{"paper_id":"02a7cc72-c443-495c-ae2e-b4d4c57e8d9e","body_text":"MLDP-AS: An Optimized Next-Generation Sequencing Assay for Enhanced Detection of Technically Challenging Variants in Expanded Carrier Screening | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MLDP-AS: An Optimized Next-Generation Sequencing Assay for Enhanced Detection of Technically Challenging Variants in Expanded Carrier Screening Zhenhua Zhao, Ganye Zhao, Shaojun Li, Chao Yuan, Jun Feng, Weiqin Tang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6880217/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Feb, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background Next-generation sequencing (NGS) facilitates simultaneous carrier screening for multiple single-gene disorders. However, conventional NGS methods struggle to detect complex variants, such as F8 inversions, CYP21A2 variations, and single-exon copy number variations (CNVs), resulting in residual risk. Methods We developed and validated MLDP-AS (Multiplex Long-Distance PCR followed by Amplicon Sequencing), a novel NGS assay, to screen for pathogenic variants in ten prevalent single-gene disorders in the Chinese population, including alpha- and beta-thalassemia, non-syndromic hearing loss, spinal muscular atrophy, Duchenne muscular dystrophy, phenylketonuria, 21-hydroxylase deficiency, Wilson disease, methylmalonic acidemia, and Hemophilia A. MLDP-AS detects routine variants and technically challenging types, such as F8 intron 22 inversions, CYP21A2 variations, and single-exon CNVs, in a single test. The assay was optimized using positive clinical samples, with sensitivity validated against gold-standard methods. Clinical applicability was evaluated through a prospective study of couples planning or undergoing pregnancy, with positive results confirmed by gold-standard techniques. Results MLDP-AS achieved 100% sensitivity, identifying all 255 pathogenic variants in known positive samples, including 37 technically challenging variants. In a prospective trial involving 5,209 individuals, carriers for all targeted disorders were detected, with an overall carrier rate of 22.15%. Thirty-four couples (1.09%) were identified as high-risk, spanning eight of the ten diseases. A total of 289 pathogenic variants were detected 1,290 times, including 169 technically challenging variants. The assay demonstrated a positive predictive value of 99.7% compared to gold-standard methods. MLDP-AS is rapid and cost-effective, completing testing within three days at a cost under $ 25. Conclusions MLDP-AS integrates detection of multiple complex variants into a single, comprehensive assay, overcoming limitations of conventional NGS. It significantly enhances variant detection and provides an efficient, cost-effective tool for carrier screening in the Chinese population. single-gene disorders next-generation sequencing long-distance PCR structural variations expanded carrier screening Figures Figure 1 Figure 2 Figure 3 Introduction Single-gene disorders (SGDs) are predominantly caused by pathogenic variants in individual genes, with negligible contribution from environmental factors[ 1 ]. Expanded carrier screening (ECS) serves as a pivotal strategy for reducing the burden of inherited SGDs. Current guidelines from the American College of Medical Genetics and Genomics (ACMG) emphasize prioritizing diseases with high carrier frequencies, severe clinical manifestations, early onset, and potential for prevention or management[ 2 , 3 ]. Population-specific ECS panels targeting prevalent and severe SGDs have emerged as a pragmatic and cost-efficient approach for large-scale screening. This is particularly relevant given the heterogeneity in carrier rates across ethnic groups and disparities in healthcare infrastructure, including laboratory capabilities, genetic counseling accessibility, and public awareness[ 4 – 6 ]. Notably, studies indicate that screening 22 genes can identify 95% of high-risk couples[ 7 ], while a focused panel of 11 diseases achieves comparable clinical utility to broader panels[ 8 ], underscoring the importance of strategic panel design. Next-generation sequencing (NGS) has transformed SGD diagnostics by enabling high-throughput, multiplexed variant detection[ 9 ]. Despite its advantages, conventional NGS workflows exhibit limitations in resolving complex genomic alterations. Key challenges include pseudogene interference (e.g., distinguishing CYP21A2 from its pseudogene CYP21A1P [ 10 ], inadequate detection of large structural variants such as F8 intron 22 inversions (INV22)[ 11 ], and incomplete capture of single-exon copy number variations (CNVs) in genes like DMD [ 12 – 16 ]. These technical gaps may result in false-negative screening outcomes, leaving residual risk for families despite negative results[ 17 ]. To address these limitations, complementary approaches have been explored, including third-generation sequencing with long-read capabilities[ 10 , 11 , 18 , 19 ], capillary electrophoresis, and algorithm-enhanced NGS pipelines[ 20 – 27 ]. For instance, Wang et al. combined long-distance PCR (LD-PCR) with NGS to improve CYP21A2 variant resolution[ 26 ], while Johnsen et al. integrated enzymatic digestion and molecular inversion probes for F8 INV22 detection[ 28 ]. However, such methods are often disease-specific and lack scalability for comprehensive ECS panels. Consequently, a clinically adaptable solution capable of harmonizing diverse variant types—including pseudogene-derived variants, structural rearrangements, and CNVs—into a unified workflow remains an unmet need. Here, we present the development and validation of Multiplex Long-Distance PCR followed by Amplicon Sequencing (MLDP-AS), a novel NGS assay designed for expanded carrier screening of ten high-penetrance SGDs prevalent in the Chinese population. By integrating optimized long-range PCR, amplicon sequencing, and bioinformatics pipelines, MLDP-AS enables simultaneous detection of routine variants and technically challenging alterations—such as CYP21A2 variations, F8 INV22, and single-exon CNVs—within a single assay. This approach aims to bridge the gap between diagnostic accuracy and clinical feasibility, offering a streamlined, cost-effective solution for population-scale carrier screening. Method Study Design The study comprises three sequential phases: (1) experimental parameter optimization, (2) analytical validation with positive controls, and (3) prospective clinical validation. In Phase 1, assay parameters were established using reference samples (positive and negative) to refine performance metrics. Phase 2 validated the method’s sensitivity using clinical samples pre-characterized by gold-standard techniques (e.g., Sanger sequencing, MLPA). Phase 3 prospectively enrolled couples planning pregnancy or in early gestation (≤24+6 weeks). Positive screening results were confirmed via gold-standard methods. Clinical feasibility was assessed by analyzing carrier rates for pathogenic/likely pathogenic (P/LP) variants and the proportion of high-risk couples. Sample Collection The study utilized two sample cohorts: a validation cohort and a prospective cohort. The validation cohort consisted of pre-characterized positive samples confirmed to harbor specific pathogenic variants by gold-standard methods, including Sanger sequencing and MLPA. The prospective cohort included couples recruited between July 2023 and January 2024. Inclusion criteria were phenotypically normal individuals planning pregnancy or in early pregnancy. Exclusion criteria included: (a) a prior history of bearing a child with any of the ten target diseases; and (b) recent allogeneic blood transfusion, transplantation, or cell therapy within the past 12 months. All participants provided written informed consent, and the study protocol was approved by the Zhengzhou University Ethics Committee (KS-2018-KY-36). Panel Design The ten autosomal or X-linked recessive single-gene disorders (SGDs) selected for screening were based on seven criteria: (a) a carrier frequency of 1/100 or higher in the Chinese population; (b) high-frequency pathogenic variants identified in the Chinese or East/Southeast Asian populations; (c) well-defined phenotypic characteristics; (d) severe impact on quality of life; (e) association with cognitive or physical disability; (f) requirement for medical or surgical intervention; and (g) necessity for delivery management adjustments to improve neonatal outcomes. The final panel included the following diseases and associated genes: Alpha-thalassemia ( HBA1 , HBA2 ), Beta-thalassemia ( HBB ), Non-syndromic hearing loss ( GJB2 , SLC26A4 ), Spinal muscular atrophy ( SMN1 , SMN2 ), Duchenne muscular dystrophy ( DMD ), Phenylketonuria ( PAH ), 21-hydroxylase deficiency ( CYP21A2 ), Wilson disease ( ATP7B ), Methylmalonic acidemia ( MMACHC , MMUT ), and Hemophilia A ( F8 ). A 1,621-plex multiplex PCR assay was designed to amplify target regions, including the full protein-coding sequences and ±10 bp splice regions of the 14 target genes, locus-specific regions upstream of HBA1 and HBA2 , differentiation sites between SMN1 and SMN2 , additional intronic regions of DMD , the F8 INV22 sequence, and intronic pathogenic variants with at least two stars in the ClinVar database (release 20230115)[29]. Amplicon lengths ranged from 81 to 298 bp, with an average length of 182 bp. The MLDP-AS Workflow The MLDP-AS workflow (Figure 1) involved three key steps. First, genomic DNA (gDNA) was split into two aliquots. One aliquot underwent multiplex long-distance PCR (LD-PCR) to enrich CYP21A2 and F8 INV22 regions. Second, LD-PCR products were mixed with the second gDNA aliquot at predefined ratios, optimized through calibration experiments. The combined template was amplified in five parallel multiplex PCR reactions using a tiling amplicon design. Third, pooled PCR products were purified, ligated with barcoded adapters, quantified, and sequenced on an Ion Proton™ sequencer (Thermo Fisher Scientific, USA). Variant calling and annotation were performed using a custom bioinformatics pipeline (Figure 2; Supplementary Methods). Validation of Pathogenic Variations All pathogenic variants identified by MLDP-AS were confirmed using orthogonal methods. Small variants were validated by Sanger sequencing, while exonic or gene-level copy number variations (CNVs) were validated using MLPA or quantitative PCR (qPCR). Result Overview The experimental results align with the three phases of the study design. In the first phase, we established an optimal mixing ratio of 10:1 for LD-PCR products to genomic DNA using positive reference samples. The second phase involved evaluating sensitivity with a set of 255 positive samples containing various pathogenic variants, demonstrating 100% concordance with prior molecular diagnostic results. In the third phase, we prospectively screened 5,209 individuals, achieving an overall carrier detection rate of 22.15% across the 14 genes tested. A total of 34 high-risk couples were identified, involving 8 of the 10 diseases under investigation. The detected variants showed 99.7% concordance with gold-standard validation results. Optimization of the Mixing Ratio of Pre-Amplified Products and gDNA To detect complex variants such as CYP21A2 and F8 inversions, the MLDP-AS method employs LD-PCR to pre-amplify target regions before mixing with gDNA for amplicon sequencing. The ratio of pre-amplified products to gDNA is crucial: a high ratio can reduce coverage of other gene regions, while a low ratio may compromise the enrichment of the pre-amplified targets. To optimize this ratio, we evaluated five positive reference samples—representing SMN1 deletion, CYP21A2 large deletion, F8 INV22, HBA1/2 deletions, and DMD exon deletions—and one negative sample across four mixing ratios: 0:1, 5:1, 10:1, and 20:1 (pre-amplified product:gDNA ), with five replicates per condition. Using the segregation index, we assessed the differentiation between positive and negative samples under these varying conditions. All samples included in the study achieved an average total read count of 1,681,107 ± 451,264. The mean coverage across the panel was 215 ± 43, with an average proportion of ≥30X coverage reaching 99.9%. Gradient experiments using Pre-PCR product-to-gDNA mixing ratios of 0:1, 5:1, 10:1, and 20:1 yielded average pre-amplification ratios of 0.28, 4.6, 9.9, and 21, with respective ranges of 0.02–0.45, 4.3–5.5, 9.0–11.3, and 16.3–23.6. The distribution of segregation index values for positive samples and negative controls under each mixing condition is presented in Figure 2. The mean segregation index values were 26.97, 34.85, 50.47, and 64.09, with ranges of 2–40, 16–55, 24–141, and 22–205, respectively. As the pre-amplification ratio increased, the segregation index values for CYP21A2 large deletions and F8 INV22 variants demonstrated enhanced discrimination. Conversely, the segregation index values for variants involving 1–2 exon deletions/duplications, SMN1/2 copy number variations, and HBA1/2 large deletions exhibited decreased discrimination. Among the evaluated conditions, the 10:1 Pre-PCR product-to-gDNA ratio produced the highest average segregation index values for all five variant types, offering the best relative discrimination. Based on these findings, the 10:1 mixing ratio was selected as the optimal condition for the final experimental setup. Evaluation of Assay Sensitivity To evaluate the accuracy of the MLDP-AS method in detecting variants, its performance was