Noninvasive prenatal diagnosis (NIPD) of non-syndromic hearing loss (NSHL) for singleton and twin pregnancies in the first trimester

Noninvasive prenatal diagnosis (NIPD) of non-syndromic hearing loss (NSHL) for singleton and twin pregnancies in the first trimester | 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 Noninvasive prenatal diagnosis (NIPD) of non-syndromic hearing loss (NSHL) for singleton and twin pregnancies in the first trimester Huanyun Li, Shaojun Li, Zhenhua Zhao, Lingrong Kong, Xinyu Fu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4008906/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2025 Read the published version in Orphanet Journal of Rare Diseases → Version 1 posted 5 You are reading this latest preprint version Abstract Background Noninvasive prenatal diagnosis (NIPD) has been proven available for non-syndromic hearing loss (NSHL) in singleton pregnancies. However, previous research is limited to the second trimester and the application in twin pregnancies is blank. Here we provide a novel algorithmic approach to assess singleton and twin pregnancies in the first trimester. Results All of the recruited participants, comprising sixteen women with singleton pregnancies and one woman with a twin pregnancy, had a proband with NSHL caused by GJB2 gene or SLC26A4 gene mutations. The twin pregnancy was a dichorionic diamniotic twin (DCDA). NIPD confirmed one fetus is affected, and another is a carrier with c.299_300delAT of GJB2 gene. Among the 16 singleton pregnancies, NIPD was successfully applied in 15 families and the coincidence rate with invasive prenatal diagnosis was 100% (15/15). Only one family NIPD result is no call because the imbalance distribution of SNP sites makes it difficult to estimate recombination events. Most (13/15) of pregnant women were in the first trimester and the earliest gestation week was the 7th week. Conclusion This study represents the pioneering evidence in the field, demonstrating the feasibility of NIPD for NSHL in twin pregnancies. Moreover, it provides a novel and advanced diagnostic approach for families at high risk of NSHL during pregnancy, offering earlier detection, enhanced safety, and improved accuracy. These findings significantly contribute to the scientific understanding and clinical management of hearing loss in multiple pregnancies. Noninvasive prenatal diagnosis Non-Syndromic Hearing Loss Haplotype construction Bayes factors Twin Figures Figure 1 Figure 2 Figure 3 Background Congenital hearing loss is one of the most frequent sensorineural disorders, affecting 1–2 in every 1,000 neonates( 1 ). The epidemiological data revealed that genetic causes account for up to 80% of congenital hearing loss, and most of them (70%) are regarded as nonsyndromic hearing loss (NSHL) without other medical anomalies( 2 , 3 ). Hereditary deafness is heterogeneous in both clinical and genetic aspects. More than 300 genetic loci linked to hereditary hearing loss and > 100 causative genes have also been identified( 4 ). In the Chinese population, the most frequent pathogenic NSHL mutations reside in the GJB2 gene and SLC26A4 gene( 5 , 6 ). For deaf patients, effective treatment is the hearing aids and cochlear implants. A delayed diagnosis can lead to life-long health issues that could be ameliorated with early intervention and treatment. For those couples known as pathogenic GJB2 gene or SLC26A4 gene mutation carriers, prenatal diagnosis is an essential way to assess the risk of fertility and guide rehabilitation treatment. As is known to all, invasive prenatal diagnosis is the gold standard. However, chorionic villus sampling (CVS) and amniocentesis have a risk of miscarriage or stillbirth (incidence: 0.1–0.3%)( 7 ). In 1997, the discovery of the cell-free fetal DNA (cffDNA) in maternal plasma laid a foundation for non-invasive prenatal diagnosis (NIPD)( 8 ). Until now, the existing NIPD approaches can be divided into two categories in the diagnosis of NHLS. The first one is relative haplotype dose (RHDO) analysis. Combining high-throughput sequencing of targeted regions and hidden Markov models, Duan et.al realized the first NIPD for a family with GJB2 gene mutation in 2014( 9 ). The second way is relative variant dose (RMD) analysis by circulating single-molecule amplification and resequencing technology (cSMART)( 10 )or digital PCR( 11 ). Both cSMART and digital PCR rely on the dosage changes of hotspot mutations between wild-type and mutant alleles to determine the fetal genotype. In that case, RMD requires a relatively higher fetal fraction (FF) and stricter experiment requirements. RHDO does not detect variants itself but infers the inheritance of parental haplotypes by counting the numerous specific SNP alleles dose changes around the pathogenic genes. In this context, RHDO is not limited by the type of mutation and has a wider range of applications. Under maternal DNA background noise, the detection of multiple alleles to infer genotypes is more accurate and feasible than directly detecting a single pathogenic variant, especially for recessive hereditary diseases like NSHL. The earliest detection of non-invasive prenatal testing based on fetal cells is at 8 weeks of gestation( 12 ). However, the rarity of fetal cells and the complexity of the experimental process limit its clinical application. The published reports based on cffDNA were limited to singleton pregnancies in the second trimester, the twin pregnancies and the first-trimester NIPD remain unknown in NSHL. With the increasing use of ovulation drugs and advancing maternal age, the frequency of twin pregnancies is increasing( 13 ). Miscarriage risk associated with twin pregnancies is greater when compared to singleton pregnancies, potentially increasing the risk for pregnancy loss if invasive prenatal diagnostic procedures are performed( 14 ). As such, to develop a comprehensive and accurate NIPD assay for determining fetal NSHL genotypes in at-risk pregnancies, we performed non-invasive twin detection in NSHL with the GJB2 gene for the first time based on our successful experience in DMD twin detection( 15 ). By target sequencing of the GJB2 gene and SLC26A4 gene of the trio blood sample, selecting informative SNP, and calculating Bayes factor (BF), the modified prototype assay successfully diagnosed the dichorionic diamniotic twin (DCDA) genotype. Additionally, this study has achieved earlier NIPD for pathogenic GJB2 gene and SLC26A4 gene carrier couples, which shows great potential and promise for clinical application. Materials and Methods Sample collection and detection workflow Seventeen families with pathogenic GJB2 gene or SLC26A4 gene mutation were enrolled from March 2021 to October 2023 after genetic counseling and a receipt of informed consent. Sixteen singletons and one twin pregnancy of DCDA were confirmed by ultrasound. For every family, peripheral blood was collected from the pregnant mother (10ml), father (2ml), and proband (2ml). The gDNA was extracted by Nucleic Acid Extraction or Purification Kit (NaHaiTM, China). The study was approved by the Ethics Committee of First Affiliated Hospital of Zhengzhou University. Library preparation and target sequencing The gDNA of the trio family was broken into fragments with an average length of ~ 200bp by the sonicator (Bioruptor Pico). Maternal plasma was isolated using a two-step centrifugation protocol (See details in Supplementary materials). Then the fragmented gDNA and cfDNA completed end-repaired