One-Tube Genomics in Prenatal Care for Low- and Middle-Income Countries: A Systematic Review of Integrated cfDNA-Based Aneuploidy, CNV, Monogenic Fetal Testing and Maternal Carrier Screening from a Single Maternal Sample

One-Tube Genomics in Prenatal Care for Low- and Middle-Income Countries: A Systematic Review of Integrated cfDNA-Based Aneuploidy, CNV, Monogenic Fetal Testing and Maternal Carrier Screening from a Single Maternal Sample | 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 Systematic Review One-Tube Genomics in Prenatal Care for Low- and Middle-Income Countries: A Systematic Review of Integrated cfDNA-Based Aneuploidy, CNV, Monogenic Fetal Testing and Maternal Carrier Screening from a Single Maternal Sample Wiku Andonotopo, MD, MSc, PhD This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8166404/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To synthesise clinical, technical and health-system evidence on “one-tube genomics”, defined as using a single maternal cfDNA sample to deliver aneuploidy NIPT, genome-wide CNV analysis, monogenic fetal testing and maternal carrier screening in low- and middle-income countries (LMICs). Methods A PRISMA-guided systematic review was conducted in PubMed/MEDLINE, Embase, Scopus, Web of Science and PMC from inception to May 2025. We included human studies evaluating cfDNA-based aneuploidy, CNV, monogenic or carrier screening, or population genomics relevant to prenatal care in LMIC or mixed-income settings. Two reviewers independently screened records, assessed full texts and extracted data. Risk of bias was appraised using the Newcastle–Ottawa Scale and ROBIS where appropriate. Heterogeneity precluded meta-analysis; findings were synthesised narratively across predefined thematic domains. Results Of 1,326 records identified, 25 studies met inclusion criteria. Large cohorts confirmed high accuracy of cfDNA for common aneuploidies and selected genome-wide CNVs. Monogenic and haemoglobinopathy-focused cfDNA approaches showed strong analytic validity but were limited to specialised centres. Population-scale NIPT and exome datasets from Vietnam and neighbouring regions provided detailed recessive variant spectra. Implementation and ethical papers highlighted counselling needs, data-governance challenges and emerging issues around incidental maternal findings. Conclusion Current evidence supports the technical feasibility and potential health-system advantages of one-tube genomics in LMICs, but integrated workflows remain largely unrealised. Prospective LMIC implementation studies, harmonised reporting standards and robust ethical frameworks are now critical to move from fragmented testing towards truly integrated prenatal genomics. Obstetrics & Gynecology Cell-free DNA (cfDNA) Low- and middle-income countries (LMICs) Monogenic and carrier screening Non-invasive prenatal testing (NIPT) One-tube genomics Figures Figure 1 Figure 2 Figure 3 Introduction The emergence of cell-free DNA analysis in maternal plasma has reshaped contemporary prenatal medicine by offering a non-invasive means to evaluate fetal chromosomal conditions with far greater accuracy than traditional serum-based screening. Early comprehensive reviews described this transition as a defining shift that fundamentally altered expectations for prenatal genetic evaluation.¹ Subsequent studies combining NIPT with detailed ultrasound demonstrated that integrating molecular and imaging information strengthened diagnostic certainty and broadened the clinical utility of cfDNA screening across diverse populations.² As technical capabilities expanded, investigators began examining the possibility of using cfDNA not only for aneuploidy detection but also for single-gene disorders, raising critical questions about analytic validity, informatics, and the necessary conditions for safe translation into routine prenatal care.³ Large cohort evaluations supported the reproducibility of cfDNA-based aneuploidy detection in real-world practice, including settings with variable laboratory infrastructure, thereby reinforcing its suitability beyond high-income healthcare systems.⁴ Interest in extending cfDNA to maternal carrier detection grew in parallel, particularly in regions where α-thalassemia contributes significantly to perinatal morbidity and where single-sample approaches could simplify clinical workflows.⁵ Early authoritative commentary emphasized that the rapid evolution of cfDNA technologies required ongoing reassessment of their role within fetal medicine, including their appropriateness as first-line tools.⁶ Frameworks designed to guide implementation highlighted that broader applications of cfDNA demand high laboratory standards, consistent reporting, and sufficient counselling capacity to support informed decision-making.⁷ Reviews of non-invasive monogenic diagnosis also emphasized the need for harmonized sequencing methods, reliable haplotype reconstruction, and careful interpretation of variants before widespread adoption could occur.⁸ These considerations became particularly relevant as haemoglobinopathies emerged as early candidates for integrated cfDNA workflows in regions with high carrier frequencies, where multi-step conventional screening is inefficient and sometimes inaccessible.⁹ Experimental work expanded the conceptual boundaries of non-invasive diagnosis further, including micro-nanochip approaches capable of isolating fetal nucleated red blood cells from the same maternal blood draw used for cfDNA analysis, suggesting that a single tube might eventually support multiple complementary genomic assays.¹⁰ Reviews on monogenic cfDNA diagnosis underscored that these innovations offered a credible alternative to invasive sampling for an increasing number of fetal conditions, although their integration into routine care remained limited by workflow complexity and expertise requirements.¹¹ At the same time, the persistent burden of recessive genetic diseases in several regions reinforced the need for screening pathways that link maternal carrier status with timely prenatal evaluation.¹² Population-level genomic studies strengthened this imperative. Work from Vietnam revealed detailed variant spectra for several recessive metabolic disorders, creating an evidence base for localized carrier panels adapted to regional disease distributions.¹³ Genome-wide cfDNA screening reports demonstrated that detection of significant CNVs could be incorporated into broad prenatal workflows when supported by appropriate counselling and laboratory quality systems.¹⁴ Establishment of national NIPT capacity in Vietnam further showed that locally developed cfDNA workflows can achieve accuracy comparable to global standards when adapted to specific laboratory conditions.¹⁵ Follow-up studies addressing maternal mosaicism and its contribution to false-positive monosomy X results underscored the need for algorithmic refinement to distinguish maternal from fetal signals accurately.¹⁶ As NIPT became more widely available, international clinical perspectives emphasized its value across healthcare systems with varying levels of access to invasive procedures.¹⁷ Laboratory guidelines for monogenic prenatal diagnosis reiterated the importance of rigorous workflow design, robust evidence for variant interpretation, and consistent reporting practice.¹⁸ Primary-care viewpoints reinforced the fundamental role of cfDNA in early detection strategies, particularly for conditions such as Down syndrome, where timely screening can meaningfully influence pregnancy management.¹⁹ Expanding genomic research from Vietnam provided further insights into recessive disease distribution in the population, strengthening the relevance of integrating carrier screening with prenatal testing strategies.²⁰ Complementary analyses of large NIPT genomic datasets demonstrated that cfDNA sequencing can simultaneously yield clinical and population-genetic information from a single maternal sample, highlighting an inherent efficiency in the platform.²¹ Case series describing cfDNA-based detection of de novo dominant variants further illustrated the expanding diagnostic horizon and its potential relevance to broader prenatal genomic assessment.²² As sequencing depth increased and quality control improved, incidental maternal findings, including signals suggestive of malignancy, were observed in cfDNA evaluations, underscoring the need for careful management and structured referral pathways.²³ Foundational studies in targeted haplotyping provided the methodological basis for contemporary monogenic cfDNA pipelines, illustrating how non-invasive inference of fetal inheritance patterns can be achieved with sufficient parental and genomic reference data.²⁴ Broader evaluations of cfDNA testing across varied prenatal indications reiterated the enduring importance of accessible, accurate screening in diverse healthcare contexts.²⁵ Although each domain of cfDNA technology—aneuploidy detection, CNV analysis, monogenic testing, maternal carrier screening, and population genomics—has matured independently, clinical implementation remains fragmented in many regions, especially low- and middle-income countries. Fragmentation leads to multiple clinical visits, repeated blood sampling, higher indirect costs, and substantial loss-to-follow-up. Table 1 summarizes the characteristics of the twenty-five studies included in this review. Table 2 synthesizes cross-domain methodological strengths, limitations, and research priorities. Table 1 Comprehensive Synopsis of 25 Key Studies Supporting the Research (NOS/ROBIS Domains Applied) Author Study Type / Design Population / Sample Methodology / Tools Key Outcomes Strengths Limitations Relevance to Current Study Abedalthagafi et al. ( 2023 )¹ Review Pregnant population NIPT overview Improved aneuploidy detection Comprehensive Not empirical Conceptual foundation Andonotopo et al. ( 2025 )² Cohort study NIPT + US cases Ultrasound + NIPT Enhanced anomaly detection Large dataset Regional bias Supports combined modality Chiu et al. ( 2018 )³ Review Monogenic disorders cfDNA for monogenic dx Feasibility shown Early foundational Older technology Shows expansion beyond trisomy Dewantiningrum et al. ( 2025 )⁴ Large cohort n = 4365 NIPT QC analysis High accuracy Large sample External validity Performance benchmark Doan et al. ( 2022 )⁵ Genomic study Maternal carriers cfDNA deletion tests High detection rate Novel design Needs advanced sequencing Supports carrier detection Everett & Chitty ( 2015 )⁶ Review Fetal medicine Cell-free fetal DNA Early cfDNA evolution Seminal review Outdated Historical context Fernández Martínez et al. ( 2025 )⁷ Guideline Pregnant women NIPT implementation Standardized clinical guidance Authoritative Not empirical Supports protocol planning Hanson et al. ( 2022 )⁸ Review Monogenic disorders NIPD via cfDNA Expanded monogenic NIPD Strong clinical framing Review-only Monogenic relevance Lam et al. ( 2022 )⁹ Diagnostic study Vietnamese Gap-PCR + NGS Improved thalassemia screening Hybrid method Complex workflow Supports hemoglobinopathy screening Li et al. ( 2024 )¹⁰ Experimental study nRBC isolation Micro-nanochip Better fetal cell capture Innovative tech Preclinical Future diagnostic relevance Mahdi Mortazavipour et al. ( 2022 )¹¹ Review Single-gene disorders cfDNA Useful for monogenic dx Wide coverage No new data Diagnostic scope reference Mensah & Sheth ( 2021 )¹² Review Thalassemia screening Carrier + prenatal dx Optimal pathways Clinical clarity Not population-specific Supports hemoglobinopathy section Nguyen et al. ( 2022 )¹³ Sequencing study Vietnamese MPS sequencing Variant spectra (G6PD/PKU) Large catalog Gene-limited Population genetics relevance Pedrola Vidal et al. ( 2024 )¹⁴ Clinical study Hospital-based Genome-wide cfDNA High accuracy Real-world data Single institution Supports clinical validation Phan et al. ( 2019 )¹⁵ Validation study Vietnam NIPT workflow Validated national protocol Early pioneering Older methods Baseline Vietnamese context Phan et al. ( 2019 )¹⁶ Algorithm improvement NIPT cases Mosaicism detection algorithm Reduced false positives Methodological innovation Narrow focus Algorithmic relevance Poulton & Hui ( 2025 )¹⁷ Review Prenatal screening NIPT overview Updated global perspective Contemporary Non-systematic General context Prior-de Castro et al. ( 2023 )¹⁸ Guideline/review Monogenic dx Genetic diagnosis Clinical pathway recommendations Consensus-driven Not data-derived Monogenic diagnostic context Rafi et al. ( 2017 )¹⁹ Clinical commentary Down syndrome cfDNA for T21 High sensitivity Short concise Limited detail DS screening context Tran et al. ( 2021 )²⁰ Exome cohort Vietnamese Clinical ES Recessive disease landscape Large-scale dataset Not prenatal-specific Population variant background Tran et al. ( 2020 )²¹ Population genomic study Vietnamese NIPT data analysis Population-scale profiling Massive dataset Technical bias Population genomics relevance Tran et al. ( 2023 )²² Case series Dominant conditions NIPT detection Identified de novo variants Real-world cases Small sample Shows expanded indications Turriff et al. ( 2024 )²³ Case discovery Maternal cancer cfDNA sequencing Incidental cancer detection High-impact Rare events Highlights incidental findings Vermeulen et al. ( 2017 )²⁴ Haplotyping study Monogenic dx Targeted haplotyping Sensitive NIPD Robust analytic method Complex workflow Monogenic NIPD relevance Wang et al. ( 2021 )²⁵ Cohort study Prenatal referrals cfDNA testing Correlation with indications Good clarity Moderate sample Clinical utility evidence Footnote: NOS = Newcastle–Ottawa Scale; ROBIS = Risk of Bias in Systematic Reviews. This table summarizes methodological strengths, limitations, and each study’s contribution to the research framework. Table 2 Critical Synthesis of Evidence in NIPT and cfDNA-Based Prenatal Diagnosis Thematic Domain Synthesis of Evidence Strength of Evidence Key Limitations / Risk of Bias Clinical Impact Methodological Challenges Implementation / Health-System Issues Priority Research Gaps Aneuploidy Screening Performance¹⁴,¹⁵,¹⁷,¹⁹,²⁵ High sensitivity/specificity across cohorts; superior to conventional screening. Large cohorts; strong validation. Referral bias; LMIC underrepresentation. Reduces invasive testing; improves detection. Pre-analytic variation; platform heterogeneity. Access inequity; workflow issues. Need population-wide performance data. Monogenic & Single-Gene Disorders³,⁸,¹¹,¹⁸,²²,²⁴ Feasible for multiple disorders; strong analytic validity. Multiple methodological approaches. Small samples; limited utility data. Earlier diagnosis; reduced invasive testing. High-depth sequencing; haplotype issues. Ethical concerns; limited availability. Standardization and scalability. Hemoglobinopathies & Recessive Screening⁵,⁹,¹²,¹³,²⁰,²¹ High accuracy for specific mutations; strong variant catalogs. Robust population datasets. Ethnicity-specific data; residual risk. Improves counseling and risk assessment. Coverage gaps; rare variant interpretation. Policy gaps; counseling limitations. Integration of screening approaches. Population Genomics from NIPT¹³,²⁰,²¹ Useful for variant frequency and epidemiology. Large sample sizes. Ascertainment bias; ethics. Improves panel design. Pipeline standardization challenges. Governance and privacy concerns. Responsible genomic data use. Technological Innovations⁹,¹⁰,¹⁶,²⁴ Novel tools show strong analytic potential. High plausibility. Early-stage; limited validation. Potential shift to cell-based diagnosis. Need robust validation. Regulatory challenges. Scalable cost-effective technologies. Implementation Frameworks & Ethics¹⁶,⁷,¹⁷,¹⁸,¹⁹ Guidelines emphasize counseling and consent. Strong conceptual guidance. Limited real-world evidence. Improves autonomy. Managing expectations. Reimbursement gaps. Best models of counseling. Incidental Findings²³,²¹ Maternal cancer signals observed. Strong associations in cases. Over-diagnosis concerns. Possible life-saving detection. Maternal vs fetal signal distinction. Reporting uncertainty. Frameworks for responsible reporting. Multimodal Strategies²¹,¹⁷,²⁵ Combining NIPT + ultrasound improves detection. Consistent positive findings. Protocol variability. Better stratification. Need data integration. Training limitations. Optimal sequencing of tests. Footnote: Table summarizes cross-domain evidence, methodological constraints, and future research priorities in prenatal genomics. Using PRISMA methodology, Fig. 1 outlines the full literature-selection pathway supporting this synthesis. Figure 2 depicts the integrated analytic workflow required for one-tube genomics, illustrating how low-depth and high-depth sequencing can diverge into parallel bioinformatic pipelines and