da Silva (INCA). Estimativa 2023: incidência de câncer no Brasil . Rio de Janeiro: INCA; 2022. Malard F, Mohty M. Acute lymphoblastic leukaemia. Lancet . 2020;395(10230):1146–62. Inaba H, Mullighan CG. Pediatric acute lymphoblastic leukemia. Haematologica . 2020;105(11):2524–39. Boutelle AM, Attardi LD. p53 and tumor suppression: it takes a network. Trends Cell Biol . 2021;31(4):298–310. Ounalli A, Moumni I, Mechaal A, Chakroun A, Barmat M, Rhim RE, et al. TP53 gene 72 Arg/Pro (rs1042522) single nucleotide polymorphism increases the risk and severity of chronic lymphocytic leukemia. Front Oncol . 2023;13:1272876. Tripon F, Iancu M, Trifa A, Crauciuc GA, Boglis A, Balla B, et al. Association analysis of TP53 rs1042522, MDM2 rs2279744, rs3730485, MDM4 rs4245739 variants and acute myeloid leukemia susceptibility and clinical features. J Clin Med . 2020;9(6):1–16. Bilous N, Abramenko I, Saenko V, Chumak A, Dyagil I, Martina Z, et al. Clinical relevance of TP53 polymorphic genetic variations in chronic lymphocytic leukemia. Leuk Res . 2017;58:1–8. Alves FS, Xabregas LA, Kerr MWA, Souza GL, Pereira DS, Magalhães-Gama F, et al. Genetic polymorphisms of inflammasome genes associated with pediatric acute lymphoblastic leukemia and clinical prognosis in the Brazilian Amazon. Sci Rep . 2021;11:9869. Zhang J, Mullighan CG, Harvey RC, Wu G, Chen X, Edmonson M, et al. Key pathways are frequently mutated in high-risk childhood acute lymphoblastic leukemia. Blood . 2011;118(11):3080–7. Pui CH, Yang JJ, Hunger SP, Pieters R, Schrappe M, Biondi A, et al. Childhood acute lymphoblastic leukemia: progress through collaboration. J Clin Oncol . 2015;33(27):2938–48. Silva-Junior AL, Alves FS, Kerr MWA, Xabregas LA, Gama FM, Rodrigues MGA, et al. Acute lymphoid and myeloid leukemia in a Brazilian Amazon population. PLoS One . 2019;14(8):e0221518. Skhoun H, Khattab M, Belkhayat A, Takki Chebihi Z, Bakri Y, Dakka N, et al. Association of TP53 gene polymorphisms with the risk of acute lymphoblastic leukemia in Moroccan children. Mol Biol Rep . 2022;49(9):8291–300. Doffe F, Carbonnier V, Tissier M, Leroy B, Martins I, Mattsson JSM, et al. Identification and functional characterization of new missense SNPs in TP53. Cell Death Differ . 2021;28(5):1477–92. de Lourdes Perim A, Guembarovski RL, Oda JMM, Lopes LF, Ariza CB, Amarante MK, et al. CXCL12 and TP53 polymorphisms as markers of susceptibility in Brazilian children with ALL. Mol Biol Rep . 2013;40(7):4591–6. Tian X, Dai S, Sun J, Jiang S, Jiang Y. TP53 Arg72Pro polymorphism and leukemia risk: a meta-analysis. Sci Rep . 2016;6:24097. Mohammed Basabaeen AA, Abdelgader EA, Babekir EA, Abdelrahim SO, Eltayeb NH, Altayeb OA, et al. TP53 Arg72Pro polymorphism and B-CLL risk in Sudanese population. Asian Pac J Cancer Prev . 2019;20(5):1579–85. Alhadheq AM, Parine NR, Shaik JP, Alhadheq R, Alkahtani S, Aljarba NH, et al. TP53 polymorphisms and colorectal cancer risk. J King Saud Univ Sci . 2023;35(10):102936. Hamadou WS, Besbes S, Bourdon V, Ben Youssef Y, Laatiri MA, Noguchi T, et al. TP53 mutations in familial and sporadic hematological malignancies. Fam Cancer . 2017;16(1):153–7. Abramenko I, Bilous N, Chumak A, Dyagil I, Martina Z. Lipoprotein lipase expression and TP53 polymorphisms in CLL. Exp Oncol . 2021;43(3):224–8. Perriaud L, Marcel V, Sagne C, Favaudon V, Guédin A, De Rache A, et al. Intronic polymorphisms rs17878362 and rs1642785 affect TP53 transcripts. Carcinogenesis . 2014;35(12):2706–15. Hernández Borrero LJ, El-Deiry WS. Tumor suppressor p53: biology and therapeutic targeting. Biochim Biophys Acta Rev Cancer . 2021;1876(1):188556. Jha P, Pathak P, Chosdol K, Suri V, Sharma MC, Mahapatra AK, et al. TP53 polymorphisms in gliomas from Indian patients. Exp Mol Pathol . 2011;90(2):167–72. Secolin R, Mas-Sandoval A, Arauna LR, Torres FR, Araujo TK, Santos ML, et al. Local ancestry and adaptation in admixed populations. Sci Rep . 2019;9:13900. Jacovas VC, Rovaris DL, Pérez O, Azevedo S, Macedo GS, Sandoval JR, et al. TP53 pathway adaptations in Native Americans from the Andes. PLoS One . 2015;10(9):e0137823. Hattinger CM, Patrizio MP, Luppi S, Serra M. Pharmacogenomics in osteosarcoma. Int J Mol Sci . 2020;21(13):4659. Popek-Marciniec S, Styk W, Wojcierowska-Litwin M, Chocholska S, Szudy-Szczyrek A, Samardakiewicz M, et al. TP53 rs1042522 and multiple myeloma risk and response. Cancers (Basel) . 2023;15(19):4747. Floris M, Pira G, Castiglia P, Idda ML, Steri M, de Miglio MR, et al. TP53 polymorphisms and cancer susceptibility. Oncol Lett . 2022;24(4):331. Iacobucci I, Ferrari A, Kohlmann A, Papayannidis C, Venturi C, Perricone M, et al. Loss of heterozygosity at rs1042522 in BCR-ABL1+ ALL. Blood . 2012;120(21):2497. Kodal JB, Vedel-Krogh S, Kobylecki CJ, Nordestgaard BG, Bojesen SE. TP53 Arg72Pro and mortality after cancer. Sci Rep . 2017;7:1–8. Shen CC, Cheng WY, Lee CH, Dai XJ, Chiao MT, Liang YJ, et al. TP53 codon 72 polymorphism and glioblastoma prognosis. BMC Cancer . 2020;20:1–13. Zmorzynski S, Korszen-Pilecka I, Wojcierowska-Litwin M, Kwiatkowska-Drabik B, Luterek M, Chocholska S, et al. TP53 polymorphism in plasma cell myeloma. Folia Histochem Cytobiol . 2017;55(4):203–11. Hattinger CM, Biason P, Iacoboni E, Gagno S, Fanelli M, Tavanti E, et al. Germline polymorphisms and survival in osteosarcoma. Oncotarget . 2016;7(38):61970. Tran TH, Hunger SP. Genomic landscape of pediatric ALL. Semin Cancer Biol . 2022;84:144–52. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryFigure1.tiff SupplementaryFigure2.tiff SupplementaryFigure3.tiff Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2026 Read the published version in BMC Medical Genomics → Version 1 posted Editorial decision: Revision requested 17 Mar, 2026 Reviews received at journal 17 Mar, 2026 Reviews received at journal 17 Mar, 2026 Reviews received at journal 16 Mar, 2026 Reviews received at journal 15 Mar, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers invited by journal 23 Feb, 2026 Submission checks completed at journal 22 Feb, 2026 First submitted to journal 22 Feb, 2026 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-8679558","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595943072,"identity":"8bd21c66-7c26-4dab-80c0-d8973987057a","order_by":0,"name":"Glenda Menezes Nogueira","email":"","orcid":"","institution":"University of the State of Amazonas","correspondingAuthor":false,"prefix":"","firstName":"Glenda","middleName":"Menezes","lastName":"Nogueira","suffix":""},{"id":595943075,"identity":"95a8c98f-dd7b-44e4-ab10-9a4ca8c5e352","order_by":1,"name":"Luca Gabriel Marques Gonçalves","email":"","orcid":"","institution":"Amazonas Hematology and Hemotherapy Hospital Foundation","correspondingAuthor":false,"prefix":"","firstName":"Luca","middleName":"Gabriel Marques","lastName":"Gonçalves","suffix":""},{"id":595943077,"identity":"22608da3-d428-4d74-a1d2-951e88b107bd","order_by":2,"name":"Thaís Lohana Pereira-Ribeiro","email":"","orcid":"","institution":"University of the State of Amazonas","correspondingAuthor":false,"prefix":"","firstName":"Thaís","middleName":"Lohana","lastName":"Pereira-Ribeiro","suffix":""},{"id":595943078,"identity":"16842dea-a05b-4d78-b535-e494a6b90dbe","order_by":3,"name":"Larissa Silva Santos","email":"","orcid":"","institution":"Federal University of Amazonas","correspondingAuthor":false,"prefix":"","firstName":"Larissa","middleName":"Silva","lastName":"Santos","suffix":""},{"id":595943079,"identity":"f7341c55-5c4c-42aa-a95a-24896dd40a38","order_by":4,"name":"Fábio Magalhães-Gama","email":"","orcid":"","institution":"Oswaldo Cruz Foundation","correspondingAuthor":false,"prefix":"","firstName":"Fábio","middleName":"","lastName":"Magalhães-Gama","suffix":""},{"id":595943083,"identity":"7525039b-a591-4a5f-b43a-61fe5cdb9e66","order_by":5,"name":"Nilberto Dias Araújo","email":"","orcid":"","institution":"Amazonas Hematology and Hemotherapy Hospital Foundation","correspondingAuthor":false,"prefix":"","firstName":"Nilberto","middleName":"Dias","lastName":"Araújo","suffix":""},{"id":595943084,"identity":"0a6a72ba-1f6f-40c2-bef8-f1e501cdf0f2","order_by":6,"name":"Adriana Malheiro","email":"","orcid":"","institution":"Federal University of Amazonas","correspondingAuthor":false,"prefix":"","firstName":"Adriana","middleName":"","lastName":"Malheiro","suffix":""},{"id":595943085,"identity":"0212b823-6c29-40b0-a9d0-4fddad6b9521","order_by":7,"name":"Andréa Monteiro Tarragô","email":"","orcid":"","institution":"Amazonas Hematology and Hemotherapy Hospital Foundation","correspondingAuthor":false,"prefix":"","firstName":"Andréa","middleName":"Monteiro","lastName":"Tarragô","suffix":""},{"id":595943086,"identity":"d6220064-ee87-4283-94c2-7adbd07fe112","order_by":8,"name":"Fabíola Silva Alves-Hanna","email":"","orcid":"","institution":"Amazonas Hematology and Hemotherapy Hospital Foundation","correspondingAuthor":false,"prefix":"","firstName":"Fabíola","middleName":"Silva","lastName":"Alves-Hanna","suffix":""},{"id":595943087,"identity":"8230ca85-4d02-4f10-88cf-c2cc12b07c8c","order_by":9,"name":"Allyson Guimarães Costa","email":"data:image/png;base64,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","orcid":"","institution":"Federal University of Amazonas","correspondingAuthor":true,"prefix":"","firstName":"Allyson","middleName":"Guimarães","lastName":"Costa","suffix":""}],"badges":[],"createdAt":"2026-01-23 13:11:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8679558/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8679558/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12920-026-02371-0","type":"published","date":"2026-04-14T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103567764,"identity":"8d8168c4-1008-463d-94a5-467959fcde4c","added_by":"auto","created_at":"2026-02-27 07:35:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":166526,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival of patients with acute lymphoblastic leukemia according to genotypes.\u003c/p\u003e\n\u003cp\u003ep-value (log-rank): \u0026lt; 0.05; HR: Hazard Ratio.\u003c/p\u003e","description":"","filename":"Figure1Revised.png","url":"https://assets-eu.researchsquare.com/files/rs-8679558/v1/b9c01696170e8ed3039635cf.png"},{"id":104398380,"identity":"b101f36a-24f7-4d52-b440-32e98bb0709a","added_by":"auto","created_at":"2026-03-11 12:02:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1071806,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency and association of the haplotypes identified in the studied population.