and added A-tailing. After ligating the barcode, the PCR amplification was performed to enrich the library. A 750ng library was added to the designed probe panel, incubated at 80°C for 5 minutes on a PCR instrument, and maintained at 50°C for 18 hours for the hybridization reaction. Then target region was captured, and the captured library was further PCR amplified. The amplified libraries were quantified using Qubit3.0 (Invitrogen, Breda, Netherlands) and sequenced on the Ion Proton platform (Thermo Fisher Scientific, Lithuania). Targeted sequencing design A 324.614kb capture panel TargetSeq® One kit (iGeneTech, China) was designed to selectively enrich target regions based on the reference genome (GRCh37/hg19). The panel covered GJB2 and SLC26A4 genes in all exon regions (including untranslated regions), 500 bp intronic regions adjacent to exons, and 10,000 bp upstream or downstream of the target gene. In addition, there were 203 highly heterozygous SNPs (MAF > 0.45) located on 1–22 autosomes, respectively. (Fig. 2 ). Classification of SNPs and fetal fraction calculation Haplotype phasing was performed using trio family samples based on Mendel's law. The maternal pathogenic haplotype was defined as HM1 and the wild-type haplotype was defined as HM2. Similarly, paternal haplotypes were named HF1 and HF2. For singleton pregnancy, Informative SNP sites were those homozygous for one parent and heterozygous for another parent. The sType1 allele would show an imbalance if the fetus inherited HM1 and similarly the sType2 allele would change if the fetus inherited HM2. Paternal inheritance could be judged through sType3 and sType4 SNP sites. The fetal fraction (FF) was calculated via the parent's homozygous SNP but with different genotypes in maternal plasma (f) by the following equation: f = 2a ⁄ ((a + b)), where “a” is the read depth of the fetal inherited paternal allele and “b” is the read depth of the allele shared by the fetus and pregnant. For twin pregnancy, SNPs were classified into six categories, named tType 1 to tType 6 (Table 1 ). tType 1 SNPs were able to measure sequencing error and sample cross-contamination. tType 2–4 SNPs were used to calculate infer fetal paternal-inherited haplotype and fetal fraction (named ff1 for the lower fetal fraction, ff2 for the higher fetal fraction, and FFtotal for the total fetal fraction, respectively). tType 5 and 6 SNPs were dedicated to determining fetal-maternal inherited haplotypes. Table 1 The principle and function of informative SNP classification. Father Mother Proband Classification and function of informative SNPs SNP Type HF1 HF2 HM1 HM2 HF1 HM1 sType1 A A A a A A Father: homozygous Mother: heterozygous SNP allele imbalance: inherit HM1 a a a A a a sType2 A A a A A a Father: homozygous Mother: heterozygous. SNP allele imbalance: inherit HM2 a a A a a A sType3 A a a a A a Father: heterozygous Mother: homozygous New SNP allele: inherit HF1 a A A A a A sType4 A a A A A A Father: heterozygous Mother: homozygous New SNP allele: inherit HF2 a A a a a a tType1 A A A A A A Father: homozygous Mother: homozygous QC a a a a a a tType2 a A A A a A Father: heterozygous Mother: homozygous New SNP allele: inherit HF1; ff1 A a a a A a tType3 A a A A A A Father: heterozygous Mother: homozygous New SNP allele: inherit HF2; ff2 a A a a a a tType4 a a A A a A Father: homozygous Mother: homozygous FFtotal A A a a A a tType5 A A A a A A Father: homozygous Mother: heterozygous SNP allele imbalance: inherit HM1 a a a A a a tTpye6 a a A a a A Father: homozygous Mother: heterozygous. SNP imbalance: inherit HM2 A A a A A a Measurement of genotype The allele frequency of informative SNPs was used to calculate the dosage change of the pathogenic haplotype and the wild-type haplotype. Based on allele frequency imbalance, we estimated the probability of fetal inherited pathogenic or wild-type haplotypes using the BF as described previously( 16 ). For singleton pregnancy, if BF ≥ 10, the result indicates the fetus inherits HF1/HM1. If BF ≤ 0.1, it indicates the fetus inherits HF2/HM2. When BF fell between 0.1 to 10, the NIPD result was no call. For twin pregnancy, the maternal-inherited haplotype was deduced using RHDO through a two‐step Bayes factor, as we described before( 15 ). The first step was to determine if the twins inherited the same maternal haplotype. In the second step, the inherited maternal haplotype for each fetus was deduced based on the decision of the first step. Bayes factor was calculated at each step (BF1 for step 1 and BF2 for step 2) by dividing the likelihood of obtaining the observed difference of RHDO between type 5 and type 6 SNPs under two opposite hypotheses. Paternal inheritance was deduced by the dose change of tType2 and tType3. Besides, all the fetal haplotype speculations were tested by the CBS algorithm to exclude the influence of recombination events on the NIPD results. Invasive prenatal diagnosis The twin received a double separate amniocentesis and the other fifteen singleton pregnant women also received amniocentesis at 18 to 24 weeks. One pregnant woman had a cerclage of cervix procedure in the second trimester, which made it unsuitable for invasive prenatal diagnosis, so the neonate was sampled after birth. All amniotic fluid samples and neonatal samples were processed by Sanger sequence to verify the accuracy of the NIPD. Results 1. Trio family information A total of 17 pregnant women were recruited. 14 singleton pregnant women collected blood in the first trimester, from 7 to 12 + 5 weeks. Only two women had blood collected after 13 weeks. One woman sampled blood at 19 + 1 to test panel feasibility and another pregnant woman collected the blood sample at 28 weeks because she just received a cerclage of cervix procedure to prevent miscarriages, which is forbidden for amniocentesis. The twin pregnant woman had NIPD at 17 + 3 weeks. Among the 17 families, 7 families had a proband with GJB2 gene mutations, and 10 families with SLC26A4 gene mutations (see details in supplementary table). 2. NIPD results 2.1 Sequencing information The prepared gDNA and cfDNA of 17 families were sequenced by target region capture, and the average of total reads is 3,245,415 (957,564-7,114,056). The average sequencing depth of each sample range from 62x to 746x (average: 273x) and the ratio of more than 300x ranges from 25.36–87.56% (average: 50.90%). The singleton families’ informative SNP range from 8 to 196 for sType1and sType2, 8 to 178 for sType3 and sType4. The twin family SNP is 50, 46, 57, 70, 15, 86 from tType1 to tType6, respectively. 2.2 Sixteen singleton NIPD result Fifteen families were successfully diagnosed, and a total of 3 patients, 4 carriers, and 8 normal fetuses were diagnosed accurately. In the successful 15 families, two fetuses (P15, P17) had recombination events. Luckily, the breaking point of the recombination location estimated by the CBS algorithm was far from the mutation site, so the NIPD results were not affected (Fig. S1 ). There is only one failed family (P6), as the distribution of SNP sites was imbalanced, with most of them located downstream of the maternal pathogenic variant (Fig. S2 ). In this case, it was impossible to tell whether recombination had occurred and resulted in maternal haplotype with “no call”. 