Genetic polymorphism of SNPs rs9399137 and rs4895441 in HBS1L-MYB and SNP rs766432 in BCL11A among β-thalassemia Egyptian patients

Genetic polymorphism of SNPs rs9399137 and rs4895441 in HBS1L-MYB and SNP rs766432 in BCL11A among β-thalassemia Egyptian patients | 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 Genetic polymorphism of SNPs rs9399137 and rs4895441 in HBS1L-MYB and SNP rs766432 in BCL11A among β-thalassemia Egyptian patients Abdelrahman A. Abdelrahman, Randa M. Talaat, Ibrahim Abdo El-Halfawy, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8951386/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background This study aimed to evaluate the role of two genetic polymorphisms (rs9399137 and rs4895441) in the HBS1L-MYB and rs766432 of BCL11A genes as determinant HbF level and disease severity in Egyptian β-thalassemia patients. Methods Blood samples were obtained from 100 participants, comprising 50 individuals with β-thalassemia and 50 healthy controls. Hematological assessment was conducted through complete blood count (CBC), ferritin measurement, and hemoglobin electrophoresis. After genomic DNA extraction was done, HBS1L-MYB (rs9399137 and rs4895441) SNPs and rs766432 of BCL11A were all assessed by ARMS-PCR .The HBS1L-MYB rs4895441 assessed by RFLP-PCR with the restriction enzyme (RSA1). Results The genotypic frequencies of the rs766432 SNP of BCL11A and rs9399137 indicated that the genotype frequencies of these SNPs were significantly associated with severity of β thalassemia but there was no significant association between β-thalassemia severity and rs895441 SNP of HSB1L-MYB. Conclusions In Egyptian β-thalassemia patients, rs766432 in BCL11A and rs9399137 in HBSIL-MYB were found to significantly increase HbF levels, but rs4895441 was not. New tailored management techniques to decrease transfusion needs can be developed based on these findings, which show how genetic and clinical factors affect the severity of β-thalassemia. Further research involving a larger sample size and additional modifier genes is needed. BCL11A gene HSB1L-MYB gene β-thalassemia rs766432 rs9399137 rs4895441 Figures Figure 1 1. Background Thalassaemia is a genetic haematological illness marked by a partial or complete decrease in the production of one or more globin chains. β-thalassemia arises from mutations in the β-globin gene, resulting in the missing or reduced synthesis of β-globin chains. This deficiency causes ineffective erythropoiesis, subsequently resulting in anemia. Hemoglobin E (HbE) represents an abnormal hemoglobin variant caused by a single point mutation in the β-globin chain [ 1 ]. According to the World Health Organization (WHO), approximately 1.5% of the global population are carriers of β-thalassemia, and an estimated 68,000 children are born annually with different forms of thalassemia syndromes [ 2 ]. Haemoglobin E (HbE) is an atypical haemoglobin variation distinguished by a single point mutation in the β-globin chain. This mutation results in the substitution of glutamate with lysine (E26K) at position 26. The alteration influences β-globin gene expression by establishing an alternative splicing site within the mRNA at codons 25–27 of the β-globin gene, ultimately leading to the production of the abnormal HbE variant [ 3 ]. β-thalassemia/HbE disease exhibits a wide array of clinical phenotypes, ranging from asymptomatic individuals to those necessitating frequent blood transfusions. Patients exhibiting mild to moderate forms generally do not necessitate transfusion therapy, however those with severe manifestations display pronounced anaemia, hepatosplenomegaly, and considerable iron overload, requiring regular transfusions akin to persons with β-thalassemia major [ 4 ]. The thalassemia results from defective globin chain production. Based on the defectively synthesized globin chain, it is classified into α, β, δβ, and δβγ-thalassemia. From a public health perspective, only the α- and β-thalassemias are prevalent enough to warrant significance [ 5 ]. For hemoglobin production, β -globin chains make a tetramer with the α-globin chains. The decrease or lack of these chains causes β -Thalassemia. Mutations that completely inactivate the β gene and cause no β-globin production are referred to as β 0 mutations, while mutations that permit the production of some β globin are categorized according to the degree of quantitative reduction in the output of the β chains as β + or β ++ [ 6 ]. In β-thalassemia minor, the mutation is very mild and patients typically exhibit no indications or symptoms or may present with slight changes in RBCs count or volume. The level of Hb might be > 10 g/dL in β-thalassemia minor or carrier patients. This represents the predominant variant of β-thalassemia [ 1 ]. In β-thalassemia intermediate, β-globin synthesis is decreased. This results in mild anemia, decreased RBCs indices, and increased Hb A2 and/or HbF levels in hemoglobin electrophoresis. In β-thalassemia major, β-globin is not synthesized. So, the marrow produces δ-globin and more α-globin. Untreated patients exhibit severe anaemia requiring transfusion, significant splenomegaly, skeletal abnormalities, growth retardation, and distinctive facial traits [ 7 ]. Three principal quantitative trait loci QTLs for HbF exist: the β-cell lymphoma/leukemia (BCL11A) gene, the HBS1L-MYB intragenic area, and Krüppel-like factor 1 (KLF). In the fetal stage, hypermethylated in cancer 2 protein (HIC2) represses BCL11A by blocking chromatin access, while GATA1 and NF-Y activate γ-globin expression [ 8 ]. ZBTB7A (LRF) and BCL11A silence γ-globin through the nucleosome remodeling and deacetylase (NuRD) complex, aided by ZNF410, which activates CHD4 [ 9 ]. KLF1 and MYB repress γ-globin indirectly by activating BCL11A, while NFIA/NFIX reinforce this repression [ 10 ]. BCL11A, a zinc-finger transcription factor on chromosome 2p16.1, is the main silencer of HbF in adults [ 9 ]. HPFH results from mutations disrupting BCL11A or ZBTB7A binding or creating new activator sites for GATA1 or KLF1 [ 11 ]. The presence of single nucleotide polymorphisms (SNPs), transcription factors, and a therapeutic agent alter the regulation of β-globin XMN1-HBG2, BCL11A pathway, and possibly increase haemoglobin F (HbF) levels [ 12 ]. This study focusses on the impact of gene polymorphisms rs9399137 and rs4895441 in HBS1L-MYB, as well as rs766432 in BCLIIA, on HbF level among Egyptian patients with β-thalassemia. 2. Methods This case-control study was conducted over the course of one year, beginning in October 2024 and ending in January 2025. 50 β-thalassemia patients aged 5–45 years old, alongside 50 healthy, age- and sex-matched controls were collected from the Shebin El-Kom Teaching Hospital. Based on the levels of haemoglobin A2 and haemoglobin F, the thalassemic group was further divided into three subgroups as per Keohane et al., 2015 [ 13 ]: the thalassaemia minor subgroup, where HbA2 ranged from 3.5% to 7.0% and HbF from 1–5%; the thalassemia intermediate subgroup, where HbA2 was greater than 7.0% and HbF from 5–70%; and the thalassemia major subgroup, where HbF levels were between 70 and 90%.. Pregnant ladies & old age participants were excluded. All subjects underwent comprehensive history taking, a physical examination emphasizing facial morphology with documenting of alterations in facial shape, blood count, hemoglobin electrophoresis, and ferritin. Blood samples were obtained from all participants: a serum tube for ferritin level assessment and an EDTA tube for a blood count, haemoglobin electrophoresis, and DNA extraction. 2.1. Methods 2.1.1 Serum Ferritin : Ferritin was assayed using Mybiosource Enzyme-Linked Immunosorbent Assay (ELISA) kits, San Diego, California, U.S.A. (code: MBS2514182). The kit used a double-antibody sandwich ELISA to assay the serum ferritin level. 