Exploring DNA methylation profiles in the pathogenesis of human osteoporosis via whole-genome bisulfite sequencing

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This condition significantly impairs patients' quality of life and increases mortality risk. Emerging evidence suggests that DNA methylation may play a crucial role in regulating the expression of genes related to bone metabolism, thereby influencing the development of osteoporosis. However, the precise relationship between DNA methylation and osteoporosis remains unclear and warrants further investigation. Results Our study revealed significant differences in both the quantity and ratio of DNA methylation between individuals with osteoporosis and healthy controls, with differences predominantly occurring in CpG islands. GO/KEGG enrichment analyses highlighted distinct osteoporosis-related gene pathways. Notably, we identified two genes, TF and TGFB1, located on chromosomes 3 and 19, respectively, that are potentially involved in the pathogenesis of osteoporosis and are broadly involved in various diseases and biological processes. Conclusions These findings indicate distinct methylation patterns between osteoporosis patients and healthy individuals, with differential methylation levels in genes associated with osteoporosis. This research offers new insights into the epigenetic mechanisms underlying osteoporosis. DNA methylation WGBS Osteoporosis Human Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introductions Osteoporosis is a widespread metabolic bone disorder characterized by reduced bone mass and deterioration of bone tissue, resulting in an increased risk of fractures. Currently, osteoporosis affects approximately 200 million people worldwide, with one-third of women over the age of 50 and one-fifth of men at risk of osteoporotic fractures. The incidence of osteoporosis is increasing annually due to the aging population and lifestyle changes, making it a significant public health challenge with high morbidity, mortality, and associated healthcare costs[ 1 ]. Hip fractures, in particular, are a major concern, with a mortality rate of 20–24% within the first year and a substantial risk of long-term disability. Approximately 33% of hip fracture patients become fully dependent or require institutional care within a year after the fracture[ 2 ]. Despite extensive research, the pathogenesis of osteoporosis is complex and not fully understood and involves genetic, environmental, and hormonal factors. Recent studies have increasingly focused on the role of epigenetic mechanisms, particularly DNA methylation, in the development and progression of osteoporosis[ 3 ]. DNA methylation is a critical epigenetic modification in which a methyl group is added to the 5' position of the cytosine residue in CpG dinucleotides, influencing gene expression without altering the DNA sequence. This modification is crucial for regulating various biological processes, including embryonic development, X chromosome inactivation, gene imprinting, and transposon suppression[ 4 ]. Abnormal DNA methylation patterns have been linked to numerous diseases, including cancer, neurological disorders, and metabolic conditions such as osteoporosis[ 5 , 6 ]. Studies indicate that changes in DNA methylation can impact the expression of genes involved in bone formation and resorption, contributing to osteoporosis and an increased fracture risk[ 7 – 9 ]. For example, hypermethylation of bone-forming genes can reduce their expression, impair osteoblast function, and hinder bone formation, whereas hypomethylation of bone resorption-related genes can increase osteoclast activity and accelerate bone loss. Thus, DNA methylation plays a vital role in regulating genes crucial for bone stability, and the aberrant methylation patterns observed in osteoporosis underscore the importance of epigenetic modifications in disease pathogenesis. Moreover, DNA methylation profiles could serve as potential biomarkers for the early diagnosis and prognosis of osteoporosis. Epigenome-wide association studies (EWAS) have identified specific differentially methylated sites (DMS) and regions (DMR) associated with bone mineral density (BMD) and fracture risk, which are closely linked to osteoporosis[ 10 ]. These findings suggest that DNA methylation markers could help identify high-risk populations and enable personalized treatment strategies. Additionally, the DNA methylation pattern offers novel therapeutic avenues, such as the use of epigenetic drugs such as DNA methyltransferase inhibitors, which can restore normal bone metabolism by modulating gene expression and have shown promising therapeutic effects in osteoporosis patients[ 11 ]. Whole-genome bisulfite sequencing (WGBS) is an advanced high-throughput technology used for comprehensive analysis of DNA methylation across the entire genome. By combining bisulfite conversion with next-generation sequencing, WGBS enables the detection of methylation states at single-base resolution[ 12 ]. During bisulfite treatment, unmethylated cytosines are converted to uracil, whereas methylated cytosines remain unchanged. Subsequent PCR amplification converts uracil into thymine, and high-throughput sequencing of the PCR products, compared with the reference sequence, allows for the precise determination of methylation at the CpG, CHG, and CHH sites[ 13 ]. This study aimed to investigate differences in DNA methylation levels between individuals with and without osteoporosis through whole-genome DNA methylation analysis. By comparing differential methylation sites and regions, we sought to identify genes closely associated with the development of osteoporosis and further explore the relationship between DNA methylation and this disease. Methods Study Subjects and Sample Collection This study included 20 subjects aged 50–75 years, comprising 10 patients with osteoporosis and 10 without osteoporosis. The participants were divided into two groups: the non-osteoporosis group (N group, n = 10) and the osteoporosis group (O group, n = 10). The bone mineral density (BMD) of all the subjects was measured by a dual-energy X-ray absorptiometry (DXA) system (Hologic Discovery, USA). Informed consent was obtained from all participants prior to the study. Age and body mass index (BMI) were controlled to ensure that there were no significant differences between the groups. Bone tissue samples were collected during lumbar spine surgery, and extraneous tissues were removed before the samples were stored in liquid nitrogen at -80°C for DNA methylation analysis ( Fig. 1 ) . Sample DNA Extraction Sample DNA Extraction The bone samples were first cleaned with deionized water and detergent to remove impurities and contaminants. Sterile tools were then used to excise a thin surface layer, ensuring a purer internal sample. After freezing, the bone samples were ground into a fine powder in liquid nitrogen. The powder was immersed in EDTA (ethylenediaminetetraacetic acid) solution to decalcify the bone, softening the tissue and releasing DNA. The decalcified samples were lysed in lysis buffer, and the DNA was purified using a DNA extraction kit for subsequent sequencing. Library construction and sequencing Genomic DNA was fragmented according to the experimental protocol, followed by end repair, 3’ end A-tailing, adaptor ligation, hybrid capture, bisulfite conversion, and enrichment to construct sequencing libraries. The library concentration was measured by a Qubit® 2.0 fluorometer, and the library size was assessed with an Agilent 2100 Bioanalyzer. Libraries were pooled and quality-checked through an Agilent 4200 Bioanalyzer. Cluster generation and initial sequencing primer hybridization were performed on the cBot using the Illumina NovaSeq 6000 sequencer, following the procedures outlined in the cBot User Guide. The sequencing reagents were prepared according to the Illumina User Guide, and the flow cell containing the clusters was loaded onto the sequencer. Paired-end sequencing was conducted with real-time monitoring by Illumina's data collection software, which also handled on-the-fly data analysis. ( Table 1 ). Tabel 1 Reagents for library construction and quality inspection Product/Reagent Manufacturer Catalog No. SureSelectXT Methyl Reagent kit, HSQ,16 Agilent G9651A EZ-DNA Methylation-Gold Kit Zymo D5006 SureSelectXT Human Methyl-Seq Capture Library Agilent 5190 − 4661 ExKubit dsDNA HS Analysis Kit Excell NGS00-3012 Agilent High Sensitivity DNA Kit Agilent 5067 − 4626 Sequencing Data Evaluation Sequencing read quality was evaluated by a Q value box plot, where the relationship between the Q value and sequencing error rate (E value) was defined as Q = -10Log10 E. We also analyzed the base composition of the sequences. Theoretically, on the basis of complementary base pairing, the GC and AT contents should be equal and consistent throughout sequencing. However, bisulfite sequencing (BS-seq) typically results in a lower C content and higher T content. The final sequencing quality criterion included generating approximately 20 Gb of base data per sample, with a read length of 150 bp, and ensuring that at least 90% of the bases had a Q value greater than 20 (indicating an error rate of less than 1%). Sequencing Data Processing To minimize the impact of low-quality reads on the experimental results, the sequencing data were filtered out with Trim Galore v0.4.1. The filtering process included the following steps: 1. Remove reads with overall low quality, particularly those in which fewer than 50% of the bases had a quality score greater than 20. 2. Trimming bases from the 3′ end with a quality score below 20, corresponding to a base error rate greater than 0.01 (where Q = -10 log10 error ratio). 3. Remove adaptor sequences from the reads. 4. Sequencing fragments (reads) shorter than 70 bases and unpaired reads were discarded. Gene alignment The preprocessed reads were aligned to the human reference genome (hg19) using Bismark (v0.15.0; bowtie v2.2.9) [ 14 ]. PCR duplicates were removed with the deduplicate bismark function. Subsequent statistical analyses were conducted to assess read coverage, capture efficiency, and average sequencing depth. Following alignment, the average methylation level for each sample was evaluated. Detection of Methylation Sites and Methylation Levels Methylation data were extracted using Bison v0.4.0 with the bison methylation extractor and bedGraph2methylKit tools[ 15 ], generating text files for direct analysis by methylKit[ 16 ]. The R package methylKit v0.9.5 was used for statistical analysis of sequencing depth and methylation levels across all the genomic methylation sites[ 17 ]. Sample comparisons were performed by way of unsupervised clustering, PCA, and correlation statistics on the basis of the methylation levels of all the CpG sites. Identification of Differentially Methylated Sites and Regions Differentially methylated sites (DMS) were identified with the Dispersion Shrinkage for Sequencing (DMS) tool from the Bioconductor package, with thresholds set at a p value 10%. CpG island (CGI) annotation was obtained by the cpgplot tool from the EMBOSS toolkit. Differentially methylated regions (DMR) were identified by the DMS R package’s call DMR function, with analysis criteria requiring sites to have a p value < 0.05 in the DMS analysis. CGI annotation information was similarly obtained by the EMBOSS toolkit. Functional enrichment analysis Differentially methylated sites and regions were mapped to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database terms, and the number of associated genes was calculated. A hypergeometric test was applied to identify GO/KEGG terms significantly enriched with genes related to differentially methylated sites compared with the entire genomic background. P values were corrected with the Bonferroni method, with a corrected p value (FDR) ≤ 0.05 indicating significant enrichment of differentially expressed genes. Statistical analysis Data analysis was conducted using SPSS 26.0 statistical software. The normality of the measurement data was assessed with the Kolmogorov‒Smirnov test. For normally distributed data, the results are expressed as the means ± standard deviations (x̄± s), and differences between the two groups were evaluated by independent sample t tests. Categorical data were analyzed with the χ² test. Spearman's rank correlation was used to examine correlations between variables. Biological replicates were analyzed through principal component analysis (PCA), cluster analysis, and sample correlation analysis. A P and Q value of less than 0.05 was considered statistically significant. Results DNA and Data Quality Control The average DNA concentration in group N was 3.7900 ± 0.91822, with an average peak length of 303.20 ± 2.781 bp, whereas that in group O was 3.9820 ± 0.98099, with an average peak length of 300.80 ± 10.942 bp. No significant differences were found between the two groups (p > 0.05), confirming their suitability for subsequent methylation analysis. Quality control of the sequencing data revealed that all 20 samples had a Q20 ratio greater than 90% and a clean ratio exceeding 99%. Gene alignment revealed a mapping ratio greater than 80% for all the samples, with a duplication ratio less than 50% (Table 2 , 3 ). Statistical analysis of the mapped reads indicated a capture efficiency exceeding 75%, coverage greater than 98%, and an average sequencing depth of more than 120 across all samples. These findings validate the reliability and suitability of the samples and sequencing data for further analysis (Table 4 ). Table 2 Sequencing data quality control Sample Name Con.