2.3 One Twin NIPD results For the twin pregnancy parents, the mother is a GJB2 gene carrier of the c.299_300delAT mutation, and the father is a GJB2 gene carrier of the c.235delC mutation. The designed panel would capture both the SLC26A4 gene and GJB2 gene region no matter the parent's gene mutation type. From the informative SNP scattered in the SLC26A4 gene panel, the twin’s zygosity could be deduced. tType2 and tType3 elevated in both SCL26A4 gene and GJB2 gene panel, which means the twin inherited HF1 and HF2, respectively. It also proved the twin was a fraternal twin and the fetal fraction of the two fetuses is similar, about 6%. Correspondently, we could see clearly that FFtotal is about 12% from tType4 SNPs. The dosage changes of the GJB2 gene tType5 and tType6 SNPs together with the two-step Bayes factor revealed the two fetuses inherit pathogenic haplotype (HM1) simultaneously from the mother. For this GJB2 mutation carrier twin family, one fetus is a maternal carrier and another is a GJB2 gene carrier of the c.299_300delAT mutation. 3. Validation of NIPD results All 16 pregnant women underwent invasive prenatal diagnosis. For the woman who had a cerclage of cervix procedure, the fetus underwent peripheral Sanger sequencing after birth. The results showed that the accuracy of all NIPD was 100% (16/16). For the twin, the double separate amniocentesis and Sanger sequencing results are also coordinated with NIPD (Fig S3). Discussion Appropriate prenatal diagnosis of hearing loss could give carrier couples more options for future family planning and probably the preparation for the health and educational needs of the affected neonates( 17 ). In our study, as research for NIPD of NSHL, the earliest testing week is 7 weeks. Among the sixteen singleton pregnant women, NIPD was successfully applied in 93.75% (15/16) of families and the coincidence rate with invasive prenatal diagnosis was 100% (15/15). Only one NIPD result is no call because the imbalance distribution of SNP sites makes it difficult to estimate recombination events. Most (13/15) of pregnant women were in the first trimester and the earliest gestation week was the 7th week. Besides, due to the wide application of reproductive technology, the probability of multiple pregnancies is increasing. The singleton NIPD algorithms may lead to inaccurate results in dizygotic twins since the fetal fraction of the affected fetus could be lower and result in a dosage change not as considerable as expected. In this case, we proposed a two-step Bayes factor with the first step to distinguish whether the twins inherit different haplotypes. The second step could indicate whether the pathogenic haplotype was inherited for every fetus. Furthermore, if the first step indicates the twin inherited an identical haplotype, the invasive procedure could just need one puncture operation, which reduces the risk of miscarriage. Whether singleton or twin pregnancy, genomic DNA target sequencing requires no complicated experimental procedure, such as the previously reported haplotype-assisted methods, and is cost-effective if the appropriate array is designed. Moreover, the turnaround time, including the sampling process and sequencing on the Ion Proton platform, can be as short as 1 week. Then the bioinformatics analysis can be accomplished within 1 day, which lends this type of procedure to large-scale clinical applications. However, there were several shortcomings, and the relevant solution was made to secure NIPD accuracy. First, the traditional proband-based haplotype needs a complete trio family to construct the parent haplotype. However, no proband is also available in our study design. Families with a previous reproductive history, whether normal patients or carriers, can be used to construct haplotypes( 9 ). Families with no reproductive history can also construct haplotypes through grandparents. Second, the NIPD results might be disturbed by recombination events. The CBS algorithm could predict the recombination event, which is used to estimate copy number variation (CNV) data and identify the reasonable breakpoint( 18 ). Then the researchers determined whether the recombinational break point affected the identification of pathogenic variants. Third, for consanguineous marriage, homozygous regions of the gene will increase and result in an insufficient number of informative SNPs, which is not suitable for this method. For the twin pregnancy with GJB2 gene mutation, the twin’s fetal fraction is coincidentally almost identical. Luckily, the GJB2 gene mutation inheritance is diagnosed clearly in this case. However, if the family was carriers with SLC26A4 gene mutation, the result would be ambiguous. For the SLC26A4 gene, the fraternal twins inherited four parents’ haplotypes (Fig. 3 ). In this circumstance, the two fetal fractions were identical, and the four haplotypes were inherited, there are two possible inheritance situations. One, it could be two pathogenic mutation carriers. Two, it could be an affected fetus and an unaffected fetus. Invasive prenatal diagnosis is essential in this situation. Conclusion Prenatal diagnosis is an important step for couples with an established pregnancy at risk for NSHL to determine at an early stage whether their fetus is affected by a sensory disability, allowing the couple to predict fertility risk. If the couple decides to continue the pregnancy, their clinician will be much better informed to manage and treat the condition from birth. If the couple intends to give up by their own volition, a diagnosis in the first trimester allows the mother to undergo a medical abortion without enduring the trauma of additional surgery( 19 ). Our algorithm also proved the NIPT efficiency of monogenic disorders in dizygotic twin pregnancies. For this goal, the availability of a reliable and accurate NIPD genotyping method would provide a more convenient prenatal option and reduce risks posed to the mother and fetus by invasive test procedures. Abbreviations NIPD noninvasive prenatal diagnosis NSHL non-syndromic hearing loss SNP single nucleotide polymorphisms RHDO relative haplotype dosage change BF bayes factor FF fetal fraction DCDA dichorionic diamniotic twin CVS chorionic villus sampling cffDNA cell-free fetal DNA RMD relative variant dose cSMART circulating single-molecule amplification and resequencing technology CNV copy number variation. Declarations Ethics approval and consent to participate The project passed the ethics committee review by the Ethics Committee for Scientific Research and Clinical Trials of the First Affiliated Hospital of Zhengzhou University. All patients and their family members signed informed consent. Consent for publication Not applicable. Availability of data and materials The datasets for this article are not publicly available due to concerns regarding participant/patient anonymity. The datasets used during the current study are only available from the corresponding author on reasonable request. Competing interests The authors have no conflicts of interest to disclose. Author Di Wu, Shaojun Li, Jun Feng and Weiqin Tang 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. Funding Statement Funding support was given to X.K. by 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). Authors' contributions Conceptualization: XK, DW; Validation: ZZ, DW; Formal Analysis: HL, LK, and ZZ; Investigation: HL; Resources: LK, HL, JZ; Data curation: ZZ, SL, and XF; Experiment curation: JF, WT; Writing—original draft: HL; Writing—review and editing: HL, DW; Visualization: SL, HL; Funding acquisition: XK; All authors contributed to the article and approved the submitted version. Acknowledgments Not applicable. References Morton CC, Nance WE. Newborn hearing screening–a silent revolution. N Engl J Med. 2006;354(20):2151–64. Xiao J, Liu X, Cheng W, Liu J, Jiang J, Li H, et al. 