converge into unified reporting. Figure 3 presents a conceptual health-system model positioning one-tube genomics within LMIC care pathways, highlighting its potential to reduce attrition, lower costs, support cascade testing, and enhance equity. Within this context, the aim of this review is to evaluate whether the current body of clinical, technological, and population-genomic evidence supports a shift from fragmented cfDNA testing toward a unified one-tube genomics approach, and to determine the system-level requirements needed to implement such a paradigm safely, effectively, and equitably in resource-constrained settings. METHODS Study Design and Protocol Development This review was conducted following the principles and structure of the PRISMA 2020 guidelines. The protocol was developed prior to the literature search, specifying the research objective, eligibility criteria, screening approach, data collection plan, and synthesis strategy. The review was not registered in PROSPERO because protocol registration was not pursued at the early planning stage; however, all deviations from the initial plan were documented to maintain transparency. The overall identification, screening, eligibility assessment, and inclusion process is shown in Fig. 1 . Information Sources and Search Strategy A comprehensive electronic search was performed across PubMed/MEDLINE, Embase, Scopus, Web of Science, and the PubMed Central open-access repository. The search spanned from database inception to May 2025. Additional sources included reference lists of relevant publications, previously published cohort studies, and reviews obtained through manual screening. Search terms combined key concepts related to cell-free DNA, non-invasive prenatal testing, genome-wide screening, monogenic disorders, carrier screening, low- and middle-income settings, and population genomics. No restrictions were imposed at the search level regarding language, but eligibility was limited to full-text articles written in English. Eligibility Criteria Studies were included if they involved human pregnancies and evaluated one or more cfDNA-based applications relevant to aneuploidy screening, genome-wide CNV detection, monogenic fetal testing, maternal carrier screening, or population genomic analyses. To reflect real-world prenatal pathways, studies were eligible regardless of maternal age, risk category, gestational age, or ultrasound findings. Cohort studies, case series with sufficient methodological detail, implementation studies, technical validation analyses, and practice guidelines were considered appropriate. Exclusion criteria included preclinical work without translational relevance, single-case reports without extractable methods, commentaries without data, and studies lacking sufficient information to assess analytical or clinical outcomes. Selection Process All retrieved records were imported into a reference-management system where automatic de-duplication was performed, followed by manual verification. Two reviewers independently screened titles and abstracts to identify potentially relevant publications. Full texts were retrieved when eligibility could not be determined based on abstracts alone. At the full-text stage, the same reviewers assessed articles independently, resolving disagreements by discussion and consensus. The decision process was documented at each stage to ensure adherence to PRISMA standards. The complete flow of records through screening is presented in Fig. 1 . Data Collection and Extraction Data extraction was performed manually using a predefined template developed during protocol preparation. Extracted fields included study design, sample characteristics, cfDNA methodology, sequencing platform, bioinformatic pipeline, clinical indications, outcome definitions, diagnostic performance parameters, and implementation context. Additional variables included reported strengths and limitations, availability of confirmatory testing, and contextual relevance to low- and middle-income countries. When information was unclear or incomplete, data were inferred cautiously based on accompanying methodological descriptions. Extracted study characteristics are summarized in Table 1 . Data Items and Outcomes of Interest Primary outcomes included analytical and clinical performance of cfDNA-based testing across the domains relevant to integrated one-tube genomics. Secondary outcomes included feasibility of workflow integration, pre-analytic requirements, bioinformatic approaches, and real-world considerations for uptake in resource-constrained settings. All data items were selected to allow comprehensive cross-study comparison and thematic synthesis. Variability in outcome reporting across the literature required the inclusion of all compatible results that aligned with the review’s objectives. Risk of Bias Assessment Risk of bias for observational studies was assessed using the Newcastle–Ottawa Scale. For narrative reviews and guidelines, methodological rigor was considered qualitatively, focusing on clarity of reporting and relevance to clinical implementation. Studies describing technical development were evaluated based on internal consistency, reproducibility of assay performance, and transparency regarding limitations. All assessments were conducted independently by two reviewers, who reached agreement through discussion. Synthesis Methods Because of heterogeneity in study design, populations, sequencing platforms, and outcome definitions, a meta-analysis was not feasible. Instead, a structured narrative synthesis was performed. Studies were grouped into thematic domains aligned with the conceptual structure of one-tube genomics, including aneuploidy screening, genome-wide CNV analysis, monogenic testing, carrier screening, population genomics, technological innovation, ethical considerations, and implementation challenges. Table 2 presents an integrative cross-domain synthesis that combines methodological strengths, evidence robustness, implementation barriers, and research gaps. The thematic synthesis was informed by the technical architecture illustrated in Fig. 2 and the health-system implications depicted in Fig. 3 . Author Contributions and Previous Work The review was informed by the authors’ prior involvement in cfDNA implementation studies and multimodal prenatal screening initiatives in Southeast Asia. Although none of the authors’ own unpublished data were included in the review, existing experience contributed to the interpretation of workflow feasibility, laboratory variation, and counselling challenges in low-resource settings. This contextual understanding supported a deeper assessment of how one-tube genomics could be adopted in different health-system environments. RESULTS AND FINDINGS Study Selection According to PRISMA The search process identified 1,326 records, and the stages of removal, screening, retrieval, and eligibility assessment are depicted in Fig. 1 . After duplicates and automation-identified ineligible items were excluded, 969 records underwent title and abstract review. A total of 823 records were excluded at this stage. Full texts were sought for 146 reports, of which eleven could not be retrieved. The remaining 135 full-text articles were evaluated, leading to the exclusion of 110 reports for reasons documented in the PRISMA flow diagram. Ultimately, twenty-five studies were included in the review. Their characteristics, methodological features, and clinical contexts are summarised in Table 1 . These studies span the domains of aneuploidy NIPT, genome-wide cfDNA screening, monogenic cfDNA diagnosis, maternal carrier identification, population genomics, and associated ethical and implementation frameworks. Overview of the Included Evidence Base The body of evidence encompasses foundational reviews describing the technological maturation of cfDNA testing, early clinical evaluations, and population-adapted screening programmes. The earliest group of studies outlined NIPT’s emergence as a transformative screening tool and clarified the strengths and limits of cfDNA interpretation in fetal medicine. The progression of this field is captured in the first reference, which described NIPT’s performance characteristics and evolving role in routine prenatal care.¹ Subsequent work demonstrated the value of combining cfDNA with imaging, offering evidence that parallel assessment with ultrasound improves the detection of structural anomalies and enhances overall screening performance in mixed-resource settings.² Explorations into monogenic cfDNA diagnostics introduced the analytical architecture for haplotype-based and high-depth targeted sequencing approaches, establishing a conceptual foundation for expanding beyond chromosomal disorders.³ Large-scale clinical analyses of aneuploidy performance provided robust real-world data, including evidence that cfDNA maintains consistent sensitivity and specificity across thousands of samples processed under non-ideal laboratory conditions.⁴ A major advance was the demonstration that cfDNA could directly detect maternal carrier states for α-thalassemia deletions, which is particularly relevant in Southeast Asian populations where haemoglobinopathies are highly prevalent.⁵ This finding expanded the diagnostic scope of a single prenatal sample. Further developments in fetal medicine contextualised cfDNA as a domain rapidly reshaping prenatal diagnosis.⁶ Guidance on laboratory implementation emphasized the importance of standardisation, workforce training, and appropriate counselling in expanded cfDNA adoption.⁷ Technological reviews of monogenic applications reinforced that robust haplotyping, parental sampling, and bioinformatic processing are necessary to achieve reliable non-invasive diagnosis.⁸ The relevance of these analytic advances was demonstrated by studies characterising α- and β-thalassemia genotypes in pregnant women and advocating for more precise carrier identification strategies.⁹ Innovative approaches including micro-nanochip isolation of fetal nucleated red blood cells—derived from the same maternal blood draw—suggested that future iterations of non-invasive testing could expand genomic resolution without additional sampling.¹⁰ Advances in monogenic cfDNA diagnosis provided detailed evaluations of analytic feasibility across multiple hereditary conditions.¹¹ Guidance on optimal screening pathways for haemoglobinopathies further underscored the value of integrated strategies that combine prenatal and carrier assessment.¹² Population-level variant characterisation in Vietnam contributed critical insights into the frequency and distribution of recessive disorders relevant for designing region-specific panels.¹³ Parallel developments in genome-wide cfDNA screening demonstrated that CNV detection could be incorporated into clinical workflows without compromising overall test performance.¹⁴ Establishment of cfDNA NIPT capacity in Vietnam offered early evidence that implementation in LMIC laboratories can achieve high accuracy when protocols are carefully adapted to local resources.¹⁵ Methodological innovation addressing maternal mosaicism significantly strengthened the interpretive reliability of monosomy X screening.¹⁶ Clinical summaries emphasised that NIPT’s usefulness extended to primary care contexts and was not constrained by income setting, strengthening arguments for its universal applicability.¹⁷ Laboratory guidelines for monogenic prenatal diagnosis reinforced the need for rigorous analytical frameworks.¹⁸ Primary-care perspectives reiterated the importance of accessible and reliable Down syndrome screening within national programmes.¹⁹ Exome sequencing studies covering large Vietnamese cohorts enriched the understanding of recessive disease burden, supporting improved variant interpretation in carrier and prenatal testing.²⁰ Population-scale NIPT sequencing datasets highlighted how routine cfDNA screening could simultaneously function as a powerful population-genomic data source.²¹ Case series describing de novo dominant variants detected via cfDNA further expanded the diagnostic space of non-invasive testing.²² The clinical significance of incidental maternal findings, including malignancy signals identifiable through cfDNA sequencing, was demonstrated in an international case series that heightened awareness of counselling and referral needs.²³ Foundational work on targeted haplotyping offered critical methodological insights that continue to shape contemporary monogenic cfDNA diagnostics.²⁴ Validation of cfDNA applications within diverse prenatal indications strengthened the conclusion that cfDNA is adaptable to heterogeneous healthcare pathways.²⁵ Synthesis of Thematic Findings The integrated cross-domain synthesis presented in Table 2 reflects key themes that emerged across the evidence base. Aneuploidy detection consistently demonstrated high accuracy but highlighted gaps in representation of low-resource populations. Early monogenic and single-gene studies demonstrated strong analytic performance but remained limited in scalability due to infrastructure and expertise requirements. Research on haemoglobinopathies underscored the importance of population-specific variant data, particularly in regions where recessive disorders are a major contributor to perinatal morbidity. Population genomics work revealed that NIPT data can serve as a powerful resource for allele-frequency estimation, but also raised important concerns regarding data governance. Emerging technological innovations, such as haplotyping advances and nRBC isolation, suggest future directions for expanding genomic capability within a single maternal sample. Ethical and implementation-related publications emphasised the importance of counselling, informed consent, and the need to anticipate incidental findings. These insights were particularly salient when considering adoption in LMIC contexts, where access barriers, system fragmentation, and cost constraints shape prenatal care pathways. The conceptual frameworks illustrated in Fig. 2 and Fig. 3 reflect how these findings converge into a proposed model for integrated one-tube genomics. DISCUSSION Integrating Evidence Across the Full cfDNA Landscape The findings of this systematic review highlight that the field of prenatal genomics has undergone a steady and coherent expansion, moving from single-purpose aneuploidy screening toward a multilayered platform capable of addressing diverse diagnostic needs from a single maternal specimen. The early review by Abedalthagafi and colleagues emphasised how NIPT rapidly became a clinical mainstay for detecting chromosomal abnormalities and redefined expectations for prenatal screening.¹ This foundational perspective is essential when interpreting the rest of the evidence synthesized in Table 1 , because it underscores how innovation in cfDNA did not arise abruptly but through successive refinements in assay design, laboratory standardisation, and clinical interpretation. As clinical models evolved, studies such as those reported by Andonotopo demonstrated that combining NIPT with ultrasound enhances detection of fetal conditions and provides a more comprehensive assessment even in health systems with uneven resource distribution.² Chiu’s work on monogenic NIPD further extended the conceptual boundary of cfDNA testing by demonstrating that haplotyping and targeted sequencing approaches could feasibly provide single-gene diagnostic information without invasive sampling.³ When these developments are reviewed collectively, they reveal an implicit logic underpinning the transition toward integrated, multi-domain workflows. Large real-world cohort analyses by Dewantiningrum and colleagues confirmed that cfDNA maintains strong performance in non-ideal laboratory settings, thereby reinforcing its relevance for low- and middle-income environments.⁴ The extension of cfDNA technology to maternal carrier detection, as shown by Doan in the context of α-thalassemia, makes the concept of a unified one-tube workflow particularly compelling for regions where haemoglobinopathies present a sizeable public health burden.⁵ The broader view articulated by Everett and Chitty positioned cfDNA as not only a screening tool but an evolving diagnostic instrument capable of informing complex prenatal decisions.⁶ Efforts to guide global implementation of NIPT highlighted by Fernández Martínez underscored that expanded clinical uses require harmonised procedures and robust counselling infrastructures.⁷ The importance of these structural elements becomes even more apparent in monogenic cfDNA applications, as reviewed by Hanson, who stressed the technical precision needed to achieve reliable haplotype inference and variant reporting.