Unmasking Rare Thalassemia Variants through Whole-Exome Sequencing in Huadu District, China: Clinical Insights

Unmasking Rare Thalassemia Variants through Whole-Exome Sequencing in Huadu District, China: Clinical Insights | 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 Unmasking Rare Thalassemia Variants through Whole-Exome Sequencing in Huadu District, China: Clinical Insights Guowei Run, Yan Jiang, Jingxia Xu, Changlv Jiang, Lihua Zeng, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7708021/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background/Objectives : Whole-exome sequencing (WES) enhances the detection of thalassemia-associated variants beyond conventional methods, particularly in high-prevalence regions, facilitating precise genotype-phenotype correlations. This study aimed to establish a model for precision prevention in endemic regions. Methods : WES was performed on 21 patients with clinically suspected thalassemia from Huadu District, Guangzhou, stratified by severity. Variant analysis encompassed both coding and non-coding regions. Results : On average, 5440.7±94.3 insertions/deletions (INDELs) and 49,719.3±492.5 single-nucleotide variants (SNVs) per individual were detected. Frameshift INDELs were predominantly localized to the HBB gene (85.3±5.0 variants). Non-coding SNVs in 3’ untranslated regions correlated with reduced mean corpuscular hemoglobin concentration (MCHC: 301±25 g/L vs. normal >320 g/L, p T) β^+) accounted for 85.7% of severe cases. Three rare non-β-globin variants were detected: MYB c.107G>A (p.R36H) in case ZZF, associated with potential thalidomide responsiveness; HBD c.440A>T (p.H147L) in case YGX, causing artificially normalized HbA₂ (3.1%) and risk of β-thalassemia misdiagnosis; and HBG1 c.364G>T (p.E122) in case PCH, co-occurring with α-thalassemia and iron deficiency (MCHC: 285 g/L), necessitating iron repletion assessment. Hemoglobin levels declined significantly with increasing severity (mild: 99.7±2.5 g/L; very severe: 31.0±26.1 g/L, p < 0.05). Conclusions : WES significantly enhanced the diagnostic yield by identifying causative variants in all 21 cases, including coding and regulatory mutations undetectable by conventional screening. This approach facilitates comprehensive genetic profiling essential for accurate genotype-phenotype correlations and individualized management. Our findings support the integration of WES into thalassemia diagnostics and genetic counseling in high-prevalence regions. thalassemia whole-exome sequencing INDELs SNVs HBB genetic screening Guangzhou Figures Figure 1 Figure 2 1. Introduction Thalassemia poses a significant public health challenge in Guangdong Province, China, and is characterized by a markedly elevated and regionally heterogeneous carrier prevalence. Provincial carrier rates exceed 6.8%, with some studies estimating an overall frequency approaching 11%. Significant disparities exist; Huadu District reports a prevalence of approximately 8.3% ( 1 ) while Dongguan shows α-thalassemia and β-thalassemia carrier rates of 7.6% and 3.8%, respectively ( 2 ). In contrast, western regions such as Yangjiang exhibit exceptionally high overall frequencies, reaching up to 20%, Southern China, particularly Guangdong, remains a well-established hotspot, with certain localities exceeding 19% ( 1 ). These figures are further compounded by population migration and genetic heterogeneity. Conventional diagnostic techniques, including hemoglobin (HGB) electrophoresis, gap-PCR, and Multiplex Ligation-dependent Probe Amplification are limited to detecting predefined common mutations. Province-wide studies have estimated that 10–20% of thalassemia carriers remain undiagnosed using conventional methods due to rare variants ( 1 – 2 ). These methods often fail to detect non-coding variants, novel mutations, structural rearrangements, or variants beyond their target regions (deep intronic or regulatory single-nucleotide polymorphisms [SNPs]) ( 1 , 3 ), leading to diagnostic omissions, particularly among silent carriers and those with atypical mutations. Whole-exome sequencing (WES) provides a comprehensive alternative, enabling investigation of the entire protein-coding genome. This facilitates the detection of a broad spectrum of pathogenic variants, including coding, splice-site, insertions/deletions (INDEL), and regulatory single-nucleotide variants (SNVs), in both globin (HBA1/2, HBB) and modifier genes (BCL11A, KLF1) ( 4 ). Consequently, WES enhances diagnostic accuracy by identifying variants undetectable by traditional methods, thereby providing a stronger foundation for genetic counseling and prenatal diagnosis ( 1 ). Despite extensive provincial screening, WES has not been implemented in high-risk subregions, such as Huadu District, where carrier rates reach 8.3%. This study represents the first comprehensive WES-based profiling of rare thalassemia variants in Huadu District, decoding a unique mutational spectrum distinct from provincial patterns. Our findings establish a model for precision prevention in endemic regions and provide a framework for cost-effective WES integration in resource-limited settings. 2. Patients and Methods 2.1. Patients This retrospective study included 21 patients with a high clinical suspicion of thalassemia gene mutations, recruited from Guangzhou Huadu District People’s Hospital. studies were conducted under the Declaration of Helsinki. All participants or their legal guardians provided written informed consent before inclusion in the study. Ethical approval was obtained from the Institutional Review Board of the hospital (Approval No. 2021117). The cohort comprised 11 females and 10 males, aged 14–73 years. Patients were stratified into four groups based on disease severity as determined by HGB levels: Mild (M): HGB > 90 g/L (n = 3); Moderate (Mo): HGB 60–90 g/L (n = 7) Severe (S): HGB 30–60 g/L (n = 8) Very Severe (VS): HGB < 30 g/L (n = 3) Demographic and clinical characteristics are summarized in Table 1 . Table 1 Basic patient information NO. Gender Age Grade HGB (g/L) HCT (%) MCV (fL) MCH (pg) MCHC (g/L) FXY Female 46 M 100 31.2 62.8 20.1 321 LZR Male 73 M 99 30.6 69.4 22.4 324 YJW Male 38 M 104 34.6 50.4 15.1 301 ZHS Female 26 Mo 88 29.2 63.3 19.1 301 ZZF Male 25 Mo 79 23 83 27.5 343 BWS Male 14 Mo 98 30 89.7 29.2 326 QZY Female 14 Mo 99 31.3 88.4 28 317 WKT Male 20 Mo 84 27.8 72 21.8 302 YGX Male 55 Mo 77 22.5 56.8 19.4 342 PCH Female 31 Mo 81 28.6 52.8 15 285 CXM Male 22 S 39 13.7 72.5 20.6 285 DDN Female 27 S 51 16.9 73.2 22.1 302 FDQ Female 63 S 53 22.5 79 20.3 236 ZWZ Female 43 S 60 19.2 72.7 22.7 313 SY Male 18 S 56 16.7 70.5 23.6 335 LRX Female 13 S 56 16.5 72 24.3 337 CWY Female 19 S 49 14.8 74.3 24.7 332 HWL Female 14 S 55 19.8 76.7 21.3 278 HDC Male 29 VS 21 6.2 51.2 17.4 339 LJC Male 14 VS 9 3.2 64 18 281 CHH Male 19 VS 63 20.7 79.8 26.5 304 Noted: M: Mild, Mo: Moderate, S: Severe, VS: Very Severe, HGB: Hemoglobin, HCT: haematocrit, MCV: Mean corpuscular volume, MCH: Mean corpuscular hemoglobin, MCHC: Mean corpuscular hemoglobin concentration. 2.2. Blood Count Analysis Peripheral blood samples (2 mL) were collected from each participant into EDTA-anticoagulant tubes. Complete blood count analyses were performed using the Sysmex XN-1000 automated hematology analyzer (Sysmex Corporation, Kobe, Japan). The measured parameters included mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and HGB concentration. 2.3. WES, Data Processing, and Analysis The peripheral blood samples (2 mL) collected were transported to the DAAN Clinical Laboratory Center (Guangzhou, China) for processing. WES was conducted using the Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA), generating 150-bp paired-end reads at an average coverage depth of 100×. Variant pathogenicity was classified following the American College of Medical Genetics and Genomics (ACMG) guidelines ( 5 ). Briefly, variants were filtered using population frequency ( 20), and co-segregation with clinical phenotypes where applicable. 2.3.1. Data preprocessing: Raw sequencing data generated by the Illumina platform were processed using fastp (v0.12.6, https://github.com/OpenGene/fastp ) to remove adapter sequences and low-quality reads, producing high-quality clean data for downstream analyses. 2.3.2. Sequence alignment and processing: Quality-filtered reads were aligned to the reference genome using Sentieon BWA ( https://www.sentieon.com ) with default parameters. Post-alignment processing, including read sorting and duplicate removal, was performed using the Sentieon driver ( https://www.sentieon.com ). Mapping quality metrics, including sequencing depth and coverage, were calculated using bamdst ( https://github.com/shiquan/bamdst ). 2.3.3. Variant calling and analysis: Initial variant calling for SNPs and INDELS was performed using Sentieon DNAseq ( https://support.sentieon.com/manual/DNAseq_usage/dnaseq/ ). Somatic SNPs and INDELS were identified using Mutect2( https://software.broadinstitute.org/gatk/documentation/article?id=11077 ), whereas somatic copy number variations (CNVs) were detected using CNVkit ( https://cnvkit.readthedocs.io/en/stable/index.html ). All identified variants were functionally annotated using ANNOVAR ( http://annovar.openbioinformatics.org/en/latest/ ). 2.3.4. Mutational signature analysis: Somatic mutation features were extracted and analyzed using the R package Sigminer ( https://shixiangwang.github.io/sigminer/index.html ), enabling characterization of mutational patterns ( 6 ). 2.4. Statistical analysis Genotypic profiling results of thalassemia, including variant spectrum characterization and genotype frequency distribution, were systematically analyzed as the primary study endpoint. Continuous variables are reported as mean ± standard deviation. For between-group comparisons, we performed a one-way analysis of variance (ANOVA), followed by Tukey's post-hoc test for multiple comparisons (IBM SPSS Statistics, version 25.0; IBM Corp., Armonk, NY, USA). A p-value < 0.05 (two-tailed) was considered statistically significant. 3. Results 3.1. Hematological Characteristics by Disease Severity A significant inverse correlation was observed between disease severity and HGB levels (p < 0.05). HGB concentrations declined progressively across severity groups: the M group (99.67 ± 2.52 g/L) maintained near-normal values, while the Mo (86.14 ± 8.01 g/L), S (53.38 ± 5.72 g/L), and VS groups (31.00 ± 26.11 g/L) demonstrated increasingly impaired erythropoiesis. Notably, the VS group exhibited substantially greater HGB variability (SD: 26.11 g/L) compared to the other groups (SD range: 2.52–8.01 g/L). Comparative analysis revealed no intergroup variations in erythrocyte indices (MCV), MCH, mean corpuscular hemoglobin concentration (MCHC) or reticulocyte counts among the different severity classifications (all p-values > 0.05). However, Red Cell Distribution Width-Coefficient of Variation, RDW-CV, a measure of erythrocyte volumetric variation, was significantly elevated in the VS group compared to both the M and Mo groups (p < 0.05), and HGB level was inversely correlated with severity (p Mo (86.14 ± 8.01 g/L) > S (53.38 ± 5.72 g/L) > VS (31.00 ± 26.11 g/L). Notably, the VS group showed higher HGB variability (SD: 26.11 g/L vs. 2.52–8.01 g/L in others). (p < 0.05) (Table 1 , Fig. 1 ). 