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Supplementary Files Supplementarymethod.docx Supplementarytable.xlsx Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2025 Read the published version in Orphanet Journal of Rare Diseases → Version 1 posted Editorial decision: Minor revision 28 Dec, 2024 Reviewers agreed at journal 28 Nov, 2024 Reviewers invited by journal 25 Jul, 2024 Editor assigned by journal 12 Mar, 2024 First submitted to journal 11 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4008906","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331577275,"identity":"c712c27e-f0ec-4f14-a3e8-b81d85092af6","order_by":0,"name":"Huanyun Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Huanyun","middleName":"","lastName":"Li","suffix":""},{"id":331577276,"identity":"8cd81c57-818c-40f9-b46c-77ebb838e7da","order_by":1,"name":"Shaojun Li","email":"","orcid":"","institution":"Celula (China) Medical Technology Co","correspondingAuthor":false,"prefix":"","firstName":"Shaojun","middleName":"","lastName":"Li","suffix":""},{"id":331577277,"identity":"5a622210-bc20-4d48-b767-f9437309e51a","order_by":2,"name":"Zhenhua Zhao","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"Zhao","suffix":""},{"id":331577278,"identity":"f8e62678-10fd-423c-a113-fd0e1487cf67","order_by":3,"name":"Lingrong Kong","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Lingrong","middleName":"","lastName":"Kong","suffix":""},{"id":331577279,"identity":"22a089dc-e13a-4b94-8089-386271f23b64","order_by":4,"name":"Xinyu Fu","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Fu","suffix":""},{"id":331577280,"identity":"bbf82619-699f-4b3b-bd9d-27d7434ee2bb","order_by":5,"name":"Jingqi Zhu","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jingqi","middleName":"","lastName":"Zhu","suffix":""},{"id":331577281,"identity":"0157952f-2bf2-4198-b523-f664fc1e2643","order_by":6,"name":"Jun Feng","email":"","orcid":"","institution":"Celula (China) MEdical Technology Co","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Feng","suffix":""},{"id":331577282,"identity":"e06d0e83-3f26-4d7f-bdd9-33b7b1748644","order_by":7,"name":"Weiqin Tang","email":"","orcid":"","institution":"Celula (China) Medical Technology","correspondingAuthor":false,"prefix":"","firstName":"Weiqin","middleName":"","lastName":"Tang","suffix":""},{"id":331577283,"identity":"96170c52-9d11-4bd4-aff0-4487c255306e","order_by":8,"name":"Di Wu","email":"","orcid":"","institution":"Celula (China) Medical Technology","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wu","suffix":""},{"id":331577284,"identity":"a140b2af-62a4-4f06-a34a-f522f4b5efb1","order_by":9,"name":"Xiangdong Kong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACCTDJZiPHz99AmpY0Y8kZB4CMBOK1HE7c0JBApBbJ9t7Dr3nKzjNuYDjA9uDjDyK0SPOcS7Occe42szlzA7vhDGJskZPIMTP42HabzbLhAJs0D9FaEtvO8RgcSGCT/kOMFmmJHOMHH9sOSIC1EOf9njNmjDPOJRtIzjjYJtmTRoQWieM9xp95yuzq+/mbj0n8sCFCCxCwQeKGgbGBOPVAwPyBaKWjYBSMglEwMgEA1lc1Bsnz0CgAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-4322-914X","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Xiangdong","middleName":"","lastName":"Kong","suffix":""}],"badges":[],"createdAt":"2024-03-03 14:33:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4008906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4008906/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13023-025-03558-x","type":"published","date":"2025-01-27T15:57:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63314190,"identity":"6340f49f-73ea-4a07-a477-cc9af229dd7e","added_by":"auto","created_at":"2024-08-26 21:07:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75004,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe workflow of NIPD of NSHL families.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NSHL trio family samples were collected and processed after genetic counseling. Then the samples were sequenced on the designed panel. First, the parental haplotypes were identified through trio family sequencing information. The maternal haplotype with the pathogenic mutation was named HM1, and the haplotype with a wide-type allele was named HM2. Similarly for paternal haplotype, HF1 is a pathogenic haplotype. Then the informative SNPs were selected with specific functions as illustrated. RHDO and Bayes factor were performed to identify fetal genotypes inherited from parents. CBS algorithm was used to judge recombination events. Fetal fraction (FF), sequencing depth, and SNP number set quality control to ensure diagnostic accuracy (See details in Supplementary Materials). All the pregnant women received invasive diagnoses to identify NIPD results.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4008906/v1/63ff61ec4dcbc50a039c2c79.png"},{"id":63313786,"identity":"cbc42034-c35b-4605-9db2-de81088d6d38","added_by":"auto","created_at":"2024-08-26 20:59:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":948770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustration of the capture panel design.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe capture region, SNP site frequency, and GC content were illustrated from outside to inside. Purple and green denote the \u003cem\u003eSLC26A4\u003c/em\u003e and \u003cem\u003eGJB2\u003c/em\u003e gene and their spanning 1Mb region, respectively. Blue indicates the distribution at other autosomal loci, used to calculate fetal scores as well as for quality control.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4008906/v1/fe1ae20273364d8ae10a6c12.png"},{"id":63313785,"identity":"fab0464a-e329-45d7-b5f0-d9d22b110a36","added_by":"auto","created_at":"2024-08-26 20:59:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":599160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSNP classification and Dosage change\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe horizontal coordinates represent SNP sites and are sorted by SNP type and genome coordinates, with SNP types color-coded. The vertical axis represents the dose change for each SNP site, and the black horizontal line represents the mean dose change for each SNP type. The parent haplotype is at the bottom, with light blue representing the reference base and dark blue representing the variant base.