Clinical application of CNV-Seq in the diagnosis of children with abnormal brain development in 130 cases

Clinical application of CNV-Seq in the diagnosis of children with abnormal brain development in 130 cases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Clinical application of CNV-Seq in the diagnosis of children with abnormal brain development in 130 cases Shaohua Zhu, Shibing Cheng, Chunyang Jia, Pengwu Lin, Peng Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4669074/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Background To evaluate the diagnostic value of applying genome copy number variation sequencing (CNV-seq) in the genetic etiology of abnormal brain development (ABD). Methods We selected 130 ABD patients discovered in Gansu Maternal and Child Health Hospital from December 2018 to October 2023 as the research subjects, divided into non-syndrome ABD group and syndrome ABD group, performed CNV-seq testing and analyzed the genetic causes of copy number variation. Results In the 130 cases, we detected a total of 42 abnormal samples, with an abnormal detection rate of 32.3%, included 3 cases (2.3%) of aneuploidy and 39 cases (30%) of CNVs; of which 15 cases were detected in the non-syndrome ABD group, included 1 case (6.67%, 1/15) of aneuploidy, 4 cases (26.7%，4/15) of pathogenic CNVs (pCNVs) and 10 case (66.7%，10/15) of variant of uncertain significance (VUS), and 27 cases were detected in the syndrome-type ABD group, including 2 cases (7.4%, 2/27) of aneuploidy, 19 cases (70.4%, 19/27) of pCNVs and 6 cases (22.2%, 6/27) of VUS. Chi-square test suggested that the difference in detection rate was statistically significant (P<0.05). Conclusion In current study, the application of CNV-Seq in all ABD patient groups has a high abnormal detection rate, especially in the population of syndrome ABD, the detection rate was higher than that of non-syndrome ABD, but in the population of non-syndrome ABD patients, due to the low positive detection rate and the lack of obvious clinical phenotypes, such population were more likely to be neglected in clinical practice. Accordingly, more attention should be paid to population with non-syndrome ABD. In addition, prenatal diagnosis and genetic counseling should be performed in a timely manner for these of patients. Biological sciences/Genetics Biological sciences/Molecular biology Health sciences/Medical research abnormal brain development (ABD) Aneuploidy Copy number variation (CNV) CNV-seq Figures Figure 1 Figure 2 1. Introduction Abnormal brain development (ABD) refers to cognitive function defects and lag in social adaptation awareness or behavior during the developmental process (children become abnormal before adulthood). The total incidence rate of the disease was within 3%, of which the incidence rate of ABD was 0.3 ~ 0.4% for heavy and medium-sized diseases, and the prevalence ratio of women and men in ABD is about 0.71 ~ 0.63:1[1; 2]. ABD was divided into non-syndrome type (Non-syndrome NS-ABD) and syndrome type (Syndrome ABD, S-ABD) according to clinical manifestations, S-ABD was also accompanied by special features and organ deformities[ 3 ]. As a result, the etiology of ABD was extremely complicated, included factors of genetic, nutritional, and endocrine, they could all be the cause of the ABD[4; 5]. Copy number variation (CNVs) was a common occurrence in chromosomes, which referred to the replication or deletion of DNA fragments larger than 1kb on chromosomes, and when these replicated or deleted fragments caused abnormalities in the function of the genome, they were classified as pathogenic CNVs (pCNVs), they could be divided into micro-deletions, micro-repetitions, complex rearrangements, and other more complicated variations. In most cases, CNV exists in a normal polymorphic form, and only a few CNVs were related to genetic diseases. However, micro-deletion/replication syndrome caused by pCNVs still played an important role in human genetic diseases. For common chromosomal MMS[2; 6], such as Williams-Beuren syndrome (WBS, around 1.6MB heterozygous deletion at 7q11.23), Angelman/Prader-Willi syndrome (AS/PWS, 15q11-13), DGS (22q11.2 deletion of heterozygosity was caused by CNVs of chromosomal segments[7; 8; 9]. In these chromosomal diseases, the location of CNV was distributed in different regions of the genome, and the size of the fragments was also different. The technology of CNV-Seq adopted next generation sequencing to perform low-depth whole-genome sequencing of the DNA of samples, which could detect abnormal fragments with a size of larger than 0.1MB, and it had become one of the most suitable methods for prenatal diagnosis because of its large detection throughput, high resolution, easy operation, and short detection time[ 10 ]. The current study performed CNV-seq on the sample of 130 ABD fetuses (amniotic fluid) and children (peripheral blood) diagnosed from Gansu Maternal and Child Health Hospital to detect chromosomal aneuploidy and CNVs associated with brain development. Based on the different phenotypic characteristics of the patient population, we divided the research population into group of S-ABD and group of NS-ABD, then we compared the sequencing results between 2 groups. The fragment abnormalities in the genome were detected and analyzed via CNV-seq, and we performed genetic and statistical analyzes on the clinical manifestations and CNVs abnormalities in the population, based on this information to evaluate the diagnostic and application value of CNV-seq for cases with ABD, so as to deepen and consolidate clinicians' understanding of the disease. The ultimate purpose of this study was to provide theoretical data reference for the population of patients especially for children with ABD in Northwest China, in order to strengthen the prevention and treatment ideas and methods for ABD. 2. Materials and Methods 2.1 Study oversight The Medical Genetics Center where the research was located and authorized by the Gansu Provincial Health Commission and has testing qualifications. All the patients were registered at the Medical Genetics Center according to CNV-seq standard procedures and signed informed consent forms. The informed consent form details the testing method, the type of sample required, the restricted population, and the potential risks. In addition, the informed consent form also contains insurance plans, legal declarations, national ethics declarations, laboratory application processes, and some columns of information for consultation before testing. In order to ensure the fairness and reliability of the research results, we provided each subject with information such as the positive predicted value (PPV), negative predicted value (NPV), sensitivity and specificity of the CNV test results. 2.2 Sample collection and DNA extraction After informing the patient or their family members and obtaining knowledge and consent, the informed consent form was signed according to the standard, and the family members of the patient express their understanding and agree to publish it as a study object. For peripheral blood samples: collect peripheral blood (5 mL) from the child and transfer it to an anticoagulant blood collection vessel containing EDTA (KIRGEN Medical Equipment Co., Ltd. China) ;For amniotic fluid samples: puncture according to ultrasonic positioning assistance to take 15 mL of amniotic fluid in a centrifuge tube༛For fetal villi tissue samples: short-term culture in the laboratory after the villi was absorbed by the syringe. Adopted the QIA amp DNA Micro kit (Germany, Qiagen Biological Company) to extract sample genomic DNA according to the Standard Operating Procedure (SOP) contained in the manual. In addition, the Qubit 3.0 fluorescence photometer (Thermo Life Technologies, USA) was used to perform DNA concentration determination. 2.3 Sequencing and analysis Based on the CN-500 NGS sequencer (United States, Illuminina Biological Company) for low-depth genome sequencing, the raw-data obtained by sequencing is cleaned and determined. After filtering low-quality READS through quality control (quality control, QC), CLEAN data is generated with a reading length of 36bp and an average sequencing depth of 0.1×. It is exported in BAM format to the CNV version 2.0 analysis system (Beijing Berry-Genomics Biotechnology Co., Ltd.) for chromosome copy number analysis. Sequence comparison of each READ after QC with the corresponding human standard chromosome group, standardized analysis of the data, then identified whether there was CNV and abnormal types of chromosomes in the detection fragment. According to public mutation databases and population frequency databases such as ClinVar, HGMD, gnomAD, DECIPHER, and 1000 Genome Project, the detected variants are bioinformatics annotated to determine the clinical significance of CNVs, and the data in the database is used to predict whether CNVs fragments have pathogenic. It was worth noting that in order to ensure the reliability of the results, this study only compared CNV fragments larger than 100 Kb. The pathogenicity of CNV was graded and judged according to the guidelines on CNV pathogenicity issued by the American College of Medical Genetics and Genomics (ACMG) and ClinGen[11; 12]. Statistical analysis was performed using R software (version 4.2.1; https://www.r-project.org/ ). The chi-square test (χ²) was employed within the R package "chisq.test" function to evaluate associations between variables. Statistical results are presented as percentages (%) and p-values. A significance level of p < 0.05 was used to determine statistically significant differences[ 13 ]. 3. RESULTS 3.1 CNV-seq The study consisted of 130 sample groups, involving 42 patients, accounting for 32.31% (42/130), and a total of 50 abnormal CNVs fragments were detected, included aneuploidy and CNVs, of which there were 3 cases of aneuploidy (two cases of trisomy 21 and one case of trisomy 18) and 39 cases of CNVs exceeding the patient count due to multiple pCNVs in some individuals. Among the detected CNVs, 23 (17.69%) were classified as pathogenic (P) or likely pathogenic (LP) based on available evidence (Supplementary Table S1 for details). Eight cases involved fragments exceeding 10 Mb, encompassing both deletions and duplications at various loci. Three cases had fragments between 5–10 Mb, and 12 cases had fragments less than 5 Mb. Notably, some patients carried multiple pCNVs, resulting in a total number of identified CNVs exceeding the number of patients (Fig. 1 ). The distribution of pCNVs across chromosomes was depicted in Fig. 2 , with ChrX, Chr15, Chr 2, and Chr 17 exhibiting higher detection rates. 