The genetic landscape of copy number variation in a Vietnamese cohort of 5008 fetuses with clinical anomalies during pregnancy

The genetic landscape of copy number variation in a Vietnamese cohort of 5008 fetuses with clinical anomalies during pregnancy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The genetic landscape of copy number variation in a Vietnamese cohort of 5008 fetuses with clinical anomalies during pregnancy Danh-Cuong Tran, Hong-Thuy Thi Dao, Hong-Dang Luu Nguyen, Duy-Anh Nguyen, and 23 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2410361/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 Copy number variation (CNV) analysis is a powerful tool for discovering structural genomic variation. Still, no program uses this tool to analyze chromosomal aneuploidies in the Vietnamese population. Pregnant women attending routine prenatal checkups in Vietnam from October 2018 to May 2021 were included in this study and contributed fetal tissue to test the utility of CNV analysis for prenatal screening. Among 5,008 women screened, 958 (19.13%) harbored at least one CNV, comprising segmental aneuploidy (8.49%), trisomy (6.91%), multiple anomalies (2.10%), and sex chromosome abnormality (1.64%). The rate of segmental aneuploidy detection increased with gestational age, but trisomy and sex chromosomal abnormalities detection decreased as the pregnancy continued. This study also found an association between abnormal CNVs and several phenotypic markers. For ultrasound soft markers, an increased nuchal fold thickness correlated with a higher risk of abnormal CNVs. In addition, many soft indicators or structural abnormalities were significantly associated with an increased likelihood of abnormal CNVs. This work highlights the importance of CNV analysis for the early detection of prenatal congenital abnormalities, especially in the first trimester. This study’s findings will meaningfully aid policymakers in developing cost-effective genetic prenatal screening programs. Health sciences/Health care/Diagnosis/Genetic testing Health sciences/Biomarkers/Diagnostic markers Figures Figure 1 Figure 2 Introduction Prenatal screening is an important tool for diagnosing fetuses with chromosomal abnormalities. Sonographic scans have become a standard tool in antenatal care to detect congenital fetal malformations in pregnant women 1 . However, it is challenging to sonographically diagnose structural anomalies in the early stages of pregnancy, particularly in the first trimester 2 . The obstetric ultrasound to screen fetal structural anomalies is usually done from 16 to 20 weeks of gestational age to minimize false positive and negative findings of soft markers 3 . When ultrasound soft markers are detected, comprehensive chromosomal analysis is crucial to examine the genetic etiology, particularly submicroscopic chromosomal aneuploidies 3 . Currently, conventional fetal karyotyping from chorionic villus sampling and amniocentesis is the gold diagnostic standard 4 . However, this traditional method must be conducted in late pregnancy and has limited diagnostic ranges of chromosomal anomalies 5 , 6 . In addition, late diagnosis of severe congenital anomalies in fetuses might make the tough decision to terminate a pregnancy even more difficult for mothers 7 . For these reasons, early detection, accurate diagnosis, and noninvasive tests are essential to detect fetal chromosomal abnormalities in the early stage of pregnancy. Noninvasive prenatal testing (NIPT) made it possible to examine multiple fetal chromosomal abnormalities, particularly chromosomal aneuploidies 4 , 8 , 9 . However, NIPT has a limited role in detecting abnormal microdeletions and microduplications 10 . Recent advancements in chromosomal microarray analysis have enabled the chromosomal copy number variation (CNV) analysis to overcome the limitations of NIPT 11 – 13 . CNV analysis is a diagnostic test performed on fetal samples obtained through chorionic villus sampling (CVS) or amniocentesis. If ultrasounds detect abnormal soft markers during routine prenatal visits, doctors will recommend this CNV analysis for further diagnosis. Because of its high throughput and visualization resolution 13 , 14 . CNV analysis can detect submicroscopic microdeletions and microduplications from 50 to 100 kilobases, mostly missed by conventional karyotyping and NIPT techniques. Recently, to maximize diagnostic accuracy, CNV analysis has been applied to identify and confirm chromosomal abnormalities among fetuses with sonography-detected soft markers and fetal structural anomalies 15 , 16 . Many ultrasound soft markers such as echogenic intracardiac focus, echogenic bowel, fetal ventriculomegaly, hypoplastic/aplastic nasal bone, and nuchal translucency have been investigated for their relationships with abnormal CNVs 17 – 25 . Multiple congenital structural anomalies have been studied for their correlations with CNVs in early prenatal diagnosis 26 – 37 . In addition, CNV has also shown its applicability among fetuses with sonographic abnormalities but normal karyotypes from traditional diagnostic methods in late pregnancy 38 . Whether there are any associations between CNV anomalies and soft markers or between CNV anomalies and sonography-based fetal structural malformations is still unclear due to limitations in previous reports, such as retrospective study design, small sample size, or inadequate statistical power and diversity of participants 26 , 33 . Comprehensive data about pathogenic CNVs will assist clinicians in providing better patient counseling about the risk of genetic etiologies, enabling early detection and prediction of fetal outcomes, and proactively planning interventions. On this basis, we conducted a large-scale study using CNV analysis to screen 5,008 Vietnamese pregnant women in outpatient settings. This study aimed to determine the frequency of CNVs, sonography-based soft markers, and fetal structural abnormalities. Additionally, we investigated the relationships between aberrant CNVs, ultrasonography soft markers, and fetal phenotypic abnormalities. Materials And Methods Ethics statement The ethics and scientific committee of the University of Medicine and Pharmacy, Ho Chi Minh City, Vietnam, approved this study. The study complied with the guidelines set by the University of Medicine and Pharmacy, Ho Chi Minh City, in handling the human genetic data of all participants. All written informed consent forms were collected from the study participants after a session of genetic counseling and with their agreement. Study design and participants A large-scale, multicenter, cross-sectional descriptive study was conducted in multiple hospitals in Vietnam from October 2018 to May 2021 (Fig. 1 a ) . Pregnant Vietnamese women were recommended for prenatal testing during routine check-ups due to structural anomalies detected in detailed ultrasound scanning. Genetic clinicians counseled them about the risk of fetal chromosomal abnormality. If parents opted for invasive testing, they were offered the option to participate in this study. Sample collection Prenatal biological samples—including amniotic fluid, chorionic villus, umbilical cord blood, and fetal tissues—were acquired and stored in collection tubes according to the manufacturer's instructions for chromosomal analysis using copy number variation sequencing (CNV-Seq). Fetal genomic DNA extraction DNA collected from blood or fluid was extracted using GeneJET Whole Blood Genomic DNA Purification Mini Kit (Thermo Fisher Scientific). In addition, DNA from selected tissues were extracted with Relia Prep™ gDNA Tissue mini prep and quantified with the QuantiFlour dsDNA system (Promega). Library preparation and sequencing DNA fragmentation and library preparation were performed with the NEBNext Ultra II FS DNA library prep kit (New England Biolabs) following the manufacturer's protocol. Subsequently, DNA library concentrations were quantified with a QuantiFlour dsDNA system (Promega). DNA samples were sequenced on the NextSeq 550 platform using a paired-end 2×75 bp Reagent Kit (Illumina). Submicroscopic chromosomal copy number variation analysis Sequencing data quality control was conducted with the FastQC (version 0.11.9). Raw sequences from each sample were aligned to the reference human genome from the University of California, Santa Cruz Genome Browser (NCBI build GRCh37) using Burrows-Wheeler Aligner and SAMtools packages. Duplicate reads were removed by using Picard tools (Broad Institute). The final output data were collated on OMIM and DECIPHER databases to classify CNV variants. CNVs were classified as pathogenic, benign, or variants of unknown significance (VUS). To assess the statistical association between genotype and phenotype, we combined pathogenic and VUS CNVs in a single category of abnormal CNVs. Statistical analysis Descriptive analyses were used to describe the frequencies and proportions of CNVs. The bivariate logistic regression was conducted to analyze the associations between CNVs, sonography soft markers, and fetal structural anomalies. The P values < 0.05 were considered statistically significant. Stata statistical software version 16.0 was used for data analysis. Results Characteristics of the study cohort Fetal biological samples (including amniotic fluid, chorionic villus, umbilical cord blood, and fetal tissue) from 5,008 pregnant Vietnamese women were tested for chromosomal anomalies using next-generation sequencing (NGS). CNV-seq analysis was performed on fetal samples collected during the first (15.24%), second (66.39%), and third trimesters (18.37%) (Fig. 1 b). A total of 958 (19.13%) fetal samples carried at least one CNV (Fig. 1 a). [Please insert Fig. 1 here.] Genetic landscape of copy number variation in a cohort of 5008 fetal samples in Vietnam Overall, segmental aneuploidy (8.49%) and trisomy (6.91%) accounted for the largest number of abnormal CNV detected across the pregnancy trimesters. They were followed by multiple chromosomal anomalies (2.10%) and abnormal sex chromosomes (2.1%)(Fig. 2 a). Among autosomal aneuploidies, we observed the highest rate in chromosomes 21, 18, and 13 (Table 1 ). These findings are consistent with those reported in Asian 23 and English 33 