Haplotype-based noninvasive prenatal diagnoses of 36 fetuses with spinal muscular atrophy in the real clinical environment at early gestation age 

Haplotype-based noninvasive prenatal diagnoses of 36 fetuses with spinal muscular atrophy in the real clinical environment at early gestation age | 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 Haplotype-based noninvasive prenatal diagnoses of 36 fetuses with spinal muscular atrophy in the real clinical environment at early gestation age Huanyun Li, Shaojun Li, Zhenhua Zhao, Xinyu Fu, Jingqi Zhu, Jun Feng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3123735/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To explore the feasibility of noninvasive prenatal diagnoses (NIPD) based on haplotype construction and Bayes factor (BF) for spinal muscular atrophy (SMA) in clinical application. Methods 36 singleton families with pregnancy risk of SMA were recruited and all the recruited members were conducted MLPA to validate the copy number of exons 7 and 8 in SMN1 and SMN2 genes. The designed capture panel covered the entire SMN1/2 genes, including all exon and intron regions of the two genes. To ensure the NIPD accuracy, four quality control standards were set: sequencing depth, the number of informative SNPs, cell-free DNA fetal fraction, and the recombination event assessment. By enriching targeting genes and informative SNP sites in adjacent regions, the family haplotype was constructed and the fetal genotype was determined based on the dose change of the informative SNPs in cfDNA combined with BF algorithm. All NIPD results were verified by chorionic villus sampling (CVS), amniocentesis, or apoblema testing. Results In the 36 recruited SMA families, 34 (94.4%) families were successfully tested for NIPD, and 2 (5.56%) families could not be determined exactly because of the recombination event near the pathogenic mutations. In successful families, the earliest gestational week for NIPD was 7 + 3 weeks, and the lowest free fetal DNA fraction was 1.9%. A total of 8 affected fetuses, 6 paternal carriers, 8 maternal carriers, and 12 unaffected fetuses were detected. The consistency between NIPD results and invasive MLPA diagnosis was 100%. Four (11.1%) of the families obtained accurate results after redrawing blood samples due to low fetal fraction or insufficiency of informative SNPs. Follow-up results showed that all families with affected NIPD results underwent abortions, and the families with carrier and normal results chose to deliver the fetus. Conclusion NIPD is a noninvasive, high-accuracy, early pregnancy detection and cost-controllable technical method. The haplotype construction and BF analysis have considerable reliability and feasibility and could be used in the detection of other recessive monogenic diseases. Spinal muscular atrophy Noninvasive prenatal diagnoses Bayes factors Haplotype construction Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Spinal muscular atrophy (SMA) is an autosomal-recessive neurodegenerative disease characterized by progressive symmetrical muscle weakness and early death[1]. It is the second most common fatal autosomal recessive disorder after cystic fibrosis, with an estimated incidence of approximately 1 in 10,000 live births[2]. Typically, people have two copies of the SMN1 gene and one to two copies of the SMN2 gene in the 5q13 region[3]. The most striking difference between SMN1 and SMN2 is the 6th nucleotide of exon 7 (C-to‐T transition)[4]. SMN protein is encoded by these two almost identical genes but most functional SMN protein is produced by the SMN1 gene. Mutations in SMN1 can decrease the amount of SMN protein and cause progressive muscle atrophy and paralysis. It is estimated that approximately 95% of patients were caused by the homozygous absence of the SMN1 exon 7[5], while others have non-sense, frameshift, or missense mutations within the gene[6]. Prenatal diagnosis is an essential way to prevent the birth of infants with hereditary diseases. Traditional prenatal diagnoses include chorionic villus sampling (CVS) and amniocentesis which are invasive and have a risk of miscarriage or stillbirth (incidence: 0.1–0.3%)[7, 8]. Nevertheless, since the discovery of the cell-free fetal DNA (cffDNA) in maternal plasma in 1997[9], scientists have created several new approaches for noninvasive prenatal diagnosis (NIPD). Compared with the traditional invasive methods, NIPD is much safer and can help to diagnose congenital anomalies in the early gestational week. It is well known that the whole cell-free DNA (cfDNA) consists of cffDNA originating from the placental trophoblast and cfDNA from maternal cells. However, it is difficult to detect maternal genetic locus in the context of maternal cfDNA, especially for autosomal-recessive diseases. In clinical, the most effective and commonly used NIPD methods are relative mutation dose (RMD) analysis and relative haplotype dose (RHDO) analysis[10]. The ratio allele frequency or haplotype dosage can be calculated to infer fetal genotypes. Compared with RMD, RHDO is no longer dependent on the