compared to clinical gold-standard results using 255 carrier samples with known pathogenic variants (Table 1, Supplemental Tables 1 and 2). The average total read count across all samples was 1,957,549 ± 598,056, with a mean coverage of 230 ± 51 within the panel range and an average proportion of ≥30X coverage reaching 99.9%. The MLDP-AS method successfully identified all 255 known pathogenic variants, achieving a sensitivity of 100%. These variants included 151 small variants and 104 structural variations (Figure 3). Among the 255 samples, 15 (5.88%, 15/255) carried pathogenic variants in CYP21A2 , including 4 samples with large deletions or fusions. Additionally, 7 samples (2.75%, 7/255) carried pathogenic variants in F8 INV22, 43 samples exhibited exon 7 copy number deletions in SMN1 , 32 samples harbored exon deletions or duplications in DMD , and 9 samples had large deletions associated with alpha-thalassemia. Beyond the accurate detection of these known pathogenic variants, MLDP-AS identified 24 additional pathogenic variants not previously detected (Table 1). Among these, 4 (16.67%, 4/24) were pathogenic variants in CYP21A2 , highlighting the enhanced capability of this method to detect clinically relevant variants. Prospective Clinical Study A prospective clinical study was conducted involving 5,209 recruited samples, comprising 3,067 female and 2,142 male participants. The MLDP-AS assay identified a total of 1,290 pathogenic variants, including 1,160 small variants and 130 structural variants (SVs), as detailed in Table 2. The average total read count across all samples was 1,745,287 ± 483,216. Within the panel range, the average coverage was 219 ± 44, with 99.9% of bases covered at ≥30X. Gold-standard validation confirmed 1,290 true positives out of 1,294 detections, yielding an overall positive predictive value (PPV) of 99.7%. The PPV for SNVs or small indels was 99.9% (1,160/1,161), and for CNVs it was 98.0% (130/133). Among the detected pathogenic variants, 166 (12.8%) were in CYP21A2 , and 1 (0.08%) was an F8 INV22 in a male sample. The combined carrier frequency for the 14 genes screened was 22.15% (1,154/5,209). Of these carriers, 88.6% (1,023/1,154) carried a single pathogenic variant, while 11.4% (131/1,154) carried variants associated with two or more diseases. The highest carrier rates were observed for MMACHC (3.78%, 197/5,209), ATP7B (3.42%, 178/5,209), CYP21A2 (3.21%, 167/5,209), and GJB2 (3.15%, 164/5,209). The carrier frequency for CYP21A2 was 3.21%, ranking the third among all genes, while that for F8 was 0.12% (6/5,209). Additionally, 0.19% (10/5,209) of participants carried pathogenic variants in DMD , including one case of a single exon deletion. Thirty-four couples (1.09%) were identified as high-risk, with eight-in-ten of the diseases having high-risk couples identified. Methylmalonic acidemia ranked first with 12 high-risk families (35.29%), followed by Duchenne muscular dystrophy with 8 families (23.53%). Among the high-risk couples, 22 (64.7%) were at risk for autosomal recessive diseases, and 12 couples (35.3%) were at risk for X-linked diseases. Notably, one family was classified as high-risk for CYP21A2 , a finding often missed by conventional NGS-based carrier screening. Discussion In this study, we developed and validated MLDP-AS, an optimized NGS assay designed for comprehensive carrier screening of prevalent and severe single-gene disorders in the Chinese population. By integrating the detection of multiple technically challenging variants—including CYP21A2 variations, F8 inversions, and single-exon CNVs—into a unified workflow, MLDP-AS demonstrated exceptional sensitivity (100%) in identifying 255 pre-characterized pathogenic variants. Prospective screening of 5,209 individuals revealed a 22.15% carrier rate and identified 34 high-risk couples (1.09%) across 8 of the 10 target diseases, with 99.7% concordance to gold-standard methods. Notably, 3.24% (169/5,209) of participants carried variants undetectable by conventional NGS panels, translating to one missed carrier in every 31 individuals. Incorporating these challenging variants increased the positive detection rate by 14.34% and identified two additional high-risk couples (5.88% of total), underscoring MLDP-AS’s ability to reduce residual risk and enhance screening reliability. Approximately 95% of congenital adrenal hyperplasia (CAH) cases are caused by pathogenic variants in CYP21A2 , leading to 21-hydroxylase deficiency[ 30 ]. Detection of CYP21A2 variants is technically challenging due to pseudogene interference ( CYP21A1P ) and the presence of complex variant types such as large deletions and gene fusions[ 7 , 31 ]. Notably, ~ 25% of pathogenic mutations in CAH patients are attributed to these large deletions or fusions[ 32 – 34 ]. Such technical limitations have historically resulted in the exclusion of CYP21A2 from carrier screening panels or underreporting of its carrier frequency (e.g., 2.09%[ 7 ] and 1.95%[ 31 ] in prior studies). In contrast, our study identified a CYP21A2 carrier frequency of 3.18%, ranking it third among all tested genes. Importantly, one high-risk couple carrying pathogenic CYP21A2 variants was identified, emphasizing the clinical consequence of omitting this gene from screening panels[ 35 ]. To address pseudogene interference, Wang et al.[ 26 ] also employed LD-PCR to amplify CYP21A2 prior to NGS analysis. While this method effectively reduced pseudogene-derived artifacts, it required separate sequencing workflows for the LD-PCR product and lacked the ability to detect CYP21A2 variants concurrently with other panel genes. In contrast, MLDP-AS integrates LD-PCR into a unified workflow, enabling simultaneous detection of CYP21A2 variants (including large deletions, fusions, and CNVs) alongside other target genes without additional processing steps. This integration eliminates the risk of overlooking CYP21A2 -related risks while maintaining assay scalability. Hemophilia A, a hereditary bleeding disorder primarily caused by mutations in the F8 gene, poses significant diagnostic challenges due to large inversions such as the F8 intron 22 inversion (INV22). Nearly all patients with F8 INV22 exhibit severe hemophilia[ 36 , 37 ], with detection rates of this inversion ranging from 40–50% in affected populations[ 38 – 40 ]. Conventional carrier screening panels often exclude F8 INV22 detection or rely on supplementary techniques such as capillary electrophoresis. In our cohort, 0.12% of screened individuals (6/5,209) carried pathogenic F8 variants, including one male with the F8 INV22 inversion. This finding underscores the importance of comprehensive NGS-based screening to identify F8 variants that may otherwise be missed[ 41 – 43 ]. Johnsen et al.[ 28 ] developed an NGS-based method for detecting F8 INV22 and other pathogenic variants in hemophilia A and B, using Ksp22I enzyme digestion followed by molecular inversion probe capture. While effective, this approach fails to address pseudogene interference like CYP21A1P . MLDP-AS overcomes these limitations through multiplex LD-PCR, which simultaneously enriches F8 INV22 and CYP21A2 targets. By converting F8 INV22 detection into a quantitative analysis of intron 22 copy number and standardizing the mixing ratio of LD-PCR products to gDNA (10:1), MLDP-AS enables the concurrent detection of F8 INV22, CYP21A2 deletions/fusions, and other variants within a single library preparation and sequencing workflow. This integrated approach eliminates the need for separate assays or additional enzymatic steps, streamlining clinical implementation. Cost and turnaround time are important considerations in carrier screening[ 44 ]. MLDP-AS offers significant cost savings, with an experimental cost of $ 25 per sample and a testing price of less than $ 150. This affordability makes MLDP-AS a more accessible option for carrier screening compared to conventional NGS-based panels, which cost $ 300–400 per test in China, with an additional ~ $ 20 for F8 INV22 detection. Moreover, MLDP-AS boasts a faster turnaround time, with the entire experimental process completed within three days and reports available within one week for samples collected and processed in the same laboratory, compared to the usual 2–3 weeks turnaround time. This made MLDP-AS adapt to prenatal carrier screening in early gestation. A notable limitation is the low detection of F8 INV22 carriers (n = 1) in our cohort, necessitating validation in larger populations. Additionally, MLDP-AS cannot currently identify “2 + 0” SMA carriers or certain deep intronic variants due to technical constraints in SMN1/SMN2 haplotype resolution. Future optimizations should target these gaps, potentially leveraging population-specific haplotype markers or advanced long-read sequencing. In conclusion, MLDP-AS represents a significant advancement in carrier screening, enabling simultaneous detection of SNVs, CNVs, and technically challenging variants (e.g., CYP21A2 deletions, F8 INV22) within a single assay. Its high sensitivity, cost-efficiency, and rapid workflow position it as a clinically feasible solution for reducing residual risk and improving genetic screening outcomes in the Chinese population. Abbreviations SGDs, single-gene disorders; SNVs, single nucleotide variations; NGS, Next-generation sequencing; INV22, intron 22 inversion; CNVs, copy number variations; MLDP-AS, multiplex long-distance PCR followed by amplicon sequencing; gDNA, genomic DNA; LD-PCR, long-distance PCR; TVC, Torrent Variant Caller; MLPA, Multiplex Ligation-dependent Probe Amplification; qPCR, quantitative PCR. Human Genes: HBA1 , hemoglobin subunit alpha 1; HBA2 , hemoglobin subunit alpha 2; HBB , hemoglobin subunit beta; GJB2 , gap junction protein beta 2; SLC26A4 , solute carrier family 26 member 4; SMN1 , survival of motor neuron 1 (telomeric); SMN2 , survival of motor neuron 2 (centromeric); DMD , dystrophin; PAH, phenylalanine hydroxylase; CYP21A2 , cytochrome P450 family 21 subfamily A member 2; CYP21A1P , cytochrome P450 family 21 subfamily A member 1 (pseudogene); ATP7B , ATPase copper transporting beta; MMACHC , metabolism of cobalamin associated C; MMUT , methylmalonyl-CoA mutase; F8 , coagulation factor VIII. Declarations Acknowledgments We are grateful to all the team members for their contributions to data collection and integrity. Consent for publication Not applicable Funding Key Scientific Research Projects in Colleges and Universities of Henan Province (22A320075),Science and Technology Huimin Project of Zhengzhou (2021KJHM0003), Henan Province Medical Science and Technique Foundation (SBGJ202102097) and the Science and Technology Research Program of Henan Province (222102520018), Science and Technology Research Program of Henan Province (Grant Number 242102311087). Competing interests Author S.J. Li, C. Yuan, J. Feng, W.Q. Tang, D. Wu are employed by Celula (China) Medical Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Availability of data and material The datasets during and/or analysed during the current study available from the corresponding author on reasonable request. Authors' contributions We declare that his manuscript is original , has not been published before and is not currently being considered for publication elsewhere .We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed . We further confirm that the order of authors listed in the manuscript has been approved by all of us .We understand that the Corresponding Author is the sole contact for the Editorial process . She is responsible for communicating with the other authors about progress , submissions of revisions and final approval of proofs . All authors as follows : Zhenhua Zhao & Ganye Zhao: Conceptualization, Methodology, Investigation, Data Curation, Writing – Original Draft. Shaojun Li, Chao Yuan, & Jun Feng: Formal Analysis, Validation, Software, Visualization. Weiqin Tang & Xinyu Fu: Resources, Project Administration, Supervision. Huanyun Li, Jingqi Zhu, & Xueyang Zhao: Investigation, Experimental Execution, Data Collection. Di Wu: Funding Acquisition, Writing – Review & Editing, Supervision. Xiangdong Kong: Conceptualization, Funding Acquisition, Supervision, Writing – Review & Editing. References Beauchamp KA, Muzzey D, Wong KK, Hogan GJ, Karimi K, Candille SI, Mehta N, Mar-Heyming R, Kaseniit KE, Kang HP, et al: Systematic design and comparison of expanded carrier screening panels. Genet Med 2018, 20: 55-63. Gregg AR, Aarabi M, Klugman S, Leach NT, Bashford MT, Goldwaser T, Chen E, Sparks TN, Reddi HV, Rajkovic A, et al: Screening for autosomal recessive and X-linked conditions during pregnancy and preconception: a practice resource of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2021, 23: 1793-1806. Murray MF, Giovanni MA, Doyle DL, Harrison SM, Lyon E, Manickam K, Monaghan KG, Rasmussen SA, Scheuner MT, Palomaki GE, et al: DNA-based screening and population health: a points to consider statement for programs and sponsoring organizations from the American College of Medical Genetics and Genomics (ACMG). Genet Med 2021, 23: 989-995. Chokoshvili D, Vears DF, Borry P: Growing complexity of (expanded) carrier screening: Direct-to-consumer, physician-mediated, and clinic-based offers. Best Pract Res Clin Obstet Gynaecol 2017, 44: 57-67. Wienke S, Brown K, Farmer M, Strange C: Expanded carrier screening panels-does bigger mean better? J Community Genet 2014, 5: 191-198. Kihlbom U: Ethical issues in preconception genetic carrier screening. Ups J Med Sci 2016, 121: 