(ng/µL) Peak Lenth. (bp) Total Base(Gb) Q20 O1 5.50 313 36.47 97.34 O2 3.96 313 32.03 97.21 O3 4.68 313 35.09 97.36 O4 4.02 313 35.92 97.25 O5 5.18 290 36.26 97.19 O6 2.74 290 28.42 97.15 O7 4.28 290 21.86 96.01 O8 2.68 290 18.41 96.00 O9 3.84 298 19.16 96.26 O10 2.94 298 21.39 95.97 N1 2.82 298 18.29 96.03 N2 3.68 298 19.51 95.97 N3 5.12 304 22.51 95.97 N4 3.44 304 24.36 95.91 N5 5.30 304 21.41 96.27 N6 2.90 304 23.47 96.18 N7 3.38 305 19.89 96.20 N8 4.74 305 21.76 96.19 N9 3.40 305 19.89 96.28 N10 3.12 305 20.25 96.14 Table 3 Statistical analysis of genomic alignment information Sample Name Total Reads Clean Reads Clean Ratio multi mapped reads mapped ratio duplicated reads duplicated ratio O1 116361854 115799390 0.99516625 1339674 0.80674962 47459198 0.411819 O2 130035744 129384645 0.99499292 1597480 0.81911752 52362498 0.406732 O3 142709534 141971101 0.99482562 1677912 0.81194759 48623638 0.344262 O4 150072328 149335135 0.99508775 1821776 0.82258934 65158250 0.438467 O5 156461806 155617301 0.99460249 1851884 0.81658059 57213768 0.369643 O6 132591510 131863663 0.99451061 1595538 0.81388444 53365624 0.406925 O7 145079128 144309330 0.99469394 1731120 0.81630214 53823144 0.374951 O8 162410238 161576415 0.99486595 1885138 0.82120864 64905914 0.403768 O9 132579180 131871865 0.99466496 1627672 0.81871903 47184400 0.359715 O10 135007410 134259732 0.99446195 1583326 0.82419823 52382060 0.392316 N1 243113064 241934720 0.99515310 3189740 0.86702121 106438860 0.442081 N2 213560502 212618273 0.99558800 2846212 0.86392935 90939128 0.429594 N3 233958778 232798441 0.99504042 3032640 0.85860158 108501466 0.468380 N4 239441608 238393093 0.99562100 3250154 0.86617050 116544370 0.491014 N5 241708672 240553086 0.99521910 3144656 0.86342742 107123364 0.447445 N6 189477048 188660736 0.99569176 2574192 0.86247185 93278652 0.496551 N7 145745030 145071541 0.99537899 1836572 0.82573719 57872422 0.400767 N8 122726610 122138410 0.99520723 1498154 0.82233997 53291670 0.438412 N9 127744250 127181116 0.99559171 1544824 0.82629766 52933930 0.418049 N10 142593722 141911644 0.99521663 1710880 0.82496117 65877026 0.466433 Table 4 Capture efficiency statistics Sample Mapped Reads target read ratio target reads All reads capture ratio Coverage Mean depth N1 91632310 115242722 86650202 0.945629353 0.751893052 98.83% 124.96 N2 103855168 128739580 98048814 0.944091814 0.761605825 99.01% 141.83 N3 113001598 141240114 107290702 0.949461812 0.759633357 99.19% 155.93 N4 120418618 148604518 114093355 0.947472716 0.767765049 99.07% 166.41 N5 124539468 154781328 117262327 0.941567592 0.757599954 99.18% 170.29 N6 105140248 131143728 98187425 0.933870966 0.748700883 99.09% 143.63 N7 115446612 143547112 108795701 0.942389726 0.757909368 99.14% 158.06 N8 130124500 160750544 122794304 0.943667826 0.763881110 99.14% 178.66 N9 105764912 131171516 100050652 0.945972063 0.762746784 99.15% 145.84 N10 108463696 133520132 102051487 0.940881519 0.764315354 99.10% 147.94 O1 205560918 240767848 195101329 0.949116840 0.810329662 99.22% 281.21 O2 180035710 211686294 172277532 0.956907560 0.813834135 99.14% 245.31 O3 195864602 231652752 186514745 0.952263671 0.805147979 99.13% 268.17 O4 202338990 237354262 193428078 0.955960480 0.814934084 99.16% 275.41 O5 203569380 239411138 195168121 0.958730242 0.815200674 99.16% 278.21 O6 159443804 187853262 149975347 0.940615711 0.798364348 99.01% 213.04 O7 117403160 144404106 111370911 0.948619364 0.771244766 99.12% 161.1 O8 98462340 121556256 93339606 0.947972656 0.767871676 98.97% 135.25 O9 103082080 126621440 97203476 0.942971620 0.767669962 99.04% 140.83 O10 114803072 141235748 109047657 0.949867065 0.772096714 99.03% 157.34 Sample correlation analysis To minimize the impact of random biological variation on the results, we performed PCA, clustering, and correlation analysis between all samples. I In the PCA analysis, the cluster level was selected as 3, with the first two principal components selected for primary analysis based on the results. The variance explained was 15.86% for PC1 and 6.4% for PC2. In the PCA diagram, samples that are closer together indicate higher similarity. Notably, several samples from both groups, such as O3, O5, N5, and N8, displayed high similarity (Fig. 2 ). Clustering analysis revealed that the samples "O1, O2, N10, N8, N9, O3, N5, O5, N1, O8, O6, N3, N7" formed one group, while "O10, O9, M1, O7, N2, N4, N6" constituted another group. This clustering suggests that some osteoporosis samples grouping with normal samples may not cluster separately. However, when considered alongside the PCA results, there remained a significant difference between the sums of intra-group distances and inter-group distances for osteoporosis and normal samples. This indicates that, substantial differences still exist between the two groups, and the apparent similarities among certain inter-group samples can likely be attributed to the considerable variation within each group (Fig. 3 ). Correlation analysis also show that while some samples from the two groups are resemble, notable discrepancy still exist overall (Fig. 4 ). Average DNA Methylation Level Statistics Statistically significant differences were observed between groups N and O in the number of cytosine methylation events within the CHG and CHH contexts, as well as in the methylation rates in the CpG, CHG, and CHH contexts (Table 5 , 6 , all p < 0.05). In group N, the number of cytosine methylation events in the CHG and CHH contexts was 6,948,060.80 ± 918,560.150 and 16,649,499.20 ± 2,080,533.162, respectively, with average methylation rates in the CpG (Fig. 5 ), CHG, and CHH contexts of 46.84% ± 1.01566%, 2.17% ± 0.04830%, and 2.04% ± 0.06992%, respectively. In group O, the number of cytosine methylation events in the CHG and CHH contexts was 9,064,707.80 ± 2,641,711.069 and 21,586,625.00 ± 6,184,204.460, with average methylation rates in the CpG, CHG, and CHH contexts of 48.99% ± 2.46326%, 2.41% ± 0.08756%, and 2.23% ± 0.06749%, respectively. These results indicate that methylation levels differ between osteoporosis patients and non-osteoporotic individuals, particularly in CpG islands. Previous research has shown that CpG methylation is common in mammals and plants, whereas CHG/CHH methylation is more frequently studied in plants[ 18 ]. Table 5 Methylation levels in the CpG contexts Sample Total C CpG C CpG Methylated C CpG Methylated ratio N1 958040908 107917919 51865305 48.10% N2 1111044838 127618631 58130006 45.50% N3 1407171374 166715728 77565163 46.50% N4 1215024865 141439836 65977928 46.60% N5 1532936995 178896920 86226058 48.20% N6 1177328916 137700187 62360228 45.30% N7 1375816047 158953181 73983550 46.50% N8 1459444132 167285247 78104281 46.70% N9 1332058523 157807944 74205880 47.00% N10 1301224184 149706772 71896654 48.00% O1 2131735290 246384668 126047903 51.20% O2 1793186598 208489004 105589340 50.60% O3 1820792619 204196648 104075557 51.00% O4 1737156455 202060342 100376657 49.70% O5 1937102969 224421484 116883994 52.10% O6 1310886755 150025303 75086052 50.00% O7 1280444668 149801029 70496425 47.10% O8 978216988 112641268 53350186 47.40% O9 1119187777 129391176 58588323 45.30% O10 1041778936 122155901 55580146 45.50% Table 6 Methylation levels in the CHG and CHH contexts Sample CHG C CHG Methylated C CHG Methylated ratio CHH C CHH Methylated C CHH Methylated ratio N1 236510664 5311499 2.20% 613612325 12839096 2.10% N2 273207442 6002882 2.20% 710218765 14573220 2.10% N3 349779996 7811419 2.20% 890675650 18543996 2.10% N4 301618371 6614605 2.20% 771966658 15882789 2.10% N5 380436139 8342061 2.20% 973603936 19761516 2.00% N6 291243699 6207068 2.10% 748385030 14992439 2.00% N7 340102467 7247082 2.10% 876760399 17427880 2.00% N8 360732918 7523946 2.10% 931425967 18046163 1.90% N9 331580697 7449694 2.20% 842669882 17617833 2.10% N10 322699921 6970352 2.20% 828817491 16810060 2.00% O1 529070318 13021759 2.50% 1356280304 30981640 2.30% O2 446208608 11009144 2.50% 1138488986 25996681 2.30% O3 449489827 10875967 2.40% 1167106144 26171866 2.20% O4 431031165 10581646 2.50% 1104064948 25080829 2.30% O5 483227944 11734755 2.40% 1229453541 27577789 2.20% O6 324438950 7547288 2.30% 836422502 18025463 2.20% O7 318678535 7252420 2.30% 811965104 17185278 2.10% O8 240551121 5602604 2.30% 625024599 13580043 2.20% O9 276585632 6650743 2.40% 713210969 16029628 2.20% O10 258242844 6370752 2.50% 661380191 15237033 2.30% Statistics of Differentially Methylated Sites and Regions We identified a total of 38,319 differentially methylated sites (DMS) and 2,484 differentially methylated regions (DMR), with 25,335 sites and 2,060 regions showing upregulated methylation and 12,984 sites and 424 regions showing downregulated methylation. Most differential methylation occurred in CpG islands, affecting 10,532 sites (27.6%) and 1,145 regions (46.4%), particularly concentrated at transcription start sites. DMS were predominantly located in introns (13,649 sites, 35.6%), followed by intergenic regions (9,030 sites, 23.6%), promoter/enhancer regions (8,172 sites, 21.3%), exons (3,867 sites, 10.1%), terminators (2,405 sites, 6.3%), 3' UTR regions (702 sites, 1.8%), and 5' UTR regions (494 sites, 1.3%) (Figure 6A) . Similarly, DMR were mainly found in introns (887 regions, 35.6%), followed by promoter/enhancer regions (618 regions, 24.9%), intergenic regions (460 regions, 18.5%), exons (311 regions, 12.5%), terminators (165 regions, 6.6%), 5' UTR regions (51 regions, 2.1%), and 3' UTR regions (41 regions, 1.7%) (Figure 6B) . The number of DMR decreased as their length increased (Figure 7A) , suggesting that methylation plays a crucial role in gene expression regulation, with CpG islands frequently enriched in CpG islands.( Figure 7B ) GO/KEGG Enrichment Analysis The genes corresponding to significantly differentially methylated sites were mapped to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database entries, and the number of associated genes was calculated. A hypergeometric test was applied to identify GO/KEGG terms significantly enriched with genes related to differentially methylated sites compared with the entire genomic background. A total of 5,705 DMS and 985 DMR were annotated with GO terms, whereas 2,756 DMS and 436 DMR were annotated with KEGG terms. Differential gene screening From the selected gene entries, we identified 382 osteoporosis-related genes from DMS and 68 from DMR in the GO terms, whereas 92 genes from DMS and 166 from DMR were identified in the KEGG terms (all p < 0.05). Among these genes, two common genes, TF (transferrin) and TGFB1 (transforming growth factor-β1), were identified across all four categories (both p < 0.05). These genes are located on chromosomes 3 and 19, respectively. In the DMS category, TF and TGFB1 each had six differentially methylated sites, whereas in the DMR category, each gene had one differentially methylated region, mostly located near transcription start sites and within CpG islands. Further analysis of the GENE CARDS database revealed scores of 40.53 for TF and 76.70 for TGFB1, indicating their extensive involvement in various diseases and biological processes. These findings suggest a strong potential association between these genes and the development of osteoporosis. Discussion The concept of epigenetics, introduced by the British biologist Conrad Hal Waddington in 1942[ 19 ], describes how interactions between genes and their products influence cellular development without altering the DNA sequence itself. Epigenetic modifications affect gene activity by altering DNA and chromatin chemical modifications, primarily through DNA methylation, histone modification, noncoding RNA regulation, and chromatin remodeling. As research has advanced, epigenetics has been implicated in the onset and progression of various diseases, including cancer, neurological disorders, and metabolic diseases. In cancer, for example, hypermethylation of tumor suppressor gene promoters can lead to gene silencing and cancer development, whereas hypomethylation of oncogenes may contribute directly to tumor formation[ 20 , 21 ]. In amyotrophic lateral sclerosis (ALS), a progressive motor neuron disease, differences in the expression of specific miRNAs, such as miR-7-2-3p and miR-26a-1-3p, between healthy individuals and ALS patients have been identified, potentially aiding early diagnosis and timely treatment[ 22 ]. Similarly, the development of metabolic diseases such as obesity and diabetes is believed to result from a complex interplay of genetic and epigenetic factors, environmental influences, and nutrition. Adverse intrauterine environments during fetal development, which are influenced by maternal conditions, can increase susceptibility to metabolic diseases and other chronic conditions in adulthood[ 23 ]. DNA methylation is a key epigenetic modification in which a methyl group (-CH3) is added to the 5th carbon atom of cytosine within DNA, forming 5-methylcytosine (5-mC). This process is catalyzed by DNA methyltransferases (DNMT), primarily at CpG dinucleotides[ 24 ]. DNMT1 maintains methylation patterns during DNA replication, transferring methylation marks from the parent strand to the daughter strand. DNMT3A and DNMT3B establish new methylation patterns during development and differentiation, whereas DNMT3L facilitates DNMT3A and DNMT3B but lacks catalytic activity itself[ 25 ]. DNA methylation is generally associated with gene silencing, particularly when promoter regions are highly methylated[ 26 ], which suppresses transcription. During development, specific methylation patterns change in response to gene expression needs, and methylation helps suppress transposons and repetitive sequences, maintaining genomic stability. Conversely, DNA demethylation, which removes methyl groups from DNA, can occur passively during DNA replication or actively through TET enzymes, which oxidize 5-mC to 5-hydroxymethylcytosine and eventually restore it to unmethylated cytosine through a multistep reaction. Osteoporosis development is characterized by an imbalance in bone remodeling, with decreased bone formation and increased bone resorption. Factors such as genetics, the environment, hormonal changes, nutrition, and physical activity influence this balance[ 27 ]. DNA methylation also plays a critical role in osteoporosis by regulating the expression of genes related to bone metabolism. The methylation status of specific genes can alter osteoblast and osteoclast function, impacting osteoporosis development. For instance[ 28 ], hypermethylation of the RUNX2 gene, which is associated with osteoblast differentiation, can inhibit osteoblast function and reduce bone formation. Conversely, hypomethylation of the RANKL gene, which is linked to osteoclast differentiation, can increase osteoclast activity and increase bone resorption. Inflammatory cytokines such as IL-6 and TNF-α can also influence osteoblast and osteoclast function