Understanding Pathogenic Detection Rates of CNVs in Prenatal Diagnosis: Insights from a Cohort Study

Understanding Pathogenic Detection Rates of CNVs in Prenatal Diagnosis: Insights from a Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Understanding Pathogenic Detection Rates of CNVs in Prenatal Diagnosis: Insights from a Cohort Study Shaohua Zhu, Shibing Cheng, Chunyang Jia, Furong liu, Shengju Hao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5287476/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The current study aimed to explore the clinical utility of CNV-seq in prenatal diagnosis by comparing the pathogenic detection rates of copy number variants (CNVs) in pregnant women who underwent amniocentesis for various indications and across different CNV fragment sizes. A total of 1,118 pregnant women who received CNV-seq testing were included in the cohort and categorized into eight groups (Groups A to H) based on their testing indications, with the mixed group (Group H) serving as the reference. The top three groups with the highest pathogenic detection rates were high-risk NIPT (Group A), parental chromosomal abnormalities (Group D), and high-risk trisomy 18 (Group F), with detection rates of 92.16% (141/153), 80% (4/5), and 70% (14/20), respectively. The pathogenic CNV detection rate in our reference group (Group H) was 69.02% (127/184). A binary logistic regression analysis comparing the other seven groups against Group H showed that the detection rate in Group A was significantly higher than that in Group H (P < 0.05), while Groups B, C, E, and G had significantly lower detection rates. Furthermore, based on CNV fragment size, cutoffs were established at 1 Mb and 5 Mb, and the study cohort was further divided into four groups (Groups I to IV). Using the group with multiple CNVs (Group IV) as the reference, the pathogenic detection rates for each group were calculated. Binary logistic regression analysis revealed that Group I had a significantly lower detection rate than Group IV (P < 0.05), while Group III exhibited a significantly higher detection rate (P < 0.05). These findings suggest that abnormal NIPT results are often associated with a higher pathogenic detection rate, and larger CNV fragments exceeding 5 Mb are more likely to be pathogenic. This study provides crucial insights into the clinical application of CNV-seq in prenatal diagnosis, emphasizing the importance of abnormal NIPT results and CNV fragment size in clinical interpretation, thereby facilitating more accurate prenatal risk assessment. Copy number variation (CNV) Prenatal diagnosis Abnormal prenatal signs Cohort study Figures Figure 1 Figure 2 1. Introduction Congenital malformations caused by genetic diseases constitute a significant global public health concern, imposing a substantial burden on families and societies[ 1 ]. Although the incidence of neonatal mortality rate in Northwest China has been steadily declining, it remains higher than in southern China and other developed countries[ 2 ]. With increasing public awareness of prenatal care and family planning, prenatal diagnosis has become increasingly important in reducing the prevalence of birth defects. Prenatal diagnosis enables the detection and diagnosis of various genetic disorders before birth, offering pregnant women the option of selective termination, thereby lowering the birth rate of infants with birth defects. The causes of birth defects are complex and multifaceted, encompassing genetic factors, environmental influences, nutritional status, and maternal health. Therefore, early detection and accurate diagnosis of potential fetal genetic abnormalities are crucial for reducing the incidence of birth defects. Advances in genomic technologies, particularly copy number variation (CNV) detection, have played a key role in prenatal diagnosis[ 3 ]. In recent years, with the rapid advancement of high-throughput sequencing technologies, copy number variation sequencing (CNV-seq) has become widely used in prenatal diagnosis[ 4 ]. CNV-seq enables the detection of copy number variations across the entire genome with high sensitivity, specificity, and resolution. It offers several key advantages: it could identify microdeletions and duplications of chromosomal segments, significantly improving the diagnostic rate for genetic disorders such as submicroscopic deletion/duplication syndromes. The high throughput of CNV-seq allows for the assessment of copy number variations across the genome in a single experiment, greatly enhancing detection efficiency[ 5 ]. Additionally, its high resolution enables the detection of even small copy number variations, further increasing diagnostic accuracy. Moreover, CNV-seq provides a broad detection range, capable of identifying both chromosomal number and structural abnormalities, offering more comprehensive information for diagnosing genetic[ 6 , 7 ]. Amniocentesis is one of the most commonly used methods for prenatal diagnosis while its indications are complex. The primary reasons for pregnant women to undergo amniocentesis include: Advanced maternal age: Advanced maternal age is associated with a higher risk of chromosomal abnormalities, making these women one of the key groups for amniocentesis. Previous child or family history of chromosomal abnormalities: Pregnant women with a family history of chromosomal abnormalities have a higher risk of having offspring with similar conditions[ 8 ]. Abnormal findings from non-invasive prenatal testing (NIPT): Abnormal indications from NIPT may suggest potential genetic or chromosomal issues in the fetus, necessitating further prenatal diagnostic procedures[ 9 , 10 ]. Ultrasound detection of fetal structural abnormalities: Structural anomalies, such as anencephaly or open spina bifida, detected via ultrasound require further confirmation through amniocentesis[ 11 ]. Parental chromosomal abnormalities: Couples carrying abnormal chromosomes face a higher risk of chromosomal abnormalities in their offspring[ 12 ]. Other factors, such as serum screening indicating chromosomal abnormalities, a family history of genetic diseases, unexplained infertility, multiple miscarriages, or maternal exposure to harmful substances during pregnancy, may also lead to the decision for amniocentesis[ 13 – 15 ]. Therefore, understanding the characteristics of different groups of pregnant women, particularly in terms of CNV detection, holds significant clinical importance for optimizing prenatal diagnostic strategies. While there is a general correlation between the size of a copy number variant (CNV) and its pathogenicity, this relationship is not absolute. Larger CNVs are more likely to cause phenotypic abnormalities, whereas smaller ones may be benign or of uncertain clinical significance. However, CNV size is not the sole determinant of pathogenicity; the location and function of the genes involved, the type of CNV (deletion or duplication), and the patient's clinical phenotype must also be considered. Additionally, the pathogenicity of CNVs can be influenced by other genetic and quantitative factors[ 16 ]. For example, the same CNV could manifest with varying clinical symptoms or even be asymptomatic in different individuals[ 17 ]. The number of CNVs carried by an individual and the severity of the phenotype can also be correlated. Therefore, analyzing the size and potential pathogenicity of CNVs is crucial in prenatal diagnosis. In current study, we categorized the primary reasons for amniocentesis among pregnant women in the Northwestern region of China based on CNV pathogenicity assessments. We compared the association between different reasons and pathogenicity and explored the potential correlation between CNV size and pathogenicity by categorizing CNV fragment size and copy number. We aim to enhance our understanding of CNV pathogenicity through this research, promote the application of CNV-seq in birth defect prevention in the Northwestern region of China, and ultimately contribute to reducing birth defects and improving population health. 2. Materials and Methods 2.1 Ethics Statement and study oversight The sample collection procedures employed in current study strictly adhere to national ethical guidelines and have received approval from the Ethics Committee of the Gansu Provincial Maternal and Infant Health Hospital (license number: 2021GSFY Ethical Review). Furthermore, the study aligns with the Helsinki Declaration of the World Medical Association. The hospital's Medical Genetics Center, accredited by the Gansu Provincial Health Commission, is fully qualified to conduct the tests. Pregnant women registered at the Medical Genetics Center, following standard CNV-seq procedures, and provided informed consent. The consent form detailed the testing methodology, sample requirements, and potential risks for specific groups. It also outlined the associated insurance plan, legal and ethical declarations, laboratory application processes, and pre-testing consultation. Each participant was given access to comprehensive information about the type of CNV test result (missing or duplicate), fragment size, and pathogenicity grade, ensuring fairness and reliability of the study results. All personnel conducting tests in the Medical Genetics Center laboratory are skilled professionals who ensure thorough comprehension of the informed consent form. Based on the cohort data we collected, CNV-seq was performed on a total of 1118 pregnant women. Of these samples, 31 were collected from prenatal villi, 8 from umbilical cord blood, and 1088 from amniotic fluid of pregnant women. In order to make the study more targeted, we classified the sequences involved in this study according to different reasons for undergoing puncture surgery, and divided them into 8 groups, including: High-risk noninvasive prenatal testing (group A), poor pregnancy history (group B), abnormal ultrasound (group C), chromosomal abnormalities in couples (group D), advanced