2.1.2. DNA Extraction Following the directions provided by QIAGEN (Hilden, Germany), genomic DNA was extracted from an EDTA blood sample using the QIAamp® DSP Virus Spin Kit Version 1. DNA purity and concentration were determined by the spectrophotometer measurement of absorbance at 260 and 280 nm. 2.1.3. Molecular Genotyping BCL11A, rs766432 (A / C), and HBSIL-MYB, rs9399137 (T / C) polymorphisms were genotyped using tetra-primer amplification refractory mutation system-polymerase chain reaction (ARMS-PCR) with amplicons corresponding to each allelic variant. To enhance the specificity of the reaction, a mismatch was incorporated at the 3' end of each of the two allele-specific primers. To confirm primer specificity, we employed the 'BLAST' instrument available at http://www.ncbi.nlm.nih.gov/blast . Gradient PCR was performed on the Biometra T Professional thermal cycler (Biometra, Germany) to identify the optimal annealing temperature for each primer set. The PCR reaction volume was 20 µL, comprising 2X ReddyMix PCR master mix (Thermo Scientific Inc., USA), genomic DNA at a concentration of 50–100 ng/µL, primers, and MgCl₂, with a final reaction concentration of 2.5 mM. Optimal PCR conditions comprised an initial denaturation step of 10 minutes at 95°C, followed by 30 cycles of denaturation at 95°C, annealing at 58°C, and extension at 72°C, culminating in a final extension at 72°C for 7 minutes. HBS1L-MYB (rs4895441 A/G) SNP was tested using polymerase chain reaction-restriction fragment length polymorphism (PCR RFLP) assay. PCR-RFLP was performed on the Biometra T Professional thermal cycler (Biometra, Germany) using the PCR Master Mix (Cat. No 101005-Bioron GmbH, Germany) . Identification of the alleles was performed by incubating the PCR product Rsa1 GT \(\downarrow\) AC restriction endoneuclease enzyme (Thermo Scientific Cat. No ER 1121). Table (1) details the primer sequences employed in the methodology. DNA fragments were subjected to electrophoresis on a 2% agarose gel and visualized using ethidium bromide. Figure 1 .a shows the PCR products for HBSIL-MYB rs9399137 SNP where band at175 bp represented AA genotype, 117, 175 bands represented AC genotype, and 117 bands for CC genotype. Figure 1 .b shows the PCR products for HBSIL-MYB, rs9399137 (T / C): band at 254 bp represented TT genotype, 254, 173 bands represented AC genotype, and 173 band represented CC genotype. Figure 1 .c shows the PCR products for HBSIL-MYB rs4895441 SNP, the wild allele (A) was restricted into one fragment, 325 bp, while the variant allele (G) was restricted into 2 fragments 223, bp and 173 bp. 2.2. Statistical analysis SPSS version 29, developed in Chicago, Illinois, USA, was used in the statistical study. The Chi-square test and the Mann-Whitney U test were used to analyze the significance between different variables. The statistical significance was set at P > 0.05. 2.3. In Silico Analysis The functional effects of this study's single nucleotide variations (SNVs) were predicted utilizing combined annotation dependent depletion (CADD) prediction tool. CADD C-Scores > 10 indicate pathogenic variations. 3. Results 3.1. Clinical Parameters of study population Table 2 summarizes the clinical data of the study group. There is no significant difference in age or sex, but the levels of haemoglobin F (HbF), haemoglobin A2 (HbA2), hemoglobin, and serum ferritin were considerably higher in the patients group compared to the control group. The patient group had a considerably lower MCV. Table 1 Primer sequences of studied polymorphisms Gene Polymorphism Primer Sequence Methodology BCL11A rs766432 Forward (A) : 5΄-TTGTTTCGCTTTAGCTTTATTAAGGTACAA-3΄ ARMS-PCR Reverse (A) : 5΄GACGTGTTCTGTATCTTGATTTTGGT-3՛ Forward (C) : 5՛-CCAAACAGTTTAAAGGTTACAGACAGACT-3՛ Reverse (C) : 5՛-AAAATGAATGACTTTTGTTGTATGTAGAG-3՛ HBS1L-MYB rs9399137 Forward (C) : 5΄-AATGTAATTAACTGAACATATGGTTAGTC-3΄ ARMS-PCR Reverse (C) : 5΄TTTATTGTTACAAGGTTAATTCACTGCC-3՛ Forward (T) : 5՛-GAAATACCATCACTGAGAAAAGCATAAG-3՛ Reverse (T) :5՛-CAGCAGGGTTGCTTGTGAAAAAACTTTA-3՛ rs4895441 Forward : 5՛-ATATTCTGGCTGGGGGAGACAAA − 3՛ RFLP Reverse : 5՛-GCCCTACAGGATCTCACTGCTAC − 3՛ ARMS: Amplification Refractory Mutation System; RFLP: Restriction Fragment Length Polymorphism Table 2 The demographic and haematological parameters of study group Gender Patients (N = 50) Mean ± SD Controls (N = 50) Mean ± SD p value Male: 24 (48%) Female: 26 (52%) Male: 34 (68%) Female: 16 (32%) 0.068 Age (years) 17.33 ± 11.99 25.23 ± 11.53 0.386 Hb F 49.05 ± 32.25 0.78 ± 0.52 0.001 HbA2 11.12 ± 8.33 2.75 ± 0.45 0.001 Haemoglobin Level 8.17 ± 1.49 13.44 ± 1.28 0. 003 MCV 67.68 ± 7.51 82.3 ± 2.33 0. 001 Serum Ferritin 1087.74 ± 535.05 97.94 ± 50.22 0. 180 3.2. Genetic Polymorphisms of study cohort The comparison of the genotype and allele frequencies between patient and control groups indicated that AC (heterozygous) and CC (homozygous wild) of the BCL11A gene, rs766432 (A > C) and HBSIL-MYB gene, rs9399137 (T > C) polymorphisms, had a significant difference of these variants between both groups as the p-value is > 0.05. On the other hand, negative and positive genotypes of the HBSIL-MYB gene, rs4895441 polymorphism, had a non-significant difference across the same groups, as the p-value is < 0.05 (Table 3 ). Table 3 Genotype and allele frequency for gene polymorphisms among control and patient groups. Patients (N = 50) Controls (N = 50) χ2 p value BCL11A, rs766432 (A / C) Genotype AA 19 (38%) 32 (64%) 7.321 0.026 AC 19 (38%) 13 (26%) CC 12 (24%) 5 (10%) Allele Frequency A 57 (57%) 77 (77%) 12.265 0.001 C 43 (43%) 23 (23%) HBSIL-MYB, rs9399137 (T / C) Genotype TT 14 (28%) 41 (82%) 30.610 0.001 TC 32 (64%) 9 (18%) CC 4 (8%) 0 (0%) Allele Frequency T 60 (60%) 91 (91%) 25.976 0.001 C 40 (40%) 9 (9%) HBSIL-MYB rs4895441 (A/G) Genotype AA 25 (50%) 32 (64%) 3.938 0.140 AG 19 (38%) 10 (20%) GG 6 (12%) 8 (16%) Allele Frequency A 69 (69%) 74 (74%) 0.613 0.434 G 31 (31%) 26 (26%) χ2 = Chi-Square 3.3. Clinical and Laboratory features of Thalassemia patients This study conducted a comparative analysis among thalassaemia subgroups. Table (4) indicates that there was no significant difference in age and sex among the thalassaemia subgroups. Conversely, a significant difference was recorded about the percentages of HbF and HbA2 values. Although there was no statistically significant association regarding haemoglobin and ferritin levels across thalassaemia subgroups, haemoglobin levels were relatively higher in the thalassaemia minor subgroup. In contrast, ferritin levels were higher in the thalassaemia major subgroup Additionally, thalassaemia face features, splenomegaly, splenectomy, and blood transfusion rates were prevalent within the thalassaemia intermediate and major subgroups with a significant association, as shown in table (5). 3.4. Genetic Polymorphisms of Thalassemia patients The analysis of genotype and allele frequency distributions among thalassaemia major, thalassaemia intermediate, and thalassaemia minor patient groups revealed a significant difference of the genotypes AC and CC of the BCL11A gene, rs766432 (A > C) polymorphism across the three groups, as the p-value is > 0.05. TC and TT genotypes of the HBSIL-MYB gene, rs9399137 (T > C) polymorphism, exhibited a significant difference of this variant, as the p-value is > 0.05. Negative and positive genotypes of the HBSIL-MYB gene, rs4895441 (A > G) polymorphism, had a non-significant difference across groups, as the p-value is < 0.05 (Table 6). Table 6 Genotype and allele frequency for gene polymorphisms among thalassemia patient groups. Thalassemia Minor (N = 10) Thalassemia Intermediate (N = 17) Thalassemia Major (N = 23) χ2 p value BCL11A, rs766432 (A / C) Genotype AA 5 (50%) 7 (41.1%) 4 (17.4%) 10.705 0.030 AC 4 (40%) 8 (47.0%) 7 (30.4%) CC 1 (10%) 2 (11.9%) 12 (52.2%) Allele Frequency A 14 (70%) 22 (64.7%) 15 (32.6%) 11.671 0.03 C 6 (30%) 12 (35.3%) 31 (67.4%) HBSIL-MYB, rs9399137 (T / C) Genotype TT 5 (50%) 7 (41.2%) 2 (8.6%) 9.272 0.03 TC 5 (50%) 8 (47%) 19 (82.8%) CC 0 (0%) 2 (11.8%) 2 (8.6%) Allele Frequency T 15 (75%) 22 (64.7%) 23 (50.0%) 4.105 0.128 C 5 (25%) 12 (35.3%) 23 (50.0%) HBSIL-MYB rs4895441 (A/G) Genotype AA 6 (60%) 8 (47.1%) 11 (47.8%) 2.704 0.722 AG 2 (20%) 7 (41.2%) 10 (43.5%) GG 2 (20%) 2 (11.7%) 2 (8.7%) Allele Frequency A 14 (70%) 23 (67.7%) 32 (69.6%) 0.045 0.978 G 6 (30%) 11 (32.3%) 14 (30.4%) χ2 = Chi-Square 3.5. In Silico Analysis for All Variants of Modifier Genes Among the analyzed SNVs, one SNV on the HBS1L-MYB gene (rs9399137) was predicted to be pathogenic using the CADD tool. Nonetheless, one SNV on BCL11A (rs766432) and the HBS1L-MYB gene (rs4895441) SNPs were predicted to be benign using the same scoring systems (Table 7). Table 7 Pathogenicity of variants of modifier gene using CADD Gene SNP rs ID Chromosome Position Reference Alternate CADD C-Score BCl11A rs766432 Chr.2 60719970 C A 3.196 HBSIL-MYB rs9399137 