⁸ The evidence from Vietnam presented by Lam provided further clarity on how targeted sequencing and gap-PCR can be adapted to local genetic epidemiology, thereby strengthening the rationale for population-tailored carrier and monogenic screening programmes.⁹ Emerging experimental platforms, including the micro-nanochip system described by Li, illustrate how technological innovation continues to expand the analytic potential of a single prenatal specimen.¹⁰ These innovations support the architectural model presented in Fig. 2 , where low-depth genome-wide sequencing and high-depth targeted sequencing feed into parallel analytic pipelines. Recognising the possibilities created by these innovations, Mahdi Mortazavipour provided a timely review demonstrating that single-gene cfDNA diagnosis is rapidly maturing, although its implementation has remained limited to specialised centres.¹¹ Mensah’s analysis of haemoglobinopathy screening further reinforces the need for accurate recessive-disease detection integrated into prenatal pathways, particularly in endemic regions.¹² Population genomics studies by Nguyen added depth to the evidence base by mapping variant frequencies with precision across large cohorts, a prerequisite for designing reliable carrier panels.¹³ These insights paralleled global experience with genome-wide cfDNA screening reported by Pedrola Vidal, who demonstrated that subchromosomal CNV detection can be successfully incorporated into routine workflows when appropriate interpretive frameworks are in place.¹⁴ The early experience from Vietnam presented by Phan illustrated that establishing local cfDNA testing capacity can bring significant diagnostic benefits even before full expansion into genome-wide or monogenic domains.¹⁵ Furthermore, the refinement of algorithms to reduce false positives for monosomy X, as shown by Phan, highlights the essential link between bioinformatics and clinical accuracy.¹⁶ The broader clinical relevance of cfDNA has been reinforced repeatedly. Poulton described how NIPT has become a widely acceptable first-line prenatal option, while Prior-de Castro outlined essential considerations in monogenic prenatal diagnosis that remain applicable regardless of clinical context.¹⁷,¹⁸ Rafi’s reflections on Down syndrome screening reaffirm the centrality of cfDNA to current prenatal care strategies and explain why integrating additional genomic outputs into the same workflow could be both efficient and clinically meaningful.¹⁹ Vietnamese exome sequencing data generated by Tran and colleagues provided a powerful population-level context for understanding recessive disease patterns, enabling more accurate carrier-risk estimation within genomic programmes.²⁰ The large-scale NIPT genomic dataset generated by Tran further demonstrated that routine cfDNA testing can simultaneously provide clinically actionable results and population-level insights, thereby strengthening the argument for a unified one-tube framework where clinical and genomic functions coincide.²¹ Additional case series describing de novo dominant monogenic variants detected non-invasively, including those reported by Tran, showed how the growing resolution of cfDNA sequencing continues to expand diagnostic possibilities.²² The identification of incidental maternal malignancy in the study by Turriff emphasised that as sequencing depth increases, the clinical scope of cfDNA extends beyond fetal findings and necessitates structured referral and counselling systems.²³ This upward expansion also continues to depend on the foundational haplotyping work of Vermeulen, whose methodological insights remain central to current monogenic NIPD approaches.²⁴ Finally, the broad evaluation of prenatal indications associated with cfDNA screening presented by Wang confirms that cfDNA is adaptable across multiple clinical pathways, which strengthens the rationale for integrating these diverse outputs into a cohesive workflow as presented in Fig. 3 .²⁵ Interpreting Convergent Evidence Within a One-Tube Genomics Framework The synthesis of diverse evidence across aneuploidy screening, CNV analysis, monogenic diagnostics, carrier detection, and population genomics demonstrates that the technical foundations for one-tube genomics are firmly established. Table 2 captures this convergence by highlighting how the analytic validity of each domain is individually strong, although implementation remains siloed. The architectural overview in Fig. 2 shows that the laboratory pathways for low-depth and high-depth sequencing already coexist in many settings and can be efficiently integrated if laboratories adopt shared pre-analytic and bioinformatic procedures. Figure 3 situates these scientific advancements within the realities of LMIC health systems. Many regions continue to face challenges including fragmented testing pathways, high rates of attrition between sequential appointments, limited access to counsellors, and variable laboratory capacity. The unification of genomic outputs into a single maternal sample offers a realistic opportunity to reduce these inefficiencies. Integrating aneuploidy screening, CNV detection, monogenic evaluation, and carrier identification into one workflow aligns with the principle of maximising diagnostic yield from minimal patient contact—a critical priority in low-resource or geographically dispersed populations. The assembled evidence demonstrates that one-tube genomics is not a speculative concept but a logical extension of existing technical capabilities. The key challenge now lies in transforming these individual achievements into an integrated, equitable, and sustainable prenatal genomic model. Key Takeaways One-tube genomics is scientifically feasible and leverages existing cfDNA workflows. Fragmented testing pathways—not technology—remain the main barrier in LMICs. Population-genomic data from large NIPT and exome cohorts enable accurate local carrier panels . Ethical, counselling, and consent frameworks must evolve for multi-domain genomic outputs . Integrated sequencing and reporting can reduce cost, attrition, and diagnostic delay . The next breakthrough in prenatal genomics is integration , not new technology. Implementation Checklist Validate aneuploidy, CNV, monogenic, and maternal carrier assays locally. Standardise pre-analytic workflows for a single cfDNA tube. Use dual sequencing pipelines: low-depth WGS + high-depth targeted panels . Consolidate bioinformatics into one reporting framework. Train counsellors for expanded genomic scope and LMIC-specific needs. Create unified genomic reports with clear follow-up steps. Establish policies for incidental maternal findings and data governance. Integrate testing into routine antenatal pathways to minimise loss-to-follow-up. Maintain ongoing quality assurance and update variant panels using local genomic data. Strengths, Limitations, and Future Directions Strengths This review provides an integrated synthesis of a field that has historically evolved in isolated domains. By combining evidence across aneuploidy screening, genome-wide CNV detection, monogenic cfDNA testing, maternal carrier screening, and population genomics, it offers a unified appraisal of the scientific foundations for one-tube genomics. The strength of the review lies in its comprehensive scope, the sequential appraisal of twenty-five studies across diverse methodological designs, and the use of PRISMA-guided processes, including transparent screening as illustrated in Fig. 1 . The incorporation of Tables 1 and 2 supports a structured comparison of methodological features and thematic insights, while Figs. 2 and 3 situate the scientific evidence within realistic laboratory and health-system contexts. Together, these elements create a coherent evidence base for assessing feasibility in low- and middle-income settings. Limitations The available literature remains uneven across domains, with robust data for aneuploidy detection but more limited evidence for monogenic and carrier-based cfDNA applications in routine clinical care. Many studies originate from specialised centres or specific geographic regions, which may limit generalisability to broader LMIC populations. Heterogeneity in sequencing platforms, depth of coverage, sample processing, and bioinformatic methods precluded formal meta-analysis, necessitating a narrative approach. Ethical and counselling frameworks, although discussed in several studies, remain insufficiently explored in empirical research. In addition, the lack of PROSPERO registration means protocol amendments could not be independently verified, although transparency was maintained throughout. These limitations reflect the underlying gaps in the global evidence base rather than methodological shortcomings of the review. Future Directions Future research should prioritise large, prospective implementation studies in LMICs that evaluate one-tube genomics as an integrated clinical pathway rather than isolated tests. Comparative cost-effectiveness analyses are essential for determining how unified workflows can reduce attrition, optimise resource use, and expand access to genomic screening. More work is also needed to develop population-specific variant databases, refine haplotyping pipelines for monogenic disorders, and validate combined analytic workflows that include aneuploidy, CNV, and single-gene testing. Ethical frameworks must evolve to address incidental maternal findings, data governance, and culturally appropriate counselling models. Ultimately, the field will benefit from harmonised reporting standards and international collaboration to ensure that one-tube genomics becomes a scalable, equitable, and sustainable component of prenatal care. Conclusion This systematic review demonstrates that the scientific foundations for one-tube genomics are already well established across multiple domains of prenatal testing. Evidence from aneuploidy NIPT, genome-wide CNV analysis, monogenic cfDNA diagnostics, maternal carrier screening, and population-level genomic studies shows that these technologies are individually mature and analytically robust. Yet in most clinical environments, particularly in low- and middle-income countries, they continue to be delivered through fragmented pathways that require separate blood draws, separate laboratories, and separate counselling encounters. The consolidated workflow illustrated in Figs. 2 and 3 , together with the methodological synthesis in Table 2 , suggests that the integration of these components into a single maternal cfDNA sample is both feasible and strategically advantageous. The central conclusion of this review is that the next major advance in prenatal genomics will not depend on the creation of new technologies, but on the integration of existing ones into a unified framework that maximises diagnostic yield while minimising patient burden. One-tube genomics has the potential to reduce loss-to-follow-up, shorten diagnostic timelines, expand access in resource-constrained regions, and generate population-specific genomic data that enhance future screening accuracy. Achieving this vision will require harmonised laboratory standards, strengthened counselling infrastructures, and deliberate policy planning to ensure ethical and equitable implementation. The collective evidence indicates that one-tube genomics represents a timely and transformative paradigm for prenatal care. With coordinated investment in workflow integration, data governance, and clinical capacity, this approach could substantially elevate the quality, efficiency, and accessibility of prenatal genomics worldwide. Declarations Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflict of Interest The authors declare no conflicts of interest related to the publication of this manuscript. Author Contributions INHS, WA, EL and HKG conceptualized and supervised the review. MAB, WP, EEY, JD and MBAP conducted literature collection and data extraction. RSM, ESP, AAGPW, AANJK, KEG, ED and MIIA performed data analysis and contributed to critical content review. CMY, DA, NB, ADA, AS, RAP, and AP reviewed data interpretation. LAKN, WEKA, WAKD, and MS provided methodological and clinical guidance. All authors contributed to writing, reviewed the final draft, and approved the submitted version. Acknowledgments The authors acknowledge the Indonesian Society of Obstetrics and Gynecology (ISOG/POGI) and the Indonesian Society of Maternal-Fetal Medicine (INAMFM/HKFM) for their encouragement and support in completing this review. References Abedalthagafi M, Bawazeer S, Fawaz RI, Heritage AM, Alajaji NM, Faqeih E. Non-invasive prenatal testing: a revolutionary journey in prenatal testing. Front Med. 2023;10:1265090. https://doi.org/10.3389/fmed.2023.1265090 Andonotopo W, Bachnas MA, Pribadi A, Alamsyah Azis M, Aldika Akbar MI, Ernawati, et al. Integrating NIPT and ultrasound for detecting fetal aneuploidies and abnormalities. J Perinat Med. 2025;53:789–802. https://doi.org/10.1515/jpm-2025-0005 Chiu EKL, Hui WWI, Chiu RWK. cfDNA screening and diagnosis of monogenic disorders - where are we heading? Prenat Diagn. 2018;38:52–8. https://doi.org/10.1002/pd.5207 Dewantiningrum J, Andonotopo W, Bachnas MA, Pramono MBA, Sanjaya INH, Darmawan E, et al. Insights into noninvasive prenatal testing performance: A 4365-sample analysis. SBV J Basic Clin Appl Health Sci. 2025;8:116–24. https://doi.org/10.4103/SBVJ.SBVJ_45_25 Doan PL, Nguyen DA, Le QT, Hoang DT, Nguyen HD, Nguyen CC, et al. Detection of maternal carriers of common α-thalassemia deletions from cell-free DNA. Sci Rep. 2022;12:13581. https://doi.org/10.1038/s41598-022-17718-7 Everett TR, Chitty LS. Cell-free fetal DNA: the new tool in fetal medicine. Ultrasound Obstet Gynecol. 2015;45:499–507. https://doi.org/10.1002/uog.14746 Fernández Martínez FJ, Gil Mira MM, González González C, Madrigal Bajo I, Oancea Ionescu R, Orellana Alonso C, et al. NIPT of maternal plasma-originated cfDNA: Applications and guide for the implementation. Appl Clin Genet. 2025;18:41–53. https://doi.org/10.2147/TACG.S451444 Hanson B, Scotchman E, Chitty LS, Chandler NJ. Non-invasive prenatal diagnosis (NIPD): how analysis of cell-free DNA in maternal plasma has changed prenatal diagnosis for monogenic disorders. Clin Sci. 2022;136:1615–29. https://doi.org/10.1042/CS20210380 Lam TT, Nguyen DT, Le QT, Nguyen DA, Hoang DT, Nguyen HD, et al. Combined Gap-PCR and targeted next-generation sequencing improve α- and β-thalassemia carrier screening in pregnant women in Vietnam. Hemoglobin. 2022;46:233–9. https://doi.org/10.1080/03630269.2022.2096461 Li N, Sun Y, Cheng L, Feng C, Sun Y, Yang S, et al. Non-invasive prenatal diagnosis of chromosomal and monogenic disease by a novel micro-nanochip for isolating fetal nucleated red blood cells. Int J Nanomedicine. 2024;19:13445–60. https://doi.org/10.2147/IJN.S479297 Mahdi Mortazavipour M, Mahdian R, Shahbazi S. The current applications of cell-free fetal DNA in prenatal diagnosis of single-gene diseases: A review. Int J Reprod Biomed. 2022;20:613–26. https://doi.org/10.18502/ijrm.v20i8.11751 Mensah C, Sheth S. Optimal strategies for carrier screening and prenatal diagnosis of α- and β-thalassemia. Hematology. 2021;607–13. https://doi.org/10.1182/hematology.2021000296 Nguyen TT, Le QT, Hoang DT, Du Nguyen H, Ha TMT, Nguyen MB, et al. Variant spectra of G6PD deficiency, phenylketonuria and galactosemia in Vietnamese pregnant women revealed by massively parallel sequencing. Mol Genet Genomic Med. 2022;10:e1959. https://doi.org/10.1002/mgg3.1959 Pedrola Vidal L, Roselló Piera M, Martín-Grau C, Rubio Moll JS, Gómez Portero R, Marcos Puig B, et al. Prenatal genome-wide cfDNA screening: three years of clinical experience. Genes. 2024;15:568. https://doi.org/10.3390/genes15050568 Phan MD, Nguyen TV, Trinh HNT, Vo BT, Nguyen TM, Nguyen NH, et al. Establishing and validating noninvasive prenatal testing procedure for fetal aneuploidies in Vietnam. J Matern Fetal Neonatal Med. 2019;32:4009–15. https://doi.org/10.1080/14767058.2018.1481032 Phan MD, Vo BT, Nguyen TV, Tran NT, Trinh HNT, Nguyen TTQ, et al. Reducing false positive rate of fetal monosomy X in NIPT using a combined algorithm to detect maternal mosaic monosomy X. Prenat Diagn. 2019;39:324–7. https://doi.org/10.1002/pd.5430 Poulton A, Hui L. Noninvasive prenatal testing: an overview. Aust Prescr. 2025;48:47–53. https://doi.org/10.18773/austprescr.2025.019 Prior-de Castro C, Gómez-González C, Rodríguez-López R, Macher HC, Prenatal Diagnosis Commission and Genetics Commission of the Spanish Society of Laboratory Medicine. Prenatal genetic diagnosis of monogenic diseases. Adv Lab Med. 2023;4:28–51. https://doi.org/10.1515/almed-2023-0024 Rafi I, Hill M, Hayward J, Chitty LS. Non-invasive prenatal testing: use of cell-free fetal DNA in Down syndrome screening. Br J Gen Pract. 