pregnancy (group E), high-risk T18 syndrome (group F), high-risk Down syndrome (group G), and mixed group (group H). It is worth noting that the mixed group refers to those who undergo puncture surgery for many different reasons. As far as we know, these of groups were indeed some of the major and common causes of puncture surgery and prenatal diagnosis in the Northwest Territories[ 2 , 18 ]. Considering that the patient population in Group H performs amniocentesis experiments for a variety of reasons, it has the highest heterogeneity among all research groups. adopting Group H as a reference group can better comprehensively consider the impact of multiple factors and improve the reliability of the research results. To investigate the effect of fragment size on CNV pathogenicity, cutoff values were established based on the size of the sequences detected by CNV-seq. Refer to the classic cut-off value in CNVs fragment size in previous studies and combining the population involved in the current study[ 19 , 20 ]. The patient cohort involved in this study was categorized into different groups according to these size thresholds, defined as fragments smaller than 1 Mb, fragments between 1.0 and 5.0 Mb, and fragments larger than 5.0 Mb. Additionally, considering the complexity of certain patients in this cohort who carried multiple CNVs, we included the number of CNVs carried as another important factor influencing pathogenicity[ 19 ]. Patients with multiple CNVs were classified into a separate group. Ultimately, based on these criteria, all individuals in the study were divided into four groups: Group I, carrying a single fragment smaller than 1 Mb; Group II, carrying a single fragment between 1.0 and 5.0 Mb; Group III, carrying a single fragment larger than 5.0 Mb; and Group IV, carrying multiple CNVs. Among these groups, Group IV exhibited the highest heterogeneity and was therefore used as the reference group for this study. 2.2 Methods involved in current study CNV-seq Peripheral blood samples (5 mL) were collected from diagnosed patients and transferred to specialized anticoagulant blood collection tubes containing EDTA (KIRGEN Medical Equipment, China). For amniotic fluid specimens, 15 mL of fluid was obtained using ultrasound-assisted puncture and stored in centrifuge tubes. Villi tissues were briefly cultured in the laboratory after being aspirated with a syringe[ 21 ]. DNA extraction was performed using the QIAamp DNA Micro Kit (Qiagen, Germany) following the Standard Operating Procedure (SOP) from the gDNA extraction reference manual. DNA concentration was determined using the Qubit 3.0 fluorometer (Thermo Fisher Scientific, United States). Low-depth genome sequencing was conducted with the CN-500 NGS sequencer (Illumina, U.S.). 2.3 Data analysis Following CNV sequencing, raw data underwent quality control (QC) to remove low-quality reads and generate clean data. Each read was 36 base pairs (bp) long, with an average sequencing depth of 0.1x. Clean data in BAM format were imported into the second-generation CNV analysis platform for analysis. The platform performed sequence mapping against the human reference genome (GRCh38), standardized data analysis, and identified and interpreted potential pCNVs. Bioinformatics annotation and pathogenicity assessment of CNV regions were conducted using public biological databases, including gnomAD, ClinVar, and DECIPHER. To ensure data reliability and avoid false positives, the study only included CNV-seq analyses for deletions or duplications larger than 100 kb. The classification of CNV pathogenicity followed the guidelines established by the American College of Medical Genetics and Genomics (ACMG) and ClinGen[ 22 , 23 ] Data processing and statistical analysis were conducted using IBM SPSS Statistics (version 21.0; https://www.ibm.com/cn-zh/products/spss-statistics/ ) and R software (version 4.2.1; https://www.r-project.org/ ). The R package "Function.r" was used to configure reference and comparison group parameters. Additionally, SPSS was employed to perform data weighting for patients with different etiologies to assess the associations between variables, and SPSS's built-in binary logistic regression function was used for group comparisons. Statistical results are presented as percentages (%) and p-values, with a significance level of p < 0.05 used to determine statistically significant differences. 3. Results 3.1 Population statistics A total of 1,118 pregnant women who perform CNV-seq testing at the Medical Genetics Center of Gansu Province were included in the current study cohort. Based on the different indications for CNV-seq testing, these individuals were categorized into eight groups (Groups A to H). Analysis of the data revealed that the three most represented groups were Group C, Group H, and Group G, with 329, 231, and 180 participants, respectively. In contrast, only six individuals perform CNV-seq testing due to parental chromosomal abnormalities, making this the smallest group in our study cohort (Fig. 1). Based on the CNV-seq results of the cohort, abnormal CNVs were identified in 798 pregnant women, accounting for 71.38% (798/1118) of the entire cohort. It is important to note that, according to the American College of Medical Genetics and Genomics (ACMG) guidelines for CNV pathogenicity classification, abnormal CNVs include three types: pathogenic (P), likely pathogenic (LP), and variants of uncertain significance (VUS), correspondingly, non-abnormal CNVs include benign (B) and Likely benign (LB)[ 23 ]. In current study, we adhered to these guidelines when categorizing the sequencing results. However, we combined the P and LP categories into a single group, referred to as the pathogenic group, while VUS was categorized separately as the uncertain significance group. Given the inherent uncertainties associated with individuals in the VUS category, we excluded them from the pathogenicity rate calculations to ensure higher reliability and accuracy in our analysis. After excluding VUS individuals, the study cohort consisted of 836 individuals classified into either the pathogenic or non-pathogenic groups (The total number of patients in the group before excluding was 1,118). Among those with abnormal CNVs, the top three groups in terms of pathogenicity rates were Group A, Group D, and Group F, with rates of 92.16% (141/153), 80% (4/5), and 70% (14/20), respectively. The reference group (Group H) exhibited a pathogenicity rate of 69.02% (127/184). A binary logistic regression analysis comparing the remaining groups with Group H revealed that Group A had a significantly higher pathogenic CNV detection rate than the reference group (P < 0.05). Although Groups D and F showed high pathogenic CNV detection rates, their P values were both greater than 0.05, indicating no significant difference compared to Group H. Additionally, the other groups, including Groups B, C, E, and G, showed significantly lower pathogenicity rates than Group H (Table 1 ). Table 1 Comparison of CNVs abnormality rates for different prenatal diagnosis indications Group Sample Size (n) Abnormal Size (n) VUS Size (n) Non-Abnormal Size (n) Pathogenic Proportion (%) Comparison with Group H Exp(B) H(Mixed, reference group) 231 127 47 57 69.02% - null A (High-risk NIPT) 178 141 25 12 92.16% * ↑ 5.274 B (Poor pregnancy history) 56 11 25 20 35.48% * ↓ 0.247 C(abnormal ultrasound ) 329 135 80 114 54.22% * ↓ 1.795 D(chromosomal abnormalities in couples) 6 4 1 1 80% - 0.313 E(advanced pregnancy) 111 30 38 43 41.1% * ↓ 1.047 F(high-risk T18 syndrome) 27 14 7 6 70% - 0.362 G(high-risk Down syndrome) 180 54 59 67 44.63% * ↓ 2.228 Note : (1). Abnormal Size: includes all pregnant women with P and LP; VUS: Variant of Uncertain Significance; Non-Abnormal Size: includes all pregnant women with B and LB. (2). The superscript* indicates that compared with the reference group H, the difference in the proportion of pathogenic is statistically significant. (3). ↑ means significantly higher than the H group; ↓ means significantly lower than the H group. (4). The Pathogenic Proportion is the ratio of Abnormal Size to the Abnormal Size + Non-Abnormal Size. (5). Exp(B) represents the Odds Ratio, it could reflect which is a multiple of the increase or decrease in risk of other groups relative to the reference group. 3.2 Association between CNV fragment size and pathogenicity The current study conducted a comparative analysis of the impact of CNV fragment sizes on pathogenicity. Similar to the analysis of pathogenic CNV detection rates in the previous paragraph, we considered the complexity and unknown mechanisms behind the pathogenicity of CNVs with uncertain clinical significance. Although these CNVs are currently classified as variants of uncertain significance (VUS), it could be acknowledged that there is no definitive evidence indicating their pathogenicity. In other words, there is considerable uncertainty surrounding these VUS fragments, which undoubtedly introduces challenges and biases to the reliability of the study's data. As a result, all CNV fragments classified as VUS were excluded from the groups, and we focused solely on the comparative analysis between pathogenic CNVs and benign CNVs (Table 2 ). According to the statistical results: the detection rate in Group I was only 14.70% (55/374), Group II was 34.59% (101/292), Group III was 89.19% (33/37), and the reference group (Group IV) had a detection rate of 45.83% (22/48). The results of binary logistic regression analysis revealed that the detection rate in Group I was significantly lower than that in Group IV (P < 0.05), while the detection rate in Group III was significantly higher than that in Group IV (P < 0.05). No significant difference was observed between Group II and the reference Group IV. Although the abnormality detection rate of group I (<1Mb) is low, several smaller fragments but pathogenic CNVs have been detected regardless of the deletion type or