3.2. Genomic Characteristics of INDEL Variants in Thalassemia WES revealed a heterogeneous mutational spectrum across the 21 patients, with distinct patterns associated with disease severity. There were no significant differences in the INDEL/SNV burden or functional categories (frameshift, stopgain) across severity groups (all p > 0.05) (Tables 2 – 3 ). Table 2 Number of INDELs in different genome and coding regions Type M Mo S VS CDS 501.33 ± 2.89 493.29 ± 18.95 490.25 ± 8.41 490 ± 17.69 frameshift_deletion 85 ± 6.08 85.29 ± 10.03 84.75 ± 7.92 85.33 ± 5.03 frameshift_insertion 71.67 ± 1.53 71.57 ± 6.35 65.5 ± 5.13 67.33 ± 3.21 nonframeshift_deletion 120.33 ± 5.77 115.57 ± 4.79 117.13 ± 8.98 115.67 ± 6.51 nonframeshift_insertion 131.33 ± 1.15 127.29 ± 6.26 127.5 ± 6.3 126 ± 2.65 stopgain 6 ± 2.65 4.57 ± 0.98 5.75 ± 0.89 4 ± 2.65 stoploss 1 ± 0 0.86 ± 0.69 0.5 ± 0.53 0.33 ± 0.58 unknown 86 ± 2 88.14 ± 2.48 89.13 ± 2.95 91.33 ± 3.79 intronic 3772 ± 58.51 3754.71 ± 78.81 3801.63 ± 27.2 3833.67 ± 68.88 UTR’3 253.67 ± 1.15 250 ± 6.61 250.63 ± 9.86 266.67 ± 8.96 UTR’5 131.67 ± 4.93 129.43 ± 10.6 129.88 ± 9.52 124 ± 9.54 splicing 84.33 ± 3.51 80.57 ± 2.64 83.13 ± 2.59 81.33 ± 3.21 ncRNA_exonic 155.33 ± 7.57 156 ± 9.2 154.88 ± 4.61 153.33 ± 0.58 ncRNA_intronic 231 ± 12.29 242.29 ± 12.82 242.13 ± 14.44 243 ± 2.65 ncRNA_splicing 0.33 ± 0.58 0.43 ± 0.53 0.63 ± 0.52 0.33 ± 0.58 upstream 79.33 ± 4.73 76 ± 5.42 75.75 ± 3.45 79.33 ± 3.21 downstream 21 ± 5 22.57 ± 2.51 21.38 ± 2.92 20.33 ± 5.51 intergenic 210.67 ± 8.96 213.43 ± 12.23 209.38 ± 16.32 210.67 ± 7.64 Total 5440.67 ± 74.19 5418.71 ± 111.38 5459.63 ± 43.87 5502.67 ± 94.25 Noted: M: Mild, Mo: Moderate, S: Severe, VS: Very Severe, CDS: Coding sequence, UTR: Untranslated regions, ncRNA: non-coding RNA. Table 3 Characteristics of INDELs in the genome Type M Mo S VS Total 5440.67 ± 74.19 5418.71 ± 111.38 5459.63 ± 47.02 5502.67 ± 94.25 Homozygote 1926 ± 16.52 1948.57 ± 32.33 1934 ± 50.13 1982.67 ± 45.83 Heterozygote 3514.67 ± 59.1 3470.14 ± 110.31 3525.63 ± 85.02 3520 ± 51.03 dbSNP_percentage(%) 94.12 ± 0.11 94.39 ± 0.33 94.33 ± 0.3 94.06 ± 0.07 Novel 320 ± 8.54 304.29 ± 23.91 309.5 ± 18.28 327 ± 2.65 Comparative analysis of INDEL variants across thalassemia severity groups revealed no statistically significant differences in coding sequence (CDS), frameshift, or non-frameshift mutations (all p > 0.05). Likewise, total INDEL burden (p > 0.05), zygosity distribution (homozygote; heterozygote), and variant annotation characteristics (dbSNP percentage, novel variants) across severity groups showed no significant variation. While total INDEL counts were comparable (~ 5400–5500), a modest elevation in 3’ untranslated regions (UTR) variants was noted in the VS group (VS: 266.67 ± 8.96) relative to milder groups (M/Mo/S: 250–254). No severity-dependent trends were observed for functional variants (stopgain/stoploss) or non-coding regions (ncRNA, intronic). Results of INDEL detection and statistics are shown in Table 2 . Total INDEL counts remained remarkably consistent across severity groups (range: 5418.71–5502.67), with a non-statistically significant slight increase in the VS group (5502.67 ± 94.25). These findings indicate that INDELs contributed minimally to the observed heterogeneity in thalassemia severity within this cohort. 3.3. Genomic Characteristics of SNV Across Thalassemia The SNV detection and statistical results are shown in Tables 4 – 5 . CDS SNVs exhibited minimal variation across severity groups, ranging from 20,076.33 to 20,218. The ratio of synonymous to nonsynonymous variants remained consistent; synonymous variants constituted 51.1%–51.2% of CDS SNVs (10,238.67–10,328), while nonsynonymous variants represented 46.1%–46.2% (9259.33–9325). Stop-gain variants constituted 0.35% to 0.38% of CDS variants (70.75–77.33), and stop-loss variants were infrequent (7.57–9.00). Table 4 Number of single-nucleotide variants (SNVs) in different genome and coding regions. Type M Mo S VS CDS 20,218 ± 213.72 20,099.14 ± 119.3 20,126.13 ± 170.09 20,076.33 ± 38 synonymous_SNV 10,328 ± 93.95 10,242.14 ± 35.93 10,256.88 ± 60.79 10,238.67 ± 62.15 nonsynonymous_SNV 9325 ± 121.39 9304.14 ± 94.34 9314.63 ± 125.39 9259.33 ± 81.64 stopgain 77.33 ± 2.31 72.57 ± 4.72 70.75 ± 5.18 74 ± 2.65 stoploss 9 ± 1 7.57 ± 1.81 8.25 ± 2.12 7.67 ± 1.53 unknown 478.67 ± 40.53 472.71 ± 31.62 475.63 ± 36.92 496.67 ± 22.28 intronic 21,757.67 ± 218.29 21,674.86 ± 118.81 21,639 ± 147.29 21,545.67 ± 108.9 UTR’3 1448 ± 23.9 1414 ± 38.96 1418.13 ± 15.7 1423 ± 28.69 UTR’5 1068 ± 22.27 1032.43 ± 25.36 1040.5 ± 24.11 1030.33 ± 18.72 splicing 54.67 ± 4.04 53.57 ± 5.5 54 ± 3.46 49.67 ± 1.53 ncRNA_exonic 1388.33 ± 4.73 1380.86 ± 26.11 1387.5 ± 32.87 1385 ± 32.08 ncRNA_intronic 1319.33 ± 8.5 1334.57 ± 35.99 1315.13 ± 17.64 1298.33 ± 31.97 ncRNA_splicing 4.33 ± 0.58 4 ± 1.29 3.88 ± 2.42 3.33 ± 0.58 upstream 504 ± 23.81 497.29 ± 19.01 494.88 ± 22.4 477 ± 15.1 downstream 162 ± 6.56 160 ± 11.31 167.75 ± 13.59 158 ± 6.08 intergenic 1795 ± 56.03 1830 ± 56.83 1807.13 ± 47.2 1805.33 ± 41.48 Total 49,719.33 ± 451.6 49,480.71 ± 311.34 49,454 ± 287.83 49,252 ± 192.29 Noted: M: Mild, Mo: Moderate, S: Severe, VS: Very Severe, Table 5 Characterization of SNVs in the genome. Type M Mo S VS Total 49,719.33 ± 451.6 49,480.71 ± 311.34 49,454 ± 287.83 49,252 ± 192.29 Homozygote 20,322 ± 24.27 20,444.86 ± 372.95 203,86.88 ± 332.27 20,426.33 ± 427.39 Heterozygote 29,397.33 ± 475.87 29,035.86 ± 502.58 29,067.13 ± 467.45 28,825.67 ± 242.29 dbSNP_percentage 99.45 ± 0.02 99.44 ± 0.04 99.44 ± 0.04 99.45 ± 0.04 Ts 35,496.33 ± 315 35,255.57 ± 208.6 35,252.5 ± 184.51 35,120.33 ± 86.12 Tv 14,223 ± 149.61 14,225.14 ± 132.35 14,201.5 ± 113.29 14,131.67 ± 120.21 Ts/Tv 2.49 ± 0.02 2.48 ± 0.02 2.48 ± 0.01 2.48 ± 0.02 Novel 274.33 ± 6.81 277.57 ± 19.48 277.75 ± 21.89 271.67 ± 22.81 Novel_Ts 166.67 ± 9.71 162.14 ± 14.45 163.25 ± 10.28 161.33 ± 14.01 Novel_Tv 107.67 ± 9.24 115.43 ± 8 114.5 ± 14.19 110.33 ± 10.07 Novel_Ts/Tv 1.56 ± 0.22 1.41 ± 0.12 1.44 ± 0.16 1.46 ± 0.09 Noted: M: Mild, Mo: Moderate, S: Severe, VS: Very Severe, Intronic variants constituted the most abundant category (21,545.67–21,757.67). Variants in UTRs showed minimal fluctuation: 3' UTR variants numbered between 1414 and 1448, and 5' UTR variants between 1030.33 and 1068. Non-coding RNA (ncRNA) exonic variants also maintained stable counts (1385–1388.33). A modest reduction in total SNVs was observed in the VS group (49,252 ± 192.29) compared to the M group (49,719.33 ± 451.60; p T/G > A and T > C/A > G were the most frequent SNP mutation types across all four groups. No significant differences in the SNP mutation spectrum were observed among the four groups, M, MO, S, and VS, indicating that different treatments or groupings had minimal impact on the distribution of mutation types (Fig. 2 ). Heterozygous SNVs were predominant (58.1–59.1%). Annotation against dbSNPs exceeded 99.4% in all groups. The global transition/transversion (Ts/Tv) ratios were also consistently high, ranging from 2.48 to 2.49. Novel variants comprised 0.55%– 0.56% of the total SNVs (271.67–277.75) and displayed a lower Ts/Tv ratio (1.41–1.56) than the overall genomic ratio. Notably, the VS group showed a 2.9% reduction in total SNVs and a 1.9% reduction in heterozygous variants compared to the M group. 3.4. Mutation types of thalassemia identified through the application of WES The gene mutation profiles of the 21 cases analyzed by WES are summarized in Table 6 . Classical HBB mutations were identified in 18 of 21 cases (85.7%). Table 6 Mutation types in patients with thalassemia from Huadu identified by WES. NO. Gene of Mutation Results FXY HBB CD41/42(-TTCT)β0 LZR HBB Codon 17(A > T)β0 YJW HBB Initiation codon ATG > AGG β0 ZHS HBB c.-28A > Gβ+ ZZF* HBB /MYB CD41/42(-TTCT)β0,IVS-Ⅱ-654(C > T)β+ / NM_001130173.2:c.107G > A(p.R36H) BWS HBB CD41/42(-TTCT)β0, IVS-Ⅱ-654(C > T)β+ QZY HBB CD41/42(-TTCT)β0 WKT HBB CD41/42(-TTCT)β0 YGX* HBB /HBD IVS-Ⅱ-654(C > T)β+ / NM_000519.4: c.440A > T(p.H147L) PCH* HBG1 NM_000559.3:c.364G > T:p.E122* CXM HBA2 Hb Constant Spring (CS)α+ DDN HBB CD 108(A > C)Hb Shizuoka FDQ HBA2 Hb Constant Spring (CS)α+ ZWZ HBB Codons 41/42(-TTCT)β0, Codon 26(G > A)Hb E SY HBB Codons 41/42(-TTCT)β0, c.-28A > Gβ+ LRX HBB IVS-Ⅱ-654(C > T)β+ CWY HBB CD41/42(-TTCT)β0, CD27/28(+ C)β0 HWL HBA2 Hb Constant Spring (CS) α+ HDC HBB CD41/42(-TTCT)β0, c.-28A > Gβ+ LJC HBB IVS-Ⅱ-654(C > T)β+, c.-28A > Gβ+ CHH HBB c.-28A > Gβ+ Noted: * Rare Occurrence Additionally, three rare variants were detected: Case ZZF: HBB gene mutations were identified, including a heterozygous CD41/42(-TTCT) β0-type mutation and a heterozygous IVS-II-654(C > T) β+-type mutation. An MYB mutation, NM_001130173.2:c.107G > A (p.R36H), was also detected. Case YGX: A heterozygous IVS-II-654(C > T) β+-type variant in the HBB gene was observed, along with a mutation in the HBD gene, NM_000519.4:c.440A > T (p.H147L). Case PCH: A heterozygous mutation in HBG1, NM_000559.3:c.364G > T (p.E122*), was identified. 4. Discussion This study used WES to investigate the genetic landscape of thalassemia in the Huadu population, revealing classical HBB mutations and rare variants in modifier genes that contribute to phenotypic heterogeneity. A significant inverse correlation was observed between disease severity and HGB levels (p < 0.05), consistent with established clinical paradigms in which progressive erythropoietic failure culminates in severe anemia. Notably, the VS group exhibited greater variability in HGB levels, reflecting the complex clinical heterogeneity observed in advanced thalassemia phenotypes. Genomic analyses revealed a largely uniform burden of INDELs and SNVs across all severity groups, with no statistically significant differences in coding or functional variant categories. This indicates that the overall mutational burden alone does not fully account for the variability in clinical severity. Nevertheless, subtle differences such as increased 3’ UTR INDELs and reduced total SNVs in VS cases highlight potential regulatory region contributions that warrant further functional investigation. Importantly, WES uncovered rare mutations in three modifier genes—MYB, HBD, and HBG1—in cases ZZF, YGX, and PCH, respectively, potentially influencing disease expression independently of classical β-globin mutations. The MYB gene mutation NM_001130173.2:c.107G > A (p.R36H), identified in case ZZF, represents a variant of uncertain significance that may influence fetal HGB (HbF) regulation. MYB is a key transcriptional regulator of HbF suppression, and variants in the HBS1L-MYB intergenic region are known to modulate HbF levels, potentially ameliorating β-thalassemia severity ( 7 , 8 ). Although preclinical studies have indicated that MYB inhibition can reactivate HbF expression ( 7 ), the therapeutic relevance of p.R36H remains speculative. Thus, clinical screening for this variant is not currently recommended until functional validation establishes its pathogenicity. The HBD mutation NM_000519.4:c.440A > T (p.H147L) in case YGX is a known delta-thalassemia variant that suppresses HbA₂ production ( 9 ). This reduction can result in falsely normal HbA₂ levels, leading to misdiagnosis of β-thalassemia carriers—a well-documented pitfall in hemoglobinopathy diagnostics. As demonstrated by Zakaria et al., such cases require genetic testing to avoid false negative results ( 10 ). HBG1 Mutation in Case PCH presented with Iron deficiency and α-thalassemia. Case