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4008906/v1/e251626b928d6f57a95c8460.png"},{"id":75351202,"identity":"baeb0341-4f4e-49f6-96c5-d0e76c93a4cf","added_by":"auto","created_at":"2025-02-03 16:07:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2066270,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4008906/v1/c29e335e-9807-412d-b619-65f7147a0846.pdf"},{"id":63313787,"identity":"ddd37397-d831-4e9f-bf58-36cebcf95a17","added_by":"auto","created_at":"2024-08-26 20:59:15","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":10471423,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymethod.docx","url":"https://assets-eu.researchsquare.com/files/rs-4008906/v1/769a99ae7c59cc3a52074400.docx"},{"id":63313783,"identity":"334cf1d1-c1ee-4d69-886c-84aee773f635","added_by":"auto","created_at":"2024-08-26 20:59:15","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":20706,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4008906/v1/78c5bd93180e1237dcb3d08a.xlsx"}],"financialInterests":"","formattedTitle":"Noninvasive prenatal diagnosis (NIPD) of non-syndromic hearing loss (NSHL) for singleton and twin pregnancies in the first trimester","fulltext":[{"header":"Background","content":"\u003cp\u003eCongenital hearing loss is one of the most frequent sensorineural disorders, affecting 1\u0026ndash;2 in every 1,000 neonates(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The epidemiological data revealed that genetic causes account for up to 80% of congenital hearing loss, and most of them (70%) are regarded as nonsyndromic hearing loss (NSHL) without other medical anomalies(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Hereditary deafness is heterogeneous in both clinical and genetic aspects. More than 300 genetic loci linked to hereditary hearing loss and \u0026gt;\u0026thinsp;100 causative genes have also been identified(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In the Chinese population, the most frequent pathogenic NSHL mutations reside in the \u003cem\u003eGJB2\u003c/em\u003e gene and \u003cem\u003eSLC26A4\u003c/em\u003e gene(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). For deaf patients, effective treatment is the hearing aids and cochlear implants. A delayed diagnosis can lead to life-long health issues that could be ameliorated with early intervention and treatment.\u003c/p\u003e \u003cp\u003eFor those couples known as pathogenic \u003cem\u003eGJB2\u003c/em\u003e gene or \u003cem\u003eSLC26A4\u003c/em\u003e gene mutation carriers, prenatal diagnosis is an essential way to assess the risk of fertility and guide rehabilitation treatment. As is known to all, invasive prenatal diagnosis is the gold standard. However, chorionic villus sampling (CVS) and amniocentesis have a risk of miscarriage or stillbirth (incidence: 0.1\u0026ndash;0.3%)(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In 1997, the discovery of the cell-free fetal DNA (cffDNA) in maternal plasma laid a foundation for non-invasive prenatal diagnosis (NIPD)(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Until now, the existing NIPD approaches can be divided into two categories in the diagnosis of NHLS. The first one is relative haplotype dose (RHDO) analysis. Combining high-throughput sequencing of targeted regions and hidden Markov models, Duan et.al realized the first NIPD for a family with \u003cem\u003eGJB2\u003c/em\u003e gene mutation in 2014(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The second way is relative variant dose (RMD) analysis by circulating single-molecule amplification and resequencing technology (cSMART)(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)or digital PCR(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Both cSMART and digital PCR rely on the dosage changes of hotspot mutations between wild-type and mutant alleles to determine the fetal genotype. In that case, RMD requires a relatively higher fetal fraction (FF) and stricter experiment requirements. RHDO does not detect variants itself but infers the inheritance of parental haplotypes by counting the numerous specific SNP alleles dose changes around the pathogenic genes. In this context, RHDO is not limited by the type of mutation and has a wider range of applications. Under maternal DNA background noise, the detection of multiple alleles to infer genotypes is more accurate and feasible than directly detecting a single pathogenic variant, especially for recessive hereditary diseases like NSHL.\u003c/p\u003e \u003cp\u003eThe earliest detection of non-invasive prenatal testing based on fetal cells is at 8 weeks of gestation(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, the rarity of fetal cells and the complexity of the experimental process limit its clinical application. The published reports based on cffDNA were limited to singleton pregnancies in the second trimester, the twin pregnancies and the first-trimester NIPD remain unknown in NSHL. With the increasing use of ovulation drugs and advancing maternal age, the frequency of twin pregnancies is increasing(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Miscarriage risk associated with twin pregnancies is greater when compared to singleton pregnancies, potentially increasing the risk for pregnancy loss if invasive prenatal diagnostic procedures are performed(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs such, to develop a comprehensive and accurate NIPD assay for determining fetal NSHL genotypes in at-risk pregnancies, we performed non-invasive twin detection in NSHL with the \u003cem\u003eGJB2\u003c/em\u003e gene for the first time based on our successful experience in DMD twin detection(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). By target sequencing of the \u003cem\u003eGJB2\u003c/em\u003e gene and \u003cem\u003eSLC26A4\u003c/em\u003e gene of the trio blood sample, selecting informative SNP, and calculating Bayes factor (BF), the modified prototype assay successfully diagnosed the dichorionic diamniotic twin (DCDA) genotype. Additionally, this study has achieved earlier NIPD for pathogenic \u003cem\u003eGJB2\u003c/em\u003e gene and \u003cem\u003eSLC26A4\u003c/em\u003e gene carrier couples, which shows great potential and promise for clinical application.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and detection workflow\u003c/h2\u003e \u003cp\u003eSeventeen families with pathogenic \u003cem\u003eGJB2\u003c/em\u003e gene or \u003cem\u003eSLC26A4\u003c/em\u003e gene mutation were enrolled from March 2021 to October 2023 after genetic counseling and a receipt of informed consent. Sixteen singletons and one twin pregnancy of DCDA were confirmed by ultrasound. For every family, peripheral blood was collected from the pregnant mother (10ml), father (2ml), and proband (2ml). The gDNA was extracted by Nucleic Acid Extraction or Purification Kit (NaHaiTM, China). The study was approved by the Ethics Committee of First Affiliated Hospital of Zhengzhou University.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eLibrary preparation and target sequencing\u003c/h2\u003e \u003cp\u003eThe gDNA of the trio family was broken into fragments with an average length of ~\u0026thinsp;200bp by the sonicator (Bioruptor Pico). Maternal plasma was isolated using a two-step centrifugation protocol (See details in Supplementary materials). Then the fragmented gDNA and cfDNA completed end-repaired and added A-tailing. After ligating the barcode, the PCR amplification was performed to enrich the library. A 750ng library was added to the designed probe panel, incubated at 80\u0026deg;C for 5 minutes on a PCR instrument, and maintained at 50\u0026deg;C for 18 hours for the hybridization reaction. Then target region was captured, and the captured library was further PCR amplified. The amplified libraries were quantified using Qubit3.0 (Invitrogen, Breda, Netherlands) and sequenced on the Ion Proton platform (Thermo Fisher Scientific, Lithuania).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTargeted sequencing design\u003c/h2\u003e \u003cp\u003eA 324.614kb capture panel TargetSeq\u0026reg; One kit (iGeneTech, China) was designed to selectively enrich target regions based on the reference genome (GRCh37/hg19). The panel covered \u003cem\u003eGJB2\u003c/em\u003e and \u003cem\u003eSLC26A4\u003c/em\u003e genes in all exon regions (including untranslated regions), 500 bp intronic regions adjacent to exons, and 10,000 bp upstream or downstream of the target gene. In addition, there were 203 highly heterozygous SNPs (MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.45) located on 1\u0026ndash;22 autosomes, respectively. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eClassification of SNPs and fetal fraction calculation\u003c/h2\u003e \u003cp\u003eHaplotype phasing was performed using trio family samples based on Mendel's law. The maternal pathogenic haplotype was defined as HM1 and the wild-type haplotype was defined as HM2. Similarly, paternal haplotypes were named HF1 and HF2. For singleton pregnancy, Informative SNP sites were those homozygous for one parent and heterozygous for another parent. The sType1 allele would show an imbalance if the fetus inherited HM1 and similarly the sType2 allele would change if the fetus inherited HM2. Paternal inheritance could be judged through sType3 and sType4 SNP sites. The fetal fraction (FF) was calculated via the parent's homozygous SNP but with different genotypes in maternal plasma (f) by the following equation: f\u0026thinsp;=\u0026thinsp;2a \u0026frasl; ((a\u0026thinsp;+\u0026thinsp;b)), where \u0026ldquo;a\u0026rdquo; is the read depth of the fetal inherited paternal allele and \u0026ldquo;b\u0026rdquo; is the read depth of the allele shared by the fetus and pregnant. For twin pregnancy, SNPs were classified into six categories, named tType 1 to tType 6 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). tType 1 SNPs were able to measure sequencing error and sample cross-contamination. tType 2\u0026ndash;4 SNPs were used to calculate infer fetal paternal-inherited haplotype and fetal fraction (named ff1 for the lower fetal fraction, ff2 for the higher fetal fraction, and FFtotal for the total fetal fraction, respectively). tType 5 and 6 SNPs were dedicated to determining fetal-maternal inherited haplotypes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe principle and function of informative SNP classification.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFather\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eProband\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClassification and function of informative SNPs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esType1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFather: homozygous\u003c/p\u003e \u003cp\u003eMother: heterozygous\u003c/p\u003e \u003cp\u003eSNP allele imbalance: inherit HM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esType2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFather: homozygous\u003c/p\u003e \u003cp\u003eMother: heterozygous.\u003c/p\u003e \u003cp\u003eSNP allele imbalance: inherit HM2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esType3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFather: heterozygous\u003c/p\u003e \u003cp\u003eMother: homozygous\u003c/p\u003e \u003cp\u003eNew SNP allele: inherit HF1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esType4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFather: heterozygous\u003c/p\u003e \u003cp\u003eMother: homozygous\u003c/p\u003e \u003cp\u003eNew SNP allele: inherit HF2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etType1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFather: homozygous\u003c/p\u003e \u003cp\u003eMother: homozygous\u003c/p\u003e \u003cp\u003eQC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etType2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFather: heterozygous\u003c/p\u003e \u003cp\u003eMother: homozygous\u003c/p\u003e \u003cp\u003eNew SNP allele: inherit HF1; ff1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etType3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFather: heterozygous\u003c/p\u003e \u003cp\u003eMother: homozygous\u003c/p\u003e \u003cp\u003eNew SNP allele: inherit HF2; ff2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etType4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFather: homozygous\u003c/p\u003e \u003cp\u003eMother: homozygous\u003c/p\u003e \u003cp\u003eFFtotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etType5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFather: homozygous\u003c/p\u003e \u003cp\u003eMother: heterozygous\u003c/p\u003e \u003cp\u003eSNP allele imbalance: inherit HM1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etTpye6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFather: homozygous\u003c/p\u003e \u003cp\u003eMother: heterozygous.\u003c/p\u003e \u003cp\u003eSNP imbalance: inherit HM2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of genotype\u003c/h2\u003e \u003cp\u003eThe allele frequency of informative SNPs was used to calculate the dosage change of the pathogenic haplotype and the wild-type haplotype. Based on allele frequency imbalance, we estimated the probability of fetal inherited pathogenic or wild-type haplotypes using the BF as described previously(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). For singleton pregnancy, if BF\u0026thinsp;\u0026ge;\u0026thinsp;10, the result indicates the fetus inherits HF1/HM1. If BF\u0026thinsp;\u0026le;\u0026thinsp;0.1, it indicates the fetus inherits HF2/HM2. When BF fell between 0.1 to 10, the NIPD result was no call. For twin pregnancy, the maternal-inherited haplotype was deduced using RHDO through a two‐step Bayes factor, as we described before(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The first step was to determine if the twins inherited the same maternal haplotype. In the second step, the inherited maternal haplotype for each fetus was deduced based on the decision of the first step. Bayes factor was calculated at each step (BF1 for step 1 and BF2 for step 2) by dividing the likelihood of obtaining the observed difference of RHDO between type 5 and type 6 SNPs under two opposite hypotheses. Paternal inheritance was deduced by the dose change of tType2 and tType3. Besides, all the fetal haplotype speculations were tested by the CBS algorithm to exclude the influence of recombination events on the NIPD results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInvasive prenatal diagnosis\u003c/h2\u003e \u003cp\u003eThe twin received a double separate amniocentesis and the other fifteen singleton pregnant women also received amniocentesis at 18 to 24 weeks. One pregnant woman had a cerclage of cervix procedure in the second trimester, which made it unsuitable for invasive prenatal diagnosis, so the neonate was sampled after birth. All amniotic fluid samples and neonatal samples were processed by Sanger sequence to verify the accuracy of the NIPD.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e1. Trio family information\u003c/h2\u003e \u003cp\u003eA total of 17 pregnant women were recruited. 14 singleton pregnant women collected blood in the first trimester, from 7 to 12\u003csup\u003e+\u0026thinsp;5\u003c/sup\u003e weeks. Only two women had blood collected after 13 weeks. One woman sampled blood at 19\u003csup\u003e+\u0026thinsp;1\u003c/sup\u003e to test panel feasibility and another pregnant woman collected the blood sample at 28 weeks because she just received a cerclage of cervix procedure to prevent miscarriages, which is forbidden for amniocentesis. The twin pregnant woman had NIPD at 17\u003csup\u003e+\u0026thinsp;3\u003c/sup\u003e weeks. Among the 17 families, 7 families had a proband with \u003cem\u003eGJB2\u003c/em\u003e gene mutations, and 10 families with \u003cem\u003eSLC26A4\u003c/em\u003e gene mutations (see details in supplementary table).