Chr.6 135419018 T C 12.68 HBSIL-MYB rs4895441 Chr.6 135426573 A G 2.619 CADD = Combined Annotation Dependent Depletion 4. Discussion Beta-thalassemia is a prevalent genetic condition, resulting from impaired synthesis of the β-globin chain or an unbalanced α/β chain ratio [ 14 ], and presenting clinically from asymptomatic carriers to transfusion-dependent patients [ 15 ]. The genetic variables influencing the imbalance between α- and non-α-haemoglobin chains dictate the clinical severity of β-thalassemia. Reduced globin chain imbalance promotes the preferential survival of erythroid precursors, thereby reducing ineffective erythropoiesis [ 16 , 17 ]. This study offered comprehensive insights into the clinical and haematological parameters, as well as genotype polymorphisms, of Egyptian β-thalassemia patients, clarifying the impact of BCL11A and HBS1L-MYB intergenic region gene polymorphisms on HBF levels in this cohort. In this study, blood transfusion rate, splenomegaly and facial changes were significantly higher in the β-thalassaemia major group. These findings correspond with Langhi et al. [ 18 ], who reported that β-thalassemia major patients undergo regular blood transfusions throughout their lives, typically provided every 2 to 5 weeks based on transfusion requirements. Serum ferritin levels were found to be higher in the thalassaemia major group with a significant difference when compared with other groups in this study. Taher et al. [ 19 ] attributed this to iron loading through blood transfusion in this group. In β-thalassaemia minor and intermediate groups, increased ferritin level is elucidated by Hsu et al., 2022 article, which indicated that serum ferritin levels may influenced by various non-iron-related physiological and pathological situations, such as ineffective erythropoiesis, enhanced intestinal absorption, and hepcidin suppression [ 20 ]. The zinc finger protein encoded by the BCL11A gene plays a crucial role in human HbF expression repression. It does this by binding to specific regions within the β-globin gene cluster and forming a complex with NuRD, which inhibits the expression of γ-globin gene [ 21 , 22 , 23 ]. In different ethnic groups, HbF levels are significantly correlated with polymorphisms in intron 2 of the BCL11A gene, as previously documented. This elucidates the variability in allele frequencies concerning rs766432, rs4671393, rs1427407, and rs11886868 SNPs [ 21 , 24 ]. Several researches studied the association between BCL11A gene polymorphisms and fluctuations in HbF levels; nevertheless, these studies exhibited significant diversity across different ethnic groups Some authors [ 21 , 25 , 26 , 27 , 28 , 29 ] have documented the presence of such an association for the variants rs766432, rs4671393, rs1427407, rs11886868, rs10189857, and rs7557939, whereas others have refuted this association regarding rs766432, rs4671393, rs1427407, rs11886868, and rs10189857 SNPs [ 30 ] and rs7557939 [ 17 , 31 ] polymorphisms with variation in HbF levels in thalassaemia major disease. To ascertain their association with HbF levels and, consequently, the severity of disease, we investigated the status of the rs766432 SNP located on the BCL11A gene, rs9399137, and rs4895441 SNPs located on the HBS1L-MYB gene in β-thalassaemia patients in this study. A robust association between these genetic variants and increased HbF levels in thalassaemia major patients was shown in recent studies, which supported the SNPs selection. For the SNP rs766432, located in the BCL11A gene, the frequency of AC (heterozygous mutant) genotype was significantly higher than that of the genotype AA (wild type) and the C allele frequency of was significantly higher in the thalassaemia major group when compared to the A allele in this study. This outcome indicates a possible correlation between the C allele and thalassaemia, particularly given that BCL11A is known to regulate foetal haemoglobin production and may be may contribute to the pathophysiology of thalassaemia. These findings are in line with prior research conducted by Sales et al. (2020) and Muszlak et al. (2020), who found that the BCL11A gene polymorphism with rs766432 was associated with increased HbF levels [ 28 , 29 ]. This conclusion, in contrast, contradicts that of Bhanushali et al., 2016, who reported no association between the intronic region BCL11A gene polymorphism and fluctuations in HbF levels [ 30 ]. Moreover, our results contrasted with those of Suali et al. (2025), who reported that neither the XmnI rs7482144 nor the BCL11A rs766432 polymorphisms exhibited a significant influence on the clinical phenotype of β-thalassaemia patients [ 31 ]. Primarily expressed in erythroid precursor cells, the HBS1L-MYB gene is the next regulator of HbF levels [ 32 ]. Decreased MYB levels are linked to slower cell proliferation and faster erythroid differentiation, which raises the possibility that changes in intrinsic MYB levels can influence HbF through the cell cycle [ 33 ]. This region's polymorphisms accounted for 17.6% of the phenotypic variance in healthy Europeans and 3–7% in African-Americans and Brazilians with sickle cell anaemia [ 34 , 35 ]. For rs9399137 on the HBS1L-MYB gene, our findings indicated that There was a significantly higher prevalence of the TC (heterozygous mutant) genotype compared to the TT (wild type) among the β-thalassaemia major group. Additionally, the C allele frequency exhibited a gradual increase among β-thalassaemia patients, and consequently, we ascertain that the HBS1L-MYB gene variant (rs9399137) contributes to elevated HbF levels in thalassaemia major patients. Our findings correspond with those recorded in other research [ 21 , 30 , 36 , 37 ]. Bashir et al. (2024) reported that the heterozygous CT genotype of rs9399137 was found to be associated with a two-fold increased risk of thalassaemia in both sexes, indicating a significant association of this SNP with elevated HbF levels as well as modulation of disease severity [ 38 ]. Conversely, several studies demonstrated that this intergenic area had a negligible impact on patients' phenotypic expression [ 39 , 40 ]. The in silico analysis conducted using the CADD tool indicated that the HBS1L-MYB gene (rs9399137) variant was predicted to be pathogenic. Consequently, assessing the status of genetic markers of modifier genes and forecasting their pathogenic impact is beneficial for the management of thalassaemia major patients. Additional studies are required to determine the precise functional mechanisms linking these modifier gene genetic markers to HbF levels. The third polymorphism included in this study was HBS1L-MYB for the rs4895441 SNP. This variant was determined to be unassociated with variations in HbF levels in TM patients. This finding corresponds with the observations reported by Omar et al., 2020 [ 41 ], who revealed no significant differences were found between β-thalassaemia major patients and healthy controls in terms of HBS1L–MYB (rs4895441) gene polymorphism or allele frequency. Additionally, no association was identified between this genetic variant and disease severity indicators, including age at disease onset, transfusion frequency, splenectomy status, or HbF levels. Conversely, Cyrus et al. (2017) reported that a significant association was identified between β-thalassaemia major and the HBS1L-MYB (rs4895441 and rs9376090) genetic polymorphisms among Saudi Arabian patients, suggesting that these variants may contribute to disease susceptibility or modulation in this population [ 42 ]. 