PCH carries a heterozygous HBG1 mutation NM_000559.3:c.364G > T (p.E122*), which primarily affects HbF function or levels; hence, the patient was diagnosed with α-thalassemia. Literature suggests that HBG1 mutations have minimal impact on the clinical severity of α-thalassemia but may influence HbF expression, playing a role in modulating disease phenotype. Given the concurrent iron deficiency, it is essential to reassess HGB levels following iron repletion to distinguish the contributions of iron deficiency versus thalassemia to anemia. For genetic counseling, comprehensive screening of both α- and β-thalassemia genes in the patient’s spouse is advised to accurately assess the risk of thalassemia inheritance in offspring ( 10 ). WES detected rare gene mutations in three cases, ZZF, YGX, and PCH, which may modulate disease expression beyond classical HBB mutations. Case ZZF harbored a heterozygous CD41/42(-TTCT) β0 mutation and IVS-II-654(C > T) β + mutation in HBB, alongside a MYB variant (NM_001130173.2:c.107G > A, p.R36H). Although MYB mutations are not typically associated with severe anemia, their known role in modulating HbF implies potential therapeutic implications. This is particularly noteworthy considering reports of thalidomide responsiveness in such contexts. Case YGX presented with a heterozygous IVS-II-654(C > T) β + variant and a delta-thalassemia-associated HBD mutation (NM_000519.4:c.440A > T, p.H147L). Although delta-thalassemia rarely exacerbates anemia, it can complicate diagnosis by causing falsely normal or reduced HbA₂ levels, emphasizing the need for comprehensive genetic testing rather than reliance on HGB electrophoresis alone. Case PCH carried a heterozygous HBG1 mutation (NM_000559.3:c.364G > T, p.E122*), which primarily affects HbF function; literature suggests minimal clinical impact when combined with α-thalassemia, but iron deficiency correction is necessary to accurately assess anemia severity. Collectively, these findings highlight the complex genetic architecture underlying thalassemia severity in the Huadu population. Rare variants in modifier genes such as MYB, HBD, and HBG1 contribute to nuanced effects that may alter clinical presentation, diagnostic accuracy, and therapeutic responsiveness. The implementation of WES in routine diagnostics facilitates the detection of such variants, supporting precision medicine approaches tailored to individual genetic profiles ( 11 ). This study had certain limitations. First, the small cohort size (n = 21) limited the statistical power to correlate rare variants with clinical phenotypes. Second, the functional consequence of prioritized variants (e.g., MYB p.R36H) remains unvalidated by in vitro or in vivo models. Third, as a single-center study from Huadu District, the findings may not fully represent the broader genetic heterogeneity across Guangdong Province. Finally, no cost-effectiveness analysis of WES implementation was conducted, which is a critical consideration for resource-limited endemic regions. In such regions, conventional screening costs $ 15–30 per test compared to $ 300–500 for WES ( 11 ). Future multicenter studies with larger sample sizes and health-economic evaluations are warranted. 5. Conclusions This study demonstrates the utility of WES in identifying both classical and rare modifier gene mutations that contribute to the clinical heterogeneity of thalassemia in the Huadu population. Our findings highlight a significant inverse relationship between HGB levels and disease severity, and reveal that mutational burden alone is insufficient to explain phenotypic variability. Notably, rare variants in MYB, HBD, and HBG1 were identified as potential modulators of disease expression, with implications for diagnosis, treatment, and genetic counseling. These results support the integration of WES into standard thalassemia diagnostic workflows to enhance precision diagnosis and inform individualized management strategies. Future research should prioritize functional characterization of non-coding variants and modifier genes to further elucidate genotype-phenotype correlations and refine therapeutic interventions. Abbreviations WES, Whole-exome sequencing; INDELs, insertions/deletions; SNVs, single-nucleotide variants; HGB, hemoglobin; HCT, haematocrit; MCV, Mean corpuscular volume; MCH, Mean corpuscular hemoglobin; MCHC, Mean corpuscular hemoglobin concentration; CDS, coding sequence Declarations Conflicts of Interest: The authors declare no conflict of interest. All participants or their legal guardians provided written informed consent before inclusion in the study. Funding: This work was supported by the Guangzhou Huadu District People's Hospital Institutional Research Fund Project (2021-A01). Institutional Review Board Statement: Ethical approval was obtained from the Institutional Review Board of the hospital (Approval No. 2021117). Acknowledgment: The authors wish to express their heartfelt gratitude for the facility support by the Guangzhou Huadu District People's Hospital, China. Author Contributions: Guowei Run and Yan Jiang: Conceptualization, Methodology, Formal analysis, Data curation, Writing- original draft. Jingxia Xu, changlv Jiang, and Lihua Zeng: Methodology. Bizhen Yu, Jingnan Bi, Cuijin Tan and HaoHao Lei: Formal analysis, Data curation. Huang yulan: Conceptualization. Linhua Ji: Conceptualization, Methodology, Formal analysis, Funding acquisition,Writing-review and editing, Supervision. All authors have read and agreed to the published version of the manuscript. Data Availability Statement: Not applicable References Xian J, Wang Y, He J, Li S, He W, Ma X, Li Q (2022)Molecular epidemiology and hematologic characterization of thalassemia in Guangdong Province, Southern China. Clinical and Applied Thrombosis/Hemostasis 28:10760296221119807. https://doi.org/10.1177/10760296221119807. Peng Q, Zhang Z, Li S, Cheng C, Li W, Rao C, Zhong B, Lu X(2021) Molecular epidemiological and hematological profile of thalassemia in the Dongguan Region of Guangdong Province, Southern China. J Clin Lab Anal 35(2):e23596. https://doi.org/10.1002/jcla.23596. Segarra-Casas A, Yépez VA, Demidov G, Laurie S, Esteve-Codina A, Gagneur J, Parkhurst Y, Muni-Lofra R, Harris E, Marini-Bettolo C, Straub V, Töpf A(2024) An Integrated Transcriptomics and Genomics Approach Detects an X/Autosome Translocation in a Female with Duchenne Muscular Dystrophy. Int J Mol Sci 25(14). https://doi.org/10.3390/ijms25147793. Hantaweepant C, Suktitipat B, Pithukpakorn M, Chinthammitr Y, Limwongse C, Tansiri N, Sawatnatee S, Takpradit C, Rotchanapanya W, Pongudom S, Charoenprasert K, Paiboonsukwong K, Thamprasert W, Nolwachai N, Rattanasawat W, Sae-Aeng B, Khorwanichakij N, Saetow P, Saengboon S, Kamjornpreecha K, Pholmoo W, Dujjawan B, Siritanaratkul N(2023) Whole exome sequencing and rare variant association study to identify genetic modifiers, KLF1 mutations, and a novel double mutation in Thai patients with hemoglobin E/beta-thalassemia. Hematology 28(1):2187155. https://doi.org/10.1080/16078454.2023.2187155. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL(2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17(5):405-424. https://doi.org/10.1038/gim.2015.30. Chen S (2023). Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. Imeta 2(2):e107. https://doi.org/10.1002/imt2.107. Bashir K, Niazi UK, Shahzadi R, Azam K, Idrees A, Ain QU, Alamin AA(2024) Associations between BCL11A and HBS1L-MYB polymorphisms and thalassemia risk. Journal of Taibah University Medical Sciences 19(5):1039-1048. https://doi.org/10.1016/j.jtumed.2024.10.004. Ferdous J, Tasnim M, Qadri F, Hosen MI, Chowdhury EK, Shekhar HU(2024). Disease-Modifying Effect of HBS1L-MYB in HbE/β-Thalassemia Patients in Bangladeshi Population. Thalassemia Reports. 14(4):103-117. https://doi.org/10.3390/thalassrep14040011. Zakaria NA, Islam MA, Abdullah WZ, Bahar R, Mohamed Yusoff AA, Abdul Wahab R, Shamsuddin S, Johan MF(2021). Epigenetic insights and potential modifiers as therapeutic targets in β–thalassemia. Biomolecules 11(5):755. https://doi.org/10.3390/biom11050755 Shabaan HM, Ahmed SS, Shalaby MA, Fallah AA(2023). The role of HBG2, BCL11A, and HBS1L-MYB in early diagnosis of transfusion-dependent thalassemia among Egyptian children. The Egyptian Journal of Haematology. 48(1):37-46. https://doi.org/10.4103/ejh.ejh_22_22 Nawaz K, Khan SN, Bashir A, Rehman A, Tariq Masood Khan M, Mir A, Ahmad S(2024). Unraveling Impact of Hemoglobin F and A2 Levels: Correlation With Disease Severity and Treatment Response in Transfusion-Dependent Beta-Thalassemia. Cureus 16(1):e52002. https://doi.org/10.7759/cureus.52002. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7708021","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529241654,"identity":"9a603f0d-1a52-4257-a82d-c732084b145c","order_by":0,"name":"Guowei Run","email":"","orcid":"","institution":"Huadu Institute of Medical Sciences, Huadu District People’s Hospital of Guangzhou","correspondingAuthor":false,"prefix":"","firstName":"Guowei","middleName":"","lastName":"Run","suffix":""},{"id":529241655,"identity":"9eb00050-4fb5-41c0-b572-75d080217e6b","order_by":1,"name":"Yan Jiang","email":"","orcid":"","institution":"Capital Medical 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15:55:30","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116138,"visible":true,"origin":"","legend":"","description":"","filename":"72a28b12aa2947da92df840ca5d809b41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7708021/v1/209b05917d08fb6097b3df0e.xml"},{"id":93797139,"identity":"cbace1c4-d150-4c13-a741-5bfb005a0bb1","added_by":"auto","created_at":"2025-10-17 15:55:30","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121571,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7708021/v1/b29f20728c9ae3c392fce9bd.html"},{"id":93797765,"identity":"324b3914-2134-48f0-a41e-2f57077a0fbf","added_by":"auto","created_at":"2025-10-17 16:03:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":202415,"visible":true,"origin":"","legend":"\u003cp\u003eRoutine blood characteristics across thalassemia severity levels.\u003c/p\u003e\n\u003cp\u003eNote: M: Mild, Mo: Moderate, S: Severe, VS: Very Severe, * Comparisons between the two groups, P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7708021/v1/57d2af3bb4e904194de6a186.png"},{"id":93797124,"identity":"af56434c-32d6-4b42-9ae8-35cf9c889c2f","added_by":"auto","created_at":"2025-10-17 15:55:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82775,"visible":true,"origin":"","legend":"\u003cp\u003eStatistics of point mutation types. M: Mild, Mo: Moderate, S: Severe, VS: Very Severe.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7708021/v1/aaec88d5de1d05611c757579.png"},{"id":96363358,"identity":"6555671f-27fd-4f4b-8ba7-b20d4906349f","added_by":"auto","created_at":"2025-11-20 10:06:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1279962,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7708021/v1/8c1643fe-2e63-4840-8a1e-c43996044974.