3.2 Comparison of positivity rates between NS-ABD and S-ABD groups Analysis of the 130 patients with ABD identified abnormalities in 42 individuals. Within the NS-ABD group (n = 15), 15 abnormalities were detected, including 1 case of aneuploidy (6.67%), four pCNVs (26.67%), and 10 CNVs of unknown clinical significance (66.67%). The S-ABD group (n = 27) harbored abnormalities in 27 patients, encompassing 2 cases of aneuploidy (7.41%), 19 PCNVs (70.37%), and 6 CNVs of unknown clinical significance (22.22%). Chi-square analysis revealed a significant difference (χ² = 40.03, p < 0.05) in the detection rate of pCNVs between the NS-ABD and S-ABD groups. Additionally, the overall positive detection rate was significantly higher in the S-ABD group (77.78%) compared to the NS-ABD group (33.33%) (χ² = 40.97, p < 0.05) (details in Table 1 ). Table 1 CNV-seq test and statistical results of 130 patients with ABD Name Group (%) χ 2 P Value NS-BD (%) S-BD (%) Type Aneuploidy 6.67 7.41 1.46 0.226 Pathogenic CNVs 26.67 70.37 41.79 0.000** VUS 66.67 22.22 3.82 0,102 positive cases 33.3 77.8 40.99 0.000** 3.3 Screening of candidate genes associated with ABD in CNVs region Based on the detected pCNVs segment, we screened the ABD candidate gene. First, according to the physical location of the candidate gene, the genes associated with ABD were retrieved in the OMIM database. Second, considering the level of gene expression and its function, 8 candidate genes that may affect ABD were finally selected. They were UBE3A, AUTS4, SATB2, GLSS, SMCR, ARSL and CDPX1 (Table 2 ) Table2 Copy number variation analysis of candidate genes Case Type of sample age CNVs Type of CNVs Fragment length Syndrome Pathogenicity Candidate gene 1 Peripheral blood 2Y and 2M 15q11.2-q13.2 duplication 7.64Mb 15q11-q13 duplication syndrome P UBE3A 、 AUTS4 2 Peripheral blood 9M 2q33.1-q33.3 deletion 9.56Mb 2q32-q33 deletion syndrome P SATB2 、 GLSS 3 Peripheral blood 2Y 17p11.2 duplication 3.88Mb Smith-Magenis syndrome P SMCR 4 Amniotic fluid 29W Xp22.33-p11.1 deletion 55.86Mb Chondrodysplasia Punctata P ARSL, CDPX1 4. Discussion With the continuous development of next-generation sequencing (NGS), more research and medical testing in recent years have begun to adopt NGS to explore sequence variation[14; 15]. Compared with methods of microarray, NGS has the advantages of greater single throughput, faster detection speed, higher resolution, lower cost, and high repeatability. For the identification of chromosomal abnormalities at the sub-microscopic level, the most common method was CNV-seq, it was based on estimating the extension of different statistical models in the confidence interval, and was suitable for the comparison and recognition of the proportion of CNVs[ 16 ]. Different from microarray genotyping of target fragments, CNV-seq adopted the READS reference sequence as a template[ 17 ]. When comparing sequences, double of Shotgun sequences were adopted for pairing, and two-dimensional sequence comparisons were performed with the templates. The data reading method was Slide Window Mode, finally, we compared the confidence level of the calculation results with above method was not suitable for long sequences and the accuracy of analysis of large fragments needs to be improved[ 18 ]. ABD is actually an adaptive behavior defect and cognitive impairment accompanied by human development. The prevalence rate of this disease is about 6%-8%. It is one of the important diseases that endanger physical and mental health. The main causes include genetic and environmental factors. Chromosomal abnormalities, gene mutations, and pCNVs might all lead to the occurrence of ABD. About 30–40% of ABD patients were caused by chromosomal abnormalities. In addition, the prevalence of ABD in the general population about was 1%~3%[ 19 ]. China has a very large population, so the number of ABD patients is correspondingly considerable. According to the current situation, most patients with ABD lack effective and targeted prevention and treatment. In other words, it is very important to clarify the cause of ABD. However, the etiology of ABD is very complicated. Based on data from the World Health Organization, it is reported that more than 50% of ABD patients have unknown causes of disease. In China, this rate is even more serious, accounting for about 67%[ 20 ]. In fact, genetic factors play a key role in ABD. ABD was divided into S-ABD and NS-ABD. The clinical phenotypes of patients with NS-ABD mainly include typical language disorders, motor developmental delays, and mental decline; unlike NS-ABD, patients with S-ABD included the above abnormalities, but also combine other diseases or systemic deformities, included congenital heart disease, abnormal face, and cleft lip and palate[ 21 ]. In current study, 130 ABD patients were selected for CNV-seq analysis. In addition, we combined the clinical phenotypes and ultrasound indicators of the patients involved in the study to group the ABD patient population into NS-ABD group and S-ABD group. Through data analysis, it was suggested that there were 42 cases of abnormal results (42/130). It was worth noting that among these patients, there have been cases where the same patient contains multiple CNVs regional abnormalities. In other words, the same patient carries 2 or more abnormal CNVs at the same time. More and more studies have shown that the adopt of CNV-seq method in genetic diseases that might be caused by chromosomal aberrations such as mental developmental delay and sexual dysplasia could be very effective in improving the detection rate. Therefore, the method was indeed worthy of being recommended as one of the suitable methods for clinical diagnosis [ 22 ], Based on the statistical results of this study, our detection rate is as high as 32.3%, which also supports the above view. In current study, the cases detected by CNV-seq included ABD patients with aneuploidy abnormalities, and in addition, they also included ABD patients caused by pCNVs. According to the classification of ABD phenotypes, we further performed an intergroup comparison between NS-ABD and S-ABD, and the results showed that there was a significant difference ( χ 2 = 40.03, P<0.05) in the detection rate of pathogenic CNVs between the these of two groups. What’s more, based on the results of inter-group comparison, this study continued to analyze the difference in the positivity rate (total detection rate) of the two pathogenic factors aneuploidy abnormality and pCNVs. The results suggested that in the above-mentioned patient groups, the positivity rate of the NS-ABD group was 33.33%, and the positivity rate of the S-ABD group was 77.78%, obviously, the positivity rate of the two groups of patients was also significantly different (χ2 = 40.97, P < 0.05). According to the results above, the current study showed that chromosomal abnormalities and pCNVS were indeed more likely to appear among S-ABD patients. And there were limitations of the above methods: considering that many patients refuse to provide their own information for the current study, the data volume of this study was not large, the current rate of detection and positivity could be fluctuating or uncertain. We would further increase the number of samples or patient groups in future studies. In the current study, a total of 27 kinds of pCNVs included 15q11.2-q13.2, 2q33.1-q33.3, 17p11.2 and Xp22.33-p11.1 were found, pCNVs such as 15q11.2-q13.2 micro-deletion syndrome[ 23 ] and 22q11.21 microdeletion syndrome[ 24 ], which have been reported various times and might cause ABD, have been discovered in the current studies, What's more, we also found rarely reported pCNV fragments associated with ABD such as p11.23. There was no obvious pattern in the distribution of these of pCNVs within the chromosomes, which further indicated the widespread of the ABD-associated CNVs region, and also suggested the complexity of the ABD genetic mechanism. Therefore, we adopted the information included in the such four public databases DECIPHER, OMIM, ClinGen and PubMed, and then investigated the highly associated genes located close to the region where the pCNVs fragments appeared, based on the above-mentioned pCNVs fragments. a total of 7 candidate genes associated with ABD were selected. Within the 15q11.2-q13.2 chromosomal region, mutations in the UBE3A and AUTS2 genes were associated with two distinct neurodevelopmental disorders: 15q11-q13 microdeletion syndrome and Angelman syndrome. These syndromes share a common clinical presentation characterized by intellectual disability, ataxia, and motor delays[ 25 ], and it was true that many studies have proved that UBE3A was the essential gene for Angelman syndrome[ 26 ]; The GLSS and SATB2 contained within the 2q33.1-q33.3 region were involved in Glass Syndrome. Common clinical manifestations of this syndrome include growth delay and severe intellectual backwardness, and these of phenotypes would continue to extend to the prenatal and postpartum stages[27; 28]; The SMCR contained within the 17p11.2 region involved Smith-Magenis Syndrome, and its main clinical manifestations included mild to moderate intellectual disability and lag of reflexes[ 29 ]; It should be noted that the ARSL and CDPX1 contained within the Xp22.33 region were closely associated with dot-shaped cartilage dysplasia typeⅠ. Although the phenotypes of the syndrome mainly include short phalangeal cartilage dysplasia and distal phalangeal hypoplasia of the fingers, many previous reports have also found that the syndrome also has common manifestations including developmental delays and extreme mental decline in infancy[30; 31]. In short, the candidate genes discovered in these of crucial pCNVs regions were closely associated with the occurrence of ABD. With the continuous development of next-generation sequencing (NGS), more research and medical testing in recent years have begun to adopt NGS to explore sequence variation[14; 15]. Compared with methods of microarray, NGS has the advantages of greater single throughput, faster detection speed, higher resolution, lower cost, and high repeatability. For the identification of chromosomal abnormalities at the sub-microscopic level, the most common method was CNV-seq, it was based on estimating the extension of different statistical models in the confidence interval, and was suitable for the comparison and recognition of the proportion of CNVs[ 16 ]. Different from microarray genotyping of target fragments, CNV-seq adopted the READS reference sequence as a template[ 17 ]. When comparing sequences, double of Shotgun sequences were adopted for pairing, and two-dimensional sequence comparisons were performed with the templates. The data