by modifying the DNA methylation status[ 29 ], thereby exacerbating osteoporosis progression. Environmental and lifestyle factors, such as diet, exercise, smoking, and alcohol consumption, may indirectly influence osteoporosis by affecting DNA methylation. For example[ 30 ], insufficient nutrients such as calcium and vitamin D in the diet can impact bone metabolism by altering the methylation status of related genes. The VDR gene, encoding the vitamin D receptor, plays a crucial role in calcium absorption and bone health. Studies have shown that promoter methylation of the VDR gene is significantly associated with osteoporosis development[ 31 ], with higher promoter methylation levels in osteoporosis patients leading to decreased VDR gene expression, subsequently affecting calcium absorption and bone density. Recent studies have also implicated mitochondrial DNA (mtDNA) methylation in osteoporosis[ 32 ]. Although mtDNA methylation levels are generally low, its role in energy metabolism and oxidative stress is significant. Elevated mtDNA methylation levels have been observed in osteoporosis patients, potentially affecting mitochondrial function, which in turn influences energy metabolism and oxidative stress responses in bone cells, thereby exacerbating osteoporosis progression. Our results suggest that DNA methylation is involved in various biological processes, cellular functions, and disease progression. Although this study cannot directly establish a causal relationship between DNA methylation and osteoporosis, evidence points to a significant association between the two. We observed differences in the methylation levels and expression of methylation-related genes between the osteoporosis and non-osteoporosis groups. Enrichment analysis further revealed differential methylation of several genes associated with osteoporosis, indicating that altered methylation levels of certain genes may be associated with this disease. Notably, we identified two genes, TF and TGFB1, which are potentially related to osteoporosis development. Both genes are closely associated with bone development and were found to have DMS and DMR by means of both GO and KEGG enrichment analyses. Our subsequent experiments will validate these two models in animal models to assess whether the methylation levels of these two genes are associated with the development of osteoporosis and explore their potential role. Transferrin[ 33 ], a plasma protein that is involved primarily in iron ion transport, plays a crucial role in iron metabolism and storage, which is vital for bone formation. Iron is involved in the growth and differentiation of bone cells, and studies suggest that transferrin may influence bone remodeling by affecting the balance between osteoclasts and osteoblasts. TGFB1 is a multifunctional cytokine involved in cell growth[ 34 ], differentiation, apoptosis, and immune regulation. TGF-β promotes osteoblast proliferation and differentiation, enhancing bone matrix production. Low concentrations of TGFB1 inhibit osteoclast formation, whereas high concentrations can increase osteoclast activity by stimulating osteoclast precursor cell proliferation. Research indicates that regulating the TGF-β signaling pathway can affect bone mass and strength, with serum TGF-β levels in osteoporosis patients potentially being correlated with bone density and fracture risk. Conclusion This study utilized whole-genome DNA methylation sequencing to identify differences in genomic methylation levels between osteoporosis (OP) patients and healthy individuals. Compared with the non-osteoporosis group, the OP group presented higher methylation levels and a greater number of methylated genes. Enrichment analysis highlighted two key genes, TF and TGFB1, which may contribute to osteoporosis development through distinct pathways. However, owing to the limited sample size and the large number of genes analyzed, there may be biases in identifying common differential genes, and further validation of these findings is needed. Experiments are also needed to test their hypothesis based on the methylome datasets. Nevertheless, we believe that ongoing research will continue to clarify the relationship between DNA methylation and osteoporosis, offering new approaches for clinical diagnosis and treatment. Abbreviations EWAS Epigenome-wide association studies DMS Differentially methylated sites DMR Differentially methylated regions WGBS Whole-genome bisulfite sequencing BMD Bone mineral density DXA Dual-energy X-ray absorptiometry BMI Body mass index CGI CpG island GO Kyoto Encyclopedia of Genes and Genomes KEGG Kyoto Encyclopedia of Genes and Genomes FDR False discovery rate PCA Principal component analysis TF Transferrin TGFB1 Transforming growth factor-β1 mtDNA Mitochondrial DNA DNMT Mitochondrial DNA Declarations Ethics approval All experimental procedures, including human care and tissue sample collection, were approved by and conducted in accordance with the guidelines of the Medical Ethics Committee of the Third Affiliated Hospital of Guangzhou University of Chinese Medicine, China (Approval ID: PJ-XS-20240531-009). Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This work was financially supported by the National Natural Science Foundation of China (NSFC-82374482) and the Guangdong Provincial Administration of Traditional Chinese Medicine (GPATCM-202205030934309730). The funding bodies had no role in the design of the study, the collection, analysis, and interpretation of data, or in writing the manuscript. Author Contribution YYZ and YLZ conducted the experiments and data analysis. YL designed and supervised the experiment and assisted with manuscript revisions. CML provided technical support. YKZ and SSX coordinated the research. QHL and HWG performed the surgeries and provided the samples. YYZ drafted the manuscript. All authors read and approved the final version of the manuscript. Acknowledgement We sincerely thank all members of Ying Li's group for their contributions to sample collection and technical assistance. All images were created by the author team. Data Availability Sequence data that support the findings of this study have been deposited in the NCBI with the primary accession code PRJNA1154659 References Johnston CB, Dagar M: Osteoporosis in Older Adults. The Medical clinics of North America 2020, 104(5):873–884. Ebeling PR, Nguyen HH, Aleksova J, Vincent AJ, Wong P, Milat F: Secondary Osteoporosis. Endocrine reviews 2022, 43(2):240–313. Xu Z, Yu Z, Chen M, Zhang M, Chen R, Yu H, Lin Y, Wang D, Li S, Huang L et al : Mechanisms of estrogen deficiency-induced osteoporosis based on transcriptome and DNA methylation. Frontiers in cell and developmental biology 2022, 10:1011725. Moore LD, Le T, Fan G: DNA methylation and its basic function. Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology 2013, 38(1):23–38. 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Science (New York, NY) 2022, 378(6623):983–989. Wang P, Cao Y, Zhan D, Wang D, Wang B, Liu Y, Li G, He W, Wang H, Xu L: Influence of DNA methylation on the expression of OPG/RANKL in primary osteoporosis. International journal of medical sciences 2018, 15(13):1480–1485. Galbraith K, Snuderl M: DNA methylation as a diagnostic tool. Acta neuropathologica communications 2022, 10(1):71. Zhang L, Zheng YL, Wang R, Wang XQ, Zhang H: Exercise for osteoporosis: A literature review of pathology and mechanism. Frontiers in immunology 2022, 13:1005665. Łoboś P, Regulska-Ilow B: Link between methyl nutrients and the DNA methylation process in the course of selected diseases in adults. Roczniki Panstwowego Zakladu Higieny 2021, 72(2):123–136. Wang B, Li H, Yang C, Nie R, Zhang X, Pu C: VDR gene ApaI polymorphism and risk of postmenopausal osteoporosis: a meta-analysis from 22 studies. Climacteric: the journal of the International Menopause Society 2023, 26(6):583–593. Liu J, Gao Z, Liu X: Mitochondrial dysfunction and therapeutic perspectives in osteoporosis. Frontiers in endocrinology 2024, 15:1325317. Geiser DL, Winzerling JJ: Insect transferrins: multifunctional proteins. Biochimica et biophysica acta 2012, 1820(3):437–451. Hansamuit K, Osathanon T, Suwanwela J: Effect of Jagged1 on the expression of genes in regulation of osteoblast differentiation and bone mineralization ontology in human dental pulp and periodontal ligament cells. Journal of oral biology and craniofacial research 2020, 10(2):233–237. Additional Declarations No competing interests reported. Supplementary Files TreatvsNormalCpGdiffsite.xlsx TreatvsNormalCpGdiffsitebedanno.xlsx TreatvsNormalCpGdifftile.xlsx TreatvsNormalCpGdifftileanno.xlsx allsample.CpG.txt Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5397269","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":380635215,"identity":"7aa7d322-2fa2-49f3-98c1-0d5525f67c72","order_by":0,"name":"Yinyin Zhang","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yinyin","middleName":"","lastName":"Zhang","suffix":""},{"id":380635216,"identity":"94da4663-24eb-406e-8621-1a67061f9b5b","order_by":1,"name":"Yeling Zhong","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yeling","middleName":"","lastName":"Zhong","suffix":""},{"id":380635217,"identity":"87e53baf-16dc-4130-8c73-ef2eb329f5ac","order_by":2,"name":"Chunmei Li","email":"","orcid":"","institution":"The Third Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chunmei","middleName":"","lastName":"Li","suffix":""},{"id":380635218,"identity":"2ec4d72c-32a9-43dd-b8b2-77b9841ed271","order_by":3,"name":"Yukai Zhang","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yukai","middleName":"","lastName":"Zhang","suffix":""},{"id":380635219,"identity":"62286d8c-1423-464c-a810-1b5d2f439606","order_by":4,"name":"Shishuo Xiong","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shishuo","middleName":"","lastName":"Xiong","suffix":""},{"id":380635220,"identity":"094f48c3-dd2c-4fd5-8c3b-9c2c811e4e07","order_by":5,"name":"Qihuo Li","email":"","orcid":"","institution":"The Third Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qihuo","middleName":"","lastName":"Li","suffix":""},{"id":380635221,"identity":"d8ed39f6-8b18-4b5b-871a-9a5ab07e9fb5","order_by":6,"name":"Haiwei Guo","email":"","orcid":"","institution":"The Third Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haiwei","middleName":"","lastName":"Guo","suffix":""},{"id":380635222,"identity":"62f85485-689a-4049-9dac-e785b3f9e885","order_by":7,"name":"Ying Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACPhCRwGDBwM/MfPgBUVrYIFokGCTb2dIMiNfCANRicJ5HQYI4LezNxyQe7pBI3HyYh8GAocYmmrAWnmNpEolnJBK3HeY98IDhWFpuA0EtEjlmNxLbQFr4EgwYGw4ToUX+/Tewls3NPAYSxGmR4GEDa9nATLQWnjTzH0AtxjMOAwM5gRi/8LMffmz4s81Gtr//8OEHH2psCGuBAUewygRilYOAPSmKR8EoGAWjYIQBABypO14ETpjrAAAAAElFTkSuQmCC","orcid":"","institution":"The Third Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-11-05 17:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5397269/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5397269/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70405515,"identity":"9e04607c-720a-406b-a919-ee7ae15dbf54","added_by":"auto","created_at":"2024-12-02 23:13:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114813,"visible":true,"origin":"","legend":"\u003cp\u003eWhole-genome bisulfite sequencing analysis flowchart\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/a30a651dc56255841afc624a.png"},{"id":70405516,"identity":"4a44bf32-809b-4527-8679-7fecbc80b092","added_by":"auto","created_at":"2024-12-02 23:13:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":144273,"visible":true,"origin":"","legend":"\u003cp\u003ePCA diagram of each sample. Component analysis (PCA) was conducted on the basis of the methylation levels of all the CpG sites in each sample. The distance between points (samples) in the graph reflects their similarity or dissimilarity.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/06ee44502e9e7e0730668a07.jpg"},{"id":70405973,"identity":"4291dd0d-75c5-4ece-b693-34f1c04dee12","added_by":"auto","created_at":"2024-12-02 23:21:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163291,"visible":true,"origin":"","legend":"\u003cp\u003eSample clustering diagram. Unsupervised cluster analysis was conducted on the basis of the methylation levels of all the CpG sites in each sample, and similar samples were grouped into the same cluster.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/ac6a3c64d7cc237283a22361.jpg"},{"id":70405519,"identity":"cbe459fd-5ecb-497b-afa9-3d84923d5cc8","added_by":"auto","created_at":"2024-12-02 23:13:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":299397,"visible":true,"origin":"","legend":"\u003cp\u003eCluster diagram of each sample. Correlation analysis was performed on the basis of the methylation levels of all the CpG sites in each sample. The lower left section shows the scatter heatmap of CpG site methylation between pairs of samples, whereas the upper right section displays the correlation coefficients between the corresponding samples.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/c3db8f00459c06cdac46b956.png"},{"id":70406325,"identity":"285036a5-a104-4a14-86ff-2146889b46ba","added_by":"auto","created_at":"2024-12-02 23:29:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68857,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall methylation levels of the samples in the CpG context are presented. In the figure above, the horizontal axis represents the sample names, and the vertical axis represents the sample beta values (sorted from smallest to largest). The rectangular box indicates the interquartile range (IQR), with the upper and lower quartiles (Q1 and Q3) represented by the edges. The line within the box marks the median, whereas the whiskers extend to Q1 - 1.5IQR and Q3 + 1.5IQR, where IQR = Q3 - Q1.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/267902d9b090ae2c79f02969.png"},{"id":70405527,"identity":"cd6f1e1f-6937-4e6c-8b5b-9340dade5b63","added_by":"auto","created_at":"2024-12-02 23:13:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":193373,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of differentially methylated sites and regions across different gene elements.