\u003c/p\u003e\n\u003cp\u003eALL: Acute lymphoblastic leukemia; OR: Odds Ratio; 95% confidence interval; p-value: \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8679558/v1/cc523d66630723bc75b10ac7.png"},{"id":107351914,"identity":"fd80413e-5bc1-4000-8a10-d67ac9e6a1fa","added_by":"auto","created_at":"2026-04-20 16:12:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2619420,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8679558/v1/c82bc3fa-dd53-47bf-abf3-5a13b463fc9e.pdf"},{"id":103567763,"identity":"7d7209e3-486b-4dd1-868e-bcafc1cb603b","added_by":"auto","created_at":"2026-02-27 07:35:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15543,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8679558/v1/985749a444bcfc66e3cced94.docx"},{"id":103567765,"identity":"a2492d98-fef5-4697-b17f-4134f874b625","added_by":"auto","created_at":"2026-02-27 07:35:34","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20185,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8679558/v1/a42fc8b53027b7b2a15fd883.docx"},{"id":103567769,"identity":"4e861564-5403-4677-be0f-a381d7674d75","added_by":"auto","created_at":"2026-02-27 07:35:35","extension":"tiff","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":841345,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-8679558/v1/62601c2fefbac992cfb983de.tiff"},{"id":103567768,"identity":"cf3b45a1-f21b-40fe-a35e-4dbf5ee5c00e","added_by":"auto","created_at":"2026-02-27 07:35:34","extension":"tiff","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":897407,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-8679558/v1/33ec15a4dc413977bd032c0b.tiff"},{"id":103567767,"identity":"a9509957-996d-45b4-b6bd-a7092522a6b6","added_by":"auto","created_at":"2026-02-27 07:35:34","extension":"tiff","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":830091,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure3.tiff","url":"https://assets-eu.researchsquare.com/files/rs-8679558/v1/066adbe272f45aa87b7b3de4.tiff"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of TP53 polymorphic variants rs1042522 and rs1642785 with susceptibility and prognosis of acute lymphoblastic leukemia in a Brazilian Amazon population","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAcute lymphoblastic leukemia (ALL) is a lymphoproliferative hematological malignancy that predominantly affects children, with a peak incidence between 2 and 5 years of age\u0026nbsp;\u003csup\u003e(1,2)\u003c/sup\u003e. \u0026nbsp;In Brazil, approximately 11,540 cases of leukemia (acute and chronic) are estimated for each three-year period from 2023 to 2025. In the Northern region of the country, leukemia ranks as the sixth most frequent cancer among both men (4.53 per 100,000) and women (3.64 per 100,000), excluding non-melanoma skin cancers\u003csup\u003e(3)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe etiology of ALL remains largely unknown; however, environmental factors such as ionizing radiation, as well as infections and genetic susceptibility, have been implicated in disease development. Although isolated genetic events do not fully explain leukemogenesis, the association of ALL with congenital syndromes, chromosomal translocations, and polymorphic genetic variants underscores the central role of genetic factors in its pathogenesis. Accordingly, genomic studies have increasingly focused on the impact of gene variations in pathways related to inflammation and tumor suppression\u003csup\u003e(4,5)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA key gene involved in tumor suppression and activated in response to cellular stress signals, such as DNA damage, is tumor protein 53 (\u003cem\u003eTP53\u003c/em\u003e), which encodes the p53 phosphoprotein. \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003eplays a central role in safeguarding genomic integrity by regulating the cellular response to damage and preventing malignant transformation. Through its function as a transcription factor, \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003econtrols the expression of multiple genes involved in critical cellular processes, including apoptosis, cell-cycle arrest, senescence, and DNA repair\u0026nbsp;\u003csup\u003e(6)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eGiven the indispensable role of \u003cem\u003eTP53\u003c/em\u003e, the presence of single nucleotide variants (SNVs), particularly \u003cem\u003ers1042522\u003c/em\u003e, has been reported to partially impair specific p53 functions, such as apoptotic induction, potentially favoring sustained cellular proliferation\u003csup\u003e(7)\u003c/sup\u003e.\u0026nbsp;In addition, \u003cem\u003ers1042522\u003c/em\u003e has been described as a potential risk factor for acute myeloid leukemia (AML)\u003csup\u003e(8)\u003c/sup\u003e.\u0026nbsp;In chronic lymphocytic leukemia (CLL), the \u003cem\u003eTP53\u003c/em\u003e variants \u003cem\u003ers1042522\u003c/em\u003e, \u003cem\u003ers1642785\u003c/em\u003e, and \u003cem\u003ers2909430\u003c/em\u003e have been associated with an increased frequency of somatic \u003cem\u003eTP53\u003c/em\u003e mutations\u003csup\u003e(9)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHowever, data regarding hematological malignancies such as ALL\u0026mdash;especially concerning \u003cem\u003eTP53\u003c/em\u003e polymorphic variants\u0026mdash;remain limited in Brazil, with a marked scarcity of information from the Amazon region. To address this gap, the present study investigated the frequency and prognostic relevance of the \u003cem\u003eTP53\u003c/em\u003e variants \u003cem\u003ers1042522\u003c/em\u003e and \u003cem\u003ers1642785\u003c/em\u003e in patients with ALL from the Brazilian Amazon, supporting their potential role as prognostic biomarkers in this population.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy design and study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis case\u0026ndash;control study included 193 patients diagnosed with acute lymphoblastic leukemia (ALL) who were treated at the Funda\u0026ccedil;\u0026atilde;o Hospitalar de Hematologia e Hemoterapia do Amazonas (HEMOAM). Patients of all age groups, both sexes and unrelated individuals, were eligible for inclusion. The control group comprised 215 healthy individuals who underwent blood donation or routine screening at HEMOAM. All controls were screened by serological testing for HIV, hepatitis C virus (HCV), hepatitis B virus (HBV), human T-lymphotropic virus types 1 and 2 (HTLV-1/2), syphilis, and Chagas disease to ensure their healthy status. In addition, participants were interviewed to assess the presence of comorbidities and other risk factors, in accordance with technical guidelines established by the Brazilian Ministry of Health. Individuals with familial relationships were excluded from the study. Patients who underwent bone marrow transplantation during the follow-up period and those diagnosed with other hematological malignancies were also excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical issues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee of HEMOAM under approval number 3,335,123/2019 (CAAE: 12615918.9.0000.0009), in accordance with the principles of the Declaration of Helsinki and Resolution No. 466/12 of the Brazilian National Health Council, which governs research involving human subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiological sample collection and data acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproximately 2 mL of peripheral blood was collected by venipuncture into vacuum tubes containing EDTA from participants in both groups. Samples were obtained from the Biorepository of Cellular and Molecular Biomarkers in Acute Lymphoblastic Leukemia (BCM-ALL) at HEMOAM. Sociodemographic data (age, sex, race/ethnicity, and place of origin), clinical data (treatment protocol, risk group, post-induction relapse, and death), and laboratory data (immunophenotype) were retrieved from physical medical records archived in the Medical and Statistical Care System (SAME) and from electronic medical records (iDoctor and Softlab systems) at HEMOAM. Race/ethnicity was defined by self-reported skin color or race, or by parental/legal guardian report for minors, according to the classification of the Brazilian Institute of Geography and Statistics (IBGE): White, Black, Brown (Mixed-race), Yellow (Asian), and Indigenous. In addition, relapse was defined as treatment failure occurring after completion of remission induction therapy (day 35), according to the treatment protocol. Death was assessed over a minimum follow-up period of five years after diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction and assessment of nucleic acid purity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was extracted using a commercial column-based method with the PureLink\u0026trade; Genomic DNA Mini Kit (Invitrogen\u0026trade;), according to the manufacturer\u0026rsquo;s instructions. DNA concentration and purity were assessed by measuring absorbance ratios at 260/280 nm and 260/230 nm using a NanoDrop\u0026trade; 2000/2000c spectrophotometer (Thermo Scientific\u0026trade;).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNV selection criteria and genotyping by quantitative PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePolymorphic variants were selected based on information retrieved from the Cancer Genome Anatomy Project and SNP500 databases. The selected variants included the \u003cem\u003eTP53\u003c/em\u003e missense variant \u003cem\u003ers1042522\u003c/em\u003e, corresponding to a c.215C\u0026gt;G substitution in the coding sequence that results in an amino acid change (p.Pro72Arg), as well as two intronic variants, \u003cem\u003eTP53\u003c/em\u003e \u003cem\u003ers1642785\u0026nbsp;\u003c/em\u003e(C\u0026gt;G) and \u003cem\u003ers2909430\u003c/em\u003e (C\u0026gt;T), located in exon 4 and introns 2 and 4, respectively. The criteria for SNV selection included their predicted functional effects, minor allele frequency (MAF \u0026ge; 3%), and previously reported associations with hematological malignancies, including leukemia. Genotyping of the selected variants was performed using quantitative real-time PCR (qPCR) with allele-specific TaqMan probes (Applied Biosystems) (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e), following the protocol described by\u0026nbsp;Alves et al. (2021)\u003csup\u003e(10)\u003c/sup\u003e. Amplification was carried out using the StepOnePlus\u0026trade; Real-Time PCR System, and genotype assignment was performed with StepOne Software v2.3, which allowed genotype discrimination based on the amplification curves generated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis and statistical methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive and statistical analyses were performed using GraphPad Prism version 10 (San Diego, CA, USA). Comparisons between groups were conducted using Fisher\u0026rsquo;s exact test, with results expressed as odds ratios and 95% confidence intervals (95% CI). Overall survival (OS) was defined as the time from diagnosis to death from any cause and was estimated using the Kaplan\u0026ndash;Meier method, with differences between groups assessed by the log-rank test. Hardy\u0026ndash;Weinberg equilibrium (HWE) for all SNVs was evaluated using R software version 4.4.1 (www.r-project.org) with the \u003cem\u003eSNPassoc\u003c/em\u003e package. The same package was used to assess associations between allele and genotype frequencies and susceptibility to ALL, relapse, and death under four genetic inheritance models: codominant, dominant, recessive, and overdominant. The best-fitting genetic model was selected based on the Akaike Information Criterion (AIC). P-values were adjusted for multiple comparisons using the Bonferroni correction, and statistical significance was defined as p \u0026lt;0.025. Combined analyses (gene\u0026ndash;gene interactions) of the SNVs were also performed using the \u003cem\u003eSNPassoc\u003c/em\u003e package, considering p \u0026lt; 0.05 as statistically significant. Linkage disequilibrium (LD) was assessed using the \u003cem\u003ehaplo.stats\u003c/em\u003e package. LD was evaluated using D\u0026prime; to estimate the strength of disequilibrium and pairwise r\u0026sup2; to indicate the degree of correlation between allele frequencies, with r\u0026sup2; values \u0026gt; 0.8 indicating strong LD, values \u0026lt; 0.8 indicating weak LD, and values \u0026lt; 0.1 indicating negative LD. Only haplotypes with a frequency greater than 5% were included in comparative analyses.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eSociodemographic, clinical, and epidemiological profile of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 215 healthy individuals were included in the control group, with a median age of 29 years. The case group comprised 193 patients diagnosed with acute lymphoblastic leukemia (ALL), with a median age of 9 years. In both groups, males were predominant, accounting for 67% of the control group and 59% of the ALL group. Regarding racial distribution, 82% of patients with ALL self-identified as Brown (Mixed-race). All individuals in the control group (100%) resided in Manaus, while 71% of patients in the ALL group also reported residence in the capital city. Among 70 patients with available information, 56% reported no family history of cancer (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eWith respect to immunophenotype, 91% of patients with ALL presented with B-cell ALL (B-ALL), whereas 9% had T-cell ALL (T-ALL). Additionally, 68% of patients were classified into the high-risk group. Most patients were treated according to the Berlin\u0026ndash;Frankfurt\u0026ndash;M\u0026uuml;nster (BFM) European Group protocol (50%), while 32% received treatment following the Brazilian Childhood Leukemia Treatment Group (GBTLI) protocol. During treatment, 53% of patients did not relapse after remission induction, and 71% were alive at the end of follow-up (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Sociodemographic, laboratory and clinical characteristics of the study population.