reading method was Slide Window Mode, finally, we compared the confidence level of the calculation results with above method was not suitable for long sequences and the accuracy of analysis of large fragments needs to be improved[ 18 ]. ABD is actually an adaptive behavior defect and cognitive impairment accompanied by human development. The prevalence rate of this disease is about 6%-8%. It is one of the important diseases that endanger physical and mental health. The main causes include genetic and environmental factors. Chromosomal abnormalities, gene mutations, and pCNVs might all lead to the occurrence of ABD. About 30–40% of ABD patients were caused by chromosomal abnormalities. In addition, the prevalence of ABD in the general population about was 1%~3%[ 19 ]. China has a very large population, so the number of ABD patients is correspondingly considerable. According to the current situation, most patients with ABD lack effective and targeted prevention and treatment. In other words, it is very important to clarify the cause of ABD. However, the etiology of ABD is very complicated. Based on data from the World Health Organization, it is reported that more than 50% of ABD patients have unknown causes of disease. In China, this rate is even more serious, accounting for about 67%[ 20 ]. In fact, genetic factors play a key role in ABD. ABD was divided into S-ABD and NS-ABD. The clinical phenotypes of patients with NS-ABD mainly include typical language disorders, motor developmental delays, and mental decline; unlike NS-ABD, patients with S-ABD included the above abnormalities, but also combine other diseases or systemic deformities, included congenital heart disease, abnormal face, and cleft lip and palate[ 21 ]. In current study, 130 ABD patients were selected for CNV-seq analysis. In addition, we combined the clinical phenotypes and ultrasound indicators of the patients involved in the study to group the ABD patient population into NS-ABD group and S-ABD group. Through data analysis, it was suggested that there were 42 cases of abnormal results (42/130). It was worth noting that among these patients, there have been cases where the same patient contains multiple CNVs regional abnormalities. In other words, the same patient carries 2 or more abnormal CNVs at the same time. More and more studies have shown that the adopt of CNV-seq method in genetic diseases that might be caused by chromosomal aberrations such as mental developmental delay and sexual dysplasia could be very effective in improving the detection rate. Therefore, the method was indeed worthy of being recommended as one of the suitable methods for clinical diagnosis [ 22 ], Based on the statistical results of this study, our detection rate is as high as 32.3%, which also supports the above view. In current study, the cases detected by CNV-seq included ABD patients with aneuploidy abnormalities, and in addition, they also included ABD patients caused by pCNVs. According to the classification of ABD phenotypes, we further performed an intergroup comparison between NS-ABD and S-ABD, and the results showed that there was a significant difference ( χ 2 = 40.03, P<0.05) in the detection rate of pathogenic CNVs between the these of two groups. What’s more, based on the results of inter-group comparison, this study continued to analyze the difference in the positivity rate (total detection rate) of the two pathogenic factors aneuploidy abnormality and pCNVs. The results suggested that in the above-mentioned patient groups, the positivity rate of the NS-ABD group was 33.33%, and the positivity rate of the S-ABD group was 77.78%, obviously, the positivity rate of the two groups of patients was also significantly different (χ2 = 40.97, P < 0.05). According to the results above, the current study showed that chromosomal abnormalities and pCNVS were indeed more likely to appear among S-ABD patients. And there were limitations of the above methods: considering that many patients refuse to provide their own information for the current study, the data volume of this study was not large, the current rate of detection and positivity could be fluctuating or uncertain. We would further increase the number of samples or patient groups in future studies. In the current study, a total of 27 kinds of pCNVs included 15q11.2-q13.2, 2q33.1-q33.3, 17p11.2 and Xp22.33-p11.1 were found, pCNVs such as 15q11.2-q13.2 micro-deletion syndrome[ 23 ] and 22q11.21 microdeletion syndrome[ 24 ], which have been reported various times and might cause ABD, have been discovered in the current studies, What's more, we also found rarely reported pCNV fragments associated with ABD such as p11.23. There was no obvious pattern in the distribution of these of pCNVs within the chromosomes, which further indicated the widespread of the ABD-associated CNVs region, and also suggested the complexity of the ABD genetic mechanism. Therefore, we adopted the information included in the such four public databases DECIPHER, OMIM, ClinGen and PubMed, and then investigated the highly associated genes located close to the region where the pCNVs fragments appeared, based on the above-mentioned pCNVs fragments. a total of 7 candidate genes associated with ABD were selected. Within the 15q11.2-q13.2 chromosomal region, mutations in the UBE3A and AUTS2 genes were associated with two distinct neurodevelopmental disorders: 15q11-q13 microdeletion syndrome and Angelman syndrome. These syndromes share a common clinical presentation characterized by intellectual disability, ataxia, and motor delays[ 25 ], and it was true that many studies have proved that UBE3A was the essential gene for Angelman syndrome[ 26 ]; The GLSS and SATB2 contained within the 2q33.1-q33.3 region were involved in Glass Syndrome. Common clinical manifestations of this syndrome include growth delay and severe intellectual backwardness, and these of phenotypes would continue to extend to the prenatal and postpartum stages[27; 28]; The SMCR contained within the 17p11.2 region involved Smith-Magenis Syndrome, and its main clinical manifestations included mild to moderate intellectual disability and lag of reflexes[ 29 ]; It should be noted that the ARSL and CDPX1 contained within the Xp22.33 region were closely associated with dot-shaped cartilage dysplasia typeⅠ. Although the phenotypes of the syndrome mainly include short phalangeal cartilage dysplasia and distal phalangeal hypoplasia of the fingers, many previous reports have also found that the syndrome also has common manifestations including developmental delays and extreme mental decline in infancy[30; 31]. In short, the candidate genes discovered in these of crucial pCNVs regions were closely associated with the occurrence of ABD. 5. Conclusions In summary, the current research shows that the occurrence of ABD was closely related to chromosomal aneuploidy and pCNVs, the detection rate of pCNVs and positive detection rate of chromosomal in population of patients with S-ABD type were significantly higher than that of NS-ABD type. Combined with the current research results, patients with NSABD were indeed more likely to be ignored in clinical diagnosis. Therefore, we recommended that more attention should be paid to patients with NS-ABD types and these of patients should be reminded to receive appropriate tests. In addition, the detection of submicroscopic chromosomal aberrations through CNV-seq could significantly improved the detection efficiency of diagnosis, performing analysis on the correlation between genotypes and phenotypes could also provide the reliable genetic reference for prenatal diagnosis related to ABD, and of course, it could also provide basic theoretical basis and application guidance for eugenics and postnatal care. Declarations Conflicts of Interest: The authors declare no conflict of interest. Ethical Statements: All sample collection procedures in the study strictly complied with the national ethical guidelines and were approved by the Ethics Committee of Gansu Maternal and Child Health Hospital (Batch No.: 2021GSFY Ethical Review). The study also complied with the World Medical Association Declaration of Helsinki. Funding: This research: including experimental design, sample collection, data analysis, and manuscript writing, was funded by the Gansu Provincial Department of Science and Technology Innovation Base and Talent Plan (21JR7RA680), the Major project of Gansu Maternal and Child Health Hospital (GSFY-2021), and Clinical application of non-invasive prenatal genetic testing technology in chromosomal microdeletion and microduplication syndrome (2017-04-50). Author Contribution We acknowledge Mr Xuan Feng and Furong Liu for reading through the manuscript and giving valuable comments Conceptualization, Shaohua Zhu and ; Data curation, Shaohua Zhu and Chunyang Jia; Formal analysis, Shaohua Zhu and Shibing Cheng; Funding acquisition, Xuan Feng; Investigation, YuanYuan Guo and PengwuLin; Methodology, Xuan Feng; Project administration, Xuan Feng and Furong Liu; Resources, Yuanyuan Guo and Chunyang Jia; Software, Shaohua Zhu and Peng Zhang; Validation, Shibing Cheng; Visualization, Pengwu Lin; Writing – original draft, Shaohua Zhu and Shibing Cheng; Writing – review & editing, Furong Liu and Xuan Feng. Data Availability The datasets generated and/or analysed during the current study are not publicly available due to the data were sourced from the internal LAN database of the medical department(Gansu Provincial Clinical Research Center for Birth Defects and Rare Diseases), and the current study has only obtained ethical permission, but has not obtained permission for all patient public data, and it could be noted that the data were available from the corresponding author on reasonable request. References J.B. Moeschler, and M. Shevell, Comprehensive evaluation of the child with intellectual disability or global developmental delays. Pediatrics 134 (2014) e903-18. J. Sebat, B. Lakshmi, J. Troge, J. Alexander, J. Young, P. Lundin, S. Månér, H. Massa, M. Walker, M. Chi, N. Navin, R. Lucito, J. Healy, J. Hicks, K. Ye, A. Reiner, T.C. Gilliam, B. Trask, N. Patterson, A. Zetterberg, and M. Wigler, Large-scale copy number polymorphism in the human genome. Science 305 (2004) 525-8. H. 