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unmasking Rare Thalassemia Variants through Whole-Exome Sequencing in Huadu District, China: Clinical Insights","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e Thalassemia poses a significant public health challenge in Guangdong Province, China, and is characterized by a markedly elevated and regionally heterogeneous carrier prevalence. Provincial carrier rates exceed 6.8%, with some studies estimating an overall frequency approaching 11%. Significant disparities exist; Huadu District reports a prevalence of approximately 8.3% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) while Dongguan shows α-thalassemia and β-thalassemia carrier rates of 7.6% and 3.8%, respectively (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In contrast, western regions such as Yangjiang exhibit exceptionally high overall frequencies, reaching up to 20%, Southern China, particularly Guangdong, remains a well-established hotspot, with certain localities exceeding 19% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). These figures are further compounded by population migration and genetic heterogeneity.\u003c/p\u003e\u003cp\u003eConventional diagnostic techniques, including hemoglobin (HGB) electrophoresis, gap-PCR, and Multiplex Ligation-dependent Probe Amplification are limited to detecting predefined common mutations. Province-wide studies have estimated that 10\u0026ndash;20% of thalassemia carriers remain undiagnosed using conventional methods due to rare variants (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These methods often fail to detect non-coding variants, novel mutations, structural rearrangements, or variants beyond their target regions (deep intronic or regulatory single-nucleotide polymorphisms [SNPs]) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), leading to diagnostic omissions, particularly among silent carriers and those with atypical mutations.\u003c/p\u003e\u003cp\u003eWhole-exome sequencing (WES) provides a comprehensive alternative, enabling investigation of the entire protein-coding genome. This facilitates the detection of a broad spectrum of pathogenic variants, including coding, splice-site, insertions/deletions (INDEL), and regulatory single-nucleotide variants (SNVs), in both globin (HBA1/2, HBB) and modifier genes (BCL11A, KLF1) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Consequently, WES enhances diagnostic accuracy by identifying variants undetectable by traditional methods, thereby providing a stronger foundation for genetic counseling and prenatal diagnosis (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite extensive provincial screening, WES has not been implemented in high-risk subregions, such as Huadu District, where carrier rates reach 8.3%. This study represents the first comprehensive WES-based profiling of rare thalassemia variants in Huadu District, decoding a unique mutational spectrum distinct from provincial patterns. Our findings establish a model for precision prevention in endemic regions and provide a framework for cost-effective WES integration in resource-limited settings.\u003c/p\u003e"},{"header":"2. Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Patients\u003c/h2\u003e\u003cp\u003eThis retrospective study included 21 patients with a high clinical suspicion of thalassemia gene mutations, recruited from Guangzhou Huadu District People\u0026rsquo;s Hospital. studies were conducted under the Declaration of Helsinki. All participants or their legal guardians provided written informed consent before inclusion in the study. Ethical approval was obtained from the Institutional Review Board of the hospital (Approval No. 2021117). The cohort comprised 11 females and 10 males, aged 14\u0026ndash;73 years. Patients were stratified into four groups based on disease severity as determined by HGB levels:\u003c/p\u003e\u003cp\u003eMild (M): HGB\u0026thinsp;\u0026gt;\u0026thinsp;90 g/L (n\u0026thinsp;=\u0026thinsp;3);\u003c/p\u003e\u003cp\u003eModerate (Mo): HGB 60\u0026ndash;90 g/L (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e\u003cp\u003eSevere (S): HGB 30\u0026ndash;60 g/L (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\u003cp\u003eVery Severe (VS): HGB\u0026thinsp;\u0026lt;\u0026thinsp;30 g/L (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\u003cp\u003eDemographic and clinical characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBasic patient information\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHGB (g/L)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHCT (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMCV (fL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMCH (pg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMCHC (g/L)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFXY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e62.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e321\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLZR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e324\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYJW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZHS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZZF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e27.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e343\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e89.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e29.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQZY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e88.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e317\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWKT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYGX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e342\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e285\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCXM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e285\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDDN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e73.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZWZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e313\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e70.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e335\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLRX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e337\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCWY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e332\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHWL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e76.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e339\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLJC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e281\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e79.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eNoted: M: Mild, Mo: Moderate, S: Severe, VS: Very Severe, HGB: Hemoglobin, HCT: haematocrit, MCV: Mean corpuscular volume, MCH: Mean corpuscular hemoglobin, MCHC: Mean corpuscular hemoglobin concentration.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Blood Count Analysis\u003c/h2\u003e\u003cp\u003ePeripheral blood samples (2 mL) were collected from each participant into EDTA-anticoagulant tubes. Complete blood count analyses were performed using the Sysmex XN-1000 automated hematology analyzer (Sysmex Corporation, Kobe, Japan). The measured parameters included mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and HGB concentration.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. WES, Data Processing, and Analysis\u003c/h2\u003e\u003cp\u003eThe peripheral blood samples (2 mL) collected were transported to the DAAN Clinical Laboratory Center (Guangzhou, China) for processing. WES was conducted using the Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA), generating 150-bp paired-end reads at an average coverage depth of 100\u0026times;. Variant pathogenicity was classified following the American College of Medical Genetics and Genomics (ACMG) guidelines (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Briefly, variants were filtered using population frequency (\u0026lt;\u0026thinsp;0.1% in the gnomAD East Asian database), in silico prediction tools (SIFT, PolyPhen-2, CADD\u0026thinsp;\u0026gt;\u0026thinsp;20), and co-segregation with clinical phenotypes where applicable.\u003c/p\u003e\u003cp\u003e\u003cspan\u003e2.3.1. Data preprocessing: Raw sequencing data generated by the Illumina platform were processed using fastp (v0.12.6,\u0026nbsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/OpenGene/fastp\u003c/span\u003e\u003c/span\u003e) to remove adapter sequences and low-quality reads, producing high-quality clean data for downstream analyses.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e2.3.2. Sequence alignment and processing: Quality-filtered reads were aligned to the reference genome using Sentieon BWA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sentieon.com\u003c/span\u003e\u003c/span\u003e) with default parameters. Post-alignment processing, including read sorting and duplicate removal, was performed using the Sentieon driver (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sentieon.com\u003c/span\u003e\u003c/span\u003e). Mapping quality metrics, including sequencing depth and coverage, were calculated using bamdst (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/shiquan/bamdst\u003c/span\u003e\u003c/span\u003e).\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e2.3.3. Variant calling and analysis: Initial variant calling for SNPs and INDELS was performed using Sentieon DNAseq (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://support.sentieon.com/manual/DNAseq_usage/dnaseq/\u003c/span\u003e\u003c/span\u003e). Somatic SNPs and INDELS were identified using Mutect2(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://software.broadinstitute.org/gatk/documentation/article?id=11077\u003c/span\u003e\u003c/span\u003e), whereas somatic copy number variations (CNVs) were detected using CNVkit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cnvkit.readthedocs.io/en/stable/index.html\u003c/span\u003e\u003c/span\u003e). All identified variants were functionally annotated using ANNOVAR (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://annovar.openbioinformatics.org/en/latest/\u003c/span\u003e\u003c/span\u003e).\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e2.3.4. Mutational signature analysis: Somatic mutation features were extracted and analyzed using the R package Sigminer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://shixiangwang.github.io/sigminer/index.html\u003c/span\u003e\u003c/span\u003e), enabling characterization of mutational patterns (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003cp\u003e2.4. Statistical analysis\u003c/p\u003e\n \u003cp\u003eGenotypic profiling results of thalassemia, including variant spectrum characterization and genotype frequency distribution, were systematically analyzed as the primary study endpoint. Continuous variables are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. For between-group comparisons, we performed a one-way analysis of variance (ANOVA), followed by Tukey\u0026apos;s post-hoc test for multiple comparisons (IBM SPSS Statistics, version 25.0; IBM Corp., Armonk, NY, USA). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed) was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Hematological Characteristics by Disease Severity\u003c/h2\u003e\u003cp\u003eA significant inverse correlation was observed between disease severity and HGB levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). HGB concentrations declined progressively across severity groups: the M group (99.