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables (completeness)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e215 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e191 (98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003cstrong\u003e, years, median [IQR]\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e29 [4-65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e9 [0-69]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e215 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e193 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e145 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e114 (59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e70 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e79 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e120 (62)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eWhite, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e12 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eAdmixed, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e99 (82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eOther, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e9 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmunophenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e193 (100)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eB-ALL, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e176 (91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eT-ALL, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e17 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e191 (98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eManaus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e215 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e136 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eInterior of Amazonas, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e53 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eOther state, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e2 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e179 (92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eLow Risk, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e58 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eHigh Risk, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e121 (68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTreatment protocol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e179 (92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eGBTLI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e57 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eBFM, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e90 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eGMALL, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e22 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eHyperCVAD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e3 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eGMALL + HyperCVAD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e7 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelapse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e145 (75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eYes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e68 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNo, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e77 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e185 (96)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eYes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e54 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNo, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e131 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily history of cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e70 (36)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eYes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e31 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNo, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e39 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIQR\u0026thinsp;=\u0026thinsp; Interquartile Range. Other: black 0 (0%), indigenous 8 (6.67%) and yellow 1 (0.83%). Other states: Par\u0026aacute; 1 (0.52%) and Roraima 1 (0.52%). European Group Berlin-Frankfurt-M\u0026uuml;nster (BFM). Brazilian Group for the Treatment of Leukemia in Childhood (GBTLI). Group for Adult Acute Lymphoblastic Leukemia (GMALL). Ethnicity was defined by self-declaration of color or race or declaration by parents/guardians for minors, following the classification used by the Brazilian Institute of Geography and Statistics (IBGE): white, black, brown, yellow, and indigenous.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAllele frequencies and their association with prognosis in patients with acute lymphoblastic leukemia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAllele frequency analysis revealed a higher prevalence of the C allele in both study groups. However, comparative analysis showed that the C allele was more frequent in the case group than in the control group, whereas a higher proportion of the G allele was observed among healthy individuals (\u003cstrong\u003eSupplementary Figures 1A and 1B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAllelic association analysis demonstrated that the C allele of \u003cem\u003ers1042522\u0026nbsp;\u003c/em\u003ewas associated with an increased risk of developing ALL compared with the G allele (OR: 1.44; 95% CI: 1.06\u0026ndash;1.96; p = 0.021) (\u003cstrong\u003eTable 2\u003c/strong\u003e). In addition, the \u003cem\u003ers1042522\u003c/em\u003e-C was associated with a higher risk of relapse (OR: 1.97; 95% CI: 1.14\u0026ndash;3.32; p= 0.016) (\u003cstrong\u003eTable 3\u003c/strong\u003e) and death (OR: 2.15; 95% CI: 1.21\u0026ndash;2.87; p= 0.009) (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eMultivariate analysis adjusted for sex and age for the association of single nucleotide variants (SNVs) with the \u003cem\u003erisk\u003c/em\u003e of acute lymphoblastic leukemia.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"110%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31.8039%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic models\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.2332%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en= 215 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en= 193 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eadj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 28.1503%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ers1042522\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eC\u0026gt;G\u003cem\u003e\u0026nbsp;\u003c/em\u003e(HWE:1.0000)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCodominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e96 (44.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e106 (54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e95 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e75 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003cp\u003e(0.47\u0026nbsp;\u0026ndash; 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e564.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003cp\u003e(0.46 \u0026ndash; 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e443.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e24 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e12 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003cp\u003e(0.21\u0026nbsp;\u0026ndash; 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003cp\u003e(0.14\u0026nbsp;\u0026ndash; 0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e96 (44.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e106 (54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/G-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e119 (55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e87 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.66\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.45\u0026nbsp;\u0026ndash; 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e564.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003cp\u003e(0.41 \u0026ndash; 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e445.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecessive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/C-C/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e191 (88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e181 (93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e24 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e12 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.53\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.26\u0026nbsp;\u0026ndash; 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e565.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003cp\u003e(0.17 \u0026ndash; 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.019\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e443.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverdominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/C-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e120 (55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e118 (61.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e95 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e75 (38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003cp\u003e(0.54\u0026nbsp;\u0026ndash; 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e567.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003cp\u003e(0.56 \u0026ndash; 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e448.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e287 (66.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e287 (74.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003cp\u003e(1.06 \u0026ndash; 1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.021\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e143 (33.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e99 (25.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog-Additive\u0026nbsp;\u003c/strong\u003e0,1,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e215 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e193 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003cp\u003e(0.51 \u0026ndash; 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.017\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e562.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003cp\u003e(0.45 \u0026ndash; 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.014\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e442.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 28.1503%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ers1642785\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eC\u0026gt;G\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e(HWE: 0.5363)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCodominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e101 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e111 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e95 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e73 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.70\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.47\u0026nbsp;\u0026ndash; 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e564.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.65\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.40\u0026nbsp;\u0026ndash; 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.013\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e441.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e19 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e9 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.43\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.19\u0026nbsp;\u0026ndash; 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.28\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.11\u0026nbsp;\u0026ndash; 0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e101 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e111 (57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/G-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e114 (53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e82 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.65\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.44\u0026nbsp;\u0026ndash; 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e563.