The Diagnostic Value of Whole Exome Sequencing in Prenatal Fetal Structural Anomalies: A Study Based on the North-Central Region of Guangxi

The Diagnostic Value of Whole Exome Sequencing in Prenatal Fetal Structural Anomalies: A Study Based on the North-Central Region of Guangxi | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Diagnostic Value of Whole Exome Sequencing in Prenatal Fetal Structural Anomalies: A Study Based on the North-Central Region of Guangxi Meiyu Zhang, Lizhu Chen, Xiaobao Wei, Yan Mei, Bailing Liu, Hui Chen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8559221/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objective: Prenatal fetal structural abnormalities are one of the serious birth defect disease groups that endanger the life and health of newborns. Whole exome sequencing (WES) has been widely applied in the diagnosis of pediatric genetic diseases in clinical practice. This study is based on a cohort study in the central and northern regions of Guangxi, aiming to evaluate the efficiency of WES in diagnosing prenatal fetal congenital structural abnormalities. Method: 160 pregnant women whose fetal structural abnormalities were found by ultrasound examination and whose results were negative by karyotype analysis and CNV-seq testing, were collected. Further WES testing was performed to identify the related pathogenic genes. The pregnancy outcomes of pregnant women were collected. Result: This cohort study included 160 couples. Through karyotype analysis and CNV-seq testing, 138 (86.25%, 138/160) cases without detected abnormalities, WES identified pathogenic or likely pathogenic variants (P/LP) in 35 (25.36%, 35/138) cases, variants of uncertain significance (VUS) in 4 (2.90%, 4/138) cases. In WES testing, the most frequently affected organs was the skeletal system (19.38%). Among them, FGFR3 was the most common pathogenic mutant gene, followed by ACE, ASPM, CHD3, etc. The turnaround time (TAT) of WES synchronized with conventional prenatal diagnostic techniques was 22.46 days, much shorter than the 39.02 days of traditional sequential testing. A total of 154 pregnancy outcome data of pregnant women were obtained, among which 81 cases terminated their pregnancies. Conclusion: WES plays an important role in prenatal diagnosis and fetal structural abnormality diagnosis. Conducting WES in parallel with conventional prenatal diagnosis technology can significantly reduce TAT of prenatal diagnosis, improve the efficiency of clinical prenatal diagnosis of abnormal fetus. At the same time, the newly discovered gene variants expand the expression spectrum of genetic diseases and enrich the prenatal diagnosis database in central and northern region of Guangxi. Prenatal diagnosis Prenatal Fetal Structural Anomalies Whole Exome Sequencing (WES) Turnaround time (TAT) Figures Figure 1 Figure 2 Figure 3 1. Introduction Fetal congenital anomalies occur in 2–3% of pregnancies, ranging from isolated minor defects to severe multisystem conditions, and account for approximately 21% of perinatal deaths [ 1 ] . Such anomalies impose both physical and psychological distress on families while increasing economic strain on households and society. Timely and precise etiological diagnosis, together with appropriate intervention, is therefore essential for affected fetuses. Prenatal ultrasound represents the primary tool for assessing fetal growth and detecting major structural anomalies. Nevertheless, factors such as fetal position and frequent movement limit its ability to provide a comprehensive phenotypic evaluation. Fetal anomalies detected by ultrasound may arise from diverse genetic alterations, including chromosomal aneuploidy, copy number variation (CNV), and single base substitution. Historically, clinical evaluation relied primarily on conventional prenatal cytogenetic testing. However, the diagnostic yield of chromosomal karyotyping for fetuses with structural anomalies has been reported at only 3.81% to 15.5% [ 2 ] , falling significantly short of clinical requirements. The advent of chromosomal microarray analysis (CMA) enables identification of CNV at the submicroscopic scale [ 3 – 5 ] . Subsequent progress in high-throughput sequencing demonstrates that CNV sequencing (CNV-seq) provides more robust detection of fetal copy number alterations in prenatal samples. Wang J et al. suggested that CNV-seq represented an appropriate first-line diagnostic approach for pregnancies with suspected chromosomal abnormalities in the general population [ 6 ] . Both CMA and CNV-seq are now widely applied in prenatal diagnosis, markedly enhancing the detection of fetal structural anomalies. Nevertheless, a subset of fetuses with such anomalies remains unexplained, with some cases attributable to underlying single gene mutations, which remain undetectable by the aforementioned methods. Whole exome sequencing (WES) employs sequence capture technology to enrich DNA from all exonic regions of the genome, enabling the detection of single nucleotide variants (SNVs), small insertions or deletions, and exon-level CNVs. This approach has proven highly effective in diagnosing single-gene disorders [ 7 – 9 ] . Its application has expanded into prenatal diagnostics, where it serves as an important method for evaluating fetal structural anomalies. Nonetheless, restricted prenatal phenotypic data and the frequent identification of variants of uncertain significance (VUS) continue to hinder its broader clinical adoption. Integrative clinical datasets linking fetal genotype and phenotype remain essential for improving diagnostic precision and genetic counseling. Evidence indicates that WES has become a first-line diagnostic method for children presenting with neurodevelopmental disorders [ 10 ] . A large-scale prenatal cohort study in 2019 reported a diagnostic yield of 8.5% for fetuses with structural anomalies [ 11 ] . More recently, repeated clinical validations have established WES as a valuable approach for investigating the genetic etiology of both structural anomalies and soft-index anomalies in fetuses [ 12 – 15 ] . Findings from Chen, X et al. indicate that sequential testing strategies are often applied in cases of fetal structural anomalies; when CNV-seq identifies a pathogenic variant, WES is commonly omitted. Such practice eliminates the opportunity to detect additional relevant genetic variants, thereby increasing the risk of diagnostic errors and delaying appropriate clinical management [ 16 ] . The conventional sequential testing approach for pregnant women presenting with prenatal fetal structural anomalies relies on concurrent chromosomal karyotyping and CNV-seq to determine whether the anomaly originates from chromosomal number or structural anomalies. When neither method accounts for the observed anomaly, WES is subsequently employed to detect pathogenic variants and additional disease-associated mutations. In Guangxi, the distinctive karst terrain poses challenges for patient travel, and multiple consultations required by sequential testing impose considerable inconvenience. To address this limitation, alternative diagnostic strategies offering greater efficiency warrant evaluation. In this study, 160 cases of fetal structural anomalies were analyzed. Participants voluntarily chose between two diagnostic pathways: sequential testing with chromosomal karyotyping and CNV-seq followed by WES, or a simultaneous application of all three techniques. The objective was to delineate the genetic alterations responsible for fetal anomalies and to provide precise guidance for fetal prognosis, perinatal management, reproductive decisions, and future pregnancies. In addition, the diagnostic performance and clinical utility of WES in prenatal anomaly assessment were systematically assessed. Follow-up data included pregnancy outcomes and fetal clinical phenotypes after either continuation or termination of pregnancy. An effort was also undertaken to establish a regional phenotype–genotype database, forming a theoretical foundation for the broader implementation of WES in prenatal diagnostics and enhancing its diagnostic yield. 2. Materials and Methods 2.1 Cohort All experiments in this retrospective study received approval from the Institutional Review Board of Guangxi Liuzhou Maternity and Child Healthcare Hospital, and written informed consent for invasive prenatal diagnosis was obtained from the parents of each fetus. Eligibility criteria included: (1) fetuses with congenital structural anomalies or abnormal ultrasound soft markers—such as increased nuchal translucency (NT ≥ 3.5 mm), fetal growth restriction (FGR), or fetal edema—who underwent invasive prenatal diagnosis at this hospital; and (2) availability of complete parental clinical data with samples fulfilling testing requirements. At present, invasive prenatal diagnosis for congenital structural anomalies in this hospital routinely involves concurrent chromosomal karyotyping and CNV-seq testing, with WES offered as an optional procedure. The cohort therefore comprised cases that underwent karyotype analysis, CNV-seq, and WES simultaneously, or cases in which WES was performed following negative results from both karyotype analysis and CNV-seq. A schematic overview of the study design is presented in Fig. 1 . Clinical samples were obtained from 160 pregnant women between March 2020 and February 2023. Demographic and clinical variables were systematically documented, including maternal and paternal age, gravidity and parity history, consanguinity, adverse pregnancy or birth outcomes, mode of conception (natural or in vitro fertilization), and family history of genetic disorders. 2.2 Chromosome karyotyping Samples of chorionic villus, amniotic fluid, and umbilical cord blood (10 mL/tube) were collected and cultured following standardized protocols. After sequential treatment with colchicine, hypotonic solution, fixation, and centrifugation, chromosomes were stained with Giemsa and analyzed for cytogenetic karyotypes [ 17 ] . The classification of chromosomal abnormalities was conducted in accordance with the International System for Human Cytogenomic Nomenclature (ISCN, 2020). 2.3 CNV-seq Chorionic villus, amniotic fluid, and umbilical cord blood specimens (10 mL/tube) were processed under standardized protocols. Genomic DNA was extracted and purified using a genomic DNA kit, followed by fragmentation and library preparation according to the chromosome CNV detection kit (Reversible Termination Sequencing). DNA libraries were subsequently purified, quantified, and sequenced with the NextSeq CN500 High-Throughput Sequencing Kit on the NextSeq CN500 platform (Berry Genomics Biotechnology Co., Ltd., China). Bioinformatics analysis was applied to identify CNVs, and pathogenic variants were classified in line with the international guidelines of the ACMG. 2.4 WES WES was conducted using a parent–child trio detection approach. Depending on gestational age, chorionic villus, amniotic fluid cells, or umbilical cord blood were obtained, together with 2 mL EDTA-K2–anticoagulated peripheral blood from immediate family members. Genomic DNA was isolated using the QIAamp Whole Blood DNA Extraction Kit and subsequently fragmented ultrasonically to 150–200 bp. Whole-genome libraries were generated with the NEB Next DNA Library Preparation Kit for Illumina (NEB, USA). Library quality was evaluated using a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, USA) and an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Exonic regions of human genes were enriched by liquid-phase hybridization capture (Berry Genomics Biotechnology Co., Ltd., China) and subjected to paired-end sequencing (150 bp read length) on the Illumina Nextseq 6000 platform. Post-sequencing processing involved removal of adapters and low-quality reads, alignment to the reference genome with BWA, and statistical assessment of sequencing depth, uniformity, and probe specificity. Variant calling and annotation were performed with the Verita Trekker® Variant Detection System and the Enliven® Variant Annotation System. single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) were profiled, with candidate pathogenic loci defined as variants with a frequency < 0.05 in the 1000 Genomes, ESP6500si, ExAC_ALL, ExAC_EAS, and Berry Genomics databases, and predicted as pathogenic by SIFT, PolyPhen2, or MutationTaster. Suspected mutations in fetuses and parents were validated by Sanger sequencing. According to ACMG and ClinGen variant curation expert panel recommendations, single nucleotide variants (SNVs) and indels were categorized into five groups: pathogenic (P), likely pathogenic (LP), uncertain significance (VUS), likely benign (LB), and benign (B). Only variants classified as P or LP were included in this study. 