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Nassisi, C. Mainetti, A. Aretti, A. Sperti, V. Nicotra, B. Rinaldi, F. Natacci, M.F. Bedeschi, and F. Viola, Ocular features in Williams-Beuren syndrome: a review of the literature. Current opinion in ophthalmology 34 (2023) 514-521. A.E. Lackey, and M.R. Muzio, DiGeorge Syndrome, StatPearls, StatPearls Publishing Copyright © 2024, StatPearls Publishing LLC., Treasure Island (FL) ineligible companies. Disclosure: Maria Rosaria Muzio declares no relevant financial relationships with ineligible companies., 2024. P. Shi, Y. Xia, Q. Li, and X. Kong, Usefulness of copy number variant detection following monogenic disease exclusion in prenatal diagnosis. The journal of obstetrics and gynaecology research 47 (2021) 1002-1008. E.R. Riggs, E.F. Andersen, A.M. Cherry, S. Kantarci, H. Kearney, A. Patel, G. Raca, D.I. Ritter, S.T. South, E.C. Thorland, D. Pineda-Alvarez, S. Aradhya, and C.L. 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Omlin, How to read a next-generation sequencing report-what oncologists need to know. ESMO open 7 (2022) 100570. J. Xuan, Y. Yu, T. Qing, L. Guo, and L. Shi, Next-generation sequencing in the clinic: promises and challenges. Cancer letters 340 (2013) 284-95. H. Luo, Q. Wang, D. Fu, J. Gao, and D. Lu, Additional diagnostic value of CNV-seq over conventional karyotyping in prenatal diagnosis: A systematic review and meta-analysis. The journal of obstetrics and gynaecology research 49 (2023) 1641-1650. N. Li, L. Wang, H. Wang, M. Ma, X. Wang, Y. Li, W. Zhang, J. Zhang, D.S. Cram, and Y. Yao, The Performance of Whole Genome Amplification Methods and Next-Generation Sequencing for Pre-Implantation Genetic Diagnosis of Chromosomal Abnormalities. Journal of genetics and genomics = Yi chuan xue bao 42 (2015) 151-9. J. Muys, B. Blaumeiser, K. Janssens, P. Loobuyck, and Y. Jacquemyn, Chromosomal microarray analysis in prenatal diagnosis: ethical considerations of the Belgian approach. Journal of medical ethics 46 (2020) 104-109. A.J. Iafrate, L. Feuk, M.N. Rivera, M.L. Listewnik, P.K. Donahoe, Y. Qi, S.W. Scherer, and C. Lee, Detection of large-scale variation in the human genome. Nat Genet 36 (2004) 949-51. J.M. Kidd, G.M. Cooper, W.F. Donahue, H.S. Hayden, N. Sampas, T. Graves, N. Hansen, B. Teague, C. Alkan, F. Antonacci, E. Haugen, T. Zerr, N.A. Yamada, P. Tsang, T.L. Newman, E. Tüzün, Z. Cheng, H.M. Ebling, N. Tusneem, R. David, W. Gillett, K.A. Phelps, M. Weaver, D. Saranga, A. Brand, W. Tao, E. Gustafson, K. McKernan, L. Chen, M. Malig, J.D. Smith, J.M. Korn, S.A. McCarroll, D.A. Altshuler, D.A. Peiffer, M. Dorschner, J. Stamatoyannopoulos, D. Schwartz, D.A. Nickerson, J.C. Mullikin, R.K. Wilson, L. Bruhn, M.V. Olson, R. Kaul, D.R. Smith, and E.E. Eichler, Mapping and sequencing of structural variation from eight human genomes. Nature 453 (2008) 56-64. P. Stankiewicz, and J.R. Lupski, Genome architecture, rearrangements and genomic disorders. Trends in genetics : TIG 18 (2002) 74-82. Q. Wang, T. Hu, L. Chen, J. Wang, Y. Zeng, D. Yin, J. Wang, Z. Zhang, and S. Liu, [Application of copy number variation squencing for prenatal diagnosis]. Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics 39 (2022) 949-953. M.G. Butler, Prader-Willi Syndrome and Chromosome 15q11.2 BP1-BP2 Region: A Review. International journal of molecular sciences 24 (2023). G. Leader, A. Curtin, R.J. Shprintzen, S. Whelan, R. Coyne, and A. Mannion, Adaptive living skills, sleep problems, and mental health disorders in adults with 22q11.21 deletion syndrome. Research in developmental disabilities 136 (2023) 104491. X.L. Jiang, B. Liang, W.T. Zhao, N. Lin, H.L. Huang, M.Y. Cai, and L.P. Xu, Prenatal diagnosis of 15q11.2 microdeletion fetuses in Eastern China: 21 case series and literature review. The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstet 36 (2023) 2262700. M.A. Hicks, E. Lalonde, J. Zoladz, B. Gonik, and S. Ebrahim, A Diagnosis of Maternal 22q Duplication and Mosaic Deletion following Prenatal Cell-Free DNA Screening. Case reports in genetics 2023 (2023) 9127430. L. Yang, X. Shu, S. Mao, Y. Wang, X. Du, and C. Zou, Genotype-Phenotype Correlations in Angelman Syndrome. Genes (Basel) 12 (2021). J. Duis, M. Nespeca, J. Summers, L. Bird, K. Bindels-de Heus, M.J. Valstar, M.Y. de Wit, C. Navis, M. Ten Hooven-Radstaake, B.M. van Iperen-Kolk, S. Ernst, M. Dendrinos, T. Katz, G. Diaz-Medina, A. Katyayan, S. Nangia, R. Thibert, D. Glaze, C. Keary, K. Pelc, N. Simon, A. Sadhwani, H. Heussler, A. Wheeler, C. Woeber, M. DeRamus, A. Thomas, E. Kertcher, L. DeValk, K. Kalemeris, K. Arps, C. Baym, N. Harris, J.P. Gorham, B.L. Bohnsack, R.C. Chambers, S. Harris, H.G. Chambers, K. Okoniewski, E.R. Jalazo, A. Berent, C.A. Bacino, C. Williams, and A. Anderson, A multidisciplinary approach and consensus statement to establish standards of care for Angelman syndrome. Mol Genet Genomic Med 10 (2022) e1843. H. Gregoric Kumperscak, D. Krgovic, and N.K. Vokac, Specific behavioural phenotype and secondary cognitive decline as a result of an 8.6 Mb deletion of 2q32.2q33.1. The Journal of international medical research 44 (2016) 395-402. H. Lewis, D. Samanta, J.L. Örsell, K.A. Bosanko, A. Rowell, M. Jones, R.C. Dale, S. Taravath, C.D. Hahn, D. Krishnakumar, S. Chagnon, S. Keller, E. Hagebeuk, S. Pathak, E.M. Bebin, D.H. Arndt, J.J. Alexander, G. Mainali, G. Coppola, J. Maclean, S. Sparagana, N. McNamara, D.M. Smith, V. Raggio, M. Cruz, A. Fernández-Jaén, M.P. Kava, L. Emrick, J.L. Fish, A. Vanderver, G. Helman, T.M. Pierson, and Y.A. Zarate, Epilepsy and Electroencephalographic Abnormalities in SATB2-Associated Syndrome. Pediatric neurology 112 (2020) 94-100. A.C.M. Smith, K.E. Boyd, C. Brennan, J. Charles, S.H. Elsea, B.M. Finucane, R. Foster, A. Gropman, S. Girirajan, and B. Haas-Givler, Smith-Magenis Syndrome. in: M.P. Adam, J. Feldman, G.M. Mirzaa, R.A. Pagon, S.E. Wallace, L.J.H. Bean, K.W. Gripp, and A. Amemiya, (Eds.), GeneReviews(®), University of Washington, Seattle Copyright © 1993-2023, University of Washington, Seattle. GeneReviews is a registered trademark of the University of Washington, Seattle. All rights reserved., Seattle (WA), 1993. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4669074","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":330035328,"identity":"e160a414-d206-4e93-bcb7-c3b7c5ee7c1d","order_by":0,"name":"Shaohua Zhu","email":"","orcid":"","institution":"Gansu Maternity and Child-care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shaohua","middleName":"","lastName":"Zhu","suffix":""},{"id":330035330,"identity":"1d854bce-0aba-4f2b-b77e-8b33a705acb4","order_by":1,"name":"Shibing Cheng","email":"","orcid":"","institution":"Gansu Provincial Clinical Research Center for Birth Defects and Rare Diseases","correspondingAuthor":false,"prefix":"","firstName":"Shibing","middleName":"","lastName":"Cheng","suffix":""},{"id":330035331,"identity":"74170067-93e3-4866-931f-85191a1af95b","order_by":2,"name":"Chunyang Jia","email":"","orcid":"","institution":"Gansu Maternity and Child-care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunyang","middleName":"","lastName":"Jia","suffix":""},{"id":330035334,"identity":"76e41d45-6362-415d-ab78-789b84e1b370","order_by":3,"name":"Pengwu Lin","email":"","orcid":"","institution":"Gansu Provincial Clinical Research Center for Birth Defects and Rare Diseases","correspondingAuthor":false,"prefix":"","firstName":"Pengwu","middleName":"","lastName":"Lin","suffix":""},{"id":330035336,"identity":"b58b9b4b-34ea-4a14-bcba-43ac6f044498","order_by":4,"name":"Peng Zhang","email":"","orcid":"","institution":"Gansu Maternity and Child-care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhang","suffix":""},{"id":330035338,"identity":"5d3ecf88-1579-4b5b-9919-45dc811d4a30","order_by":5,"name":"YuanYuan Guo","email":"","orcid":"","institution":"Gansu Provincial Clinical Research Center for Birth Defects and Rare Diseases","correspondingAuthor":false,"prefix":"","firstName":"YuanYuan","middleName":"","lastName":"Guo","suffix":""},{"id":330035339,"identity":"2615124d-bb8d-41bc-89f6-7531270425ea","order_by":6,"name":"Furong Liu","email":"","orcid":"","institution":"Gansu Provincial Clinical Research Center for Birth Defects and Rare Diseases","correspondingAuthor":false,"prefix":"","firstName":"Furong","middleName":"","lastName":"Liu","suffix":""},{"id":330035340,"identity":"85188394-c7dc-487d-8e32-d2deb5500aac","order_by":7,"name":"Xuan Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBAC9gYog429sfHBBwYJwlp4DkAZfDyHmw1nkKRFTiK9TZqHGIfxsPcefs3bZpfHxpDYIG3zxyKPv4H54aMb+LTwnEuznNmWXMzGcLDBOLdNoljiAJuxcQ4eLfYSOWYGH9uYE9sYGxuScxskEhsO8LBJ49PCI//GzCCxrT6xjZmx4bDFH4nE+QS1SPAYP/jYdjixjY2xsZmBTSJxA0EtPDlmjDPOHU9s42FsZuxtk0jceJiAX3jYzxh/5imrTpw///nzHz/+1CXOO9788DE+LUDAhhZ9zPiVg5V8IKxmFIyCUTAKRjQAAFvYStf24BkZAAAAAElFTkSuQmCC","orcid":"","institution":"Gansu Maternity and Child-care Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2024-07-01 15:23:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4669074/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4669074/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-18350-x","type":"published","date":"2025-09-29T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61178117,"identity":"aea49d63-ad4f-443a-b44a-69e0d9fc6c3f","added_by":"auto","created_at":"2024-07-26 15:57:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74881,"visible":true,"origin":"","legend":"\u003cp\u003eCNV-seq detection distribution. B/LB represents Benign or Likely Benign; T21 represents Trisomy 21; T18 represents Trisomy 18; VUS represents Variant of Unknown Significance; Au CNVs represents Autosomal CNVs; S chr CNVs represents Sex chromosome CNVs; S chr CNVs mos. represents Sex chromosome CNVs mosaicism.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4669074/v1/0c6ebb41f4246691613390a7.png"},{"id":61178919,"identity":"5c00ca4e-846d-4b32-a50d-efaf4edc438c","added_by":"auto","created_at":"2024-07-26 16:05:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95291,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of CNVs in each chromosome\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4669074/v1/152d24e105a188580d676550.png"},{"id":92883783,"identity":"d228554b-ecec-4507-bb5c-0555223ed97d","added_by":"auto","created_at":"2025-10-06 16:09:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":767244,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4669074/v1/7268f049-bf74-4373-938f-3fe9e4fb545a.pdf"},{"id":61178920,"identity":"ede67f69-da68-4965-adcd-902c652a9b0c","added_by":"auto","created_at":"2024-07-26 16:05:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18666,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4669074/v1/2c2ffd9ac62124a0a1a8f721.