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.58\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.36\u0026nbsp;\u0026ndash; 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.018\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e443.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecessive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/C-C/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e196 (91.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e184 (95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e19 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e9 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.50\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.22\u0026nbsp;\u0026ndash;1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e565.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.34\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.14\u0026nbsp;\u0026ndash; 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.018\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e442.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverdominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/C-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e120 (55.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e120 (62.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.77\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.52\u0026nbsp;\u0026ndash; 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e566.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.77\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.48\u0026nbsp;\u0026ndash; 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e447.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e95 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e73 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e270 (69.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e295 (76.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e1.45\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1.06 \u0026ndash; 1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.022\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e116 (30.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e91 (23.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog-Additive\u0026nbsp;\u003c/strong\u003e0,1,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e215 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.5434%;\"\u003e\n \u003cp\u003e193 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.68\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.49\u0026nbsp;\u0026ndash; 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.016\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e562.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.3541%;\"\u003e\n \u003cp\u003e0.59\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(0.40\u0026nbsp;\u0026ndash; 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.6381%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.004\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.5022%;\"\u003e\n \u003cp\u003e440.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100%;\" colspan=\"9\"\u003eAdjusted for sex and age (p-value adj, OR adj); OR: Odds Ratio; p-value: \u0026lt; 0.025; 95% confidence interval; AIC: Akaike Criterion Value; HWE: Hardy-Weinberg equilibrium.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSimilarly, the \u003cem\u003ers1642785\u003c/em\u003e-C was associated with increased susceptibility to ALL compared with the G allele (OR: 1.45; 95% CI: 1.06\u0026ndash;1.97; p= 0.022). However, no statistically significant associations were observed between \u003cem\u003ers1642785\u003c/em\u003e and relapse (OR: 1.57; 95% CI: 0.91\u0026ndash;2.73; p= 0.063) or death (OR: 1.78; 95% CI: 0.98\u0026ndash;3.32; p= 0.058).\u003c/p\u003e\n\u003cp\u003eAssessment of Hardy\u0026ndash;Weinberg equilibrium indicated that \u003cem\u003ers2909430\u003c/em\u003e was not in equilibrium and was monomorphic in the association analyses. Consequently, case\u0026ndash;control comparative analysis for this SNV was not performed. Nevertheless, allele frequency comparison across populations revealed similarity with the low frequency reported in East Asian populations (\u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e). Furthermore, genotype frequencies of \u003cem\u003ers1042522\u0026nbsp;\u003c/em\u003eand r\u003cem\u003es1642785\u003c/em\u003e in the study population were comparable to those observed in admixed American populations (\u003cstrong\u003eSupplementary Figure 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenotype frequencies and their association with prognosis in patients with acute lymphoblastic leukemia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distribution of genotype frequencies for the polymorphic variants showed a similar pattern for both SNVs (\u003cem\u003ers1042522\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;rs1642785\u003c/em\u003e), with the CC, CG, and GG genotypes observed, respectively. Despite this shared distribution pattern, the CC genotype was more prevalent in cases than in controls, whereas the GG genotype was more frequent among controls for both \u003cem\u003ers1042522\u003c/em\u003e and \u003cem\u003ers1642785\u003c/em\u003e (\u003cstrong\u003eSupplementary Figures 1C and 1D\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAdjusted analyses revealed that the \u003cem\u003eTP53\u003c/em\u003e SNVs \u003cem\u003ers1042522\u003c/em\u003e (\u003csub\u003eadj\u003c/sub\u003eOR: 0.39; 95% CI: 0.17\u0026ndash;0.88; p= 0.019) and \u003cem\u003ers1642785\u003c/em\u003e (\u003csub\u003eadj\u003c/sub\u003eOR: 0.65; 95% CI: 0.40\u0026ndash;1.06; p= 0.013) were associated with a protective effect against the development of ALL after adjustment for age and sex (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e presents the association analyses between genotypes and relapse following remission induction. In this cohort, \u003cem\u003ers1042522\u003c/em\u003e was significantly associated with protection against relapse (adjusted OR: 0.51; 95% CI: 0.29\u0026ndash;0.92; adjusted p= 0.022), whereas \u003cem\u003ers1642785\u003c/em\u003e showed no statistically significant association (adjusted OR: 0.52; 95% CI: 0.26\u0026ndash;1.05; adjusted p= 0.067).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eAssociation analysis of SNVs in the \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003egene with \u003cem\u003erelapse\u003c/em\u003e in patients with acute lymphoblastic leukemia.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic models\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=77 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=68 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eadj \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eadj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;rs1042522\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;C\u0026gt;G\u003cem\u003e\u0026nbsp;\u003c/em\u003e(HWE:1.0000)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCodominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e33 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e43 (63.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e36 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e23 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.52 (0.25 \u0026ndash; 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e196.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.26 (0.05 \u0026ndash; 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e33 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e43 (63.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/G-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e43 (56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e25 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.48 (0.24 \u0026ndash; 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e195.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecessive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/C-C/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e69 (90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e66 (97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e7 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.35 (0.07 \u0026ndash; 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e197.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverdominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/C-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e40 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e45 (66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e36 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e23 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.59 (0.30 \u0026ndash; 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e197.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e102 (67.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e109 (80.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1.97 (1.14\u0026ndash; 3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.016\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e50 (32.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e27 (19.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog-Additive\u0026nbsp;\u003c/strong\u003e0,1,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e76 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e68 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.51 (0.29 \u0026ndash; 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.022\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e194.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ers1642785\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eC\u0026gt;G\u003cem\u003e\u0026nbsp;\u003c/em\u003e(HWE:0.5553)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCodominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e37 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e45 (66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e35 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e20 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.51 (0.25 \u0026ndash; 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003e198.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e4 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e3 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.80 (0.16 \u0026ndash; 3.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e37 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e45 (66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.54 (0.27 \u0026ndash; 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e196.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/G-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e39 (51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e23 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecessive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/C-C/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e72 (94.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e65 (95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e4 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e3 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.07 (0.22 \u0026ndash; 5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e199.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverdominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/C-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e41 (53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e48 (70.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e35 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e20 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.52 (0.26 \u0026ndash; 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e196.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e109 (71.