5. Conclusions This present study has shown that there is a substantial association between the severity of beta-thalassaemia and BCL11A rs766432 and rs9399137 in HBS1L-MYB SNPs. In Egyptian patients with HBS1L-MYB, rs4895441 did not significantly affect beta-thalassaemia severity, demonstrating that this mutation does not affect HbF independently. These genes regulate haemoglobin synthesis, however their effect on HbF levels may be indirect or impacted by factors not observed in this study. These findings enhance our comprehension of the genetic determinants affecting clinical outcomes in thalassaemia and may inform the creation of more individualized therapeutic approaches to diminish transfusion dependency. Despite the small sample size, we contend that our findings still offer significant insights, as they correspond with existing research and reveal new genetic connections that merit future exploration. Additional studies are needed to clarify the functional mechanisms that underpin these genetic associations and to evaluate their potential applicability in clinical practice. Abbreviations QTLs: Ouantitative Trait; BCL11A : β-cell lymphoma/leukemia; KLF : Krüppel-like factor; HIC2: Hypermethylated In Cancer 2; NuRD: Nucleosome Remodeling and Deacetylase; SNPs: Single Nucleotide Polymorphisms; HbF: Haemoglobin F (HbF); ARMS: Amplification Refractory Mutation System; RFLP: Restriction Fragment Length Polymorphism Declarations Ethics approval and consent to participate : This study was approved by the Ethics Committee of Shebin El-Kom Teaching Hospital (HSH00072) and was performed in accordance with the relevant guidelines and regulations of the Shebin El-Kom Teaching Hospital. Informed consent was obtained from all individual participants included in the study. Consent for publication: Not applicable Availability of data and materials : All data generated or analysed during this study are included in this published article. Competing interests : The authors declare that they have no competing interests. Funding: Open access funding will be provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). This work received no funds from any funding bodies. Authors' contributions: Randa M. Talaat and Ibrahim Abdo El-Halfawydesigned the study. Mohamed Ramzy Alalwi and Dina Malak Yousef Rizk collected and extracted data. Abdelrahman A. Abdelrahman, Ibrahim Abdo El-Halfawy and Dina Malak Yousef Rizk performed the in vitro and in vivo experiments. Abdelrahman A. Abdelrahman and Dina Malak Yousef Rizk analyzed and interpreted the data. Randa M. Talaat, Ibrahim Abdo El-Halfawy critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgements: The authors extend their appreciation to the doctors and patients who took part in this study. References Ali S, Mumtaz S, Shakir HA, Khan M, Tahir HM, Mumtaz S, Mughal TA, Hassan A, Kazmi SAR. & Sadia. 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Abdelrahman","email":"data:image/png;base64,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","orcid":"","institution":"University of Sadat City","correspondingAuthor":true,"prefix":"","firstName":"Abdelrahman","middleName":"A.","lastName":"Abdelrahman","suffix":""},{"id":608496048,"identity":"57e835b1-1858-479d-a0c6-be641b48a525","order_by":1,"name":"Randa M. Talaat","email":"","orcid":"","institution":"University of Sadat City","correspondingAuthor":false,"prefix":"","firstName":"Randa","middleName":"M.","lastName":"Talaat","suffix":""},{"id":608496049,"identity":"8af178a1-8b7e-441b-97ce-e8345fcba772","order_by":2,"name":"Ibrahim Abdo El-Halfawy","email":"","orcid":"","institution":"University of Sadat City","correspondingAuthor":false,"prefix":"","firstName":"Ibrahim","middleName":"Abdo","lastName":"El-Halfawy","suffix":""},{"id":608496050,"identity":"fe8ad4bd-f7cd-4ba6-8546-bc7aa31e397d","order_by":3,"name":"Mohamed Ramzy Alalwi","email":"","orcid":"","institution":"Shebin Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Ramzy","lastName":"Alalwi","suffix":""},{"id":608496051,"identity":"10476a5c-7003-4fe3-adec-847fcd43cd1e","order_by":4,"name":"Dina Malak Yousef Rizk","email":"","orcid":"","institution":"University of Sadat City","correspondingAuthor":false,"prefix":"","firstName":"Dina","middleName":"Malak Yousef","lastName":"Rizk","suffix":""}],"badges":[],"createdAt":"2026-02-24 00:53:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8951386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8951386/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104999124,"identity":"26406342-61f6-4738-b870-b5f8d1115e14","added_by":"auto","created_at":"2026-03-19 16:33:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":515974,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8951386/v1/3e072b5cd1d4e11344048453.png"},{"id":105035633,"identity":"a9f77907-007d-4e53-97bf-06d43b65b5cc","added_by":"auto","created_at":"2026-03-20 07:26:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2050038,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8951386/v1/e61d7fe9-76c7-4aa9-a760-46305feb5bc5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGenetic polymorphism of SNPs rs9399137 and rs4895441 in HBS1L-MYB and SNP rs766432 in BCL11A among β-thalassemia Egyptian patients\u003c/p\u003e","fulltext":[{"header":"1. Background","content":"\u003cp\u003eThalassaemia is a genetic haematological illness marked by a partial or complete decrease in the production of one or more globin chains. β-thalassemia arises from mutations in the β-globin gene, resulting in the missing or reduced synthesis of β-globin chains. This deficiency causes ineffective erythropoiesis, subsequently resulting in anemia. Hemoglobin E (HbE) represents an abnormal hemoglobin variant caused by a single point mutation in the β-globin chain [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the World Health Organization (WHO), approximately 1.5% of the global population are carriers of β-thalassemia, and an estimated 68,000 children are born annually with different forms of thalassemia syndromes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHaemoglobin E (HbE) is an atypical haemoglobin variation distinguished by a single point mutation in the β-globin chain. This mutation results in the substitution of glutamate with lysine (E26K) at position 26. The alteration influences β-globin gene expression by establishing an alternative splicing site within the mRNA at codons 25\u0026ndash;27 of the β-globin gene, ultimately leading to the production of the abnormal HbE variant [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eβ-thalassemia/HbE disease exhibits a wide array of clinical phenotypes, ranging from asymptomatic individuals to those necessitating frequent blood transfusions. Patients exhibiting mild to moderate forms generally do not necessitate transfusion therapy, however those with severe manifestations display pronounced anaemia, hepatosplenomegaly, and considerable iron overload, requiring regular transfusions akin to persons with β-thalassemia major [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe thalassemia results from defective globin chain production. Based on the defectively synthesized globin chain, it is classified into α, β, δβ, and δβγ-thalassemia. From a public health perspective, only the α- and β-thalassemias are prevalent enough to warrant significance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor hemoglobin production, β -globin chains make a tetramer with the α-globin chains. The decrease or lack of these chains causes β -Thalassemia. Mutations that completely inactivate the β gene and cause no β-globin production are referred to as β\u003csup\u003e0\u003c/sup\u003e mutations, while mutations that permit the production of some β globin are categorized according to the degree of quantitative reduction in the output of the β chains as β\u003csup\u003e+\u003c/sup\u003e or β\u003csup\u003e++\u003c/sup\u003e [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn β-thalassemia minor, the mutation is very mild and patients typically exhibit no indications or symptoms or may present with slight changes in RBCs count or volume. The level of Hb might be \u0026gt;\u0026thinsp;10 g/dL in β-thalassemia minor or carrier patients. This represents the predominant variant of β-thalassemia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn β-thalassemia intermediate, β-globin synthesis is decreased. This results in mild anemia, decreased RBCs indices, and increased Hb A2 and/or HbF levels in hemoglobin electrophoresis.\u003c/p\u003e \u003cp\u003eIn β-thalassemia major, β-globin is not synthesized. So, the marrow produces δ-globin and more α-globin. Untreated patients exhibit severe anaemia requiring transfusion, significant splenomegaly, skeletal abnormalities, growth retardation, and distinctive facial traits [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThree principal quantitative trait loci QTLs for HbF exist: the β-cell lymphoma/leukemia (BCL11A) gene, the HBS1L-MYB intragenic area, and Kr\u0026uuml;ppel-like factor 1 (KLF).