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/b957de19f3bcd69149521efb.png"},{"id":70405976,"identity":"7ba40563-4c2e-4864-9fcc-09f19e60e7fe","added_by":"auto","created_at":"2024-12-02 23:21:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":44068,"visible":true,"origin":"","legend":"\u003cp\u003eThe statistical distribution of the relative positions of DMR from CpG islands and the length distribution of DMR. The horizontal axis represents the length of the DMR and its upstream and downstream positions relative to the CpG island, while the vertical axis indicates the frequency of DMR occurrence at specific lengths and the frequency of differential methylation segments at corresponding locations.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/24f9326a1d0acc233adbd8a0.png"},{"id":70405521,"identity":"16dea7af-f3b8-4033-94e7-87b52fba9142","added_by":"auto","created_at":"2024-12-02 23:13:18","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":357360,"visible":true,"origin":"","legend":"\u003cp\u003eTop 30 enrichment maps of GO and KEGG genes in DMS (A and B) and DMR (C and D). In the figure, a higher Rich factor value indicates more significant enrichment. Only the top 30 terms ranked by Rich factor are displayed for each project\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/bf662f3f4ed27acd7dfc138f.png"},{"id":73429818,"identity":"f4d09531-d7d9-4ccc-acc3-89fc19197df0","added_by":"auto","created_at":"2025-01-09 23:16:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2833684,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/a89a2f4e-57d6-4b04-bb25-8c22b44ed10d.pdf"},{"id":70405977,"identity":"97c3ab9c-8331-43b2-81cd-e01c124dcc36","added_by":"auto","created_at":"2024-12-02 23:21:19","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5549674,"visible":true,"origin":"","legend":"","description":"","filename":"TreatvsNormalCpGdiffsite.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/0c56805571cfdfc9bfdd313a.xlsx"},{"id":70405524,"identity":"0ed47f13-9472-4b0e-b88a-fb6f926521cb","added_by":"auto","created_at":"2024-12-02 23:13:18","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8180836,"visible":true,"origin":"","legend":"","description":"","filename":"TreatvsNormalCpGdiffsitebedanno.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/42d341e4f404ae7bfb00ce05.xlsx"},{"id":70405975,"identity":"5ca8a64e-ed06-49ae-b635-edbab6e5c6e0","added_by":"auto","created_at":"2024-12-02 23:21:18","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":240632,"visible":true,"origin":"","legend":"","description":"","filename":"TreatvsNormalCpGdifftile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/91dbbe6140eacf4d611cb8e3.xlsx"},{"id":70405525,"identity":"7e5fa5a0-cec3-4879-8c30-8467da0c1390","added_by":"auto","created_at":"2024-12-02 23:13:19","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":475245,"visible":true,"origin":"","legend":"","description":"","filename":"TreatvsNormalCpGdifftileanno.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/d8058779edc315665bd3e9eb.xlsx"},{"id":70405529,"identity":"dde292d3-ebc6-432d-8596-71b6926c795a","added_by":"auto","created_at":"2024-12-02 23:13:20","extension":"txt","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":100021946,"visible":true,"origin":"","legend":"","description":"","filename":"allsample.CpG.txt","url":"https://assets-eu.researchsquare.com/files/rs-5397269/v1/f4aa973850fca7c2ebc00ea7.txt"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring DNA methylation profiles in the pathogenesis of human osteoporosis via whole-genome bisulfite sequencing","fulltext":[{"header":"Introductions","content":"\u003cp\u003eOsteoporosis is a widespread metabolic bone disorder characterized by reduced bone mass and deterioration of bone tissue, resulting in an increased risk of fractures. Currently, osteoporosis affects approximately 200\u0026nbsp;million people worldwide, with one-third of women over the age of 50 and one-fifth of men at risk of osteoporotic fractures. The incidence of osteoporosis is increasing annually due to the aging population and lifestyle changes, making it a significant public health challenge with high morbidity, mortality, and associated healthcare costs[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Hip fractures, in particular, are a major concern, with a mortality rate of 20\u0026ndash;24% within the first year and a substantial risk of long-term disability. Approximately 33% of hip fracture patients become fully dependent or require institutional care within a year after the fracture[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite extensive research, the pathogenesis of osteoporosis is complex and not fully understood and involves genetic, environmental, and hormonal factors. Recent studies have increasingly focused on the role of epigenetic mechanisms, particularly DNA methylation, in the development and progression of osteoporosis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. DNA methylation is a critical epigenetic modification in which a methyl group is added to the 5' position of the cytosine residue in CpG dinucleotides, influencing gene expression without altering the DNA sequence. This modification is crucial for regulating various biological processes, including embryonic development, X chromosome inactivation, gene imprinting, and transposon suppression[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAbnormal DNA methylation patterns have been linked to numerous diseases, including cancer, neurological disorders, and metabolic conditions such as osteoporosis[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Studies indicate that changes in DNA methylation can impact the expression of genes involved in bone formation and resorption, contributing to osteoporosis and an increased fracture risk[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For example, hypermethylation of bone-forming genes can reduce their expression, impair osteoblast function, and hinder bone formation, whereas hypomethylation of bone resorption-related genes can increase osteoclast activity and accelerate bone loss. Thus, DNA methylation plays a vital role in regulating genes crucial for bone stability, and the aberrant methylation patterns observed in osteoporosis underscore the importance of epigenetic modifications in disease pathogenesis.\u003c/p\u003e \u003cp\u003eMoreover, DNA methylation profiles could serve as potential biomarkers for the early diagnosis and prognosis of osteoporosis. Epigenome-wide association studies (EWAS) have identified specific differentially methylated sites (DMS) and regions (DMR) associated with bone mineral density (BMD) and fracture risk, which are closely linked to osteoporosis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These findings suggest that DNA methylation markers could help identify high-risk populations and enable personalized treatment strategies. Additionally, the DNA methylation pattern offers novel therapeutic avenues, such as the use of epigenetic drugs such as DNA methyltransferase inhibitors, which can restore normal bone metabolism by modulating gene expression and have shown promising therapeutic effects in osteoporosis patients[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhole-genome bisulfite sequencing (WGBS) is an advanced high-throughput technology used for comprehensive analysis of DNA methylation across the entire genome. By combining bisulfite conversion with next-generation sequencing, WGBS enables the detection of methylation states at single-base resolution[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. During bisulfite treatment, unmethylated cytosines are converted to uracil, whereas methylated cytosines remain unchanged. Subsequent PCR amplification converts uracil into thymine, and high-throughput sequencing of the PCR products, compared with the reference sequence, allows for the precise determination of methylation at the CpG, CHG, and CHH sites[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to investigate differences in DNA methylation levels between individuals with and without osteoporosis through whole-genome DNA methylation analysis. By comparing differential methylation sites and regions, we sought to identify genes closely associated with the development of osteoporosis and further explore the relationship between DNA methylation and this disease.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Subjects and Sample Collection\u003c/h2\u003e \u003cp\u003eThis study included 20 subjects aged 50\u0026ndash;75 years, comprising 10 patients with osteoporosis and 10 without osteoporosis. The participants were divided into two groups: the non-osteoporosis group (N group, n\u0026thinsp;=\u0026thinsp;10) and the osteoporosis group (O group, n\u0026thinsp;=\u0026thinsp;10). The bone mineral density (BMD) of all the subjects was measured by a dual-energy X-ray absorptiometry (DXA) system (Hologic Discovery, USA). Informed consent was obtained from all participants prior to the study. Age and body mass index (BMI) were controlled to ensure that there were no significant differences between the groups. Bone tissue samples were collected during lumbar spine surgery, and extraneous tissues were removed before the samples were stored in liquid nitrogen at -80\u0026deg;C for DNA methylation analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample DNA Extraction\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eSample DNA Extraction\u003c/div\u003e \u003cp\u003eThe bone samples were first cleaned with deionized water and detergent to remove impurities and contaminants. Sterile tools were then used to excise a thin surface layer, ensuring a purer internal sample. After freezing, the bone samples were ground into a fine powder in liquid nitrogen. The powder was immersed in EDTA (ethylenediaminetetraacetic acid) solution to decalcify the bone, softening the tissue and releasing DNA. The decalcified samples were lysed in lysis buffer, and the DNA was purified using a DNA extraction kit for subsequent sequencing.\u003c/p\u003e\n\u003ch3\u003eLibrary construction and sequencing\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was fragmented according to the experimental protocol, followed by end repair, 3\u0026rsquo; end A-tailing, adaptor ligation, hybrid capture, bisulfite conversion, and enrichment to construct sequencing libraries. The library concentration was measured by a Qubit\u0026reg; 2.0 fluorometer, and the library size was assessed with an Agilent 2100 Bioanalyzer. Libraries were pooled and quality-checked through an Agilent 4200 Bioanalyzer. Cluster generation and initial sequencing primer hybridization were performed on the cBot using the Illumina NovaSeq 6000 sequencer, following the procedures outlined in the cBot User Guide. The sequencing reagents were prepared according to the Illumina User Guide, and the flow cell containing the clusters was loaded onto the sequencer. Paired-end sequencing was conducted with real-time monitoring by Illumina's data collection software, which also handled on-the-fly data analysis. (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTabel 1\u003c/b\u003e Reagents for library construction and quality inspection\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProduct/Reagent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManufacturer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCatalog No.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSureSelectXT Methyl Reagent kit, HSQ,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgilent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG9651A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEZ-DNA Methylation-Gold Kit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZymo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD5006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSureSelectXT Human Methyl-Seq Capture Library\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgilent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5190\u0026thinsp;\u0026minus;\u0026thinsp;4661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExKubit dsDNA HS Analysis Kit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNGS00-3012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgilent High Sensitivity DNA Kit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgilent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5067\u0026thinsp;\u0026minus;\u0026thinsp;4626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSequencing Data Evaluation\u003c/h3\u003e\n\u003cp\u003eSequencing read quality was evaluated by a Q value box plot, where the relationship between the Q value and sequencing error rate (E value) was defined as Q = -10Log10 E. We also analyzed the base composition of the sequences. Theoretically, on the basis of complementary base pairing, the GC and AT contents should be equal and consistent throughout sequencing. However, bisulfite sequencing (BS-seq) typically results in a lower C content and higher T content. The final sequencing quality criterion included generating approximately 20 Gb of base data per sample, with a read length of 150 bp, and ensuring that at least 90% of the bases had a Q value greater than 20 (indicating an error rate of less than 1%).\u003c/p\u003e\n\u003ch3\u003eSequencing Data Processing\u003c/h3\u003e\n\u003cp\u003eTo minimize the impact of low-quality reads on the experimental results, the sequencing data were filtered out with Trim Galore v0.4.1. The filtering process included the following steps: 1. Remove reads with overall low quality, particularly those in which fewer than 50% of the bases had a quality score greater than 20. 2. Trimming bases from the 3\u0026prime; end with a quality score below 20, corresponding to a base error rate greater than 0.01 (where Q = -10 log10 error ratio). 3. Remove adaptor sequences from the reads. 4. Sequencing fragments (reads) shorter than 70 bases and unpaired reads were discarded.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene alignment\u003c/h2\u003e \u003cp\u003eThe preprocessed reads were aligned to the human reference genome (hg19) using Bismark (v0.15.0; bowtie v2.2.9) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. PCR duplicates were removed with the deduplicate bismark function. Subsequent statistical analyses were conducted to assess read coverage, capture efficiency, and average sequencing depth. Following alignment, the average methylation level for each sample was evaluated.