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2. NIPD results\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.1 Sequencing information\u003c/h2\u003e \u003cp\u003eThe prepared gDNA and cfDNA of 17 families were sequenced by target region capture, and the average of total reads is 3,245,415 (957,564-7,114,056). The average sequencing depth of each sample range from 62x to 746x (average: 273x) and the ratio of more than 300x ranges from 25.36\u0026ndash;87.56% (average: 50.90%). The singleton families\u0026rsquo; informative SNP range from 8 to 196 for sType1and sType2, 8 to 178 for sType3 and sType4. The twin family SNP is 50, 46, 57, 70, 15, 86 from tType1 to tType6, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sixteen singleton NIPD result\u003c/h2\u003e \u003cp\u003eFifteen families were successfully diagnosed, and a total of 3 patients, 4 carriers, and 8 normal fetuses were diagnosed accurately. In the successful 15 families, two fetuses (P15, P17) had recombination events. Luckily, the breaking point of the recombination location estimated by the CBS algorithm was far from the mutation site, so the NIPD results were not affected (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). There is only one failed family (P6), as the distribution of SNP sites was imbalanced, with most of them located downstream of the maternal pathogenic variant (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). In this case, it was impossible to tell whether recombination had occurred and resulted in maternal haplotype with \u0026ldquo;no call\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.3 One Twin NIPD results\u003c/h2\u003e \u003cp\u003eFor the twin pregnancy parents, the mother is a \u003cem\u003eGJB2\u003c/em\u003e gene carrier of the c.299_300delAT mutation, and the father is a \u003cem\u003eGJB2\u003c/em\u003e gene carrier of the c.235delC mutation. The designed panel would capture both the \u003cem\u003eSLC26A4\u003c/em\u003e gene and \u003cem\u003eGJB2\u003c/em\u003e gene region no matter the parent's gene mutation type. From the informative SNP scattered in the \u003cem\u003eSLC26A4\u003c/em\u003e gene panel, the twin\u0026rsquo;s zygosity could be deduced. tType2 and tType3 elevated in both SCL26A4 gene and GJB2 gene panel, which means the twin inherited HF1 and HF2, respectively. It also proved the twin was a fraternal twin and the fetal fraction of the two fetuses is similar, about 6%. Correspondently, we could see clearly that FFtotal is about 12% from tType4 SNPs. The dosage changes of the \u003cem\u003eGJB2\u003c/em\u003e gene tType5 and tType6 SNPs together with the two-step Bayes factor revealed the two fetuses inherit pathogenic haplotype (HM1) simultaneously from the mother. For this GJB2 mutation carrier twin family, one fetus is a maternal carrier and another is a \u003cem\u003eGJB2\u003c/em\u003e gene carrier of the c.299_300delAT mutation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3. Validation of NIPD results\u003c/h2\u003e \u003cp\u003eAll 16 pregnant women underwent invasive prenatal diagnosis. For the woman who had a cerclage of cervix procedure, the fetus underwent peripheral Sanger sequencing after birth. The results showed that the accuracy of all NIPD was 100% (16/16). For the twin, the double separate amniocentesis and Sanger sequencing results are also coordinated with NIPD (Fig S3).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAppropriate prenatal diagnosis of hearing loss could give carrier couples more options for future family planning and probably the preparation for the health and educational needs of the affected neonates(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In our study, as research for NIPD of NSHL, the earliest testing week is 7 weeks. Among the sixteen singleton pregnant women, NIPD was successfully applied in 93.75% (15/16) of families and the coincidence rate with invasive prenatal diagnosis was 100% (15/15). Only one NIPD result is no call because the imbalance distribution of SNP sites makes it difficult to estimate recombination events. Most (13/15) of pregnant women were in the first trimester and the earliest gestation week was the 7th week.\u003c/p\u003e \u003cp\u003eBesides, due to the wide application of reproductive technology, the probability of multiple pregnancies is increasing. The singleton NIPD algorithms may lead to inaccurate results in dizygotic twins since the fetal fraction of the affected fetus could be lower and result in a dosage change not as considerable as expected. In this case, we proposed a two-step Bayes factor with the first step to distinguish whether the twins inherit different haplotypes. The second step could indicate whether the pathogenic haplotype was inherited for every fetus. Furthermore, if the first step indicates the twin inherited an identical haplotype, the invasive procedure could just need one puncture operation, which reduces the risk of miscarriage.\u003c/p\u003e \u003cp\u003eWhether singleton or twin pregnancy, genomic DNA target sequencing requires no complicated experimental procedure, such as the previously reported haplotype-assisted methods, and is cost-effective if the appropriate array is designed. Moreover, the turnaround time, including the sampling process and sequencing on the Ion Proton platform, can be as short as 1 week. Then the bioinformatics analysis can be accomplished within 1 day, which lends this type of procedure to large-scale clinical applications.\u003c/p\u003e \u003cp\u003eHowever, there were several shortcomings, and the relevant solution was made to secure NIPD accuracy. First, the traditional proband-based haplotype needs a complete trio family to construct the parent haplotype. However, no proband is also available in our study design. Families with a previous reproductive history, whether normal patients or carriers, can be used to construct haplotypes(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Families with no reproductive history can also construct haplotypes through grandparents. Second, the NIPD results might be disturbed by recombination events. The CBS algorithm could predict the recombination event, which is used to estimate copy number variation (CNV) data and identify the reasonable breakpoint(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Then the researchers determined whether the recombinational break point affected the identification of pathogenic variants. Third, for consanguineous marriage, homozygous regions of the gene will increase and result in an insufficient number of informative SNPs, which is not suitable for this method.\u003c/p\u003e \u003cp\u003eFor the twin pregnancy with \u003cem\u003eGJB2\u003c/em\u003e gene mutation, the twin\u0026rsquo;s fetal fraction is coincidentally almost identical. Luckily, the \u003cem\u003eGJB2\u003c/em\u003e gene mutation inheritance is diagnosed clearly in this case. However, if the family was carriers with \u003cem\u003eSLC26A4\u003c/em\u003e gene mutation, the result would be ambiguous. For the \u003cem\u003eSLC26A4\u003c/em\u003e gene, the fraternal twins inherited four parents\u0026rsquo; haplotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In this circumstance, the two fetal fractions were identical, and the four haplotypes were inherited, there are two possible inheritance situations. One, it could be two pathogenic mutation carriers. Two, it could be an affected fetus and an unaffected fetus. Invasive prenatal diagnosis is essential in this situation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePrenatal diagnosis is an important step for couples with an established pregnancy at risk for NSHL to determine at an early stage whether their fetus is affected by a sensory disability, allowing the couple to predict fertility risk. If the couple decides to continue the pregnancy, their clinician will be much better informed to manage and treat the condition from birth. If the couple intends to give up by their own volition, a diagnosis in the first trimester allows the mother to undergo a medical abortion without enduring the trauma of additional surgery(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Our algorithm also proved the NIPT efficiency of monogenic disorders in dizygotic twin pregnancies. For this goal, the availability of a reliable and accurate NIPD genotyping method would provide a more convenient prenatal option and reduce risks posed to the mother and fetus by invasive test procedures.