duplication type (Fig. 2). Moreover, pathogenic CNVs were detected across all other groups, including well-characterized pathogenic variants (Table 3 ). These findings highlight the representative nature of our sample cohort. Table 2 Association analysis of CNV fragment size and pathogenicity Group Sample Size (n) Abnormal Size (n) VUS Size (n) Non-Abnormal Size (n) Pathogenic Proportion (%) Comparison with Group IV Exp(B) Group IV (multiple CNVs, reference group) 48 22 21 5 45.83% - null Group I (single CNV 5Mb) 37 33 4 0 89.19% * ↑ 9.75 Note: (1). Abnormal Size: includes all pregnant women with P and LP; VUS: Variant of Uncertain Significance; Non-Abnormal Size: includes all pregnant women with B and LB. (2). The superscript* indicates that compared with the reference group IV, the difference in the proportion of pathogenic is statistically significant through binary logit regression analysis. (3). Comparison with group IV: Compare the pathogenic proportion with the group IV, and adopt arrows to indicate the direction of difference (↑indicates significantly higher than group IV and ↓ indicates significantly lower than group IV). (4). The Pathogenic Proportion is the ratio of Abnormal Size to the Abnormal Size + Non-Abnormal Size. (5). Exp(B) represents the Odds Ratio, it could reflect which is a multiple of the increase or decrease in risk of other groups relative to the reference group. Table 3 Typical pathogenic CNVs of different fragment sizes Sample Abnormal prenatal signs CNV-seq detection results CNV type Fragment size (Mb) Syndrome involved 1 abnormal ultrasound (C) seq[hg19] del(X)(p21.1) Deletion 0.22 MUSCULAR DYSTROPHY 2 abnormal ultrasound (C) seq[hg19] del(16)(p11.2) Deletion 0.56 CHROMOSOME 16p11.2 DELETION SYNDROME 3 advanced pregnancy (E) seq[hg19] dup(16)(p11.2) Duplication 0.54 CHROMOSOME 16p11.2 DUPLICATION SYNDROME 4 Mixed (H) seq[hg19] del(X)(p22.31) Deletion 1.78 ICHTHYOSIS, X-LINKED 5 High-risk NIPT (A) seq[hg19] dup(1)(q21.1-q21.2) Duplication 1.28 CHROMOSOME 1q21.1 DUPLICATION SYNDROME 6 abnormal ultrasound (C) seq[hg19] del(17)(q12) Deletion 1.46 CHROMOSOME 17q12 DELETION SYNDROME 7 Mixed (H) seq[hg19] del(11)(p15.1-p11.2) Deletion 26.14 WAGR SYNDROME / POTOCKI-SHAFFER SYNDROME 8 High-risk NIPT (A) seq[hg19] dup(X)(q28) Duplication 5.92 CHROMOSOME Xq28 DUPLICATION SYNDROME / MECP2 DUPLICATION SYNDROME 9 High-risk NIPT (A) seq[hg19] del(18)(q12.2-q12.3) Deletion 6.92 MENTAL RETARDATION, AUTOSOMAL DOMINANT 29(MRD29) (1). The brackets in the Abnormal prenatal signs column show the taxon to which the patient belongs. (2). The disease names displayed in the Syndrome involved column refer to the standard syndrome names in the OMIM database. https://omim.org/ . 4. Discussion In current study, pregnant women who underwent amniocentesis for different reasons were classified, with the top three groups exhibiting the highest pathogenicity rates being Group A, Group D, and Group F. Pairwise comparisons with the reference group revealed that the detection rate of pathogenic variants in the group undergoing amniocentesis due to high-risk NIPT was as high as 92.16%, significantly higher than that of the reference group (69.02%). Previous studies have shown that NIPT demonstrates a high positive predictive value (PPV) for common aneuploidies, consistent with our findings, indicating that NIPT is highly sensitive in screening for chromosomal abnormalities[ 24 , 25 ]. As a non-invasive test, NIPT analyzes cell-free fetal DNA (cffDNA) in maternal blood to detect potential fetal chromosomal abnormalities early in pregnancy, particularly showing high accuracy for common trisomies (such as trisomy 21, trisomy 18, and trisomy 13)[ 26 ]. Our results further confirm that patients undergoing amniocentesis due to abnormal NIPT findings are significantly more likely to detect pathogenic CNVs upon diagnosis, correlating with NIPT's efficacy in aneuploidy screening. In addition, the results indicated that the proportion of women undergoing amniocentesis due to high-risk trisomy 18 syndrome or parental chromosomal abnormalities was low within the overall cohort. Although no significant difference was observed compared to the reference group, the detection rate of pathogenic CNVs in these groups was indeed high, suggesting that these factors may play a critical role in the prenatal detection of CNV abnormalities. By contrast, other groups, such as advanced maternal age, abnormal ultrasound findings, or a history of adverse pregnancy, exhibited significantly lower pathogenicity rates compared to the reference group. This finding suggests that, in the absence of abnormal NIPT guidance, patients undergoing amniocentesis for conventional indications face a lower detection rate of pathogenic CNVs despite being at risk for chromosomal abnormalities, and the result aligns with previous studies, both domestically and internationally[ 27 , 28 ]. For example, some studies have noted that the primary purpose of amniocentesis in advanced maternal age is to screen for aneuploidy (such as Down syndrome), yet the detection rate of chromosomal abnormalities is relatively low, particularly for small copy number variants[ 29 , 30 ]. Similarly, women with a history of adverse pregnancy may have a pathogenic cause rooted in monogenic disorders rather than chromosomal structural abnormalities, resulting in fewer detectable copy number variants through CNV-seq[ 31 , 32 ]. In current study, patients with detected copy number variants (CNVs) were divided into four groups based on the size of the CNV fragments. The group with multiple CNVs was adopted as the reference group, and a comparative analysis of pathogenic detection rates across the four groups was conducted. The results demonstrated a strong correlation between the size of the CNV fragments and their pathogenicity. Patients with larger CNV fragments (greater than 5.0 Mb) exhibited a higher detection rate of pathogenic variants, which aligns with findings from existing literature. Numerous studies have reported that larger CNV fragments are typically associated with more severe phenotypes and pathogenicity. For instance, deletions or duplications exceeding 5 Mb often impact the expression of multiple genes, leading to severe developmental defects or syndromes, such as DiGeorge syndrome (22q11.2 deletion syndrome) and Williams syndrome (7q11.23 deletion), which are frequently accompanied by larger fragment deletions or duplications[ 33 – 35 ]. This is consistent with our findings, where Group Ⅲ showed a significantly higher pathogenic detection rate compared to the reference group, further supporting the notion that large-scale genomic structural variations have a substantial impact on fetal health. In contrast, the group carrying CNV fragments smaller than 1 Mb had a notably lower pathogenic detection rate. Small-scale CNVs, particularly those under 1 Mb, are relatively common in the general population and do not necessarily lead to overt clinical symptoms. The finding is consistent with several international cohort studies, which indicate that smaller CNV fragments are often benign variants with no significant clinical impact and are detected at higher frequencies in healthy individuals. For CNV fragments between 1.0 and 5.0 Mb, our study found no significant difference in pathogenic detection rates compared to the reference group. It suggests that medium-sized CNV fragments may exhibit heterogeneity, potentially leading to severe clinical phenotypes in some cases, while representing moderate-risk variants in others[ 36 , 37 ]. The pathogenicity of these variants may depend on the specific genes involved, the nature of the variation (deletion or duplication), and the functional roles of the affected genes. Some studies have highlighted the need for special attention to CNV fragments between 1 Mb and 5 Mb in prenatal diagnosis, particularly when they involve key genes related to development[ 38 , 39 ]. The current study perform a comparative analysis of pathogenic detection rates for different reasons of prenatal amniocentesis and varying sizes of CNV fragments, provides important clinical reference points for the interpretation of CNVs in prenatal diagnosis. Patients who underwent amniocentesis due to abnormal NIPT results exhibited a significantly higher pathogenic detection rate compared to other groups, highlighting the need for clinicians to pay close attention to positive NIPT findings and promptly pursue confirmatory testing. Moreover, for patients carrying larger CNV fragments, clinicians should be vigilant regarding their potential pathogenicity and make timely intervention decisions in conjunction with other imaging or clinical phenotypes. In contrast, while smaller CNVs are frequently detected, most are benign or neutral variants, and over-interpretation should be avoided in clinical practice to reduce unnecessary psychological stress. In clinical settings, the size of CNV fragments should be considered an important reference for assessing pathogenicity, but it is not the sole criterion. Gene function, CNV type (deletion or duplication), genomic location, and other genomic characteristics must also be taken into account. It is worth mentioning that as we continue to accumulate genomic and phenotypic data from the Northwestern population, we will further adopt larger datasets for more in-depth study, aiming to make the results more reliable and optimize clinical decisions for prenatal diagnosis 5. Conclusions The current study demonstrates that the detection rate of pathogenic CNVs in prenatal diagnosis is significantly influenced by both the reasons for amniocentesis and the size of CNV fragments. Pregnant women with positive non-invasive prenatal testing (NIPT) exhibit a higher likelihood of harboring pathogenic CNVs, and larger fragments (> 5.0 Mb) are more strongly associated with pathogenic variants. Consequently, clinical prenatal diagnosis should prioritize CNV-seq testing for NIPT-positive patients. When interpreting CNV results, a comprehensive evaluation of fragment size and clinical significance is essential for accurate prenatal risk assessment. Declarations Conflicts of Interest: The authors declare no conflict of interest. Funding: The Study: 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); The preliminary study on non-invasive prenatal diagnosis of genetic diseases based on droplet digital PCR technology (2023-2-61). Author Contribution Conceptualization, Shaohua Zhu and Qinghua Zhang; Data curation, Shaohua Zhu and Chunyang Jia; Formal analysis, Shaohua Zhu and Jing He; Funding acquisition, Xuan Feng and Furong Liu; Investigation, Shibing Cheng and Qinghua Zhang; Methodology, Shengju Hao; Project administration, Shengju Hao and Xuan Feng; Resources, Shibing Cheng and Chunyang Jia; Software, Shaohua Zhu; Validation, Shibing Cheng; Visualization, Jing he; Writing – original draft, Shaohua Zhu and Chunyang Jia; Writing – review & editing, Furong Liu and Xuan Feng. References Wilhelm M, Gatt M, Hrzic R, Calleja N, Zeeb H. Evaluating neonatal mortality in Malta compared with other EU countries: Exploring the influence of congenital anomalies and maternal risk factors. Paediatr Perinat Epidemiol. 