2.5 Pregnancy outcome follow-up Pregnancy outcomes were obtained through telephone interviews and hospital information systems up to November 2023. Collected data comprised fetal status, phenotypic characteristics, and clinical implications, which were subsequently subjected to statistical evaluation using T-tests. 3. Results 3.1 Cohort Characteristics Between March 2020 and February 2023, 160 families with fetuses exhibiting ultrasound-detected anomalies were recruited at Liuzhou Maternity and Child Healthcare Hospital in Guangxi. All participants underwent invasive testing: 83 cases received concurrent chromosomal karyotyping, CNV-seq, and WES, whereas 77 cases underwent sequential testing, with Trio-WES performed only when both karyotype and CNV-seq results were negative. Demographic data were collected at the time of maternal WES. The mean maternal age was 31.12 years (range, 19–49 years), with a mean gestational age of 23.86 weeks (range, 11.43–38 weeks). The mean paternal age was 34.21 years (range, 24–51 years). Based on ultrasound phenotypes, the 160 fetuses were classified into 12 categories (Table 1 ), comprising increased NT and cystic hygroma, multisystem anomalies, skeletal system, cardiovascular system, gastrointestinal tract and abdomen, central nervous system, genitourinary system, edema, facial anomalies, amniotic fluid abnormalities, growth restriction (FGR), and others. Within this cohort, skeletal system anomalies were most frequent (19.38%), followed by multisystem anomalies (16.25%), cardiovascular system (13.75%), and central nervous system (11.88%). Table 1 Demographic characteristics of the participants in this study cohort Cases (trios) Percentage Gestational week (weeks) Maternal age (years) Paternal age (years) Total 160 Fetal growth restriction 9 5.63% 26.29(22.29–29.71) 29.78(20–36) 35.38(28–41) Increased NT and cystic hygroma 17 10.63% 15.89(11.43-28.0) 29.00(23–36) 35.38(28–42) Multisystem 26 16.25% 23.48(12-33.43) 31.08(19–42) 33.91(27–44) Gastrointestinal tract and AW 6 3.75% 24.11(16.57–32.29) 36.5(32–40) 39(35–41) Skeletal system 31 19.38% 25.51(13.86-38) 30.87(23–48) 34.24(26–50) Genitourinary system 6 3.75% 25.83(20-33.71) 30.33(24–37) 34(25–43) Others 2 1.25% 17.43(16-18.86) 33.5(32–35) 41.5(36–47) Hydrops 4 2.50% 24(16.57–32.14) 33.5(32–35) 35.5(35–36) Cardiovascular system 22 13.75% 22.86(13.14–33.14) 30.81(21–41) 32.65(22–44) Facial 12 7.50% 25.30(13.71–36.14) 32.67(27–49) 36.92(26–51) Amniotic fluid volume abnormality 6 3.75% 28.95(24.57–32.71) 33.17(21–40) 35.5(24–45) Central nervous system 19 11.88% 26.09(16.57–37.86) 30.68(19–41) 33.57(25–44) 3.2 Diagnostic yield of chromosome karyotyping and CNV-seq Chromosomal karyotyping identified structural or numerical abnormalities in 15 fetal samples. Among the numerical abnormalities, four cases presented with 47,XY + 21, one with 45,X, one with 47,XYY, one with 45,XN,der(13;14)(q10;q10), and one with 45,XN,der(14)t(14;15)(p12;q15),-15. The remaining seven samples exhibited structural aberrations, including inversions (inv) and derivative chromosomes (der). CNV-seq detected pathogenic variants in 10 samples, whereas 8 additional cases showed VUS. 3.3 WES positive diagnostic results identified In this study, 22 fetal samples with chromosomal numerical or structural anomalies were excluded, and the remaining 138 underwent WES. P/LP variants were identified in 35 samples (25.36%, 35/138), indicating that WES substantially enhances the diagnostic yield of prenatal structural anomalies, with an overall detection rate of 21.88% (35/160) when compared with conventional approaches (Table 2 ). The 35 positive cases involved 35 P/LP variants distributed across 27 genes. Among them, 17 cases (48.57%, 17/35) represented de novo mutations affecting genes such as TRPV4, FGFR3, TUBB, RAF1, COL1A1, ACTA1, PTEN, CHD3 , and LZTR1 . Another 12 cases (34.29%, 12/35) were inherited from both parents, involving genes including LBR, CC2D2A, WDR62, RAB3GAP2, NEB, LZTR1, KLF1, GUSB, ACE, PIEZO1, LAMA3 , and ASPM , while 4 cases carried variants inherited from a single parent ( KCNJ1, NOTCH1, TSC2 , and GLI2 ). FGFR3 accounted for the highest frequency of mutations, detected in 7 cases (20%, 7/35), with three instances of c.1138G > A (p.G380R), three of c.742C > T (p.Arg248Cys), and one of c.2421A > G (p.Ter807TrpextTer101). RAF1 variants were also common, found in 2 cases (5.71%, 2/35) with c.770C > T (p.S257L) and c.788T > A (p.V263D). LZTR1 mutations were identified in 2 cases, including c.851G > A (p.R284H), c.1822T > C (p.F608L), and c.794G > A (p.W265*). Each of the remaining 24 genes was implicated by a single variant (Table 2 ). Table 2 Summary of variant cases detected by WES Case Gene Alterration Amino acid change Pathogenicity Origin System affected Pregnancy outcome 1 TRPV4 c.1412_1414del, het p.F471del P Denovo Skeletal System Induction of labor 2 LBR c.166-1G > C, hom / LP Pat + Mat Skeletal System Induction of labor 3 KCNJ1 c.346_357del, hom p.A116_T119del LP Pat + Mat Amniotic fluid abnormalities Intrauterine Fetal Demise 4 FGFR3 c.1138G > A, het p.G380R P Denovo Skeletal System Intrauterine Fetal Demise 5 TUBB c.895A > G, het p.M299V LP Denovo Central nervous System Stillbirth 7 FGFR3 c.742C > T, het p.R248C P Denovo Multisystem Stillbirth 8 FGFR3 c.1138G > A, het p.G380R P Denovo Skeletal System Intrauterine Fetal Demise 9 CC2D2A c.2848C > T, het p.R950* P Pat Central nervous System Induction of labor CC2D2A c.1267C > T, het p.R423* P Mat 10 RAF1 c.770C > T, het p.S257L P Denovo Genitourinary System Live Birth 11 WDR62 c.4556A > G, het p.K1519R LP Pat Central nervous System Intrauterine Fetal Demise WDR62 c.1836 + 1G > A, het LP Mat 12 TSC2 c.4138C > T, het p.Q1380* LP Mat Cardiovascular System Induction of labor 13 FGFR3 c.742C > T, het p.R248C P Denovo Skeletal System Induction of labor 14 FGFR3 c.2421A > G, het p.Ter807WextTer101 LP Denovo Skeletal System Induction of labor 15 COL1A1 c.2015_2016delinsT, het p.P672Lfs*94 P Denovo Skeletal System Induction of labor 16 RAB3GAP2 c.812-2A > G, het / LP Pat Facial Induction of labor c.304 + 5G > T, het / VUS Mat 17 TGIF1 c.268C > T, het p.R90C LP NA Multisystem Induction of labor 18 NEB c.24871G > T, het p.V8291F VUS Pat Skeletal System Induction of labor c.24871-10C > G, het / VUS c.10612C > T, het p.R3538* LP Mat 19 ACTA1 c.49G > A, het p.G17S LP Denovo Multisystem Induction of labor 20 LZTR1 c.1822T > C, het p.F608L VUS Pat Increased NT Induction of labor c.794G > A, het p.W265* LP Mat 21 KLF1 c.519_525dup, het p.G176Rfs*179 P Pat Cardiovascular System Induction of labor c.1012C > A, het p.P338T LP Mat 22 GUSB c.863G > C, het p.W288S LP Pat Edema Induction of labor c.1687A > T, het p.K563Ter LP Mat 23 FGFR3 c.742C > T, het p.R248C P Denovo Multisystem Induction of labor 24 RAF1 c.788T > A, het p.V263D LP Denovo Multisystem Induction of labor 25 ACE c.1540G > A, het p.D514N VUS Pat Genitourinary System Live Birth c.1324del, het p.R442Vfs*14 LP Mat 26 PTEN c.144C > G, het p.N48K P Denovo Central nervous System Induction of labor 27 FGFR3 c.1138G > A, het p.G380R P Denovo Skeletal System Stillbirth 28 LZTR1 c.851G > A, het p.R284H LP Denovo Multisystem Intrauterine Fetal Demise 29 PIEZO1 c.4483G > T, het p.E1495* P Pat Edema Induction of labor c.3490C > T, het p.R1164* P Mat 30 XIAP c.612_614del, het p.G205del LP NA Gastrointestinal/Abdominal Live Birth 31 CHD3 c.5456G > A, het p.W1819* P Denovo Facial Induction of labor 32 LAMA3 c.2422T > C, het p.S808P VUS Pat Facial Induction of labor c.4750C > T, het p.R1584* LP Mat 33 GLI2 c.1189del, het p.V397Cfs*124 LP Pat Facial Live Birth 34 ASPM c.6015_6016del,het p.Arg2005fs P Pat Central nervous System Induction of labor c.2751_2757del,het p.T918Pfs P Mat 35 COL2A1 c.3481G > A, het p.G1161S P Denovo Skeletal System Termination of Pregnancy Analysis of WES-positive cases across 12 phenotypic categories revealed variable diagnostic yields among different fetal systems. The highest yield was observed in the edema group at 50.00% (2/4), followed by the genitourinary system (33.33%, 2/6), facial anomalies (33.33%, 4/12), skeletal system (32.26%, 10/32), central nervous system (26.32%, 5/19), multisystem anomalies (23.08%, 6/26), gastrointestinal/abdominal group (16.67%, 1/6), amniotic fluid abnormalities (16.67%, 1/6), cardiovascular system (13.64%, 3/22), and increased NT with cystic hygroma (5.88%, 1/17) (Fig. 2 ). Evaluation of turnaround time (TAT) demonstrated that simultaneous testing required markedly less time (22.46 days) compared with sequential testing (39.02 days), with statistical significance ( P < 0.001) (Fig. 3 ). 3.4 Pregnancy outcomes and clinical impact assessment All 160 pregnant women in the cohort underwent WES testing. Follow-up was completed for 154 cases (96.88%, 155/160), while 5 (3.12%, 5/160) were lost to follow-up. Among the 48 women with a confirmed molecular diagnosis, 42 chose pregnancy termination, 6 continued their pregnancies, and none were lost to follow-up. Among the 112 women without a definitive diagnosis, 42 opted for termination, 65 continued their pregnancies, and 5 were lost to follow-up (Table 3 ). Table 3 Pregnancy outcomes among 160 cases with WES results. Groups Number of Cases Termination of Pregnancy Continuing Pregnancy Loss to follow up Number of Cases Number of Cases Number of Cases Diagnosed 48 42(87.5%) 6(12.5%) 0(10.87%) Undiagnosed 112 42(37.5%) 65(58.04%) 5(4.46%) Overall 160 84(52.5%) 71(44.38%) 5(3.13%) 4. Discussion Congenital anomalies represent a major contributor to birth defects and exert a substantial impact on population health. Prenatal ultrasound remains the primary method for assessing fetal growth and identifying severe structural anomalies in clinical settings. The integration of chromosomal karyotyping and CNV-seq into prenatal diagnostics has markedly enhanced diagnostic accuracy. In this study, 160 pregnant women with abnormal ultrasound findings underwent chromosomal karyotyping, CNV-seq, and WES. Chromosomal karyotyping and CNV-seq identified 22 fetal structural anomalies, yet both methods exhibited limitations in capturing the full spectrum of phenotypic abnormalities, leaving a subset of cases without definitive diagnoses. Advances in genomic technology have expanded the application of WES in prenatal diagnostics, and it is now recognized as a valuable method for detecting genetic causes of fetal structural anomalies, with clear improvements in diagnostic yield [ 11 ] . Within this cohort, 138 anomalies remained unresolved after chromosomal karyotyping and CNV-seq; subsequent WES testing revealed pathogenic variants in 35 cases, highlighting the superior diagnostic capacity of WES relative to conventional approaches. Nonetheless, four cases remained inconclusive, likely attributable to insufficient gene coverage in current databases, and 99 