295-298. Hou W, Fu X, Xie X, Zhang C, Bian J, Mao X, Wen J, Luo C, Jin H, Zhu Q, et al: [Carrier screening for 223 monogenic diseases in Chinese population: a multi-center study in 33 104 individuals]. Nan Fang Yi Ke Da Xue Xue Bao 2024, 44: 1015-1023. Shi M, Liauw AL, Tong S, Zheng Y, Leung TY, Chong SC, Cao Y, Lau TK, Choy KW, Chung JPW: Clinical Implementation of Expanded Carrier Screening in Pregnant Women at Early Gestational Weeks: A Chinese Cohort Study. Genes (Basel) 2021, 12 . Prior TW: Next-generation carrier screening: are we ready? Genome Med 2014, 6: 62. Li H, Zhu X, Yang Y, Wang W, Mao A, Li J, Bao S, Li J: Long-read sequencing: An effective method for genetic analysis of CYP21A2 variation in congenital adrenal hyperplasia. Clin Chim Acta 2023, 547: 117419. Liu Y, Li D, Yu D, Liang Q, Chen G, Li F, Gao L, Li Z, Xie T, Wu L, et al: Comprehensive analysis of hemophilia A (CAHEA): towards full characterization of the F8 gene variants by long-read sequencing. Thromb Haemost 2023. Lao Q, Zhou K, Parker M, Faucz FR, Merke DP: Pseudogene TNXA Variants May Interfere with the Genetic Testing of CAH-X. Genes (Basel) 2023, 14 . Abou Tayoun AN, Krock B, Spinner NB: Sequencing-based diagnostics for pediatric genetic diseases: progress and potential. Expert Rev Mol Diagn 2016, 16: 987-999. Truty R, Paul J, Kennemer M, Lincoln SE, Olivares E, Nussbaum RL, Aradhya S: Prevalence and properties of intragenic copy-number variation in Mendelian disease genes. Genet Med 2019, 21: 114-123. Li W, Freudenberg J: Mappability and read length. Front Genet 2014, 5: 381. Mandelker D, Schmidt RJ, Ankala A, McDonald Gibson K, Bowser M, Sharma H, Duffy E, Hegde M, Santani A, Lebo M, Funke B: Navigating highly homologous genes in a molecular diagnostic setting: a resource for clinical next-generation sequencing. Genet Med 2016, 18: 1282-1289. Kraft SA, Duenas D, Wilfond BS, Goddard KAB: The evolving landscape of expanded carrier screening: challenges and opportunities. Genet Med 2019, 21: 790-797. Liu Y, Chen M, Liu J, Mao A, Teng Y, Yan H, Zhu H, Li Z, Liang D, Wu L: Comprehensive Analysis of Congenital Adrenal Hyperplasia Using Long-Read Sequencing. Clin Chem 2022, 68: 927-939. Chen X, Harting J, Farrow E, Thiffault I, Kasperaviciute D, Genomics England Research C, Hoischen A, Gilissen C, Pastinen T, Eberle MA: Comprehensive SMN1 and SMN2 profiling for spinal muscular atrophy analysis using long-read PacBio HiFi sequencing. Am J Hum Genet 2023, 110: 240-250. Lopez-Lopez D, Loucera C, Carmona R, Aquino V, Salgado J, Pasalodos S, Miranda M, Alonso A, Dopazo J: SMN1 copy-number and sequence variant analysis from next-generation sequencing data. Hum Mutat 2020, 41: 2073-2077. Kozareva V, Stroff C, Silver M, Freidin JF, Delaney NF: Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model. BMC Med Genomics 2018, 11: 91. Guzel F, Romano M, Keles E, Piskin D, Ozen S, Poyrazoglu H, Kasapcopur O, Demirkaya E: Next Generation Sequencing Based Multiplex Long-Range PCR for Routine Genotyping of Autoinflammatory Disorders. Front Immunol 2021, 12: 666273. Shum BOV, Henner I, Cairns A, Pretorius C, Wilgen U, Barahona P, Ungerer JPJ, Bennett G: Technical feasibility of newborn screening for spinal muscular atrophy by next-generation DNA sequencing. Front Genet 2023, 14: 1095600. Ren Y, Lian Y, Yan Z, Zhai F, Yang M, Zhu X, Wang Y, Nie Y, Guan S, Kuo Y, et al: Clinical application of an NGS-based method in the preimplantation genetic testing for Duchenne muscular dystrophy. J Assist Reprod Genet 2021, 38: 1979-1986. Rojahn S, Hambuch T, Adrian J, Gafni E, Gileta A, Hatchell H, Johnson B, Kallman B, Karfilis K, Kautzer C, et al: Scalable detection of technically challenging variants through modified next-generation sequencing. Mol Genet Genomic Med 2022 : e2072. Wang W, Han R, Yang Z, Zheng S, Li H, Wan Z, Qi Y, Sun S, Ye L, Ning G: Targeted gene panel sequencing for molecular diagnosis of congenital adrenal hyperplasia. J Steroid Biochem Mol Biol 2021, 211: 105899. Hassan S, Bahar R, Johan MF, Mohamed Hashim EK, Abdullah WZ, Esa E, Abdul Hamid FS, Zulkafli Z: Next-Generation Sequencing (NGS) and Third-Generation Sequencing (TGS) for the Diagnosis of Thalassemia. Diagnostics (Basel) 2023, 13: 373. Johnsen JM, Fletcher SN, Huston H, Roberge S, Martin BK, Kircher M, Josephson NC, Shendure J, Ruuska S, Koerper MA, et al: Novel approach to genetic analysis and results in 3000 hemophilia patients enrolled in the My Life, Our Future initiative. Blood Adv 2017, 1: 824-834. Landrum MJ, Chitipiralla S, Brown GR, Chen C, Gu B, Hart J, Hoffman D, Jang W, Kaur K, Liu C, et al: ClinVar: improvements to accessing data. Nucleic Acids Res 2020, 48: D835-D844. Pignatelli D, Carvalho BL, Palmeiro A, Barros A, Guerreiro SG, Macut D: The Complexities in Genotyping of Congenital Adrenal Hyperplasia: 21-Hydroxylase Deficiency. Front Endocrinol (Lausanne) 2019, 10: 432. Huang Q, Wen J, Zhang H, Teng Y, Zhang W, Zhu H, Liang D, Wu L, Li Z: Comprehensive analysis of NGS-based expanded carrier screening and follow-up in southern and southwestern China: results from 3024 Chinese individuals. Hum Genomics 2024, 18: 111. Xia Y, Shi P, Gao S, Liu N, Zhang H, Kong X: Genetic analysis and novel variation identification in Chinese patients with congenital adrenal hyperplasia due to 21-hydroxylase deficiency. J Steroid Biochem Mol Biol 2022, 222: 106156. Xu J, Li P: Identification of novel and rare CYP21A2 variants in Chinese patients with congenital adrenal hyperplasia due to 21-hydroxylase deficiency. Clin Biochem 2019, 68: 44-49. Luo C, Jiang T, Zhang J, Li L, Sun Y, Liu G, Wang Y, Cheng J, Ma D, Xu Z: [Genetic analysis and prenatal diagnosis for 25 Chinese pedigrees affected with congenital adrenal hyperplasia due to 21-hydroxylase deficiency]. Zhonghua Yi Xue Yi Chuan Xue Za Zhi 2018, 35: 832-835. Zhang X, Chen Q, Li J, Luo X, Luo J, Li J, Zeng Z, Wu Y, Zhang H, Dong Y: The effectiveness of expanded carrier screening based on next-generation sequencing for severe monogenic genetic diseases. Hum Genomics 2024, 18: 9. Luna-Zaizar H, Gonzalez-Alcazar JA, Evangelista-Castro N, Aguilar-Lopez LB, Ruiz-Quezada SL, Beltran-Miranda CP, Jaloma-Cruz AR: F8 inversions of introns 22 and 1 confer a moderate risk of inhibitors in Mexican patients with severe hemophilia A. Concordance analysis and literature review. Blood Cells Mol Dis 2018, 71: 45-52. Li F, He L, Chen G, Lu Y, Li R, Zhang Y, Jing X, Ling R, Li D, Liao C: Variant spectrum of F8 and F9 in hemophilia patients from southern China and 26 novel variants. Front Genet 2023, 14: 1254265. Antonarakis SE, Rossiter JP, Young M, Horst J, de Moerloose P, Sommer SS, Ketterling RP, Kazazian HH, Jr., Negrier C, Vinciguerra C, et al: Factor VIII gene inversions in severe hemophilia A: results of an international consortium study. Blood 1995, 86: 2206-2212. Lakich D, Kazazian HH, Jr., Antonarakis SE, Gitschier J: Inversions disrupting the factor VIII gene are a common cause of severe haemophilia A. Nat Genet 1993, 5: 236-241. Feng Y, Li Q, Shi P, Liu N, Kong X, Guo R: Mutation analysis in the F8 gene in 485 families with haemophilia A and prenatal diagnosis in China. Haemophilia 2021, 27: e88-e92. Zhang X, Chen K, Bian S, Wang G, Qin X, Zhang R, Yang L: Molecular Diagnosis of Hemophilia A and Pathogenesis of Novel F8 Variants in Shanxi, China. Glob Med Genet 2023, 10: 247-262. Bai H, Xue X, Tian L, Liu XT, Li Q: Case Report: Identification of a de novo Missense Mutation in the F8 Gene, p.(Phe690Leu)/c.2070C > A, Causing Hemophilia A: A Case Report. Front Genet 2020, 11: 589899. Chen J, Li Q, Lin S, Li F, Huang L, Jin W, Yang X, Li Y, Li K, Xiong Y, et al: The spectrum of FVIII gene variants detected by next generation sequencing in 236 Chinese non-inversion hemophilia A pedigrees. Thromb Res 2021, 202: 8-13. Pereira N, Wood M, Luong E, Briggs A, Galloway M, Maxwell RA, Lindheim SR: Expanded genetic carrier screening in clinical practice: a current survey of patient impressions and attitudes. J Assist Reprod Genet 2019, 36: 709-716. Tables Table 1 Pathogenic Variations Discovered by MLDP-AS Disease Gene Estimated Percent of SVs a Number of Pathogenic Variations Sensitivity (%) Specificity (%) Already Known b New Discovery Small Variants SVs Small Variants SVs Alpha-thalassemia HBA1 / HBA2 >96 5/5 9/9 0 0 100 100 Beta-thalassemia HBB Rare 7/7 1/1 0 0 100 100 Non-syndromic hearing loss GJB2 Rare 6/6 0/0 1 0 100 100 SLC26A4 Rare 13/13 0/0 6 0 100 100 Spinal muscular atrophy SMN1 / SMN2 95-98 3/3 43/43 0 1 100 100 Duchenne muscular dystrophy DMD 65-80 14/14 32/32 0 0 100 100 Phenylketonuria PAH 1-3 38/38 3/3 4 0 100 100 21-hydroxylase deficiency CYP21A2 20-30 11/11 4/4 3 1 100 100 Wilson disease ATP7B Rare 11/11 2/2 4 0 100 100 Methylmalonic acidemia MMACHC 2-4 19/19 2/2 3 0 100 100 MMUT ~1 16/16 1/1 1 0 100 100 Hemophilia A F8 ~50 8/8 7/7 0 0 100 100 Total 151/151 104/104 22 2 100 100 SVs represent structural variations. The denominator represents the number of known pathogenic variations, while the numerator represents those discovered by MLDP-AS. Table 2 Genes with carrier frequency and At-risk couples Gene Variants Total carrier frequency b (n=5209) At-risk couples b (n=3117) Small Variants SVs a N % 1 in _ N % 1 in _ MMACHC 197 / 197 3.78 26.4 9 0.29 346.3 ATP7B 177 1 178 3.42 29.3 1 0.03 3117.0 CYP21A2 166 1 167 3.21 31.2 1 0.03 3117.0 GJB2 164 / 164 3.15 31.8 1 0.03 3117.0 PAH 157 / 157 3.01 33.2 4 0.13 779.3 SLC26A4 151 1 152 2.92 34.3 3 0.10 1039.0 MMUT 109 / 109 2.09 47.8 3 0.10 1039.0 SMN1 1 78 79 1.52 65.9 0 0.00 / HBA1/HBA2 4 38 42 0.81 124.0 0 0.00 / HBB 28 / 28 0.54 186.0 0 0.00 / DMD / 10 10 0.19 520.9 8 0.26 389.6 F8 6 1 7 0.13 744.1 4 0.13 779.3 Total 1160 130 1154 22.15 4.5 34 1.09 77.5 SVs represent structural variations. Only P/LP variants are considered. Cite Share Download PDF Status: Published Journal Publication published 14 Feb, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted Reviewers agreed at journal 10 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 17 Jun, 2025 First submitted to journal 16 Jun, 2025 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-6880217\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":483137572,\"identity\":\"605e828c-4220-4912-b713-99c8389f33a8\",\"order_by\":0,\"name\":\"Zhenhua Zhao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Zhengzhou University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhenhua\",\"middleName\":\"\",\"lastName\":\"Zhao\",\"suffix\":\"\"},{\"id\":483137573,\"identity\":\"b78d4f69-0d34-4b8d-a8d6-7de8b47d7204\",\"order_by\":1,\"name\":\"Ganye Zhao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Zhengzhou University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ganye\",\"middleName\":\"\",\"lastName\":\"Zhao\",\"suffix\":\"\"},{\"id\":483137574,\"identity\":\"74286e1b-7d6e-48c4-b901-316e5c6c4105\",\"order_by\":2,\"name\":\"Shaojun Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Celula Medical Technology Co.,Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shaojun\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":483137575,\"identity\":\"cdcedaa8-d6e1-4a73-ae85-828b6e5ec1a9\",\"order_by\":3,\"name\":\"Chao Yuan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Celula Medical Technology Co.,Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chao\",\"middleName\":\"\",\"lastName\":\"Yuan\",\"suffix\":\"\"},{\"id\":483137576,\"identity\":\"d0e9e7cf-bcb4-4604-9999-3784022005f2\",\"order_by\":4,\"name\":\"Jun Feng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Celula(China) Medical Technology Co.,Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jun\",\"middleName\":\"\",\"lastName\":\"Feng\",\"suffix\":\"\"},{\"id\":483137577,\"identity\":\"9000449a-7313-460c-8ea0-6451bd9fbef1\",\"order_by\":5,\"name\":\"Weiqin Tang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Celula(China) Medical Technology Co.,Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Weiqin\",\"middleName\":\"\",\"lastName\":\"Tang\",\"suffix\":\"\"},{\"id\":483137578,\"identity\":\"5691b914-7de8-4b02-b0e8-b6f97b69882b\",\"order_by\":6,\"name\":\"Xinyu Fu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Zhengzhou University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xinyu\",\"middleName\":\"\",\"lastName\":\"Fu\",\"suffix\":\"\"},{\"id\":483137579,\"identity\":\"0659f148-6a91-4af3-89ef-25ee7a737013\",\"order_by\":7,\"name\":\"Huanyun Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Zhengzhou University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Huanyun\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":483137580,\"identity\":\"18b674d3-481d-4d88-a119-2dfb574e9f1d\",\"order_by\":8,\"name\":\"Jingqi Zhu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Zhengzhou University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jingqi\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"},{\"id\":483137581,\"identity\":\"bb5a25cb-0e68-4f47-8ad7-07c786ae1c31\",\"order_by\":9,\"name\":\"Xueyang Zhao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Zhengzhou University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xueyang\",\"middleName\":\"\",\"lastName\":\"Zhao\",\"suffix\":\"\"},{\"id\":483137582,\"identity\":\"8dd5460a-3927-4335-94f3-2fc02e4444d6\",\"order_by\":10,\"name\":\"Di Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Celula(China) Medical Technology Co.,Ltd\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Di\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":483137583,\"identity\":\"9ccac237-e87f-4fef-a9ed-383aa4525e4f\",\"order_by\":11,\"name\":\"Xiangdong Kong\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACCTDJZiPHz99AmpY0Y8kZB4CMBOK1HE7c0JBApBbJ9t7Dr3nKzjNuYDjA9uDjDyK0SPOcS7Occe42szlzA7vhDGJskZPIMTP42HabzbLhAJs0D9FaEtvO8RgcSGCT/kOMFmmJHOMHH9sOSIC1EOf9njNmjDPOJRtIzjjYJtmTRoQWieM9xp95yuzq+/mbj0n8sCFCCxCwQeKGgbGBOPVAwPyBaKWjYBSMglEwMgEA1lc1Bsnz0CgAAAAASUVORK5CYII=\",\"orcid\":\"https://orcid.org/0000-0003-0030-7638\",\"institution\":\"The First Affiliated Hospital of Zhengzhou University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Xiangdong\",\"middleName\":\"\",\"lastName\":\"Kong\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-06-12 12:09:07\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6880217/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6880217/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12967-026-07735-9\",\"type\":\"published\",\"date\":\"2026-02-14T15:57:16+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":86678288,\"identity\":\"5f1f99b2-51a3-47a9-aa01-8068d1e9af38\",\"added_by\":\"auto\",\"created_at\":\"2025-07-14 12:32:40\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":271093,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFlow diagram of participant recruitment and screening methodology. \\u003c/strong\\u003eTwo cohorts were included in this study: (A) Clinically validated samples used for optimizing the pre-amplified product-to-gDNA mixing ratio and comparing MLDP-AS performance against gold-standard diagnostics. (B) Prospective clinical screening samples tested using MLDP-AS. All detected pathogenic variants were confirmed by orthogonal methods, including Sanger sequencing, multiplex ligation-dependent probe amplification (MLPA), or quantitative PCR (qPCR), to evaluate the specificity of MLDP-AS.