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52 g/L) maintained near-normal values, while the Mo (86.14\u0026thinsp;\u0026plusmn;\u0026thinsp;8.01 g/L), S (53.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72 g/L), and VS groups (31.00\u0026thinsp;\u0026plusmn;\u0026thinsp;26.11 g/L) demonstrated increasingly impaired erythropoiesis. Notably, the VS group exhibited substantially greater HGB variability (SD: 26.11 g/L) compared to the other groups (SD range: 2.52\u0026ndash;8.01 g/L).\u003c/p\u003e\u003cp\u003eComparative analysis revealed no intergroup variations in erythrocyte indices (MCV), MCH, mean corpuscular hemoglobin concentration (MCHC) or reticulocyte counts among the different severity classifications (all p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, Red Cell Distribution Width-Coefficient of Variation, RDW-CV, a measure of erythrocyte volumetric variation, was significantly elevated in the VS group compared to both the M and Mo groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and HGB level was inversely correlated with severity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05): M (99.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52 g/L)\u0026thinsp;\u0026gt;\u0026thinsp;Mo (86.14\u0026thinsp;\u0026plusmn;\u0026thinsp;8.01 g/L)\u0026thinsp;\u0026gt;\u0026thinsp;S (53.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.72 g/L)\u0026thinsp;\u0026gt;\u0026thinsp;VS (31.00\u0026thinsp;\u0026plusmn;\u0026thinsp;26.11 g/L). Notably, the VS group showed higher HGB variability (SD: 26.11 g/L vs. 2.52\u0026ndash;8.01 g/L in others). (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Genomic Characteristics of INDEL Variants in Thalassemia\u003c/h2\u003e\u003cp\u003eWES revealed a heterogeneous mutational spectrum across the 21 patients, with distinct patterns associated with disease severity. There were no significant differences in the INDEL/SNV burden or functional categories (frameshift, stopgain) across severity groups (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of INDELs in different genome and coding regions\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e501.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e493.29\u0026thinsp;\u0026plusmn;\u0026thinsp;18.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e490.25\u0026thinsp;\u0026plusmn;\u0026thinsp;8.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e490\u0026thinsp;\u0026plusmn;\u0026thinsp;17.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eframeshift_deletion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e85\u0026thinsp;\u0026plusmn;\u0026thinsp;6.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e85.29\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e84.75\u0026thinsp;\u0026plusmn;\u0026thinsp;7.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e85.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eframeshift_insertion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e71.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e71.57\u0026thinsp;\u0026plusmn;\u0026thinsp;6.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e65.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e67.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonframeshift_deletion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e120.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e115.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e117.13\u0026thinsp;\u0026plusmn;\u0026thinsp;8.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e115.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonframeshift_insertion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e131.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e127.29\u0026thinsp;\u0026plusmn;\u0026thinsp;6.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e127.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e126\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estopgain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e5.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estoploss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eunknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e86\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e88.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e89.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e91.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3772\u0026thinsp;\u0026plusmn;\u0026thinsp;58.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3754.71\u0026thinsp;\u0026plusmn;\u0026thinsp;78.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e3801.63\u0026thinsp;\u0026plusmn;\u0026thinsp;27.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e3833.67\u0026thinsp;\u0026plusmn;\u0026thinsp;68.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUTR\u0026rsquo;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e253.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e250\u0026thinsp;\u0026plusmn;\u0026thinsp;6.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e250.63\u0026thinsp;\u0026plusmn;\u0026thinsp;9.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e266.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUTR\u0026rsquo;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e131.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e129.43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e129.88\u0026thinsp;\u0026plusmn;\u0026thinsp;9.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e124\u0026thinsp;\u0026plusmn;\u0026thinsp;9.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esplicing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e84.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e80.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e83.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e81.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003encRNA_exonic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e155.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e156\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e154.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e153.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003encRNA_intronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e231\u0026thinsp;\u0026plusmn;\u0026thinsp;12.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e242.29\u0026thinsp;\u0026plusmn;\u0026thinsp;12.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e242.13\u0026thinsp;\u0026plusmn;\u0026thinsp;14.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e243\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003encRNA_splicing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eupstream\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e79.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e75.75\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e79.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edownstream\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e21\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e22.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e21.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e20.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eintergenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e210.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e213.43\u0026thinsp;\u0026plusmn;\u0026thinsp;12.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e209.38\u0026thinsp;\u0026plusmn;\u0026thinsp;16.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e210.67\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e5440.67\u0026thinsp;\u0026plusmn;\u0026thinsp;74.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e5418.71\u0026thinsp;\u0026plusmn;\u0026thinsp;111.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e5459.63\u0026thinsp;\u0026plusmn;\u0026thinsp;43.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e5502.67\u0026thinsp;\u0026plusmn;\u0026thinsp;94.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eNoted: M: Mild, Mo: Moderate, S: Severe, VS: Very Severe, CDS: Coding sequence, UTR: Untranslated regions, ncRNA: non-coding RNA.\u003c/p\u003e\n\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of INDELs in the genome\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e5440.67\u0026thinsp;\u0026plusmn;\u0026thinsp;74.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e5418.71\u0026thinsp;\u0026plusmn;\u0026thinsp;111.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e5459.63\u0026thinsp;\u0026plusmn;\u0026thinsp;47.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e5502.67\u0026thinsp;\u0026plusmn;\u0026thinsp;94.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomozygote\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1926\u0026thinsp;\u0026plusmn;\u0026thinsp;16.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1948.57\u0026thinsp;\u0026plusmn;\u0026thinsp;32.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1934\u0026thinsp;\u0026plusmn;\u0026thinsp;50.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e1982.67\u0026thinsp;\u0026plusmn;\u0026thinsp;45.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeterozygote\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3514.67\u0026thinsp;\u0026plusmn;\u0026thinsp;59.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3470.14\u0026thinsp;\u0026plusmn;\u0026thinsp;110.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e3525.63\u0026thinsp;\u0026plusmn;\u0026thinsp;85.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e3520\u0026thinsp;\u0026plusmn;\u0026thinsp;51.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edbSNP_percentage(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e94.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e94.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e94.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e94.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNovel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e320\u0026thinsp;\u0026plusmn;\u0026thinsp;8.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e304.29\u0026thinsp;\u0026plusmn;\u0026thinsp;23.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e309.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e327\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eComparative analysis of INDEL variants across thalassemia severity groups revealed no statistically significant differences in coding sequence (CDS), frameshift, or non-frameshift mutations (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Likewise, total INDEL burden (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), zygosity distribution (homozygote; heterozygote), and variant annotation characteristics (dbSNP percentage, novel variants) across severity groups showed no significant variation. While total INDEL counts were comparable (~\u0026thinsp;5400\u0026ndash;5500), a modest elevation in 3\u0026rsquo; untranslated regions (UTR) variants was noted in the VS group (VS: 266.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.96) relative to milder groups (M/Mo/S: 250\u0026ndash;254). No severity-dependent trends were observed for functional variants (stopgain/stoploss) or non-coding regions (ncRNA, intronic). Results of INDEL detection and statistics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Total INDEL counts remained remarkably consistent across severity groups (range: 5418.71\u0026ndash;5502.67), with a non-statistically significant slight increase in the VS group (5502.67\u0026thinsp;\u0026plusmn;\u0026thinsp;94.25).