2024. 10.1111/ppe.13106 . Li C, Yan H, Zeng L, Dibley MJ, Wang D. Predictors for neonatal death in the rural areas of Shaanxi Province of Northwestern China: a cross-sectional study. 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BJOG: Int J Obstet Gynecol. 2017;124:32–46. Bijok J, Dąbkowska S, Kucińska-Chahwan A, Massalska D, Nowakowska B, Gawlik-Zawiślak S, Panek G, Roszkowski T. Prenatal diagnosis of acrania/exencephaly/anencephaly sequence (AEAS): additional structural and genetic anomalies. Arch Gynecol Obstet. 2023;307:293–9. 10.1007/s00404-022-06584-3 . Li S, Chen M, Zheng PS. Analysis of parental abnormal chromosomal karyotype and subsequent live births in Chinese couples with recurrent pregnancy loss. Sci Rep. 2021;11:20298. 10.1038/s41598-021-98606-4 . Hui L, Hutchinson B, Poulton A, Halliday J. Population-based impact of noninvasive prenatal screening on screening and diagnostic testing for fetal aneuploidy. Genet medicine: official J Am Coll Med Genet. 2017;19:1338–45. 10.1038/gim.2017.55 . Zemet R, Van den Veyver IB. Impact of prenatal genomics on clinical genetics practice. Best Pract Res Clin Obstet Gynecol. 2024;97:102545. 10.1016/j.bpobgyn.2024.102545 . Sapantzoglou I, Giourga M, Pergialiotis V, Mantzioros R, Daskalaki MA, Papageorgiou D, Antsaklis P, Theodora M, Thomakos N, Daskalakis G. Low fetal fraction and adverse pregnancy outcomes- systematic review of the literature and metanalysis. Arch Gynecol Obstet. 2024;310:1343–54. 10.1007/s00404-024-07638-4 . Drakulic D, Djurovic S, Syed YA, Trattaro S, Caporale N, Falk A, Ofir R, Heine VM, Chawner SJ, Rodriguez-Moreno A. Copy number variants (CNVs): a powerful tool for iPSC-based modelling of ASD. Mol autism. 2020;11:1–18. Chawner S, Owen MJ, Holmans P, Raymond FL, Skuse D, Hall J, van den Bree MBM. Genotype-phenotype associations in children with copy number variants associated with high neuropsychiatric risk in the UK (IMAGINE-ID): a case-control cohort study. lancet Psychiatry. 2019;6:493–505. 10.1016/s2215-0366(19)30123-3 . Huang LL, Chen HF, Huang Y, Wei YN, Tong JR, Chen Y, Luo J, Liao S, Wei LL, Deng L, et al. Analysis results of 579 cases of genomic copy number variation sequencing of pregnant women in prenatal diagnosis. Eur Rev Med Pharmacol Sci. 2022;26:7572–9. 10.26355/eurrev_202210_30032 . Wayhelova M, Smetana J, Vallova V, Hladilkova E, Filkova H, Hanakova M, Vilemova M, Nikolova P, Gromesova B, Gaillyova R, et al. The clinical benefit of array-based comparative genomic hybridization for detection of copy number variants in Czech children with intellectual disability and developmental delay. BMC Med Genom. 2019;12. 10.1186/s12920-019-0559-7 . Soster E, Tynan J, Gibbons C, Meschino W, Wardrop J, Almasri E, Schwartz S, McLennan G. Laboratory performance of genome-wide cfDNA for copy number variants as compared to prenatal microarray. Mol Cytogenet. 2023;16. 10.1186/s13039-023-00642-4 . Dizon-Townson DS, Lu J, Morgan TK, Ward KJ. Genetic expression by fetal chorionic villi during the first trimester of human gestation. Am J Obstet Gynecol. 2000;183:706–11. 10.1067/mob.2000.106583 . Riggs ER, Andersen EF, Cherry AM, Kantarci S, Kearney H, Patel A, Raca G, Ritter DI, South ST, Thorland EC, et al. Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen). Genet medicine: official J Am Coll Med Genet. 2020;22:245–57. 10.1038/s41436-019-0686-8 . Kearney HM, Thorland EC, Brown KK, Quintero-Rivera F, South ST. American College of Medical Genetics standards and guidelines for interpretation and reporting of postnatal constitutional copy number variants. Genet medicine: official J Am Coll Med Genet. 2011;13:680–5. 10.1097/GIM.0b013e3182217a3a . Liu S, Yang F, Chang Q, Jia B, Xu Y, Wu R, Li L, Chen W, Yin A, Huang F, et al. Positive predictive value estimates for noninvasive prenatal testing from data of a prenatal diagnosis laboratory and literature review. Mol Cytogenet. 2022;15. 10.1186/s13039-022-00607-z . Hu H, Wang L, Wu J, Zhou P, Fu J, Sun J, Cai W, Liu H, Yang Y. Noninvasive prenatal testing for chromosome aneuploidies and subchromosomal microdeletions/microduplications in a cohort of 8141 single pregnancies. Hum Genomics. 2019;13:14. 10.1186/s40246-019-0198-2 . Li C, Xiong M, Zhan Y, Zhang J, Qiao G, Li J, Yang H. Clinical Potential of Expanded Noninvasive Prenatal Testing for Detection of Aneuploidies and Microdeletion/Microduplication Syndromes. Mol Diagn Ther. 2023;27:769–79. 10.1007/s40291-023-00674-x . Lu S, Kakongoma N, Hu WS, Zhang YZ, Yang NN, Zhang W, Mao AF, Liang Y, Zhang ZF. Detection rates of abnormalities in over 10,000 amniotic fluid samples at a single laboratory. BMC Pregnancy Childbirth. 2023;23. 10.1186/s12884-023-05428-5 . Meng X, Jiang L. Prenatal detection of chromosomal abnormalities and copy number variants in fetuses with congenital gastrointestinal obstruction. BMC Pregnancy Childbirth. 2022;22. 10.1186/s12884-022-04401-y . Srebniak MI, Joosten M, Knapen M, Arends LR, Polak M, van Veen S, Go A, Van Opstal D. Frequency of submicroscopic chromosomal aberrations in pregnancies without increased risk for structural chromosomal aberrations: systematic review and meta-analysis. Ultrasound Obstet gynecology: official J Int Soc Ultrasound Obstet Gynecol. 2018;51:445–52. 10.1002/uog.17533 . Zhu H, Jin X, Xu Y, Zhang W, Liu X, Jin J, Qian Y, Dong M. Efficiency of non-invasive prenatal screening in pregnant women at advanced maternal age. BMC Pregnancy Childbirth. 2021;21:86. 10.1186/s12884-021-03570-6 . Zhang J, Tang X, Hu J, He G, Wang J, Zhu Y, Zhu B. Investigation on combined copy number variation sequencing and cytogenetic karyotyping for prenatal diagnosis. BMC Pregnancy Childbirth. 2021;21:496. 10.1186/s12884-021-03918-y . Chen C, Chen M, Zhu Y, Jiang L, Li J, Wang Y, Lu Z, Guo F, Wang H, Peng Z, et al. Noninvasive prenatal diagnosis of monogenic disorders based on direct haplotype phasing through targeted linked-read sequencing. BMC Med Genom. 2021;14. 10.1186/s12920-021-01091-x . Vysotskiy M, Zhong X, Miller-Fleming TW, Zhou D, Cox NJ, Weiss LA. Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes. Genome Med. 2021;13:172. 10.1186/s13073-021-00972-1 . Zhou J, Zheng Y, Liang G, Xu X, Liu J, Chen S, Ge T, Wen P, Zhang Y, Liu X, et al. Atypical deletion of Williams-Beuren syndrome reveals the mechanism of neurodevelopmental disorders. BMC Med Genom. 2022;15. 10.1186/s12920-022-01227-7 . Coughlin CR 2nd;, Scharer GH, Shaikh TH. Clinical impact of copy number variation analysis using high-resolution microarray technologies: advantages, limitations and concerns. Genome Med. 2012;4. 10.1186/gm381 . Tilemis FN, Marinakis NM, Veltra D, Svingou M, Kekou K, Mitrakos A, Tzetis M, Kosma K, Makrythanasis P, Traeger-Synodinos J et al. Germline CNV Detection through Whole-Exome Sequencing (WES) Data Analysis Enhances Resolution of Rare Genetic Diseases. Genes (Basel) 2023, 14 , 10.3390/genes14071490 Auwerx C, Jõeloo M, Sadler MC, Tesio N, Ojavee S, Clark CJ, Mägi R, Reymond A, Kutalik Z. Rare copy-number variants as modulators of common disease susceptibility. Genome Med. 2024;16. 10.1186/s13073-023-01265-5 . Hamanaka K, Miyake N, Mizuguchi T, Miyatake S, Uchiyama Y, Tsuchida N, Sekiguchi F, Mitsuhashi S, Tsurusaki Y, Nakashima M, et al. Large-scale discovery of novel neurodevelopmental disorder-related genes through a unified analysis of single-nucleotide and copy number variants. Genome Med. 2022;14. 10.1186/s13073-022-01042-w . Safizadeh Shabestari SA, Nassir N, Sopariwala S, Karimov I, Tambi R, Zehra B, Kosaji N, Akter H, Berdiev BK, Uddin M. Overlapping pathogenic de novo CNVs in neurodevelopmental disorders and congenital anomalies impacting constraint genes regulating early development. Hum Genet. 2023;142:1201–13. 