Abstract

Introduction: This study aimed to compare the efficacy of Copy Number Variation sequencing (CNV-Seq) with that of traditional karyotyping in prenatal diagnostics by assessing their concordance and ability to identify aneuploidies and structural abnormalities in fetal chromosomes.

Methodology: We analyzed 177 amniotic fluid samples from pregnant women who were at or beyond 16 weeks of gestation, utilizing both CNV-Seq and karyotyping to evaluate their detection capabilities.

Results: CNV-Seq identified chromosomal abnormalities in 46 cases (26.0%), demonstrating a higher detection rate compared to karyotyping, which found abnormalities in 40 cases (22.6%). CNV-Seq showed 100% concordance in identifying conditions such as trisomy 21, 18, 13, monosomy X, and 47, XXY. It also detected three mosaic cases and 13 copy number variations (CNVs) involving deletions or duplications that were not fully concordant with karyotyping results. Notably, CNV-Seq had a detection rate of 3.95% (7/177) for pathogenic or likely pathogenic chromosomal anomalies, and variants of uncertain significance (VUS) constituted 3.39% (6/177) of the findings.

Conclusion: CNV-Seq improves the precision of prenatal diagnostics and broadens the scope for informed clinical decision-making, especially in managing pregnancies with detected abnormalities. The integration of CNV-Seq with traditional karyotyping addresses gaps in detection and supports a more comprehensive approach to prenatal care. Further studies should aim to include a broader and more diverse population to validate and expand upon these results.

Introduction

The emergence of genomic technologies has dramatically revolutionized the field of prenatal diagnostics, significantly advancing our comprehension of fetal chromosomal abnormalities. Whereas traditional karyotyping has played a pivotal role in identifying aneuploidies and significant chromosomal rearrangements, it is hindered by its low resolution and the protracted periods required for cell culture1, 2, 3, 4. These constraints are particularly critical in contexts where prompt and accurate prenatal decision-making is paramount. To address these limitations, sophisticated methodologies such as Copy Number Variation sequencing (CNV-Seq) have been introduced, providing expedited and exhaustive genomic analyses4, 5.

CNV-Seq is superior in detecting submicroscopic chromosomal anomalies that remain undetected by traditional karyotyping, thereby significantly augmenting the diagnostic yield for chromosomal abnormalities6, 7, 8, 9. Luo et al. (2023) conducted a meta-analysis of eight studies, encompassing 11,091 pregnant women identified as high-risk or bearing fetuses with structural abnormalities detected through ultrasound. This systematic review demonstrated that CNV-Seq uncovered an additional 2% (95% CI, 0% to 4%) of chromosomal anomalies beyond what was detected by traditional karyotyping across six series. Moreover, a pooled mean incremental yield of 4% (95% CI, 3%–6%) in pathogenic CNVs was reported, with a range spanning 1%–16%. The findings underscored the enhanced capability of CNV-Seq in prenatal diagnosis, highlighting its expansive coverage, high throughput, elevated resolution, culture-independent process, excellent compatibility, and adjustable sequencing depth as factors contributing to its significant value in prenatal diagnostics10.

Nevertheless, CNV-Seq presents its own set of challenges. Although it can identify copy number variations at the kilobase level, it is incapable of detecting balanced translocations and inversions that do not result in copy number changes. Therefore, interpreting results from CNV-Seq necessitates a sophisticated understanding of genomic contexts and often requires the complementary use of traditional diagnostic methods to achieve a thorough genetic evaluation10, 11, 12, 13.

The Clinical Genome Resource (ClinGen) and the American College of Medical Genetics and Genomics (ACMG) have issued guidelines to classify CNVs based on their pathogenicity, ranging from benign to pathogenic4. These classifications assist clinicians in making well-informed decisions; however, the variability in the expressivity and penetrance of CNVs presents considerable challenges in counseling14, 15, 16. The detection of variants of uncertain significance further emphasizes the importance of a meticulous correlation between genotype and phenotype, a task complicated by the wide spectrum of clinical manifestations associated with CNVs17, 18, 19.

In Vietnam, where congenital anomalies considerably affect neonatal health, adopting CNV-Seq in prenatal care could potentially diminish the incidence of genetic disorders and enhance pregnancy outcomes. Annually, approximately 40,000 newborns in Vietnam suffer from congenital anomalies, constituting 1.5-2% of births. Given the lack of specific treatments for most of these anomalies, prenatal screening and diagnosis become crucial for genetic counseling, pregnancy management, and postnatal care20, 21, 22.

Our investigation aims to assess the efficacy of CNV-Seq relative to traditional karyotyping within a clinical context, evaluating their concordance and the incremental diagnostic value CNV-Seq might offer. By analyzing amniotic fluid samples from pregnant women at or beyond a gestational age of 16 weeks, we seek to elucidate the implications of CNV-Seq for prenatal diagnosis, focusing on its impact on clinical practice and genetic counseling.