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6880217/v1/fa24f62436eeaae222f81e58.png\"},{\"id\":86678292,\"identity\":\"b1269ae1-3ac7-489f-a56c-b0a502e506c3\",\"added_by\":\"auto\",\"created_at\":\"2025-07-14 12:32:41\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1256333,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eWorkflow of MLDP-AS.\\u003c/strong\\u003e The left side of the figure illustrates the experimental steps, while the right side shows the primer design strategies for different genes.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6880217/v1/212b87fe0c543a8a9301c155.png\"},{\"id\":86679101,\"identity\":\"22d65a8e-b190-48df-85ee-34b45fff001a\",\"added_by\":\"auto\",\"created_at\":\"2025-07-14 12:40:41\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":200736,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDetection of structural variations in known positive sample sets.\\u003c/strong\\u003e (A) Copy number analysis of \\u003cem\\u003eCYP21A2\\u003c/em\\u003e and \\u003cem\\u003eCYP21A1P\\u003c/em\\u003e genes in \\u003cem\\u003eCYP21A2\\u003c/em\\u003e deletion carriers and a negative control. Boxplots depict the distribution of amplicon copy numbers. (B) Copy number analysis of \\u003cem\\u003eF8\\u003c/em\\u003e INV22 between carriers and normal samples. (C) Copy number analysis of \\u003cem\\u003eHBA2\\u003c/em\\u003e Specific, \\u003cem\\u003eHBA2\\u003c/em\\u003e gene, \\u003cem\\u003eHBA1\\u003c/em\\u003e Specific, and \\u003cem\\u003eHBA1\\u003c/em\\u003e gene in alpha-thalassemia large deletion carriers and a negative control. Blue hollow points represent individual amplicons; red horizontal lines indicate the average copy number for each region. Gray vertical bars denote exonic regions of \\u003cem\\u003eHBA2\\u003c/em\\u003e and \\u003cem\\u003eHBA1\\u003c/em\\u003e, while gray horizontal bars represent deletion regions (-α\\u003csup\\u003e3.7\\u003c/sup\\u003e, -α\\u003csup\\u003e4.2\\u003c/sup\\u003e, and --\\u003csup\\u003eSEA\\u003c/sup\\u003e). (D) Copy number analysis of \\u003cem\\u003eSMN1\\u003c/em\\u003e and \\u003cem\\u003eSMN2\\u003c/em\\u003e genes. Hollow points represent individual samples, with colors indicating different \\u003cem\\u003eSMN1\\u003c/em\\u003e/\\u003cem\\u003eSMN2\\u003c/em\\u003e copy number combinations. (E) Exonic copy number variations (CNVs) in a subset of carrier samples. For each gene, the x-axis represents exon indices, blue hollow points denote amplicons, and red horizontal lines indicate the average copy number per exon. Sample IDs are labeled in the upper-left corner of each subplot.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6880217/v1/6a8e62720b85328a9e36f245.png\"},{\"id\":102785388,\"identity\":\"f620dd46-cbd9-4d71-bcf2-3f8bb4fffeb8\",\"added_by\":\"auto\",\"created_at\":\"2026-02-16 16:06:04\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4508000,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6880217/v1/7b3aed85-9191-483c-ac9f-11cb8696dfa7.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"MLDP-AS: An Optimized Next-Generation Sequencing Assay for Enhanced Detection of Technically Challenging Variants in Expanded Carrier Screening\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eSingle-gene disorders (SGDs) are predominantly caused by pathogenic variants in individual genes, with negligible contribution from environmental factors[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Expanded carrier screening (ECS) serves as a pivotal strategy for reducing the burden of inherited SGDs. Current guidelines from the American College of Medical Genetics and Genomics (ACMG) emphasize prioritizing diseases with high carrier frequencies, severe clinical manifestations, early onset, and potential for prevention or management[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Population-specific ECS panels targeting prevalent and severe SGDs have emerged as a pragmatic and cost-efficient approach for large-scale screening. This is particularly relevant given the heterogeneity in carrier rates across ethnic groups and disparities in healthcare infrastructure, including laboratory capabilities, genetic counseling accessibility, and public awareness[\\u003cspan additionalcitationids=\\\"CR5\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Notably, studies indicate that screening 22 genes can identify 95% of high-risk couples[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e], while a focused panel of 11 diseases achieves comparable clinical utility to broader panels[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e], underscoring the importance of strategic panel design.\\u003c/p\\u003e\\u003cp\\u003eNext-generation sequencing (NGS) has transformed SGD diagnostics by enabling high-throughput, multiplexed variant detection[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Despite its advantages, conventional NGS workflows exhibit limitations in resolving complex genomic alterations. Key challenges include pseudogene interference (e.g., distinguishing \\u003cem\\u003eCYP21A2\\u003c/em\\u003e from its pseudogene \\u003cem\\u003eCYP21A1P\\u003c/em\\u003e[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e], inadequate detection of large structural variants such as F8 intron 22 inversions (INV22)[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e], and incomplete capture of single-exon copy number variations (CNVs) in genes like \\u003cem\\u003eDMD\\u003c/em\\u003e[\\u003cspan additionalcitationids=\\\"CR13 CR14 CR15\\\" citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. These technical gaps may result in false-negative screening outcomes, leaving residual risk for families despite negative results[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eTo address these limitations, complementary approaches have been explored, including third-generation sequencing with long-read capabilities[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e], capillary electrophoresis, and algorithm-enhanced NGS pipelines[\\u003cspan additionalcitationids=\\\"CR21 CR22 CR23 CR24 CR25 CR26\\\" citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. For instance, Wang et al. combined long-distance PCR (LD-PCR) with NGS to improve \\u003cem\\u003eCYP21A2\\u003c/em\\u003e variant resolution[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e], while Johnsen et al. integrated enzymatic digestion and molecular inversion probes for F8 INV22 detection[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. However, such methods are often disease-specific and lack scalability for comprehensive ECS panels. Consequently, a clinically adaptable solution capable of harmonizing diverse variant types\\u0026mdash;including pseudogene-derived variants, structural rearrangements, and CNVs\\u0026mdash;into a unified workflow remains an unmet need.\\u003c/p\\u003e\\u003cp\\u003eHere, we present the development and validation of Multiplex Long-Distance PCR followed by Amplicon Sequencing (MLDP-AS), a novel NGS assay designed for expanded carrier screening of ten high-penetrance SGDs prevalent in the Chinese population. By integrating optimized long-range PCR, amplicon sequencing, and bioinformatics pipelines, MLDP-AS enables simultaneous detection of routine variants and technically challenging alterations\\u0026mdash;such as \\u003cem\\u003eCYP21A2\\u003c/em\\u003e variations, \\u003cem\\u003eF8\\u003c/em\\u003e INV22, and single-exon CNVs\\u0026mdash;within a single assay. This approach aims to bridge the gap between diagnostic accuracy and clinical feasibility, offering a streamlined, cost-effective solution for population-scale carrier screening.\\u003c/p\\u003e\"},{\"header\":\"Method\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eStudy Design\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study comprises three sequential phases: (1) experimental parameter optimization, (2) analytical validation with positive controls, and (3) prospective clinical validation. In Phase 1, assay parameters were established using reference samples (positive and negative) to refine performance metrics. Phase 2 validated the method\\u0026rsquo;s sensitivity using clinical samples pre-characterized by gold-standard techniques (e.g., Sanger sequencing, MLPA). Phase 3 prospectively enrolled couples planning pregnancy or in early gestation (\\u0026le;24+6 weeks). Positive screening results were confirmed via gold-standard methods. Clinical feasibility was assessed by analyzing carrier rates for pathogenic/likely pathogenic (P/LP) variants and the proportion of high-risk couples.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSample Collection\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study utilized two sample cohorts: a validation cohort and a prospective cohort. The validation cohort consisted of pre-characterized positive samples confirmed to harbor specific pathogenic variants by gold-standard methods, including Sanger sequencing and MLPA. The prospective cohort included couples recruited between July 2023 and January 2024. Inclusion criteria were phenotypically normal individuals planning pregnancy or in early pregnancy. Exclusion criteria included: (a) a prior history of bearing a child with any of the ten target diseases; and (b) recent allogeneic blood transfusion, transplantation, or cell therapy within the past 12 months. All participants provided written informed consent, and the study protocol was approved by the Zhengzhou University Ethics Committee (KS-2018-KY-36).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePanel Design\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe ten autosomal or X-linked recessive single-gene disorders (SGDs) selected for screening were based on seven criteria: (a) a carrier frequency of 1/100 or higher in the Chinese population; (b) high-frequency pathogenic variants identified in the Chinese or East/Southeast Asian populations; (c) well-defined phenotypic characteristics; (d) severe impact on quality of life; (e) association with cognitive or physical disability; (f) requirement for medical or surgical intervention; and (g) necessity for delivery management adjustments to improve neonatal outcomes.\\u003c/p\\u003e\\n\\u003cp\\u003eThe final panel included the following diseases and associated genes: Alpha-thalassemia (\\u003cem\\u003eHBA1\\u003c/em\\u003e, \\u003cem\\u003eHBA2\\u003c/em\\u003e), Beta-thalassemia (\\u003cem\\u003eHBB\\u003c/em\\u003e), Non-syndromic hearing loss (\\u003cem\\u003eGJB2\\u003c/em\\u003e, \\u003cem\\u003eSLC26A4\\u003c/em\\u003e), Spinal muscular atrophy (\\u003cem\\u003eSMN1\\u003c/em\\u003e, \\u003cem\\u003eSMN2\\u003c/em\\u003e), Duchenne muscular dystrophy (\\u003cem\\u003eDMD\\u003c/em\\u003e), Phenylketonuria (\\u003cem\\u003ePAH\\u003c/em\\u003e), 21-hydroxylase deficiency (\\u003cem\\u003eCYP21A2\\u003c/em\\u003e), Wilson disease (\\u003cem\\u003eATP7B\\u003c/em\\u003e), Methylmalonic acidemia (\\u003cem\\u003eMMACHC\\u003c/em\\u003e, \\u003cem\\u003eMMUT\\u003c/em\\u003e), and Hemophilia A (\\u003cem\\u003eF8\\u003c/em\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003eA 1,621-plex multiplex PCR assay was designed to amplify target regions, including the full protein-coding sequences and \\u0026plusmn;10 bp splice regions of the 14 target genes, locus-specific regions upstream of \\u003cem\\u003eHBA1\\u003c/em\\u003e and \\u003cem\\u003eHBA2\\u003c/em\\u003e, differentiation sites between \\u003cem\\u003eSMN1\\u003c/em\\u003e and \\u003cem\\u003eSMN2\\u003c/em\\u003e, additional intronic regions of \\u003cem\\u003eDMD\\u003c/em\\u003e, the \\u003cem\\u003eF8\\u003c/em\\u003e INV22 sequence, and intronic pathogenic variants with at least two stars in the ClinVar database (release 20230115)[29]. Amplicon lengths ranged from 81 to 298 bp, with an average length of 182 bp.