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical application of CNV-Seq in the diagnosis of children with abnormal brain development in 130 cases","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAbnormal brain development (ABD) refers to cognitive function defects and lag in social adaptation awareness or behavior during the developmental process (children become abnormal before adulthood). The total incidence rate of the disease was within 3%, of which the incidence rate of ABD was 0.3\u0026thinsp;~\u0026thinsp;0.4% for heavy and medium-sized diseases, and the prevalence ratio of women and men in ABD is about 0.71\u0026thinsp;~\u0026thinsp;0.63:1[1; 2]. ABD was divided into non-syndrome type (Non-syndrome NS-ABD) and syndrome type (Syndrome ABD, S-ABD) according to clinical manifestations, S-ABD was also accompanied by special features and organ deformities[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As a result, the etiology of ABD was extremely complicated, included factors of genetic, nutritional, and endocrine, they could all be the cause of the ABD[4; 5].\u003c/p\u003e \u003cp\u003eCopy number variation (CNVs) was a common occurrence in chromosomes, which referred to the replication or deletion of DNA fragments larger than 1kb on chromosomes, and when these replicated or deleted fragments caused abnormalities in the function of the genome, they were classified as pathogenic CNVs (pCNVs), they could be divided into micro-deletions, micro-repetitions, complex rearrangements, and other more complicated variations. In most cases, CNV exists in a normal polymorphic form, and only a few CNVs were related to genetic diseases. However, micro-deletion/replication syndrome caused by pCNVs still played an important role in human genetic diseases. For common chromosomal MMS[2; 6], such as Williams-Beuren syndrome (WBS, around 1.6MB heterozygous deletion at 7q11.23), Angelman/Prader-Willi syndrome (AS/PWS, 15q11-13), DGS (22q11.2 deletion of heterozygosity was caused by CNVs of chromosomal segments[7; 8; 9]. In these chromosomal diseases, the location of CNV was distributed in different regions of the genome, and the size of the fragments was also different. The technology of CNV-Seq adopted next generation sequencing to perform low-depth whole-genome sequencing of the DNA of samples, which could detect abnormal fragments with a size of larger than 0.1MB, and it had become one of the most suitable methods for prenatal diagnosis because of its large detection throughput, high resolution, easy operation, and short detection time[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current study performed CNV-seq on the sample of 130 ABD fetuses (amniotic fluid) and children (peripheral blood) diagnosed from Gansu Maternal and Child Health Hospital to detect chromosomal aneuploidy and CNVs associated with brain development. Based on the different phenotypic characteristics of the patient population, we divided the research population into group of S-ABD and group of NS-ABD, then we compared the sequencing results between 2 groups. The fragment abnormalities in the genome were detected and analyzed via CNV-seq, and we performed genetic and statistical analyzes on the clinical manifestations and CNVs abnormalities in the population, based on this information to evaluate the diagnostic and application value of CNV-seq for cases with ABD, so as to deepen and consolidate clinicians' understanding of the disease. The ultimate purpose of this study was to provide theoretical data reference for the population of patients especially for children with ABD in Northwest China, in order to strengthen the prevention and treatment ideas and methods for ABD.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study oversight\u003c/h2\u003e \u003cp\u003eThe Medical Genetics Center where the research was located and authorized by the Gansu Provincial Health Commission and has testing qualifications. All the patients were registered at the Medical Genetics Center according to CNV-seq standard procedures and signed informed consent forms. The informed consent form details the testing method, the type of sample required, the restricted population, and the potential risks. In addition, the informed consent form also contains insurance plans, legal declarations, national ethics declarations, laboratory application processes, and some columns of information for consultation before testing. In order to ensure the fairness and reliability of the research results, we provided each subject with information such as the positive predicted value (PPV), negative predicted value (NPV), sensitivity and specificity of the CNV test results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample collection and DNA extraction\u003c/h2\u003e \u003cp\u003e After informing the patient or their family members and obtaining knowledge and consent, the informed consent form was signed according to the standard, and the family members of the patient express their understanding and agree to publish it as a study object. For peripheral blood samples: collect peripheral blood (5 mL) from the child and transfer it to an anticoagulant blood collection vessel containing EDTA (KIRGEN Medical Equipment Co., Ltd. China) ;For amniotic fluid samples: puncture according to ultrasonic positioning assistance to take 15 mL of amniotic fluid in a centrifuge tube༛For fetal villi tissue samples: short-term culture in the laboratory after the villi was absorbed by the syringe.\u003c/p\u003e \u003cp\u003e Adopted the QIA amp DNA Micro kit (Germany, Qiagen Biological Company) to extract sample genomic DNA according to the Standard Operating Procedure (SOP) contained in the manual. In addition, the Qubit 3.0 fluorescence photometer (Thermo Life Technologies, USA) was used to perform DNA concentration determination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sequencing and analysis\u003c/h2\u003e \u003cp\u003eBased on the CN-500 NGS sequencer (United States, Illuminina Biological Company) for low-depth genome sequencing, the raw-data obtained by sequencing is cleaned and determined. After filtering low-quality READS through quality control (quality control, QC), CLEAN data is generated with a reading length of 36bp and an average sequencing depth of 0.1\u0026times;. It is exported in BAM format to the CNV version 2.0 analysis system (Beijing Berry-Genomics Biotechnology Co., Ltd.) for chromosome copy number analysis. Sequence comparison of each READ after QC with the corresponding human standard chromosome group, standardized analysis of the data, then identified whether there was CNV and abnormal types of chromosomes in the detection fragment. According to public mutation databases and population frequency databases such as ClinVar, HGMD, gnomAD, DECIPHER, and 1000 Genome Project, the detected variants are bioinformatics annotated to determine the clinical significance of CNVs, and the data in the database is used to predict whether CNVs fragments have pathogenic. It was worth noting that in order to ensure the reliability of the results, this study only compared CNV fragments larger than 100 Kb. The pathogenicity of CNV was graded and judged according to the guidelines on CNV pathogenicity issued by the American College of Medical Genetics and Genomics (ACMG) and ClinGen[11; 12].\u003c/p\u003e \u003cp\u003eStatistical analysis was performed using R software (version 4.2.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). The chi-square test (χ\u0026sup2;) was employed within the R package \"chisq.test\" function to evaluate associations between variables. Statistical results are presented as percentages (%) and p-values. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used to determine statistically significant differences[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 CNV-seq\u003c/h2\u003e \u003cp\u003eThe study consisted of 130 sample groups, involving 42 patients, accounting for 32.31% (42/130), and a total of 50 abnormal CNVs fragments were detected, included aneuploidy and CNVs, of which there were 3 cases of aneuploidy (two cases of trisomy 21 and one case of trisomy 18) and 39 cases of CNVs exceeding the patient count due to multiple pCNVs in some individuals. Among the detected CNVs, 23 (17.69%) were classified as pathogenic (P) or likely pathogenic (LP) based on available evidence (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for details). Eight cases involved fragments exceeding 10 Mb, encompassing both deletions and duplications at various loci. Three cases had fragments between 5–10 Mb, and 12 cases had fragments less than 5 Mb. Notably, some patients carried multiple pCNVs, resulting in a total number of identified CNVs exceeding the number of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The distribution of pCNVs across chromosomes was depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, with ChrX, Chr15, Chr 2, and Chr 17 exhibiting higher detection rates.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of positivity rates between NS-ABD and S-ABD groups\u003c/h2\u003e \u003cp\u003eAnalysis of the 130 patients with ABD identified abnormalities in 42 individuals. Within the NS-ABD group (n = 15), 15 abnormalities were detected, including 1 case of aneuploidy (6.67%), four pCNVs (26.67%), and 10 CNVs of unknown clinical significance (66.67%). The S-ABD group (n = 27) harbored abnormalities in 27 patients, encompassing 2 cases of aneuploidy (7.41%), 19 PCNVs (70.37%), and 6 CNVs of unknown clinical significance (22.22%). Chi-square analysis revealed a significant difference (χ² = 40.03, p \u0026lt; 0.05) in the detection rate of pCNVs between the NS-ABD and S-ABD groups. Additionally, the overall positive detection rate was significantly higher in the S-ABD group (77.78%) compared to the NS-ABD group (33.33%) (χ² = 40.97, p \u0026lt; 0.05) (details in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCNV-seq test and statistical results of 130 patients with ABD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGroup (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS-BD (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS-BD (%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAneuploidy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePathogenic CNVs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,102\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive cases\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \n\u003ch3\u003e3.3 Screening of candidate genes associated with ABD in CNVs region\u003c/h3\u003e\n\u003cp\u003eBased on the detected pCNVs segment, we screened the ABD candidate gene. First, according to the physical location of the candidate gene, the genes associated with ABD were retrieved in the OMIM database. Second, considering the level of gene expression and its function, 8 candidate genes that may affect ABD were finally selected. They were \u003cem\u003eUBE3A, AUTS4, SATB2, GLSS, SMCR, ARSL\u003c/em\u003e and \u003cem\u003eCDPX1\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable2\u0026nbsp;\u003c/strong\u003eCopy