populations. Table 1 Frequencies and proportions of CNVs detected by gestational age (N = 5,008) Type Total participants (%) Gestational age = 28 weeks CNV abnormalities 958 (19.13) 146 (33.33) 636 (18.97) 176 (14.45) Trisomy 346 (6.91) 86 (19.63) 222 (6.62) 38 (3.12) 13 25 (0.5) 06 (1.37) 15 (0.45) 04 (0.33) 18 84 (1.68) 19 (4.34) 56 (1.67) 09 (0.74) 21 203 (4.05) 45 (10.27) 138 (4.12) 20 (1.64) Other trisomy 05 (0.1) 05 (1.14) - - Autosomal mosaics 29 (0.58) 11 (2.51) 13 (0.39) 05 (0.41) Sex chromosome 82 (1.64) 22 (5.02) 49 (1.46) 11 (0.9) Mono X 18 (0.36) 06 (1.37) 09 (0.27) 03 (0.25) 47 XXY 13 (0.26) 03 (0.68) 08 (0.24) 02 (0.16) 47 XYY 06 (0.12) 01 (0.23) 04 (0.12) 01 (0.08) 47 XXX 07 (0.14) - 05 (0.15) 02 (0.16) 48 XXXY 01 (0.02) - 01 (0.03) - Sex mosaics 37 (0.74) 12 (2.74) 22 (0.66) 03 (0.25) Segmental aneuploidies 425 (8.49) 27 (6.16) 295 (8.8) 103 (8.46) Del 258 (5.15) 13 (2.97) 182 (5.43) 63 (5.17) Dup 167 (3.33) 14 (3.2) 113 (3.37) 40 (3.28) Multiple anomalies 105 (2.1) 11 (2.51) 70 (2.09) 24 (1.97) No CNV detected 4050 (80.87) 292 (66.67) 2716 (81.03) 1042 (85.55) Total 5008 (100) 438 (100) 3352 (100) 1218 (100) [Please insert Table 1 here.] [Please insert Fig. 2 here] While sex chromosome abnormalities had the lowest detected percentage overall, they were found at a higher rate in the first trimester than in the second or third trimester. In addition, chromosomal mosaicism was observed with low frequencies for both autosomal and sex chromosome aneuploidies. Although close to 85% of prenatal biological samples were collected and analyzed in the second and third trimesters of pregnancy, a significantly higher proportion of abnormal CNVs were detected in the first trimester (33.33%) than in the second (18.97%) and third trimesters (14.45%) (Table 1 ). Another finding in our study is the contradictory trend of different types of CNV abnormalities during pregnancy trimesters. Specifically, the detection of segmental aneuploidies and multiple chromosomal anomalies rose across pregnancy trimesters, in contrast to the declining trend in the detection of trisomy and sex chromosome anomalies as gestational age increased (Fig. 2 b). A statistical link between gestational age and CNVs was discovered using bivariate logistic regression analysis, with a prevalence odds ratio of 0.94 (95% confidence interval, 0.93–0.95, P value < 0.001) ( Supplementary Table 1 ) . Microdeletion mutations were most observed in the autosomal chromosomes 1, 4, 5, 7, 9, 10, 18, and 22, with prevalence ranging from 4.5–13.0% (Table 2 and Fig. 2 c). Microduplication mutations were found in a similar prevalence (4.0–13%) and frequently occurred in the autosomal chromosomes 2, 3, 5, 7, 8, 13, 14, 16, and 18 (Table 2 and Fig. 2 d). Table 2 Frequencies and percentage (%) of microdeletions and microduplications by chromosomes and pregnancy trimesters Chr Gestational age Total = 28 weeks Del Dup Del Dup Del Dup Del Dup 1 03 (16.67) - 15 (6.15) 03 (1.7) 05 (5.26) 03 (4.92) 23 (6.44) 06 (2.37) 2 02 (11.11) 02 (12.5) 09 (3.69) 25 (14.2) 05 (5.26) 06 (9.84) 16 (4.48) 33 (13.04) 3 01 (5.56) 01 (6.25) 10 (4.1) 15 (8.52) 03 (3.16) - 14 (3.92) 16 (6.32) 4 02 (11.11) 01 (6.25) 33 (13.52) 05 (2.84) 10 (10.53) 03 (4.92) 45 (12.61) 09 (3.56) 5 01 (5.56) 01(6.25) 15 (6.15) 06 (3.41) 05 (5.26) 07 (11.48) 21 (5.88) 14 (5.53) 6 01 (5.56) - 09 (3.69) 04 (2.27) 01 (1.05) - 11 (3.08) 04 (1.58) 7 01 (5.56) - 17 (6.97) 11(6.25) 03 (3.16) 03 (4.92) 21 (5.88) 14 (5.53) 8 - 01 (6.25) 09 (3.69) 14 (7.95) 04 (4.21) 08 (13.11) 13 (3.64) 23 (9.09) 9 - 01 (6.25) 13 (5.33) 08 (4.55) 03 (3.16) 02 (3.28) 16 (4.48) 11 (4.35) 10 - - 13 (5.33) 08 (4.55) 04 (4.21) 04 (6.56) 17 (4.76) 12 (4.74) 11 - - 03 (1.23) 05 (2.84) 02 (2.11) 03 (4.92) 05 (1.4) 08 (3.16) 12 01 (5.56) 01 (6.25) 06 (2.46) 07 (3.98) 04 (4.21) - 11 (3.08) 08 (3.16) 13 02 (11.11) 02 (12.5) 10 (4.1) 07 (3.98) 01 (1.05) 01 (1.64) 13 (3.64) 10 (3.95) 14 - - 09 (3.69) 05 (2.84) 07 (7.37) 05 (8.2) 16 (4.48) 10 (3.95) 15 02 (11.11) 01 (6.25) 10 (4.1) 05 (2.84) 05 (5.26) 02 (3.28) 17 (4.76) 08 (3.16) 16 - 01(6.25) 11 (4.51) 10 (5.68) 05 (5.26) 07 (11.48) 16 (4.48) 18 (7.11) 17 01 (5.56) - 14 (5.74) 01 (0.57) 09 (9.47) 01 (1.64) 24 (6.72) 02 (0.79) 18 - 02 (12.5) 15 (6.15) 17 (9.66) 04 (4.21) 03 (4.92) 19 (5.32) 22 (8.7) 19 - - 02 (0.82) - - 01 (1.64) 02 (0.56) 01 (0.4) 20 - - 01 (0.41) 02 (1.14) - - 01 (0.28) 02 (0.79) 21 - - 01 (0.41) 08 (4.55) 03 (3.16) 01 (1.64) 04 (1.12) 09 (3.56) 22 01 (5.56) 02 (12.5) 19 (7.79) 07 (3.98) 11 (11.58) - 31 (8.68) 09 (3.56) X - - - 03 (1.7) 01 (1.05) 01 (1.64) 01 (0.28) 04 (1.58) Y - - - - - - - - Total 18 (100) 16 (100) 244 (100) 176 (100) 95 (100) 61 (100) 357 (100) 253 (100) Note: Statistics are summarized in frequency (%). Abbreviations: Chr, chromosome; Del, microdeletions; Dup, microduplications. Dash lines denote microdeletions and microduplications not found in those populations. [Please insert Table 2 here.] Association between abnormal CNVs and sonography-detected soft markers and structural malformations To investigate the association between abnormal CNV and fetal clinical manifestation, we first categorized the samples using the ultrasound soft markers currently used by most clinicians. Our study found an association between abnormal CNVs and multiple ultrasound soft markers, including increased nuchal fold thickness and fetal ventriculomegaly ( P values < 0.01) (Table 3 ). Interestingly, while increased nuchal fold thickness correlated with a higher risk of having abnormal CNVs, fetal ventriculomegaly was linked with a lower risk of having abnormal CNVs. We also found that multiple soft makers were statistically associated with a higher risk of abnormal CNVs (Table 3 ). Conversely, cerebral ventriculomegaly, enlarged cisterna magna, echogenic bowel, and fetal pyelectasis showed no statistical associations with abnormal CNVs. (Table 3 ).In addition to ultrasound soft markers, we used clinical information to categorize structural anomalies for each case. Overall, 3,393 (67.75%) fetuses were identified to have structural anomalies in at least one system. Our study revealed that the prevalence of abnormal CNVs from congenital structural anomalies in the cardiovascular system (18.91%), skeletomuscular system (10.48%), central nervous system (11.11%), craniofacial structures (11.59%), urogenital system (18.48%), and digestive system (15.69%) were consistent with previously reported data from other populations (Table 3 ) 29 – 35 . Next, we investigated the association between abnormal CNVs and multiple types of structural anomalies. Interestingly, only craniofacial malformations were significantly associated with lower odds of carrying abnormal CNVs ( P values < 0.01). However, we found that multiple malformations were statistically associated with a higher risk of abnormal CNVs (Table 3 ), consistent with the previous reports 26 , 38 , 39 . Table 3 The associations between CNV types and ultrasound soft markers and sonography-detected structural anomalies Group Total cohort (N = 5,008) Abnormal CNV (N = 949) Benign CNV /Negative (N = 4059) Prevalence odd ratio (95% CI) P values Ultrasound soft markers Increased Nuchal thickness 494 120 (24.29%) 374 (75.71%) 1.43 (1.15–1.78) < 0.01 Fetal ventriculomegaly 196 15 (7.65%) 181 (92.35%) 0.34 (0.2–0.59) < 0.01 Hypoplastic/absent nasal bone 51 12 (23.53%) 39 (76.47%) 1.32 (0.69–2.53) 0.4016 Echogenic intracardiac focus 3 0 (0%) 3 (100%) Choroid plexus cysts 15 0 (0%) 15 (100%) Echogenic bowel 45 4 (8.89%) 41 (91.11%) 0.41 (0.15–1.16) 0.08366 Aberrant subclavian artery 4 1 (25%) 3 (75%) 1.43 (0.15–13.72) 0.7574 Fetal pyelectasis 68 6 (8.82%) 62 (91.18%) 0.41 (0.18–0.95) 0.03193 Single umbilical artery 8 3 (37.5%) 5 (62.5%) 2.57 (0.61–10.78) 0.1805 Enlarged cisterna magna 43 4 (9.3%) 39 (90.7%) 0.44 (0.16–1.22) 0.105 Multiple soft markers 120 37 (30.83%) 83 (69.17%) 1.94 (1.31–2.88) < 0.01 Structural anomalies on sonography Central nervous system 54 6 (11.11%) 48 (88.89%) 0.53 (0.23–1.25) 0.1395 Craniofacial malformation 276 32 (11.59%) 244 (88.41%) 0.55 (0.37–0.79) < 0.01 Cardiovascular anomaly 201 38 (18.91%) 163 (81.09%) 1 (0.7–1.43) 0.987 Respiratory anomaly 29 1 (3.45%) 28 (96.55%) 0.15 (0.02–1.12) 0.03266 Digestive malformation 51 8 (15.69%) 43 (84.31%) 0.79 (0.37–1.69) 0.55 Skeletal muscular malformation 124 13 (10.48%) 111 (89.52%) 0.49 (0.28–0.88) 0.01486 Urogenital anomaly 92 17 (18.48%) 75 (81.52%) 0.97 (0.57–1.65) 0.9073 Multiple malformations 2004 469 (23.4%) 1535 (76.6%) 1.61 (1.39–1.85) < 0.01 Other fetal anomalies Fetal growth restriction 481 48 (9.98%) 433 (90.02%) 0.45 (0.33–0.61) < 0.01 Amniotic fluid abnormalities 61 11 (18.03%) 50 (81.97%) 0.94 (0.49–1.81) 0.8541 Placenta anomaly 17 2 (11.76%) 15 (88.24%) 0.57 (0.13–2.49) 0.4489 Umbilical cord anomaly 18 2 (11.11%) 16 (88.89%) 0.53 (0.12–2.33) 0.3953 Miscarriage/Stillbirth 29 8 (27.59%) 21 (72.41%) 1.64 (0.72–3.7) 0.2346 Maternal obstetric history Non-invasive prenatal screening 20 5 (25%) 15 (75%) 1.43 (0.52–3.94) 0.4891 Other high-risk factors 504 87 (17.26%) 417 (82.74%) 0.88 (0.69–1.12) 0.3099 Note: Statistics are summarized in frequency (%). Abbreviations: CNV, copy number variants; VUS, variants of uncertain significance. a Abnormal CNVs included 659 pathogenic CNV and 314 VUS CNV. b,c Prevalence odd ratios and P values were withdrawn from logistic regression analyses. [Please insert Table 3 here.] Discussion This study investigated the prevalence and characteristics of abnormal CNVs in 5008 fetal biological samples when Vietnamese pregnant women were subject to invasive prenatal diagnostic tests. We comprehensively characterized the spectrum of abnormal CNVs and examined the associations between CNVs and sonography-based structural malformations. This study will fill in the knowledge gaps in genetics and further inform prenatal screening programs and policies. Recent technological advancements in low-coverage genome sequencing have significantly improved diagnostic values for detecting submicroscopic segmental aneuploidies 13 , 14 , 40 . We performed CNV-seq analysis on fetal biological samples from 5,008 unrelated Vietnamese pregnant women: 958 (19.13%) had CNV anomalies, including 659 pathogenic or likely pathogenic, 290 VUS, and 9 benign/likely benign CNVs. Our