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eThe MLDP-AS Workflow\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe MLDP-AS workflow (Figure 1) involved three key steps. First, genomic DNA (gDNA) was split into two aliquots. One aliquot underwent multiplex long-distance PCR (LD-PCR) to enrich \\u003cem\\u003eCYP21A2\\u003c/em\\u003e and \\u003cem\\u003eF8\\u003c/em\\u003e INV22 regions. Second, LD-PCR products were mixed with the second gDNA aliquot at predefined ratios, optimized through calibration experiments. The combined template was amplified in five parallel multiplex PCR reactions using a tiling amplicon design. Third, pooled PCR products were purified, ligated with barcoded adapters, quantified, and sequenced on an Ion Proton\\u0026trade; sequencer (Thermo Fisher Scientific, USA). Variant calling and annotation were performed using a custom bioinformatics pipeline (Figure 2; Supplementary Methods).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eValidation of Pathogenic Variations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll pathogenic variants identified by MLDP-AS were confirmed using orthogonal methods. Small variants were validated by Sanger sequencing, while exonic or gene-level copy number variations (CNVs) were validated using MLPA or quantitative PCR (qPCR).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Result\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eOverview\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe experimental results align with the three phases of the study design. In the first phase, we established an optimal mixing ratio of 10:1 for LD-PCR products to genomic DNA using positive reference samples. The second phase involved evaluating sensitivity with a set of 255 positive samples containing various pathogenic variants, demonstrating 100% concordance with prior molecular diagnostic results. In the third phase, we prospectively screened 5,209 individuals, achieving an overall carrier detection rate of 22.15% across the 14 genes tested. A total of 34 high-risk couples were identified, involving 8 of the 10 diseases under investigation. The detected variants showed 99.7% concordance with gold-standard validation results.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eOptimization of the Mixing Ratio of Pre-Amplified Products and gDNA\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo detect complex variants such as CYP21A2 and F8 inversions, the MLDP-AS method employs LD-PCR to pre-amplify target regions before mixing with gDNA for amplicon sequencing. The ratio of pre-amplified products to gDNA is crucial: a high ratio can reduce coverage of other gene regions, while a low ratio may compromise the enrichment of the pre-amplified targets. To optimize this ratio, we evaluated five positive reference samples\\u0026mdash;representing \\u003cem\\u003eSMN1\\u003c/em\\u003e deletion, \\u003cem\\u003eCYP21A2\\u003c/em\\u003e large deletion, \\u003cem\\u003eF8\\u003c/em\\u003e INV22, \\u003cem\\u003eHBA1/2\\u003c/em\\u003e deletions, and \\u003cem\\u003eDMD\\u003c/em\\u003e exon deletions\\u0026mdash;and one negative sample across four mixing ratios: 0:1, 5:1, 10:1, and 20:1 (pre-amplified product:gDNA ), with five replicates per condition. Using the segregation index, we assessed the differentiation between positive and negative samples under these varying conditions.\\u003c/p\\u003e\\n\\u003cp\\u003eAll samples included in the study achieved an average total read count of 1,681,107 \\u0026plusmn; 451,264. The mean coverage across the panel was 215 \\u0026plusmn; 43, with an average proportion of \\u0026ge;30X coverage reaching 99.9%. Gradient experiments using Pre-PCR product-to-gDNA mixing ratios of 0:1, 5:1, 10:1, and 20:1 yielded average pre-amplification ratios of 0.28, 4.6, 9.9, and 21, with respective ranges of 0.02\\u0026ndash;0.45, 4.3\\u0026ndash;5.5, 9.0\\u0026ndash;11.3, and 16.3\\u0026ndash;23.6. The distribution of segregation index values for positive samples and negative controls under each mixing condition is presented in Figure 2. The mean segregation index values were 26.97, 34.85, 50.47, and 64.09, with ranges of 2\\u0026ndash;40, 16\\u0026ndash;55, 24\\u0026ndash;141, and 22\\u0026ndash;205, respectively. As the pre-amplification ratio increased, the segregation index values for \\u003cem\\u003eCYP21A2\\u003c/em\\u003e large deletions and \\u003cem\\u003eF8\\u003c/em\\u003e INV22 variants demonstrated enhanced discrimination. Conversely, the segregation index values for variants involving 1\\u0026ndash;2 exon deletions/duplications, \\u003cem\\u003eSMN1/2\\u003c/em\\u003e copy number variations, and \\u003cem\\u003eHBA1/2\\u003c/em\\u003e large deletions exhibited decreased discrimination. Among the evaluated conditions, the 10:1 Pre-PCR product-to-gDNA ratio produced the highest average segregation index values for all five variant types, offering the best relative discrimination. Based on these findings, the 10:1 mixing ratio was selected as the optimal condition for the final experimental setup.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEvaluation of Assay Sensitivity\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo evaluate the accuracy of the MLDP-AS method in detecting variants, its performance was compared to clinical gold-standard results using 255 carrier samples with known pathogenic variants (Table 1, Supplemental Tables 1 and 2). The average total read count across all samples was 1,957,549 \\u0026plusmn; 598,056, with a mean coverage of 230 \\u0026plusmn; 51 within the panel range and an average proportion of \\u0026ge;30X coverage reaching 99.9%.\\u003c/p\\u003e\\n\\u003cp\\u003eThe MLDP-AS method successfully identified all 255 known pathogenic variants, achieving a sensitivity of 100%. These variants included 151 small variants and 104 structural variations (Figure 3). Among the 255 samples, 15 (5.88%, 15/255) carried pathogenic variants in \\u003cem\\u003eCYP21A2\\u003c/em\\u003e, including 4 samples with large deletions or fusions. Additionally, 7 samples (2.75%, 7/255) carried pathogenic variants in \\u003cem\\u003eF8\\u003c/em\\u003e INV22, 43 samples exhibited exon 7 copy number deletions in \\u003cem\\u003eSMN1\\u003c/em\\u003e, 32 samples harbored exon deletions or duplications in \\u003cem\\u003eDMD\\u003c/em\\u003e, and 9 samples had large deletions associated with alpha-thalassemia. Beyond the accurate detection of these known pathogenic variants, MLDP-AS identified 24 additional pathogenic variants not previously detected (Table 1). Among these, 4 (16.67%, 4/24) were pathogenic variants in \\u003cem\\u003eCYP21A2\\u003c/em\\u003e, highlighting the enhanced capability of this method to detect clinically relevant variants.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eProspective Clinical Study\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA prospective clinical study was conducted involving 5,209 recruited samples, comprising 3,067 female and 2,142 male participants. The MLDP-AS assay identified a total of 1,290 pathogenic variants, including 1,160 small variants and 130 structural variants (SVs), as detailed in Table 2. The average total read count across all samples was 1,745,287 \\u0026plusmn; 483,216. Within the panel range, the average coverage was 219 \\u0026plusmn; 44, with 99.9% of bases covered at \\u0026ge;30X. Gold-standard validation confirmed 1,290 true positives out of 1,294 detections, yielding an overall positive predictive value (PPV) of 99.7%. The PPV for SNVs or small indels was 99.9% (1,160/1,161), and for CNVs it was 98.0% (130/133).\\u003c/p\\u003e\\n\\u003cp\\u003eAmong the detected pathogenic variants, 166 (12.8%) were in \\u003cem\\u003eCYP21A2\\u003c/em\\u003e, and 1 (0.08%) was an \\u003cem\\u003eF8\\u003c/em\\u003e INV22 in a male sample. The combined carrier frequency for the 14 genes screened was 22.15% (1,154/5,209). Of these carriers, 88.6% (1,023/1,154) carried a single pathogenic variant, while 11.4% (131/1,154) carried variants associated with two or more diseases. The highest carrier rates were observed for \\u003cem\\u003eMMACHC\\u003c/em\\u003e (3.78%, 197/5,209), \\u003cem\\u003eATP7B\\u003c/em\\u003e (3.42%, 178/5,209), \\u003cem\\u003eCYP21A2\\u003c/em\\u003e (3.21%, 167/5,209), and \\u003cem\\u003eGJB2\\u003c/em\\u003e (3.15%, 164/5,209). The carrier frequency for \\u003cem\\u003eCYP21A2\\u003c/em\\u003e was 3.21%, ranking the third among all genes, while that for \\u003cem\\u003eF8\\u003c/em\\u003e was 0.12% (6/5,209). Additionally, 0.19% (10/5,209) of participants carried pathogenic variants in \\u003cem\\u003eDMD\\u003c/em\\u003e, including one case of a single exon deletion.\\u003c/p\\u003e\\n\\u003cp\\u003eThirty-four couples (1.09%) were identified as high-risk, with eight-in-ten of the diseases having high-risk couples identified. Methylmalonic acidemia ranked first with 12 high-risk families (35.29%), followed by Duchenne muscular dystrophy with 8 families (23.53%). Among the high-risk couples, 22 (64.7%) were at risk for autosomal recessive diseases, and 12 couples (35.3%) were at risk for X-linked diseases. Notably, one family was classified as high-risk for \\u003cem\\u003eCYP21A2\\u003c/em\\u003e, a finding often missed by conventional NGS-based carrier screening.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn this study, we developed and validated MLDP-AS, an optimized NGS assay designed for comprehensive carrier screening of prevalent and severe single-gene disorders in the Chinese population. By integrating the detection of multiple technically challenging variants\\u0026mdash;including \\u003cem\\u003eCYP21A2\\u003c/em\\u003e variations, \\u003cem\\u003eF8\\u003c/em\\u003e inversions, and single-exon CNVs\\u0026mdash;into a unified workflow, MLDP-AS demonstrated exceptional sensitivity (100%) in identifying 255 pre-characterized pathogenic variants. Prospective screening of 5,209 individuals revealed a 22.15% carrier rate and identified 34 high-risk couples (1.09%) across 8 of the 10 target diseases, with 99.7% concordance to gold-standard methods. Notably, 3.24% (169/5,209) of participants carried variants undetectable by conventional NGS panels, translating to one missed carrier in every 31 individuals. Incorporating these challenging variants increased the positive detection rate by 14.34% and identified two additional high-risk couples (5.88% of total), underscoring MLDP-AS\\u0026rsquo;s ability to reduce residual risk and enhance screening reliability.\\u003c/p\\u003e\\u003cp\\u003eApproximately 95% of congenital adrenal hyperplasia (CAH) cases are caused by pathogenic variants in \\u003cem\\u003eCYP21A2\\u003c/em\\u003e, leading to 21-hydroxylase deficiency[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Detection of \\u003cem\\u003eCYP21A2\\u003c/em\\u003e variants is technically challenging due to pseudogene interference (\\u003cem\\u003eCYP21A1P\\u003c/em\\u003e) and the presence of complex variant types such as large deletions and gene fusions[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. Notably, ~\\u0026thinsp;25% of pathogenic mutations in CAH patients are attributed to these large deletions or fusions[\\u003cspan additionalcitationids=\\\"CR33\\\" citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. Such technical limitations have historically resulted in the exclusion of \\u003cem\\u003eCYP21A2\\u003c/em\\u003e from carrier screening panels or underreporting of its carrier frequency (e.g., 2.09%[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e] and 1.95%[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e] in prior studies). In contrast, our study identified a \\u003cem\\u003eCYP21A2\\u003c/em\\u003e carrier frequency of 3.18%, ranking it third among all tested genes. Importantly, one high-risk couple carrying pathogenic \\u003cem\\u003eCYP21A2\\u003c/em\\u003e variants was identified, emphasizing the clinical consequence of omitting this gene from screening panels[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. To address pseudogene interference, Wang et al.[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e] also employed LD-PCR to amplify \\u003cem\\u003eCYP21A2\\u003c/em\\u003e prior to NGS analysis. While this method effectively reduced pseudogene-derived artifacts, it required separate sequencing workflows for the LD-PCR product and lacked the ability to detect \\u003cem\\u003eCYP21A2\\u003c/em\\u003e variants concurrently with other panel genes. In contrast, MLDP-AS integrates LD-PCR into a unified workflow, enabling simultaneous detection of \\u003cem\\u003eCYP21A2\\u003c/em\\u003e variants (including large deletions, fusions, and CNVs) alongside other target genes without additional processing steps. This integration eliminates the risk of overlooking \\u003cem\\u003eCYP21A2\\u003c/em\\u003e-related risks while maintaining assay scalability.