10.1007/s00439-022-02482-5 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5287476","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369698933,"identity":"8e618158-8488-432b-a026-3552560f57c2","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":369698935,"identity":"5d3df0f3-faab-43ae-be97-788bbd24e7af","order_by":1,"name":"Shibing Cheng","email":"","orcid":"","institution":"Gansu Maternity and Child-care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shibing","middleName":"","lastName":"Cheng","suffix":""},{"id":369698936,"identity":"5fc5b369-dd6c-40b1-85bf-161ecf081285","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":369698937,"identity":"e705b30a-ad39-4183-9a6d-4240f24f5067","order_by":3,"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":369698938,"identity":"bbec547f-9c73-4869-9f3f-9e45861cf583","order_by":4,"name":"Shengju Hao","email":"","orcid":"","institution":"Gansu Maternity and Child-care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shengju","middleName":"","lastName":"Hao","suffix":""},{"id":369698939,"identity":"0dcebd88-28a4-47e5-970f-643889460742","order_by":5,"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":369698940,"identity":"e8efc30b-b608-4289-8536-ce858e4c56c9","order_by":6,"name":"Qinghua Zhang","email":"","orcid":"","institution":"Gansu Provincial Clinical Research Center for Birth Defects and Rare Diseases","correspondingAuthor":false,"prefix":"","firstName":"Qinghua","middleName":"","lastName":"Zhang","suffix":""},{"id":369698941,"identity":"70c166eb-4ae6-4767-9a79-d5938f1ba771","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":"2024-10-18 07:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5287476/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5287476/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67924425,"identity":"1eff0cfc-9653-48a0-9643-91b577969529","added_by":"auto","created_at":"2024-10-31 08:38:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":292327,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProportion of puncture surgery for different reasons\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5287476/v1/bc3a8f3ffb72875f0fc5e0ea.png"},{"id":67924427,"identity":"f702d2d1-9afc-496d-8b37-67a772e8b5e2","added_by":"auto","created_at":"2024-10-31 08:38:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":467097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCNV-seq of group I from Table 3\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5287476/v1/863d97369cfa0ad8bf9aec58.png"},{"id":70960654,"identity":"0ad0cc83-47f7-4654-a03f-d7af570f9bb2","added_by":"auto","created_at":"2024-12-09 15:17:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1337396,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5287476/v1/c42182cd-d961-4d9b-896e-233328da7b7e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding Pathogenic Detection Rates of CNVs in Prenatal Diagnosis: Insights from a Cohort Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCongenital malformations caused by genetic diseases constitute a significant global public health concern, imposing a substantial burden on families and societies[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although the incidence of neonatal mortality rate in Northwest China has been steadily declining, it remains higher than in southern China and other developed countries[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With increasing public awareness of prenatal care and family planning, prenatal diagnosis has become increasingly important in reducing the prevalence of birth defects. Prenatal diagnosis enables the detection and diagnosis of various genetic disorders before birth, offering pregnant women the option of selective termination, thereby lowering the birth rate of infants with birth defects. The causes of birth defects are complex and multifaceted, encompassing genetic factors, environmental influences, nutritional status, and maternal health. Therefore, early detection and accurate diagnosis of potential fetal genetic abnormalities are crucial for reducing the incidence of birth defects. Advances in genomic technologies, particularly copy number variation (CNV) detection, have played a key role in prenatal diagnosis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, with the rapid advancement of high-throughput sequencing technologies, copy number variation sequencing (CNV-seq) has become widely used in prenatal diagnosis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. CNV-seq enables the detection of copy number variations across the entire genome with high sensitivity, specificity, and resolution. It offers several key advantages: it could identify microdeletions and duplications of chromosomal segments, significantly improving the diagnostic rate for genetic disorders such as submicroscopic deletion/duplication syndromes. The high throughput of CNV-seq allows for the assessment of copy number variations across the genome in a single experiment, greatly enhancing detection efficiency[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, its high resolution enables the detection of even small copy number variations, further increasing diagnostic accuracy. Moreover, CNV-seq provides a broad detection range, capable of identifying both chromosomal number and structural abnormalities, offering more comprehensive information for diagnosing genetic[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmniocentesis is one of the most commonly used methods for prenatal diagnosis while its indications are complex. The primary reasons for pregnant women to undergo amniocentesis include: Advanced maternal age: Advanced maternal age is associated with a higher risk of chromosomal abnormalities, making these women one of the key groups for amniocentesis. Previous child or family history of chromosomal abnormalities: Pregnant women with a family history of chromosomal abnormalities have a higher risk of having offspring with similar conditions[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Abnormal findings from non-invasive prenatal testing (NIPT): Abnormal indications from NIPT may suggest potential genetic or chromosomal issues in the fetus, necessitating further prenatal diagnostic procedures[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Ultrasound detection of fetal structural abnormalities: Structural anomalies, such as anencephaly or open spina bifida, detected via ultrasound require further confirmation through amniocentesis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Parental chromosomal abnormalities: Couples carrying abnormal chromosomes face a higher risk of chromosomal abnormalities in their offspring[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Other factors, such as serum screening indicating chromosomal abnormalities, a family history of genetic diseases, unexplained infertility, multiple miscarriages, or maternal exposure to harmful substances during pregnancy, may also lead to the decision for amniocentesis[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, understanding the characteristics of different groups of pregnant women, particularly in terms of CNV detection, holds significant clinical importance for optimizing prenatal diagnostic strategies. While there is a general correlation between the size of a copy number variant (CNV) and its pathogenicity, this relationship is not absolute. Larger CNVs are more likely to cause phenotypic abnormalities, whereas smaller ones may be benign or of uncertain clinical significance. However, CNV size is not the sole determinant of pathogenicity; the location and function of the genes involved, the type of CNV (deletion or duplication), and the patient's clinical phenotype must also be considered. Additionally, the pathogenicity of CNVs can be influenced by other genetic and quantitative factors[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For example, the same CNV could manifest with varying clinical symptoms or even be asymptomatic in different individuals[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The number of CNVs carried by an individual and the severity of the phenotype can also be correlated. Therefore, analyzing the size and potential pathogenicity of CNVs is crucial in prenatal diagnosis.\u003c/p\u003e \u003cp\u003eIn current study, we categorized the primary reasons for amniocentesis among pregnant women in the Northwestern region of China based on CNV pathogenicity assessments. We compared the association between different reasons and pathogenicity and explored the potential correlation between CNV size and pathogenicity by categorizing CNV fragment size and copy number. We aim to enhance our understanding of CNV pathogenicity through this research, promote the application of CNV-seq in birth defect prevention in the Northwestern region of China, and ultimately contribute to reducing birth defects and improving population health.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ethics Statement and study oversight\u003c/h2\u003e \u003cp\u003e The sample collection procedures employed in current study strictly adhere to national ethical guidelines and have received approval from the Ethics Committee of the Gansu Provincial Maternal and Infant Health Hospital (license number: 2021GSFY Ethical Review). Furthermore, the study aligns with the Helsinki Declaration of the World Medical Association. The hospital's Medical Genetics Center, accredited by the Gansu Provincial Health Commission, is fully qualified to conduct the tests. Pregnant women registered at the Medical Genetics Center, following standard CNV-seq procedures, and provided informed consent. The consent form detailed the testing methodology, sample requirements, and potential risks for specific groups. It also outlined the associated insurance plan, legal and ethical declarations, laboratory application processes, and pre-testing consultation. Each participant was given access to comprehensive information about the type of CNV test result (missing or duplicate), fragment size, and pathogenicity grade, ensuring fairness and reliability of the study results.\u003c/p\u003e \u003cp\u003e All personnel conducting tests in the Medical Genetics Center laboratory are skilled professionals who ensure thorough comprehension of the informed consent form. Based on the cohort data we collected, CNV-seq was performed on a total of 1118 pregnant women. Of these samples, 31 were collected from prenatal villi, 8 from umbilical cord blood, and 1088 from amniotic fluid of pregnant women. In order to make the study more targeted, we classified the sequences involved in this study according to different reasons for undergoing puncture surgery, and divided them into 8 groups, including: High-risk noninvasive prenatal testing (group A), poor pregnancy history (group B), abnormal ultrasound (group C), chromosomal abnormalities in couples (group D), advanced pregnancy (group E), high-risk T18 syndrome (group F), high-risk Down syndrome (group G), and mixed group (group H). It is worth noting that the mixed group refers to those who undergo puncture surgery for many different reasons. As far as we know, these of groups were indeed some of the major and common causes of puncture surgery and prenatal diagnosis in the Northwest Territories[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Considering that the patient population in Group H performs amniocentesis experiments for a variety of reasons, it has the highest heterogeneity among all research groups. adopting Group H as a reference group can better comprehensively consider the impact of multiple factors and improve the reliability of the research results.