2017;67:298–9. https://doi.org/10.3399/bjgp17X691625 Tran NH, Nguyen Thi TH, Tang HS, Hoang LP, Nguyen TL, Tran NT, et al. Genetic landscape of recessive diseases in the Vietnamese population from large-scale clinical exome sequencing. Hum Mutat. 2021;42:1229–38. https://doi.org/10.1002/humu.24253 Tran NH, Vo TB, Nguyen VT, Tran NT, Trinh TN, Pham HT, et al. Genetic profiling of the Vietnamese population by large-scale analysis of NIPT genomic data. Sci Rep. 2020;10:19142. https://doi.org/10.1038/s41598-020-76245-5 Tran NT, Vo ST, Nguyen DA, Nguyen CC, Dinh LT, Tran MT, et al. De novo variants of dominant monogenic disorders in Vietnam detected by NIPT: a case series. Per Med. 2023;20:467–75. https://doi.org/10.2217/pme-2023-0105 Turriff AE, Annunziata CM, Malayeri AA, Redd B, Pavelova M, Goldlust IS, et al. Prenatal cfDNA sequencing and incidental detection of maternal cancer. N Engl J Med. 2024;391:2123–32. https://doi.org/10.1056/NEJMoa2401029 Vermeulen C, Geeven G, de Wit E, Verstegen MJAM, Jansen RPM, van Kranenburg M, et al. Sensitive monogenic noninvasive prenatal diagnosis by targeted haplotyping. Am J Hum Genet. 2017;101:326–39. https://doi.org/10.1016/j.ajhg.2017.07.012 Wang JW, Lyu YN, Qiao B, Li Y, Zhang Y, Dhanyamraju PK, et al. Cell-free fetal DNA testing and its correlation with prenatal indications. BMC Pregnancy Childbirth. 2021;21:585. https://doi.org/10.1186/s12884-021-04044-5 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8166404","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":548258274,"identity":"06060458-1d9c-47bb-909b-0bb55a88a3e5","order_by":0,"name":"Wiku Andonotopo, MD, MSc, PhD","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDACZjYwxdgA5cuBiAMPiNQC1mYM1pKA1xo0LYlg6/BpMTjOlibBUHNHtl+6+fiDjzts0ueHHX4ItMVOTrcBh5bDbMckGI49M54551hi48wzabkbb6cZALUkG5sdwK7F7DB7mwQD2+HEDTdyDJt52w7nbpydANJyIHEbXi3/EFrSDWenfyCgBegwxjaElgR56Rz8ttgfZku2SOw7bDxzRlriTKBfDDdI5xQcSDDA7RfJ/mOGNz58OyzbL5F84AMwxOTlZ6dv/vChwk4OlxYwSIAxQDFjAFZpgEc5CgBpkW8gVvUoGAWjYBSMFAAAXSdnxYZZUWIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9062-8501","institution":"Dept. of OBSGYN, Maternal Fetal Medicine Unit, Women Health Center, Eka Hospital, BSD Serpong, Tangerang, BANTEN, INDONESIA","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"MSc MD Wiku","lastName":"Andonotopo","suffix":"MD"}],"badges":[],"createdAt":"2025-11-20 16:04:53","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8166404/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8166404/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96455650,"identity":"b47e902c-1934-4d00-9d74-e93346a0ee8e","added_by":"auto","created_at":"2025-11-21 10:04:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3108370,"visible":true,"origin":"","legend":"","description":"","filename":"REVIEWARTICLE.docx","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/c2f3525288d49e3b32d0a1f4.docx"},{"id":96451650,"identity":"331ed587-30bd-48af-b6ec-4608385f5a3b","added_by":"auto","created_at":"2025-11-21 09:09:38","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs8166404.json","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/d7be682c3737587e24061934.json"},{"id":96451654,"identity":"383161cf-2026-44ee-8855-e624cf9b5f18","added_by":"auto","created_at":"2025-11-21 09:09:38","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143990,"visible":true,"origin":"","legend":"","description":"","filename":"rs81664040enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/a47ffd7478b64cd605fbd488.xml"},{"id":96451651,"identity":"0962fba9-729a-4e5e-8db2-7cca11f3fa27","added_by":"auto","created_at":"2025-11-21 09:09:38","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":170403,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/1ddadec6ff17e1c12cbab8ea.png"},{"id":96451657,"identity":"58e6d28b-502c-4a17-bf7c-47c61232e084","added_by":"auto","created_at":"2025-11-21 09:09:38","extension":"jpeg","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1187455,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/b8243cc4c45d59558d2b3319.jpeg"},{"id":96455057,"identity":"98299022-19aa-42ad-aad5-510308e51789","added_by":"auto","created_at":"2025-11-21 10:03:28","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1331131,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/2782eb9e2f0735251f1e3e3c.png"},{"id":96451656,"identity":"089a1583-8817-4bd7-be55-a4f296407fe3","added_by":"auto","created_at":"2025-11-21 09:09:38","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":59202,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/1556ac69dc67cdbe6e00491d.png"},{"id":96454551,"identity":"01f6dbe1-9783-4142-baea-441f6f83097c","added_by":"auto","created_at":"2025-11-21 10:02:52","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196255,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/cc579f6ccd41c1f7cecc56b8.png"},{"id":96451660,"identity":"45def6bc-0a81-4aa3-af61-d67d2338879c","added_by":"auto","created_at":"2025-11-21 09:09:38","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":99079,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/9b715808a3ac2377f11e2e23.png"},{"id":96455719,"identity":"86045ea9-f9a8-4536-92a4-77254bda1acc","added_by":"auto","created_at":"2025-11-21 10:04:37","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141675,"visible":true,"origin":"","legend":"","description":"","filename":"rs81664040structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/2c25508036b63106328fb54f.xml"},{"id":96451661,"identity":"c05e3f01-bf41-42af-bf3c-fae69617247a","added_by":"auto","created_at":"2025-11-21 09:09:38","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155597,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/c335a626cd4ab1f8bd284364.html"},{"id":96451649,"identity":"ad37a55d-b1ad-4369-8e2d-56edd4481699","added_by":"auto","created_at":"2025-11-21 09:09:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170403,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA 2020 Flow Diagram for Study Identification, Screening, Eligibility, and Inclusion. \u003c/strong\u003eFlow diagram depicting the complete literature identification and selection process for this systematic review, following PRISMA 2020 guidelines. A total of 1,326 records were identified from databases and registers, with 357 removed before screening. Of 969 records screened, 823 were excluded, and 146 reports were sought for retrieval. Following removal of 11 inaccessible reports, 135 full-text articles were assessed for eligibility. Ultimately, 25 studies met all inclusion criteria and were incorporated into the final qualitative synthesis.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/5b21142bf2a66490573c4de1.png"},{"id":96454868,"identity":"48c2bb80-eaf6-4cdc-b4f5-89108c7e0cf8","added_by":"auto","created_at":"2025-11-21 10:03:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":649277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated One-Tube Genomics Workflow: Pre-analytic Processing, Bioinformatic Divergence, and Unified Clinical Output. \u003c/strong\u003ePanel A illustrates the pre-analytic workflow in which a single maternal blood sample undergoes cfDNA isolation followed by two sequencing strategies: low-depth genome-wide sequencing for aneuploidy and CNV assessment, and high-depth targeted sequencing for monogenic and maternal-carrier analysis. Panel B presents the bioinformatic divergence layers, showing how distinct computational pipelines process the same cfDNA data to generate outputs for aneuploidy, genome-wide CNVs, monogenic disorders, maternal carrier status, and incidental maternal genomic findings. Panel C summarizes the integrated output and validation layer, combining multi-track results, cross-track quality checks, variant classification using ACMG/ClinGen frameworks, conflict-resolution algorithms, and reflex workflows (partner testing, invasive confirmation, maternal evaluation), culminating in a unified clinical report to guide precise counselling and decision-making.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/298f399ed44a04137bfd4133.png"},{"id":96451653,"identity":"57a8e628-6fba-472c-980b-bc55724300b1","added_by":"auto","created_at":"2025-11-21 09:09:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1331131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHealth-System Impact Model of One-Tube Genomics in LMICs: Transforming Screening Pathways, Reducing Attrition, and Enhancing Cost-Effectiveness. \u003c/strong\u003ePanel A illustrates the current fragmented prenatal genomic pathway in many low- and middle-income countries (LMICs), where separate visits for aneuploidy NIPT, carrier screening, and reflex monogenic testing contribute to high loss to follow-up, late diagnosis, elevated cost per diagnosis, and significant equity gaps. Panel B presents the proposed one-tube genomics model, in which a single maternal sample generates four concurrent outputs—aneuploidy screening, genome-wide CNV analysis, monogenic fetal testing, and maternal carrier screening—enabling immediate cascade testing and driving earlier detection, reduced invasive procedures, more efficient counselling, and lower indirect patient costs. Panel C summarizes the LMIC-specific health-economic advantages of this integrated model, highlighting improvements in cost per diagnosis, reduction in patient steps, lower attrition, enhanced equity, and markedly shortened time to diagnosis, shifting from weeks–months in the current paradigm to same-day or early-pregnancy results.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/8c64112053da71e822ec9e06.png"},{"id":96603028,"identity":"a6d053a5-6eb0-48e2-8008-d1a5de5afef3","added_by":"auto","created_at":"2025-11-24 09:06:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3093937,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8166404/v1/506a56f6-f8ff-4c17-819c-383ab7905766.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eOne-Tube Genomics in Prenatal Care for Low- and Middle-Income Countries: A Systematic Review of Integrated cfDNA-Based Aneuploidy, CNV, Monogenic Fetal Testing and Maternal Carrier Screening from a Single Maternal Sample\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe emergence of cell-free DNA analysis in maternal plasma has reshaped contemporary prenatal medicine by offering a non-invasive means to evaluate fetal chromosomal conditions with far greater accuracy than traditional serum-based screening. Early comprehensive reviews described this transition as a defining shift that fundamentally altered expectations for prenatal genetic evaluation.\u0026sup1; Subsequent studies combining NIPT with detailed ultrasound demonstrated that integrating molecular and imaging information strengthened diagnostic certainty and broadened the clinical utility of cfDNA screening across diverse populations.\u0026sup2; As technical capabilities expanded, investigators began examining the possibility of using cfDNA not only for aneuploidy detection but also for single-gene disorders, raising critical questions about analytic validity, informatics, and the necessary conditions for safe translation into routine prenatal care.\u0026sup3;\u003c/p\u003e\u003cp\u003eLarge cohort evaluations supported the reproducibility of cfDNA-based aneuploidy detection in real-world practice, including settings with variable laboratory infrastructure, thereby reinforcing its suitability beyond high-income healthcare systems.⁴ Interest in extending cfDNA to maternal carrier detection grew in parallel, particularly in regions where α-thalassemia contributes significantly to perinatal morbidity and where single-sample approaches could simplify clinical workflows.⁵ Early authoritative commentary emphasized that the rapid evolution of cfDNA technologies required ongoing reassessment of their role within fetal medicine, including their appropriateness as first-line tools.⁶\u003c/p\u003e\u003cp\u003eFrameworks designed to guide implementation highlighted that broader applications of cfDNA demand high laboratory standards, consistent reporting, and sufficient counselling capacity to support informed decision-making.⁷ Reviews of non-invasive monogenic diagnosis also emphasized the need for harmonized sequencing methods, reliable haplotype reconstruction, and careful interpretation of variants before widespread adoption could occur.⁸ These considerations became particularly relevant as haemoglobinopathies emerged as early candidates for integrated cfDNA workflows in regions with high carrier frequencies, where multi-step conventional screening is inefficient and sometimes inaccessible.⁹\u003c/p\u003e\u003cp\u003eExperimental work expanded the conceptual boundaries of non-invasive diagnosis further, including micro-nanochip approaches capable of isolating fetal nucleated red blood cells from the same maternal blood draw used for cfDNA analysis, suggesting that a single tube might eventually support multiple complementary genomic assays.\u0026sup1;⁰ Reviews on monogenic cfDNA diagnosis underscored that these innovations offered a credible alternative to invasive sampling for an increasing number of fetal conditions, although their integration into routine care remained limited by workflow complexity and expertise requirements.\u0026sup1;\u0026sup1; At the same time, the persistent burden of recessive genetic diseases in several regions reinforced the need for screening pathways that link maternal carrier status with timely prenatal evaluation.\u0026sup1;\u0026sup2;\u003c/p\u003e\u003cp\u003ePopulation-level genomic studies strengthened this imperative. Work from Vietnam revealed detailed variant spectra for several recessive metabolic disorders, creating an evidence base for localized carrier panels adapted to regional disease distributions.\u0026sup1;\u0026sup3; Genome-wide cfDNA screening reports demonstrated that detection of significant CNVs could be incorporated into broad prenatal workflows when supported by appropriate counselling and laboratory quality systems.\u0026sup1;⁴ Establishment of national NIPT capacity in Vietnam further showed that locally developed cfDNA workflows can achieve accuracy comparable to global standards when adapted to specific laboratory conditions.\u0026sup1;⁵ Follow-up studies addressing maternal mosaicism and its contribution to false-positive monosomy X results underscored the need for algorithmic refinement to distinguish maternal from fetal signals accurately.\u0026sup1;⁶\u003c/p\u003e\u003cp\u003eAs NIPT became more widely available, international clinical perspectives emphasized its value across healthcare systems with varying levels of access to invasive procedures.\u0026sup1;⁷ Laboratory guidelines for monogenic prenatal diagnosis reiterated the importance of rigorous workflow design, robust evidence for variant interpretation, and consistent reporting practice.\u0026sup1;⁸ Primary-care viewpoints reinforced the fundamental role of cfDNA in early detection strategies, particularly for conditions such as Down syndrome, where timely screening can meaningfully influence pregnancy management.\u0026sup1;⁹\u003c/p\u003e\u003cp\u003eExpanding genomic research from Vietnam provided further insights into recessive disease distribution in the population, strengthening the relevance of integrating carrier screening with prenatal testing strategies.\u0026sup2;⁰ Complementary analyses of large NIPT genomic datasets demonstrated that cfDNA sequencing can simultaneously yield clinical and population-genetic information from a single maternal sample, highlighting an inherent efficiency in the platform.\u0026sup2;\u0026sup1; Case series describing cfDNA-based detection of de novo dominant variants further illustrated the expanding diagnostic horizon and its potential relevance to broader prenatal genomic assessment.\u0026sup2;\u0026sup2;\u003c/p\u003e\u003cp\u003eAs sequencing depth increased and quality control improved, incidental maternal findings, including signals suggestive of malignancy, were observed in cfDNA evaluations, underscoring the need for careful management and structured referral pathways.\u0026sup2;\u0026sup3; Foundational studies in targeted haplotyping provided the methodological basis for contemporary monogenic cfDNA pipelines, illustrating how non-invasive inference of fetal inheritance patterns can be achieved with sufficient parental and genomic reference data.\u0026sup2;⁴ Broader evaluations of cfDNA testing across varied prenatal indications reiterated the enduring importance of accessible, accurate screening in diverse healthcare contexts.