\u003c/p\u003e \u003cp\u003eIn the fetal stage, hypermethylated in cancer 2 protein (HIC2) represses BCL11A by blocking chromatin access, while GATA1 and NF-Y activate γ-globin expression [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. ZBTB7A (LRF) and BCL11A silence γ-globin through the nucleosome remodeling and deacetylase (NuRD) complex, aided by ZNF410, which activates CHD4 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKLF1 and MYB repress γ-globin indirectly by activating BCL11A, while NFIA/NFIX reinforce this repression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. BCL11A, a zinc-finger transcription factor on chromosome 2p16.1, is the main silencer of HbF in adults [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. HPFH results from mutations disrupting BCL11A or ZBTB7A binding or creating new activator sites for GATA1 or KLF1 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe presence of single nucleotide polymorphisms (SNPs), transcription factors, and a therapeutic agent alter the regulation of β-globin XMN1-HBG2, BCL11A pathway, and possibly increase haemoglobin F (HbF) levels [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study focusses on the impact of gene polymorphisms rs9399137 and rs4895441 in HBS1L-MYB, as well as rs766432 in BCLIIA, on HbF level among Egyptian patients with β-thalassemia.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis case-control study was conducted over the course of one year, beginning in October 2024 and ending in January 2025. 50 β-thalassemia patients aged 5\u0026ndash;45 years old, alongside 50 healthy, age- and sex-matched controls were collected from the Shebin El-Kom Teaching Hospital. Based on the levels of haemoglobin A2 and haemoglobin F, the thalassemic group was further divided into three subgroups as per Keohane et al., 2015 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]: the thalassaemia minor subgroup, where HbA2 ranged from 3.5% to 7.0% and HbF from 1\u0026ndash;5%; the thalassemia intermediate subgroup, where HbA2 was greater than 7.0% and HbF from 5\u0026ndash;70%; and the thalassemia major subgroup, where HbF levels were between 70 and 90%.. Pregnant ladies \u0026amp; old age participants were excluded. All subjects underwent comprehensive history taking, a physical examination emphasizing facial morphology with documenting of alterations in facial shape, blood count, hemoglobin electrophoresis, and ferritin. Blood samples were obtained from all participants: a serum tube for ferritin level assessment and an EDTA tube for a blood count, haemoglobin electrophoresis, and DNA extraction.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Methods\u003c/h2\u003e \u003cp\u003e\u003cstrong\u003e2.1.1 Serum Ferritin\u003c/strong\u003e: Ferritin was assayed using Mybiosource Enzyme-Linked Immunosorbent Assay (ELISA) kits, San Diego, California, U.S.A. (code: MBS2514182). The kit used a double-antibody sandwich ELISA to assay the serum ferritin level.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. DNA Extraction\u003c/h2\u003e \u003cp\u003e Following the directions provided by QIAGEN (Hilden, Germany), genomic DNA was extracted from an EDTA blood sample using the QIAamp\u0026reg; DSP Virus Spin Kit Version 1. DNA purity and concentration were determined by the spectrophotometer measurement of absorbance at 260 and 280 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3. Molecular Genotyping\u003c/h2\u003e \u003cp\u003eBCL11A, rs766432 (A / C), and HBSIL-MYB, rs9399137 (T / C) polymorphisms were genotyped using tetra-primer amplification refractory mutation system-polymerase chain reaction (ARMS-PCR) with amplicons corresponding to each allelic variant. To enhance the specificity of the reaction, a mismatch was incorporated at the 3' end of each of the two allele-specific primers. To confirm primer specificity, we employed the 'BLAST' instrument available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/blast\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/blast\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Gradient PCR was performed on the Biometra T Professional thermal cycler (Biometra, Germany) to identify the optimal annealing temperature for each primer set. The PCR reaction volume was 20 \u0026micro;L, comprising 2X ReddyMix PCR master mix (Thermo Scientific Inc., USA), genomic DNA at a concentration of 50\u0026ndash;100 ng/\u0026micro;L, primers, and MgCl₂, with a final reaction concentration of 2.5 mM.\u003c/p\u003e \u003cp\u003eOptimal PCR conditions comprised an initial denaturation step of 10 minutes at 95\u0026deg;C, followed by 30 cycles of denaturation at 95\u0026deg;C, annealing at 58\u0026deg;C, and extension at 72\u0026deg;C, culminating in a final extension at 72\u0026deg;C for 7 minutes. HBS1L-MYB (rs4895441 A/G) SNP was tested using polymerase chain reaction-restriction fragment length polymorphism (PCR RFLP) assay. PCR-RFLP was performed on the Biometra T Professional thermal cycler (Biometra, Germany) using the PCR Master Mix \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(Cat. No 101005-Bioron GmbH, Germany)\u003c/span\u003e. Identification of the alleles was performed by incubating the PCR product Rsa1 GT\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\downarrow\\)\u003c/span\u003e\u003c/span\u003eAC restriction endoneuclease enzyme (Thermo Scientific Cat. No ER 1121). Table\u0026nbsp;(1) details the primer sequences employed in the methodology.\u003c/p\u003e \u003cp\u003eDNA fragments were subjected to electrophoresis on a 2% agarose gel and visualized using ethidium bromide. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.a shows the PCR products for HBSIL-MYB rs9399137 SNP where band at175 bp represented AA genotype, 117, 175 bands represented AC genotype, and 117 bands for CC genotype. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.b shows the PCR products for HBSIL-MYB, rs9399137 (T / C): band at 254 bp represented TT genotype, 254, 173 bands represented AC genotype, and 173 band represented CC genotype. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.c shows the PCR products for HBSIL-MYB rs4895441 SNP, the wild allele (A) was restricted into one fragment, 325 bp, while the variant allele (G) was restricted into 2 fragments 223, bp and 173 bp.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Statistical analysis\u003c/h2\u003e \u003cp\u003eSPSS version 29, developed in Chicago, Illinois, USA, was used in the statistical study. The Chi-square test and the Mann-Whitney U test were used to analyze the significance between different variables. The statistical significance was set at P\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. In Silico Analysis\u003c/h2\u003e \u003cp\u003eThe functional effects of this study's single nucleotide variations (SNVs) were predicted utilizing combined annotation dependent depletion (CADD) prediction tool. CADD C-Scores\u0026thinsp;\u0026gt;\u0026thinsp;10 indicate pathogenic variations.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Clinical Parameters of study population\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the clinical data of the study group. There is no significant difference in age or sex, but the levels of haemoglobin F (HbF), haemoglobin A2 (HbA2), hemoglobin, and serum ferritin were considerably higher in the patients group compared to the control group. The patient group had a considerably lower MCV.