\\u003c/p\\u003e\\u003cp\\u003eHemophilia A, a hereditary bleeding disorder primarily caused by mutations in the \\u003cem\\u003eF8\\u003c/em\\u003e gene, poses significant diagnostic challenges due to large inversions such as the \\u003cem\\u003eF8\\u003c/em\\u003e intron 22 inversion (INV22). Nearly all patients with \\u003cem\\u003eF8\\u003c/em\\u003e INV22 exhibit severe hemophilia[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e], with detection rates of this inversion ranging from 40\\u0026ndash;50% in affected populations[\\u003cspan additionalcitationids=\\\"CR39\\\" citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. Conventional carrier screening panels often exclude \\u003cem\\u003eF8\\u003c/em\\u003e INV22 detection or rely on supplementary techniques such as capillary electrophoresis. In our cohort, 0.12% of screened individuals (6/5,209) carried pathogenic \\u003cem\\u003eF8\\u003c/em\\u003e variants, including one male with the \\u003cem\\u003eF8\\u003c/em\\u003e INV22 inversion. This finding underscores the importance of comprehensive NGS-based screening to identify \\u003cem\\u003eF8\\u003c/em\\u003e variants that may otherwise be missed[\\u003cspan additionalcitationids=\\\"CR42\\\" citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. Johnsen et al.[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e] developed an NGS-based method for detecting \\u003cem\\u003eF8\\u003c/em\\u003e INV22 and other pathogenic variants in hemophilia A and B, using Ksp22I enzyme digestion followed by molecular inversion probe capture. While effective, this approach fails to address pseudogene interference like \\u003cem\\u003eCYP21A1P\\u003c/em\\u003e. MLDP-AS overcomes these limitations through multiplex LD-PCR, which simultaneously enriches \\u003cem\\u003eF8\\u003c/em\\u003e INV22 and \\u003cem\\u003eCYP21A2\\u003c/em\\u003e targets. By converting F8 INV22 detection into a quantitative analysis of intron 22 copy number and standardizing the mixing ratio of LD-PCR products to gDNA (10:1), MLDP-AS enables the concurrent detection of \\u003cem\\u003eF8\\u003c/em\\u003e INV22, \\u003cem\\u003eCYP21A2\\u003c/em\\u003e deletions/fusions, and other variants within a single library preparation and sequencing workflow. This integrated approach eliminates the need for separate assays or additional enzymatic steps, streamlining clinical implementation.\\u003c/p\\u003e\\u003cp\\u003eCost and turnaround time are important considerations in carrier screening[\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. MLDP-AS offers significant cost savings, with an experimental cost of \\u003cspan\\u003e$\\u003c/span\\u003e25 per sample and a testing price of less than \\u003cspan\\u003e$\\u003c/span\\u003e150. This affordability makes MLDP-AS a more accessible option for carrier screening compared to conventional NGS-based panels, which cost \\u003cspan\\u003e$\\u003c/span\\u003e300\\u0026ndash;400 per test in China, with an additional ~\\u003cspan\\u003e$\\u003c/span\\u003e20 for \\u003cem\\u003eF8\\u003c/em\\u003e INV22 detection. Moreover, MLDP-AS boasts a faster turnaround time, with the entire experimental process completed within three days and reports available within one week for samples collected and processed in the same laboratory, compared to the usual 2\\u0026ndash;3 weeks turnaround time. This made MLDP-AS adapt to prenatal carrier screening in early gestation.\\u003c/p\\u003e\\u003cp\\u003eA notable limitation is the low detection of \\u003cem\\u003eF8\\u003c/em\\u003e INV22 carriers (n\\u0026thinsp;=\\u0026thinsp;1) in our cohort, necessitating validation in larger populations. Additionally, MLDP-AS cannot currently identify \\u0026ldquo;2\\u0026thinsp;+\\u0026thinsp;0\\u0026rdquo; SMA carriers or certain deep intronic variants due to technical constraints in \\u003cem\\u003eSMN1/SMN2\\u003c/em\\u003e haplotype resolution. Future optimizations should target these gaps, potentially leveraging population-specific haplotype markers or advanced long-read sequencing.\\u003c/p\\u003e\\u003cp\\u003eIn conclusion, MLDP-AS represents a significant advancement in carrier screening, enabling simultaneous detection of SNVs, CNVs, and technically challenging variants (e.g., \\u003cem\\u003eCYP21A2\\u003c/em\\u003e deletions, \\u003cem\\u003eF8\\u003c/em\\u003e INV22) within a single assay. Its high sensitivity, cost-efficiency, and rapid workflow position it as a clinically feasible solution for reducing residual risk and improving genetic screening outcomes in the Chinese population.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eSGDs, single-gene disorders; SNVs, single nucleotide variations; NGS, Next-generation sequencing; INV22, intron 22 inversion; CNVs, copy number variations; MLDP-AS, multiplex long-distance PCR followed by amplicon sequencing; gDNA, genomic DNA; LD-PCR, long-distance PCR; TVC, Torrent Variant Caller; MLPA, Multiplex Ligation-dependent Probe Amplification; qPCR, quantitative PCR.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHuman Genes:\\u003c/strong\\u003e\\u003cem\\u003e\\u0026nbsp;HBA1\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003ehemoglobin subunit alpha 1; \\u003cem\\u003eHBA2\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003ehemoglobin subunit alpha 2; \\u003cem\\u003eHBB\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003ehemoglobin subunit beta; \\u003cem\\u003eGJB2\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003egap junction protein beta 2; \\u003cem\\u003eSLC26A4\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003esolute carrier family 26 member 4; \\u003cem\\u003eSMN1\\u003c/em\\u003e, survival of motor neuron 1 (telomeric); \\u003cem\\u003eSMN2\\u003c/em\\u003e, survival of motor neuron 2 (centromeric); \\u003cem\\u003eDMD\\u003c/em\\u003e, dystrophin; \\u003cem\\u003ePAH,\\u0026nbsp;\\u003c/em\\u003ephenylalanine hydroxylase; \\u003cem\\u003eCYP21A2\\u003c/em\\u003e, cytochrome P450 family 21 subfamily A member 2; \\u003cem\\u003eCYP21A1P\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003ecytochrome P450 family 21 subfamily A member 1 (pseudogene); \\u003cem\\u003eATP7B\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003eATPase copper transporting beta; \\u003cem\\u003eMMACHC\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003emetabolism of cobalamin associated C; \\u003cem\\u003eMMUT\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003emethylmalonyl-CoA mutase; \\u003cem\\u003eF8\\u003c/em\\u003e,\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003ecoagulation factor VIII.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe are grateful to all the team members for their contributions to data collection and integrity.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eKey Scientific Research Projects in Colleges and Universities of Henan Province (22A320075),Science and Technology Huimin Project of Zhengzhou (2021KJHM0003), Henan Province Medical Science and Technique Foundation\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;(SBGJ202102097) and the Science and Technology Research Program of Henan Province (222102520018), Science and Technology\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eResearch Program of Henan Province (Grant Number 242102311087).\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAuthor S.J. Li, C. Yuan, J. Feng, W.Q. Tang, D. Wu are employed by Celula (China) Medical Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and material\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets during and/or analysed during the current study available from the corresponding author on reasonable request.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe declare that his manuscript is original , has not been published before and is not currently being considered for publication elsewhere .We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed . We further confirm that the order of authors listed in the manuscript has been approved by all of us .We understand that the Corresponding Author is the sole contact for the Editorial process . She is responsible for communicating with the other authors about progress , submissions of revisions and final approval of proofs .\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors as follows :\\u003c/p\\u003e\\n\\u003cp\\u003eZhenhua Zhao \\u0026amp; Ganye Zhao: Conceptualization, Methodology, Investigation, Data Curation, Writing \\u0026ndash; Original Draft.\\u003c/p\\u003e\\n\\u003cp\\u003eShaojun Li, Chao Yuan, \\u0026amp; Jun Feng: Formal Analysis, Validation, Software, Visualization.\\u003c/p\\u003e\\n\\u003cp\\u003eWeiqin Tang \\u0026amp; Xinyu Fu: Resources, Project Administration, Supervision.\\u003c/p\\u003e\\n\\u003cp\\u003eHuanyun Li, Jingqi Zhu, \\u0026amp; Xueyang Zhao: Investigation, Experimental Execution, Data Collection.\\u003c/p\\u003e\\n\\u003cp\\u003eDi Wu: Funding Acquisition, Writing \\u0026ndash; Review \\u0026amp; Editing, Supervision.\\u003c/p\\u003e\\n\\u003cp\\u003eXiangdong Kong: Conceptualization, Funding Acquisition, Supervision, Writing \\u0026ndash; Review \\u0026amp; Editing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eBeauchamp KA, Muzzey D, Wong KK, Hogan GJ, Karimi K, Candille SI, Mehta N, Mar-Heyming R, Kaseniit KE, Kang HP, et al: \\u003cstrong\\u003eSystematic design and comparison of expanded carrier screening panels.\\u003c/strong\\u003e \\u003cem\\u003eGenet Med \\u003c/em\\u003e2018, \\u003cstrong\\u003e20:\\u003c/strong\\u003e55-63.\\u003c/li\\u003e\\n\\u003cli\\u003eGregg AR, Aarabi M, Klugman S, Leach NT, Bashford MT, Goldwaser T, Chen E, Sparks TN, Reddi HV, Rajkovic A, et al: \\u003cstrong\\u003eScreening for autosomal recessive and X-linked conditions during pregnancy and preconception: a practice resource of the American College of Medical Genetics and Genomics (ACMG).\\u003c/strong\\u003e \\u003cem\\u003eGenet Med \\u003c/em\\u003e2021, \\u003cstrong\\u003e23:\\u003c/strong\\u003e1793-1806.\\u003c/li\\u003e\\n\\u003cli\\u003eMurray MF, Giovanni MA, Doyle DL, Harrison SM, Lyon E, Manickam K, Monaghan KG, Rasmussen SA, Scheuner MT, Palomaki GE, et al: \\u003cstrong\\u003eDNA-based screening and population health: a points to consider statement for programs and sponsoring organizations from the American College of Medical Genetics and Genomics (ACMG).\\u003c/strong\\u003e \\u003cem\\u003eGenet Med \\u003c/em\\u003e2021, \\u003cstrong\\u003e23:\\u003c/strong\\u003e989-995.\\u003c/li\\u003e\\n\\u003cli\\u003eChokoshvili D, Vears DF, Borry P: \\u003cstrong\\u003eGrowing complexity of (expanded) carrier screening: Direct-to-consumer, physician-mediated, and clinic-based offers.\\u003c/strong\\u003e \\u003cem\\u003eBest Pract Res Clin Obstet Gynaecol \\u003c/em\\u003e2017, \\u003cstrong\\u003e44:\\u003c/strong\\u003e57-67.\\u003c/li\\u003e\\n\\u003cli\\u003eWienke S, Brown K, Farmer M, Strange C: \\u003cstrong\\u003eExpanded carrier screening panels-does bigger mean better?\\u003c/strong\\u003e \\u003cem\\u003eJ Community Genet \\u003c/em\\u003e2014, \\u003cstrong\\u003e5:\\u003c/strong\\u003e191-198.\\u003c/li\\u003e\\n\\u003cli\\u003eKihlbom U: \\u003cstrong\\u003eEthical issues in preconception genetic carrier screening.\\u003c/strong\\u003e \\u003cem\\u003eUps J Med Sci \\u003c/em\\u003e2016, \\u003cstrong\\u003e121:\\u003c/strong\\u003e295-298.\\u003c/li\\u003e\\n\\u003cli\\u003eHou W, Fu X, Xie X, Zhang C, Bian J, Mao X, Wen J, Luo C, Jin H, Zhu Q, et al: \\u003cstrong\\u003e[Carrier screening for 223 monogenic diseases in Chinese population: a multi-center study in 33 104 individuals].\\u003c/strong\\u003e \\u003cem\\u003eNan Fang Yi Ke Da Xue Xue Bao \\u003c/em\\u003e2024, \\u003cstrong\\u003e44:\\u003c/strong\\u003e1015-1023.\\u003c/li\\u003e\\n\\u003cli\\u003eShi M, Liauw AL, Tong S, Zheng Y, Leung TY, Chong SC, Cao Y, Lau TK, Choy KW, Chung JPW: \\u003cstrong\\u003eClinical Implementation of Expanded Carrier Screening in Pregnant Women at Early Gestational Weeks: A Chinese Cohort Study.\\u003c/strong\\u003e \\u003cem\\u003eGenes (Basel) \\u003c/em\\u003e2021, \\u003cstrong\\u003e12\\u003c/strong\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003ePrior TW: \\u003cstrong\\u003eNext-generation carrier screening: are we ready?\\u003c/strong\\u003e \\u003cem\\u003eGenome Med \\u003c/em\\u003e2014, \\u003cstrong\\u003e6:\\u003c/strong\\u003e62.\\u003c/li\\u003e\\n\\u003cli\\u003eLi H, Zhu X, Yang Y, Wang W, Mao A, Li J, Bao S, Li J: \\u003cstrong\\u003eLong-read sequencing: An effective method for genetic analysis of CYP21A2 variation in congenital adrenal hyperplasia.\\u003c/strong\\u003e \\u003cem\\u003eClin Chim Acta \\u003c/em\\u003e2023, \\u003cstrong\\u003e547:\\u003c/strong\\u003e117419.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu Y, Li D, Yu D, Liang Q, Chen G, Li F, Gao L, Li Z, Xie T, Wu L, et al: \\u003cstrong\\u003eComprehensive analysis of hemophilia A (CAHEA): towards full characterization of the F8 gene variants by long-read sequencing.\\u003c/strong\\u003e \\u003cem\\u003eThromb Haemost \\u003c/em\\u003e2023.\\u003c/li\\u003e\\n\\u003cli\\u003eLao Q, Zhou K, Parker M, Faucz FR, Merke DP: \\u003cstrong\\u003ePseudogene TNXA Variants May Interfere with the Genetic Testing of CAH-X.\\u003c/strong\\u003e \\u003cem\\u003eGenes (Basel) \\u003c/em\\u003e2023, \\u003cstrong\\u003e14\\u003c/strong\\u003e.\\u003c/li\\u003e\\n\\u003cli\\u003eAbou Tayoun AN, Krock B, Spinner NB: \\u003cstrong\\u003eSequencing-based diagnostics for pediatric genetic diseases: progress and potential.\\u003c/strong\\u003e \\u003cem\\u003eExpert Rev Mol Diagn \\u003c/em\\u003e2016, \\u003cstrong\\u003e16:\\u003c/strong\\u003e987-999.\\u003c/li\\u003e\\n\\u003cli\\u003eTruty R, Paul J, Kennemer M, Lincoln SE, Olivares E, Nussbaum RL, Aradhya S: \\u003cstrong\\u003ePrevalence and properties of intragenic copy-number variation in Mendelian disease genes.\\u003c/strong\\u003e \\u003cem\\u003eGenet Med \\u003c/em\\u003e2019, \\u003cstrong\\u003e21:\\u003c/strong\\u003e114-123.