\u003c/p\u003e \u003cp\u003eTo investigate the effect of fragment size on CNV pathogenicity, cutoff values were established based on the size of the sequences detected by CNV-seq.\u0026nbsp;Refer to the classic cut-off value in CNVs fragment size in previous studies and combining the population involved in the current study[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The patient cohort involved in this study was categorized into different groups according to these size thresholds, defined as fragments smaller than 1 Mb, fragments between 1.0 and 5.0 Mb, and fragments larger than 5.0 Mb. Additionally, considering the complexity of certain patients in this cohort who carried multiple CNVs, we included the number of CNVs carried as another important factor influencing pathogenicity[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Patients with multiple CNVs were classified into a separate group. Ultimately, based on these criteria, all individuals in the study were divided into four groups: Group I, carrying a single fragment smaller than 1 Mb; Group II, carrying a single fragment between 1.0 and 5.0 Mb; Group III, carrying a single fragment larger than 5.0 Mb; and Group IV, carrying multiple CNVs. Among these groups, Group IV exhibited the highest heterogeneity and was therefore used as the reference group for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methods involved in current study\u003c/h2\u003e \u003cp\u003eCNV-seq\u003c/p\u003e \u003cp\u003ePeripheral blood samples (5 mL) were collected from diagnosed patients and transferred to specialized anticoagulant blood collection tubes containing EDTA (KIRGEN Medical Equipment, China). For amniotic fluid specimens, 15 mL of fluid was obtained using ultrasound-assisted puncture and stored in centrifuge tubes. Villi tissues were briefly cultured in the laboratory after being aspirated with a syringe[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. DNA extraction was performed using the QIAamp DNA Micro Kit (Qiagen, Germany) following the Standard Operating Procedure (SOP) from the gDNA extraction reference manual. DNA concentration was determined using the Qubit 3.0 fluorometer (Thermo Fisher Scientific, United States). Low-depth genome sequencing was conducted with the CN-500 NGS sequencer (Illumina, U.S.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data analysis\u003c/h2\u003e \u003cp\u003eFollowing CNV sequencing, raw data underwent quality control (QC) to remove low-quality reads and generate clean data. Each read was 36 base pairs (bp) long, with an average sequencing depth of 0.1x. Clean data in BAM format were imported into the second-generation CNV analysis platform for analysis. The platform performed sequence mapping against the human reference genome (GRCh38), standardized data analysis, and identified and interpreted potential pCNVs. Bioinformatics annotation and pathogenicity assessment of CNV regions were conducted using public biological databases, including gnomAD, ClinVar, and DECIPHER. To ensure data reliability and avoid false positives, the study only included CNV-seq analyses for deletions or duplications larger than 100 kb. The classification of CNV pathogenicity followed the guidelines established by the American College of Medical Genetics and Genomics (ACMG) and ClinGen[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eData processing and statistical analysis were conducted using IBM SPSS Statistics (version 21.0; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ibm.com/cn-zh/products/spss-statistics/\u003c/span\u003e\u003cspan address=\"https://www.ibm.com/cn-zh/products/spss-statistics/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and 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 R package \"Function.r\" was used to configure reference and comparison group parameters. Additionally, SPSS was employed to perform data weighting for patients with different etiologies to assess the associations between variables, and SPSS's built-in binary logistic regression function was used for group comparisons. Statistical results are presented as percentages (%) and p-values, with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 used to determine statistically significant differences.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Population statistics\u003c/h2\u003e \u003cp\u003eA total of 1,118 pregnant women who perform CNV-seq testing at the Medical Genetics Center of Gansu Province were included in the current study cohort. Based on the different indications for CNV-seq testing, these individuals were categorized into eight groups (Groups A to H). Analysis of the data revealed that the three most represented groups were Group C, Group H, and Group G, with 329, 231, and 180 participants, respectively. In contrast, only six individuals perform CNV-seq testing due to parental chromosomal abnormalities, making this the smallest group in our study cohort (Fig.\u0026nbsp;1). Based on the CNV-seq results of the cohort, abnormal CNVs were identified in 798 pregnant women, accounting for 71.38% (798/1118) of the entire cohort. It is important to note that, according to the American College of Medical Genetics and Genomics (ACMG) guidelines for CNV pathogenicity classification, abnormal CNVs include three types: pathogenic (P), likely pathogenic (LP), and variants of uncertain significance (VUS), correspondingly, non-abnormal CNVs include benign (B) and Likely benign (LB)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In current study, we adhered to these guidelines when categorizing the sequencing results. However, we combined the P and LP categories into a single group, referred to as the pathogenic group, while VUS was categorized separately as the uncertain significance group. Given the inherent uncertainties associated with individuals in the VUS category, we excluded them from the pathogenicity rate calculations to ensure higher reliability and accuracy in our analysis. After excluding VUS individuals, the study cohort consisted of 836 individuals classified into either the pathogenic or non-pathogenic groups (The total number of patients in the group before excluding was 1,118). Among those with abnormal CNVs, the top three groups in terms of pathogenicity rates were Group A, Group D, and Group F, with rates of 92.16% (141/153), 80% (4/5), and 70% (14/20), respectively. The reference group (Group H) exhibited a pathogenicity rate of 69.02% (127/184). A binary logistic regression analysis comparing the remaining groups with Group H revealed that Group A had a significantly higher pathogenic CNV detection rate than the reference group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although Groups D and F showed high pathogenic CNV detection rates, their P values were both greater than 0.05, indicating no significant difference compared to Group H. Additionally, the other groups, including Groups B, C, E, and G, showed significantly lower pathogenicity rates than Group H (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of CNVs abnormality rates for different prenatal diagnosis indications\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample Size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbnormal Size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVUS Size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-Abnormal Size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePathogenic Proportion (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eComparison with Group H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH(Mixed, reference group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enull\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA (High-risk NIPT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.16% *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB (Poor pregnancy history)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.48% *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC(abnormal ultrasound )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.22% *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(chromosomal abnormalities in couples)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE(advanced pregnancy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.1% *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF(high-risk T18 syndrome)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG(high-risk Down syndrome)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.63% *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: (1). Abnormal Size: includes all pregnant women with P and LP; VUS: Variant of Uncertain Significance; Non-Abnormal Size: includes all pregnant women with B and LB. (2). The superscript* indicates that compared with the reference group H, the difference in the proportion of pathogenic is statistically significant. (3). \u0026uarr; means significantly higher than the H group; \u0026darr; means significantly lower than the H group. (4). The Pathogenic Proportion is the ratio of Abnormal Size to the Abnormal Size\u0026thinsp;+\u0026thinsp;Non-Abnormal Size. (5). Exp(B) represents the Odds Ratio, it could reflect which is a multiple of the increase or decrease in risk of other groups relative to the reference group.