Methods Study Design and Participant Overview Study Setting and Duration

The study was conducted at a specialized Center for Prenatal Screening, Diagnosis, and Neonatology in a major regional hospital, equipped with state-of-the-art genetic screening technology. It commenced in January 2021 and concluded in December 2022, aiming to collect a comprehensive dataset throughout different seasons.

Participants

Eligible participants were pregnant women who were at least 16 weeks into their gestation, deemed an optimal period for amniocentesis, which ensures the availability of sufficient amniotic fluid for genetic testing. Enrollment was limited to those with singleton pregnancies to reduce genetic variability and exclude the confounding variables associated with multiples. Inclusion criteria included elevated risk for chromosomal abnormalities indicated by abnormal prenatal screening results, concerning ultrasound findings, or a family history of genetic disorders. Exclusion criteria encompassed incomplete data for CNV-Seq and karyotyping and multiple pregnancies, maintaining the genetic analysis's focus and integrity.

Procedures and Genetic Analysis Amniocentesis and Sample Preparation

Amniocentesis was carried out between 16 and 18 weeks of gestation under ultrasound guidance, withdrawing approximately 20-30 mL of amniotic fluid. This timeframe minimizes risk to both the mother and fetus while providing an adequate sample volume for genetic testing.

Karyotyping

Approximately 10 mL of the collected fluid was treated with colchicine, subjected to hypotonic treatment, fixation, and centrifugation. Chromosomes were stained using G-banding techniques and analyzed with the GSL-120 automatic metaphase chromosome analysis system (Leica Microsystems, Deerfield, IL, USA), with chromosomal structures cataloged according to the 2016 International System of Human Cytogenetic Nomenclature23, 24.

CNV-Sequencing (CNV-seq)

The leftover sample was allocated for CNV-sequencing by Berry Genomics Co., including quality control measures like the usage of STR markers to ensure genetic integrity and prevent maternal DNA contamination. Genomic DNA was extracted using the QIAamp DNA Mini Kit (Qiagen, Valencia, CA, USA), prepared into a sequencing library with the NEBNext Ultra II DNA Library Prep Kit for Illumina, and sequenced on the NextSeq 500 platform (Illumina, San Diego, CA, USA). The produced data were aligned to human genome references hg19 and updated to hg38 (GRCh38) using the DECIPHER database, with CNVs interpreted in alignment with public databases and evaluated according to ACMG guidelines for clinical relevance.

Data Collection and Statistical Analysis Data Collection and Management

Clinical and genetic data collection adhered to standardized protocols for accuracy and reliability. High-resolution ultrasound results and genetic test findings from karyotyping and CNV-sequencing were meticulously recorded and entered into an electronic database. Genetic specialists reviewed the data, cross-referencing each finding with reputable genomic databases like DECIPHER and OMIM to guarantee consistent and reliable data interpretation.

Statistical Analysis

The statistical analysis aimed to accurately represent the data, focusing on demographic characteristics and chromosomal abnormality incidences. Data management and analysis were conducted using IBM SPSS Statistics (version 22.0, IBM Corp., Armonk, NY, USA), STATA (version 17.0, StataCorp LLC, College Station, TX, USA), and R software (version 4.3.2), calculating means, standard deviations, and percentages for maternal and gestational ages at diagnosis. Genetic findings were categorized and quantitatively expressed as percentages, including normal findings, duplications, deletions, mosaicism, and aneuploidy.

Ethical Considerations

The study was approved by the Institutional Review Board of Nghe An Maternity and Pediatric Hospital (Code 13/QĐ-BVSN dated 16/01/2023). Informed consent was obtained from all participants, detailing the study's purpose, procedures, and potential risks, ensuring informed decision-making. Participant privacy was safeguarded with unique identification codes, maintaining confidentiality. Amniocentesis was performed by certified professionals following clinical standards to minimize risks, with continuous monitoring to address any complications promptly, ensuring participant safety and study integrity.

Table 1.

Characteristics of Study Participants

Characteristics Number (n) Percentage (%) Maternal Age 117 66.1 ≥ 35 years 60 33.9 Average (years) Mean ± SD: 31.3 ± 6.9 Range: 15-47 years Gestational Age 97 54.8 ≥ 20 weeks 80 45.2 Average (weeks) Mean ± SD: 20.5 ± 4.0 Range: 16-33 months

Table 2.

Indications for amniotic fluid testing

Indication Number (n) Percentage (%) Abnormal ultrasound morphology 120 67.8 High-risk maternal serum screening 19 10.7 High-risk NIPT (Non-invasive prenatal test) 5 2.8 History of pregnancies with abnormalities 4 2.3 Abnormal ultrasound + High-risk maternal serum screening 11 6.2 Abnormal ultrasound + High-risk NIPT 14 7.9 Abnormal ultrasound + History of pregnancies with abnormalities 4 2.3 Total 177 100

Table 3.

Results of CNV-sequencing and karyotyping on amniotic fluid samples

Results CNV-seq Number (n) CNV-seq Percentage (%) Karyotype Number (n) Karyotype Percentage (%)