analysis combined pathogenic CNVs and VUS into a single category of abnormal CNVs. The prevalence of the abnormal CNVs in our study was consistent with a report from a cohort study among a Korean population; however, our study figures are higher than previous reports in the Chinese population 15 , 26 , 27 . Our study also showed that submicroscopic segmental aneuploidies were the predominant CNVs, followed by trisomy. In particular, 425 (8.49%) submicroscopic segmental aneuploidies were identified, and microdeletions had a somewhat higher frequency than microduplications (5.15% versus 3.33%). Overall, the prevalence of segmental aneuploidies in our study was significantly higher than that reported prevalence by Wang J et al. 15 . One explanation for this disparity might be due to the difference in sampling strategies. Wang J et al. used a broadly defined eligibility to recruit pregnant women, so 77% of the participants had normal ultrasound findings (2616 out of 3398). Meanwhile, all participants in our study were referred to invasive prenatal testing due to ultrasound soft marker anomalies. Our analysis also revealed that trisomy was the second most common CNV, accounting for 36.1% of all CNVs identified. Trisomy 21 shared the largest proportions (approximately 60%) of the trisomy, followed by trisomy 18 and 13. Autosomal and sex chromosome mosaics were also seen in our study with low frequencies. Our findings were congruous with previous reports from large cohorts among different populations 26 , 41 , 42 . A comprehensive understanding of the associations between ultrasound soft markers and abnormal CNVs will aid clinicians in approaching genetic etiology, proactively determining better therapeutic interventions, and counseling pregnant women. Wang J et al. showed that fetuses with multiple ultrasound soft markers had an increased risk of pathogenic CNVs compared to the general fetal population. Consistently, our study found a statistically significant correlation between abnormal CNVs and multiple commonly applicable soft markers. When we further investigated each solitary soft marker, we only found increased nuchal fold thickness to be highly associated with a higher risk of abnormal CNVs. Even though our study showed no statistical significance due to the small sample size, several soft makers, such as a single umbilical artery or hypoplastic/absent nasal bone, showed increased odds of associated abnormal CNVs. Our study also found that isolated fetal ventriculomegaly, a standard measure in prenatal screening, was associated with lower odds of abnormal CNVs. Our findings were congruent with results from previous studies, although those reported studies had relatively small sample sizes to determine such significant associations 18 – 24 . The most observed structural abnormalities for phenotypic anomalies were in the cardiovascular, skeletal, muscular, central nervous, digestive, and urogenital systems. These findings are similar to the report by Donnelly et al. among North Americans 28 . Major fetal structural anomalies, including central nervous system anomalies, cardiovascular anomalies, craniofacial malformations, digestive malformation, skeletal muscular malformation, and urogenital anomalies, were not associated with abnormal CNVs. However, when multiple malformations were present, the fetus had significantly higher odds of harboring abnormal CNV. Noticeably, there were slightly higher proportions of abnormal CNVs in fetuses of pregnant women who experienced miscarriage or stillbirth. Still, our study showed no statistical association even though several small studies supported such a relationship 36 , 37 . While advanced maternal age may lead to higher risks of trisomy 21 (Down syndrome), we found that maternal history of obstetric risks other than non-invasive prenatal screening was not associated with an increased prevalence of abnormal CNVs. Nevertheless, our study has one major limitation. Sampling bias is inherent in the cross-sectional study design. This study only recruited participants from highly specialized tertiary obstetric hospitals in Vietnam, reducing the diversity of the population studied. In summary, in the presence of fetal structural malformations detected on routine obstetric ultrasound, CNV analysis is crucial to detect whole chromosomal abnormalities and elucidate these phenotypic anomalies' genetic etiology. We demonstrated the statistically significant associations between abnormal CNVs, and multiple soft markers and fetal structural anomalies. Most importantly, we presented a large database study to provide a comprehensive spectrum of submicroscopic chromosomal aneuploidies in Vietnamese pregnant women. Hence, our study results provide strong evidence to support CNV-seq analysis as a powerful diagnostic tool for identifying genetic etiology in fetuses with abnormal ultrasound soft markers. Conclusion Our study results demonstrate that CNV analysis is a powerful diagnostic and confirmatory tool in prenatal genetic diagnosis, particularly in the first trimester of pregnancy. This large-scale study characterized the prevalence of submicroscopic chromosomal aneuploidies and the spectrum of CNVs among fetuses with sonography-detected soft markers and phenotypic anomalies. Our study results allow clinicians in Vietnam to devise the most appropriate strategies for prenatal diagnostic programs and counseling plans for genetic diseases. Declarations Data availability statement The data that support the findings of this study are available at the Sequence Read Archive at https://www.ncbi.nlm.nih.gov/bioproject/923276. The data supporting this study's findings are available from the corresponding authors, [HST, HG], upon reasonable request. Acknowledgments We thank all patients who participated in this study and gave consent to report findings in this paper. We thank Angela Jansen, Ph.D., MHS of Angela Jansen & Associates, for her editorial services in preparing the manuscript for publication. Author contributions Study concept and design: HG, HST, PMD, HNN; Obtaining Funding: NHN, HG; Data acquisition: DCT, DAN, QTL, DTTH, TNT, TMTH, TLD, CCN, KPTD, LATL, TSV, THNT, VTN, TTTD, QTTN, DKT; Laboratory work: HTTD, PANV, YNN, MAD; Data analysis: HTTD, HDLN, MNP, PLD, TNT, HT, MDP, HG. Drafting the manuscript: HG; Critical revision of the manuscript for intellectual content: PMD, HG. All authors contributed to and approved the final manuscript. Funding Gene Solutions, Vietnam funded this study. The funder did not have any role in the study design, data collection, analysis, publishing decision, or manuscript preparation. Conflicts of interest We declare that there is no conflict of interest. References Whitworth, M., Bricker, L. & Mullan, C. Ultrasound for fetal assessment in early pregnancy. Cochrane Database Syst Rev, CD007058 (2015). https://doi.org:10.1002/14651858.CD007058.pub3 Doubilet, P. M. Ultrasound evaluation of the first trimester. 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The use of chromosomal microarray for prenatal diagnosis. Am J Obstet Gynecol 215 , B2-9 (2016). https://doi.org:10.1016/j.ajog.2016.07.016 Martin, C. L., Kirkpatrick, B. E. & Ledbetter, D. H. Copy number variants, aneuploidies, and human disease. Clin Perinatol 42 , 227–242, vii (2015). https://doi.org:10.1016/j.clp.2015.03.001 Zhao, M., Wang, Q., Wang, Q., Peilin, J. & Zhao, Z. in The Second Workshop on Data Mining of Next-Generation Sequencing in Conjunction with the 2012 IEEE International Conference on Bioinformatics and Biomedicine. (BMC Bioinformatics). Wang, J. et al. Identification of copy number variations among fetuses with ultrasound soft markers using next-generation sequencing. Sci Rep 8 , 8134 (2018). https://doi.org:10.1038/s41598-018-26555-6 Cai, M. et al. Evaluation of chromosomal abnormalities and copy number variations in fetuses with ultrasonic soft markers. BMC Med Genomics 14 , 19 (2021). https://doi.org:10.1186/s12920-021-00870-w Hu, T. et al. Prenatal chromosomal microarray analysis in 2466 fetuses with ultrasonographic soft markers: a prospective cohort study. Am J Obstet Gynecol 224 , 516 e511-516 e516 (2021). https://doi.org:10.1016/j.ajog.2020.10.039 Hu, P. et al. Copy Number Variations with Isolated Fetal Ventriculomegaly. Curr Mol Med 17 , 133–139 (2017). https://doi.org:10.2174/1566524017666170303125529 Cai, M. et al. Choroid Plexus Cysts: Single Nucleotide Polymorphism Array Analysis of Associated Genetic Anomalies and Resulting Obstetrical Outcomes. Risk Manag Healthc Policy 14 , 2491–2497 (2021). https://doi.org:10.2147/RMHP.S312813 Singer, A. et al. Microarray analysis in pregnancies with isolated echogenic bowel. Early Hum Dev 119 , 25–28 (2018). https://doi.org:10.1016/j.earlhumdev.2018.02.014 Gu, Y. Z., Nisbet, D. L., Reidy, K. L. & Palma-Dias, R. Hypoplastic nasal bone: A potential marker for facial dysmorphism associated with pathogenic copy number variants on microarray. Prenat Diagn 39 , 116–123 (2019). https://doi.org:10.1002/pd.5410 He, M., Zhang, Z., Hu, T. & Liu, S. Chromosomal microarray analysis for the detection of chromosome abnormalities in fetuses with echogenic intracardiac focus in women without high-risk factors. Medicine (Baltimore) 99 , e19014 (2020). https://doi.org:10.1097/MD.0000000000019014 Su, J. et al. The correlations of prenatal renal ultrasound abnormalities with pathogenic CNVs in a large Chinese cohort. Ultrasound Obstet Gynecol (2021). https://doi.org:10.1002/uog.23702 An, G. et al. Application of chromosomal microarray to investigate genetic causes of isolated fetal growth restriction. Mol Cytogenet 11 , 33 (2018). https://doi.org:10.1186/s13039-018-0382-4 Angras, K., A. Bailey, L., K. Singh, P., J. Young, A. & Ross, J. A Retrospective Review of Copy Number Variants and Ultrasound-Detected Soft Markers. Molecular and Genetic Medicine 14 (2020). https://doi.org:10.37421/jmgm.2020.14.448 Wang, J. et al. Prospective chromosome analysis of 3429 amniocentesis samples in China using copy number variation sequencing. Am J Obstet Gynecol 219 , 287 e281-287 e218 (2018). https://doi.org:10.1016/j.ajog.2018.05.030 Jang, W. et al. Chromosomal Microarray Analysis as a First-Tier Clinical Diagnostic Test in Patients With Developmental Delay/Intellectual