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e104 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e1.57 (0.91 \u0026ndash; 2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e43 (28.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e26 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog-Additive\u0026nbsp;\u003c/strong\u003e0,1,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e76 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e68 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0.64 (0.35 \u0026ndash; 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e197.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdjusted for sex and age (p-value adj, OR adj); OR: Odds Ratio; p-value: \u0026lt; 0.025; 95% confidence interval; AIC: Akaike Criterion Value; HWE: Hardy-Weinberg equilibrium.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, both \u003cem\u003ers1042522\u0026nbsp;\u003c/em\u003e(adjusted OR: 0.45; 95% CI: 0.25\u0026ndash;0.81; adjusted p= 0.005) and \u003cem\u003ers1642785\u003c/em\u003e (adjusted OR: 0.52; 95% CI: 0.29\u0026ndash;0.94; adjusted p= 0.024) were associated with a protective effect against death (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eAssociation analysis of SNVs in the \u003cem\u003eTP53\u0026nbsp;\u003c/em\u003egene with \u003cem\u003edeath\u0026nbsp;\u003c/em\u003ein patients with acute lymphoblastic leukemia.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic models\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=131 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=54 (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eadj \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ers1042522\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eC\u0026gt;G\u003cem\u003e\u0026nbsp;\u003c/em\u003e(HWE:1.0000)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCodominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e63 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e37 (68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e57 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e16 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.48 (0.24 \u0026ndash; 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.018\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e221.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e11 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.15 (0.02 \u0026ndash; 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e63 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e37 (68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/G-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e68 (51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e17 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.43 (0.22 \u0026ndash; 0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.010\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e220.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecessive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/C-C/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e120 (91.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e53 (98.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e11 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.21 (0.03 \u0026ndash; 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e224.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverdominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/C-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e72 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e38 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.55 (0.28 \u0026ndash; 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e224.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e57 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e16 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e183 (69.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e90 (83.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e2.15 (1.21 \u0026ndash; 3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.009\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e79 (30.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e18 (16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog-Additive\u0026nbsp;\u003c/strong\u003e0,1,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e131 (70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e54 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.45 (0.25 \u0026ndash; 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.005\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e219.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ers1642785\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eC\u0026gt;G\u003cem\u003e\u0026nbsp;\u003c/em\u003e(HWE:0.5553)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCodominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e68 (51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e37 (68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e55 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e16 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.53 (0.27 \u0026ndash; 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e224.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.23 (0.03 \u0026ndash; 1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e68 (51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e37 (68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.50 (0.25 \u0026ndash; 0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e223.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/G-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e63 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e17 (31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecessive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/C-C/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e123 (93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e53 (98.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e8 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.29 (0.04 \u0026ndash; 2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e225.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverdominant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/C-G/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e76 (58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e38 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0.58 (0.29 \u0026ndash; 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e224.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e55 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e16 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlleles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e191 (73.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e90 (83.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1.78 (0.98 \u0026ndash; 3.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e68 (26.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e18 (16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog-Additive\u0026nbsp;\u003c/strong\u003e0,1,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e131 (70.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e54 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.52 (0.29 \u0026ndash; 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.024\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e222.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAdjusted for sex and age (p-value adj, OR adj); OR: Odds Ratio; p-value: \u0026lt; 0.025; 95% confidence interval; AIC: Akaike Criterion Value; HWE: Hardy-Weinberg equilibrium.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction Analysis of \u003cem\u003ers1042522\u003c/em\u003e and \u003cem\u003ers1642785\u003c/em\u003e on Clinical Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate interactions between the SNVs, a combined analysis of \u003cem\u003ers1042522\u003c/em\u003e and \u003cem\u003ers1642785\u003c/em\u003e was performed using a logistic regression model adjusted for age and sex. The results showed that, among the analyzed combinations and interactions, individuals heterozygous for \u003cem\u003ers1042522\u003c/em\u003e (CG) exhibited a protective effect against relapse compared with CC homozygotes (adjusted OR: 0.82; 95% CI: 0.25\u0026ndash;0.98; p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eNo statistically significant associations were observed between genotype combinations and susceptibility to ALL or death. However, age emerged as a significant covariate influencing clinical outcomes. Increasing age was associated with an 8% reduction in the risk of ALL susceptibility per year (adjusted OR: 0.92; 95% CI: 0.90\u0026ndash;0.93; p \u0026lt; 0.001), while it was also associated with a 4% increase in the risk of relapse following remission induction therapy (adjusted OR: 1.04; 95% CI: 1.00\u0026ndash;1.07; p= 0.026), as shown in \u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of \u003cem\u003eTP53\u003c/em\u003e \u003cem\u003ers1042522\u003c/em\u003e and \u003cem\u003ers1642785\u0026nbsp;\u003c/em\u003eon Overall Survival\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall survival (OS) analysis according to \u003cem\u003eTP53\u003c/em\u003e SNV genotypes revealed that specific \u003cem\u003eTP53\u003c/em\u003e genotypes significantly influenced survival outcomes in patients with ALL. For \u003cem\u003ers1042522\u003c/em\u003e, individuals with the CC genotype were associated with a 2.08-fold higher mortality risk compared with patients harboring the CG genotype (p= 0.030; \u003cstrong\u003eFigure 1A\u003c/strong\u003e), with a median survival of only 22 months (\u003cstrong\u003eTable 5\u003c/strong\u003e). In contrast, heterozygous CG individuals showed significantly prolonged overall survival, with a median of 75 months (\u003cstrong\u003eTable 5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eSimilar findings were observed for \u003cem\u003eTP53 rs1642785\u003c/em\u003e. Patients homozygous for the C allele (CC vs. CG) demonstrated a strong association with poorer survival (HR: 2.36; 95% CI: 1.24\u0026ndash;4.50; p= 0.012), with a median survival of only 20 months (\u003cstrong\u003eTable 5\u003c/strong\u003e). Conversely, individuals with the heterozygous CG genotype of \u003cem\u003ers1642785\u003c/em\u003e exhibited a protective advantage (\u003cstrong\u003eFigure 1B\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eOverall survival analysis according to genotypes of the evaluated single nucleotide variants.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"108%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.7333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenetic models\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8387%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALL Deaths\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en= 44 (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9276%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian Survival (months)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.853%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog-rank\u003cem\u003e\u0026nbsp;p\u0026nbsp;\u003c/em\u003evalue\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 5.4938%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ers1042522\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eC\u0026gt;G\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.7333%;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8387%;\"\u003e\n \u003cp\u003e30 (68.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9276%;\"\u003e\n \u003cp\u003e22.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13.2866%;\"\u003e\n \u003cp\u003e2.08 (1.09\u0026nbsp;\u0026ndash; 3.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.030\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.7333%;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8387%;\"\u003e\n \u003cp\u003e13 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9276%;\"\u003e\n \u003cp\u003e75.