\\u003c/li\\u003e\\n\\u003cli\\u003eLi W, Freudenberg J: \\u003cstrong\\u003eMappability and read length.\\u003c/strong\\u003e \\u003cem\\u003eFront Genet \\u003c/em\\u003e2014, \\u003cstrong\\u003e5:\\u003c/strong\\u003e381.\\u003c/li\\u003e\\n\\u003cli\\u003eMandelker D, Schmidt RJ, Ankala A, McDonald Gibson K, Bowser M, Sharma H, Duffy E, Hegde M, Santani A, Lebo M, Funke B: \\u003cstrong\\u003eNavigating highly homologous genes in a molecular diagnostic setting: a resource for clinical next-generation sequencing.\\u003c/strong\\u003e \\u003cem\\u003eGenet Med \\u003c/em\\u003e2016, \\u003cstrong\\u003e18:\\u003c/strong\\u003e1282-1289.\\u003c/li\\u003e\\n\\u003cli\\u003eKraft SA, Duenas D, Wilfond BS, Goddard KAB: \\u003cstrong\\u003eThe evolving landscape of expanded carrier screening: challenges and opportunities.\\u003c/strong\\u003e \\u003cem\\u003eGenet Med \\u003c/em\\u003e2019, \\u003cstrong\\u003e21:\\u003c/strong\\u003e790-797.\\u003c/li\\u003e\\n\\u003cli\\u003eLiu Y, Chen M, Liu J, Mao A, Teng Y, Yan H, Zhu H, Li Z, Liang D, Wu L: \\u003cstrong\\u003eComprehensive Analysis of Congenital Adrenal Hyperplasia Using Long-Read Sequencing.\\u003c/strong\\u003e \\u003cem\\u003eClin Chem \\u003c/em\\u003e2022, \\u003cstrong\\u003e68:\\u003c/strong\\u003e927-939.\\u003c/li\\u003e\\n\\u003cli\\u003eChen X, Harting J, Farrow E, Thiffault I, Kasperaviciute D, Genomics England Research C, Hoischen A, Gilissen C, Pastinen T, Eberle MA: \\u003cstrong\\u003eComprehensive SMN1 and SMN2 profiling for spinal muscular atrophy analysis using long-read PacBio HiFi sequencing.\\u003c/strong\\u003e \\u003cem\\u003eAm J Hum Genet \\u003c/em\\u003e2023, \\u003cstrong\\u003e110:\\u003c/strong\\u003e240-250.\\u003c/li\\u003e\\n\\u003cli\\u003eLopez-Lopez D, Loucera C, Carmona R, Aquino V, Salgado J, Pasalodos S, Miranda M, Alonso A, Dopazo J: \\u003cstrong\\u003eSMN1 copy-number and sequence variant analysis from next-generation sequencing data.\\u003c/strong\\u003e \\u003cem\\u003eHum Mutat \\u003c/em\\u003e2020, \\u003cstrong\\u003e41:\\u003c/strong\\u003e2073-2077.\\u003c/li\\u003e\\n\\u003cli\\u003eKozareva V, Stroff C, Silver M, Freidin JF, Delaney NF: \\u003cstrong\\u003eClinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model.\\u003c/strong\\u003e \\u003cem\\u003eBMC Med Genomics \\u003c/em\\u003e2018, \\u003cstrong\\u003e11:\\u003c/strong\\u003e91.\\u003c/li\\u003e\\n\\u003cli\\u003eGuzel F, Romano M, Keles E, Piskin D, Ozen S, Poyrazoglu H, Kasapcopur O, Demirkaya E: \\u003cstrong\\u003eNext Generation Sequencing Based Multiplex Long-Range PCR for Routine Genotyping of Autoinflammatory Disorders.\\u003c/strong\\u003e \\u003cem\\u003eFront Immunol \\u003c/em\\u003e2021, \\u003cstrong\\u003e12:\\u003c/strong\\u003e666273.\\u003c/li\\u003e\\n\\u003cli\\u003eShum BOV, Henner I, Cairns A, Pretorius C, Wilgen U, Barahona P, Ungerer JPJ, Bennett G: \\u003cstrong\\u003eTechnical feasibility of newborn screening for spinal muscular atrophy by next-generation DNA sequencing.\\u003c/strong\\u003e \\u003cem\\u003eFront Genet \\u003c/em\\u003e2023, \\u003cstrong\\u003e14:\\u003c/strong\\u003e1095600.\\u003c/li\\u003e\\n\\u003cli\\u003eRen Y, Lian Y, Yan Z, Zhai F, Yang M, Zhu X, Wang Y, Nie Y, Guan S, Kuo Y, et al: \\u003cstrong\\u003eClinical application of an NGS-based method in the preimplantation genetic testing for Duchenne muscular dystrophy.\\u003c/strong\\u003e \\u003cem\\u003eJ Assist Reprod Genet \\u003c/em\\u003e2021, \\u003cstrong\\u003e38:\\u003c/strong\\u003e1979-1986.\\u003c/li\\u003e\\n\\u003cli\\u003eRojahn S, Hambuch T, Adrian J, Gafni E, Gileta A, Hatchell H, Johnson B, Kallman B, Karfilis K, Kautzer C, et al: \\u003cstrong\\u003eScalable detection of technically challenging variants through modified next-generation sequencing.\\u003c/strong\\u003e \\u003cem\\u003eMol Genet Genomic Med \\u003c/em\\u003e2022\\u003cstrong\\u003e:\\u003c/strong\\u003ee2072.\\u003c/li\\u003e\\n\\u003cli\\u003eWang W, Han R, Yang Z, Zheng S, Li H, Wan Z, Qi Y, Sun S, Ye L, Ning G: \\u003cstrong\\u003eTargeted gene panel sequencing for molecular diagnosis of congenital adrenal hyperplasia.\\u003c/strong\\u003e \\u003cem\\u003eJ Steroid Biochem Mol Biol \\u003c/em\\u003e2021, \\u003cstrong\\u003e211:\\u003c/strong\\u003e105899.\\u003c/li\\u003e\\n\\u003cli\\u003eHassan S, Bahar R, Johan MF, Mohamed Hashim EK, Abdullah WZ, Esa E, Abdul Hamid FS, Zulkafli Z: \\u003cstrong\\u003eNext-Generation Sequencing (NGS) and Third-Generation Sequencing (TGS) for the Diagnosis of Thalassemia.\\u003c/strong\\u003e \\u003cem\\u003eDiagnostics (Basel) \\u003c/em\\u003e2023, \\u003cstrong\\u003e13:\\u003c/strong\\u003e373.\\u003c/li\\u003e\\n\\u003cli\\u003eJohnsen JM, Fletcher SN, Huston H, Roberge S, Martin BK, Kircher M, Josephson NC, Shendure J, Ruuska S, Koerper MA, et al: \\u003cstrong\\u003eNovel approach to genetic analysis and results in 3000 hemophilia patients enrolled in the My Life, Our Future initiative.\\u003c/strong\\u003e \\u003cem\\u003eBlood Adv \\u003c/em\\u003e2017, \\u003cstrong\\u003e1:\\u003c/strong\\u003e824-834.\\u003c/li\\u003e\\n\\u003cli\\u003eLandrum MJ, Chitipiralla S, Brown GR, Chen C, Gu B, Hart J, Hoffman D, Jang W, Kaur K, Liu C, et al: \\u003cstrong\\u003eClinVar: improvements to accessing data.\\u003c/strong\\u003e \\u003cem\\u003eNucleic Acids Res \\u003c/em\\u003e2020, \\u003cstrong\\u003e48:\\u003c/strong\\u003eD835-D844.\\u003c/li\\u003e\\n\\u003cli\\u003ePignatelli D, Carvalho BL, Palmeiro A, Barros A, Guerreiro SG, Macut D: \\u003cstrong\\u003eThe Complexities in Genotyping of Congenital Adrenal Hyperplasia: 21-Hydroxylase Deficiency.\\u003c/strong\\u003e \\u003cem\\u003eFront Endocrinol (Lausanne) \\u003c/em\\u003e2019, \\u003cstrong\\u003e10:\\u003c/strong\\u003e432.\\u003c/li\\u003e\\n\\u003cli\\u003eHuang Q, Wen J, Zhang H, Teng Y, Zhang W, Zhu H, Liang D, Wu L, Li Z: \\u003cstrong\\u003eComprehensive analysis of NGS-based expanded carrier screening and follow-up in southern and southwestern China: results from 3024 Chinese individuals.\\u003c/strong\\u003e \\u003cem\\u003eHum Genomics \\u003c/em\\u003e2024, \\u003cstrong\\u003e18:\\u003c/strong\\u003e111.\\u003c/li\\u003e\\n\\u003cli\\u003eXia Y, Shi P, Gao S, Liu N, Zhang H, Kong X: \\u003cstrong\\u003eGenetic analysis and novel variation identification in Chinese patients with congenital adrenal hyperplasia due to 21-hydroxylase deficiency.\\u003c/strong\\u003e \\u003cem\\u003eJ Steroid Biochem Mol Biol \\u003c/em\\u003e2022, \\u003cstrong\\u003e222:\\u003c/strong\\u003e106156.\\u003c/li\\u003e\\n\\u003cli\\u003eXu J, Li P: \\u003cstrong\\u003eIdentification of novel and rare CYP21A2 variants in Chinese patients with congenital adrenal hyperplasia due to 21-hydroxylase deficiency.\\u003c/strong\\u003e \\u003cem\\u003eClin Biochem \\u003c/em\\u003e2019, \\u003cstrong\\u003e68:\\u003c/strong\\u003e44-49.\\u003c/li\\u003e\\n\\u003cli\\u003eLuo C, Jiang T, Zhang J, Li L, Sun Y, Liu G, Wang Y, Cheng J, Ma D, Xu Z: \\u003cstrong\\u003e[Genetic analysis and prenatal diagnosis for 25 Chinese pedigrees affected with congenital adrenal hyperplasia due to 21-hydroxylase deficiency].\\u003c/strong\\u003e \\u003cem\\u003eZhonghua Yi Xue Yi Chuan Xue Za Zhi \\u003c/em\\u003e2018, \\u003cstrong\\u003e35:\\u003c/strong\\u003e832-835.\\u003c/li\\u003e\\n\\u003cli\\u003eZhang X, Chen Q, Li J, Luo X, Luo J, Li J, Zeng Z, Wu Y, Zhang H, Dong Y: \\u003cstrong\\u003eThe effectiveness of expanded carrier screening based on next-generation sequencing for severe monogenic genetic diseases.\\u003c/strong\\u003e \\u003cem\\u003eHum Genomics \\u003c/em\\u003e2024, \\u003cstrong\\u003e18:\\u003c/strong\\u003e9.\\u003c/li\\u003e\\n\\u003cli\\u003eLuna-Zaizar H, Gonzalez-Alcazar JA, Evangelista-Castro N, Aguilar-Lopez LB, Ruiz-Quezada SL, Beltran-Miranda CP, Jaloma-Cruz AR: \\u003cstrong\\u003eF8 inversions of introns 22 and 1 confer a moderate risk of inhibitors in Mexican patients with severe hemophilia A. Concordance analysis and literature review.\\u003c/strong\\u003e \\u003cem\\u003eBlood Cells Mol Dis \\u003c/em\\u003e2018, \\u003cstrong\\u003e71:\\u003c/strong\\u003e45-52.\\u003c/li\\u003e\\n\\u003cli\\u003eLi F, He L, Chen G, Lu Y, Li R, Zhang Y, Jing X, Ling R, Li D, Liao C: \\u003cstrong\\u003eVariant spectrum of F8 and F9 in hemophilia patients from southern China and 26 novel variants.\\u003c/strong\\u003e \\u003cem\\u003eFront Genet \\u003c/em\\u003e2023, \\u003cstrong\\u003e14:\\u003c/strong\\u003e1254265.\\u003c/li\\u003e\\n\\u003cli\\u003eAntonarakis SE, Rossiter JP, Young M, Horst J, de Moerloose P, Sommer SS, Ketterling RP, Kazazian HH, Jr., Negrier C, Vinciguerra C, et al: \\u003cstrong\\u003eFactor VIII gene inversions in severe hemophilia A: results of an international consortium study.\\u003c/strong\\u003e \\u003cem\\u003eBlood \\u003c/em\\u003e1995, \\u003cstrong\\u003e86:\\u003c/strong\\u003e2206-2212.\\u003c/li\\u003e\\n\\u003cli\\u003eLakich D, Kazazian HH, Jr., Antonarakis SE, Gitschier J: \\u003cstrong\\u003eInversions disrupting the factor VIII gene are a common cause of severe haemophilia A.\\u003c/strong\\u003e \\u003cem\\u003eNat Genet \\u003c/em\\u003e1993, \\u003cstrong\\u003e5:\\u003c/strong\\u003e236-241.\\u003c/li\\u003e\\n\\u003cli\\u003eFeng Y, Li Q, Shi P, Liu N, Kong X, Guo R: \\u003cstrong\\u003eMutation analysis in the F8 gene in 485 families with haemophilia A and prenatal diagnosis in China.\\u003c/strong\\u003e \\u003cem\\u003eHaemophilia \\u003c/em\\u003e2021, \\u003cstrong\\u003e27:\\u003c/strong\\u003ee88-e92.\\u003c/li\\u003e\\n\\u003cli\\u003eZhang X, Chen K, Bian S, Wang G, Qin X, Zhang R, Yang L: \\u003cstrong\\u003eMolecular Diagnosis of Hemophilia A and Pathogenesis of Novel F8 Variants in Shanxi, China.\\u003c/strong\\u003e \\u003cem\\u003eGlob Med Genet \\u003c/em\\u003e2023, \\u003cstrong\\u003e10:\\u003c/strong\\u003e247-262.\\u003c/li\\u003e\\n\\u003cli\\u003eBai H, Xue X, Tian L, Liu XT, Li Q: \\u003cstrong\\u003eCase Report: Identification of a de novo Missense Mutation in the F8 Gene, p.(Phe690Leu)/c.2070C \\u0026gt; A, Causing Hemophilia A: A Case Report.\\u003c/strong\\u003e \\u003cem\\u003eFront Genet \\u003c/em\\u003e2020, \\u003cstrong\\u003e11:\\u003c/strong\\u003e589899.\\u003c/li\\u003e\\n\\u003cli\\u003eChen J, Li Q, Lin S, Li F, Huang L, Jin W, Yang X, Li Y, Li K, Xiong Y, et al: \\u003cstrong\\u003eThe spectrum of FVIII gene variants detected by next generation sequencing in 236 Chinese non-inversion hemophilia A pedigrees.\\u003c/strong\\u003e \\u003cem\\u003eThromb Res \\u003c/em\\u003e2021, \\u003cstrong\\u003e202:\\u003c/strong\\u003e8-13.\\u003c/li\\u003e\\n\\u003cli\\u003ePereira N, Wood M, Luong E, Briggs A, Galloway M, Maxwell RA, Lindheim SR: \\u003cstrong\\u003eExpanded genetic carrier screening in clinical practice: a current survey of patient impressions and attitudes.\\u003c/strong\\u003e \\u003cem\\u003eJ Assist Reprod Genet \\u003c/em\\u003e2019, \\u003cstrong\\u003e36:\\u003c/strong\\u003e709-716.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eTable 1 Pathogenic Variations Discovered by MLDP-AS\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"1002\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eDisease\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003eGene\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eEstimated\\u003cbr\\u003ePercent of SVs\\u003cem\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd colspan=\\\"5\\\" style=\\\"width: 378px;\\\"\\u003e\\n \\u003cp\\u003eNumber of Pathogenic Variations\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003eSensitivity\\u003c/p\\u003e\\n \\u003cp\\u003e(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003eSpecificity\\u003c/p\\u003e\\n \\u003cp\\u003e(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eAlready Known\\u003cem\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 180px;\\\"\\u003e\\n \\u003cp\\u003eNew Discovery\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003eSmall Variants\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eSVs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003eSmall Variants\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eSVs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eAlpha-thalassemia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eHBA1\\u003c/em\\u003e/\\u003cem\\u003eHBA2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;96\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e5/5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e9/9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eBeta-thalassemia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eHBB\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eRare\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e7/7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e1/1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eNon-syndromic hearing loss\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eGJB2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eRare\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e6/6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0/0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSLC26A4\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eRare\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e13/13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0/0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eSpinal muscular atrophy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSMN1\\u003c/em\\u003e/\\u003cem\\u003eSMN2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e95-98\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e3/3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e43/43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eDuchenne