\u003c/p\u003e\u003cp\u003eThese findings indicate that INDELs contributed minimally to the observed heterogeneity in thalassemia severity within this cohort.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Genomic Characteristics of SNV Across Thalassemia\u003c/h2\u003e\u003cp\u003eThe SNV detection and statistical results are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. CDS SNVs exhibited minimal variation across severity groups, ranging from 20,076.33 to 20,218. The ratio of synonymous to nonsynonymous variants remained consistent; synonymous variants constituted 51.1%\u0026ndash;51.2% of CDS SNVs (10,238.67\u0026ndash;10,328), while nonsynonymous variants represented 46.1%\u0026ndash;46.2% (9259.33\u0026ndash;9325). Stop-gain variants constituted 0.35% to 0.38% of CDS variants (70.75\u0026ndash;77.33), and stop-loss variants were infrequent (7.57\u0026ndash;9.00).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of single-nucleotide variants (SNVs) in different genome and coding regions.\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e20,218\u0026thinsp;\u0026plusmn;\u0026thinsp;213.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e20,099.14\u0026thinsp;\u0026plusmn;\u0026thinsp;119.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e20,126.13\u0026thinsp;\u0026plusmn;\u0026thinsp;170.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e20,076.33\u0026thinsp;\u0026plusmn;\u0026thinsp;38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esynonymous_SNV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e10,328\u0026thinsp;\u0026plusmn;\u0026thinsp;93.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e10,242.14\u0026thinsp;\u0026plusmn;\u0026thinsp;35.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e10,256.88\u0026thinsp;\u0026plusmn;\u0026thinsp;60.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e10,238.67\u0026thinsp;\u0026plusmn;\u0026thinsp;62.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enonsynonymous_SNV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e9325\u0026thinsp;\u0026plusmn;\u0026thinsp;121.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e9304.14\u0026thinsp;\u0026plusmn;\u0026thinsp;94.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e9314.63\u0026thinsp;\u0026plusmn;\u0026thinsp;125.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e9259.33\u0026thinsp;\u0026plusmn;\u0026thinsp;81.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estopgain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e77.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e72.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e70.75\u0026thinsp;\u0026plusmn;\u0026thinsp;5.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e74\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estoploss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e9\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e8.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e7.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eunknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e478.67\u0026thinsp;\u0026plusmn;\u0026thinsp;40.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e472.71\u0026thinsp;\u0026plusmn;\u0026thinsp;31.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e475.63\u0026thinsp;\u0026plusmn;\u0026thinsp;36.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e496.67\u0026thinsp;\u0026plusmn;\u0026thinsp;22.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e21,757.67\u0026thinsp;\u0026plusmn;\u0026thinsp;218.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e21,674.86\u0026thinsp;\u0026plusmn;\u0026thinsp;118.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e21,639\u0026thinsp;\u0026plusmn;\u0026thinsp;147.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e21,545.67\u0026thinsp;\u0026plusmn;\u0026thinsp;108.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUTR\u0026rsquo;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1448\u0026thinsp;\u0026plusmn;\u0026thinsp;23.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1414\u0026thinsp;\u0026plusmn;\u0026thinsp;38.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1418.13\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e1423\u0026thinsp;\u0026plusmn;\u0026thinsp;28.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUTR\u0026rsquo;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1068\u0026thinsp;\u0026plusmn;\u0026thinsp;22.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1032.43\u0026thinsp;\u0026plusmn;\u0026thinsp;25.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1040.5\u0026thinsp;\u0026plusmn;\u0026thinsp;24.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e1030.33\u0026thinsp;\u0026plusmn;\u0026thinsp;18.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esplicing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e54.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e53.57\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e49.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003encRNA_exonic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1388.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1380.86\u0026thinsp;\u0026plusmn;\u0026thinsp;26.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1387.5\u0026thinsp;\u0026plusmn;\u0026thinsp;32.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e1385\u0026thinsp;\u0026plusmn;\u0026thinsp;32.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003encRNA_intronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1319.33\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1334.57\u0026thinsp;\u0026plusmn;\u0026thinsp;35.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1315.13\u0026thinsp;\u0026plusmn;\u0026thinsp;17.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e1298.33\u0026thinsp;\u0026plusmn;\u0026thinsp;31.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003encRNA_splicing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eupstream\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e504\u0026thinsp;\u0026plusmn;\u0026thinsp;23.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e497.29\u0026thinsp;\u0026plusmn;\u0026thinsp;19.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e494.88\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e477\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edownstream\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e162\u0026thinsp;\u0026plusmn;\u0026thinsp;6.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e160\u0026thinsp;\u0026plusmn;\u0026thinsp;11.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e167.75\u0026thinsp;\u0026plusmn;\u0026thinsp;13.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e158\u0026thinsp;\u0026plusmn;\u0026thinsp;6.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eintergenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1795\u0026thinsp;\u0026plusmn;\u0026thinsp;56.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1830\u0026thinsp;\u0026plusmn;\u0026thinsp;56.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1807.13\u0026thinsp;\u0026plusmn;\u0026thinsp;47.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e1805.33\u0026thinsp;\u0026plusmn;\u0026thinsp;41.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e49,719.33\u0026thinsp;\u0026plusmn;\u0026thinsp;451.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e49,480.71\u0026thinsp;\u0026plusmn;\u0026thinsp;311.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e49,454\u0026thinsp;\u0026plusmn;\u0026thinsp;287.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e49,252\u0026thinsp;\u0026plusmn;\u0026thinsp;192.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cp\u003eNoted: M: Mild, Mo: Moderate, S: Severe, VS: Very Severe,\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacterization of SNVs in the genome.\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e49,719.33\u0026thinsp;\u0026plusmn;\u0026thinsp;451.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e49,480.71\u0026thinsp;\u0026plusmn;\u0026thinsp;311.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e49,454\u0026thinsp;\u0026plusmn;\u0026thinsp;287.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e49,252\u0026thinsp;\u0026plusmn;\u0026thinsp;192.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomozygote\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e20,322\u0026thinsp;\u0026plusmn;\u0026thinsp;24.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e20,444.86\u0026thinsp;\u0026plusmn;\u0026thinsp;372.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e203,86.88\u0026thinsp;\u0026plusmn;\u0026thinsp;332.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e20,426.33\u0026thinsp;\u0026plusmn;\u0026thinsp;427.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeterozygote\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e29,397.33\u0026thinsp;\u0026plusmn;\u0026thinsp;475.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e29,035.86\u0026thinsp;\u0026plusmn;\u0026thinsp;502.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e29,067.13\u0026thinsp;\u0026plusmn;\u0026thinsp;467.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e28,825.67\u0026thinsp;\u0026plusmn;\u0026thinsp;242.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edbSNP_percentage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e99.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e99.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e99.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e99.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e35,496.33\u0026thinsp;\u0026plusmn;\u0026thinsp;315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e35,255.57\u0026thinsp;\u0026plusmn;\u0026thinsp;208.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e35,252.5\u0026thinsp;\u0026plusmn;\u0026thinsp;184.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e35,120.33\u0026thinsp;\u0026plusmn;\u0026thinsp;86.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e14,223\u0026thinsp;\u0026plusmn;\u0026thinsp;149.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e14,225.14\u0026thinsp;\u0026plusmn;\u0026thinsp;132.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e14,201.5\u0026thinsp;\u0026plusmn;\u0026thinsp;113.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e14,131.67\u0026thinsp;\u0026plusmn;\u0026thinsp;120.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTs/Tv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNovel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e274.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e277.57\u0026thinsp;\u0026plusmn;\u0026thinsp;19.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e277.75\u0026thinsp;\u0026plusmn;\u0026thinsp;21.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e271.67\u0026thinsp;\u0026plusmn;\u0026thinsp;22.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNovel_Ts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e166.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e162.14\u0026thinsp;\u0026plusmn;\u0026thinsp;14.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e163.25\u0026thinsp;\u0026plusmn;\u0026thinsp;10.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e161.33\u0026thinsp;\u0026plusmn;\u0026thinsp;14.