number variation analysis of candidate genes\u003c/p\u003e\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd width=\"4.2105263157894735%\" style=\"width: 3.2888%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.368421052631579%\" style=\"width: 6.4809%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.368421052631579%\" style=\"width: 4.1594%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"12.631578947368421%\" colspan=\"\" style=\"width: 7.9319%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCNVs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"9.473684210526315%\" colspan=\"\" style=\"width: 8.3188%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of CNVs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.368421052631579%\" colspan=\"\" style=\"width: 6.6744%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFragment length\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"21.05263157894737%\" style=\"width: 12.8652%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSyndrome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"11.578947368421053%\" colspan=\"\" style=\"width: 9.1894%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathogenicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"18.94736842105263%\" style=\"width: 3.7338%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCandidate gene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"4.2105263157894735%\" style=\"width: 3.2888%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.368421052631579%\" style=\"width: 6.4809%;\"\u003e\n \u003cp\u003ePeripheral blood\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.368421052631579%\" style=\"width: 4.1594%;\"\u003e\n \u003cp\u003e2Y and 2M\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"12.631578947368421%\" colspan=\"\" style=\"width: 7.9319%;\"\u003e\n \u003cp\u003e15q11.2-q13.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"9.473684210526315%\" colspan=\"\" style=\"width: 8.3188%;\"\u003e\n \u003cp\u003eduplication\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.368421052631579%\" colspan=\"\" style=\"width: 6.6744%;\"\u003e\n \u003cp\u003e7.64Mb\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"21.05263157894737%\" style=\"width: 12.8652%;\"\u003e\n \u003cp\u003e15q11-q13 duplication syndrome\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"11.578947368421053%\" colspan=\"\" style=\"width: 9.1894%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"18.94736842105263%\" style=\"width: 3.7338%;\"\u003e\n \u003cp\u003e\u003cem\u003eUBE3A\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eAUTS4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"4.2105263157894735%\" style=\"width: 3.2888%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.368421052631579%\" style=\"width: 6.4809%;\"\u003e\n \u003cp\u003ePeripheral blood\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.368421052631579%\" style=\"width: 4.1594%;\"\u003e\n \u003cp\u003e9M\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"12.631578947368421%\" colspan=\"\" style=\"width: 7.9319%;\"\u003e\n \u003cp\u003e2q33.1-q33.3\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"9.473684210526315%\" colspan=\"\" style=\"width: 8.3188%;\"\u003e\n \u003cp\u003edeletion\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.368421052631579%\" colspan=\"\" style=\"width: 6.6744%;\"\u003e\n \u003cp\u003e9.56Mb\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"21.05263157894737%\" style=\"width: 12.8652%;\"\u003e\n \u003cp\u003e2q32-q33 deletion syndrome\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"11.578947368421053%\" colspan=\"\" style=\"width: 9.1894%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"18.94736842105263%\" style=\"width: 3.7338%;\"\u003e\n \u003cp\u003e\u003cem\u003eSATB2\u003c/em\u003e\u003cem\u003e、\u003c/em\u003e\u003cem\u003eGLSS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"4.166666666666667%\" style=\"width: 3.2888%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.291666666666667%\" style=\"width: 6.4809%;\"\u003e\n \u003cp\u003ePeripheral blood\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.291666666666667%\" style=\"width: 4.1594%;\"\u003e\n \u003cp\u003e2Y\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"11.458333333333334%\" style=\"width: 6.9646%;\"\u003e\n \u003cp\u003e17p11.2\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"8.333333333333334%\" colspan=\"\" style=\"width: 7.6417%;\"\u003e\n \u003cp\u003eduplication\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"9.375%\" colspan=\"\" style=\"width: 8.1254%;\"\u003e\n \u003cp\u003e3.88Mb\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"21.875%\" colspan=\"\" style=\"width: 13.0586%;\"\u003e\n \u003cp\u003eSmith-Magenis syndrome\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"11.458333333333334%\" style=\"width: 8.9959%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"18.75%\" colspan=\"\" style=\"width: 7.1581%;\"\u003e\n \u003cp\u003e\u003cem\u003eSMCR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd width=\"4.166666666666667%\" style=\"width: 3.2888%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.291666666666667%\" style=\"width: 6.4809%;\"\u003e\n \u003cp\u003eAmniotic fluid\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"7.291666666666667%\" style=\"width: 4.1594%;\"\u003e\n \u003cp\u003e29W\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"11.458333333333334%\" style=\"width: 6.9646%;\"\u003e\n \u003cp\u003eXp22.33-p11.1\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"8.333333333333334%\" colspan=\"\" style=\"width: 7.6417%;\"\u003e\n \u003cp\u003e\u0026nbsp;deletion\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"9.375%\" colspan=\"\" style=\"width: 8.1254%;\"\u003e\n \u003cp\u003e55.86Mb\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"21.875%\" colspan=\"\" style=\"width: 13.0586%;\"\u003e\n \u003cp\u003eChondrodysplasia Punctata\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"11.458333333333334%\" style=\"width: 8.9959%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\u003ctd width=\"18.75%\" colspan=\"\" style=\"width: 7.1581%;\"\u003e\n \u003cp\u003e\u003cem\u003eARSL, CDPX1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith the continuous development of next-generation sequencing (NGS), more research and medical testing in recent years have begun to adopt NGS to explore sequence variation[14; 15]. Compared with methods of microarray, NGS has the advantages of greater single throughput, faster detection speed, higher resolution, lower cost, and high repeatability. For the identification of chromosomal abnormalities at the sub-microscopic level, the most common method was CNV-seq, it was based on estimating the extension of different statistical models in the confidence interval, and was suitable for the comparison and recognition of the proportion of CNVs[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Different from microarray genotyping of target fragments, CNV-seq adopted the READS reference sequence as a template[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. When comparing sequences, double of Shotgun sequences were adopted for pairing, and two-dimensional sequence comparisons were performed with the templates. The data reading method was Slide Window Mode, finally, we compared the confidence level of the calculation results with above method was not suitable for long sequences and the accuracy of analysis of large fragments needs to be improved[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eABD is actually an adaptive behavior defect and cognitive impairment accompanied by human development. The prevalence rate of this disease is about 6%-8%. It is one of the important diseases that endanger physical and mental health. The main causes include genetic and environmental factors. Chromosomal abnormalities, gene mutations, and pCNVs might all lead to the occurrence of ABD. About 30–40% of ABD patients were caused by chromosomal abnormalities. In addition, the prevalence of ABD in the general population about was 1%~3%[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. China has a very large population, so the number of ABD patients is correspondingly considerable. According to the current situation, most patients with ABD lack effective and targeted prevention and treatment. In other words, it is very important to clarify the cause of ABD. However, the etiology of ABD is very complicated. Based on data from the World Health Organization, it is reported that more than 50% of ABD patients have unknown causes of disease. In China, this rate is even more serious, accounting for about 67%[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In fact, genetic factors play a key role in ABD. ABD was divided into S-ABD and NS-ABD. The clinical phenotypes of patients with NS-ABD mainly include typical language disorders, motor developmental delays, and mental decline; unlike NS-ABD, patients with S-ABD included the above abnormalities, but also combine other diseases or systemic deformities, included congenital heart disease, abnormal face, and cleft lip and palate[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn current study, 130 ABD patients were selected for CNV-seq analysis. In addition, we combined the clinical phenotypes and ultrasound indicators of the patients involved in the study to group the ABD patient population into NS-ABD group and S-ABD group. Through data analysis, it was suggested that there were 42 cases of abnormal results (42/130). It was worth noting that among these patients, there have been cases where the same patient contains multiple CNVs regional abnormalities. In other words, the same patient carries 2 or more abnormal CNVs at the same time. More and more studies have shown that the adopt of CNV-seq method in genetic diseases that might be caused by chromosomal aberrations such as mental developmental delay and sexual dysplasia could be very effective in improving the detection rate. Therefore, the method was indeed worthy of being recommended as one of the suitable methods for clinical diagnosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Based on the statistical results of this study, our detection rate is as high as 32.3%, which also supports the above view. In current study, the cases detected by CNV-seq included ABD patients with aneuploidy abnormalities, and in addition, they also included ABD patients caused by pCNVs. According to the classification of ABD phenotypes, we further performed an intergroup comparison between NS-ABD and S-ABD, and the results showed that there was a significant difference (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 40.03, P\u0026lt;0.05) in the detection rate of pathogenic CNVs between the these of two groups. What’s more, based on the results of inter-group comparison, this study continued to analyze the difference in the positivity rate (total detection rate) of the two pathogenic factors aneuploidy abnormality and pCNVs. The results suggested that in the above-mentioned patient groups, the positivity rate of the NS-ABD group was 33.33%, and the positivity rate of the S-ABD group was 77.78%, obviously, the positivity rate of the two groups of patients was also significantly different (χ2 = 40.97, P \u0026lt; 0.05). According to the results above, the current study showed that chromosomal abnormalities and pCNVS were indeed more likely to appear among S-ABD patients. And there were limitations of the above methods: considering that many patients refuse to provide their own information for the current study, the data volume of this study was not large, the current rate of detection and positivity could be fluctuating or uncertain. We would further increase the number of samples or patient groups in future studies.