detection of specific mutations and has a high sensitivity. Recent clinical applications based on haplotype in several single-gene disorder (SGD) diagnoses have been proven technically possible, such as β-thalassemia[11], congenital adrenal hyperplasia (CAH)[12], and Duchenne and Becker muscular dystrophies (DMD/BMD)[13]. Unlike most other SGDs, SMA harbors the need and potential for a specific design of the NIPD technique. First, the predominant gene mutation is the loss of SMN1 (95%) instead of point mutation, which implies it is difficult for RMD analysis and there are few clinical applications[14]. Second, the pseudogene SMN2 results in the difficulty to estimate the copy number of SMN1 accurately. In this study, we developed a high-accuracy assay based on RHDO and Bayesian to infer fetal genotype. MATERIALS AND METHODS Sample information and preparation Thirty-six SMA families with singleton pregnancies (named P1-P36) were enrolled from December 2019 to January 2023 after genetic counseling and a receipt of informed consent. The pregnancy gestational age ranged from 7 + 3 weeks to 13 + 0 weeks (Table 1 ). Thirty-four families had typical trio pedigrees (Father, Mother, and Proband) and two families had normal pedigrees (Father, Mother, and completely normal offspring). For each family, 10 ml of peripheral blood from the pregnant mother and 2 ml of peripheral blood from the offspring and father were collected. The study was approved by the Ethics Committee of First Affiliated Hospital of Zhengzhou University. All methods were carried out in accordance with relevant guidelines. Table 1 The NIPD results of 36 SMA families. Family Gestational weeks Fetal fraction Maternal inheritance Paternal inheritance NIPD results MLPA results P1 11 13.12% HM1 HF1 affected affected P2 11 + 5 5.12% HM2 HF2 unaffected unaffected P3-1 7 + 4 2.74% - - - - P3-2 9 4.59% HM1 HF1 affected affected P4 8 + 3 4.02% HM1 HF2 maternal carrier carrier P5 9 3.36% HM2 HF2 unaffected unaffected P6 13 2.79% HM1 HF2 maternal carrier carrier P7 8 + 4 4.29% HM1 HF2 maternal carrier carrier P8 8 + 5 4.86% HM1 HF1 affected affected P9 8 + 4 4.29% HM1 HF1 affected affected P10 7 + 3 3.77% HM1 HF2 maternal carrier carrier P11 12 4.99% HM1 HF2 maternal carrier carrier P12 12 + 5 3.54% HM1 HF2 maternal carrier carrier P13-1 9 + 3 < 1% - - - - P13-2 12 + 1 13.50% HM2 HF1 paternal carrier carrier P14 7 + 6 10.04% HM2 HF1 paternal carrier carrier P15 8 + 4 6.90% HM2 HF2 unaffected unaffected P16 7 + 4 2.43% HM2 HF1 paternal carrier carrier P17 8 + 1 5.71% HM1 HF1 affected affected P18 8 13.52% HM2 HF2 unaffected unaffected P19-1 9 3.00% - - - - P19-2 10 + 5 9.92% HM1 HF1 affected affected P20 8 + 1 1.94% HM2 HF2 unaffected unaffected P21 10 7.83% HM2 HF2 unaffected unaffected P22 8 7.61% HM1 HF1 affected affected P23 11 2.78% HM2 HF2 unaffected unaffected P24 7 + 5 6.25% HM2 HF2 unaffected unaffected P25 8 + 2 4.90% HM1 HF2 maternal carrier carrier P26 8 + 6 4.32% Recombination HF2 no call unaffected P27 9 9.25% HM2 HF1 paternal carrier carrier P28 8 5.78% HM2 HF2 unaffected unaffected P29 10 + 6 4.36% HM2 HF2 unaffected unaffected P30 8 + 6 7.96% HM1 HF1 affected affected P31 10 + 5 6.39% HM2 HF2 unaffected unaffected P32 8 + 3 9.56% HM2 HF2 unaffected unaffected P33 10 6.75% HM2 Recombination no call unaffected P34-1 12 2.47% - - - - P34-2 14 + 4 5.89% HM2 HF1 paternal carrier carrier P35 8 2.16% HM2 HF1 paternal carrier carrier P36 8 + 6 1.90% HM1 HF2 maternal carrier carrier Detection Workflow The workflow of the NIPD for SMA families is illustrated in Fig. 1 . First, members of at-risk of SMA families were recruited and received genetic counseling. Then, peripheral blood was collected from each family member. Then the cfDNA was extracted following the manufacturer’s instructions (Nahai Bio, Chengdu, China). gDNA was extracted from the leucocytes of three family members via the in-house protocol. Subsequently, samples were subject to end-repair, barcode ligation, PCR amplification, and target capture. The post-capture DNA libraries were subjected to another round of PCR amplification and sequenced on the Ion Proton platform. If sequencing depth met the quality control requirement, then haplotype phasing and fetal fraction calculation were conducted separately, with quality control (QC) at the end of each process. After the QC of FF and informative SNPs, the recombination event was analyzed by the circular binary segmentation (CBS) algorithm and fetal genotype was predicted using Bayes factor (BF). NIPD results were validated by invasive diagnosis and post-test genetic counseling was provided. Probe design and target sequencing A 168.736kb capture panel TargetSeq® One kit (iGeneTech, China) was designed to selectively enrich target regions based on the reference genome (GRCh37/hg19). The capture panel covered the entire SMN1/2 genes, including all exon and intron regions of the two genes. 