cases tested negative by WES, reflecting the inherent diagnostic limitations of this technology in clinical practice. Imaging analysis in 31 fetuses revealed skeletal dysplasia within the cohort. Triol-WES identified causative variants in 10 cases presenting with abnormalities such as fetal and humeral dysplasia, all of which were autosomal dominant de novo mutations. Five cases were attributed to FGFR3 mutations, while the remaining involved genes including LBR, NEB, TRPV4, COL2A1 , and COL1A1 . Previous studies have shown that FGFR3 acts as a negative regulator of skeletal development by suppressing proliferation and differentiation of growth plate chondrocytes, thereby inducing long bone growth defects such as achondroplasia and lethal skeletal dysplasia. Frequently observed variants include c.1138G > A (G380R) and c.742C > T (p.Arg248Cys) [ 18 – 21 ] , which cannot be detected through chromosomal karyotyping or CNV-seq. Among 26 fetal samples with multisystem anomalies, WES identified pathogenic mutations in six cases, two of which involved FGFR3 [ 21 ] , while the other four were associated with ACTA1, LZTR1, RAF1 , and TGIF1 . In the subgroup with facial malformations (n = 4), one fetus (case 16) carried a compound heterozygous mutation in RAB3GAP2 ( c.812-2A > G/c.304 + 5G > T ), inherited paternally and maternally, respectively. The c.812-2A > G variant disrupts splicing and alters protein function. Mutations in RAB3GAP2 are known to cause Martsolf syndrome type 1 and Warburg micro syndrome type 2, both characterized by congenital cataracts among other features [ 22 , 23 ] . Absence of bilateral lenses in the ultrasound of case 16 may therefore reflect cataracts secondary to RAB3GAP2 mutations. Another fetus (case 21) underwent invasive prenatal testing due to ultrasound evidence of cardiomegaly, right ventricular wall hypertrophy, pericardial effusion, anemia, and hypoxia. Trio-WES identified compound heterozygous mutations in the fetal KLF1 gene: a heterozygous mutation in exon 2, c.519_525dup (p.G176Rfs179 ), inherited from the father, and a heterozygous mutation in exon 3, c.1012C > A (p.P338T) , inherited from the mother. Previous studies [ 3 , 4 , 24 ] reported trans mutations involving c.519_525dup (p.G176Rfs179) in multiple patients, supporting its pathogenic role. Similarly, in two reported cases [ 25 ] , trans mutations involving c.1012C > A (p.P338T) in exon 3 were associated with pathogenic outcomes. Based on ACMG criteria, the mutation identified in this case was classified as pathogenic (P). Mutations in KLF1 are associated with congenital dyserythropoietic anemia type IV, characterized primarily by anemia and cardiomegaly [ 25 ] , and the fetal phenotype in this case aligned with this disorder. Ultrasound in another fetus within the cohort revealed strephenopodia and overlapping digits on both hands. Trio-WES detected three compound heterozygous mutations in the NEB gene. Among them, c.10612C > T was categorized as a likely pathogenic variant (LP) due to the introduction of a premature stop codon leading to loss of protein function. In contrast, c.24871G > T and c.24871-10C > G were classified as VUS. To explore potential pathogenic mechanisms, partial fetal tissue obtained post-induction was subjected to RNA sequencing, which showed no marked abnormalities, possibly reflecting limitations in available phenotypic and genetic correlation data. Within the intrauterine growth restriction and renal system subgroups of this study, no definitive pathogenic diagnosis was established. In addition, four cases involving VUS were identified. In one fetus, ultrasound demonstrated bilateral anophthalmia and tetra-amelia. WES revealed a heterozygous NSDHL mutation, c.119G > A (p.Cys40Tyr). The mutation was absent in the father but detected in heterozygous form in the mother, and was classified as VUS. NSDHL, located on chromosome Xq28, has been implicated in congenital hemidysplasia with ichthyosiform erythroderma and tetra-amelia, as well as CK syndrome [ 26 , 27 ] . Following genetic counseling, the pregnancy was terminated. In this study, statistical analysis of WES in fetuses presenting with abnormal ultrasound findings but negative chromosomal karyotyping and CNV-seq demonstrated a diagnostic yield of 25.36%. This result suggests that WES enhances the detection of abnormalities in prenatal diagnosis and provides added value for elucidating fetal structural anomalies. The diagnostic efficiency observed aligns with that reported in related studies (14.2%–34%) [ 13 , 14 ] . However, diagnostic performance varied across different malformation subgroups. Potential explanations for the absence of pathogenic variants in FGR fetuses include: (1) incomplete functional characterization of genes associated with the phenotype; (2) insufficient evidence regarding the pathogenicity of detected variants. Additional factors contributing to negative findings include incomplete phenotypic delineation by prenatal ultrasound and the absence of comprehensive genotype–phenotype correlation databases, both of which pose considerable challenges for genetic counseling. For fetuses with negative WES results, longitudinal monitoring of phenotypic progression is warranted. Reanalysis of sequencing data in light of database updates or functional validation studies [ 28 , 29 ] may allow reclassification of variants and clarification of the genetic basis of fetal abnormalities. Significant progress in prenatal imaging has improved the detection of fetal anomalies by ultrasound; however, the heterogeneity of disease phenotypes and conditions such as intellectual disability and metabolic disorders that remain undetectable through imaging necessitate complementary cytogenetic and molecular genetic analyses. The clinical relevance of prenatal WES was therefore assessed in terms of its influence on medical management, including adjustments in delivery planning and neonatal care. A comparison between sequential testing using chromosomal karyotyping, CNV-seq, and WES and simultaneous testing revealed a markedly shorter TAT in the latter group: 22.46 days for 83 women undergoing simultaneous testing versus 39.02 days for those choosing the sequential approach. This reduction in diagnostic turnaround supports earlier decision-making regarding pregnancy continuation or termination and lessens maternal anxiety as well as the risks associated with late-gestational induction. Given the broad coverage and relatively high diagnostic yield of WES in cases of fetal structural anomalies, simultaneous testing is considered more effective for minimizing loss of genetic variant data and enabling earlier intervention and timely treatment. Nonetheless, the absence of a fetal variant database comparable to postnatal resources and the limited understanding of fetal genotype-phenotype relationships continue to present challenges for prenatal WES. Data obtained from pathological examination following termination and systematic postnatal phenotypic documentation provide essential insights for refining genotype-phenotype correlations. Follow-up of women in this cohort, together with the establishment of a preliminary regional database of prenatal phenotypes and genotypes in North-Central Guangxi, offers a valuable reference framework to support the broader application of WES in prenatal diagnostics. Conclusion The extensive application of WES in clinical diagnostics has markedly accelerated the identification of pathogenic genes. Integration of WES with chromosomal karyotyping and CNV-seq enables substantial reduction in diagnostic turnaround, improves the efficiency of genetic disease detection, and deepens insight into the molecular basis of hereditary disorders. Continuous monitoring of pregnancy outcomes contributes to clarifying fetal genotype-phenotype associations and supports the establishment of a region-specific prenatal phenotype database. Such resources provide essential guidance for evaluating therapeutic prospects and prognostic expectations, informing parental decisions regarding pregnancy management, and shaping strategies for future reproductive planning. Declarations Data Availability Statement Not applicable. This manuscript does not report data generation or analysis. For more detail information, please contact the corresponding author Dejian Yuan (E-mail: [email protected] ). Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki. This research was approved by Liuzhou Maternity and Child Healthcare Hospital ethics committee on July 22, 2022, with the approval number: Quick Review-Research-2022-019. The patients had provided written informed consent for publication. Clinical trial number: not applicable. Consent for publication Not Applicable. Competing Interest declaration This study has no conflicts of interest. No potential conflict of interest was reported by the author(s). Author Contributions Meiyu Zhang and Lizhu Chen contributed equally to this work. Meiyu Zhang designed the study and performed the experiments. Lizhu Chen analyzed the data and wrote the manuscript. Xiaobao Wei, Yan Mei, Bailing Liu, Hui Chen, Xiaohui Qin, Qiurong Lai and Meiting Tan performed the experiments and provided genetic counseling. Dejian Yuan provided critical reagents and reviewed the manuscript. All authors read and approved the final manuscript. Funding This work was supported by National Natural Science Foundation of China（82060284），Guangxi Clinical Research Center for Obstetrics and Gynecology (GuikeAD22035223), Self-funded research project of Health Commission of Guangxi Zhuang Autonomous Region (Z-B20221576)； References Qiao J, Yuan J, Hu W, et al. Combined diagnosis of QF-PCR and CNV-Seq in fetal chromosomal abnormalities: A new perspective on prenatal diagnosis. J Clin Lab Anal. 2022;36(4):e24311. Huang LY, Li J, Zhang Y, Li DZ. A KLF1 gene mutation causes beta-thalassemia minor in a Chinese family. Int J Lab Hematol. 2018;40(2):e35–7. Tangsricharoen T, Natesirinilkul R, Phusua A, et al. Severe neonatal haemolytic anaemia caused by compound heterozygous KLF1 mutations: report of four families and literature review. Br J Haematol. 2021;194(3):626–34. Jiang H, Jiang F, Li J, et al. Congenital Nonspherocytic Hemolytic Anemia Caused by Krüppel-Like Factor 1 Gene Variants: Another Case Report. Hemoglobin. 2019;43(4–5):292–5. Lee HH, Mak AS, Kou KO, et al. An Unusual Hydrops Fetalis Associated with Compound Heterozygosity for Kruppel-like Factor 1 mutations. Hemoglobin. 2016;40(6):431–4. Wang J, Chen L, Zhou C et al. Prospective chromosome analysis of 3429 amniocentesis samples in China using copy number variation sequencing. Am J Obstet Gynecol, 2018. 219(3): p. 287 e1-287 e18. Pratt M, Garritty C, Thuku M, et al. Application of exome sequencing for prenatal diagnosis: a rapid scoping review. Genet Med. 2020;22(12):1925–34. Yates CL, Monaghan KG, Copenheaver D, et al. Whole-exome sequencing on deceased fetuses with ultrasound anomalies: expanding our knowledge of genetic disease during fetal development. Genet Med. 2017;19(10):1171–8. Yang Y, Muzny DM, Reid JG, et al. Clinical whole-exome sequencing for the diagnosis of mendelian disorders. N Engl J Med. 2013;369(16):1502–11. Luo C, Wen E, Liu Y, et al. Application of Whole-Exome Sequencing in the Prenatal Diagnosis of Foetuses With Central Nervous System Abnormalities. Mol Genet Genomic Med. 2024;12(10):e70016. Lord J, McMullan DJ, Eberhardt RY, et al. Prenatal exome sequencing analysis in fetal structural anomalies detected by ultrasonography (PAGE): a cohort study. Lancet. 2019;393(10173):747–57. Lai THT, Au LKS, Lau YTE et al. Application of Prenatal Whole Exome Sequencing for Structural Congenital Anomalies-Experience from a Local Prenatal Diagnostic Laboratory. Healthc (Basel), 2022. 