\u003c/p\u003e \u003cp\u003eIn the current study, a total of 27 kinds of pCNVs included 15q11.2-q13.2, 2q33.1-q33.3, 17p11.2 and Xp22.33-p11.1 were found, pCNVs such as 15q11.2-q13.2 micro-deletion syndrome[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and 22q11.21 microdeletion syndrome[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which have been reported various times and might cause ABD, have been discovered in the current studies, What's more, we also found rarely reported pCNV fragments associated with ABD such as p11.23. There was no obvious pattern in the distribution of these of pCNVs within the chromosomes, which further indicated the widespread of the ABD-associated CNVs region, and also suggested the complexity of the ABD genetic mechanism. Therefore, we adopted the information included in the such four public databases DECIPHER, OMIM, ClinGen and PubMed, and then investigated the highly associated genes located close to the region where the pCNVs fragments appeared, based on the above-mentioned pCNVs fragments. a total of 7 candidate genes associated with ABD were selected. Within the 15q11.2-q13.2 chromosomal region, mutations in the UBE3A and AUTS2 genes were associated with two distinct neurodevelopmental disorders: 15q11-q13 microdeletion syndrome and Angelman syndrome. These syndromes share a common clinical presentation characterized by intellectual disability, ataxia, and motor delays[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and it was true that many studies have proved that \u003cem\u003eUBE3A\u003c/em\u003e was the essential gene for Angelman syndrome[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]; The \u003cem\u003eGLSS\u003c/em\u003e and \u003cem\u003eSATB2\u003c/em\u003e contained within the 2q33.1-q33.3 region were involved in Glass Syndrome. Common clinical manifestations of this syndrome include growth delay and severe intellectual backwardness, and these of phenotypes would continue to extend to the prenatal and postpartum stages[27; 28]; The \u003cem\u003eSMCR\u003c/em\u003e contained within the 17p11.2 region involved Smith-Magenis Syndrome, and its main clinical manifestations included mild to moderate intellectual disability and lag of reflexes[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; It should be noted that the \u003cem\u003eARSL\u003c/em\u003e and \u003cem\u003eCDPX1\u003c/em\u003e contained within the Xp22.33 region were closely associated with dot-shaped cartilage dysplasia typeⅠ. Although the phenotypes of the syndrome mainly include short phalangeal cartilage dysplasia and distal phalangeal hypoplasia of the fingers, many previous reports have also found that the syndrome also has common manifestations including developmental delays and extreme mental decline in infancy[30; 31]. In short, the candidate genes discovered in these of crucial pCNVs regions were closely associated with the occurrence of ABD.\u003c/p\u003e \u003cp\u003eWith the continuous development of next-generation sequencing (NGS), more research and medical testing in recent years have begun to adopt NGS to explore sequence variation[14; 15]. Compared with methods of microarray, NGS has the advantages of greater single throughput, faster detection speed, higher resolution, lower cost, and high repeatability. For the identification of chromosomal abnormalities at the sub-microscopic level, the most common method was CNV-seq, it was based on estimating the extension of different statistical models in the confidence interval, and was suitable for the comparison and recognition of the proportion of CNVs[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Different from microarray genotyping of target fragments, CNV-seq adopted the READS reference sequence as a template[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. When comparing sequences, double of Shotgun sequences were adopted for pairing, and two-dimensional sequence comparisons were performed with the templates. The data reading method was Slide Window Mode, finally, we compared the confidence level of the calculation results with above method was not suitable for long sequences and the accuracy of analysis of large fragments needs to be improved[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eABD is actually an adaptive behavior defect and cognitive impairment accompanied by human development. The prevalence rate of this disease is about 6%-8%. It is one of the important diseases that endanger physical and mental health. The main causes include genetic and environmental factors. Chromosomal abnormalities, gene mutations, and pCNVs might all lead to the occurrence of ABD. About 30–40% of ABD patients were caused by chromosomal abnormalities. In addition, the prevalence of ABD in the general population about was 1%~3%[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. China has a very large population, so the number of ABD patients is correspondingly considerable. According to the current situation, most patients with ABD lack effective and targeted prevention and treatment. In other words, it is very important to clarify the cause of ABD. However, the etiology of ABD is very complicated. Based on data from the World Health Organization, it is reported that more than 50% of ABD patients have unknown causes of disease. In China, this rate is even more serious, accounting for about 67%[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In fact, genetic factors play a key role in ABD. ABD was divided into S-ABD and NS-ABD. The clinical phenotypes of patients with NS-ABD mainly include typical language disorders, motor developmental delays, and mental decline; unlike NS-ABD, patients with S-ABD included the above abnormalities, but also combine other diseases or systemic deformities, included congenital heart disease, abnormal face, and cleft lip and palate[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn current study, 130 ABD patients were selected for CNV-seq analysis. In addition, we combined the clinical phenotypes and ultrasound indicators of the patients involved in the study to group the ABD patient population into NS-ABD group and S-ABD group. Through data analysis, it was suggested that there were 42 cases of abnormal results (42/130). It was worth noting that among these patients, there have been cases where the same patient contains multiple CNVs regional abnormalities. In other words, the same patient carries 2 or more abnormal CNVs at the same time. More and more studies have shown that the adopt of CNV-seq method in genetic diseases that might be caused by chromosomal aberrations such as mental developmental delay and sexual dysplasia could be very effective in improving the detection rate. Therefore, the method was indeed worthy of being recommended as one of the suitable methods for clinical diagnosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Based on the statistical results of this study, our detection rate is as high as 32.3%, which also supports the above view. In current study, the cases detected by CNV-seq included ABD patients with aneuploidy abnormalities, and in addition, they also included ABD patients caused by pCNVs. According to the classification of ABD phenotypes, we further performed an intergroup comparison between NS-ABD and S-ABD, and the results showed that there was a significant difference (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 40.03, P\u0026lt;0.05) in the detection rate of pathogenic CNVs between the these of two groups. What’s more, based on the results of inter-group comparison, this study continued to analyze the difference in the positivity rate (total detection rate) of the two pathogenic factors aneuploidy abnormality and pCNVs. The results suggested that in the above-mentioned patient groups, the positivity rate of the NS-ABD group was 33.33%, and the positivity rate of the S-ABD group was 77.78%, obviously, the positivity rate of the two groups of patients was also significantly different (χ2 = 40.97, P \u0026lt; 0.05). According to the results above, the current study showed that chromosomal abnormalities and pCNVS were indeed more likely to appear among S-ABD patients. And there were limitations of the above methods: considering that many patients refuse to provide their own information for the current study, the data volume of this study was not large, the current rate of detection and positivity could be fluctuating or uncertain. We would further increase the number of samples or patient groups in future studies.