758 common SNPs (EAS_MAF > 0.2, 1000 Genomes Project Phase 3) within the 2Mb genomic region both upstream and downstream of the SMN1 gene were used for the target DNA capture. Besides, the panel covered 213 highly heterozygous SNPs (MAF > 0.45) scattered on chromosomes 1–22 to calculate fetal fraction. cfDNA and fragmented gDNA were captured after end repair, barcode adapter ligation, and PCR amplification. Subsequently, the post-capture libraries were subjected to PCR amplification again and sequenced on the Ion Proton platform (Thermo Fisher Scientific, Lithuania). Measurement of fetal fraction and fetal genotype 213 specific SNPs loci scattered on chromosomes 1–22 were used for calculating fetal fraction. We selected SNPs homozygous in parents but with different genotypes to calculate the fetal fraction 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. Haplotype-phasing was conducted using genotypes of the core family according to Mendel’s law ( Fig. 3 ) . The maternal pathogenic and wild-type haplotypes were defined as HM1 and HM2 separately, while the paternal pathogenic and wild-type haplotypes were named HF1 and HF2. 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[13]. If BF ≥ 10, the result indicated the fetus inherited HF1/HM1. If BF ≤ 0.1, it indicated the fetus inherited HF2/HM2. When BF fell between 0.1 to 10, the NIPD result is no call. After haplotype phasing, quality control for SNP numbers was performed. When the number of Type 1 or Type 2 alleles was less than 10, this indicated consanguineous marriage. Haplotype-based NIPD was not suitable for such families, and invasive diagnoses were suggested. Quality control (QC) Four QC criteria were implemented to ensure the reliability of the results: average sequencing depth, the fetal fraction, the number of informative SNPs, and recombination events. The average sequencing depth should be ≥ 70x for cfDNA and ≥ 30x for gDNA samples. If the sample failed the QC depth, it should be re-captured and sequenced. The lower limit is 10 for Type 1/2 SNPs in maternal haplotype phasing and 5 for Type 3/4 in parental haplotype phasing. Patients without sufficient informative SNPs would take invasive diagnoses instead. As for the minimum fetal fraction, if the fetal fraction is less than 1%, the pregnant mother needs to redraw blood samples after one or two weeks. When recombination events showed in target regions, it is necessary to combine bioinformatics analysis and clinical knowledge to judge whether the discrimination of pathogenic haplotypes is affected. MlPA analysis All the recruited trio families and NIPD results were conducted MLPA analysis to validate the copy number of exons 7 and 8 in SMN1 and SMN2 genes. Besides, the NIPD results were compared with the MLPA results of the chorionic villus sampling (CVS), amniocentesis samples, or apoblema testing. Results 1. Family information Totally, 36 trio families were recruited in the NIPD testing. Pregnant women's age ranges from 22 to 41 (median: 31) years old and the mean gestational age of NIPD blood drawing is 9 + 3 (7 + 3 -13 + 0 )weeks. 34 families consisted of a father, mother, and proband. The parents were carriers and the proband is a patient of EX7_8del. While there were two families without proband, and the offspring was confirmed in complete health without the SMA variant. These two families are speculated in the same way as trio families with proband but the results are the opposite to them. 2. NIPD results 2.1 sequencing information The prepared gDNA and cfDNA of 36 families were sequenced by target region capture, and the average of total reads is 4265958 (803485-13211730). The average sequencing depth of each sample range from 121x to 995x (average:428x) and the ratio of more than 300x ranges from 43.3–73.92% (average:59.15%). Generally, the capture ratio is around 88.00% (62.82%-98.77%). All samples met the depth quality control requirements and their average sequencing depth is more than 70x in cfDNA or 30x in gDNA samples. Hence, sufficient sequencing depth can help to screen qualified SNP sites. ( Table S1 ). 2.2 Fetal Fraction and haplotype outcomes Fetal fraction is an essential factor for haplotype-based analysis in NIPD and it is affected by the maternal age, gestational week, and other maternal factors as reported before[15]. In our testing, the average fetal fraction is 6.01% (1.9%-13.52%) and all samples are above the minimum margin at last. Among the 36 families, 34 families were successfully tested for NIPD (success rate: 94.4%), and 2 families could not be determined exactly because of the recombination event near the pathogenic mutation. According to the haplotype analysis, the maternal or paternal pathogenic haplotypes were confirmed as HF1 and HM1, respectively. The average SNP number is 91 for type 1 (18–216) and 79 for type 2 (13–341) which were used to speculate fetal-maternal inheritance. Similarly, type 3 (6-172) and type 4 (17–154) were used to infer paternal inherence (Fig. 4 ). BF is used to judge the magnitude of informative SNP imbalance and has shown great precision in predicting fetal haplotype. In this study, the result includes 7 affected fetuses (P1, P3, P8, P9, P17, P19, P21), 6 paternal carriers, 8 maternal carriers, and 13 normal fetuses. Among the 34 successful