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDetection of Methylation Sites and Methylation Levels\u003c/h3\u003e\n\u003cp\u003eMethylation data were extracted using Bison v0.4.0 with the bison methylation extractor and bedGraph2methylKit tools[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], generating text files for direct analysis by methylKit[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The R package methylKit v0.9.5 was used for statistical analysis of sequencing depth and methylation levels across all the genomic methylation sites[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Sample comparisons were performed by way of unsupervised clustering, PCA, and correlation statistics on the basis of the methylation levels of all the CpG sites.\u003c/p\u003e\n\u003ch3\u003eIdentification of Differentially Methylated Sites and Regions\u003c/h3\u003e\n\u003cp\u003eDifferentially methylated sites (DMS) were identified with the Dispersion Shrinkage for Sequencing (DMS) tool from the Bioconductor package, with thresholds set at a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a methylation difference\u0026thinsp;\u0026gt;\u0026thinsp;10%. CpG island (CGI) annotation was obtained by the cpgplot tool from the EMBOSS toolkit. Differentially methylated regions (DMR) were identified by the DMS R package\u0026rsquo;s call DMR function, with analysis criteria requiring sites to have a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the DMS analysis. CGI annotation information was similarly obtained by the EMBOSS toolkit.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eDifferentially methylated sites and regions were mapped to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database terms, and the number of associated genes was calculated. A hypergeometric test was applied to identify GO/KEGG terms significantly enriched with genes related to differentially methylated sites compared with the entire genomic background. P values were corrected with the Bonferroni method, with a corrected p value (FDR)\u0026thinsp;\u0026le;\u0026thinsp;0.05 indicating significant enrichment of differentially expressed genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis was conducted using SPSS 26.0 statistical software. The normality of the measurement data was assessed with the Kolmogorov‒Smirnov test. For normally distributed data, the results are expressed as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (x̄\u0026plusmn; s), and differences between the two groups were evaluated by independent sample t tests. Categorical data were analyzed with the χ\u0026sup2; test. Spearman's rank correlation was used to examine correlations between variables. Biological replicates were analyzed through principal component analysis (PCA), cluster analysis, and sample correlation analysis. A P and Q value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDNA and Data Quality Control\u003c/h2\u003e \u003cp\u003eThe average DNA concentration in group N was 3.7900\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91822, with an average peak length of 303.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.781 bp, whereas that in group O was 3.9820\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98099, with an average peak length of 300.80\u0026thinsp;\u0026plusmn;\u0026thinsp;10.942 bp. No significant differences were found between the two groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), confirming their suitability for subsequent methylation analysis. Quality control of the sequencing data revealed that all 20 samples had a Q20 ratio greater than 90% and a clean ratio exceeding 99%. Gene alignment revealed a mapping ratio greater than 80% for all the samples, with a duplication ratio less than 50% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e,\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Statistical analysis of the mapped reads indicated a capture efficiency exceeding 75%, coverage greater than 98%, and an average sequencing depth of more than 120 across all samples. These findings validate the reliability and suitability of the samples and sequencing data for further analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSequencing data quality control\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=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCon.(ng/\u0026micro;L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeak Lenth. (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Base(Gb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ20\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.14\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical analysis of genomic alignment information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClean Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClean Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emulti mapped reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003emapped ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eduplicated reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eduplicated ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116361854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115799390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99516625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1339674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.80674962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47459198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.411819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130035744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129384645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99499292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1597480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81911752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52362498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.406732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142709534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141971101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99482562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1677912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81194759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e48623638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.344262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150072328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149335135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99508775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1821776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82258934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65158250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.438467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156461806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155617301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99460249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1851884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81658059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57213768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.369643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132591510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131863663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99451061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1595538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81388444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53365624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.406925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145079128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144309330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99469394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1731120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81630214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53823144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.374951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e162410238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161576415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99486595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1885138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82120864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64905914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.403768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132579180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131871865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99466496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1627672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81871903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47184400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.359715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135007410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134259732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99446195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1583326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82419823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52382060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.392316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e243113064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e241934720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99515310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3189740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86702121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e106438860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.442081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e213560502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212618273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99558800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2846212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86392935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90939128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.429594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e233958778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e232798441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99504042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3032640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85860158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e108501466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.468380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e239441608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e238393093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99562100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3250154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86617050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e116544370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.491014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e241708672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240553086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99521910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3144656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86342742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e107123364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.447445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e189477048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188660736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99569176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2574192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86247185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93278652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.496551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145745030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e145071541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99537899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1836572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82573719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57872422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.400767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122726610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122138410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99520723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1498154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82233997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53291670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.438412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e127744250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127181116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99559171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1544824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82629766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52933930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.418049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142593722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141911644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99521663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1710880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82496117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65877026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.466433\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCapture efficiency statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMapped Reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003etarget read ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003etarget reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAll reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecapture ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean depth\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91632310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115242722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86650202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.945629353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.751893052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e124.