10(12). Fu F, Li R, Yu Q, et al. Application of exome sequencing for prenatal diagnosis of fetal structural anomalies: clinical experience and lessons learned from a cohort of 1618 fetuses. Genome Med. 2022;14(1):123. Tran Mau-Them F, Delanne J, Denommé-Pichon AS, et al. Prenatal diagnosis by trio exome sequencing in fetuses with ultrasound anomalies: A powerful diagnostic tool. Front Genet. 2023;14:1099995. Shi X, Ding H, Li C, et al. Clinical utility of chromosomal microarray analysis and whole exome sequencing in foetuses with oligohydramnios. Ann Med. 2023;55(1):2215539. Chen X, Jiang Y, Chen R, et al. Clinical efficiency of simultaneous CNV-seq and whole-exome sequencing for testing fetal structural anomalies. J Transl Med. 2022;20(1):10. Zhang J, Tang X, Hu J, et al. Investigation on combined copy number variation sequencing and cytogenetic karyotyping for prenatal diagnosis. BMC Pregnancy Childbirth. 2021;21(1):496. Liang H, Qi W, Jin C, et al. Evaluation of Volumetric Bone Mineral Density, Bone Microarchitecture, and Bone Strength in Patients with Achondroplasia Caused by FGFR3 c.1138G > A Mutation. Calcif Tissue Int. 2023;112(1):13–23. Ornitz DM, Legeai-Mallet L. Achondroplasia: Development, pathogenesis, and therapy. Dev Dyn. 2017;246(4):291–309. Arenas MA, Pino MD, Fano V. FGFR3-related hypochondroplasia: longitudinal growth in 57 children with the p.Asn540Lys mutation. J Pediatr Endocrinol Metab. 2018;31(11):1279–84. Chen J, Liu J, Zhou Y, et al. Molecular therapeutic strategies for FGFR3 gene-related skeletal dysplasia. J Mol Med (Berl). 2017;95(12):1303–13. Aligianis IA, Morgan NV, Mione M, et al. Mutation in Rab3 GTPase-activating protein (RAB3GAP) noncatalytic subunit in a kindred with Martsolf syndrome. Am J Hum Genet. 2006;78(4):702–7. Aligianis IA, Johnson CA, Gissen P, et al. Mutations of the catalytic subunit of RAB3GAP cause Warburg Micro syndrome. Nat Genet. 2005;37(3):221–3. Huang LY, Li J, Zhang Y, Li DZ. A KLF1 gene mutation causes β-thalassemia minor in a Chinese family. Int J Lab Hematol. 2018;40(2):e35–7. Lee HH, Mak AS, Kou KO, et al. An Unusual Hydrops Fetalis Associated with Compound Heterozygosity for Krüppel-like Factor 1 mutations. Hemoglobin. 2016;40(6):431–4. Garg M, Kulkarni SD, Sayed R, Hegde AU. CK syndrome: a rare cause of developmental delay in a young boy. Clin Dysmorphol. 2021;30(4):201–3. McLarren KW, Severson TM, du Souich C, et al. Hypomorphic temperature-sensitive alleles of NSDHL cause CK syndrome. Am J Hum Genet. 2010;87(6):905–14. Aarabi M, Sniezek O, Jiang H, et al. Importance of complete phenotyping in prenatal whole exome sequencing. Hum Genet. 2018;137(2):175–81. Eldomery MK, Coban-Akdemir Z, Harel T, et al. Lessons learned from additional research analyses of unsolved clinical exome cases. Genome Med. 2017;9(1):26. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 Feb, 2026 Reviewers invited by journal 13 Feb, 2026 Editor invited by journal 16 Jan, 2026 Editor assigned by journal 12 Jan, 2026 Submission checks completed at journal 12 Jan, 2026 First submitted to journal 09 Jan, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8559221","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590938966,"identity":"df9b25b9-4501-4735-a65f-29e755af8f5c","order_by":0,"name":"Meiyu Zhang","email":"","orcid":"","institution":"Liuzhou Maternity and Child Healthcare Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meiyu","middleName":"","lastName":"Zhang","suffix":""},{"id":590938967,"identity":"d3777a9f-ec00-4db3-aff7-b05fae9bb07c","order_by":1,"name":"Lizhu Chen","email":"","orcid":"","institution":"Liuzhou Hospital, Guangzhou Women and Children’s Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Lizhu","middleName":"","lastName":"Chen","suffix":""},{"id":590938968,"identity":"1ee55641-5f43-4c1d-b0ba-358a76edcc83","order_by":2,"name":"Xiaobao Wei","email":"","orcid":"","institution":"Liuzhou Hospital, Guangzhou Women and Children’s Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Xiaobao","middleName":"","lastName":"Wei","suffix":""},{"id":590938969,"identity":"52c27e7a-ece6-4b9d-8498-d51b8cd2807c","order_by":3,"name":"Yan Mei","email":"","orcid":"","institution":"Liuzhou Maternity and Child Healthcare Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Mei","suffix":""},{"id":590938970,"identity":"b739f726-9c80-46db-8a8b-b6d09c1ac02e","order_by":4,"name":"Bailing Liu","email":"","orcid":"","institution":"Liuzhou Maternity and Child Healthcare 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Center","correspondingAuthor":false,"prefix":"","firstName":"Qiurong","middleName":"","lastName":"Lai","suffix":""},{"id":590938974,"identity":"49f9432e-fcb8-45c6-8605-b5e15a30fcc8","order_by":8,"name":"Meiting Tan","email":"","orcid":"","institution":"Liuzhou Hospital, Guangzhou Women and Children’s Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Meiting","middleName":"","lastName":"Tan","suffix":""},{"id":590938975,"identity":"aebc5b9e-07b3-47ba-b7f5-62ce5f22d0f9","order_by":9,"name":"Dejian Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYLACHgY2HgYG5gMHPvwgTQtb4sGZPcRrAZPGhznYiFBtcCP52YM3FXwy/LN7PhwGapbnFztASEuaueGcM2w8EnfObjhcYMFgOHN2AiEtCWbSvG1Av9zI3XB4Bg9DgsFtglrSv0nz/mPjkb+R8+AwDxtRWnKAtjSw8QAZDMRpkTzzpkxyzjE2HsMbaQbAQJYg7Be+4+nbJN7UHLOXu5H8+MOHHzby/NIEtCgcAFPHYHwJ/MpBQL4BTNUQVjkKRsEoGAUjFwAAhr9FttRJNCEAAAAASUVORK5CYII=","orcid":"","institution":"Liuzhou Hospital, Guangzhou Women and Children’s Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Dejian","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2026-01-09 09:24:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8559221/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8559221/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102991157,"identity":"fceeccb7-68f0-49b3-a0c0-c7e28ceea82e","added_by":"auto","created_at":"2026-02-19 11:30:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87708,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the enrolled cases\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8559221/v1/0ba8b4d1731ce443bfa58586.png"},{"id":103049806,"identity":"7197b2f3-2c3c-4b6a-8a07-1e2b3702e869","added_by":"auto","created_at":"2026-02-20 07:46:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72756,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic rate of WES in cases of fetal structural abnormalities\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8559221/v1/8eed24c6f50519c2f5924d7e.png"},{"id":102991159,"identity":"011455c2-3c40-4432-ba8f-adf1214da14f","added_by":"auto","created_at":"2026-02-19 11:30:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21498,"visible":true,"origin":"","legend":"\u003cp\u003eThe TAT for Synchronous inspection and Sequential inspection\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8559221/v1/8f037de1337d9f56dc167d04.png"},{"id":103051124,"identity":"50c676ea-1f4d-400a-a064-cdb2f945fc5e","added_by":"auto","created_at":"2026-02-20 07:58:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1141539,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8559221/v1/e67e8d9a-bf3f-4748-b21c-64c2faf639d2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Diagnostic Value of Whole Exome Sequencing in Prenatal Fetal Structural Anomalies: A Study Based on the North-Central Region of Guangxi","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFetal congenital anomalies occur in 2\u0026ndash;3% of pregnancies, ranging from isolated minor defects to severe multisystem conditions, and account for approximately 21% of perinatal deaths \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Such anomalies impose both physical and psychological distress on families while increasing economic strain on households and society. Timely and precise etiological diagnosis, together with appropriate intervention, is therefore essential for affected fetuses. Prenatal ultrasound represents the primary tool for assessing fetal growth and detecting major structural anomalies. Nevertheless, factors such as fetal position and frequent movement limit its ability to provide a comprehensive phenotypic evaluation.\u003c/p\u003e \u003cp\u003eFetal anomalies detected by ultrasound may arise from diverse genetic alterations, including chromosomal aneuploidy, copy number variation (CNV), and single base substitution. Historically, clinical evaluation relied primarily on conventional prenatal cytogenetic testing. However, the diagnostic yield of chromosomal karyotyping for fetuses with structural anomalies has been reported at only 3.81% to 15.5% \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, falling significantly short of clinical requirements. The advent of chromosomal microarray analysis (CMA) enables identification of CNV at the submicroscopic scale \u003csup\u003e[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Subsequent progress in high-throughput sequencing demonstrates that CNV sequencing (CNV-seq) provides more robust detection of fetal copy number alterations in prenatal samples. Wang J et al. suggested that CNV-seq represented an appropriate first-line diagnostic approach for pregnancies with suspected chromosomal abnormalities in the general population \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Both CMA and CNV-seq are now widely applied in prenatal diagnosis, markedly enhancing the detection of fetal structural anomalies. Nevertheless, a subset of fetuses with such anomalies remains unexplained, with some cases attributable to underlying single gene mutations, which remain undetectable by the aforementioned methods.\u003c/p\u003e \u003cp\u003eWhole exome sequencing (WES) employs sequence capture technology to enrich DNA from all exonic regions of the genome, enabling the detection of single nucleotide variants (SNVs), small insertions or deletions, and exon-level CNVs. This approach has proven highly effective in diagnosing single-gene disorders \u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Its application has expanded into prenatal diagnostics, where it serves as an important method for evaluating fetal structural anomalies. Nonetheless, restricted prenatal phenotypic data and the frequent identification of variants of uncertain significance (VUS) continue to hinder its broader clinical adoption. Integrative clinical datasets linking fetal genotype and phenotype remain essential for improving diagnostic precision and genetic counseling. Evidence indicates that WES has become a first-line diagnostic method for children presenting with neurodevelopmental disorders \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. A large-scale prenatal cohort study in 2019 reported a diagnostic yield of 8.5% for fetuses with structural anomalies \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. More recently, repeated clinical validations have established WES as a valuable approach for investigating the genetic etiology of both structural anomalies and soft-index anomalies in fetuses \u003csup\u003e[\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Findings from Chen, X et al. indicate that sequential testing strategies are often applied in cases of fetal structural anomalies; when CNV-seq identifies a pathogenic variant, WES is commonly omitted. Such practice eliminates the opportunity to detect additional relevant genetic variants, thereby increasing the risk of diagnostic errors and delaying appropriate clinical management \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe conventional sequential testing approach for pregnant women presenting with prenatal fetal structural anomalies relies on concurrent chromosomal karyotyping and CNV-seq to determine whether the anomaly originates from chromosomal number or structural anomalies. When neither method accounts for the observed anomaly, WES is subsequently employed to detect pathogenic variants and additional disease-associated mutations. In Guangxi, the distinctive karst terrain poses challenges for patient travel, and multiple consultations required by sequential testing impose considerable inconvenience. To address this limitation, alternative diagnostic strategies offering greater efficiency warrant evaluation. In this study, 160 cases of fetal structural anomalies were analyzed. Participants voluntarily chose between two diagnostic pathways: sequential testing with chromosomal karyotyping and CNV-seq followed by WES, or a simultaneous application of all three techniques. The objective was to delineate the genetic alterations responsible for fetal anomalies and to provide precise guidance for fetal prognosis, perinatal management, reproductive decisions, and future pregnancies. In addition, the diagnostic performance and clinical utility of WES in prenatal anomaly assessment were systematically assessed. Follow-up data included pregnancy outcomes and fetal clinical phenotypes after either continuation or termination of pregnancy. An effort was also undertaken to establish a regional phenotype\u0026ndash;genotype database, forming a theoretical foundation for the broader implementation of WES in prenatal diagnostics and enhancing its diagnostic yield.