families, three of the families obtained accurate results after redrawing blood samples due to low fetal concentration or the inefficiency of informative SNPs. 2.3 Four families redrew blood samples Among the 36 families, four families (P3, P13, P19, P34) redrew the blood sample after the first blood collection. P13 took a blood sample at week 9 and attained 13.45% fetal fraction because the fetal fraction in the first blood sample was below 1% of the QC at week 7 + 4 . Even though the other three families passed the QC of FF, P3 and P19 failed the targeted capture the first time, because the site coverage was not enough to judge the inheritance of the pathogenic haplotype. In these cases, redrawing blood samples after around two weeks could increase both the maternal and paternal SNP numbers greatly. For P34, they failed the first time because the distribution of loci was not equilibrium. The type1 sites clustered around downstream and resulted in inadequacy to judge the recombination events. However, the proportion of FF increased by redrawing blood samples and help to secure the accuracy of haplotype judgment. 2.4 Three recombination families P18, P26, and P33 were identified with recombination by the CBS algorithm ( Figure S2 ). Luckily for P18, according to artificial evaluation, recombination occurred far away from downstream of the pathogenic variant and did not affect the results. However, P26 and P33 failed to get the exact accurate NIPD results because of recombination events. The maternal type1 locus recombined near the downstream of the pathogenic locus and the type2 locus recombined region crossed the SMN gene in P26. However, even if the mother's inheritance status cannot be determined, the fetus is revealed clearly with HF2 inheritance from the father. As a result, the fetus is normal or a maternal carrier without phenotype. Similarly, due to paternal recombination in P33 ( Fig. 4 ) , it failed to determine the father's inheritance, but it could ensure that the fetus inherited the normal haplotype from the mother. Although recombination events occurred, the two pregnant women chose to retain the fetus after fully informed consent that the fetus inherit one of the parent’s normal haplotypes. 3. MLPA validation and follow-up result 36 families validated the NIPD results by MLPA testing. In these families, the accuracy of NIPD was verified by CVS, amniocentesis, or apoblema testing (Table S2) . The consistency rate between NIPD results and MLPA diagnosis was 100%. Follow-up results showed that some families with affected NIPD results underwent abortions without invasive verifications, and the families with carrier and normal results chose to deliver fetuses. The two affected recombinant families that opted for retention were confirmed as normal fetuses after birth. Discussion As spinal muscular atrophy (SMA) carrier screening is commonly used in clinical practice, it gives rise to a huge demand for prenatal diagnosis in SMA carriers[16, 17]. The traditional prenatal diagnoses include CVS, amniocentesis, fetal blood sampling, and embryo scope[18]. Those methods are invasive operations and carry the risk of infection[19]. Most pregnant women worry about the risk of these invasive procedures during prenatal counseling[20]. The emergence of NIPD offers them another option with the advantages, such as early gestational age diagnosis and absolute safe operation. In our study, the accuracy of NIPD was verified by real clinical data. Compared with the earliest invasive diagnosis method CVS at 11 weeks, the earliest gestational age of blood collection for NIPD is week 7 + 3 . What’s more, the sensitivity and specificity of the NIPD were 100% with the set criterion in this study. Through MLPA verification, the NIPD results obtained by haplotype construction and Bayes factor showed a 100% accuracy rate. Up to now, SMA is one of the few SGDs that can be treated. According to the literature reported previously, FDA has approved Spinraza (nusinersen)[21], Zolgensma (onasemnogene abeparvovec-xioi)[22], and Evrysdir (risdiplam)[23] for SMA treatment. Even though there are no developed programs for intrauterine treatment, excessive treatment costs need time to raise money. As reported in our previous study, we recommend the earliest noninvasive detection of gestational age could reach 7 + 0 weeks[13]. It earns 5 weeks compared with CVS and 9 weeks compared with amniocentesis for families who wants to retain the affected fetus. For those families who want a healthy baby, the NIPD result could help them make pregnancy decisions as early as possible. Considering the high accuracy of NIPD, early medical abortion to can be performed for families who do not want invasive verification, reducing the harm of surgical abortion to the uterus and the pregnant woman[24]. In our study, the accurate, early, rapid, and safe noninvasive prenatal diagnosis of SMA is realized through targeted capture, haplotype construction, and Bayes factor calculation. Compared with the RMD, RHDO freed the dependence of the parental mutation spectrum. Besides, the MLPA test by