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103855168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128739580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98048814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.944091814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.761605825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e141.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e113001598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141240114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107290702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.949461812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.759633357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e155.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120418618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148604518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114093355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.947472716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.767765049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e166.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124539468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154781328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117262327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.941567592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.757599954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e170.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105140248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131143728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98187425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.933870966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.748700883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e143.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115446612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143547112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108795701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.942389726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.757909368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e158.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130124500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160750544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122794304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.943667826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.763881110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e178.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105764912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131171516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100050652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.945972063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.762746784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e145.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108463696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133520132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102051487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.940881519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.764315354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e147.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e205560918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240767848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195101329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.949116840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.810329662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e281.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180035710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e211686294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e172277532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.956907560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.813834135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e245.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e195864602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e231652752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186514745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.952263671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.805147979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e268.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e202338990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e237354262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193428078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.955960480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.814934084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e275.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e203569380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e239411138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195168121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.958730242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.815200674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e278.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159443804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187853262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149975347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.940615711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.798364348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e213.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117403160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144404106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111370911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.948619364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.771244766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e161.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98462340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121556256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93339606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.947972656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.767871676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e135.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103082080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126621440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97203476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.942971620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.767669962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e140.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114803072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141235748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109047657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.949867065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.772096714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e157.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSample correlation analysis\u003c/h2\u003e \u003cp\u003eTo minimize the impact of random biological variation on the results, we performed PCA, clustering, and correlation analysis between all samples. I In the PCA analysis, the cluster level was selected as 3, with the first two principal components selected for primary analysis based on the results. The variance explained was 15.86% for PC1 and 6.4% for PC2. In the PCA diagram, samples that are closer together indicate higher similarity. Notably, several samples from both groups, such as O3, O5, N5, and N8, displayed high similarity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClustering analysis revealed that the samples \"O1, O2, N10, N8, N9, O3, N5, O5, N1, O8, O6, N3, N7\" formed one group, while \"O10, O9, M1, O7, N2, N4, N6\" constituted another group. This clustering suggests that some osteoporosis samples grouping with normal samples may not cluster separately. However, when considered alongside the PCA results, there remained a significant difference between the sums of intra-group distances and inter-group distances for osteoporosis and normal samples. This indicates that, substantial differences still exist between the two groups, and the apparent similarities among certain inter-group samples can likely be attributed to the considerable variation within each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Correlation analysis also show that while some samples from the two groups are resemble, notable discrepancy still exist overall (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAverage DNA Methylation Level Statistics\u003c/h2\u003e \u003cp\u003eStatistically significant differences were observed between groups N and O in the number of cytosine methylation events within the CHG and CHH contexts, as well as in the methylation rates in the CpG, CHG, and CHH contexts (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e,\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In group N, the number of cytosine methylation events in the CHG and CHH contexts was 6,948,060.80\u0026thinsp;\u0026plusmn;\u0026thinsp;918,560.150 and 16,649,499.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2,080,533.162, respectively, with average methylation rates in the CpG (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), CHG, and CHH contexts of 46.84% \u0026plusmn; 1.01566%, 2.17% \u0026plusmn; 0.04830%, and 2.04% \u0026plusmn; 0.06992%, respectively. In group O, the number of cytosine methylation events in the CHG and CHH contexts was 9,064,707.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2,641,711.069 and 21,586,625.00\u0026thinsp;\u0026plusmn;\u0026thinsp;6,184,204.460, with average methylation rates in the CpG, CHG, and CHH contexts of 48.99% \u0026plusmn; 2.46326%, 2.41% \u0026plusmn; 0.08756%, and 2.23% \u0026plusmn; 0.06749%, respectively. These results indicate that methylation levels differ between osteoporosis patients and non-osteoporotic individuals, particularly in CpG islands. Previous research has shown that CpG methylation is common in mammals and plants, whereas CHG/CHH methylation is more frequently studied in plants[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMethylation levels in the CpG contexts\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=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCpG C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCpG Methylated C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCpG Methylated ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e958040908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107917919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51865305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1111044838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127618631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58130006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1407171374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166715728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77565163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1215024865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141439836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65977928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1532936995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178896920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86226058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1177328916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137700187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62360228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1375816047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158953181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73983550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1459444132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167285247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78104281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1332058523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157807944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74205880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1301224184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149706772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71896654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2131735290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e246384668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e126047903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1793186598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e208489004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105589340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1820792619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e204196648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104075557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1737156455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e202060342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100376657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1937102969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e224421484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116883994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1310886755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150025303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75086052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1280444668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149801029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70496425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e978216988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112641268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53350186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1119187777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129391176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58588323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1041778936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122155901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55580146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.50%\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMethylation levels in the CHG and CHH contexts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHG C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHG Methylated C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCHG Methylated ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCHH C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCHH Methylated C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCHH Methylated ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e236510664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5311499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e613612325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12839096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e273207442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6002882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e710218765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14573220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e349779996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7811419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e890675650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18543996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e301618371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6614605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e771966658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15882789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380436139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8342061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e973603936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19761516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e291243699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6207068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e748385030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14992439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e340102467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7247082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e876760399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17427880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e360732918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7523946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e931425967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18046163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e331580697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7449694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e842669882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17617833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e322699921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6970352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e828817491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16810060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e529070318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13021759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1356280304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30981640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e446208608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11009144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1138488986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25996681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e449489827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10875967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1167106144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26171866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e431031165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10581646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1104064948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25080829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e483227944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11734755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1229453541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27577789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e324438950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7547288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e836422502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18025463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e318678535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7252420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e811965104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17185278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e240551121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5602604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e625024599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13580043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e276585632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6650743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e713210969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16029628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258242844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6370752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e661380191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15237033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.30%\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 \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistics of Differentially Methylated Sites and Regions\u003c/h2\u003e \u003cp\u003eWe identified a total of 38,319 differentially methylated sites (DMS) and 2,484 differentially methylated regions (DMR), with 25,335 sites and 2,060 regions showing upregulated methylation and 12,984 sites and 424 regions showing downregulated methylation. Most differential methylation occurred in CpG islands, affecting 10,532 sites (27.6%) and 1,145 regions (46.4%), particularly concentrated at transcription start sites. DMS were predominantly located in introns (13,649 sites, 35.6%), followed by intergenic regions (9,030 sites, 23.6%), promoter/enhancer regions (8,172 sites, 21.3%), exons (3,867 sites, 10.1%), terminators (2,405 sites, 6.3%), 3\u0026apos; UTR regions (702 sites, 1.8%), and 5\u0026apos; UTR regions (494 sites, 1.3%) \u003cstrong\u003e(Figure 6A)\u003c/strong\u003e. Similarly, DMR were mainly found in introns (887 regions, 35.6%), followed by promoter/enhancer regions (618 regions, 24.9%), intergenic regions (460 regions, 18.5%), exons (311 regions, 12.5%), terminators (165 regions, 6.6%), 5\u0026apos; UTR regions (51 regions, 2.1%), and 3\u0026apos; UTR regions (41 regions, 1.7%) \u003cstrong\u003e(Figure 6B)\u003c/strong\u003e. The number of DMR decreased as their length increased\u003cstrong\u003e\u0026nbsp;(Figure 7A)\u003c/strong\u003e, suggesting that methylation plays a crucial role in gene expression regulation, with CpG islands frequently enriched in CpG islands.(\u003cstrong\u003e\u0026nbsp;Figure 7B\u003c/strong\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGO/KEGG Enrichment Analysis\u003c/h2\u003e \u003cp\u003eThe genes corresponding to significantly differentially methylated sites were mapped to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database entries, and the number of associated genes was calculated. A hypergeometric test was applied to identify GO/KEGG terms significantly enriched with genes related to differentially methylated sites compared with the entire genomic background. A total of 5,705 DMS and 985 DMR were annotated with GO terms, whereas 2,756 DMS and 436 DMR were annotated with KEGG terms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene screening\u003c/h2\u003e \u003cp\u003eFrom the selected gene entries, we identified 382 osteoporosis-related genes from DMS and 68 from DMR in the GO terms, whereas 92 genes from DMS and 166 from DMR were identified in the KEGG terms (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among these genes, two common genes, TF (transferrin) and TGFB1 (transforming growth factor-β1), were identified across all four categories (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These genes are located on chromosomes 3 and 19, respectively. In the DMS category, TF and TGFB1 each had six differentially methylated sites, whereas in the DMR category, each gene had one differentially methylated region, mostly located near transcription start sites and within CpG islands.\u003c/p\u003e \u003cp\u003eFurther analysis of the GENE CARDS database revealed scores of 40.53 for TF and 76.70 for TGFB1, indicating their extensive involvement in various diseases and biological processes. These findings suggest a strong potential association between these genes and the development of osteoporosis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe concept of epigenetics, introduced by the British biologist Conrad Hal Waddington in 1942[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], describes how interactions between genes and their products influence cellular development without altering the DNA sequence itself. Epigenetic modifications affect gene activity by altering DNA and chromatin chemical modifications, primarily through DNA methylation, histone modification, noncoding RNA regulation, and chromatin remodeling. As research has advanced, epigenetics has been implicated in the onset and progression of various diseases, including cancer, neurological disorders, and metabolic diseases.\u003c/p\u003e \u003cp\u003eIn cancer, for example, hypermethylation of tumor suppressor gene promoters can lead to gene silencing and cancer development, whereas hypomethylation of oncogenes may contribute directly to tumor formation[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In amyotrophic lateral sclerosis (ALS), a progressive motor neuron disease, differences in the expression of specific miRNAs, such as miR-7-2-3p and miR-26a-1-3p, between healthy individuals and ALS patients have been identified, potentially aiding early diagnosis and timely treatment[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, the development of metabolic diseases such as obesity and diabetes is believed to result from a complex interplay of genetic and epigenetic factors, environmental influences, and nutrition. Adverse intrauterine environments during fetal development, which are influenced by maternal conditions, can increase susceptibility to metabolic diseases and other chronic conditions in adulthood[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDNA methylation is a key epigenetic modification in which a methyl group (-CH3) is added to the 5th carbon atom of cytosine within DNA, forming 5-methylcytosine (5-mC). This process is catalyzed by DNA methyltransferases (DNMT), primarily at CpG dinucleotides[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. DNMT1 maintains methylation patterns during DNA replication, transferring methylation marks from the parent strand to the daughter strand. DNMT3A and DNMT3B establish new methylation patterns during development and differentiation, whereas DNMT3L facilitates DNMT3A and DNMT3B but lacks catalytic activity itself[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. DNA methylation is generally associated with gene silencing, particularly when promoter regions are highly methylated[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which suppresses transcription. During development, specific methylation patterns change in response to gene expression needs, and methylation helps suppress transposons and repetitive sequences, maintaining genomic stability.\u003c/p\u003e \u003cp\u003eConversely, DNA demethylation, which removes methyl groups from DNA, can occur passively during DNA replication or actively through TET enzymes, which oxidize 5-mC to 5-hydroxymethylcytosine and eventually restore it to unmethylated cytosine through a multistep reaction.\u003c/p\u003e \u003cp\u003eOsteoporosis development is characterized by an imbalance in bone remodeling, with decreased bone formation and increased bone resorption. Factors such as genetics, the environment, hormonal changes, nutrition, and physical activity influence this balance[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. DNA methylation also plays a critical role in osteoporosis by regulating the expression of genes related to bone metabolism. The methylation status of specific genes can alter osteoblast and osteoclast function, impacting osteoporosis development. For instance[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], hypermethylation of the RUNX2 gene, which is associated with osteoblast differentiation, can inhibit osteoblast function and reduce bone formation. Conversely, hypomethylation of the RANKL gene, which is linked to osteoclast differentiation, can increase osteoclast activity and increase bone resorption.\u003c/p\u003e \u003cp\u003eInflammatory cytokines such as IL-6 and TNF-α can also influence osteoblast and osteoclast function by modifying the DNA methylation status[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], thereby exacerbating osteoporosis progression. Environmental and lifestyle factors, such as diet, exercise, smoking, and alcohol consumption, may indirectly influence osteoporosis by affecting DNA methylation. For example[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], insufficient nutrients such as calcium and vitamin D in the diet can impact bone metabolism by altering the methylation status of related genes. The VDR gene, encoding the vitamin D receptor, plays a crucial role in calcium absorption and bone health. Studies have shown that promoter methylation of the VDR gene is significantly associated with osteoporosis development[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], with higher promoter methylation levels in osteoporosis patients leading to decreased VDR gene expression, subsequently affecting calcium absorption and bone density.