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cohort\u003c/h2\u003e \u003cp\u003eAll experiments in this retrospective study received approval from the Institutional Review Board of Guangxi Liuzhou Maternity and Child Healthcare Hospital, and written informed consent for invasive prenatal diagnosis was obtained from the parents of each fetus. Eligibility criteria included: (1) fetuses with congenital structural anomalies or abnormal ultrasound soft markers\u0026mdash;such as increased nuchal translucency (NT\u0026thinsp;\u0026ge;\u0026thinsp;3.5 mm), fetal growth restriction (FGR), or fetal edema\u0026mdash;who underwent invasive prenatal diagnosis at this hospital; and (2) availability of complete parental clinical data with samples fulfilling testing requirements. At present, invasive prenatal diagnosis for congenital structural anomalies in this hospital routinely involves concurrent chromosomal karyotyping and CNV-seq testing, with WES offered as an optional procedure. The cohort therefore comprised cases that underwent karyotype analysis, CNV-seq, and WES simultaneously, or cases in which WES was performed following negative results from both karyotype analysis and CNV-seq.\u0026nbsp;A schematic overview of the study design is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Clinical samples were obtained from 160 pregnant women between March 2020 and February 2023. Demographic and clinical variables were systematically documented, including maternal and paternal age, gravidity and parity history, consanguinity, adverse pregnancy or birth outcomes, mode of conception (natural or in vitro fertilization), and family history of genetic disorders.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Chromosome karyotyping\u003c/h2\u003e \u003cp\u003eSamples of chorionic villus, amniotic fluid, and umbilical cord blood (10 mL/tube) were collected and cultured following standardized protocols. After sequential treatment with colchicine, hypotonic solution, fixation, and centrifugation, chromosomes were stained with Giemsa and analyzed for cytogenetic karyotypes \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The classification of chromosomal abnormalities was conducted in accordance with the International System for Human Cytogenomic Nomenclature (ISCN, 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 CNV-seq\u003c/h2\u003e \u003cp\u003eChorionic villus, amniotic fluid, and umbilical cord blood specimens (10 mL/tube) were processed under standardized protocols. Genomic DNA was extracted and purified using a genomic DNA kit, followed by fragmentation and library preparation according to the chromosome CNV detection kit (Reversible Termination Sequencing). DNA libraries were subsequently purified, quantified, and sequenced with the NextSeq CN500 High-Throughput Sequencing Kit on the NextSeq CN500 platform (Berry Genomics Biotechnology Co., Ltd., China). Bioinformatics analysis was applied to identify CNVs, and pathogenic variants were classified in line with the international guidelines of the ACMG.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 WES\u003c/h2\u003e \u003cp\u003eWES was conducted using a parent\u0026ndash;child trio detection approach. Depending on gestational age, chorionic villus, amniotic fluid cells, or umbilical cord blood were obtained, together with 2 mL EDTA-K2\u0026ndash;anticoagulated peripheral blood from immediate family members. Genomic DNA was isolated using the QIAamp Whole Blood DNA Extraction Kit and subsequently fragmented ultrasonically to 150\u0026ndash;200 bp. Whole-genome libraries were generated with the NEB Next DNA Library Preparation Kit for Illumina (NEB, USA). Library quality was evaluated using a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, USA) and an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Exonic regions of human genes were enriched by liquid-phase hybridization capture (Berry Genomics Biotechnology Co., Ltd., China) and subjected to paired-end sequencing (150 bp read length) on the Illumina Nextseq 6000 platform. Post-sequencing processing involved removal of adapters and low-quality reads, alignment to the reference genome with BWA, and statistical assessment of sequencing depth, uniformity, and probe specificity. Variant calling and annotation were performed with the Verita Trekker\u0026reg; Variant Detection System and the Enliven\u0026reg; Variant Annotation System. single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) were profiled, with candidate pathogenic loci defined as variants with a frequency\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the 1000 Genomes, ESP6500si, ExAC_ALL, ExAC_EAS, and Berry Genomics databases, and predicted as pathogenic by SIFT, PolyPhen2, or MutationTaster. Suspected mutations in fetuses and parents were validated by Sanger sequencing. According to ACMG and ClinGen variant curation expert panel recommendations, single nucleotide variants (SNVs) and indels were categorized into five groups: pathogenic (P), likely pathogenic (LP), uncertain significance (VUS), likely benign (LB), and benign (B). Only variants classified as P or LP were included in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Pregnancy outcome follow-up\u003c/h2\u003e \u003cp\u003ePregnancy outcomes were obtained through telephone interviews and hospital information systems up to November 2023. Collected data comprised fetal status, phenotypic characteristics, and clinical implications, which were subsequently subjected to statistical evaluation using T-tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Cohort Characteristics\u003c/h2\u003e \u003cp\u003eBetween March 2020 and February 2023, 160 families with fetuses exhibiting ultrasound-detected anomalies were recruited at Liuzhou Maternity and Child Healthcare Hospital in Guangxi. All participants underwent invasive testing: 83 cases received concurrent chromosomal karyotyping, CNV-seq, and WES, whereas 77 cases underwent sequential testing, with Trio-WES performed only when both karyotype and CNV-seq results were negative. Demographic data were collected at the time of maternal WES. The mean maternal age was 31.12 years (range, 19\u0026ndash;49 years), with a mean gestational age of 23.86 weeks (range, 11.43\u0026ndash;38 weeks). The mean paternal age was 34.21 years (range, 24\u0026ndash;51 years). Based on ultrasound phenotypes, the 160 fetuses were classified into 12 categories (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), comprising increased NT and cystic hygroma, multisystem anomalies, skeletal system, cardiovascular system, gastrointestinal tract and abdomen, central nervous system, genitourinary system, edema, facial anomalies, amniotic fluid abnormalities, growth restriction (FGR), and others. Within this cohort, skeletal system anomalies were most frequent (19.38%), followed by multisystem anomalies (16.25%), cardiovascular system (13.75%), and central nervous system (11.88%).\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\u003eDemographic characteristics of the participants in this study cohort\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases (trios)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGestational week (weeks)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaternal age (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePaternal age (years)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \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\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.29(22.29\u0026ndash;29.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.78(20\u0026ndash;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.38(28\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncreased NT and cystic hygroma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.89(11.43-28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.00(23\u0026ndash;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.38(28\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultisystem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.48(12-33.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.08(19\u0026ndash;42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.91(27\u0026ndash;44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal tract and AW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.11(16.57\u0026ndash;32.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.5(32\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39(35\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkeletal system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.51(13.86-38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.87(23\u0026ndash;48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.24(26\u0026ndash;50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenitourinary system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.83(20-33.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.33(24\u0026ndash;37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34(25\u0026ndash;43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.43(16-18.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.5(32\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.5(36\u0026ndash;47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydrops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24(16.57\u0026ndash;32.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.5(32\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.5(35\u0026ndash;36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.86(13.14\u0026ndash;33.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.81(21\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.65(22\u0026ndash;44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.30(13.71\u0026ndash;36.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.67(27\u0026ndash;49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.92(26\u0026ndash;51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmniotic fluid volume abnormality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.95(24.57\u0026ndash;32.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.17(21\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.5(24\u0026ndash;45)\u003c/p\u003e \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\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.09(16.57\u0026ndash;37.