muscular dystrophy\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDMD\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e65-80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e14/14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e32/32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003ePhenylketonuria\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ePAH\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e1-3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e38/38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e3/3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003e21-hydroxylase deficiency\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCYP21A2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e20-30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e11/11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e4/4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eWilson disease\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eATP7B\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eRare\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e11/11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e2/2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eMethylmalonic acidemia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eMMACHC\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e2-4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e19/19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e2/2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eMMUT\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e~1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e16/16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e1/1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eHemophilia A\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eF8\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e~50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e8/8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e7/7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 198px;\\\"\\u003e\\n \\u003cp\\u003eTotal\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e151/151\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e104/104\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 19px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003col\\u003e\\n \\u003cli\\u003eSVs represent structural variations.\\u003c/li\\u003e\\n \\u003cli\\u003eThe denominator represents the number of known pathogenic variations, while the numerator represents those discovered by MLDP-AS.\\u0026nbsp;\\u003c/li\\u003e\\n\\u003c/ol\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2 Genes with carrier frequency and At-risk couples\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"84%\\\" class=\\\"fr-table-selection-hover\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003eGene\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 25px;\\\"\\u003e\\n \\u003cp\\u003eVariants\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 32px;\\\"\\u003e\\n \\u003cp\\u003eTotal carrier frequency\\u003cstrong\\u003e\\u003cem\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/em\\u003e\\u003c/strong\\u003e (n=5209)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\" valign=\\\"top\\\" style=\\\"width: 24px;\\\"\\u003e\\n \\u003cp\\u003eAt-risk couples\\u003cstrong\\u003e\\u003cem\\u003e\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/em\\u003e\\u003c/strong\\u003e (n=3117)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003eSmall Variants\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003eSVs\\u003cem\\u003e\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e%\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1 in _\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e%\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e1 in _\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eMMACHC\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e197\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e197\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e3.78\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e26.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e346.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eATP7B\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e177\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e178\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e3.42\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e29.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e3117.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCYP21A2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e166\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e167\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e3.21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e31.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e3117.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eGJB2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e164\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e164\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e3.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e31.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e3117.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ePAH\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e157\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e157\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e3.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e33.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e779.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSLC26A4\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e151\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e152\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e2.92\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e34.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e1039.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eMMUT\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e109\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e109\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e2.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e47.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e1039.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eSMN1\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e78\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e1.52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e65.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eHBA1/HBA2\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e42\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e0.81\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e124.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eHBB\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e0.54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e186.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDMD\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e/\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e0.19\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e520.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e389.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eF8\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e0.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e744.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e0.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e779.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003eTotal\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e1160\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e130\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 2px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 9px;\\\"\\u003e\\n \\u003cp\\u003e1154\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e22.15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 12px;\\\"\\u003e\\n \\u003cp\\u003e4.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 2px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e1.09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 8px;\\\"\\u003e\\n \\u003cp\\u003e77.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003col\\u003e\\n \\u003cli\\u003eSVs represent structural variations.\\u003c/li\\u003e\\n \\u003cli\\u003eOnly P/LP variants are considered.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"journal-of-translational-medicine\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"jtrm\",\"sideBox\":\"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/jtrm/default.aspx\",\"title\":\"Journal of Translational Medicine\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"single-gene disorders, next-generation sequencing, long-distance PCR, structural variations, expanded carrier screening\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6880217/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6880217/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e\\u003cp\\u003eNext-generation sequencing (NGS) facilitates simultaneous carrier screening for multiple single-gene disorders. However, conventional NGS methods struggle to detect complex variants, such as \\u003cem\\u003eF8\\u003c/em\\u003e inversions, \\u003cem\\u003eCYP21A2\\u003c/em\\u003e variations, and single-exon copy number variations (CNVs), resulting in residual risk.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eWe developed and validated MLDP-AS (Multiplex Long-Distance PCR followed by Amplicon Sequencing), a novel NGS assay, to screen for pathogenic variants in ten prevalent single-gene disorders in the Chinese population, including alpha- and beta-thalassemia, non-syndromic hearing loss, spinal muscular atrophy, Duchenne muscular dystrophy, phenylketonuria, 21-hydroxylase deficiency, Wilson disease, methylmalonic acidemia, and Hemophilia A. MLDP-AS detects routine variants and technically challenging types, such as \\u003cem\\u003eF8\\u003c/em\\u003e intron 22 inversions, \\u003cem\\u003eCYP21A2\\u003c/em\\u003e variations, and single-exon CNVs, in a single test. The assay was optimized using positive clinical samples, with sensitivity validated against gold-standard methods. Clinical applicability was evaluated through a prospective study of couples planning or undergoing pregnancy, with positive results confirmed by gold-standard techniques.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eMLDP-AS achieved 100% sensitivity, identifying all 255 pathogenic variants in known positive samples, including 37 technically challenging variants. In a prospective trial involving 5,209 individuals, carriers for all targeted disorders were detected, with an overall carrier rate of 22.15%. Thirty-four couples (1.09%) were identified as high-risk, spanning eight of the ten diseases. A total of 289 pathogenic variants were detected 1,290 times, including 169 technically challenging variants. The assay demonstrated a positive predictive value of 99.7% compared to gold-standard methods. MLDP-AS is rapid and cost-effective, completing testing within three days at a cost under \\u003cspan\\u003e$\\u003c/span\\u003e25.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e\\u003cp\\u003eMLDP-AS integrates detection of multiple complex variants into a single, comprehensive assay, overcoming limitations of conventional NGS. It significantly enhances variant detection and provides an efficient, cost-effective tool for carrier screening in the Chinese population.\\u003c/p\\u003e\",\"manuscriptTitle\":\"MLDP-AS: An Optimized Next-Generation Sequencing Assay for Enhanced Detection of Technically Challenging Variants in Expanded Carrier Screening\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-07-14 12:32:36\",\"doi\":\"10.21203/rs.3.rs-6880217/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2025-07-10T07:48:51+00:00\",\"index\":0,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-07-09T19:31:26+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-06-17T17:37:27+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Journal of Translational Medicine\",\"date\":\"2025-06-16T05:51:45+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"journal-of-translational-medicine\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"jtrm\",\"sideBox\":\"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/jtrm/default.aspx\",\"title\":\"Journal of Translational Medicine\",\"twitterHandle\":\"@BioMedCentral\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"6550ddf1-2a11-4ca7-ba7d-a618117c493d\",\"owner\":[],\"postedDate\":\"July 14th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-02-16T16:02:54+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-6880217\",\"link\":\"https://doi.org/10.1186/s12967-026-07735-9\",\"journal\":{\"identity\":\"journal-of-translational-medicine\",\"isVorOnly\":false,\"title\":\"Journal of Translational Medicine\"},\"publishedOn\":\"2026-02-14 15:57:16\",\"publishedOnDateReadable\":\"February 14th, 2026\"},\"versionCreatedAt\":\"2025-07-14 12:32:36\",\"video\":\"\",\"vorDoi\":\"10.1186/s12967-026-07735-9\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12967-026-07735-9\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6880217\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6880217\",\"identity\":\"rs-6880217\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}