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequences of studied polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePolymorphism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrimer Sequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMethodology\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eBCL11A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ers766432\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eForward (A)\u003c/b\u003e: 5΄-TTGTTTCGCTTTAGCTTTATTAAGGTACAA-3΄\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eARMS-PCR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eReverse (A)\u003c/b\u003e: 5΄GACGTGTTCTGTATCTTGATTTTGGT-3՛\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eForward (C)\u003c/b\u003e: 5՛-CCAAACAGTTTAAAGGTTACAGACAGACT-3՛\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eReverse (C)\u003c/b\u003e: 5՛-AAAATGAATGACTTTTGTTGTATGTAGAG-3՛\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eHBS1L-MYB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ers9399137\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eForward (C)\u003c/b\u003e: 5΄-AATGTAATTAACTGAACATATGGTTAGTC-3΄\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eARMS-PCR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eReverse (C)\u003c/b\u003e: 5΄TTTATTGTTACAAGGTTAATTCACTGCC-3՛\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eForward (T)\u003c/b\u003e: 5՛-GAAATACCATCACTGAGAAAAGCATAAG-3՛\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eReverse (T)\u003c/b\u003e:5՛-CAGCAGGGTTGCTTGTGAAAAAACTTTA-3՛\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ers4895441\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eForward\u003c/b\u003e: 5՛-ATATTCTGGCTGGGGGAGACAAA\u0026thinsp;\u0026minus;\u0026thinsp;3՛\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eRFLP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eReverse\u003c/b\u003e: 5՛-GCCCTACAGGATCTCACTGCTAC\u0026thinsp;\u0026minus;\u0026thinsp;3՛\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eARMS: Amplification Refractory Mutation System; RFLP: Restriction Fragment Length Polymorphism\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe demographic and haematological parameters of study group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients (N\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControls (N\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale: 24 (48%)\u003c/p\u003e \u003cp\u003eFemale: 26 (52%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale: 34 (68%)\u003c/p\u003e \u003cp\u003eFemale: 16 (32%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.33\u0026thinsp;\u0026plusmn;\u0026thinsp;11.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.23\u0026thinsp;\u0026plusmn;\u0026thinsp;11.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.05\u0026thinsp;\u0026plusmn;\u0026thinsp;32.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.12\u0026thinsp;\u0026plusmn;\u0026thinsp;8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaemoglobin Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.68\u0026thinsp;\u0026plusmn;\u0026thinsp;7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Ferritin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1087.74\u0026thinsp;\u0026plusmn;\u0026thinsp;535.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.94\u0026thinsp;\u0026plusmn;\u0026thinsp;50.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Genetic Polymorphisms of study cohort\u003c/h2\u003e \u003cp\u003eThe comparison of the genotype and allele frequencies between patient and control groups indicated that AC (heterozygous) and CC (homozygous wild) of the BCL11A gene, rs766432 (A\u0026thinsp;\u0026gt;\u0026thinsp;C) and HBSIL-MYB gene, rs9399137 (T\u0026thinsp;\u0026gt;\u0026thinsp;C) polymorphisms, had a significant difference of these variants between both groups as the p-value is \u0026gt;\u0026thinsp;0.05. On the other hand, negative and positive genotypes of the HBSIL-MYB gene, rs4895441 polymorphism, had a non-significant difference across the same groups, as the p-value is \u0026lt;\u0026thinsp;0.05 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eGenotype and allele frequency for gene polymorphisms among control and patient groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatients (N = 50)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControls (N = 50)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eχ2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eBCL11A, rs766432 (A / C)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e7.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele Frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e12.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eHBSIL-MYB, rs9399137 (T / C)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e30.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele Frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e25.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eHBSIL-MYB rs4895441 (A/G)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e3.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele Frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eχ2 = Chi-Square\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cdiv\u003e\n 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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.3. Clinical and Laboratory features of Thalassemia patients\u003c/h2\u003e\n \u003cp\u003eThis study conducted a comparative analysis among thalassaemia subgroups. Table\u0026nbsp;(4) indicates that there was no significant difference in age and sex among the thalassaemia subgroups. Conversely, a significant difference was recorded about the percentages of HbF and HbA2 values. Although there was no statistically significant association regarding haemoglobin and ferritin levels across thalassaemia subgroups, haemoglobin levels were relatively higher in the thalassaemia minor subgroup. In contrast, ferritin levels were higher in the thalassaemia major subgroup\u003c/p\u003e\n \u003cp\u003eAdditionally, thalassaemia face features, splenomegaly, splenectomy, and blood transfusion rates were prevalent within the thalassaemia intermediate and major subgroups with a significant association, as shown in table (5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.4. Genetic Polymorphisms of Thalassemia patients\u003c/h2\u003e\n \u003cp\u003eThe analysis of genotype and allele frequency distributions among thalassaemia major, thalassaemia intermediate, and thalassaemia minor patient groups revealed a significant difference of the genotypes AC and CC of the BCL11A gene, rs766432 (A \u0026gt; C) polymorphism across the three groups, as the p-value is \u0026gt; 0.05. TC and TT genotypes of the HBSIL-MYB gene, rs9399137 (T \u0026gt; C) polymorphism, exhibited a significant difference of this variant, as the p-value is \u0026gt; 0.05. Negative and positive genotypes of the HBSIL-MYB gene, rs4895441 (A \u0026gt; G) polymorphism, had a non-significant difference across groups, as the p-value is \u0026lt; 0.05 (Table\u0026nbsp;6).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eGenotype and allele frequency for gene polymorphisms among thalassemia patient groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThalassemia Minor (N = 10)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThalassemia Intermediate (N = 17)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThalassemia Major (N = 23)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eχ2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eBCL11A, rs766432 (A / C)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (41.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e10.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (47.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (30.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (52.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele Frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (64.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (32.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e11.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (67.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBSIL-MYB, rs9399137 (T / C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (41.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e9.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (82.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele Frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (64.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e4.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBSIL-MYB rs4895441 (A/G)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (47.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (47.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e2.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (41.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele Frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (67.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (69.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (30.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eχ2 = Chi-Square\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.5. In Silico Analysis for All Variants of Modifier Genes\u003c/h2\u003e\n \u003cp\u003eAmong the analyzed SNVs, one SNV on the HBS1L-MYB gene (rs9399137) was predicted to be pathogenic using the CADD tool. Nonetheless, one SNV on BCL11A (rs766432) and the HBS1L-MYB gene (rs4895441) SNPs were predicted to be benign using the same scoring systems (Table\u0026nbsp;7).