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association between CNV fragment size and pathogenicity\u003c/h2\u003e \u003cp\u003eThe current study conducted a comparative analysis of the impact of CNV fragment sizes on pathogenicity. Similar to the analysis of pathogenic CNV detection rates in the previous paragraph, we considered the complexity and unknown mechanisms behind the pathogenicity of CNVs with uncertain clinical significance. Although these CNVs are currently classified as variants of uncertain significance (VUS), it could be acknowledged that there is no definitive evidence indicating their pathogenicity. In other words, there is considerable uncertainty surrounding these VUS fragments, which undoubtedly introduces challenges and biases to the reliability of the study's data. As a result, all CNV fragments classified as VUS were excluded from the groups, and we focused solely on the comparative analysis between pathogenic CNVs and benign CNVs (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). According to the statistical results: the detection rate in Group I was only 14.70% (55/374), Group II was 34.59% (101/292), Group III was 89.19% (33/37), and the reference group (Group IV) had a detection rate of 45.83% (22/48). The results of binary logistic regression analysis revealed that the detection rate in Group I was significantly lower than that in Group IV (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the detection rate in Group III was significantly higher than that in Group IV (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant difference was observed between Group II and the reference Group IV. Although the abnormality detection rate of group I (\u0026lt;1Mb) is low, several smaller fragments but pathogenic CNVs have been detected regardless of the deletion type or duplication type (Fig.\u0026nbsp;2). Moreover, pathogenic CNVs were detected across all other groups, including well-characterized pathogenic variants (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings highlight the representative nature of our sample cohort.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation analysis of CNV fragment size and pathogenicity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample Size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbnormal Size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVUS Size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-Abnormal Size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePathogenic Proportion (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eComparison with Group IV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup IV (multiple CNVs, reference group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enull\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup I (single CNV\u0026thinsp;\u0026lt;\u0026thinsp;1Mb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.7% *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup II (single CNV 1\u0026thinsp;~\u0026thinsp;5Mb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup III (single CNV\u0026thinsp;\u0026gt;\u0026thinsp;5Mb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89.19% *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: (1). Abnormal Size: includes all pregnant women with P and LP; VUS: Variant of Uncertain Significance; Non-Abnormal Size: includes all pregnant women with B and LB. (2). The superscript* indicates that compared with the reference group IV, the difference in the proportion of pathogenic is statistically significant through binary logit regression analysis. (3). Comparison with group IV: Compare the pathogenic proportion with the group IV, and adopt arrows to indicate the direction of difference (\u0026uarr;indicates significantly higher than group IV and \u0026darr; indicates significantly lower than group IV). (4). The Pathogenic Proportion is the ratio of Abnormal Size to the Abnormal Size\u0026thinsp;+\u0026thinsp;Non-Abnormal Size. (5). Exp(B) represents the Odds Ratio, it could reflect which is a multiple of the increase or decrease in risk of other groups relative to the reference group.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTypical pathogenic CNVs of different fragment sizes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbnormal prenatal signs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCNV-seq detection results\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCNV type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFragment size (Mb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSyndrome involved\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eabnormal ultrasound (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eseq[hg19] del(X)(p21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMUSCULAR DYSTROPHY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eabnormal ultrasound (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eseq[hg19] del(16)(p11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCHROMOSOME 16p11.2 DELETION SYNDROME\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eadvanced pregnancy (E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eseq[hg19] dup(16)(p11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuplication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCHROMOSOME 16p11.2 DUPLICATION SYNDROME\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed (H)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eseq[hg19] del(X)(p22.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICHTHYOSIS, X-LINKED\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-risk NIPT (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eseq[hg19] dup(1)(q21.1-q21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuplication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCHROMOSOME 1q21.1 DUPLICATION SYNDROME\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eabnormal ultrasound (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eseq[hg19] del(17)(q12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCHROMOSOME 17q12 DELETION SYNDROME\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed (H)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eseq[hg19] del(11)(p15.1-p11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWAGR SYNDROME / POTOCKI-SHAFFER SYNDROME\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-risk NIPT (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eseq[hg19] dup(X)(q28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuplication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCHROMOSOME Xq28 DUPLICATION SYNDROME / MECP2 DUPLICATION SYNDROME\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-risk NIPT (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eseq[hg19] del(18)(q12.2-q12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMENTAL RETARDATION, AUTOSOMAL DOMINANT 29(MRD29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e(1). The brackets in the Abnormal prenatal signs column show the taxon to which the patient belongs. (2). The disease names displayed in the Syndrome involved column refer to the standard syndrome names in the OMIM database. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://omim.org/\u003c/span\u003e\u003cspan address=\"https://omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn current study, pregnant women who underwent amniocentesis for different reasons were classified, with the top three groups exhibiting the highest pathogenicity rates being Group A, Group D, and Group F. Pairwise comparisons with the reference group revealed that the detection rate of pathogenic variants in the group undergoing amniocentesis due to high-risk NIPT was as high as 92.16%, significantly higher than that of the reference group (69.02%). Previous studies have shown that NIPT demonstrates a high positive predictive value (PPV) for common aneuploidies, consistent with our findings, indicating that NIPT is highly sensitive in screening for chromosomal abnormalities[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. As a non-invasive test, NIPT analyzes cell-free fetal DNA (cffDNA) in maternal blood to detect potential fetal chromosomal abnormalities early in pregnancy, particularly showing high accuracy for common trisomies (such as trisomy 21, trisomy 18, and trisomy 13)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our results further confirm that patients undergoing amniocentesis due to abnormal NIPT findings are significantly more likely to detect pathogenic CNVs upon diagnosis, correlating with NIPT's efficacy in aneuploidy screening. In addition, the results indicated that the proportion of women undergoing amniocentesis due to high-risk trisomy 18 syndrome or parental chromosomal abnormalities was low within the overall cohort. Although no significant difference was observed compared to the reference group, the detection rate of pathogenic CNVs in these groups was indeed high, suggesting that these factors may play a critical role in the prenatal detection of CNV abnormalities. By contrast, other groups, such as advanced maternal age, abnormal ultrasound findings, or a history of adverse pregnancy, exhibited significantly lower pathogenicity rates compared to the reference group. This finding suggests that, in the absence of abnormal NIPT guidance, patients undergoing amniocentesis for conventional indications face a lower detection rate of pathogenic CNVs despite being at risk for chromosomal abnormalities, and the result aligns with previous studies, both domestically and internationally[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For example, some studies have noted that the primary purpose of amniocentesis in advanced maternal age is to screen for aneuploidy (such as Down syndrome), yet the detection rate of chromosomal abnormalities is relatively low, particularly for small copy number variants[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Similarly, women