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.68(19\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.57(25\u0026ndash;44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Diagnostic yield of chromosome karyotyping and CNV-seq\u003c/h2\u003e \u003cp\u003eChromosomal karyotyping identified structural or numerical abnormalities in 15 fetal samples. Among the numerical abnormalities, four cases presented with 47,XY\u0026thinsp;+\u0026thinsp;21, one with 45,X, one with 47,XYY, one with 45,XN,der(13;14)(q10;q10), and one with 45,XN,der(14)t(14;15)(p12;q15),-15. The remaining seven samples exhibited structural aberrations, including inversions (inv) and derivative chromosomes (der). CNV-seq detected pathogenic variants in 10 samples, whereas 8 additional cases showed VUS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 WES positive diagnostic results identified\u003c/h2\u003e \u003cp\u003eIn this study, 22 fetal samples with chromosomal numerical or structural anomalies were excluded, and the remaining 138 underwent WES. P/LP variants were identified in 35 samples (25.36%, 35/138), indicating that WES substantially enhances the diagnostic yield of prenatal structural anomalies, with an overall detection rate of 21.88% (35/160) when compared with conventional approaches (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The 35 positive cases involved 35 P/LP variants distributed across 27 genes. Among them, 17 cases (48.57%, 17/35) represented de novo mutations affecting genes such as \u003cem\u003eTRPV4, FGFR3, TUBB, RAF1, COL1A1, ACTA1, PTEN, CHD3\u003c/em\u003e, and \u003cem\u003eLZTR1\u003c/em\u003e. Another 12 cases (34.29%, 12/35) were inherited from both parents, involving genes including \u003cem\u003eLBR, CC2D2A, WDR62, RAB3GAP2, NEB, LZTR1, KLF1, GUSB, ACE, PIEZO1, LAMA3\u003c/em\u003e, and \u003cem\u003eASPM\u003c/em\u003e, while 4 cases carried variants inherited from a single parent (\u003cem\u003eKCNJ1, NOTCH1, TSC2\u003c/em\u003e, and \u003cem\u003eGLI2\u003c/em\u003e). FGFR3 accounted for the highest frequency of mutations, detected in 7 cases (20%, 7/35), with three instances of c.1138G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.G380R), three of c.742C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.Arg248Cys), and one of c.2421A\u0026thinsp;\u0026gt;\u0026thinsp;G (p.Ter807TrpextTer101). RAF1 variants were also common, found in 2 cases (5.71%, 2/35) with c.770C\u0026thinsp;\u0026gt;\u0026thinsp;T (p.S257L) and c.788T\u0026thinsp;\u0026gt;\u0026thinsp;A (p.V263D). LZTR1 mutations were identified in 2 cases, including c.851G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.R284H), c.1822T\u0026thinsp;\u0026gt;\u0026thinsp;C (p.F608L), and c.794G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.W265*). Each of the remaining 24 genes was implicated by a single variant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of variant cases detected by WES\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlterration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmino acid change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePathogenicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOrigin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSystem affected\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePregnancy outcome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRPV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.1412_1414del, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.F471del\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkeletal System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.166-1G\u0026thinsp;\u0026gt;\u0026thinsp;C, hom\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\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u0026thinsp;+\u0026thinsp;Mat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkeletal System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKCNJ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.346_357del, hom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.A116_T119del\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u0026thinsp;+\u0026thinsp;Mat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAmniotic fluid abnormalities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntrauterine Fetal Demise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGFR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.1138G\u0026thinsp;\u0026gt;\u0026thinsp;A, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.G380R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkeletal System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntrauterine Fetal Demise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTUBB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.895A\u0026thinsp;\u0026gt;\u0026thinsp;G, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.M299V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCentral nervous System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStillbirth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGFR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.742C\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.R248C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultisystem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStillbirth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGFR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.1138G\u0026thinsp;\u0026gt;\u0026thinsp;A, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.G380R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkeletal System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntrauterine Fetal Demise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC2D2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.2848C\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.R950*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCentral nervous System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC2D2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.1267C\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.R423*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.770C\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.S257L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGenitourinary System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLive Birth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWDR62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.4556A\u0026thinsp;\u0026gt;\u0026thinsp;G, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep.K1519R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCentral nervous System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntrauterine Fetal Demise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWDR62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.1836\u0026thinsp;+\u0026thinsp;1G\u0026thinsp;\u0026gt;\u0026thinsp;A, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.4138C\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.Q1380*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCardiovascular System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction of labor\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\u003eFGFR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.742C\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.R248C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkeletal System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGFR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.2421A\u0026thinsp;\u0026gt;\u0026thinsp;G, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.Ter807WextTer101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkeletal System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOL1A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.2015_2016delinsT,\u003c/p\u003e \u003cp\u003ehet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.P672Lfs*94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkeletal System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRAB3GAP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.812-2A\u0026thinsp;\u0026gt;\u0026thinsp;G, het\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\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFacial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.304\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T, het\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\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGIF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.268C\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.R90C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultisystem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNEB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.24871G\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.V8291F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSkeletal System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.24871-10C\u0026thinsp;\u0026gt;\u0026thinsp;G, het\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\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.10612C\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.R3538*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACTA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.49G\u0026thinsp;\u0026gt;\u0026thinsp;A, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.G17S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultisystem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLZTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.1822T\u0026thinsp;\u0026gt;\u0026thinsp;C, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.F608L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIncreased NT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.794G\u0026thinsp;\u0026gt;\u0026thinsp;A, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.W265*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKLF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.519_525dup, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.G176Rfs*179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCardiovascular System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.1012C\u0026thinsp;\u0026gt;\u0026thinsp;A, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.P338T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGUSB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.863G\u0026thinsp;\u0026gt;\u0026thinsp;C, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.W288S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEdema\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.1687A\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.K563Ter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGFR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.742C\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.R248C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultisystem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.788T\u0026thinsp;\u0026gt;\u0026thinsp;A, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.V263D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultisystem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.1540G\u0026thinsp;\u0026gt;\u0026thinsp;A, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.D514N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenitourinary System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLive Birth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.1324del, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.R442Vfs*14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.144C\u0026thinsp;\u0026gt;\u0026thinsp;G, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.N48K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCentral nervous System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGFR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.1138G\u0026thinsp;\u0026gt;\u0026thinsp;A, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.G380R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkeletal System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStillbirth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLZTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.851G\u0026thinsp;\u0026gt;\u0026thinsp;A, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.R284H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultisystem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntrauterine Fetal Demise\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePIEZO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.4483G\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.E1495*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEdema\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.3490C\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.R1164*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXIAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.612_614del, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.G205del\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGastrointestinal/Abdominal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLive Birth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.5456G\u0026thinsp;\u0026gt;\u0026thinsp;A, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.W1819*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFacial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLAMA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.2422T\u0026thinsp;\u0026gt;\u0026thinsp;C, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.S808P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFacial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.4750C\u0026thinsp;\u0026gt;\u0026thinsp;T, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.R1584*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.1189del, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.V397Cfs*124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFacial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLive Birth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eASPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.6015_6016del,het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.Arg2005fs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCentral nervous System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInduction of labor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.2751_2757del,het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.T918Pfs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOL2A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.3481G\u0026thinsp;\u0026gt;\u0026thinsp;A, het\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.G1161S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSkeletal System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTermination of Pregnancy\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\u003eAnalysis of WES-positive cases across 12 phenotypic categories revealed variable diagnostic yields among different fetal systems. The highest yield was observed in the edema group at 50.00% (2/4), followed by the genitourinary system (33.33%, 2/6), facial anomalies (33.33%, 4/12), skeletal system (32.26%, 10/32), central nervous system (26.32%, 5/19), multisystem anomalies (23.08%, 6/26), gastrointestinal/abdominal group (16.67%, 1/6), amniotic fluid abnormalities (16.67%, 1/6), cardiovascular system (13.64%, 3/22), and increased NT with cystic hygroma (5.88%, 1/17) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Evaluation of turnaround time (TAT) demonstrated that simultaneous testing required markedly less time (22.46 days) compared with sequential testing (39.02 days), with statistical significance (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Pregnancy outcomes and clinical impact assessment\u003c/h2\u003e \u003cp\u003eAll 160 pregnant women in the cohort underwent WES testing. Follow-up was completed for 154 cases (96.88%, 155/160), while 5 (3.12%, 5/160) were lost to follow-up. Among the 48 women with a confirmed molecular diagnosis, 42 chose pregnancy termination, 6 continued their pregnancies, and none were lost to follow-up. Among the 112 women without a definitive diagnosis, 42 opted for termination, 65 continued their pregnancies, and 5 were lost to follow-up (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003ePregnancy outcomes among 160 cases with WES results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTermination of Pregnancy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eContinuing Pregnancy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLoss to follow up\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of Cases\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42(87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0(10.87%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndiagnosed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42(37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65(58.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5(4.46%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84(52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71(44.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5(3.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCongenital anomalies represent a major contributor to birth defects and exert a substantial impact on population health. Prenatal ultrasound remains the primary method for assessing fetal growth and identifying severe structural anomalies in clinical settings. The integration of chromosomal karyotyping and CNV-seq into prenatal diagnostics has markedly enhanced diagnostic accuracy. In this study, 160 pregnant women with abnormal ultrasound findings underwent chromosomal karyotyping, CNV-seq, and WES. Chromosomal karyotyping and CNV-seq identified 22 fetal structural anomalies, yet both methods exhibited limitations in capturing the full spectrum of phenotypic abnormalities, leaving a subset of cases without definitive diagnoses. Advances in genomic technology have expanded the application of WES in prenatal diagnostics, and it is now recognized as a valuable method for detecting genetic causes of fetal structural anomalies, with clear improvements in diagnostic yield \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Within this cohort, 138 anomalies remained unresolved after chromosomal karyotyping and CNV-seq; subsequent WES testing revealed pathogenic variants in 35 cases, highlighting the superior diagnostic capacity of WES relative to conventional approaches. Nonetheless, four cases remained inconclusive, likely attributable to insufficient gene coverage in current databases, and 99 cases tested negative by WES, reflecting the inherent diagnostic limitations of this technology in clinical practice.\u003c/p\u003e \u003cp\u003eImaging analysis in 31 fetuses revealed skeletal dysplasia within the cohort. Triol-WES identified causative variants in 10 cases presenting with abnormalities such as fetal and humeral dysplasia, all of which were autosomal dominant de novo mutations. Five cases were attributed to FGFR3 mutations, while the remaining involved genes including \u003cem\u003eLBR, NEB, TRPV4, COL2A1\u003c/em\u003e, and \u003cem\u003eCOL1A1\u003c/em\u003e. Previous studies have shown that FGFR3 acts as a negative regulator of skeletal development by suppressing proliferation and differentiation of growth plate chondrocytes, thereby inducing long bone growth defects such as achondroplasia and lethal skeletal dysplasia. Frequently observed variants include \u003cem\u003ec.1138G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/em\u003e (G380R) and \u003cem\u003ec.742C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/em\u003e (p.Arg248Cys) \u003csup\u003e[\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, which cannot be detected through chromosomal karyotyping or CNV-seq.\u0026nbsp;Among 26 fetal samples with multisystem anomalies, WES identified pathogenic mutations in six cases, two of which involved \u003cem\u003eFGFR3\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, while the other four were associated with \u003cem\u003eACTA1, LZTR1, RAF1\u003c/em\u003e, and \u003cem\u003eTGIF1\u003c/em\u003e. In the subgroup with facial malformations (n\u0026thinsp;=\u0026thinsp;4), one fetus (case 16) carried a compound heterozygous mutation in \u003cem\u003eRAB3GAP2\u003c/em\u003e (\u003cem\u003ec.812-2A\u0026thinsp;\u0026gt;\u0026thinsp;G/c.304\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/em\u003e), inherited paternally and maternally, respectively. The \u003cem\u003ec.812-2A\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e variant disrupts splicing and alters protein function. Mutations in \u003cem\u003eRAB3GAP2\u003c/em\u003e are known to cause Martsolf syndrome type 1 and Warburg micro syndrome type 2, both characterized by congenital cataracts among other features \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Absence of bilateral lenses in the ultrasound of case 16 may therefore reflect cataracts secondary to \u003cem\u003eRAB3GAP2\u003c/em\u003e mutations. Another fetus (case 21) underwent invasive prenatal testing due to ultrasound evidence of cardiomegaly, right ventricular wall hypertrophy, pericardial effusion, anemia, and hypoxia. Trio-WES identified compound heterozygous mutations in the fetal KLF1 gene: a heterozygous mutation in exon 2, \u003cem\u003ec.519_525dup (p.G176Rfs179\u003c/em\u003e), inherited from the father, and a heterozygous mutation in exon 3, \u003cem\u003ec.1012C\u0026thinsp;\u0026gt;\u0026thinsp;A (p.P338T)\u003c/em\u003e, inherited from the mother. Previous studies \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e reported trans mutations involving c.519_525dup (p.G176Rfs179) in multiple patients, supporting its pathogenic role. Similarly, in two reported cases \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, trans mutations involving \u003cem\u003ec.1012C\u0026thinsp;\u0026gt;\u0026thinsp;A (p.P338T)\u003c/em\u003e in exon 3 were associated with pathogenic outcomes. Based on ACMG criteria, the mutation identified in this case was classified as pathogenic (P). Mutations in KLF1 are associated with congenital dyserythropoietic anemia type IV, characterized primarily by anemia and cardiomegaly \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, and the fetal phenotype in this case aligned with this disorder. Ultrasound in another fetus within the cohort revealed strephenopodia and overlapping digits on both hands. Trio-WES detected three compound heterozygous mutations in the NEB gene. Among them, c.10612C\u0026thinsp;\u0026gt;\u0026thinsp;T was categorized as a likely pathogenic variant (LP) due to the introduction of a premature stop codon leading to loss of protein function. In contrast, c.24871G\u0026thinsp;\u0026gt;\u0026thinsp;T and c.24871-10C\u0026thinsp;\u0026gt;\u0026thinsp;G were classified as VUS. To explore potential pathogenic mechanisms, partial fetal tissue obtained post-induction was subjected to RNA sequencing, which showed no marked abnormalities, possibly reflecting limitations in available phenotypic and genetic correlation data. Within the intrauterine growth restriction and renal system subgroups of this study, no definitive pathogenic diagnosis was established.\u003c/p\u003e \u003cp\u003eIn addition, four cases involving VUS were identified. In one fetus, ultrasound demonstrated bilateral anophthalmia and tetra-amelia. WES revealed a heterozygous NSDHL mutation, c.119G\u0026thinsp;\u0026gt;\u0026thinsp;A (p.Cys40Tyr). The mutation was absent in the father but detected in heterozygous form in the mother, and was classified as VUS. NSDHL, located on chromosome Xq28, has been implicated in congenital hemidysplasia with ichthyosiform erythroderma and tetra-amelia, as well as CK syndrome \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Following genetic counseling, the pregnancy was terminated.