\u003c/p\u003e \u003cp\u003eRecent studies have also implicated mitochondrial DNA (mtDNA) methylation in osteoporosis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Although mtDNA methylation levels are generally low, its role in energy metabolism and oxidative stress is significant. Elevated mtDNA methylation levels have been observed in osteoporosis patients, potentially affecting mitochondrial function, which in turn influences energy metabolism and oxidative stress responses in bone cells, thereby exacerbating osteoporosis progression.\u003c/p\u003e \u003cp\u003eOur results suggest that DNA methylation is involved in various biological processes, cellular functions, and disease progression. Although this study cannot directly establish a causal relationship between DNA methylation and osteoporosis, evidence points to a significant association between the two. We observed differences in the methylation levels and expression of methylation-related genes between the osteoporosis and non-osteoporosis groups. Enrichment analysis further revealed differential methylation of several genes associated with osteoporosis, indicating that altered methylation levels of certain genes may be associated with this disease.\u003c/p\u003e \u003cp\u003eNotably, we identified two genes, TF and TGFB1, which are potentially related to osteoporosis development. Both genes are closely associated with bone development and were found to have DMS and DMR by means of both GO and KEGG enrichment analyses. Our subsequent experiments will validate these two models in animal models to assess whether the methylation levels of these two genes are associated with the development of osteoporosis and explore their potential role. Transferrin[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], a plasma protein that is involved primarily in iron ion transport, plays a crucial role in iron metabolism and storage, which is vital for bone formation. Iron is involved in the growth and differentiation of bone cells, and studies suggest that transferrin may influence bone remodeling by affecting the balance between osteoclasts and osteoblasts. TGFB1 is a multifunctional cytokine involved in cell growth[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], differentiation, apoptosis, and immune regulation. TGF-β promotes osteoblast proliferation and differentiation, enhancing bone matrix production. Low concentrations of TGFB1 inhibit osteoclast formation, whereas high concentrations can increase osteoclast activity by stimulating osteoclast precursor cell proliferation. Research indicates that regulating the TGF-β signaling pathway can affect bone mass and strength, with serum TGF-β levels in osteoporosis patients potentially being correlated with bone density and fracture risk.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study utilized whole-genome DNA methylation sequencing to identify differences in genomic methylation levels between osteoporosis (OP) patients and healthy individuals. Compared with the non-osteoporosis group, the OP group presented higher methylation levels and a greater number of methylated genes. Enrichment analysis highlighted two key genes, TF and TGFB1, which may contribute to osteoporosis development through distinct pathways. However, owing to the limited sample size and the large number of genes analyzed, there may be biases in identifying common differential genes, and further validation of these findings is needed. Experiments are also needed to test their hypothesis based on the methylome datasets. Nevertheless, we believe that ongoing research will continue to clarify the relationship between DNA methylation and osteoporosis, offering new approaches for clinical diagnosis and treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEpigenome-wide association studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially methylated sites\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially methylated regions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWGBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhole-genome bisulfite sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBone mineral density\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDXA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDual-energy X-ray absorptiometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCGI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCpG island\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransferrin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTGFB1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransforming growth factor-β1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emtDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMitochondrial DNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDNMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMitochondrial DNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003e All experimental procedures, including human care and tissue sample collection, were approved by and conducted in accordance with the guidelines of the Medical Ethics Committee of the Third Affiliated Hospital of Guangzhou University of Chinese Medicine, China (Approval ID: PJ-XS-20240531-009).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was financially supported by the National Natural Science Foundation of China (NSFC-82374482) and the Guangdong Provincial Administration of Traditional Chinese Medicine (GPATCM-202205030934309730). The funding bodies had no role in the design of the study, the collection, analysis, and interpretation of data, or in writing the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYYZ and YLZ conducted the experiments and data analysis. YL designed and supervised the experiment and assisted with manuscript revisions. CML provided technical support. YKZ and SSX coordinated the research. QHL and HWG performed the surgeries and provided the samples. YYZ drafted the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe sincerely thank all members of Ying Li's group for their contributions to sample collection and technical assistance. All images were created by the author team.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSequence data that support the findings of this study have been deposited in the NCBI with the primary accession code PRJNA1154659\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJohnston CB, Dagar M: Osteoporosis in Older Adults. The Medical clinics of North America 2020, 104(5):873\u0026ndash;884.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbeling PR, Nguyen HH, Aleksova J, Vincent AJ, Wong P, Milat F: Secondary Osteoporosis. Endocrine reviews 2022, 43(2):240\u0026ndash;313.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Z, Yu Z, Chen M, Zhang M, Chen R, Yu H, Lin Y, Wang D, Li S, Huang L \u003cem\u003eet al\u003c/em\u003e: Mechanisms of estrogen deficiency-induced osteoporosis based on transcriptome and DNA methylation. Frontiers in cell and developmental biology 2022, 10:1011725.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore LD, Le T, Fan G: DNA methylation and its basic function. Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology 2013, 38(1):23\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang X, Aouizerat BE, So-Armah K, Cohen MH, Marconi VC, Xu K, Justice AC: DNA methylation-based telomere length is associated with HIV infection, physical frailty, cancer, and all-cause mortality. Aging cell 2024, 23(7):e14174.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHannon E, Dempster EL, Mansell G, Burrage J, Bass N, Bohlken MM, Corvin A, Curtis CJ, Dempster D, Di Forti M \u003cem\u003eet al\u003c/em\u003e: DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia. eLife 2021, 10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVisconti VV, Cariati I, Fittipaldi S, Iundusi R, Gasbarra E, Tarantino U, Botta A: DNA Methylation Signatures of Bone Metabolism in Osteoporosis and Osteoarthritis Aging-Related Diseases: An Updated Review. International journal of molecular sciences 2021, 22(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang S, Duan X: Epigenetics, Bone Remodeling and Osteoporosis. 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Trends in genetics: TIG 2022, 38(7):676\u0026ndash;707.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao J, Yan Q: Cancer Epigenetics, Tumor Immunity, and Immunotherapy. Trends in cancer 2020, 6(7):580\u0026ndash;592.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhattacharya D, Pomeroy SL, Pomeranz Krummel DA, Sengupta S: Epigenetics and survivorship in pediatric brain tumor patients. Journal of neuro-oncology 2020, 150(1):77\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYazar V, Ruf WP, Knehr A, G\u0026uuml;nther K, Ammerpohl O, Danzer KM, Ludolph AC: DNA Methylation Analysis in Monozygotic Twins Discordant for ALS in Blood Cells. Epigenetics insights 2023, 16:25168657231172159.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmail HO: The epigenetics of diabetes, obesity, overweight and cardiovascular disease. AIMS genetics 2019, 6(3):36\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones PA: Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nature reviews Genetics 2012, 13(7):484\u0026ndash;492.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrasad R, Yen TJ, Bellacosa A: Active DNA demethylation-The epigenetic gatekeeper of development, immunity, and cancer. Advanced genetics (Hoboken, NJ) 2021, 2(1):e10033.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang D, Wu W, Callen E, Pavani R, Zolnerowich N, Kodali S, Zong D, Wong N, Noriega S, Nathan WJ \u003cem\u003eet al\u003c/em\u003e: Active DNA demethylation promotes cell fate specification and the DNA damage response. Science (New York, NY) 2022, 378(6623):983\u0026ndash;989.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang P, Cao Y, Zhan D, Wang D, Wang B, Liu Y, Li G, He W, Wang H, Xu L: Influence of DNA methylation on the expression of OPG/RANKL in primary osteoporosis. International journal of medical sciences 2018, 15(13):1480\u0026ndash;1485.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalbraith K, Snuderl M: DNA methylation as a diagnostic tool. Acta neuropathologica communications 2022, 10(1):71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Zheng YL, Wang R, Wang XQ, Zhang H: Exercise for osteoporosis: A literature review of pathology and mechanism. Frontiers in immunology 2022, 13:1005665.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŁoboś P, Regulska-Ilow B: Link between methyl nutrients and the DNA methylation process in the course of selected diseases in adults. Roczniki Panstwowego Zakladu Higieny 2021, 72(2):123\u0026ndash;136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang B, Li H, Yang C, Nie R, Zhang X, Pu C: VDR gene ApaI polymorphism and risk of postmenopausal osteoporosis: a meta-analysis from 22 studies. Climacteric: the journal of the International Menopause Society 2023, 26(6):583\u0026ndash;593.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Gao Z, Liu X: Mitochondrial dysfunction and therapeutic perspectives in osteoporosis. Frontiers in endocrinology 2024, 15:1325317.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeiser DL, Winzerling JJ: Insect transferrins: multifunctional proteins. Biochimica et biophysica acta 2012, 1820(3):437\u0026ndash;451.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansamuit K, Osathanon T, Suwanwela J: Effect of Jagged1 on the expression of genes in regulation of osteoblast differentiation and bone mineralization ontology in human dental pulp and periodontal ligament cells. Journal of oral biology and craniofacial research 2020, 10(2):233\u0026ndash;237.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"DNA methylation, WGBS, Osteoporosis, Human","lastPublishedDoi":"10.21203/rs.3.rs-5397269/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5397269/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOsteoporosis is a prevalent bone metabolic disorder characterized by reduced bone mass, disruption of bone microarchitecture, and increased bone fragility, leading to a heightened risk of fracture. This condition significantly impairs patients' quality of life and increases mortality risk. Emerging evidence suggests that DNA methylation may play a crucial role in regulating the expression of genes related to bone metabolism, thereby influencing the development of osteoporosis. However, the precise relationship between DNA methylation and osteoporosis remains unclear and warrants further investigation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur study revealed significant differences in both the quantity and ratio of DNA methylation between individuals with osteoporosis and healthy controls, with differences predominantly occurring in CpG islands. GO/KEGG enrichment analyses highlighted distinct osteoporosis-related gene pathways. Notably, we identified two genes, TF and TGFB1, located on chromosomes 3 and 19, respectively, that are potentially involved in the pathogenesis of osteoporosis and are broadly involved in various diseases and biological processes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings indicate distinct methylation patterns between osteoporosis patients and healthy individuals, with differential methylation levels in genes associated with osteoporosis. This research offers new insights into the epigenetic mechanisms underlying osteoporosis.\u003c/p\u003e","manuscriptTitle":"Exploring DNA methylation profiles in the pathogenesis of human osteoporosis via whole-genome bisulfite sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 23:13:13","doi":"10.21203/rs.3.rs-5397269/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":"c55bee4b-311c-40fd-8609-9c07906d4432","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-29T12:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-02 23:13:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5397269","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5397269","identity":"rs-5397269","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}