Disability, Autism Spectrum Disorders, and Multiple Congenital Anomalies: A Prospective Multicenter Study in Korea. Ann Lab Med 39 , 299–310 (2019). https://doi.org:10.3343/alm.2019.39.3.299 Donnelly, J. C. et al. Association of copy number variants with specific ultrasonographically detected fetal anomalies. Obstet Gynecol 124 , 83–90 (2014). https://doi.org:10.1097/AOG.0000000000000336 Sun, L. et al. Prenatal Diagnosis of Central Nervous System Anomalies by High-Resolution Chromosomal Microarray Analysis. Biomed Res Int 2015, 426379 (2015). https://doi.org:10.1155/2015/426379 Xu, C. et al. Clinical application of chromosomal microarray analysis for fetuses with craniofacial malformations. Mol Cytogenet 13 , 38 (2020). https://doi.org:10.1186/s13039-020-00502-5 Mademont-Soler, I. et al. Prenatal diagnosis of chromosomal abnormalities in fetuses with abnormal cardiac ultrasound findings: evaluation of chromosomal microarray-based analysis. Ultrasound Obstet Gynecol 41 , 375–382 (2013). https://doi.org:10.1002/uog.12372 Caruana, G. et al. Copy-number variation associated with congenital anomalies of the kidney and urinary tract. Pediatr Nephrol 30 , 487–495 (2015). https://doi.org:10.1007/s00467-014-2962-9 Lord, J. et al. Prenatal exome sequencing analysis in fetal structural anomalies detected by ultrasonography (PAGE): a cohort study. The Lancet 393 , 747–757 (2019). https://doi.org:10.1016/s0140-6736(18)31940-8 Winberg, J. et al. Pathogenic copy number variants are detected in a subset of patients with gastrointestinal malformations. Mol Genet Genomic Med 8 , e1084 (2020). https://doi.org:10.1002/mgg3.1084 Weissman, A. & A, D. Sonographic findings of the umbilical cord implications for the risk of. Ultrasound Obstet Gynecol 17 , 6 (2001). Rajcan-Separovic, E. et al. Identification of copy number variants in miscarriages from couples with idiopathic recurrent pregnancy loss. Hum Reprod 25 , 2913–2922 (2010). https://doi.org:10.1093/humrep/deq202 Harris, R. A. et al. Genome-wide array-based copy number profiling in human placentas from unexplained stillbirths. Prenat Diagn 31 , 932–944 (2011). https://doi.org:10.1002/pd.2817 Cai, M. et al. Copy number variations in ultrasonically abnormal late pregnancy fetuses with normal karyotypes. Sci Rep 10 , 15094 (2020). https://doi.org:10.1038/s41598-020-72157-6 Levy, B. & Wapner, R. Prenatal diagnosis by chromosomal microarray analysis. Fertility and Sterility 109 , 201–212 (2018). https://doi.org:https://doi.org/10.1016/j.fertnstert.2018.01.005 Whitford, W., Lehnert, K., Snell, R. G. & Jacobsen, J. C. Evaluation of the performance of copy number variant prediction tools for the detection of deletions from whole genome sequencing data. J Biomed Inform 94 , 103174 (2019). https://doi.org:10.1016/j.jbi.2019.103174 Sun, Y. et al. Cytogenetic analysis of 3387 umbilical cord blood in pregnant women at high risk for chromosomal abnormalities. Mol Cytogenet 13 , 2 (2020). https://doi.org:10.1186/s13039-020-0469-6 Brison, N. et al. Predicting fetoplacental chromosomal mosaicism during non-invasive prenatal testing. Prenat Diagn 38 , 258–266 (2018). https://doi.org:10.1002/pd.5223 Additional Declarations No competing interests reported. Supplementary Files 221215CNVNSRSuppTable1.pdf 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-2410361","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":167597336,"identity":"ad5fef87-3925-4337-a56b-a0cf3eb7c1ad","order_by":0,"name":"Danh-Cuong Tran","email":"","orcid":"","institution":"National Hospital of Obstetrics and Gynecology","correspondingAuthor":false,"prefix":"","firstName":"Danh-Cuong","middleName":"","lastName":"Tran","suffix":""},{"id":167597337,"identity":"ec06bd7b-7e91-44fb-867c-5592372efcd3","order_by":1,"name":"Hong-Thuy Thi Dao","email":"","orcid":"","institution":"Gene 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Giang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACZiBmbGBg4AdxEgpI0SLZANJiQKxNIC0GB0AsYrTIuzM/e/h1h4288fnViR8eGDDI84sdwK/F8DCbubHsmTTDbTfebpYAOsxw5uwEAlqaGcykJdsOM267cXYDSEuCwW2CWti/gbTYb55xdvMPorTIM/OYSX5sO5y4gb93G3G2GDDzlEkztqUlz7jBu80iwUCCsF/k+49vk/zZZmPb3392880fFTby/NKEbDkAjE0eEEsCrFICv3KwLQ3AmPwBYvEfIKx6FIyCUTAKRiYAAMZPRG82dpbmAAAAAElFTkSuQmCC","orcid":"","institution":"Gene Solutions","correspondingAuthor":true,"prefix":"","firstName":"Hoa","middleName":"","lastName":"Giang","suffix":""}],"badges":[],"createdAt":"2022-12-24 01:44:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2410361/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2410361/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":31640890,"identity":"4b1c19c4-e9f7-469b-9ff3-7a467090ceb7","added_by":"auto","created_at":"2023-01-16 16:04:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56551,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design (a) and the proportion of prenatal samples in each pregnancy trimester (b).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: CNVs, copy number variants; Trisomy, chromosomal trisomy; Segmental, segmental aneuploidies; Multi, multiple chromosomal anomalies.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2410361/v1/1d5dd7f3988b1a6127ff4b63.jpg"},{"id":31640891,"identity":"a5f9de9b-7704-45ee-bbef-db4dc837b866","added_by":"auto","created_at":"2023-01-16 16:04:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71771,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe landscape of copy number variations in a Vietnamese cohort of fetuses with ultrasound soft markers.\u003c/strong\u003e (\u003cstrong\u003ea) \u003c/strong\u003eMultiple fetal chromosomal anomalies were identified in 958 fetuses among a total of 5008 cases. \u003cstrong\u003e(b) \u003c/strong\u003eFetal chromosomal anomalies detected in different pregnancy trimesters. (\u003cstrong\u003ec) \u003c/strong\u003eDistribution of microdeletions by chromosomes. \u003cstrong\u003e(d) \u003c/strong\u003eDistribution of microduplications by chromosomes. Abbreviations: CNVs, copy number variations.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2410361/v1/551fc1365fbd6219c6c7e979.jpg"},{"id":41165862,"identity":"56f7ec68-5158-47dc-b541-7dd21c04fd89","added_by":"auto","created_at":"2023-08-07 09:52:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":809159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2410361/v1/cda5a58a-0339-4d4c-951b-049a954f30eb.pdf"},{"id":31640892,"identity":"67806ae9-a346-42f4-af54-f015ac5485f0","added_by":"auto","created_at":"2023-01-16 16:04:26","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11016,"visible":true,"origin":"","legend":"","description":"","filename":"221215CNVNSRSuppTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2410361/v1/d6e62869c16b82f30a7c85f3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The genetic landscape of copy number variation in a Vietnamese cohort of 5008 fetuses with clinical anomalies during pregnancy","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePrenatal screening is an important tool for diagnosing fetuses with chromosomal abnormalities. Sonographic scans have become a standard tool in antenatal care to detect congenital fetal malformations in pregnant women\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, it is challenging to sonographically diagnose structural anomalies in the early stages of pregnancy, particularly in the first trimester\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The obstetric ultrasound to screen fetal structural anomalies is usually done from 16 to 20 weeks of gestational age to minimize false positive and negative findings of soft markers\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. When ultrasound soft markers are detected, comprehensive chromosomal analysis is crucial to examine the genetic etiology, particularly submicroscopic chromosomal aneuploidies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Currently, conventional fetal karyotyping from chorionic villus sampling and amniocentesis is the gold diagnostic standard\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, this traditional method must be conducted in late pregnancy and has limited diagnostic ranges of chromosomal anomalies\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In addition, late diagnosis of severe congenital anomalies in fetuses might make the tough decision to terminate a pregnancy even more difficult for mothers\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. For these reasons, early detection, accurate diagnosis, and noninvasive tests are essential to detect fetal chromosomal abnormalities in the early stage of pregnancy.\u003c/p\u003e \u003cp\u003eNoninvasive prenatal testing (NIPT) made it possible to examine multiple fetal chromosomal abnormalities, particularly chromosomal aneuploidies\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, NIPT has a limited role in detecting abnormal microdeletions and microduplications\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Recent advancements in chromosomal microarray analysis have enabled the chromosomal copy number variation (CNV) analysis to overcome the limitations of NIPT\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. CNV analysis is a diagnostic test performed on fetal samples obtained through chorionic villus sampling (CVS) or amniocentesis. If ultrasounds detect abnormal soft markers during routine prenatal visits, doctors will recommend this CNV analysis for further diagnosis. Because of its high throughput and visualization resolution\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. CNV analysis can detect submicroscopic microdeletions and microduplications from 50 to 100 kilobases, mostly missed by conventional karyotyping and NIPT techniques. Recently, to maximize diagnostic accuracy, CNV analysis has been applied to identify and confirm chromosomal abnormalities among fetuses with sonography-detected soft markers and fetal structural anomalies\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Many ultrasound soft markers such as echogenic intracardiac focus, echogenic bowel, fetal ventriculomegaly, hypoplastic/aplastic nasal bone, and nuchal translucency have been investigated for their relationships with abnormal CNVs\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21 CR22 CR23 CR24\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Multiple congenital structural anomalies have been studied for their correlations with CNVs in early prenatal diagnosis\u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In addition, CNV has also shown its applicability among fetuses with sonographic abnormalities but normal karyotypes from traditional diagnostic methods in late pregnancy\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Whether there are any associations between CNV anomalies and soft markers or between CNV anomalies and sonography-based fetal structural malformations is still unclear due to limitations in previous reports, such as retrospective study design, small sample size, or inadequate statistical power and diversity of participants\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eComprehensive data about pathogenic CNVs will assist clinicians in providing better patient counseling about the risk of genetic etiologies, enabling early detection and prediction of fetal outcomes, and proactively planning interventions. On this basis, we conducted a large-scale study using CNV analysis to screen 5,008 Vietnamese pregnant women in outpatient settings. This study aimed to determine the frequency of CNVs, sonography-based soft markers, and fetal structural abnormalities. Additionally, we investigated the relationships between aberrant CNVs, ultrasonography soft markers, and fetal phenotypic abnormalities.