\u003c/p\u003e \u003cp\u003eIn this study, statistical analysis of WES in fetuses presenting with abnormal ultrasound findings but negative chromosomal karyotyping and CNV-seq demonstrated a diagnostic yield of 25.36%. This result suggests that WES enhances the detection of abnormalities in prenatal diagnosis and provides added value for elucidating fetal structural anomalies. The diagnostic efficiency observed aligns with that reported in related studies (14.2%\u0026ndash;34%) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. However, diagnostic performance varied across different malformation subgroups. Potential explanations for the absence of pathogenic variants in FGR fetuses include: (1) incomplete functional characterization of genes associated with the phenotype; (2) insufficient evidence regarding the pathogenicity of detected variants. Additional factors contributing to negative findings include incomplete phenotypic delineation by prenatal ultrasound and the absence of comprehensive genotype\u0026ndash;phenotype correlation databases, both of which pose considerable challenges for genetic counseling. For fetuses with negative WES results, longitudinal monitoring of phenotypic progression is warranted. Reanalysis of sequencing data in light of database updates or functional validation studies \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e may allow reclassification of variants and clarification of the genetic basis of fetal abnormalities.\u003c/p\u003e \u003cp\u003eSignificant progress in prenatal imaging has improved the detection of fetal anomalies by ultrasound; however, the heterogeneity of disease phenotypes and conditions such as intellectual disability and metabolic disorders that remain undetectable through imaging necessitate complementary cytogenetic and molecular genetic analyses. The clinical relevance of prenatal WES was therefore assessed in terms of its influence on medical management, including adjustments in delivery planning and neonatal care. A comparison between sequential testing using chromosomal karyotyping, CNV-seq, and WES and simultaneous testing revealed a markedly shorter TAT in the latter group: 22.46 days for 83 women undergoing simultaneous testing versus 39.02 days for those choosing the sequential approach. This reduction in diagnostic turnaround supports earlier decision-making regarding pregnancy continuation or termination and lessens maternal anxiety as well as the risks associated with late-gestational induction. Given the broad coverage and relatively high diagnostic yield of WES in cases of fetal structural anomalies, simultaneous testing is considered more effective for minimizing loss of genetic variant data and enabling earlier intervention and timely treatment. Nonetheless, the absence of a fetal variant database comparable to postnatal resources and the limited understanding of fetal genotype-phenotype relationships continue to present challenges for prenatal WES. Data obtained from pathological examination following termination and systematic postnatal phenotypic documentation provide essential insights for refining genotype-phenotype correlations. Follow-up of women in this cohort, together with the establishment of a preliminary regional database of prenatal phenotypes and genotypes in North-Central Guangxi, offers a valuable reference framework to support the broader application of WES in prenatal diagnostics.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe extensive application of WES in clinical diagnostics has markedly accelerated the identification of pathogenic genes. Integration of WES with chromosomal karyotyping and CNV-seq enables substantial reduction in diagnostic turnaround, improves the efficiency of genetic disease detection, and deepens insight into the molecular basis of hereditary disorders. Continuous monitoring of pregnancy outcomes contributes to clarifying fetal genotype-phenotype associations and supports the establishment of a region-specific prenatal phenotype database. Such resources provide essential guidance for evaluating therapeutic prospects and prognostic expectations, informing parental decisions regarding pregnancy management, and shaping strategies for future reproductive planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not report data generation or analysis. For more detail information, please contact the corresponding author Dejian Yuan (E-mail: [email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. This research was approved by Liuzhou Maternity and Child Healthcare Hospital ethics committee on July 22, 2022, with the approval number: Quick Review-Research-2022-019. The patients had provided written informed consent for publication. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has no conflicts of interest. No potential conflict of interest was reported by the author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Meiyu Zhang and Lizhu Chen contributed equally to this work. Meiyu Zhang designed the study and performed the experiments. Lizhu Chen analyzed the data and wrote the manuscript. Xiaobao Wei, Yan Mei, Bailing Liu, Hui Chen, Xiaohui Qin, Qiurong Lai and Meiting Tan performed the experiments and provided genetic counseling. Dejian Yuan provided critical reagents and reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China（82060284），Guangxi Clinical Research Center for Obstetrics and Gynecology (GuikeAD22035223), Self-funded research project of Health Commission of Guangxi Zhuang Autonomous Region (Z-B20221576)；\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eQiao J, Yuan J, Hu W, et al. Combined diagnosis of QF-PCR and CNV-Seq in fetal chromosomal abnormalities: A new perspective on prenatal diagnosis. 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Ann Med. 2023;55(1):2215539.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Jiang Y, Chen R, et al. Clinical efficiency of simultaneous CNV-seq and whole-exome sequencing for testing fetal structural anomalies. J Transl Med. 2022;20(1):10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Tang X, Hu J, et al. Investigation on combined copy number variation sequencing and cytogenetic karyotyping for prenatal diagnosis. BMC Pregnancy Childbirth. 2021;21(1):496.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang H, Qi W, Jin C, et al. Evaluation of Volumetric Bone Mineral Density, Bone Microarchitecture, and Bone Strength in Patients with Achondroplasia Caused by FGFR3 c.1138G\u0026thinsp;\u0026gt;\u0026thinsp;A Mutation. Calcif Tissue Int. 2023;112(1):13\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrnitz DM, Legeai-Mallet L. Achondroplasia: Development, pathogenesis, and therapy. Dev Dyn. 2017;246(4):291\u0026ndash;309.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArenas MA, Pino MD, Fano V. FGFR3-related hypochondroplasia: longitudinal growth in 57 children with the p.Asn540Lys mutation. J Pediatr Endocrinol Metab. 2018;31(11):1279\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Liu J, Zhou Y, et al. Molecular therapeutic strategies for FGFR3 gene-related skeletal dysplasia. J Mol Med (Berl). 2017;95(12):1303\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAligianis IA, Morgan NV, Mione M, et al. Mutation in Rab3 GTPase-activating protein (RAB3GAP) noncatalytic subunit in a kindred with Martsolf syndrome. Am J Hum Genet. 2006;78(4):702\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAligianis IA, Johnson CA, Gissen P, et al. Mutations of the catalytic subunit of RAB3GAP cause Warburg Micro syndrome. 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Am J Hum Genet. 2010;87(6):905\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAarabi M, Sniezek O, Jiang H, et al. Importance of complete phenotyping in prenatal whole exome sequencing. Hum Genet. 2018;137(2):175\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEldomery MK, Coban-Akdemir Z, Harel T, et al. Lessons learned from additional research analyses of unsolved clinical exome cases. Genome Med. 2017;9(1):26.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prenatal diagnosis, Prenatal Fetal Structural Anomalies, Whole Exome Sequencing (WES), Turnaround time (TAT)","lastPublishedDoi":"10.21203/rs.3.rs-8559221/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8559221/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003ePrenatal fetal structural abnormalities are one of the serious birth defect disease groups that endanger the life and health of newborns. Whole exome sequencing (WES) has been widely applied in the diagnosis of pediatric genetic diseases in clinical practice. This study is based on a cohort study in the central and northern regions of Guangxi, aiming to evaluate the efficiency of WES in diagnosing prenatal fetal congenital structural abnormalities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003e160 pregnant women whose fetal structural abnormalities were found by ultrasound examination and whose results were negative by karyotype analysis and CNV-seq testing, were collected. Further WES testing was performed to identify the related pathogenic genes. The pregnancy outcomes of pregnant women were collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003eThis cohort study included 160 couples. Through karyotype analysis and CNV-seq testing, 138 (86.25%, 138/160) cases without detected abnormalities, WES identified pathogenic or likely pathogenic variants (P/LP) in 35 (25.36%, 35/138) cases, variants of uncertain significance (VUS) in 4 (2.90%, 4/138) cases. \u0026nbsp;In WES testing, the most frequently affected organs was the skeletal system (19.38%). Among them, FGFR3 was the most common pathogenic mutant gene, followed by ACE, ASPM, CHD3, etc. The turnaround time (TAT) of WES synchronized with conventional prenatal diagnostic techniques was 22.46 days, much shorter than the 39.02 days of traditional sequential testing. A total of 154 pregnancy outcome data of pregnant women were obtained, among which 81 cases terminated their pregnancies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eWES plays an important role in prenatal diagnosis and fetal structural abnormality diagnosis. \u0026nbsp;Conducting WES in parallel with conventional prenatal diagnosis technology can significantly reduce TAT of prenatal diagnosis, improve the efficiency of clinical prenatal diagnosis of abnormal fetus. At the same time, the newly discovered gene variants expand the expression spectrum of genetic diseases and enrich the prenatal diagnosis database in central and northern region of Guangxi.\u003c/p\u003e","manuscriptTitle":"The Diagnostic Value of Whole Exome Sequencing in Prenatal Fetal Structural Anomalies: A Study Based on the North-Central Region of Guangxi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 11:30:42","doi":"10.21203/rs.3.rs-8559221/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"78377129344102302235243577405299460561","date":"2026-02-13T13:53:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-13T09:44:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-16T05:45:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T01:46:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-13T01:46:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2026-01-09T09:16:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b39459e0-19d6-4f10-8e93-02d9b9f5d7d3","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-19T11:30:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 11:30:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8559221","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8559221","identity":"rs-8559221","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}