measuring the copy number of SMN could only detect variants with deletions of exons (approximately 95%) and it is not suitable for the “2 + 0” carrier. RHDO offers a solution for all kinds of variant carriers, including the “2 + 0” families and point mutation families that could not be detected in the past. Only families with exon 7 and 8 deletions were involved in our study, and no families with point mutations were found. However, these types of families could be detected quickly and accurately in principle. Nevertheless, there are also some limitations associated with RHDO diagnostic methods. First, a complete pedigree is needed to construct haplotypes. In our study, two types of pedigrees were used. One is the families with a proband and the other is the families with a completely normal child (P17, P19). Actually, if the parents are carriers of different variants, the offspring used for haplotype construction can be normal, carriers, or patients. Second, the NIPD results were disturbed by recombination events extremely. The CBS algorithm was used to predict the recombination event, which is used to estimate copy number variation (CNV) data and identify the reasonable breakpoint[25]. There are two affected families (P26, P33) that showed different parents' origins of recombination. Luckily, only one of the parents had recombination, and the other haplotype could be accurately determined as a normal haplotype. In these cases, the fetuses could be confirmed as completely normal or carriers according to the NIPD results. Neither of them would have any symptoms and the parents choose to continue the pregnancy. However, if only one parental haplotype were confirmed as HF1/HM1 and another haplotype occurs recombination events, the family still needs invasive diagnosis to distinguish the carrier and patient. If both the two haplotypes occur recombination, they also need an invasive diagnosis instead of NIPD. Besides, as we can observe in the probe design of targeted capture ( Fig. 2 ) , there is an absence of probes located around SMN1 and SMN2 genes (chromosome coordinates: 68 813 676 and 70 680 481)[26]. Because this segment of the gene is relatively conservative, unique probes were unable to design for this region, which means the recombination occurring in this region could not be judged. Third, de novo mutations could result in NIPD failure. It is estimated that de novo SMN1 deletions occur in approximately 2% of patients with SMA, most of which are paternal origination[27, 28]. In addition, we need to rule out false positives due to parental gonadal mosaicism. Taking the above events together, we recommend that all noninvasive prenatal diagnostic results should be validated at a later gestational stage. QCs are essential for improving NIPD accuracy. In this study, three thresholds were set on informative SNP numbers, fetal fraction, and average sequencing depth, at the same time recombination events were assessed. Enough sequencing depth is to guarantee enough fetal fraction to calculate dose change. The larger the number of SNPs, the more accurate the haplotype construction, which is also beneficial to the judgment of recombination. In our study, the least SNPs for type 1 to type 4 were 18 (P5), 17(P34), 6(P35), and 17(P2), respectively. The distribution of SNP sites is also crucial for the judgment of recombination events. If the number of SNPs itself is limited and most concentrated at one end of the gene, then recombination cannot be accurately determined. If we can obtain enough SNPs, recombination could be fully assessed and the no-call rate would decrease. When the recombination event is far from the key area, it will not affect the judgment of the result. That is why the recombination needs a combination of manual and algorithmic assessment. For QC failure families, the current countermeasure is to redraw blood samples after two weeks. As gestational age increases, fetal fraction also increases, which can supplement the deficiency of SNP sites and sequencing depth. There are novel methods to increase fetal fraction by enriching the amount of DNA before library establishment (two-step magnetic bead screening). In this study, the minimal number of informative SNPs and fetal fractions to accurately estimate fetal haplotype was investigated and provided a useful preliminary reference for clinical application in the case of different fetal fractions. There remains room for improvement in fetal genotype determination, especially when recombination has occurred in the target region. Additionally, the evaluation of health economics is related to formulating and enforcing clinical policy. Our study showed that haplotype-based NIPD is a cost-effective, secure, and accurate method for prenatal diagnosis. Compared with whole-exome sequencing (WES) or whole-genome sequencing (WGS) (~ 50x sequencing depth), it increased the targeted region sequencing depth to about 300x and at the same time controlling the cost below $ 500. The turnaround time of NIPD is about 7–10 days, therefore the final report can come out within the first trimester of