with a history of adverse pregnancy may have a pathogenic cause rooted in monogenic disorders rather than chromosomal structural abnormalities, resulting in fewer detectable copy number variants through CNV-seq[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn current study, patients with detected copy number variants (CNVs) were divided into four groups based on the size of the CNV fragments. The group with multiple CNVs was adopted as the reference group, and a comparative analysis of pathogenic detection rates across the four groups was conducted. The results demonstrated a strong correlation between the size of the CNV fragments and their pathogenicity. Patients with larger CNV fragments (greater than 5.0 Mb) exhibited a higher detection rate of pathogenic variants, which aligns with findings from existing literature. Numerous studies have reported that larger CNV fragments are typically associated with more severe phenotypes and pathogenicity. For instance, deletions or duplications exceeding 5 Mb often impact the expression of multiple genes, leading to severe developmental defects or syndromes, such as DiGeorge syndrome (22q11.2 deletion syndrome) and Williams syndrome (7q11.23 deletion), which are frequently accompanied by larger fragment deletions or duplications[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This is consistent with our findings, where Group Ⅲ showed a significantly higher pathogenic detection rate compared to the reference group, further supporting the notion that large-scale genomic structural variations have a substantial impact on fetal health. In contrast, the group carrying CNV fragments smaller than 1 Mb had a notably lower pathogenic detection rate. Small-scale CNVs, particularly those under 1 Mb, are relatively common in the general population and do not necessarily lead to overt clinical symptoms. The finding is consistent with several international cohort studies, which indicate that smaller CNV fragments are often benign variants with no significant clinical impact and are detected at higher frequencies in healthy individuals. For CNV fragments between 1.0 and 5.0 Mb, our study found no significant difference in pathogenic detection rates compared to the reference group. It suggests that medium-sized CNV fragments may exhibit heterogeneity, potentially leading to severe clinical phenotypes in some cases, while representing moderate-risk variants in others[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The pathogenicity of these variants may depend on the specific genes involved, the nature of the variation (deletion or duplication), and the functional roles of the affected genes. Some studies have highlighted the need for special attention to CNV fragments between 1 Mb and 5 Mb in prenatal diagnosis, particularly when they involve key genes related to development[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current study perform a comparative analysis of pathogenic detection rates for different reasons of prenatal amniocentesis and varying sizes of CNV fragments, provides important clinical reference points for the interpretation of CNVs in prenatal diagnosis. Patients who underwent amniocentesis due to abnormal NIPT results exhibited a significantly higher pathogenic detection rate compared to other groups, highlighting the need for clinicians to pay close attention to positive NIPT findings and promptly pursue confirmatory testing. Moreover, for patients carrying larger CNV fragments, clinicians should be vigilant regarding their potential pathogenicity and make timely intervention decisions in conjunction with other imaging or clinical phenotypes. In contrast, while smaller CNVs are frequently detected, most are benign or neutral variants, and over-interpretation should be avoided in clinical practice to reduce unnecessary psychological stress. In clinical settings, the size of CNV fragments should be considered an important reference for assessing pathogenicity, but it is not the sole criterion. Gene function, CNV type (deletion or duplication), genomic location, and other genomic characteristics must also be taken into account. It is worth mentioning that as we continue to accumulate genomic and phenotypic data from the Northwestern population, we will further adopt larger datasets for more in-depth study, aiming to make the results more reliable and optimize clinical decisions for prenatal diagnosis\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe current study demonstrates that the detection rate of pathogenic CNVs in prenatal diagnosis is significantly influenced by both the reasons for amniocentesis and the size of CNV fragments. Pregnant women with positive non-invasive prenatal testing (NIPT) exhibit a higher likelihood of harboring pathogenic CNVs, and larger fragments (\u0026gt;\u0026thinsp;5.0 Mb) are more strongly associated with pathogenic variants. Consequently, clinical prenatal diagnosis should prioritize CNV-seq testing for NIPT-positive patients. When interpreting CNV results, a comprehensive evaluation of fragment size and clinical significance is essential for accurate prenatal risk assessment.\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\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe Study: 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); The preliminary study on non-invasive prenatal diagnosis of genetic diseases based on droplet digital PCR technology (2023-2-61).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Shaohua Zhu and Qinghua Zhang; Data curation, Shaohua Zhu and Chunyang Jia; Formal analysis, Shaohua Zhu and Jing He; Funding acquisition, Xuan Feng and Furong Liu; Investigation, Shibing Cheng and Qinghua Zhang; Methodology, Shengju Hao; Project administration, Shengju Hao and Xuan Feng; Resources, Shibing Cheng and Chunyang Jia; Software, Shaohua Zhu; Validation, Shibing Cheng; Visualization, Jing he; Writing \u0026ndash; original draft, Shaohua Zhu and Chunyang Jia; Writing \u0026ndash; review \u0026amp; editing, Furong Liu and Xuan Feng.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWilhelm M, Gatt M, Hrzic R, Calleja N, Zeeb H. 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Genome Med. 2022;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13073-022-01042-w\u003c/span\u003e\u003cspan address=\"10.1186/s13073-022-01042-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSafizadeh Shabestari SA, Nassir N, Sopariwala S, Karimov I, Tambi R, Zehra B, Kosaji N, Akter H, Berdiev BK, Uddin M. Overlapping pathogenic de novo CNVs in neurodevelopmental disorders and congenital anomalies impacting constraint genes regulating early development. Hum Genet. 2023;142:1201\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00439-022-02482-5\u003c/span\u003e\u003cspan address=\"10.1007/s00439-022-02482-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Copy number variation (CNV), Prenatal diagnosis, Abnormal prenatal signs, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-5287476/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5287476/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe current study aimed to explore the clinical utility of CNV-seq in prenatal diagnosis by comparing the pathogenic detection rates of copy number variants (CNVs) in pregnant women who underwent amniocentesis for various indications and across different CNV fragment sizes. A total of 1,118 pregnant women who received CNV-seq testing were included in the cohort and categorized into eight groups (Groups A to H) based on their testing indications, with the mixed group (Group H) serving as the reference. The top three groups with the highest pathogenic detection rates were high-risk NIPT (Group A), parental chromosomal abnormalities (Group D), and high-risk trisomy 18 (Group F), with detection rates of 92.16% (141/153), 80% (4/5), and 70% (14/20), respectively. The pathogenic CNV detection rate in our reference group (Group H) was 69.02% (127/184). A binary logistic regression analysis comparing the other seven groups against Group H showed that the detection rate in Group A was significantly higher than that in Group H (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while Groups B, C, E, and G had significantly lower detection rates. Furthermore, based on CNV fragment size, cutoffs were established at 1 Mb and 5 Mb, and the study cohort was further divided into four groups (Groups I to IV). Using the group with multiple CNVs (Group IV) as the reference, the pathogenic detection rates for each group were calculated. Binary logistic regression analysis revealed that Group I had a significantly lower detection rate than Group IV (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while Group III exhibited a significantly higher detection rate (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings suggest that abnormal NIPT results are often associated with a higher pathogenic detection rate, and larger CNV fragments exceeding 5 Mb are more likely to be pathogenic. This study provides crucial insights into the clinical application of CNV-seq in prenatal diagnosis, emphasizing the importance of abnormal NIPT results and CNV fragment size in clinical interpretation, thereby facilitating more accurate prenatal risk assessment.\u003c/p\u003e","manuscriptTitle":"Understanding Pathogenic Detection Rates of CNVs in Prenatal Diagnosis: Insights from a Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-31 08:38:15","doi":"10.21203/rs.3.rs-5287476/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fc2b5637-2a69-48c8-91e5-2bb1ba00631e","owner":[],"postedDate":"October 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T15:09:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-31 08:38:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5287476","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5287476","identity":"rs-5287476","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}