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e The ethics and scientific committee of the University of Medicine and Pharmacy, Ho Chi Minh City, Vietnam, approved this study. The study complied with the guidelines set by the University of Medicine and Pharmacy, Ho Chi Minh City, in handling the human genetic data of all participants. All written informed consent forms were collected from the study participants after a session of genetic counseling and with their agreement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eA large-scale, multicenter, cross-sectional descriptive study was conducted in multiple hospitals in Vietnam from October 2018 to May 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Pregnant Vietnamese women were recommended for prenatal testing during routine check-ups due to structural anomalies detected in detailed ultrasound scanning. Genetic clinicians counseled them about the risk of fetal chromosomal abnormality. If parents opted for invasive testing, they were offered the option to participate in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003ePrenatal biological samples\u0026mdash;including amniotic fluid, chorionic villus, umbilical cord blood, and fetal tissues\u0026mdash;were acquired and stored in collection tubes according to the manufacturer's instructions for chromosomal analysis using copy number variation sequencing (CNV-Seq).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFetal genomic DNA extraction\u003c/h2\u003e \u003cp\u003eDNA collected from blood or fluid was extracted using GeneJET Whole Blood Genomic DNA Purification Mini Kit (Thermo Fisher Scientific). In addition, DNA from selected tissues were extracted with Relia Prep\u0026trade; gDNA Tissue mini prep and quantified with the QuantiFlour dsDNA system (Promega).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eLibrary preparation and sequencing\u003c/h2\u003e \u003cp\u003eDNA fragmentation and library preparation were performed with the NEBNext Ultra II FS DNA library prep kit (New England Biolabs) following the manufacturer's protocol. Subsequently, DNA library concentrations were quantified with a QuantiFlour dsDNA system (Promega). DNA samples were sequenced on the NextSeq 550 platform using a paired-end 2\u0026times;75 bp Reagent Kit (Illumina).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSubmicroscopic chromosomal copy number variation analysis\u003c/h2\u003e \u003cp\u003eSequencing data quality control was conducted with the FastQC (version 0.11.9). Raw sequences from each sample were aligned to the reference human genome from the University of California, Santa Cruz Genome Browser (NCBI build GRCh37) using Burrows-Wheeler Aligner and SAMtools packages. Duplicate reads were removed by using Picard tools (Broad Institute). The final output data were collated on OMIM and DECIPHER databases to classify CNV variants. CNVs were classified as pathogenic, benign, or variants of unknown significance (VUS). To assess the statistical association between genotype and phenotype, we combined pathogenic and VUS CNVs in a single category of abnormal CNVs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive analyses were used to describe the frequencies and proportions of CNVs. The bivariate logistic regression was conducted to analyze the associations between CNVs, sonography soft markers, and fetal structural anomalies. The \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Stata statistical software version 16.0 was used for data analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study cohort\u003c/h2\u003e \u003cp\u003eFetal biological samples (including amniotic fluid, chorionic villus, umbilical cord blood, and fetal tissue) from 5,008 pregnant Vietnamese women were tested for chromosomal anomalies using next-generation sequencing (NGS). CNV-seq analysis was performed on fetal samples collected during the first (15.24%), second (66.39%), and third trimesters (18.37%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). A total of 958 (19.13%) fetal samples carried at least one CNV (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e[Please insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here.]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenetic landscape of copy number variation in a cohort of 5008 fetal samples in Vietnam\u003c/h2\u003e \u003cp\u003eOverall, segmental aneuploidy (8.49%) and trisomy (6.91%) accounted for the largest number of abnormal CNV detected across the pregnancy trimesters. They were followed by multiple chromosomal anomalies (2.10%) and abnormal sex chromosomes (2.1%)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Among autosomal aneuploidies, we observed the highest rate in chromosomes 21, 18, and 13 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings are consistent with those reported in Asian\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and English\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e populations.\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\u003eFrequencies and proportions of CNVs detected by gestational age (N\u0026thinsp;=\u0026thinsp;5,008)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eparticipants (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eGestational age\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; = 13 weeks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u0026ndash;27 weeks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt; = 28 weeks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNV abnormalities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e958 (19.13)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (33.33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e636 (18.97)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e176 (14.45)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrisomy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e346 (6.91)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e86 (19.63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e222 (6.62)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e38 (3.12)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e06 (1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e04 (0.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e09 (0.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203 (4.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (10.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (1.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther trisomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e05 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e05 (1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutosomal mosaics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e05 (0.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex chromosome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e82 (1.64)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e22 (5.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e49 (1.46)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e11 (0.9)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMono X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e06 (1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e09 (0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e03 (0.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47 XXY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e03 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e08 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e02 (0.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47 XYY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e06 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e04 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e01 (0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47 XXX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e07 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e05 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e02 (0.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 XXXY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e01 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex mosaics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (2.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e03 (0.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSegmental aneuploidies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e425 (8.49)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e27 (6.16)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e295 (8.8)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e103 (8.46)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258 (5.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182 (5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (5.