\u003c/p\u003e\u003cp\u003eIn the current study, a total of 27 kinds of pCNVs included 15q11.2-q13.2, 2q33.1-q33.3, 17p11.2 and Xp22.33-p11.1 were found, pCNVs such as 15q11.2-q13.2 micro-deletion syndrome[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and 22q11.21 microdeletion syndrome[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which have been reported various times and might cause ABD, have been discovered in the current studies, What's more, we also found rarely reported pCNV fragments associated with ABD such as p11.23. There was no obvious pattern in the distribution of these of pCNVs within the chromosomes, which further indicated the widespread of the ABD-associated CNVs region, and also suggested the complexity of the ABD genetic mechanism. Therefore, we adopted the information included in the such four public databases DECIPHER, OMIM, ClinGen and PubMed, and then investigated the highly associated genes located close to the region where the pCNVs fragments appeared, based on the above-mentioned pCNVs fragments. a total of 7 candidate genes associated with ABD were selected. Within the 15q11.2-q13.2 chromosomal region, mutations in the UBE3A and AUTS2 genes were associated with two distinct neurodevelopmental disorders: 15q11-q13 microdeletion syndrome and Angelman syndrome. These syndromes share a common clinical presentation characterized by intellectual disability, ataxia, and motor delays[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and it was true that many studies have proved that \u003cem\u003eUBE3A\u003c/em\u003e was the essential gene for Angelman syndrome[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]; The \u003cem\u003eGLSS\u003c/em\u003e and \u003cem\u003eSATB2\u003c/em\u003e contained within the 2q33.1-q33.3 region were involved in Glass Syndrome. Common clinical manifestations of this syndrome include growth delay and severe intellectual backwardness, and these of phenotypes would continue to extend to the prenatal and postpartum stages[27; 28]; The \u003cem\u003eSMCR\u003c/em\u003e contained within the 17p11.2 region involved Smith-Magenis Syndrome, and its main clinical manifestations included mild to moderate intellectual disability and lag of reflexes[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]; It should be noted that the \u003cem\u003eARSL\u003c/em\u003e and \u003cem\u003eCDPX1\u003c/em\u003e contained within the Xp22.33 region were closely associated with dot-shaped cartilage dysplasia typeⅠ. Although the phenotypes of the syndrome mainly include short phalangeal cartilage dysplasia and distal phalangeal hypoplasia of the fingers, many previous reports have also found that the syndrome also has common manifestations including developmental delays and extreme mental decline in infancy[30; 31]. In short, the candidate genes discovered in these of crucial pCNVs regions were closely associated with the occurrence of ABD.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, the current research shows that the occurrence of ABD was closely related to chromosomal aneuploidy and pCNVs, the detection rate of pCNVs and positive detection rate of chromosomal in population of patients with S-ABD type were significantly higher than that of NS-ABD type. Combined with the current research results, patients with NSABD were indeed more likely to be ignored in clinical diagnosis. Therefore, we recommended that more attention should be paid to patients with NS-ABD types and these of patients should be reminded to receive appropriate tests. In addition, the detection of submicroscopic chromosomal aberrations through CNV-seq could significantly improved the detection efficiency of diagnosis, performing analysis on the correlation between genotypes and phenotypes could also provide the reliable genetic reference for prenatal diagnosis related to ABD, and of course, it could also provide basic theoretical basis and application guidance for eugenics and postnatal care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical Statements:\u003c/h2\u003e \u003cp\u003e All sample collection procedures in the study strictly complied with the national ethical guidelines and were approved by the Ethics Committee of Gansu Maternal and Child Health Hospital (Batch No.: 2021GSFY Ethical Review). The study also complied with the World Medical Association Declaration of Helsinki.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research: including experimental design, sample collection, data analysis, and manuscript writing, was funded by the Gansu Provincial Department of Science and Technology Innovation Base and Talent Plan (21JR7RA680), the Major project of Gansu Maternal and Child Health Hospital (GSFY-2021), and Clinical application of non-invasive prenatal genetic testing technology in chromosomal microdeletion and microduplication syndrome (2017-04-50).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWe acknowledge Mr Xuan Feng and Furong Liu for reading through the manuscript and giving valuable comments Conceptualization, Shaohua Zhu and ; Data curation, Shaohua Zhu and Chunyang Jia; Formal analysis, Shaohua Zhu and Shibing Cheng; Funding acquisition, Xuan Feng; Investigation, YuanYuan Guo and PengwuLin; Methodology, Xuan Feng; Project administration, Xuan Feng and Furong Liu; Resources, Yuanyuan Guo and Chunyang Jia; Software, Shaohua Zhu and Peng Zhang; Validation, Shibing Cheng; Visualization, Pengwu Lin; Writing \u0026ndash; original draft, Shaohua Zhu and Shibing Cheng; Writing \u0026ndash; review \u0026amp; editing, Furong Liu and Xuan Feng.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to the data were sourced from the internal LAN database of the medical department(Gansu Provincial Clinical Research Center for Birth Defects and Rare Diseases), and the current study has only obtained ethical permission, but has not obtained permission for all patient public data, and it could be noted that the data were available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJ.B. 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Samanta, J.L. \u0026Ouml;rsell, K.A. Bosanko, A. Rowell, M. Jones, R.C. Dale, S. Taravath, C.D. Hahn, D. Krishnakumar, S. Chagnon, S. Keller, E. Hagebeuk, S. Pathak, E.M. Bebin, D.H. Arndt, J.J. Alexander, G. Mainali, G. Coppola, J. Maclean, S. Sparagana, N. McNamara, D.M. Smith, V. Raggio, M. Cruz, A. Fern\u0026aacute;ndez-Ja\u0026eacute;n, M.P. Kava, L. Emrick, J.L. Fish, A. Vanderver, G. Helman, T.M. Pierson, and Y.A. Zarate, Epilepsy and Electroencephalographic Abnormalities in SATB2-Associated Syndrome. Pediatric neurology 112 (2020) 94-100.\u003c/li\u003e\n\u003cli\u003eA.C.M. Smith, K.E. Boyd, C. Brennan, J. Charles, S.H. Elsea, B.M. Finucane, R. Foster, A. Gropman, S. Girirajan, and B. Haas-Givler, Smith-Magenis Syndrome. in: M.P. Adam, J. Feldman, G.M. Mirzaa, R.A. Pagon, S.E. Wallace, L.J.H. Bean, K.W. Gripp, and A. Amemiya, (Eds.), GeneReviews(\u0026reg;), University of Washington, Seattle Copyright \u0026copy; 1993-2023, University of Washington, Seattle. GeneReviews is a registered trademark of the University of Washington, Seattle. All rights reserved., Seattle (WA), 1993.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"abnormal brain development (ABD), Aneuploidy, Copy number variation (CNV), CNV-seq","lastPublishedDoi":"10.21203/rs.3.rs-4669074/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4669074/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground\u003c/em\u003e To evaluate the diagnostic value of applying genome copy number variation sequencing (CNV-seq) in the genetic etiology of abnormal brain development (ABD).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods\u003c/em\u003e We selected 130 ABD patients discovered in Gansu Maternal and Child Health Hospital from December 2018 to October 2023 as the research subjects, divided into non-syndrome ABD group and syndrome ABD group, performed CNV-seq testing and analyzed the genetic causes of copy number variation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults \u003c/em\u003eIn the 130 cases, we detected a total of 42 abnormal samples, with an abnormal detection rate of 32.3%, included 3 cases (2.3%) of aneuploidy and 39 cases (30%) of CNVs; of which 15 cases were detected in the non-syndrome ABD group, included 1 case (6.67%, 1/15) of aneuploidy, 4 cases (26.7%，4/15) of pathogenic CNVs (pCNVs) and 10 case (66.7%，10/15) of variant of uncertain significance (VUS), and 27 cases were detected in the syndrome-type ABD group, including 2 cases (7.4%, 2/27) of aneuploidy, 19 cases (70.4%, 19/27) of pCNVs and 6 cases (22.2%, 6/27) of VUS. Chi-square test suggested that the difference in detection rate was statistically significant (P\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion \u003c/em\u003eIn current study, the application of CNV-Seq in all ABD patient groups has a high abnormal detection rate, especially in the population of syndrome ABD, the detection rate was higher than that of non-syndrome ABD, but in the population of non-syndrome ABD patients, due to the low positive detection rate and the lack of obvious clinical phenotypes, such population were more likely to be neglected in clinical practice. Accordingly, more attention should be paid to population with non-syndrome ABD. In addition, prenatal diagnosis and genetic counseling should be performed in a timely manner for these of patients.\u003c/p\u003e","manuscriptTitle":"Clinical application of CNV-Seq in the diagnosis of children with abnormal brain development in 130 cases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-26 15:56:56","doi":"10.21203/rs.3.rs-4669074/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-11T04:46:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-02T14:39:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254735683499502394140890072805085510039","date":"2025-06-23T11:56:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113069718495220778472299256788784996669","date":"2025-04-21T00:11:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100205905836456085796352886368910849669","date":"2025-01-15T05:00:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-23T01:54:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285822714512141715221449820210122834165","date":"2024-08-03T21:19:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111718082678682558188500617248642401469","date":"2024-08-01T16:18:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-01T06:59:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-17T08:35:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-03T15:45:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-03T03:46:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-01T15:22:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fc2b5637-2a69-48c8-91e5-2bb1ba00631e","owner":[],"postedDate":"July 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34932885,"name":"Biological sciences/Genetics"},{"id":34932886,"name":"Biological sciences/Molecular biology"},{"id":34932887,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-10-06T16:02:31+00:00","versionOfRecord":{"articleIdentity":"rs-4669074","link":"https://doi.org/10.1038/s41598-025-18350-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-29 15:57:37","publishedOnDateReadable":"September 29th, 2025"},"versionCreatedAt":"2024-07-26 15:56:56","video":"","vorDoi":"10.1038/s41598-025-18350-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-18350-x","workflowStages":[]},"version":"v1","identity":"rs-4669074","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4669074","identity":"rs-4669074","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}