pregnancy, as the NIPD can be applied as early as 7 weeks. We have to mention that when a couple of SMA carriers want to have a healthy baby, PGT-M offers another option for them[29]. However, the success rate is not as high as it would be theoretically. What’s more, as the PGT-M procedure only detects a part of embryonic cells, prenatal diagnosis is still required at a later gestational stage. No matter the success or not, the PGT-M cost is ten times more than NIPD. In summary, NIPD based on haplotype is a noninvasive, high-accuracy, early pregnancy detection and cost-controllable technical method that has considerable reliability and feasibility in early pregnancy diagnosis and screening of SMA. According to the existing successful research like DMD, PKU, and SMA, we believe that the application of NIPD for autosomal recessive genetic diseases is relatively mature. In addition, we can design appropriate probes for different diseases and perform haplotype analysis when clinically necessary[30]. Except for the design of new probes, population haplotype construction that does not depend on the trio family is also in the process of continuous development[31]. It is foreseen that many novel NIPD applications will emerge in the near future. Declarations Ethics Statement 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. Competing Interests All authors declare that they have no conflicts of interest with the contents of this article. 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. Acknowledgments Not applicable. Funding Funding support was given to XK by Key projects of medical science and technology in Henan province jointly built by the provincial departments (SBGJ202102097) and Henan province's key research and development and promotion of key scientific and technological projects (222102520018) and Key scientific research projects of colleges and universities in Henan province (22A320075). Author Contributions Conceptualization: XDK, HYL, and DW; Software: SJL; Validation: HYL; Formal Analysis: SJL, HYL, and ZHZ; Investigation: JF; Resources: XDK, HYL, JQZ, and XYF; Data curation: HYL, JQZ, and XYF; Writing—original draft: HYL; Writing—review and editing: HYL, SJL, and WQT; Visualization: HYL and SJL; Funding acquisition: XDK; All authors contributed to the article and approved the submitted version. References Mercuri E, Sumner CJ, Muntoni F, Darras BT, Finkel RS: Spinal muscular atrophy . Nat Rev Dis Primers 2022, 8 (1):52. Pearn J: Classification of spinal muscular atrophies . Lancet 1980, 1 (8174):919-922. Roy N, McLean MD, Besner-Johnston A, Lefebvre C, Salih M, Carpten JD, Burghes AH, Yaraghi Z, Ikeda JE, Korneluk RG: Refined physical map of the spinal muscular atrophy gene (SMA) region at 5q13 based on YAC and cosmid contiguous arrays . Genomics 1995, 26 (3):451-460. 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Li S, Han X, Xu Y, Chang C, Gao L, Li J, Lu Y, Mao A, Wang Y: Comprehensive Analysis of Spinal Muscular Atrophy: SMN1 Copy Number, Intragenic Mutation, and 2 + 0 Carrier Analysis by Third-Generation Sequencing . J Mol Diagn 2022, 24 (9):1009-1020. Alfirevic Z, Navaratnam K, Mujezinovic F: Amniocentesis and chorionic villus sampling for prenatal diagnosis . Cochrane Database Syst Rev 2017, 9 (9):CD003252. Tabor A, Philip J, Madsen M, Bang J, Obel EB, Nørgaard-Pedersen B: Randomised controlled trial of genetic amniocentesis in 4606 low-risk women . Lancet 1986, 1 (8493):1287-1293. Boulet SL, Kirby RS, Reefhuis J, Zhang Y, Sunderam S, Cohen B, Bernson D, Copeland G, Bailey MA, Jamieson DJ et al : Assisted Reproductive Technology and Birth Defects Among Liveborn Infants in Florida, Massachusetts, and Michigan, 2000-2010 . JAMA Pediatr 2016, 170 (6):e154934. Hagenacker T, Wurster CD, Günther R, Schreiber-Katz O, Osmanovic A, Petri S, Weiler M, Ziegler A, Kuttler J, Koch JC et al : Nusinersen in adults with 5q spinal muscular atrophy: a non-interventional, multicentre, observational cohort study . Lancet Neurol 2020, 19 (4):317-325. Strauss KA, Farrar MA, Muntoni F, Saito K, Mendell JR, Servais L, McMillan HJ, Finkel RS, Swoboda KJ, Kwon JM et al : Onasemnogene abeparvovec for presymptomatic infants with two copies of SMN2 at risk for spinal muscular atrophy type 1: the Phase III SPR1NT trial . Nat Med 2022, 28 (7):1381-1389. Markati T, Fisher G, Ramdas S, Servais L: Risdiplam: an investigational survival motor neuron 2 (SMN2) splicing modifier for spinal muscular atrophy (SMA) . Expert Opin Investig Drugs 2022, 31 (5):451-461. Winikoff B, Dzuba IG, Chong E, Goldberg AB, Lichtenberg ES, Ball C, Dean G, Sacks D, Crowden WA, Swica Y: Extending outpatient medical abortion services through 70 days of gestational age . Obstet Gynecol 2012, 120 (5):1070-1076. Lai WR, Johnson MD, Kucherlapati R, Park PJ: Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data . Bioinformatics 2005, 21 (19):3763-3770. Scheffer H, Cobben JM, Matthijs G, Wirth B: Best practice guidelines for molecular analysis in spinal muscular atrophy . Eur J Hum Genet 2001, 9 (7):484-491. Melki J, Lefebvre S, Burglen L, Burlet P, Clermont O, Millasseau