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 7\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePathogenicity of variants of modifier gene using CADD\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSNP rs ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChromosome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlternate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCADD C-Score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCl11A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers766432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChr.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60719970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHBSIL-MYB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers9399137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChr.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135419018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHBSIL-MYB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ers4895441\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChr.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135426573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eCADD = Combined Annotation Dependent Depletion\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBeta-thalassemia is a prevalent genetic condition, resulting from impaired synthesis of the β-globin chain or an unbalanced α/β chain ratio [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and presenting clinically from asymptomatic carriers to transfusion-dependent patients [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The genetic variables influencing the imbalance between α- and non-α-haemoglobin chains dictate the clinical severity of β-thalassemia. Reduced globin chain imbalance promotes the preferential survival of erythroid precursors, thereby reducing ineffective erythropoiesis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study offered comprehensive insights into the clinical and haematological parameters, as well as genotype polymorphisms, of Egyptian β-thalassemia patients, clarifying the impact of BCL11A and HBS1L-MYB intergenic region gene polymorphisms on HBF levels in this cohort.\u003c/p\u003e \u003cp\u003eIn this study, blood transfusion rate, splenomegaly and facial changes were significantly higher in the β-thalassaemia major group. These findings correspond with Langhi et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], who reported that β-thalassemia major patients undergo regular blood transfusions throughout their lives, typically provided every 2 to 5 weeks based on transfusion requirements.\u003c/p\u003e \u003cp\u003eSerum ferritin levels were found to be higher in the thalassaemia major group with a significant difference when compared with other groups in this study. Taher et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] attributed this to iron loading through blood transfusion in this group. In β-thalassaemia minor and intermediate groups, increased ferritin level is elucidated by Hsu et al., 2022 article, which indicated that serum ferritin levels may influenced by various non-iron-related physiological and pathological situations, such as ineffective erythropoiesis, enhanced intestinal absorption, and hepcidin suppression [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe zinc finger protein encoded by the BCL11A gene plays a crucial role in human HbF expression repression. It does this by binding to specific regions within the β-globin gene cluster and forming a complex with NuRD, which inhibits the expression of γ-globin gene [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn different ethnic groups, HbF levels are significantly correlated with polymorphisms in intron 2 of the BCL11A gene, as previously documented. This elucidates the variability in allele frequencies concerning rs766432, rs4671393, rs1427407, and rs11886868 SNPs [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral researches studied the association between BCL11A gene polymorphisms and fluctuations in HbF levels; nevertheless, these studies exhibited significant diversity across different ethnic groups Some authors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] have documented the presence of such an association for the variants rs766432, rs4671393, rs1427407, rs11886868, rs10189857, and rs7557939, whereas others have refuted this association regarding rs766432, rs4671393, rs1427407, rs11886868, and rs10189857 SNPs [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and rs7557939 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] polymorphisms with variation in HbF levels in thalassaemia major disease.\u003c/p\u003e \u003cp\u003eTo ascertain their association with HbF levels and, consequently, the severity of disease, we investigated the status of the rs766432 SNP located on the BCL11A gene, rs9399137, and rs4895441 SNPs located on the HBS1L-MYB gene in β-thalassaemia patients in this study. A robust association between these genetic variants and increased HbF levels in thalassaemia major patients was shown in recent studies, which supported the SNPs selection.\u003c/p\u003e \u003cp\u003eFor the SNP rs766432, located in the BCL11A gene, the frequency of AC (heterozygous mutant) genotype was significantly higher than that of the genotype AA (wild type) and the C allele frequency of was significantly higher in the thalassaemia major group when compared to the A allele in this study. This outcome indicates a possible correlation between the C allele and thalassaemia, particularly given that BCL11A is known to regulate foetal haemoglobin production and may be may contribute to the pathophysiology of thalassaemia. These findings are in line with prior research conducted by Sales et al. (2020) and Muszlak et al. (2020), who found that the BCL11A gene polymorphism with rs766432 was associated with increased HbF levels [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This conclusion, in contrast, contradicts that of Bhanushali et al., 2016, who reported no association between the intronic region BCL11A gene polymorphism and fluctuations in HbF levels [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Moreover, our results contrasted with those of Suali et al. (2025), who reported that neither the XmnI rs7482144 nor the BCL11A rs766432 polymorphisms exhibited a significant influence on the clinical phenotype of β-thalassaemia patients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrimarily expressed in erythroid precursor cells, the HBS1L-MYB gene is the next regulator of HbF levels [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Decreased MYB levels are linked to slower cell proliferation and faster erythroid differentiation, which raises the possibility that changes in intrinsic MYB levels can influence HbF through the cell cycle [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This region's polymorphisms accounted for 17.6% of the phenotypic variance in healthy Europeans and 3\u0026ndash;7% in African-Americans and Brazilians with sickle cell anaemia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor rs9399137 on the HBS1L-MYB gene, our findings indicated that There was a significantly higher prevalence of the TC (heterozygous mutant) genotype compared to the TT (wild type) among the β-thalassaemia major group. Additionally, the C allele frequency exhibited a gradual increase among β-thalassaemia patients, and consequently, we ascertain that the HBS1L-MYB gene variant (rs9399137) contributes to elevated HbF levels in thalassaemia major patients. Our findings correspond with those recorded in other research [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Bashir et al. (2024) reported that the heterozygous CT genotype of rs9399137 was found to be associated with a two-fold increased risk of thalassaemia in both sexes, indicating a significant association of this SNP with elevated HbF levels as well as modulation of disease severity [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Conversely, several studies demonstrated that this intergenic area had a negligible impact on patients' phenotypic expression [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe in silico analysis conducted using the CADD tool indicated that the HBS1L-MYB gene (rs9399137) variant was predicted to be pathogenic. Consequently, assessing the status of genetic markers of modifier genes and forecasting their pathogenic impact is beneficial for the management of thalassaemia major patients. Additional studies are required to determine the precise functional mechanisms linking these modifier gene genetic markers to HbF levels.