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNovel_Tv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e107.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e115.43\u0026thinsp;\u0026plusmn;\u0026thinsp;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e114.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e110.33\u0026thinsp;\u0026plusmn;\u0026thinsp;10.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNovel_Ts/Tv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNoted: M: Mild, Mo: Moderate, S: Severe, VS: Very Severe,\u003c/p\u003e\u003cp\u003eIntronic variants constituted the most abundant category (21,545.67\u0026ndash;21,757.67). Variants in UTRs showed minimal fluctuation: 3' UTR variants numbered between 1414 and 1448, and 5' UTR variants between 1030.33 and 1068. Non-coding RNA (ncRNA) exonic variants also maintained stable counts (1385\u0026ndash;1388.33).\u003c/p\u003e\u003cp\u003eA modest reduction in total SNVs was observed in the VS group (49,252\u0026thinsp;\u0026plusmn;\u0026thinsp;192.29) compared to the M group (49,719.33\u0026thinsp;\u0026plusmn;\u0026thinsp;451.60; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). C\u0026thinsp;\u0026gt;\u0026thinsp;T/G\u0026thinsp;\u0026gt;\u0026thinsp;A and T\u0026thinsp;\u0026gt;\u0026thinsp;C/A\u0026thinsp;\u0026gt;\u0026thinsp;G were the most frequent SNP mutation types across all four groups. No significant differences in the SNP mutation spectrum were observed among the four groups, M, MO, S, and VS, indicating that different treatments or groupings had minimal impact on the distribution of mutation types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Heterozygous SNVs were predominant (58.1\u0026ndash;59.1%). Annotation against dbSNPs exceeded 99.4% in all groups. The global transition/transversion (Ts/Tv) ratios were also consistently high, ranging from 2.48 to 2.49.\u003c/p\u003e\u003cp\u003eNovel variants comprised 0.55%\u0026ndash; 0.56% of the total SNVs (271.67\u0026ndash;277.75) and displayed a lower Ts/Tv ratio (1.41\u0026ndash;1.56) than the overall genomic ratio. Notably, the VS group showed a 2.9% reduction in total SNVs and a 1.9% reduction in heterozygous variants compared to the M group.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Mutation types of thalassemia identified through the application of WES\u003c/h2\u003e\u003cp\u003eThe gene mutation profiles of the 21 cases analyzed by WES are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Classical HBB mutations were identified in 18 of 21 cases (85.7%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMutation types in patients with thalassemia from Huadu identified by WES.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene of Mutation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eResults\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFXY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCD41/42(-TTCT)β0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLZR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCodon 17(A\u0026thinsp;\u0026gt;\u0026thinsp;T)β0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYJW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eInitiation codon ATG\u0026thinsp;\u0026gt;\u0026thinsp;AGG β0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZHS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003ec.-28A\u0026thinsp;\u0026gt;\u0026thinsp;Gβ+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZZF*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003cp\u003e/MYB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCD41/42(-TTCT)β0,IVS-Ⅱ-654(C\u0026thinsp;\u0026gt;\u0026thinsp;T)β+\u003c/p\u003e\u003cp\u003e/ NM_001130173.2:c.107G\u0026thinsp;\u0026gt;\u0026thinsp;A(p.R36H)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBWS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCD41/42(-TTCT)β0, IVS-Ⅱ-654(C\u0026thinsp;\u0026gt;\u0026thinsp;T)β+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQZY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCD41/42(-TTCT)β0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWKT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCD41/42(-TTCT)β0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYGX*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003cp\u003e/HBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eIVS-Ⅱ-654(C\u0026thinsp;\u0026gt;\u0026thinsp;T)β+\u003c/p\u003e\u003cp\u003e/ NM_000519.4: c.440A\u0026thinsp;\u0026gt;\u0026thinsp;T(p.H147L)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCH*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBG1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eNM_000559.3:c.364G\u0026thinsp;\u0026gt;\u0026thinsp;T:p.E122*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCXM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eHb Constant Spring (CS)α+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDDN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCD 108(A\u0026thinsp;\u0026gt;\u0026thinsp;C)Hb Shizuoka\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eHb Constant Spring (CS)α+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZWZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCodons 41/42(-TTCT)β0, Codon 26(G\u0026thinsp;\u0026gt;\u0026thinsp;A)Hb E\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCodons 41/42(-TTCT)β0, c.-28A\u0026thinsp;\u0026gt;\u0026thinsp;Gβ+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLRX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eIVS-Ⅱ-654(C\u0026thinsp;\u0026gt;\u0026thinsp;T)β+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCWY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCD41/42(-TTCT)β0, CD27/28(+\u0026thinsp;C)β0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHWL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eHb Constant Spring (CS) α+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eCD41/42(-TTCT)β0, c.-28A\u0026thinsp;\u0026gt;\u0026thinsp;Gβ+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLJC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eIVS-Ⅱ-654(C\u0026thinsp;\u0026gt;\u0026thinsp;T)β+, c.-28A\u0026thinsp;\u0026gt;\u0026thinsp;Gβ+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHBB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ec.-28A\u0026thinsp;\u0026gt;\u0026thinsp;Gβ+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003eNoted: * Rare Occurrence\u003cp\u003eAdditionally, three rare variants were detected: Case ZZF: HBB gene mutations were identified, including a heterozygous CD41/42(-TTCT) β0-type mutation and a heterozygous IVS-II-654(C\u0026thinsp;\u0026gt;\u0026thinsp;T) β+-type mutation. An MYB mutation, NM_001130173.2:c.107G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.R36H), was also detected. Case YGX: A heterozygous IVS-II-654(C\u0026thinsp;\u0026gt;\u0026thinsp;T) β+-type variant in the HBB gene was observed, along with a mutation in the HBD gene, NM_000519.4:c.440A\u0026thinsp;\u0026gt;\u0026thinsp;T (p.H147L). Case PCH: A heterozygous mutation in HBG1, NM_000559.3:c.364G\u0026thinsp;\u0026gt;\u0026thinsp;T (p.E122*), was identified.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study used WES to investigate the genetic landscape of thalassemia in the Huadu population, revealing classical HBB mutations and rare variants in modifier genes that contribute to phenotypic heterogeneity. A significant inverse correlation was observed between disease severity and HGB levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), consistent with established clinical paradigms in which progressive erythropoietic failure culminates in severe anemia. Notably, the VS group exhibited greater variability in HGB levels, reflecting the complex clinical heterogeneity observed in advanced thalassemia phenotypes.\u003c/p\u003e\u003cp\u003eGenomic analyses revealed a largely uniform burden of INDELs and SNVs across all severity groups, with no statistically significant differences in coding or functional variant categories. This indicates that the overall mutational burden alone does not fully account for the variability in clinical severity. Nevertheless, subtle differences such as increased 3\u0026rsquo; UTR INDELs and reduced total SNVs in VS cases highlight potential regulatory region contributions that warrant further functional investigation.\u003c/p\u003e\u003cp\u003eImportantly, WES uncovered rare mutations in three modifier genes\u0026mdash;MYB, HBD, and HBG1\u0026mdash;in cases ZZF, YGX, and PCH, respectively, potentially influencing disease expression independently of classical β-globin mutations. The MYB gene mutation NM_001130173.2:c.107G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.R36H), identified in case ZZF, represents a variant of uncertain significance that may influence fetal HGB (HbF) regulation. MYB is a key transcriptional regulator of HbF suppression, and variants in the HBS1L-MYB intergenic region are known to modulate HbF levels, potentially ameliorating β-thalassemia severity (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Although preclinical studies have indicated that MYB inhibition can reactivate HbF expression (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), the therapeutic relevance of p.R36H remains speculative. Thus, clinical screening for this variant is not currently recommended until functional validation establishes its pathogenicity.\u003c/p\u003e\u003cp\u003eThe HBD mutation NM_000519.4:c.440A\u0026thinsp;\u0026gt;\u0026thinsp;T (p.H147L) in case YGX is a known delta-thalassemia variant that suppresses HbA₂ production (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This reduction can result in falsely normal HbA₂ levels, leading to misdiagnosis of β-thalassemia carriers\u0026mdash;a well-documented pitfall in hemoglobinopathy diagnostics. As demonstrated by Zakaria et al., such cases require genetic testing to avoid false negative results (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHBG1 Mutation in Case PCH presented with Iron deficiency and α-thalassemia. Case PCH carries a heterozygous HBG1 mutation NM_000559.3:c.364G\u0026thinsp;\u0026gt;\u0026thinsp;T (p.E122*), which primarily affects HbF function or levels; hence, the patient was diagnosed with α-thalassemia. Literature suggests that HBG1 mutations have minimal impact on the clinical severity of α-thalassemia but may influence HbF expression, playing a role in modulating disease phenotype. Given the concurrent iron deficiency, it is essential to reassess HGB levels following iron repletion to distinguish the contributions of iron deficiency versus thalassemia to anemia. For genetic counseling, comprehensive screening of both α- and β-thalassemia genes in the patient\u0026rsquo;s spouse is advised to accurately assess the risk of thalassemia inheritance in offspring (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWES detected rare gene mutations in three cases, ZZF, YGX, and PCH, which may modulate disease expression beyond classical HBB mutations. Case ZZF harbored a heterozygous CD41/42(-TTCT) β0 mutation and IVS-II-654(C\u0026thinsp;\u0026gt;\u0026thinsp;T) β\u0026thinsp;+\u0026thinsp;mutation in HBB, alongside a MYB variant (NM_001130173.2:c.107G\u0026thinsp;\u0026gt;\u0026thinsp;A, p.R36H). Although MYB mutations are not typically associated with severe anemia, their known role in modulating HbF implies potential therapeutic implications. This is particularly noteworthy considering reports of thalidomide responsiveness in such contexts.