P, Reboullet S, Bénichou B, Zeviani M, Le Paslier D: De novo and inherited deletions of the 5q13 region in spinal muscular atrophies . Science 1994, 264 (5164):1474-1477. Wirth B, Schmidt T, Hahnen E, Rudnik-Schöneborn S, Krawczak M, Müller-Myhsok B, Schönling J, Zerres K: De novo rearrangements found in 2% of index patients with spinal muscular atrophy: mutational mechanisms, parental origin, mutation rate, and implications for genetic counseling . Am J Hum Genet 1997, 61 (5):1102-1111. Zhao M, Lian M, Cheah FSH, Tan ASC, Agarwal A, Chong SS: Identification of Novel Microsatellite Markers Flanking the SMN1 and SMN2 Duplicated Region and Inclusion Into a Single-Tube Tridecaplex Panel for Haplotype-Based Preimplantation Genetic Testing of Spinal Muscular Atrophy . Front Genet 2019, 10 :1105. Wang J, Gao P, Cao Q, Chen F, Song J, Wang C, Dou J, Wu Y, Niu Q, Li J et al : Haplotype-based non-invasive prenatal diagnosis of recessive dystrophic epidermolysis bullosa via targeted capture sequencing of maternal plasma . J Dermatol 2023. Chen C, Li R, Sun J, Zhu Y, Jiang L, Li J, Fu F, Wan J, Guo F, An X et al : Noninvasive prenatal testing of α-thalassemia and β-thalassemia through population-based parental haplotyping . Genome Med 2021, 13 (1):18. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3123735","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":218694061,"identity":"e29e4352-d61a-4b32-a3a2-87adfa96f18a","order_by":0,"name":"Huanyun Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huanyun","middleName":"","lastName":"Li","suffix":""},{"id":218694062,"identity":"b52032c0-926b-42bb-8a19-76b0256e4fdb","order_by":1,"name":"Shaojun Li","email":"","orcid":"","institution":"Celula (China) Medical Technology Co., Ltd","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shaojun","middleName":"","lastName":"Li","suffix":""},{"id":218694063,"identity":"c49e0173-fc53-4484-b8dd-823ab4b6d655","order_by":2,"name":"Zhenhua Zhao","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"Zhao","suffix":""},{"id":218694064,"identity":"e84e0d7d-e6c8-4c13-8d11-68cae0af7663","order_by":3,"name":"Xinyu Fu","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Fu","suffix":""},{"id":218694065,"identity":"f4649ca1-0c5d-43a5-b234-e0108ed8f019","order_by":4,"name":"Jingqi Zhu","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingqi","middleName":"","lastName":"Zhu","suffix":""},{"id":218694066,"identity":"3e31933d-ebba-4b5a-a40e-9dcdc86a5f16","order_by":5,"name":"Jun Feng","email":"","orcid":"","institution":"Celula (China) Medical Technology Co., Ltd","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Feng","suffix":""},{"id":218694067,"identity":"cea6e063-eac3-456f-9d45-90ee0d8cac45","order_by":6,"name":"Weiqin Tang","email":"","orcid":"","institution":"Celula (China) Medical Technology Co., Ltd","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weiqin","middleName":"","lastName":"Tang","suffix":""},{"id":218694068,"identity":"0f21d5d5-b490-44dc-afc3-fb47c7ba44e7","order_by":7,"name":"Di Wu","email":"","orcid":"","institution":"Celula (China) Medical Technology Co., Ltd","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wu","suffix":""},{"id":218694069,"identity":"724669b9-5ea2-4067-b240-ae6bbd27430d","order_by":8,"name":"Xiangdong Kong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACCTDJZiPHz99AmpY0Y8kZB4CMBOK1HE7c0JBApBbJ9t7Dr3nKzjNuYDjA9uDjDyK0SPOcS7Occe42szlzA7vhDGJskZPIMTP42HabzbLhAJs0D9FaEtvO8RgcSGCT/kOMFmmJHOMHH9sOSIC1EOf9njNmjDPOJRtIzjjYJtmTRoQWieM9xp95yuzq+/mbj0n8sCFCCxCwQeKGgbGBOPVAwPyBaKWjYBSMglEwMgEA1lc1Bsnz0CgAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiangdong","middleName":"","lastName":"Kong","suffix":""}],"badges":[],"createdAt":"2023-06-29 08:44:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3123735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3123735/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":40257923,"identity":"67be3179-7ffa-4097-9f2d-17bc8b3f248d","added_by":"auto","created_at":"2023-07-19 14:28:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":196090,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow of NIPD. \u003c/strong\u003eAfter genetic counseling, blood samples from the qualified 36 SMA family members were collected for noninvasive prenatal diagnosis (NIPD), and the results were reported within 1 week if samples met quality control requirements. Finally, the invasive diagnosis was applied to confirm the accuracy of NIPD testing.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3123735/v1/aa09809ac717415cb304c1f9.png"},{"id":40257924,"identity":"e728ecd4-c842-4a92-9f95-3ce5539173ed","added_by":"auto","created_at":"2023-07-19 14:28:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7012308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe probe design of the SMA region. \u003c/strong\u003eSubgraph A shows the structure within the SMA gene(70220739-70248867). The green band represents the untranslated regions (UTR) area, the blue band represents the coding sequences (CDS) area, and the pathogenic site is marked with a red dot. Subgraph B shows the designed panel coverage (67934652-71802533) and the linkage disequilibrium (LD) situation of the SMA gene within the panel range. The blue vertical line represents the panel coverage and each pink vertical line represents an SNP. The lower triangle region represents the linkage degree between SNPs, and D is the linkage metric. The redder the color is, the D is closer to 1, and the stronger linkage between SNPs is. The yellow color indicates that D is closer to 0, and the linkage between SNPs is weaker. LD blocks between adjacent SNPs are marked with black solid lines.