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113 (3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (3.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultiple anomalies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e105 (2.1)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11 (2.51)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e70 (2.09)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e24 (1.97)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo CNV detected\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4050 (80.87)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e292 (66.67)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2716 (81.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1042 (85.55)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5008 (100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e438 (100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3352 (100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1218 (100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Please insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here.]\u003c/p\u003e \u003cp\u003e[Please insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eWhile sex chromosome abnormalities had the lowest detected percentage overall, they were found at a higher rate in the first trimester than in the second or third trimester. In addition, chromosomal mosaicism was observed with low frequencies for both autosomal and sex chromosome aneuploidies. Although close to 85% of prenatal biological samples were collected and analyzed in the second and third trimesters of pregnancy, a significantly higher proportion of abnormal CNVs were detected in the first trimester (33.33%) than in the second (18.97%) and third trimesters (14.45%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Another finding in our study is the contradictory trend of different types of CNV abnormalities during pregnancy trimesters. Specifically, the detection of segmental aneuploidies and multiple chromosomal anomalies rose across pregnancy trimesters, in contrast to the declining trend in the detection of trisomy and sex chromosome anomalies as gestational age increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). A statistical link between gestational age and CNVs was discovered using bivariate logistic regression analysis, with a prevalence odds ratio of 0.94 (95% confidence interval, 0.93\u0026ndash;0.95, \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eSupplementary Table\u0026nbsp;1\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eMicrodeletion mutations were most observed in the autosomal chromosomes 1, 4, 5, 7, 9, 10, 18, and 22, with prevalence ranging from 4.5\u0026ndash;13.0% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Microduplication mutations were found in a similar prevalence (4.0\u0026ndash;13%) and frequently occurred in the autosomal chromosomes 2, 3, 5, 7, 8, 13, 14, 16, and 18 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\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\u003eFrequencies and percentage (%) of microdeletions and microduplications by chromosomes and pregnancy trimesters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eGestational age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt; = 13 weeks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e14\u0026ndash;27 weeks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026gt; = 28 weeks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDup\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e03 (16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (6.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e03 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e05 (5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e03 (4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23 (6.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e06 (2.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e02 (11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e02 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e09 (3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e05 (5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e06 (9.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16 (4.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33 (13.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01 (5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01 (6.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (8.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e03 (3.16)\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\u003e14 (3.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16 (6.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e02 (11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01 (6.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (13.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e05 (2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (10.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e03 (4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45 (12.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e09 (3.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01 (5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01(6.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (6.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e06 (3.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e05 (5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e07 (11.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21 (5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14 (5.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01 (5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e09 (3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e04 (2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e01 (1.05)\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\u003e11 (3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e 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colname=\"c4\"\u003e \u003cp\u003e10 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e07 (3.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e01 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e01 (1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 (3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10 (3.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e09 (3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e05 (2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e07 (7.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e05 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16 (4.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10 (3.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e02 (11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e01 (6.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e05 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colname=\"c6\"\u003e \u003cp\u003e05 (5.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e07 (11.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16 (4.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18 (7.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01 (5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (5.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e01 (0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09 (9.47)\u003c/p\u003e \u003c/td\u003e 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(4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19 (5.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22 (8.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e02 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e01 (1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e02 (0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e01 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e01 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e02 (1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\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\u003e01 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e02 (0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e01 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e08 (4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e03 (3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e01 (1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e04 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e09 (3.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e01 (5.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e02 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (7.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e07 (3.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (11.58)\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\u003e31 (8.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e09 (3.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e03 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e01 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e01 (1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e01 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e04 (1.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e176 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e357 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e253 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Statistics are summarized in frequency (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviations: Chr, chromosome; Del, microdeletions; Dup, microduplications. Dash lines denote microdeletions and microduplications not found in those populations.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Please insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here.]