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enoninvasive prenatal diagnosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSHL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-syndromic hearing loss\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle nucleotide polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRHDO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erelative haplotype dosage change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebayes factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efetal fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edichorionic diamniotic twin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echorionic villus sampling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecffDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecell-free fetal DNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erelative variant dose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecSMART\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecirculating single-molecule amplification and resequencing technology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecopy number variation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project passed the ethics committee review by the Ethics Committee for Scientific Research and Clinical Trials of the First Affiliated Hospital of Zhengzhou University. All patients and their family members signed informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets for this article are not publicly available due to concerns regarding participant/patient anonymity. The datasets used during the current study are only available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to disclose. Author Di Wu, Shaojun Li, Jun Feng and Weiqin Tang 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\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding support was given to X.K. by Key Scientific Research Projects in Colleges and\u003c/p\u003e\n\u003cp\u003eUniversities 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: XK, DW; Validation: ZZ, DW; Formal Analysis: HL, LK, and ZZ; Investigation: HL; Resources: LK, HL, JZ; Data curation: ZZ, SL, and XF; Experiment curation: JF, WT; Writing\u0026mdash;original draft: HL; Writing\u0026mdash;review and editing: HL, DW; Visualization: SL, HL; Funding acquisition: XK; All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMorton CC, Nance WE. 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Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics. 2005;21(19):3763\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinikoff B, Dzuba IG, Chong E, Goldberg AB, Lichtenberg ES, Ball C, et al. Extending outpatient medical abortion services through 70 days of gestational age. Obstet Gynecol. 2012;120(5):1070\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Noninvasive prenatal diagnosis, Non-Syndromic Hearing Loss, Haplotype construction, Bayes factors, Twin","lastPublishedDoi":"10.21203/rs.3.rs-4008906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4008906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNoninvasive prenatal diagnosis (NIPD) has been proven available for non-syndromic hearing loss (NSHL) in singleton pregnancies. However, previous research is limited to the second trimester and the application in twin pregnancies is blank. Here we provide a novel algorithmic approach to assess singleton and twin pregnancies in the first trimester.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAll of the recruited participants, comprising sixteen women with singleton pregnancies and one woman with a twin pregnancy, had a proband with NSHL caused by \u003cem\u003eGJB2\u003c/em\u003e gene or \u003cem\u003eSLC26A4\u003c/em\u003e gene mutations. The twin pregnancy was a dichorionic diamniotic twin (DCDA). NIPD confirmed one fetus is affected, and another is a carrier with c.299_300delAT of \u003cem\u003eGJB2\u003c/em\u003e gene. Among the 16 singleton pregnancies, NIPD was successfully applied in 15 families and the coincidence rate with invasive prenatal diagnosis was 100% (15/15). Only one family NIPD result is no call because the imbalance distribution of SNP sites makes it difficult to estimate recombination events. Most (13/15) of pregnant women were in the first trimester and the earliest gestation week was the 7th week.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study represents the pioneering evidence in the field, demonstrating the feasibility of NIPD for NSHL in twin pregnancies. Moreover, it provides a novel and advanced diagnostic approach for families at high risk of NSHL during pregnancy, offering earlier detection, enhanced safety, and improved accuracy. These findings significantly contribute to the scientific understanding and clinical management of hearing loss in multiple pregnancies.\u003c/p\u003e","manuscriptTitle":"Noninvasive prenatal diagnosis (NIPD) of non-syndromic hearing loss (NSHL) for singleton and twin pregnancies in the first trimester","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-26 20:59:10","doi":"10.21203/rs.3.rs-4008906/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2024-12-28T16:33:57+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-11-28T05:43:39+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-25T07:51:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-13T01:46:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Orphanet Journal of Rare Diseases","date":"2024-03-11T05:03:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"orphanet-journal-of-rare-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ojrd","sideBox":"Learn more about [Orphanet Journal of Rare Diseases](http://ojrd.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ojrd/default.aspx","title":"Orphanet Journal of Rare Diseases","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6550ddf1-2a11-4ca7-ba7d-a618117c493d","owner":[],"postedDate":"August 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:00:59+00:00","versionOfRecord":{"articleIdentity":"rs-4008906","link":"https://doi.org/10.1186/s13023-025-03558-x","journal":{"identity":"orphanet-journal-of-rare-diseases","isVorOnly":false,"title":"Orphanet Journal of Rare Diseases"},"publishedOn":"2025-01-27 15:57:17","publishedOnDateReadable":"January 27th, 2025"},"versionCreatedAt":"2024-08-26 20:59:10","video":"","vorDoi":"10.1186/s13023-025-03558-x","vorDoiUrl":"https://doi.org/10.1186/s13023-025-03558-x","workflowStages":[]},"version":"v1","identity":"rs-4008906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4008906","identity":"rs-4008906","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}