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.7333%;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8387%;\"\u003e\n \u003cp\u003e1 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9276%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2866%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 29.9948%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ers1642785\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eC\u0026gt;G\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.7333%;\"\u003e\n \u003cp\u003eC/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8387%;\"\u003e\n \u003cp\u003e31 (70.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9276%;\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 13.2866%;\"\u003e\n \u003cp\u003e2.36 (1.24\u0026nbsp;\u0026ndash; 4.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.012\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.7333%;\"\u003e\n \u003cp\u003eC/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8387%;\"\u003e\n \u003cp\u003e13 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9276%;\"\u003e\n \u003cp\u003e75.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.7333%;\"\u003e\n \u003cp\u003eG/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.8387%;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.9276%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2866%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99.7319%;\" colspan=\"5\"\u003eHR: Hazard Ratio (log-rank); p value log-rank (Mantel-Cox) test: \u0026lt;0.05; CI of Ratio 95%: confidence interval.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eHaplotype analysis of \u003cem\u003eTP53 v\u003c/em\u003eariants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe polymorphic variants under investigation were found to be in strong linkage disequilibrium (LD), with a D\u0026prime; value of 0.893, indicating that they are frequently inherited together. The pairwise correlation coefficient (r\u0026sup2;= 0.716) reflected a strong correlation between allele frequencies in the analysis of ALL susceptibility. Similar LD patterns were observed in analyses involving relapse and death, with D\u0026prime; = 0.891 and r\u0026sup2; = 0.711.\u003c/p\u003e\n\u003cp\u003eFour haplotypes were identified, of which two exhibited frequencies greater than 5%. The CC haplotype was the most prevalent in the studied population (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). Both common haplotypes showed significant associations with ALL. Individuals carrying the GG haplotype had a 42% lower risk of developing ALL compared with those carrying the CC haplotype (adjusted OR: 0.58; 95% CI: 0.40\u0026ndash;0.86; p = 0.007), as well as a reduced risk of death (adjusted OR: 0.44; 95% CI: 0.23\u0026ndash;0.85; p = 0.0138).\u003c/p\u003e\n\u003cp\u003eAge was identified as a significant covariate, with the risk of ALL decreasing by 8% per additional year of age (adjusted OR: 0.92; 95% CI: 0.90\u0026ndash;0.93; p \u0026lt; 0.001), whereas the risk of relapse increased by 4% per year (adjusted OR: 1.04; 95% CI: 1.00\u0026ndash;1.07; p = 0.0275).\u003c/p\u003e\n\u003cp\u003eThe remaining two haplotypes showed frequencies below the predefined threshold and were therefore classified as rare haplotypes. These haplotypes did not demonstrate statistically significant associations with ALL susceptibility or clinical outcomes (adjusted OR: 0.76; 95% CI: 0.42\u0026ndash;1.38; p = 0.368) (\u003cstrong\u003eFigure 2B\u003c/strong\u003e).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe \u003cem\u003eTP53\u003c/em\u003e gene plays a central role in maintaining genomic stability by regulating the expression of target genes involved in cell-cycle control mechanisms, thereby ensuring cellular homeostasis. However, \u003cem\u003eTP53\u003c/em\u003e alterations are frequently observed across multiple human cancers\u003csup\u003e(6)\u003c/sup\u003e. Disruption of \u003cem\u003eTP53\u003c/em\u003e may compromise the wild-type function of the p53 protein, contributing to tumor promotion and disease progression\u003csup\u003e(7)\u003c/sup\u003e. In acute lymphoblastic leukemia (ALL), alterations in the TP53/RB1 tumor suppressor pathway are commonly reported, particularly in high-risk patients\u003csup\u003e(11)\u003c/sup\u003e. Increasing evidence suggests that polymorphic variants in \u003cem\u003eTP53\u003c/em\u003e are associated with susceptibility to ALL and may provide important insights into genetic factors influencing disease incidence and heterogeneity\u003csup\u003e(5,12)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn the present study, the clinical and sociodemographic characteristics of the study population were consistent with those reported in previous epidemiological investigations conducted in the same region\u003csup\u003e(13)\u003c/sup\u003e. Our findings demonstrated that the \u003cem\u003eTP53\u003c/em\u003e \u003cem\u003ers1042522\u003c/em\u003e-GG genotype exerted a protective effect against ALL. These results are in agreement with the study by Skhoun et al. (2022), which reported that the CG and GG genotypes of \u003cem\u003ers1042522\u0026nbsp;\u003c/em\u003ewere associated with a protective effect against ALL in Moroccan children\u003csup\u003e(14)\u003c/sup\u003e.\u0026nbsp;Nevertheless, the role of \u003cem\u003ers1042522\u003c/em\u003e appears to be context-dependent, with studies reporting heterogeneous findings ranging from no association to either protective or risk effects across different cancer types\u003csup\u003e(15)\u003c/sup\u003e. In Brazil, a single study conducted in a predominantly Caucasian population from the Southeast region reported an association between the \u003cem\u003ers1042522\u003c/em\u003e-C and increased susceptibility to ALL\u003csup\u003e(16)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA meta-analysis reported that the \u003cem\u003ers1042522\u003c/em\u003e-CC is correlated with an increased risk of ALL (OR = 1.73; 95% CI = 1.07\u0026ndash;2.81)\u003csup\u003e(17)\u003c/sup\u003e. Although the specific effect of the \u003cem\u003ers1042522\u003c/em\u003e-CC was not explicitly modeled as an isolated variable in our analysis, with findings suggest that this genotype is associated with increased susceptibility to ALL when compared with the CG and GG genotypes, given its higher prevalence in the case group. Similarly, Basabaeen et al. (2019) reported up to a tenfold increase in the risk of B-cell chronic lymphocytic leukemia (B-CLL) among individuals from the Sudanese population carrying the CC genotype compared with those harboring the GG genotype\u003csup\u003e(18)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003ers1642785\u003c/em\u003e SNV also demonstrated a protective effect against ALL in our cohort. In patients with colorectal cancer diagnosed before the age of 57 years, the \u003cem\u003ers1642785-\u003c/em\u003eCG has been reported to confer protection against disease development\u003csup\u003e(19)\u003c/sup\u003e.\u0026nbsp;In contrast, the CC genotype has been associated with an increased risk of CLL (OR = 1.73; 95% CI = 1.01\u0026ndash;2.95; p = 0.048) and with a higher frequency of somatic \u003cem\u003eTP53\u003c/em\u003e mutations\u003csup\u003e(9)\u003c/sup\u003e.\u0026nbsp;The \u003cem\u003ers1642785-\u003c/em\u003eCG has also been identified in sporadic and familial cases of AML and ALL, exhibiting a similar degree of homozygosity to \u003cem\u003ers1042522\u003c/em\u003e in most cases\u003csup\u003e(20)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo our knowledge, this is the first study to investigate the impact of \u003cem\u003ers1642785\u003c/em\u003e on ALL. Considering that ALL predominantly affects pediatric populations, findings from studies on CLL and AML may reflect genetic risk patterns primarily relevant to adult disease. Furthermore, combined analysis of the SNVs adjusted for age and sex demonstrated that increasing age was strongly associated with protection against ALL susceptibility, reinforcing the well-established epidemiological observation that ALL is more prevalent in children and has a lower incidence in adults\u003csup\u003e(2,13)\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the allele-based analysis, we observed that the C allele of both variants was associated with an increased risk of developing ALL. In patients with chronic lymphocytic leukemia (CLL), the C allele of \u003cem\u003ers1042522\u003c/em\u003e and \u003cem\u003ers1642785\u003c/em\u003e has been associated with higher expression of lipoprotein lipase (LPL) in leukemic B cells. Increased LPL expression may promote neoplastic progression by supplying metabolic substrates and creating a microenvironment favorable to cell survival and proliferation. In this context, Abramenko et al. (2021) suggested that these SNVs may act as genetic modifiers, influencing the development of CLL\u003csup\u003e(21)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIt has also been reported that the \u003cem\u003ers1642785\u003c/em\u003e-C \u0026nbsp;affects mRNA stability, leading to reduced levels of p53 pre-mRNA and total \u003cem\u003eTP53\u003c/em\u003e transcripts\u003csup\u003e(22)\u003c/sup\u003e.\u0026nbsp;Reduced \u003cem\u003eTP53\u003c/em\u003e transcription or impaired activation under conditions of cellular stress may compromise tumor suppressor function, thereby facilitating tumor development and progression\u003csup\u003e(23)\u003c/sup\u003e. Conversely, the \u003cem\u003ers1642785-G\u003c/em\u003e has been described as significantly more frequent in individuals with glioma than in control populations, suggesting that this variant may also confer increased risk in other oncological contexts\u003csup\u003e(24)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo assess genetic variability, we compared allele frequencies across different populations. The frequencies of both SNVs in our cohort were similar to those reported in admixed American populations, reflecting the high degree of genetic admixture in the Brazilian population, which is characterized by ancestral contributions from Indigenous peoples, Europeans, and Africans. In Northern Brazil, the focus of the present study, Indigenous South American ancestry predominates\u003csup\u003e(25)\u003c/sup\u003e. This genetic background may partially explain discrepancies among studies in literature and the allele frequency patterns observed here in relation to ALL susceptibility. Indeed, admixed children, particularly those with Indigenous ancestry, have been reported to exhibit increased susceptibility to ALL due to the presence of ancestral-specific genetic variants\u003csup\u003e(10)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn addition, some authors have hypothesized that the frequency of the \u003cem\u003ers1042522\u003c/em\u003e variant may vary according to altitude, suggesting that Andean populations may have undergone genetic adaptation to environmental factors such as hypoxia, ultraviolet radiation, and extreme climatic conditions. Such adaptations may involve selective combinations of alleles and genotypes within the \u003cem\u003eTP53\u003c/em\u003e pathway, further reinforcing the importance of considering genetic associations within a population- and region-specific context\u003csup\u003e(26)\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further investigate the prognostic impact of the studied variants in ALL, we evaluated their association with relapses, which remains a major therapeutic challenge. Combined SNV analysis revealed that individuals heterozygous for\u003cem\u003e\u0026nbsp;rs1042522\u003c/em\u003e (CG) had a lower risk of relapse compared with those carrying the CC genotype. In high-grade osteosarcoma, \u003cem\u003ers1042522\u003c/em\u003e has been associated with an increased risk of relapse\u003csup\u003e(27)\u003c/sup\u003e,\u0026nbsp;whereas data reported by Popek-Marciniec et al. (2023) showed no association between \u003cem\u003ers1042522\u003c/em\u003e and relapse risk in patients with multiple myeloma\u003csup\u003e(28)\u003c/sup\u003e. These findings highlight the context-dependent role of this variant across different malignancies.\u003c/p\u003e\n\u003cp\u003eAllele frequency analysis further demonstrated that the C allele was associated with an increased risk of relapse. It is well established that the C allele (Pro72) has a reduced ability to induce apoptosis in damaged cells, whereas the G allele (Arg72) is more effective due to its greater mitochondrial localization and enhanced transcription of several \u003cem\u003eTP53\u003c/em\u003e-regulated pro-apoptotic genes. In contrast, the C allele is more efficient in promoting cell-cycle arrest, favoring DNA damage repair\u003csup\u003e(29)\u003c/sup\u003e.