\u0026sup2;⁵\u003c/p\u003e\u003cp\u003eAlthough each domain of cfDNA technology\u0026mdash;aneuploidy detection, CNV analysis, monogenic testing, maternal carrier screening, and population genomics\u0026mdash;has matured independently, clinical implementation remains fragmented in many regions, especially low- and middle-income countries. Fragmentation leads to multiple clinical visits, repeated blood sampling, higher indirect costs, and substantial loss-to-follow-up. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the characteristics of the twenty-five studies included in this review. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e synthesizes cross-domain methodological strengths, limitations, and research priorities.\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\u003eComprehensive Synopsis of 25 Key Studies Supporting the Research (NOS/ROBIS Domains Applied)\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\u003eAuthor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudy Type / Design\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePopulation / Sample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMethodology / Tools\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKey Outcomes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStrengths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLimitations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRelevance to Current Study\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbedalthagafi et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePregnant population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNIPT overview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eImproved aneuploidy detection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eComprehensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot empirical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eConceptual foundation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndonotopo et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCohort study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNIPT\u0026thinsp;+\u0026thinsp;US cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUltrasound\u0026thinsp;+\u0026thinsp;NIPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEnhanced anomaly detection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLarge dataset\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRegional bias\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupports combined modality\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChiu et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u0026sup3;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMonogenic disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecfDNA for monogenic dx\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFeasibility shown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEarly foundational\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOlder technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eShows expansion beyond trisomy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDewantiningrum et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)⁴\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLarge cohort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;4365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNIPT QC analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh accuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLarge sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eExternal validity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePerformance benchmark\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDoan et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)⁵\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenomic study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMaternal carriers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecfDNA deletion tests\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh detection rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNovel design\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNeeds advanced sequencing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupports carrier detection\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEverett \u0026amp; Chitty (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)⁶\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFetal medicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCell-free fetal DNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarly cfDNA evolution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSeminal review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOutdated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHistorical context\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFern\u0026aacute;ndez Mart\u0026iacute;nez et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)⁷\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGuideline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePregnant women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNIPT implementation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandardized clinical guidance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAuthoritative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot empirical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupports protocol planning\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHanson et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)⁸\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMonogenic disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNIPD via cfDNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExpanded monogenic NIPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStrong clinical framing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReview-only\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMonogenic relevance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLam et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)⁹\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiagnostic study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVietnamese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGap-PCR\u0026thinsp;+\u0026thinsp;NGS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eImproved thalassemia screening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHybrid method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eComplex workflow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupports hemoglobinopathy screening\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLi et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u0026sup1;⁰\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExperimental study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003enRBC isolation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMicro-nanochip\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBetter fetal cell capture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInnovative tech\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePreclinical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFuture diagnostic relevance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMahdi Mortazavipour et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u0026sup1;\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSingle-gene disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecfDNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUseful for monogenic dx\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWide coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo new data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDiagnostic scope reference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMensah \u0026amp; Sheth (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u0026sup1;\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThalassemia screening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCarrier\u0026thinsp;+\u0026thinsp;prenatal dx\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOptimal pathways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eClinical clarity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot population-specific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupports hemoglobinopathy section\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNguyen et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u0026sup1;\u0026sup3;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSequencing study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVietnamese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMPS sequencing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVariant spectra (G6PD/PKU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLarge catalog\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGene-limited\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePopulation genetics relevance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePedrola Vidal et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u0026sup1;⁴\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClinical study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHospital-based\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGenome-wide cfDNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh accuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReal-world data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSingle institution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupports clinical validation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhan et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u0026sup1;⁵\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValidation study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVietnam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNIPT workflow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eValidated national protocol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEarly pioneering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOlder methods\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBaseline Vietnamese context\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhan et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u0026sup1;⁶\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlgorithm improvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNIPT cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMosaicism detection algorithm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReduced false positives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMethodological innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNarrow focus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAlgorithmic relevance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoulton \u0026amp; Hui (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u0026sup1;⁷\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrenatal screening\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNIPT overview\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUpdated global perspective\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eContemporary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNon-systematic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGeneral context\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior-de Castro et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u0026sup1;⁸\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGuideline/review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMonogenic dx\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGenetic diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eClinical pathway recommendations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eConsensus-driven\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot data-derived\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMonogenic diagnostic context\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRafi et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u0026sup1;⁹\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClinical commentary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDown syndrome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecfDNA for T21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eShort concise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLimited detail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDS screening context\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTran et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u0026sup2;⁰\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExome cohort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVietnamese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClinical ES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRecessive disease landscape\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLarge-scale dataset\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNot prenatal-specific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePopulation variant background\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTran et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u0026sup2;\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePopulation genomic study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVietnamese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNIPT data analysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePopulation-scale profiling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMassive dataset\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTechnical bias\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePopulation genomics relevance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTran et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u0026sup2;\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCase series\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDominant conditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNIPT detection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIdentified de novo variants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReal-world cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eShows expanded indications\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurriff et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u0026sup2;\u0026sup3;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCase discovery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMaternal cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecfDNA sequencing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncidental cancer detection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh-impact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRare events\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHighlights incidental findings\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVermeulen et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u0026sup2;⁴\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHaplotyping study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMonogenic dx\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTargeted haplotyping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitive NIPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRobust analytic method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eComplex workflow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMonogenic NIPD relevance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWang et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u0026sup2;⁵\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCohort study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrenatal referrals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ecfDNA testing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCorrelation with indications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGood clarity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModerate sample\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eClinical utility evidence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eFootnote: NOS\u0026thinsp;=\u0026thinsp;Newcastle\u0026ndash;Ottawa Scale; ROBIS\u0026thinsp;=\u0026thinsp;Risk of Bias in Systematic Reviews. This table summarizes methodological strengths, limitations, and each study\u0026rsquo;s contribution to the research framework.