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between abnormal CNVs and sonography-detected soft markers and structural malformations\u003c/h2\u003e \u003cp\u003eTo investigate the association between abnormal CNV and fetal clinical manifestation, we first categorized the samples using the ultrasound soft markers currently used by most clinicians. Our study found an association between abnormal CNVs and multiple ultrasound soft markers, including increased nuchal fold thickness and fetal ventriculomegaly (\u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Interestingly, while increased nuchal fold thickness correlated with a higher risk of having abnormal CNVs, fetal ventriculomegaly was linked with a lower risk of having abnormal CNVs. We also found that multiple soft makers were statistically associated with a higher risk of abnormal CNVs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Conversely, cerebral ventriculomegaly, enlarged cisterna magna, echogenic bowel, and fetal pyelectasis showed no statistical associations with abnormal CNVs. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).In addition to ultrasound soft markers, we used clinical information to categorize structural anomalies for each case. Overall, 3,393 (67.75%) fetuses were identified to have structural anomalies in at least one system. Our study revealed that the prevalence of abnormal CNVs from congenital structural anomalies in the cardiovascular system (18.91%), skeletomuscular system (10.48%), central nervous system (11.11%), craniofacial structures (11.59%), urogenital system (18.48%), and digestive system (15.69%) were consistent with previously reported data from other populations (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003csup\u003e\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Next, we investigated the association between abnormal CNVs and multiple types of structural anomalies. Interestingly, only craniofacial malformations were significantly associated with lower odds of carrying abnormal CNVs (\u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, we found that multiple malformations were statistically associated with a higher risk of abnormal CNVs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), consistent with the previous reports\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\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\u003eThe associations between CNV types and ultrasound soft markers and sonography-detected structural anomalies\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\u003eTotal cohort (N\u0026thinsp;=\u0026thinsp;5,008)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbnormal CNV (N\u0026thinsp;=\u0026thinsp;949)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenign CNV /Negative (N\u0026thinsp;=\u0026thinsp;4059)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrevalence odd ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltrasound soft markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased Nuchal thickness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (24.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e374 (75.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.43 (1.15\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFetal ventriculomegaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (7.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181 (92.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34 (0.2\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoplastic/absent nasal bone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (23.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (76.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.32 (0.69\u0026ndash;2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEchogenic intracardiac focus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChoroid plexus cysts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEchogenic bowel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (8.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (91.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41 (0.15\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.08366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAberrant subclavian artery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.43 (0.15\u0026ndash;13.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFetal pyelectasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (8.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (91.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41 (0.18\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle umbilical artery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.57 (0.61\u0026ndash;10.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnlarged cisterna magna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (90.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44 (0.16\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple soft markers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (30.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (69.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.94 (1.31\u0026ndash;2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStructural anomalies on sonography\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral nervous system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (11.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (88.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53 (0.23\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCraniofacial malformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (11.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244 (88.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55 (0.37\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular anomaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (18.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (81.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (0.7\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory anomaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (96.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15 (0.02\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigestive malformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (15.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (84.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79 (0.37\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkeletal muscular malformation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (10.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (89.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.49 (0.28\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrogenital anomaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (18.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (81.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.57\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple malformations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e469 (23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1535 (76.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.61 (1.39\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther fetal anomalies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFetal growth restriction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (9.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e433 (90.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45 (0.33\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmniotic fluid abnormalities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (18.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (81.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94 (0.49\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlacenta anomaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (88.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57 (0.13\u0026ndash;2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUmbilical cord anomaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (88.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53 (0.12\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiscarriage/Stillbirth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (27.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (72.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.64 (0.72\u0026ndash;3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal obstetric history\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-invasive prenatal screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.43 (0.52\u0026ndash;3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther high-risk factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (17.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e417 (82.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88 (0.69\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Statistics are summarized in frequency (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: CNV, copy number variants; VUS, variants of uncertain significance.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Abnormal CNVs included 659 pathogenic CNV and 314 VUS CNV.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb,c\u003c/sup\u003e Prevalence odd ratios and \u003cem\u003eP\u003c/em\u003e values were withdrawn from logistic regression analyses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Please insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here.]\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the prevalence and characteristics of abnormal CNVs in 5008 fetal biological samples when Vietnamese pregnant women were subject to invasive prenatal diagnostic tests. We comprehensively characterized the spectrum of abnormal CNVs and examined the associations between CNVs and sonography-based structural malformations. This study will fill in the knowledge gaps in genetics and further inform prenatal screening programs and policies.