\u003c/p\u003e\u003cp\u003eCase YGX presented with a heterozygous IVS-II-654(C\u0026thinsp;\u0026gt;\u0026thinsp;T) β\u0026thinsp;+\u0026thinsp;variant and a delta-thalassemia-associated HBD mutation (NM_000519.4:c.440A\u0026thinsp;\u0026gt;\u0026thinsp;T, p.H147L). Although delta-thalassemia rarely exacerbates anemia, it can complicate diagnosis by causing falsely normal or reduced HbA₂ levels, emphasizing the need for comprehensive genetic testing rather than reliance on HGB electrophoresis alone. Case PCH carried a heterozygous HBG1 mutation (NM_000559.3:c.364G\u0026thinsp;\u0026gt;\u0026thinsp;T, p.E122*), which primarily affects HbF function; literature suggests minimal clinical impact when combined with α-thalassemia, but iron deficiency correction is necessary to accurately assess anemia severity.\u003c/p\u003e\u003cp\u003eCollectively, these findings highlight the complex genetic architecture underlying thalassemia severity in the Huadu population. Rare variants in modifier genes such as MYB, HBD, and HBG1 contribute to nuanced effects that may alter clinical presentation, diagnostic accuracy, and therapeutic responsiveness. The implementation of WES in routine diagnostics facilitates the detection of such variants, supporting precision medicine approaches tailored to individual genetic profiles (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study had certain limitations. First, the small cohort size (n\u0026thinsp;=\u0026thinsp;21) limited the statistical power to correlate rare variants with clinical phenotypes. Second, the functional consequence of prioritized variants (e.g., MYB p.R36H) remains unvalidated by in vitro or in vivo models. Third, as a single-center study from Huadu District, the findings may not fully represent the broader genetic heterogeneity across Guangdong Province. Finally, no cost-effectiveness analysis of WES implementation was conducted, which is a critical consideration for resource-limited endemic regions. In such regions, conventional screening costs \u003cspan\u003e$\u003c/span\u003e15\u0026ndash;30 per test compared to \u003cspan\u003e$\u003c/span\u003e300\u0026ndash;500 for WES (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Future multicenter studies with larger sample sizes and health-economic evaluations are warranted.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study demonstrates the utility of WES in identifying both classical and rare modifier gene mutations that contribute to the clinical heterogeneity of thalassemia in the Huadu population. Our findings highlight a significant inverse relationship between HGB levels and disease severity, and reveal that mutational burden alone is insufficient to explain phenotypic variability. Notably, rare variants in MYB, HBD, and HBG1 were identified as potential modulators of disease expression, with implications for diagnosis, treatment, and genetic counseling. These results support the integration of WES into standard thalassemia diagnostic workflows to enhance precision diagnosis and inform individualized management strategies. Future research should prioritize functional characterization of non-coding variants and modifier genes to further elucidate genotype-phenotype correlations and refine therapeutic interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eWES, Whole-exome sequencing; INDELs, insertions/deletions; SNVs, single-nucleotide variants; HGB, hemoglobin; HCT, haematocrit; MCV, Mean corpuscular volume; MCH, Mean corpuscular hemoglobin; MCHC, Mean corpuscular hemoglobin concentration; CDS, coding sequence\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003eAll participants or their legal guardians provided written informed consent before inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by the Guangzhou Huadu District People\u0026apos;s Hospital Institutional Research Fund Project (2021-A01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e Ethical approval was obtained from the Institutional Review Board of the hospital (Approval No. 2021117).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u003c/strong\u003e The authors wish to express their heartfelt gratitude for the facility support by the Guangzhou Huadu District People\u0026apos;s Hospital, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Guowei Run and Yan Jiang: Conceptualization, Methodology, Formal analysis, Data curation, Writing- original draft. Jingxia Xu, changlv Jiang, and Lihua Zeng: Methodology. Bizhen Yu, Jingnan Bi, Cuijin Tan and HaoHao Lei: Formal analysis, Data curation. Huang yulan: Conceptualization. Linhua Ji: Conceptualization, Methodology, Formal analysis, Funding acquisition,Writing-review and editing, Supervision. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXian J, Wang Y, He J, Li S, He W, Ma X, Li Q (2022)Molecular epidemiology and hematologic characterization of thalassemia in Guangdong Province, Southern China. Clinical and Applied Thrombosis/Hemostasis 28:10760296221119807. https://doi.org/10.1177/10760296221119807.\u003c/li\u003e\n\u003cli\u003ePeng Q, Zhang Z, Li S, Cheng C, Li W, Rao C, Zhong B, Lu X(2021) Molecular epidemiological and hematological profile of thalassemia in the Dongguan Region of Guangdong Province, Southern China. J Clin Lab Anal 35(2):e23596. https://doi.org/10.1002/jcla.23596.\u003c/li\u003e\n\u003cli\u003eSegarra-Casas A, Y\u0026eacute;pez VA, Demidov G, Laurie S, Esteve-Codina A, Gagneur J, Parkhurst Y, Muni-Lofra R, Harris E, Marini-Bettolo C, Straub V, T\u0026ouml;pf A(2024) An Integrated Transcriptomics and Genomics Approach Detects an X/Autosome Translocation in a Female with Duchenne Muscular Dystrophy. Int J Mol Sci 25(14). https://doi.org/10.3390/ijms25147793.\u003c/li\u003e\n\u003cli\u003eHantaweepant C, Suktitipat B, Pithukpakorn M, Chinthammitr Y, Limwongse C, Tansiri N, Sawatnatee S, Takpradit C, Rotchanapanya W, Pongudom S, Charoenprasert K, Paiboonsukwong K, Thamprasert W, Nolwachai N, Rattanasawat W, Sae-Aeng B, Khorwanichakij N, Saetow P, Saengboon S, Kamjornpreecha K, Pholmoo W, Dujjawan B, Siritanaratkul N(2023) Whole exome sequencing and rare variant association study to identify genetic modifiers, KLF1 mutations, and a novel double mutation in Thai patients with hemoglobin E/beta-thalassemia. Hematology 28(1):2187155. https://doi.org/10.1080/16078454.2023.2187155.\u003c/li\u003e\n\u003cli\u003eRichards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL(2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17(5):405-424. https://doi.org/10.1038/gim.2015.30.\u003c/li\u003e\n\u003cli\u003eChen S (2023). Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. Imeta 2(2):e107. https://doi.org/10.1002/imt2.107.\u003c/li\u003e\n\u003cli\u003eBashir K, Niazi UK, Shahzadi R, Azam K, Idrees A, Ain QU, Alamin AA(2024) Associations between BCL11A and HBS1L-MYB polymorphisms and thalassemia risk. Journal of Taibah University Medical Sciences 19(5):1039-1048. https://doi.org/10.1016/j.jtumed.2024.10.004.\u003c/li\u003e\n\u003cli\u003eFerdous J, Tasnim M, Qadri F, Hosen MI, Chowdhury EK, Shekhar HU(2024). Disease-Modifying Effect of HBS1L-MYB in HbE/\u0026beta;-Thalassemia Patients in Bangladeshi Population. Thalassemia Reports. 14(4):103-117. https://doi.org/10.3390/thalassrep14040011.\u003c/li\u003e\n\u003cli\u003eZakaria NA, Islam MA, Abdullah WZ, Bahar R, Mohamed Yusoff AA, Abdul Wahab R, Shamsuddin S, Johan MF(2021). Epigenetic insights and potential modifiers as therapeutic targets in \u0026beta;\u0026ndash;thalassemia. Biomolecules 11(5):755. https://doi.org/10.3390/biom11050755 \u003c/li\u003e\n\u003cli\u003eShabaan HM, Ahmed SS, Shalaby MA, Fallah AA(2023). The role of HBG2, BCL11A, and HBS1L-MYB in early diagnosis of transfusion-dependent thalassemia among Egyptian children. The Egyptian Journal of Haematology. 48(1):37-46. https://doi.org/10.4103/ejh.ejh_22_22 \u003c/li\u003e\n\u003cli\u003eNawaz K, Khan SN, Bashir A, Rehman A, Tariq Masood Khan M, Mir A, Ahmad S(2024). Unraveling Impact of Hemoglobin F and A2 Levels: Correlation With Disease Severity and Treatment Response in Transfusion-Dependent Beta-Thalassemia. Cureus 16(1):e52002. https://doi.org/10.7759/cureus.52002. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"thalassemia, whole-exome sequencing, INDELs, SNVs, HBB, genetic screening, Guangzhou","lastPublishedDoi":"10.21203/rs.3.rs-7708021/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7708021/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/Objectives\u003c/strong\u003e: Whole-exome sequencing (WES) enhances the detection of thalassemia-associated variants beyond conventional methods, particularly in high-prevalence regions, facilitating precise genotype-phenotype correlations. This study aimed to establish a model for precision prevention in endemic regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: WES was performed on 21 patients with clinically suspected thalassemia from Huadu District, Guangzhou, stratified by severity. Variant analysis encompassed both coding and non-coding regions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: On average, 5440.7±94.3 insertions/deletions (INDELs) and 49,719.3±492.5 single-nucleotide variants (SNVs) per individual were detected. Frameshift INDELs were predominantly localized to the HBB gene (85.3±5.0 variants). Non-coding SNVs in 3’ untranslated regions correlated with reduced mean corpuscular hemoglobin concentration (MCHC: 301±25 g/L vs. normal \u0026gt;320 g/L, p \u0026lt; 0.01). Compound heterozygosity involving classical β-thalassemia mutations (CD41/42(-TTCT) β^0 and IVS-II-654(C\u0026gt;T) β^+) accounted for 85.7% of severe cases. Three rare non-β-globin variants were detected: MYB c.107G\u0026gt;A (p.R36H) in case ZZF, associated with potential thalidomide responsiveness; HBD c.440A\u0026gt;T (p.H147L) in case YGX, causing artificially normalized HbA₂ (3.1%) and risk of β-thalassemia misdiagnosis; and HBG1 c.364G\u0026gt;T (p.E122) in case PCH, co-occurring with α-thalassemia and iron deficiency (MCHC: 285 g/L), necessitating iron repletion assessment. Hemoglobin levels declined significantly with increasing severity (mild: 99.7±2.5 g/L; very severe: 31.0±26.1 g/L, p \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: WES significantly enhanced the diagnostic yield by identifying causative variants in all 21 cases, including coding and regulatory mutations undetectable by conventional screening. This approach facilitates comprehensive genetic profiling essential for accurate genotype-phenotype correlations and individualized management. Our findings support the integration of WES into thalassemia diagnostics and genetic counseling in high-prevalence regions.\u003c/p\u003e","manuscriptTitle":"Unmasking Rare Thalassemia Variants through Whole-Exome Sequencing in Huadu District, China: Clinical Insights","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 15:55:25","doi":"10.21203/rs.3.rs-7708021/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f9f566f-5d18-4c60-8b37-057464c24eac","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-14T14:53:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 15:55:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7708021","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7708021","identity":"rs-7708021","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}