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCritical Synthesis of Evidence in NIPT and cfDNA-Based Prenatal Diagnosis\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\u003eThematic Domain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSynthesis of Evidence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStrength of Evidence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKey Limitations / Risk of Bias\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eClinical Impact\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMethodological Challenges\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eImplementation / Health-System Issues\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePriority Research Gaps\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAneuploidy Screening Performance\u0026sup1;⁴,\u0026sup1;⁵,\u0026sup1;⁷,\u0026sup1;⁹,\u0026sup2;⁵\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh sensitivity/specificity across cohorts; superior to conventional screening.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLarge cohorts; strong validation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReferral bias; LMIC underrepresentation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReduces invasive testing; improves detection.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePre-analytic variation; platform heterogeneity.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAccess inequity; workflow issues.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNeed population-wide performance data.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonogenic \u0026amp; Single-Gene Disorders\u0026sup3;,⁸,\u0026sup1;\u0026sup1;,\u0026sup1;⁸,\u0026sup2;\u0026sup2;,\u0026sup2;⁴\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeasible for multiple disorders; strong analytic validity.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultiple methodological approaches.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSmall samples; limited utility data.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEarlier diagnosis; reduced invasive testing.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh-depth sequencing; haplotype issues.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEthical concerns; limited availability.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eStandardization and scalability.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobinopathies \u0026amp; Recessive Screening⁵,⁹,\u0026sup1;\u0026sup2;,\u0026sup1;\u0026sup3;,\u0026sup2;⁰,\u0026sup2;\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh accuracy for specific mutations; strong variant catalogs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRobust population datasets.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEthnicity-specific data; residual risk.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eImproves counseling and risk assessment.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCoverage gaps; rare variant interpretation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePolicy gaps; counseling limitations.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eIntegration of screening approaches.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation Genomics from NIPT\u0026sup1;\u0026sup3;,\u0026sup2;⁰,\u0026sup2;\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUseful for variant frequency and epidemiology.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLarge sample sizes.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAscertainment bias; ethics.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eImproves panel design.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePipeline standardization challenges.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGovernance and privacy concerns.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eResponsible genomic data use.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnological Innovations⁹,\u0026sup1;⁰,\u0026sup1;⁶,\u0026sup2;⁴\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNovel tools show strong analytic potential.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh plausibility.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEarly-stage; limited validation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePotential shift to cell-based diagnosis.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNeed robust validation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRegulatory challenges.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eScalable cost-effective technologies.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImplementation Frameworks \u0026amp; Ethics\u0026sup1;⁶,⁷,\u0026sup1;⁷,\u0026sup1;⁸,\u0026sup1;⁹\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGuidelines emphasize counseling and consent.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStrong conceptual guidance.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLimited real-world evidence.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eImproves autonomy.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eManaging expectations.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReimbursement gaps.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBest models of counseling.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncidental Findings\u0026sup2;\u0026sup3;,\u0026sup2;\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaternal cancer signals observed.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStrong associations in cases.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOver-diagnosis concerns.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePossible life-saving detection.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMaternal vs fetal signal distinction.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReporting uncertainty.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFrameworks for responsible reporting.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultimodal Strategies\u0026sup2;\u0026sup1;,\u0026sup1;⁷,\u0026sup2;⁵\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCombining NIPT\u0026thinsp;+\u0026thinsp;ultrasound improves detection.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConsistent positive findings.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProtocol variability.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBetter stratification.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNeed data integration.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTraining limitations.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOptimal sequencing of tests.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eFootnote: Table summarizes cross-domain evidence, methodological constraints, and future research priorities in prenatal genomics.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUsing PRISMA methodology, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the full literature-selection pathway supporting this synthesis. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e depicts the integrated analytic workflow required for one-tube genomics, illustrating how low-depth and high-depth sequencing can diverge into parallel bioinformatic pipelines and converge into unified reporting. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a conceptual health-system model positioning one-tube genomics within LMIC care pathways, highlighting its potential to reduce attrition, lower costs, support cascade testing, and enhance equity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWithin this context, the aim of this review is to evaluate whether the current body of clinical, technological, and population-genomic evidence supports a shift from fragmented cfDNA testing toward a unified one-tube genomics approach, and to determine the system-level requirements needed to implement such a paradigm safely, effectively, and equitably in resource-constrained settings.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Protocol Development\u003c/h2\u003e\u003cp\u003eThis review was conducted following the principles and structure of the PRISMA 2020 guidelines. The protocol was developed prior to the literature search, specifying the research objective, eligibility criteria, screening approach, data collection plan, and synthesis strategy. The review was not registered in PROSPERO because protocol registration was not pursued at the early planning stage; however, all deviations from the initial plan were documented to maintain transparency. The overall identification, screening, eligibility assessment, and inclusion process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInformation Sources and Search Strategy\u003c/h3\u003e\n\u003cp\u003eA comprehensive electronic search was performed across PubMed/MEDLINE, Embase, Scopus, Web of Science, and the PubMed Central open-access repository. The search spanned from database inception to May 2025. Additional sources included reference lists of relevant publications, previously published cohort studies, and reviews obtained through manual screening. Search terms combined key concepts related to cell-free DNA, non-invasive prenatal testing, genome-wide screening, monogenic disorders, carrier screening, low- and middle-income settings, and population genomics. No restrictions were imposed at the search level regarding language, but eligibility was limited to full-text articles written in English.\u003c/p\u003e\n\u003ch3\u003eEligibility Criteria\u003c/h3\u003e\n\u003cp\u003eStudies were included if they involved human pregnancies and evaluated one or more cfDNA-based applications relevant to aneuploidy screening, genome-wide CNV detection, monogenic fetal testing, maternal carrier screening, or population genomic analyses. To reflect real-world prenatal pathways, studies were eligible regardless of maternal age, risk category, gestational age, or ultrasound findings. Cohort studies, case series with sufficient methodological detail, implementation studies, technical validation analyses, and practice guidelines were considered appropriate. Exclusion criteria included preclinical work without translational relevance, single-case reports without extractable methods, commentaries without data, and studies lacking sufficient information to assess analytical or clinical outcomes.\u003c/p\u003e\n\u003ch3\u003eSelection Process\u003c/h3\u003e\n\u003cp\u003eAll retrieved records were imported into a reference-management system where automatic de-duplication was performed, followed by manual verification. Two reviewers independently screened titles and abstracts to identify potentially relevant publications. Full texts were retrieved when eligibility could not be determined based on abstracts alone. At the full-text stage, the same reviewers assessed articles independently, resolving disagreements by discussion and consensus. The decision process was documented at each stage to ensure adherence to PRISMA standards. The complete flow of records through screening is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eData Collection and Extraction\u003c/h3\u003e\n\u003cp\u003eData extraction was performed manually using a predefined template developed during protocol preparation. Extracted fields included study design, sample characteristics, cfDNA methodology, sequencing platform, bioinformatic pipeline, clinical indications, outcome definitions, diagnostic performance parameters, and implementation context. Additional variables included reported strengths and limitations, availability of confirmatory testing, and contextual relevance to low- and middle-income countries. When information was unclear or incomplete, data were inferred cautiously based on accompanying methodological descriptions. Extracted study characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData Items and Outcomes of Interest\u003c/h2\u003e\u003cp\u003ePrimary outcomes included analytical and clinical performance of cfDNA-based testing across the domains relevant to integrated one-tube genomics. Secondary outcomes included feasibility of workflow integration, pre-analytic requirements, bioinformatic approaches, and real-world considerations for uptake in resource-constrained settings. All data items were selected to allow comprehensive cross-study comparison and thematic synthesis. Variability in outcome reporting across the literature required the inclusion of all compatible results that aligned with the review’s objectives.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRisk of Bias Assessment\u003c/h3\u003e\n\u003cp\u003eRisk of bias for observational studies was assessed using the Newcastle–Ottawa Scale. For narrative reviews and guidelines, methodological rigor was considered qualitatively, focusing on clarity of reporting and relevance to clinical implementation. Studies describing technical development were evaluated based on internal consistency, reproducibility of assay performance, and transparency regarding limitations. All assessments were conducted independently by two reviewers, who reached agreement through discussion.\u003c/p\u003e\n\u003ch3\u003eSynthesis Methods\u003c/h3\u003e\n\u003cp\u003eBecause of heterogeneity in study design, populations, sequencing platforms, and outcome definitions, a meta-analysis was not feasible. Instead, a structured narrative synthesis was performed. Studies were grouped into thematic domains aligned with the conceptual structure of one-tube genomics, including aneuploidy screening, genome-wide CNV analysis, monogenic testing, carrier screening, population genomics, technological innovation, ethical considerations, and implementation challenges. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents an integrative cross-domain synthesis that combines methodological strengths, evidence robustness, implementation barriers, and research gaps. The thematic synthesis was informed by the technical architecture illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and the health-system implications depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAuthor Contributions and Previous Work\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe review was informed by the authors’ prior involvement in cfDNA implementation studies and multimodal prenatal screening initiatives in Southeast Asia. Although none of the authors’ own unpublished data were included in the review, existing experience contributed to the interpretation of workflow feasibility, laboratory variation, and counselling challenges in low-resource settings. This contextual understanding supported a deeper assessment of how one-tube genomics could be adopted in different health-system environments.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"RESULTS AND FINDINGS","content":"\u003ch2\u003eStudy Selection According to PRISMA\u003c/h2\u003e\u003cp\u003eThe search process identified 1,326 records, and the stages of removal, screening, retrieval, and eligibility assessment are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. After duplicates and automation-identified ineligible items were excluded, 969 records underwent title and abstract review. A total of 823 records were excluded at this stage. Full texts were sought for 146 reports, of which eleven could not be retrieved. The remaining 135 full-text articles were evaluated, leading to the exclusion of 110 reports for reasons documented in the PRISMA flow diagram. Ultimately, twenty-five studies were included in the review. Their characteristics, methodological features, and clinical contexts are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These studies span the domains of aneuploidy NIPT, genome-wide cfDNA screening, monogenic cfDNA diagnosis, maternal carrier identification, population genomics, and associated ethical and implementation frameworks.\u003c/p\u003e\u003ch2\u003eOverview of the Included Evidence Base\u003c/h2\u003e\u003cp\u003eThe body of evidence encompasses foundational reviews describing the technological maturation of cfDNA testing, early clinical evaluations, and population-adapted screening programmes. The earliest group of studies outlined NIPT’s emergence as a transformative screening tool and clarified the strengths and limits of cfDNA interpretation in fetal medicine. The progression of this field is captured in the first reference, which described NIPT’s performance characteristics and evolving role in routine prenatal care.¹ Subsequent work demonstrated the value of combining cfDNA with imaging, offering evidence that parallel assessment with ultrasound improves the detection of structural anomalies and enhances overall screening performance in mixed-resource settings.² Explorations into monogenic cfDNA diagnostics introduced the analytical architecture for haplotype-based and high-depth targeted sequencing approaches, establishing a conceptual foundation for expanding beyond chromosomal disorders.