\u003c/p\u003e \u003cp\u003eRecent technological advancements in low-coverage genome sequencing have significantly improved diagnostic values for detecting submicroscopic segmental aneuploidies\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. We performed CNV-seq analysis on fetal biological samples from 5,008 unrelated Vietnamese pregnant women: 958 (19.13%) had CNV anomalies, including 659 pathogenic or likely pathogenic, 290 VUS, and 9 benign/likely benign CNVs. Our analysis combined pathogenic CNVs and VUS into a single category of abnormal CNVs. The prevalence of the abnormal CNVs in our study was consistent with a report from a cohort study among a Korean population; however, our study figures are higher than previous reports in the Chinese population\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Our study also showed that submicroscopic segmental aneuploidies were the predominant CNVs, followed by trisomy. In particular, 425 (8.49%) submicroscopic segmental aneuploidies were identified, and microdeletions had a somewhat higher frequency than microduplications (5.15% versus 3.33%). Overall, the prevalence of segmental aneuploidies in our study was significantly higher than that reported prevalence by Wang J et al. \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. One explanation for this disparity might be due to the difference in sampling strategies. Wang J et al. used a broadly defined eligibility to recruit pregnant women, so 77% of the participants had normal ultrasound findings (2616 out of 3398).\u003c/p\u003e \u003cp\u003eMeanwhile, all participants in our study were referred to invasive prenatal testing due to ultrasound soft marker anomalies. Our analysis also revealed that trisomy was the second most common CNV, accounting for 36.1% of all CNVs identified. Trisomy 21 shared the largest proportions (approximately 60%) of the trisomy, followed by trisomy 18 and 13. Autosomal and sex chromosome mosaics were also seen in our study with low frequencies. Our findings were congruous with previous reports from large cohorts among different populations \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA comprehensive understanding of the associations between ultrasound soft markers and abnormal CNVs will aid clinicians in approaching genetic etiology, proactively determining better therapeutic interventions, and counseling pregnant women. Wang J et al. showed that fetuses with multiple ultrasound soft markers had an increased risk of pathogenic CNVs compared to the general fetal population. Consistently, our study found a statistically significant correlation between abnormal CNVs and multiple commonly applicable soft markers. When we further investigated each solitary soft marker, we only found increased nuchal fold thickness to be highly associated with a higher risk of abnormal CNVs. Even though our study showed no statistical significance due to the small sample size, several soft makers, such as a single umbilical artery or hypoplastic/absent nasal bone, showed increased odds of associated abnormal CNVs. Our study also found that isolated fetal ventriculomegaly, a standard measure in prenatal screening, was associated with lower odds of abnormal CNVs. Our findings were congruent with results from previous studies, although those reported studies had relatively small sample sizes to determine such significant associations\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe most observed structural abnormalities for phenotypic anomalies were in the cardiovascular, skeletal, muscular, central nervous, digestive, and urogenital systems. These findings are similar to the report by Donnelly et al. among North Americans\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Major fetal structural anomalies, including central nervous system anomalies, cardiovascular anomalies, craniofacial malformations, digestive malformation, skeletal muscular malformation, and urogenital anomalies, were not associated with abnormal CNVs. However, when multiple malformations were present, the fetus had significantly higher odds of harboring abnormal CNV. Noticeably, there were slightly higher proportions of abnormal CNVs in fetuses of pregnant women who experienced miscarriage or stillbirth. Still, our study showed no statistical association even though several small studies supported such a relationship\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. While advanced maternal age may lead to higher risks of trisomy 21 (Down syndrome), we found that maternal history of obstetric risks other than non-invasive prenatal screening was not associated with an increased prevalence of abnormal CNVs.\u003c/p\u003e \u003cp\u003eNevertheless, our study has one major limitation. Sampling bias is inherent in the cross-sectional study design. This study only recruited participants from highly specialized tertiary obstetric hospitals in Vietnam, reducing the diversity of the population studied.\u003c/p\u003e \u003cp\u003eIn summary, in the presence of fetal structural malformations detected on routine obstetric ultrasound, CNV analysis is crucial to detect whole chromosomal abnormalities and elucidate these phenotypic anomalies' genetic etiology. We demonstrated the statistically significant associations between abnormal CNVs, and multiple soft markers and fetal structural anomalies. Most importantly, we presented a large database study to provide a comprehensive spectrum of submicroscopic chromosomal aneuploidies in Vietnamese pregnant women. Hence, our study results provide strong evidence to support CNV-seq analysis as a powerful diagnostic tool for identifying genetic etiology in fetuses with abnormal ultrasound soft markers.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study results demonstrate that CNV analysis is a powerful diagnostic and confirmatory tool in prenatal genetic diagnosis, particularly in the first trimester of pregnancy. This large-scale study characterized the prevalence of submicroscopic chromosomal aneuploidies and the spectrum of CNVs among fetuses with sonography-detected soft markers and phenotypic anomalies. Our study results allow clinicians in Vietnam to devise the most appropriate strategies for prenatal diagnostic programs and counseling plans for genetic diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"text-align: inherit;\"\u003eThe data that support the findings of this study are available at the Sequence Read Archive \u0026nbsp;at https://www.ncbi.nlm.nih.gov/bioproject/923276.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study\u0026apos;s findings are available from the corresponding authors, [HST, HG], upon reasonable request.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe thank all patients who participated in this study and gave consent to report findings in this paper. We thank Angela Jansen, Ph.D., MHS of Angela Jansen \u0026amp; Associates, for her editorial services in preparing the manuscript for publication.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eStudy concept and design: HG, HST,\u0026nbsp;PMD, HNN; Obtaining Funding: NHN, HG; Data acquisition: DCT, DAN, QTL, DTTH, TNT, TMTH, TLD, CCN, KPTD, LATL, TSV, THNT, VTN, TTTD, QTTN, DKT; Laboratory work: HTTD, PANV, YNN, MAD; Data analysis: HTTD, HDLN, MNP, PLD,\u0026nbsp;TNT, HT, MDP, HG. Drafting the manuscript: HG; Critical revision of the manuscript for intellectual content: PMD, HG. All authors contributed to and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eGene Solutions, Vietnam funded this study. The funder did not have any role in the study design, data collection, analysis, publishing decision, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003eConflicts of interest\u003c/p\u003e\n\u003cp\u003eWe declare that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWhitworth, M., Bricker, L. \u0026amp; Mullan, C. Ultrasound for fetal assessment in early pregnancy. Cochrane Database Syst Rev, CD007058 (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1002/14651858.CD007058.pub3\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1002/14651858.CD007058.pub3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoubilet, P. M. Ultrasound evaluation of the first trimester. 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Prenat Diagn \u003cb\u003e38\u003c/b\u003e, 258\u0026ndash;266 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1002/pd.5223\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1002/pd.5223\" 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":"","lastPublishedDoi":"10.21203/rs.3.rs-2410361/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2410361/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCopy number variation (CNV) analysis is a powerful tool for discovering structural genomic variation. Still, no program uses this tool to analyze chromosomal aneuploidies in the Vietnamese population. Pregnant women attending routine prenatal checkups in Vietnam from October 2018 to May 2021 were included in this study and contributed fetal tissue to test the utility of CNV analysis for prenatal screening. Among 5,008 women screened, 958 (19.13%) harbored at least one CNV, comprising segmental aneuploidy (8.49%), trisomy (6.91%), multiple anomalies (2.10%), and sex chromosome abnormality (1.64%). The rate of segmental aneuploidy detection increased with gestational age, but trisomy and sex chromosomal abnormalities detection decreased as the pregnancy continued. This study also found an association between abnormal CNVs and several phenotypic markers. For ultrasound soft markers, an increased nuchal fold thickness correlated with a higher risk of abnormal CNVs. In addition, many soft indicators or structural abnormalities were significantly associated with an increased likelihood of abnormal CNVs. This work highlights the importance of CNV analysis for the early detection of prenatal congenital abnormalities, especially in the first trimester. This study\u0026rsquo;s findings will meaningfully aid policymakers in developing cost-effective genetic prenatal screening programs.\u003c/p\u003e","manuscriptTitle":"The genetic landscape of copy number variation in a Vietnamese cohort of 5008 fetuses with clinical anomalies during pregnancy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-16 16:04:21","doi":"10.21203/rs.3.rs-2410361/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":"64e259b8-dda7-4e8d-8232-f21535c498a5","owner":[],"postedDate":"January 16th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":18399182,"name":"Health sciences/Health care/Diagnosis/Genetic testing"},{"id":18399183,"name":"Health sciences/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2023-08-07T09:44:33+00:00","versionOfRecord":[],"versionCreatedAt":"2023-01-16 16:04:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2410361","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2410361","identity":"rs-2410361","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}