\u003c/p\u003e \u003cp\u003eThe third polymorphism included in this study was HBS1L-MYB for the rs4895441 SNP. This variant was determined to be unassociated with variations in HbF levels in TM patients. This finding corresponds with the observations reported by Omar et al., 2020 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], who revealed no significant differences were found between β-thalassaemia major patients and healthy controls in terms of HBS1L\u0026ndash;MYB (rs4895441) gene polymorphism or allele frequency. Additionally, no association was identified between this genetic variant and disease severity indicators, including age at disease onset, transfusion frequency, splenectomy status, or HbF levels. Conversely, Cyrus et al. (2017) reported that a significant association was identified between β-thalassaemia major and the HBS1L-MYB (rs4895441 and rs9376090) genetic polymorphisms among Saudi Arabian patients, suggesting that these variants may contribute to disease susceptibility or modulation in this population [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis present study has shown that there is a substantial association between the severity of beta-thalassaemia and BCL11A rs766432 and rs9399137 in HBS1L-MYB SNPs. In Egyptian patients with HBS1L-MYB, rs4895441 did not significantly affect beta-thalassaemia severity, demonstrating that this mutation does not affect HbF independently. These genes regulate haemoglobin synthesis, however their effect on HbF levels may be indirect or impacted by factors not observed in this study. These findings enhance our comprehension of the genetic determinants affecting clinical outcomes in thalassaemia and may inform the creation of more individualized therapeutic approaches to diminish transfusion dependency.\u003c/p\u003e \u003cp\u003eDespite the small sample size, we contend that our findings still offer significant insights, as they correspond with existing research and reveal new genetic connections that merit future exploration. Additional studies are needed to clarify the functional mechanisms that underpin these genetic associations and to evaluate their potential applicability in clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eQTLs: Ouantitative Trait; BCL11A : \u0026beta;-cell lymphoma/leukemia; KLF : Kr\u0026uuml;ppel-like factor; HIC2: Hypermethylated In Cancer 2; NuRD: Nucleosome Remodeling and Deacetylase; SNPs: Single Nucleotide Polymorphisms; HbF: Haemoglobin F (HbF); ARMS: Amplification Refractory Mutation System; RFLP: Restriction Fragment Length Polymorphism\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: This study was approved by the Ethics Committee of Shebin El-Kom Teaching Hospital (HSH00072) and was performed in accordance with the relevant guidelines and regulations of the Shebin El-Kom Teaching Hospital. Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication: \u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: All data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Open access funding will be provided by The Science, Technology \u0026amp; Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). This work received no funds from any funding bodies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e Randa M. Talaat and Ibrahim Abdo El-Halfawydesigned the study. Mohamed Ramzy Alalwi and Dina Malak Yousef Rizk collected and extracted data. Abdelrahman A. Abdelrahman, Ibrahim Abdo El-Halfawy and Dina Malak Yousef Rizk performed the in vitro and in vivo experiments. Abdelrahman A. Abdelrahman and Dina Malak Yousef Rizk analyzed and interpreted the data. Randa M. Talaat, Ibrahim Abdo El-Halfawy critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The authors extend their appreciation to the doctors and patients who took part in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli S, Mumtaz S, Shakir HA, Khan M, Tahir HM, Mumtaz S, Mughal TA, Hassan A, Kazmi SAR. \u0026amp; Sadia. Current status of beta-thalassemia and its treatment strategies. 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Biomed Res Int. 2017; (1), 1972429.\u003c/span\u003e\u003c/li\u003e\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-genomic-data","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gtic","sideBox":"Learn more about [BMC Genomic Data](http://bmcgenet.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gtic/default.aspx","title":"BMC Genomic Data","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"BCL11A gene, HSB1L-MYB gene, β-thalassemia, rs766432, rs9399137, rs4895441","lastPublishedDoi":"10.21203/rs.3.rs-8951386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8951386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to evaluate the role of two genetic polymorphisms (rs9399137 and rs4895441) in the HBS1L-MYB and rs766432 of BCL11A genes as determinant HbF level and disease severity in Egyptian β-thalassemia patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e Blood samples were obtained from 100 participants, comprising 50 individuals with β-thalassemia and 50 healthy controls. Hematological assessment was conducted through complete blood count (CBC), ferritin measurement, and hemoglobin electrophoresis. After genomic DNA extraction was done, HBS1L-MYB (rs9399137 and rs4895441) SNPs and rs766432 of BCL11A were all assessed by ARMS-PCR .The HBS1L-MYB rs4895441 assessed by RFLP-PCR with the restriction enzyme (RSA1).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe genotypic frequencies of the rs766432 SNP of BCL11A and rs9399137 indicated that the genotype frequencies of these SNPs were significantly associated with severity of β thalassemia but there was no significant association between β-thalassemia severity and rs895441 SNP of HSB1L-MYB.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn Egyptian β-thalassemia patients, rs766432 in BCL11A and rs9399137 in HBSIL-MYB were found to significantly increase HbF levels, but rs4895441 was not. New tailored management techniques to decrease transfusion needs can be developed based on these findings, which show how genetic and clinical factors affect the severity of β-thalassemia. Further research involving a larger sample size and additional modifier genes is needed.\u003c/p\u003e","manuscriptTitle":"Genetic polymorphism of SNPs rs9399137 and rs4895441 in HBS1L-MYB and SNP rs766432 in BCL11A among β-thalassemia Egyptian patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 16:33:06","doi":"10.21203/rs.3.rs-8951386/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-02T07:41:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T15:02:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T09:19:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76369781253430428092220575308175461965","date":"2026-03-22T14:53:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128084282058942153499571937884730097587","date":"2026-03-18T21:10:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T13:29:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T13:20:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-16T12:29:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-13T14:43:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomic Data","date":"2026-03-12T22:53:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomic-data","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gtic","sideBox":"Learn more about [BMC Genomic Data](http://bmcgenet.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gtic/default.aspx","title":"BMC Genomic Data","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65dd4a9e-fe97-4074-bdc2-6f547971f0dc","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T07:09:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 16:33:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8951386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8951386","identity":"rs-8951386","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}