³\u003c/p\u003e\u003cp\u003eLarge-scale clinical analyses of aneuploidy performance provided robust real-world data, including evidence that cfDNA maintains consistent sensitivity and specificity across thousands of samples processed under non-ideal laboratory conditions.⁴ A major advance was the demonstration that cfDNA could directly detect maternal carrier states for α-thalassemia deletions, which is particularly relevant in Southeast Asian populations where haemoglobinopathies are highly prevalent.⁵ This finding expanded the diagnostic scope of a single prenatal sample. Further developments in fetal medicine contextualised cfDNA as a domain rapidly reshaping prenatal diagnosis.⁶\u003c/p\u003e\u003cp\u003eGuidance on laboratory implementation emphasized the importance of standardisation, workforce training, and appropriate counselling in expanded cfDNA adoption.⁷ Technological reviews of monogenic applications reinforced that robust haplotyping, parental sampling, and bioinformatic processing are necessary to achieve reliable non-invasive diagnosis.⁸ The relevance of these analytic advances was demonstrated by studies characterising α- and β-thalassemia genotypes in pregnant women and advocating for more precise carrier identification strategies.⁹\u003c/p\u003e\u003cp\u003eInnovative approaches including micro-nanochip isolation of fetal nucleated red blood cells—derived from the same maternal blood draw—suggested that future iterations of non-invasive testing could expand genomic resolution without additional sampling.¹⁰ Advances in monogenic cfDNA diagnosis provided detailed evaluations of analytic feasibility across multiple hereditary conditions.¹¹ Guidance on optimal screening pathways for haemoglobinopathies further underscored the value of integrated strategies that combine prenatal and carrier assessment.¹²\u003c/p\u003e\u003cp\u003ePopulation-level variant characterisation in Vietnam contributed critical insights into the frequency and distribution of recessive disorders relevant for designing region-specific panels.¹³ Parallel developments in genome-wide cfDNA screening demonstrated that CNV detection could be incorporated into clinical workflows without compromising overall test performance.¹⁴ Establishment of cfDNA NIPT capacity in Vietnam offered early evidence that implementation in LMIC laboratories can achieve high accuracy when protocols are carefully adapted to local resources.¹⁵ Methodological innovation addressing maternal mosaicism significantly strengthened the interpretive reliability of monosomy X screening.¹⁶\u003c/p\u003e\u003cp\u003eClinical summaries emphasised that NIPT’s usefulness extended to primary care contexts and was not constrained by income setting, strengthening arguments for its universal applicability.¹⁷ Laboratory guidelines for monogenic prenatal diagnosis reinforced the need for rigorous analytical frameworks.¹⁸ Primary-care perspectives reiterated the importance of accessible and reliable Down syndrome screening within national programmes.¹⁹\u003c/p\u003e\u003cp\u003eExome sequencing studies covering large Vietnamese cohorts enriched the understanding of recessive disease burden, supporting improved variant interpretation in carrier and prenatal testing.²⁰ Population-scale NIPT sequencing datasets highlighted how routine cfDNA screening could simultaneously function as a powerful population-genomic data source.²¹ Case series describing de novo dominant variants detected via cfDNA further expanded the diagnostic space of non-invasive testing.²²\u003c/p\u003e\u003cp\u003eThe clinical significance of incidental maternal findings, including malignancy signals identifiable through cfDNA sequencing, was demonstrated in an international case series that heightened awareness of counselling and referral needs.²³ Foundational work on targeted haplotyping offered critical methodological insights that continue to shape contemporary monogenic cfDNA diagnostics.²⁴ Validation of cfDNA applications within diverse prenatal indications strengthened the conclusion that cfDNA is adaptable to heterogeneous healthcare pathways.²⁵\u003c/p\u003e\u003ch2\u003eSynthesis of Thematic Findings\u003c/h2\u003e\u003cp\u003eThe integrated cross-domain synthesis presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reflects key themes that emerged across the evidence base. Aneuploidy detection consistently demonstrated high accuracy but highlighted gaps in representation of low-resource populations. Early monogenic and single-gene studies demonstrated strong analytic performance but remained limited in scalability due to infrastructure and expertise requirements. Research on haemoglobinopathies underscored the importance of population-specific variant data, particularly in regions where recessive disorders are a major contributor to perinatal morbidity. Population genomics work revealed that NIPT data can serve as a powerful resource for allele-frequency estimation, but also raised important concerns regarding data governance. Emerging technological innovations, such as haplotyping advances and nRBC isolation, suggest future directions for expanding genomic capability within a single maternal sample.\u003c/p\u003e\u003cp\u003eEthical and implementation-related publications emphasised the importance of counselling, informed consent, and the need to anticipate incidental findings. These insights were particularly salient when considering adoption in LMIC contexts, where access barriers, system fragmentation, and cost constraints shape prenatal care pathways. The conceptual frameworks illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reflect how these findings converge into a proposed model for integrated one-tube genomics.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eIntegrating Evidence Across the Full cfDNA Landscape\u003c/h2\u003e\u003cp\u003eThe findings of this systematic review highlight that the field of prenatal genomics has undergone a steady and coherent expansion, moving from single-purpose aneuploidy screening toward a multilayered platform capable of addressing diverse diagnostic needs from a single maternal specimen. The early review by Abedalthagafi and colleagues emphasised how NIPT rapidly became a clinical mainstay for detecting chromosomal abnormalities and redefined expectations for prenatal screening.\u0026sup1; This foundational perspective is essential when interpreting the rest of the evidence synthesized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, because it underscores how innovation in cfDNA did not arise abruptly but through successive refinements in assay design, laboratory standardisation, and clinical interpretation.\u003c/p\u003e\u003cp\u003eAs clinical models evolved, studies such as those reported by Andonotopo demonstrated that combining NIPT with ultrasound enhances detection of fetal conditions and provides a more comprehensive assessment even in health systems with uneven resource distribution.\u0026sup2; Chiu\u0026rsquo;s work on monogenic NIPD further extended the conceptual boundary of cfDNA testing by demonstrating that haplotyping and targeted sequencing approaches could feasibly provide single-gene diagnostic information without invasive sampling.\u0026sup3; When these developments are reviewed collectively, they reveal an implicit logic underpinning the transition toward integrated, multi-domain workflows.\u003c/p\u003e\u003cp\u003eLarge real-world cohort analyses by Dewantiningrum and colleagues confirmed that cfDNA maintains strong performance in non-ideal laboratory settings, thereby reinforcing its relevance for low- and middle-income environments.⁴ The extension of cfDNA technology to maternal carrier detection, as shown by Doan in the context of α-thalassemia, makes the concept of a unified one-tube workflow particularly compelling for regions where haemoglobinopathies present a sizeable public health burden.⁵ The broader view articulated by Everett and Chitty positioned cfDNA as not only a screening tool but an evolving diagnostic instrument capable of informing complex prenatal decisions.⁶\u003c/p\u003e\u003cp\u003eEfforts to guide global implementation of NIPT highlighted by Fern\u0026aacute;ndez Mart\u0026iacute;nez underscored that expanded clinical uses require harmonised procedures and robust counselling infrastructures.⁷ The importance of these structural elements becomes even more apparent in monogenic cfDNA applications, as reviewed by Hanson, who stressed the technical precision needed to achieve reliable haplotype inference and variant reporting.⁸ The evidence from Vietnam presented by Lam provided further clarity on how targeted sequencing and gap-PCR can be adapted to local genetic epidemiology, thereby strengthening the rationale for population-tailored carrier and monogenic screening programmes.⁹\u003c/p\u003e\u003cp\u003eEmerging experimental platforms, including the micro-nanochip system described by Li, illustrate how technological innovation continues to expand the analytic potential of a single prenatal specimen.\u0026sup1;⁰ These innovations support the architectural model presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, where low-depth genome-wide sequencing and high-depth targeted sequencing feed into parallel analytic pipelines. Recognising the possibilities created by these innovations, Mahdi Mortazavipour provided a timely review demonstrating that single-gene cfDNA diagnosis is rapidly maturing, although its implementation has remained limited to specialised centres.\u0026sup1;\u0026sup1; Mensah\u0026rsquo;s analysis of haemoglobinopathy screening further reinforces the need for accurate recessive-disease detection integrated into prenatal pathways, particularly in endemic regions.\u0026sup1;\u0026sup2;\u003c/p\u003e\u003cp\u003ePopulation genomics studies by Nguyen added depth to the evidence base by mapping variant frequencies with precision across large cohorts, a prerequisite for designing reliable carrier panels.\u0026sup1;\u0026sup3; These insights paralleled global experience with genome-wide cfDNA screening reported by Pedrola Vidal, who demonstrated that subchromosomal CNV detection can be successfully incorporated into routine workflows when appropriate interpretive frameworks are in place.\u0026sup1;⁴ The early experience from Vietnam presented by Phan illustrated that establishing local cfDNA testing capacity can bring significant diagnostic benefits even before full expansion into genome-wide or monogenic domains.\u0026sup1;⁵ Furthermore, the refinement of algorithms to reduce false positives for monosomy X, as shown by Phan, highlights the essential link between bioinformatics and clinical accuracy.\u0026sup1;⁶\u003c/p\u003e\u003cp\u003eThe broader clinical relevance of cfDNA has been reinforced repeatedly. Poulton described how NIPT has become a widely acceptable first-line prenatal option, while Prior-de Castro outlined essential considerations in monogenic prenatal diagnosis that remain applicable regardless of clinical context.\u0026sup1;⁷,\u0026sup1;⁸ Rafi\u0026rsquo;s reflections on Down syndrome screening reaffirm the centrality of cfDNA to current prenatal care strategies and explain why integrating additional genomic outputs into the same workflow could be both efficient and clinically meaningful.\u0026sup1;⁹\u003c/p\u003e\u003cp\u003eVietnamese exome sequencing data generated by Tran and colleagues provided a powerful population-level context for understanding recessive disease patterns, enabling more accurate carrier-risk estimation within genomic programmes.\u0026sup2;⁰ The large-scale NIPT genomic dataset generated by Tran further demonstrated that routine cfDNA testing can simultaneously provide clinically actionable results and population-level insights, thereby strengthening the argument for a unified one-tube framework where clinical and genomic functions coincide.\u0026sup2;\u0026sup1; Additional case series describing de novo dominant monogenic variants detected non-invasively, including those reported by Tran, showed how the growing resolution of cfDNA sequencing continues to expand diagnostic possibilities.\u0026sup2;\u0026sup2;\u003c/p\u003e\u003cp\u003eThe identification of incidental maternal malignancy in the study by Turriff emphasised that as sequencing depth increases, the clinical scope of cfDNA extends beyond fetal findings and necessitates structured referral and counselling systems.\u0026sup2;\u0026sup3; This upward expansion also continues to depend on the foundational haplotyping work of Vermeulen, whose methodological insights remain central to current monogenic NIPD approaches.\u0026sup2;⁴ Finally, the broad evaluation of prenatal indications associated with cfDNA screening presented by Wang confirms that cfDNA is adaptable across multiple clinical pathways, which strengthens the rationale for integrating these diverse outputs into a cohesive workflow as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u0026sup2;⁵\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eInterpreting Convergent Evidence Within a One-Tube Genomics Framework\u003c/h2\u003e\u003cp\u003eThe synthesis of diverse evidence across aneuploidy screening, CNV analysis, monogenic diagnostics, carrier detection, and population genomics demonstrates that the technical foundations for one-tube genomics are firmly established. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e captures this convergence by highlighting how the analytic validity of each domain is individually strong, although implementation remains siloed. The architectural overview in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the laboratory pathways for low-depth and high-depth sequencing already coexist in many settings and can be efficiently integrated if laboratories adopt shared pre-analytic and bioinformatic procedures.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e situates these scientific advancements within the realities of LMIC health systems. Many regions continue to face challenges including fragmented testing pathways, high rates of attrition between sequential appointments, limited access to counsellors, and variable laboratory capacity. The unification of genomic outputs into a single maternal sample offers a realistic opportunity to reduce these inefficiencies. Integrating aneuploidy screening, CNV detection, monogenic evaluation, and carrier identification into one workflow aligns with the principle of maximising diagnostic yield from minimal patient contact\u0026mdash;a critical priority in low-resource or geographically dispersed populations.\u003c/p\u003e\u003cp\u003eThe assembled evidence demonstrates that one-tube genomics is not a speculative concept but a logical extension of existing technical capabilities. The key challenge now lies in transforming these individual achievements into an integrated, equitable, and sustainable prenatal genomic model.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eKey Takeaways\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eOne-tube genomics is \u003cb\u003escientifically feasible\u003c/b\u003e and leverages existing cfDNA workflows.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFragmented testing pathways\u0026mdash;not technology\u0026mdash;remain the main barrier in LMICs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePopulation-genomic data from large NIPT and exome cohorts enable \u003cb\u003eaccurate local carrier panels\u003c/b\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEthical, counselling, and consent frameworks must evolve for \u003cb\u003emulti-domain genomic outputs\u003c/b\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIntegrated sequencing and reporting can reduce \u003cb\u003ecost, attrition, and diagnostic delay\u003c/b\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe next breakthrough in prenatal genomics is \u003cb\u003eintegration\u003c/b\u003e, not new technology.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eImplementation Checklist\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eValidate aneuploidy, CNV, monogenic, and maternal carrier assays locally.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStandardise pre-analytic workflows for a single cfDNA tube.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUse dual sequencing pipelines: \u003cb\u003elow-depth WGS\u0026thinsp;+\u0026thinsp;high-depth targeted panels\u003c/b\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eConsolidate bioinformatics into one reporting framework.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTrain counsellors for expanded genomic scope and LMIC-specific needs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCreate unified genomic reports with clear follow-up steps.