\u0026nbsp;This functional divergence may help explain our findings, as individuals carrying the C allele may exhibit increased resistance to chemotherapy in this context.\u003c/p\u003e\n\u003cp\u003eNotably, inactivation of the \u003cem\u003ers1042522\u003c/em\u003e-C has been reported to influence the progression of BCR::ABL1-positive ALL. In approximately 12% of relapse cases, loss of heterozygosity of this allele was observed in leukemic cells, suggesting functional inactivation that may contribute to treatment resistance\u003csup\u003e(30)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003ers1642785\u0026nbsp;\u003c/em\u003eSNV exhibited a protective effect with respect to relapse. When evaluating the outcome of death, the log-additive model showed that both SNVs exerted a significant protective effect. However, the C allele of \u003cem\u003ers1042522\u003c/em\u003e was associated with an increased risk for this outcome. In multiple myeloma, \u003cem\u003ers1042522\u003c/em\u003e has been reported to have no significant impact on mortality\u003csup\u003e(28)\u003c/sup\u003e. Similarly, a large cohort study reported no significant association between \u003cem\u003ers1042522\u003c/em\u003e and cancer-related mortality\u003csup\u003e(31)\u003c/sup\u003e. Overall, data regarding the specific roles of these SNVs in relapse and death remain limited, particularly in hematological malignancies. To our knowledge, this is the first study to associate \u003cem\u003ers1642785\u003c/em\u003e with these clinical outcomes in ALL.\u003c/p\u003e\n\u003cp\u003eOverall survival analysis of both SNVs revealed that the CG genotype was associated with longer survival than the CC genotype in patients with ALL. Our combined analyses further reinforced the protective role of the heterozygous CG genotype, corroborating the findings observed across different analytical approaches. Skhoun et al. (2022) also suggested that the heterozygous genotype confers a survival advantage in patients with ALL\u003csup\u003e(14)\u003c/sup\u003e. In addition, the CG and GG genotypes have been associated with improved OS in glioblastoma\u003csup\u003e(32)\u003c/sup\u003e. Regarding \u003cem\u003ers1642785\u003c/em\u003e, a study in osteosarcoma similarly reported a protective effect, with associations to improved event-free survival and OS\u003csup\u003e(27)\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;OS analysis also associated the CC genotype of both SNVs with poorer survival compared with the CG genotype. Indeed, \u003cem\u003ers1042522\u003c/em\u003e has previously been associated with reduced OS (p = 0.004), with a mean survival of only 12 months reported for patients with plasma cell myeloma carrying the CC genotype\u003csup\u003e(33)\u003c/sup\u003e. In acute myeloid leukemia (AML), genotype distributions did not show a significant impact on OS; however, \u003cem\u003ers1042522\u0026nbsp;\u003c/em\u003ehas been reported to reduce apoptotic potential, thereby increasing leukemia risk and being associated with poorer OS and higher relapse rates\u003csup\u003e(8)\u003c/sup\u003e. In addition, \u003cem\u003ers1642785\u003c/em\u003e has been shown to influence survival outcomes in patients with osteosarcoma\u003csup\u003e(34)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eGiven the close genomic proximity of the studied SNVs, linkage disequilibrium analysis enabled haplotype-based evaluation, revealing that inherited allele combinations, particularly the GG haplotype, were associated with protection against ALL and death when compared with the CC haplotype. Preliminary evidence has suggested co-segregation of \u003cem\u003ers1042522\u003c/em\u003e and \u003cem\u003ers1642785\u003c/em\u003e, supporting the presence of linkage disequilibrium between these variants\u003csup\u003e(20)\u003c/sup\u003e.\u0026nbsp;Age also emerged as an important variable in haplotype analyses, acting as an increasing risk factor for relapse with each additional year. In this regard, the literature consistently reports that older individuals with ALL tend to respond less favorably to therapy due to differences in tumor biology when compared with younger patients\u003csup\u003e(35)\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDespite the strengths of this study, some limitations should be acknowledged. The relatively modest sample size compared with large-scale polymorphism studies limited statistical power and resulted in the absence of events for certain genotype combinations. For overall survival analyses, complete information on the date of death was available for only 44 patients, limiting the number of evaluable events. The highly unbalanced genotypic distribution, including the very low frequency of the GG genotype (Table 5), reduced the statistical power to detect genotype-specific survival differences. In addition, the analyzed variants may be co-inherited as haplotypes, potentially contributing to the overlapping survival patterns observed. Larger cohorts will be necessary to independently assess the impact of these variants on overall survival. In addition, functional and gene expression data were not available, which limits direct inference regarding the biological mechanisms underlying the observed associations. Finally, differences in genetic ancestry and population structure may contribute to variability across studies, reinforcing the importance of regional and population-specific investigations. Future studies integrating larger cohorts and functional approaches will be essential to validate and expand upon these findings.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, the present study demonstrates that the \u003cem\u003eTP53\u003c/em\u003e variants \u003cem\u003ers1042522\u003c/em\u003e and \u003cem\u003ers1642785\u003c/em\u003e are significantly associated with susceptibility and clinical outcomes in patients with ALL from the Brazilian Amazon. Our findings highlight that these variants modulate the risk of disease development, relapses, and overall survival, with a notable protective effect conferred by specific heterozygous genotypes and the GG haplotype. These results support the role of germline \u003cem\u003eTP53\u003c/em\u003e variation as a relevant prognostic biomarker in ALL, especially in populations with unique genetic backgrounds. Thus, our data emphasizes the importance of regional genetic studies and warrant further investigation in larger cohorts and functional assays to explore the clinical utility of these variants in risk stratification and personalized therapy.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u0026nbsp;\u003c/strong\u003eAllyson Guimar\u0026atilde;es Costa, Glenda Menezes Nogueira, and Fab\u0026iacute;ola Silva Alves-Hanna.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData curation:\u0026nbsp;\u003c/strong\u003eGlenda Menezes Nogueira, Tha\u0026iacute;s Lohana Pereira Ribeiro, Fab\u0026iacute;ola Silva Alves-Hanna, F\u0026aacute;bio Magalh\u0026atilde;es Gama, and Nilberto Dias Ara\u0026uacute;jo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFormal analysis:\u0026nbsp;\u003c/strong\u003eGlenda Menezes Nogueira, Fab\u0026iacute;ola Silva Alves-Hanna, and Allyson Guimar\u0026atilde;es Costa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding acquisition:\u0026nbsp;\u003c/strong\u003eAdriana Malheiro, Andr\u0026eacute;a Monteiro Tarrag\u0026ocirc; and Allyson Guimar\u0026atilde;es Costa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInvestigation:\u0026nbsp;\u003c/strong\u003eGlenda Menezes Nogueira, Luca Gabriel Marques Gon\u0026ccedil;alves, Larissa Silva Santos, Tha\u0026iacute;s Lohana Pereira Ribeiro, F\u0026aacute;bio Magalh\u0026atilde;es Gama, Nilberto Dias Ara\u0026uacute;jo and Fab\u0026iacute;ola Silva Alves-Hanna.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u0026nbsp;\u003c/strong\u003eGlenda Menezes Nogueira, Luca Gabriel Marques Gon\u0026ccedil;alves, Larissa Silva Santos, Tha\u0026iacute;s Lohana Pereira Ribeiro and Fab\u0026iacute;ola Silva Alves-Hanna.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProject administration:\u0026nbsp;\u003c/strong\u003eAdriana Malheiro, Andr\u0026eacute;a Monteiro Tarrag\u0026ocirc; and Allyson Guimar\u0026atilde;es Costa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026ndash; original draft:\u0026nbsp;\u003c/strong\u003eGlenda Menezes Nogueira, Fab\u0026iacute;ola Silva Alves-Hanna, and Allyson Guimar\u0026atilde;es Costa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026ndash; review \u0026amp; editing:\u0026nbsp;\u003c/strong\u003eGlenda Menezes Nogueira, F\u0026aacute;bio Magalh\u0026atilde;es Gama, Nilberto Dias Ara\u0026uacute;jo Adriana Malheiro, Andr\u0026eacute;a Monteiro Tarrag\u0026ocirc;, Fab\u0026iacute;ola Silva Alves-Hanna, and Allyson Guimar\u0026atilde;es Costa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll study protocols and informed consent procedures were approved by the Research Ethics Committee of HEMOAM (CEP/HEMOAM; approval number 3,335,123/2019). All patients were treated in accordance with the guidelines and recommendations of the Brazilian Ministry of Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s. Due to ethical restrictions regarding patient privacy, data are available upon request. Data are available upon request from the Ethics Committee of HEMOAM (CEP-HEMOAM), [email protected], for researchers who meet the criteria for access to confidential data. Additional requests for the data may be sent to the corresponding author or coauthors Allyson G. Costa ([email protected]) and Fab\u0026iacute;ola S. Alves-Hanna ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Genomics Laboratory of the Funda\u0026ccedil;\u0026atilde;o Hospitalar de Hematologia e Hemoterapia do Amazonas (HEMOAM) for technical support and for providing the facilities necessary to conduct the experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from the Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado do Amazonas (FAPEAM) (PR\u0026Oacute;-ESTADO Program - #002/2008, #007/2018, #005/2019 and POSGRAD Program - #002/2025)\u0026nbsp;and Genomic Surveillance Network in Health of the State of Amazonas (REGESAM), Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq) (INCT-Sangue \u0026ndash; Process #405918/2022-4) e Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior (CAPES) (PDPG-CONSOLIDACAO-3-4 Program \u0026ndash; Process #88887.707248/2022-00).\u0026nbsp;GMN, LGMG, TLPR, LSS, and LSP received undergraduate and master\u0026rsquo;s scholarships from FAPEAM. FSH was supported by a CNPq Junior Postdoctoral Fellowship (PDJ).\u0026nbsp;AM is level 1B research fellow of the CNPq. AGC is a level 2 research fellow of CNPq.\u0026nbsp;The funding agencies had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFujita TC, Sousa-Pereira N, Amarante MK, Watanabe MAE. Acute lymphoid leukemia etiopathogenesis. \u003cstrong\u003eMol Biol Rep\u003c/strong\u003e. 2021;48(1):817\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eTerwilliger T, Abdul-Hay M. Acute lymphoblastic leukemia: a comprehensive review and 2017 update. \u003cstrong\u003eBlood Cancer J\u003c/strong\u003e. 2017;7(6):e577.\u003c/li\u003e\n\u003cli\u003eInstituto Nacional de C\u0026acirc;ncer Jos\u0026eacute; Alencar Gomes da Silva (INCA). \u003cem\u003eEstimativa 2023: incid\u0026ecirc;ncia de c\u0026acirc;ncer no Brasil\u003c/em\u003e. Rio de Janeiro: INCA; 2022.\u003c/li\u003e\n\u003cli\u003eMalard F, Mohty M. Acute lymphoblastic leukaemia. \u003cstrong\u003eLancet\u003c/strong\u003e. 2020;395(10230):1146\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003eInaba H, Mullighan CG. Pediatric acute lymphoblastic leukemia. \u003cstrong\u003eHaematologica\u003c/strong\u003e. 2020;105(11):2524\u0026ndash;39.\u003c/li\u003e\n\u003cli\u003eBoutelle AM, Attardi LD. p53 and tumor suppression: it takes a network. \u003cstrong\u003eTrends Cell Biol\u003c/strong\u003e. 2021;31(4):298\u0026ndash;310.