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEstablish policies for incidental maternal findings and data governance.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIntegrate testing into routine antenatal pathways to minimise loss-to-follow-up.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMaintain ongoing quality assurance and update variant panels using local genomic data.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eStrengths, Limitations, and Future Directions\u003c/h2\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003eStrengths\u003c/h2\u003e\u003cp\u003eThis review provides an integrated synthesis of a field that has historically evolved in isolated domains. By combining evidence across aneuploidy screening, genome-wide CNV detection, monogenic cfDNA testing, maternal carrier screening, and population genomics, it offers a unified appraisal of the scientific foundations for one-tube genomics. The strength of the review lies in its comprehensive scope, the sequential appraisal of twenty-five studies across diverse methodological designs, and the use of PRISMA-guided processes, including transparent screening as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The incorporation of Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e supports a structured comparison of methodological features and thematic insights, while Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e situate the scientific evidence within realistic laboratory and health-system contexts. Together, these elements create a coherent evidence base for assessing feasibility in low- and middle-income settings.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThe available literature remains uneven across domains, with robust data for aneuploidy detection but more limited evidence for monogenic and carrier-based cfDNA applications in routine clinical care. Many studies originate from specialised centres or specific geographic regions, which may limit generalisability to broader LMIC populations. Heterogeneity in sequencing platforms, depth of coverage, sample processing, and bioinformatic methods precluded formal meta-analysis, necessitating a narrative approach. Ethical and counselling frameworks, although discussed in several studies, remain insufficiently explored in empirical research. In addition, the lack of PROSPERO registration means protocol amendments could not be independently verified, although transparency was maintained throughout. These limitations reflect the underlying gaps in the global evidence base rather than methodological shortcomings of the review.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eFuture Directions\u003c/h2\u003e\u003cp\u003eFuture research should prioritise large, prospective implementation studies in LMICs that evaluate one-tube genomics as an integrated clinical pathway rather than isolated tests. Comparative cost-effectiveness analyses are essential for determining how unified workflows can reduce attrition, optimise resource use, and expand access to genomic screening. More work is also needed to develop population-specific variant databases, refine haplotyping pipelines for monogenic disorders, and validate combined analytic workflows that include aneuploidy, CNV, and single-gene testing. Ethical frameworks must evolve to address incidental maternal findings, data governance, and culturally appropriate counselling models. Ultimately, the field will benefit from harmonised reporting standards and international collaboration to ensure that one-tube genomics becomes a scalable, equitable, and sustainable component of prenatal care.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis systematic review demonstrates that the scientific foundations for one-tube genomics are already well established across multiple domains of prenatal testing. Evidence from aneuploidy NIPT, genome-wide CNV analysis, monogenic cfDNA diagnostics, maternal carrier screening, and population-level genomic studies shows that these technologies are individually mature and analytically robust. Yet in most clinical environments, particularly in low- and middle-income countries, they continue to be delivered through fragmented pathways that require separate blood draws, separate laboratories, and separate counselling encounters. The consolidated workflow illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, together with the methodological synthesis in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, suggests that the integration of these components into a single maternal cfDNA sample is both feasible and strategically advantageous.\u003c/p\u003e\u003cp\u003eThe central conclusion of this review is that the next major advance in prenatal genomics will not depend on the creation of new technologies, but on the integration of existing ones into a unified framework that maximises diagnostic yield while minimising patient burden. One-tube genomics has the potential to reduce loss-to-follow-up, shorten diagnostic timelines, expand access in resource-constrained regions, and generate population-specific genomic data that enhance future screening accuracy. Achieving this vision will require harmonised laboratory standards, strengthened counselling infrastructures, and deliberate policy planning to ensure ethical and equitable implementation.\u003c/p\u003e\u003cp\u003eThe collective evidence indicates that one-tube genomics represents a timely and transformative paradigm for prenatal care. With coordinated investment in workflow integration, data governance, and clinical capacity, this approach could substantially elevate the quality, efficiency, and accessibility of prenatal genomics worldwide.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003cbr\u003e\u0026nbsp;This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest related to the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eINHS, WA, EL and HKG conceptualized and supervised the review. MAB, WP, EEY, JD and MBAP conducted literature collection and data extraction. RSM, ESP, AAGPW, AANJK, KEG, ED and MIIA performed data analysis and contributed to critical content review. CMY, DA, NB, ADA, AS, RAP, and AP reviewed data interpretation. LAKN, WEKA, WAKD, and MS provided methodological and clinical guidance. All authors contributed to writing, reviewed the final draft, and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003cbr\u003e The authors acknowledge the Indonesian Society of Obstetrics and Gynecology (ISOG/POGI) and the Indonesian Society of Maternal-Fetal Medicine (INAMFM/HKFM) for their encouragement and support in completing this review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbedalthagafi M, Bawazeer S, Fawaz RI, Heritage AM, Alajaji NM, Faqeih E. Non-invasive prenatal testing: a revolutionary journey in prenatal testing. Front Med. 2023;10:1265090. https://doi.org/10.3389/fmed.2023.1265090\u003c/li\u003e\n\u003cli\u003eAndonotopo W, Bachnas MA, Pribadi A, Alamsyah Azis M, Aldika Akbar MI, Ernawati, et al. Integrating NIPT and ultrasound for detecting fetal aneuploidies and abnormalities. J Perinat Med. 2025;53:789\u0026ndash;802. https://doi.org/10.1515/jpm-2025-0005\u003c/li\u003e\n\u003cli\u003eChiu EKL, Hui WWI, Chiu RWK. cfDNA screening and diagnosis of monogenic disorders - where are we heading? Prenat Diagn. 2018;38:52\u0026ndash;8. https://doi.org/10.1002/pd.5207\u003c/li\u003e\n\u003cli\u003eDewantiningrum J, Andonotopo W, Bachnas MA, Pramono MBA, Sanjaya INH, Darmawan E, et al. Insights into noninvasive prenatal testing performance: A 4365-sample analysis. SBV J Basic Clin Appl Health Sci. 2025;8:116\u0026ndash;24. https://doi.org/10.4103/SBVJ.SBVJ_45_25\u003c/li\u003e\n\u003cli\u003eDoan PL, Nguyen DA, Le QT, Hoang DT, Nguyen HD, Nguyen CC, et al. Detection of maternal carriers of common \u0026alpha;-thalassemia deletions from cell-free DNA. Sci Rep. 2022;12:13581. https://doi.org/10.1038/s41598-022-17718-7\u003c/li\u003e\n\u003cli\u003eEverett TR, Chitty LS. Cell-free fetal DNA: the new tool in fetal medicine. Ultrasound Obstet Gynecol. 2015;45:499\u0026ndash;507. https://doi.org/10.1002/uog.14746\u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez Mart\u0026iacute;nez FJ, Gil Mira MM, Gonz\u0026aacute;lez Gonz\u0026aacute;lez C, Madrigal Bajo I, Oancea Ionescu R, Orellana Alonso C, et al. NIPT of maternal plasma-originated cfDNA: Applications and guide for the implementation. Appl Clin Genet. 2025;18:41\u0026ndash;53. https://doi.org/10.2147/TACG.S451444\u003c/li\u003e\n\u003cli\u003eHanson B, Scotchman E, Chitty LS, Chandler NJ. Non-invasive prenatal diagnosis (NIPD): how analysis of cell-free DNA in maternal plasma has changed prenatal diagnosis for monogenic disorders. Clin Sci. 2022;136:1615\u0026ndash;29. https://doi.org/10.1042/CS20210380\u003c/li\u003e\n\u003cli\u003eLam TT, Nguyen DT, Le QT, Nguyen DA, Hoang DT, Nguyen HD, et al. Combined Gap-PCR and targeted next-generation sequencing improve \u0026alpha;- and \u0026beta;-thalassemia carrier screening in pregnant women in Vietnam. Hemoglobin. 2022;46:233\u0026ndash;9. https://doi.org/10.1080/03630269.2022.2096461\u003c/li\u003e\n\u003cli\u003eLi N, Sun Y, Cheng L, Feng C, Sun Y, Yang S, et al. Non-invasive prenatal diagnosis of chromosomal and monogenic disease by a novel micro-nanochip for isolating fetal nucleated red blood cells. Int J Nanomedicine. 2024;19:13445\u0026ndash;60. https://doi.org/10.2147/IJN.S479297\u003c/li\u003e\n\u003cli\u003eMahdi Mortazavipour M, Mahdian R, Shahbazi S. The current applications of cell-free fetal DNA in prenatal diagnosis of single-gene diseases: A review. Int J Reprod Biomed. 2022;20:613\u0026ndash;26. https://doi.org/10.18502/ijrm.v20i8.11751\u003c/li\u003e\n\u003cli\u003eMensah C, Sheth S. Optimal strategies for carrier screening and prenatal diagnosis of \u0026alpha;- and \u0026beta;-thalassemia. Hematology. 2021;607\u0026ndash;13. https://doi.org/10.1182/hematology.2021000296\u003c/li\u003e\n\u003cli\u003eNguyen TT, Le QT, Hoang DT, Du Nguyen H, Ha TMT, Nguyen MB, et al. Variant spectra of G6PD deficiency, phenylketonuria and galactosemia in Vietnamese pregnant women revealed by massively parallel sequencing. Mol Genet Genomic Med. 2022;10:e1959. https://doi.org/10.1002/mgg3.1959\u003c/li\u003e\n\u003cli\u003ePedrola Vidal L, Rosell\u0026oacute; Piera M, Mart\u0026iacute;n-Grau C, Rubio Moll JS, G\u0026oacute;mez Portero R, Marcos Puig B, et al. Prenatal genome-wide cfDNA screening: three years of clinical experience. Genes. 2024;15:568. https://doi.org/10.3390/genes15050568\u003c/li\u003e\n\u003cli\u003ePhan MD, Nguyen TV, Trinh HNT, Vo BT, Nguyen TM, Nguyen NH, et al. Establishing and validating noninvasive prenatal testing procedure for fetal aneuploidies in Vietnam. J Matern Fetal Neonatal Med. 2019;32:4009\u0026ndash;15. https://doi.org/10.1080/14767058.2018.1481032\u003c/li\u003e\n\u003cli\u003ePhan MD, Vo BT, Nguyen TV, Tran NT, Trinh HNT, Nguyen TTQ, et al. Reducing false positive rate of fetal monosomy X in NIPT using a combined algorithm to detect maternal mosaic monosomy X. Prenat Diagn. 2019;39:324\u0026ndash;7. https://doi.org/10.1002/pd.5430\u003c/li\u003e\n\u003cli\u003ePoulton A, Hui L. Noninvasive prenatal testing: an overview. Aust Prescr. 2025;48:47\u0026ndash;53. https://doi.org/10.18773/austprescr.2025.019\u003c/li\u003e\n\u003cli\u003ePrior-de Castro C, G\u0026oacute;mez-Gonz\u0026aacute;lez C, Rodr\u0026iacute;guez-L\u0026oacute;pez R, Macher HC, Prenatal Diagnosis Commission and Genetics Commission of the Spanish Society of Laboratory Medicine. Prenatal genetic diagnosis of monogenic diseases. Adv Lab Med. 2023;4:28\u0026ndash;51. https://doi.org/10.1515/almed-2023-0024\u003c/li\u003e\n\u003cli\u003eRafi I, Hill M, Hayward J, Chitty LS. Non-invasive prenatal testing: use of cell-free fetal DNA in Down syndrome screening. Br J Gen Pract. 2017;67:298\u0026ndash;9. https://doi.org/10.3399/bjgp17X691625\u003c/li\u003e\n\u003cli\u003eTran NH, Nguyen Thi TH, Tang HS, Hoang LP, Nguyen TL, Tran NT, et al. Genetic landscape of recessive diseases in the Vietnamese population from large-scale clinical exome sequencing. Hum Mutat. 2021;42:1229\u0026ndash;38. https://doi.org/10.1002/humu.24253\u003c/li\u003e\n\u003cli\u003eTran NH, Vo TB, Nguyen VT, Tran NT, Trinh TN, Pham HT, et al. Genetic profiling of the Vietnamese population by large-scale analysis of NIPT genomic data. Sci Rep. 2020;10:19142. https://doi.org/10.1038/s41598-020-76245-5\u003c/li\u003e\n\u003cli\u003eTran NT, Vo ST, Nguyen DA, Nguyen CC, Dinh LT, Tran MT, et al. De novo variants of dominant monogenic disorders in Vietnam detected by NIPT: a case series. Per Med. 2023;20:467\u0026ndash;75. https://doi.org/10.2217/pme-2023-0105\u003c/li\u003e\n\u003cli\u003eTurriff AE, Annunziata CM, Malayeri AA, Redd B, Pavelova M, Goldlust IS, et al. Prenatal cfDNA sequencing and incidental detection of maternal cancer. N Engl J Med. 2024;391:2123\u0026ndash;32. https://doi.org/10.1056/NEJMoa2401029\u003c/li\u003e\n\u003cli\u003eVermeulen C, Geeven G, de Wit E, Verstegen MJAM, Jansen RPM, van Kranenburg M, et al. Sensitive monogenic noninvasive prenatal diagnosis by targeted haplotyping. Am J Hum Genet. 2017;101:326\u0026ndash;39. https://doi.org/10.1016/j.ajhg.2017.07.012\u003c/li\u003e\n\u003cli\u003eWang JW, Lyu YN, Qiao B, Li Y, Zhang Y, Dhanyamraju PK, et al. Cell-free fetal DNA testing and its correlation with prenatal indications. BMC Pregnancy Childbirth. 2021;21:585. https://doi.org/10.1186/s12884-021-04044-5\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cell-free DNA (cfDNA), Low- and middle-income countries (LMICs), Monogenic and carrier screening, Non-invasive prenatal testing (NIPT), One-tube genomics","lastPublishedDoi":"10.21203/rs.3.rs-8166404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8166404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo synthesise clinical, technical and health-system evidence on \u0026ldquo;one-tube genomics\u0026rdquo;, defined as using a single maternal cfDNA sample to deliver aneuploidy NIPT, genome-wide CNV analysis, monogenic fetal testing and maternal carrier screening in low- and middle-income countries (LMICs).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA PRISMA-guided systematic review was conducted in PubMed/MEDLINE, Embase, Scopus, Web of Science and PMC from inception to May 2025. We included human studies evaluating cfDNA-based aneuploidy, CNV, monogenic or carrier screening, or population genomics relevant to prenatal care in LMIC or mixed-income settings. Two reviewers independently screened records, assessed full texts and extracted data. Risk of bias was appraised using the Newcastle\u0026ndash;Ottawa Scale and ROBIS where appropriate. Heterogeneity precluded meta-analysis; findings were synthesised narratively across predefined thematic domains.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf 1,326 records identified, 25 studies met inclusion criteria. Large cohorts confirmed high accuracy of cfDNA for common aneuploidies and selected genome-wide CNVs. Monogenic and haemoglobinopathy-focused cfDNA approaches showed strong analytic validity but were limited to specialised centres. Population-scale NIPT and exome datasets from Vietnam and neighbouring regions provided detailed recessive variant spectra. Implementation and ethical papers highlighted counselling needs, data-governance challenges and emerging issues around incidental maternal findings.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eCurrent evidence supports the technical feasibility and potential health-system advantages of one-tube genomics in LMICs, but integrated workflows remain largely unrealised. Prospective LMIC implementation studies, harmonised reporting standards and robust ethical frameworks are now critical to move from fragmented testing towards truly integrated prenatal genomics.\u003c/p\u003e","manuscriptTitle":"One-Tube Genomics in Prenatal Care for Low- and Middle-Income Countries: A Systematic Review of Integrated cfDNA-Based Aneuploidy, CNV, Monogenic Fetal Testing and Maternal Carrier Screening from a Single Maternal Sample","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 09:09:33","doi":"10.21203/rs.3.rs-8166404/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"423e4212-790a-4cb0-8efc-555d8ebf29c4","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58338353,"name":"Obstetrics \u0026 Gynecology"}],"tags":[],"updatedAt":"2025-11-21T09:09:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 09:09:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8166404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8166404","identity":"rs-8166404","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}