\u003c/li\u003e\n\u003cli\u003eOunalli A, Moumni I, Mechaal A, Chakroun A, Barmat M, Rhim RE, et al. TP53 gene 72 Arg/Pro (rs1042522) single nucleotide polymorphism increases the risk and severity of chronic lymphocytic leukemia. \u003cstrong\u003eFront Oncol\u003c/strong\u003e. 2023;13:1272876.\u003c/li\u003e\n\u003cli\u003eTripon F, Iancu M, Trifa A, Crauciuc GA, Boglis A, Balla B, et al. Association analysis of TP53 rs1042522, MDM2 rs2279744, rs3730485, MDM4 rs4245739 variants and acute myeloid leukemia susceptibility and clinical features. \u003cstrong\u003eJ Clin Med\u003c/strong\u003e. 2020;9(6):1\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eBilous N, Abramenko I, Saenko V, Chumak A, Dyagil I, Martina Z, et al. Clinical relevance of TP53 polymorphic genetic variations in chronic lymphocytic leukemia. \u003cstrong\u003eLeuk Res\u003c/strong\u003e. 2017;58:1\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eAlves FS, Xabregas LA, Kerr MWA, Souza GL, Pereira DS, Magalh\u0026atilde;es-Gama F, et al. Genetic polymorphisms of inflammasome genes associated with pediatric acute lymphoblastic leukemia and clinical prognosis in the Brazilian Amazon. \u003cstrong\u003eSci Rep\u003c/strong\u003e. 2021;11:9869.\u003c/li\u003e\n\u003cli\u003eZhang J, Mullighan CG, Harvey RC, Wu G, Chen X, Edmonson M, et al. Key pathways are frequently mutated in high-risk childhood acute lymphoblastic leukemia. \u003cstrong\u003eBlood\u003c/strong\u003e. 2011;118(11):3080\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003ePui CH, Yang JJ, Hunger SP, Pieters R, Schrappe M, Biondi A, et al. Childhood acute lymphoblastic leukemia: progress through collaboration. \u003cstrong\u003eJ Clin Oncol\u003c/strong\u003e. 2015;33(27):2938\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003eSilva-Junior AL, Alves FS, Kerr MWA, Xabregas LA, Gama FM, Rodrigues MGA, et al. Acute lymphoid and myeloid leukemia in a Brazilian Amazon population. \u003cstrong\u003ePLoS One\u003c/strong\u003e. 2019;14(8):e0221518.\u003c/li\u003e\n\u003cli\u003eSkhoun H, Khattab M, Belkhayat A, Takki Chebihi Z, Bakri Y, Dakka N, et al. Association of TP53 gene polymorphisms with the risk of acute lymphoblastic leukemia in Moroccan children. \u003cstrong\u003eMol Biol Rep\u003c/strong\u003e. 2022;49(9):8291\u0026ndash;300.\u003c/li\u003e\n\u003cli\u003eDoffe F, Carbonnier V, Tissier M, Leroy B, Martins I, Mattsson JSM, et al. Identification and functional characterization of new missense SNPs in TP53. \u003cstrong\u003eCell Death Differ\u003c/strong\u003e. 2021;28(5):1477\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003ede Lourdes Perim A, Guembarovski RL, Oda JMM, Lopes LF, Ariza CB, Amarante MK, et al. CXCL12 and TP53 polymorphisms as markers of susceptibility in Brazilian children with ALL. \u003cstrong\u003eMol Biol Rep\u003c/strong\u003e. 2013;40(7):4591\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eTian X, Dai S, Sun J, Jiang S, Jiang Y. TP53 Arg72Pro polymorphism and leukemia risk: a meta-analysis. \u003cstrong\u003eSci Rep\u003c/strong\u003e. 2016;6:24097.\u003c/li\u003e\n\u003cli\u003eMohammed Basabaeen AA, Abdelgader EA, Babekir EA, Abdelrahim SO, Eltayeb NH, Altayeb OA, et al. TP53 Arg72Pro polymorphism and B-CLL risk in Sudanese population. \u003cstrong\u003eAsian Pac J Cancer Prev\u003c/strong\u003e. 2019;20(5):1579\u0026ndash;85.\u003c/li\u003e\n\u003cli\u003eAlhadheq AM, Parine NR, Shaik JP, Alhadheq R, Alkahtani S, Aljarba NH, et al. TP53 polymorphisms and colorectal cancer risk. \u003cstrong\u003eJ King Saud Univ Sci\u003c/strong\u003e. 2023;35(10):102936.\u003c/li\u003e\n\u003cli\u003eHamadou WS, Besbes S, Bourdon V, Ben Youssef Y, Laatiri MA, Noguchi T, et al. TP53 mutations in familial and sporadic hematological malignancies. \u003cstrong\u003eFam Cancer\u003c/strong\u003e. 2017;16(1):153\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eAbramenko I, Bilous N, Chumak A, Dyagil I, Martina Z. Lipoprotein lipase expression and TP53 polymorphisms in CLL. \u003cstrong\u003eExp Oncol\u003c/strong\u003e. 2021;43(3):224\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003ePerriaud L, Marcel V, Sagne C, Favaudon V, Gu\u0026eacute;din A, De Rache A, et al. Intronic polymorphisms rs17878362 and rs1642785 affect TP53 transcripts. \u003cstrong\u003eCarcinogenesis\u003c/strong\u003e. 2014;35(12):2706\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eHern\u0026aacute;ndez Borrero LJ, El-Deiry WS. Tumor suppressor p53: biology and therapeutic targeting. \u003cstrong\u003eBiochim Biophys Acta Rev Cancer\u003c/strong\u003e. 2021;1876(1):188556.\u003c/li\u003e\n\u003cli\u003eJha P, Pathak P, Chosdol K, Suri V, Sharma MC, Mahapatra AK, et al. TP53 polymorphisms in gliomas from Indian patients. \u003cstrong\u003eExp Mol Pathol\u003c/strong\u003e. 2011;90(2):167\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eSecolin R, Mas-Sandoval A, Arauna LR, Torres FR, Araujo TK, Santos ML, et al. Local ancestry and adaptation in admixed populations. \u003cstrong\u003eSci Rep\u003c/strong\u003e. 2019;9:13900.\u003c/li\u003e\n\u003cli\u003eJacovas VC, Rovaris DL, P\u0026eacute;rez O, Azevedo S, Macedo GS, Sandoval JR, et al. TP53 pathway adaptations in Native Americans from the Andes. \u003cstrong\u003ePLoS One\u003c/strong\u003e. 2015;10(9):e0137823.\u003c/li\u003e\n\u003cli\u003eHattinger CM, Patrizio MP, Luppi S, Serra M. Pharmacogenomics in osteosarcoma. \u003cstrong\u003eInt J Mol Sci\u003c/strong\u003e. 2020;21(13):4659.\u003c/li\u003e\n\u003cli\u003ePopek-Marciniec S, Styk W, Wojcierowska-Litwin M, Chocholska S, Szudy-Szczyrek A, Samardakiewicz M, et al. TP53 rs1042522 and multiple myeloma risk and response. \u003cstrong\u003eCancers (Basel)\u003c/strong\u003e. 2023;15(19):4747.\u003c/li\u003e\n\u003cli\u003eFloris M, Pira G, Castiglia P, Idda ML, Steri M, de Miglio MR, et al. TP53 polymorphisms and cancer susceptibility. \u003cstrong\u003eOncol Lett\u003c/strong\u003e. 2022;24(4):331.\u003c/li\u003e\n\u003cli\u003eIacobucci I, Ferrari A, Kohlmann A, Papayannidis C, Venturi C, Perricone M, et al. Loss of heterozygosity at rs1042522 in BCR-ABL1+ ALL. \u003cstrong\u003eBlood\u003c/strong\u003e. 2012;120(21):2497.\u003c/li\u003e\n\u003cli\u003eKodal JB, Vedel-Krogh S, Kobylecki CJ, Nordestgaard BG, Bojesen SE. TP53 Arg72Pro and mortality after cancer. \u003cstrong\u003eSci Rep\u003c/strong\u003e. 2017;7:1\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eShen CC, Cheng WY, Lee CH, Dai XJ, Chiao MT, Liang YJ, et al. TP53 codon 72 polymorphism and glioblastoma prognosis. \u003cstrong\u003eBMC Cancer\u003c/strong\u003e. 2020;20:1\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eZmorzynski S, Korszen-Pilecka I, Wojcierowska-Litwin M, Kwiatkowska-Drabik B, Luterek M, Chocholska S, et al. TP53 polymorphism in plasma cell myeloma. \u003cstrong\u003eFolia Histochem Cytobiol\u003c/strong\u003e. 2017;55(4):203\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eHattinger CM, Biason P, Iacoboni E, Gagno S, Fanelli M, Tavanti E, et al. Germline polymorphisms and survival in osteosarcoma. \u003cstrong\u003eOncotarget\u003c/strong\u003e. 2016;7(38):61970.\u003c/li\u003e\n\u003cli\u003eTran TH, Hunger SP. Genomic landscape of pediatric ALL. \u003cstrong\u003eSemin Cancer Biol\u003c/strong\u003e. 2022;84:144\u0026ndash;52.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"TP53, genetic polymorphisms, acute lymphoblastic leukemia, Prognosis, population genetics, pediatric cancer","lastPublishedDoi":"10.21203/rs.3.rs-8679558/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8679558/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eTumor suppressor genes play a central role in cancer development, and inherited genetic variation may influence both disease susceptibility and clinical outcomes. This study aimed to investigate the frequency and prognostic relevance of \u003cem\u003eTP53\u003c/em\u003e polymorphic variants in patients with acute lymphoblastic leukemia (ALL) from an admixed population in the Brazilian Amazon.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA population-based case–control study was conducted including 193 patients diagnosed with ALL and 215 healthy controls. Germline \u003cem\u003eTP53\u003c/em\u003epolymorphisms \u003cem\u003ers1042522\u003c/em\u003e and \u003cem\u003ers1642785\u003c/em\u003e were genotyped, and allele and genotype frequencies were compared between groups. Associations with ALL susceptibility, relapse, and mortality were evaluated using multiple genetic models adjusted for age and sex. Combined genotype and haplotype analyses were performed, and overall survival was estimated using Kaplan–Meier curves and log-rank tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe CC genotype and C allele were more frequent among ALL cases than controls. The C allele of \u003cem\u003ers1042522\u003c/em\u003e (p=0.021) and \u003cem\u003ers1642785\u003c/em\u003e(p=0.022) were associated with increased susceptibility to ALL. In addition, the \u003cem\u003ers1042522-\u003c/em\u003eC was associated with a higher risk of relapse (p=0.016) and death (p=0.009). Protective effects against ALL were observed under the recessive model for \u003cem\u003ers1042522\u003c/em\u003e (p=0.019) and the codominant model for \u003cem\u003ers1642785\u003c/em\u003e(p=0.013). Both variants showed protective associations with mortality under the log-additive model. Combined genotype analysis revealed that \u003cem\u003ers1042522\u003c/em\u003eCG and GG genotypes were associated with a reduced risk of relapse (p\u0026lt;0.001). Overall survival analysis revealed reduced survival associated with the CC genotype, whereas improved survival was observed for the heterozygous CG genotype. Haplotype analysis indicated that the GG haplotype conferred a reduced risk of ALL (p=0.007) and death (p=0.013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eOur findings suggest that germline \u003cem\u003eTP53\u003c/em\u003evariants \u003cem\u003ers1042522\u003c/em\u003e and \u003cem\u003ers1642785\u003c/em\u003e modulate susceptibility and clinical outcomes in ALL, supporting their potential role as prognostic biomarkers. This study highlights the importance of population-based genomic investigations in underrepresented populations.\u003c/p\u003e","manuscriptTitle":"Association of TP53 polymorphic variants rs1042522 and rs1642785 with susceptibility and prognosis of acute lymphoblastic leukemia in a Brazilian Amazon population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 07:35:28","doi":"10.21203/rs.3.rs-8679558/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-17T18:10:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T17:20:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T14:03:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T03:58:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-15T12:59:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279955403743386747160424325045294554111","date":"2026-02-25T15:28:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28195041355843837197909437247563026410","date":"2026-02-25T08:00:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279221935242785845437825550054069188944","date":"2026-02-24T00:38:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110438056826829457746412760092327342811","date":"2026-02-23T15:15:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-23T14:55:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-22T23:51:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Genomics","date":"2026-02-22T23:46:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17556cac-f930-468b-9ad9-b8b5ed706d40","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:09:54+00:00","versionOfRecord":{"articleIdentity":"rs-8679558","link":"https://doi.org/10.1186/s12920-026-02371-0","journal":{"identity":"bmc-medical-genomics","isVorOnly":false,"title":"BMC Medical Genomics"},"publishedOn":"2026-04-14 15:57:02","publishedOnDateReadable":"April 14th, 2026"},"versionCreatedAt":"2026-02-27 07:35:28","video":"","vorDoi":"10.1186/s12920-026-02371-0","vorDoiUrl":"https://doi.org/10.1186/s12920-026-02371-0","workflowStages":[]},"version":"v1","identity":"rs-8679558","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8679558","identity":"rs-8679558","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}