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3123735/v1/95eb14d0842d24c75b6696a3.png"},{"id":40257925,"identity":"541dbd06-13e9-44cc-a4cc-53e553ada718","added_by":"auto","created_at":"2023-07-19 14:28:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":625145,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrinciple of haplotype-based noninvasive prenatal diagnosis.\u003c/strong\u003eFirst, the trio family analysis is essential to distinguish the informative SNP locations. Informative SNP sites were those homozygous for one parent and heterozygous for another parent. According to the proband information, the maternal pathogenic haplotype with type1 was defined as HM1, and the wild-type haplotype with type2 was defined as HM2. Similarly, paternal haplotypes were named HF1 including type 3, and HF2 including type 4, respectively. With this principle, types 1-4 would have different expected dosages when the fetus inherited different haplotypes. The key was the balance of maternal HM1 and HM2 alleles were disturbed. For example, if the fetus inherited HM1, type 1 site A-T dosage would be imbalanced. The elevating A dosage is caused by fetal’s HM1 and HF1/HF2. For paternal inheritance, it was easier to distinguish because type 3/type 4 would appear as a novel base.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3123735/v1/5f4bfaab63b089a877cd0a7f.png"},{"id":40257926,"identity":"96272a04-d010-4ef2-9f45-a5d3a6d5ba6c","added_by":"auto","created_at":"2023-07-19 14:28:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":573693,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe RHDO result of P10 (A) and P33 (B)\u003c/strong\u003e. Scatter plot of the dosage change (DC) of each allele. The X-axis is the genomic coordinate, and the Y-axis represents DC. Red dots denote the DC of the Type 1/Type 3 allele (over-represented when the fetus inherited HM1/HF1, which carries the pathogenic variant), whereas blue dots are the DC of the Type 2/Type 4 allele (overrepresented if the fetus inherited HM2/HF2, which carries the wild-type SMA gene). The red and blue horizontal line is the center of DC returned by the CBS algorithm. When recombination occurs, both lines will cross at the switch site (P33). The dashed lines indicate the expected value of DC for Type 1 and Type 2 alleles under the assumption that the fetus inherits the maternal pathogenic and wild-type haplotype. The gray vertical dashed line marks the position of SMA exons.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3123735/v1/6285fc632ad1429423c70e86.png"},{"id":44491347,"identity":"74d13232-639a-4cd0-9312-5acccac3dcfe","added_by":"auto","created_at":"2023-10-12 08:07:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1879145,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3123735/v1/a04a2cd5-dff3-44c9-a4b7-c9ecfb556727.pdf"},{"id":40257927,"identity":"5ea44e02-9277-4d0b-ac21-d83b8334ff8f","added_by":"auto","created_at":"2023-07-19 14:28:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":607364,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-3123735/v1/5664c0eaeac40cc2d83e8ae6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Haplotype-based noninvasive prenatal diagnoses of 36 fetuses with spinal muscular atrophy in the real clinical environment at early gestation age ","fulltext":[{"header":"Background","content":"\u003cp\u003eSpinal muscular atrophy (SMA) is an autosomal-recessive neurodegenerative disease characterized by progressive symmetrical muscle weakness and early death[1]. It is the second most common fatal autosomal recessive disorder after cystic fibrosis, with an estimated incidence of approximately 1 in 10,000 live births[2]. Typically, people have two copies of the \u003cem\u003eSMN1\u003c/em\u003e gene and one to two copies of the \u003cem\u003eSMN2\u003c/em\u003e gene in the 5q13 region[3]. The most striking difference between \u003cem\u003eSMN1\u003c/em\u003e and \u003cem\u003eSMN2\u003c/em\u003e is the 6th nucleotide of exon 7 (C-to‐T transition)[4]. SMN protein is encoded by these two almost identical genes but most functional SMN protein is produced by the \u003cem\u003eSMN1\u003c/em\u003e gene. Mutations in \u003cem\u003eSMN1\u003c/em\u003e can decrease the amount of SMN protein and cause progressive muscle atrophy and paralysis. It is estimated that approximately 95% of patients were caused by the homozygous absence of the \u003cem\u003eSMN1\u003c/em\u003e exon 7[5], while others have non-sense, frameshift, or missense mutations within the gene[6].\u003c/p\u003e \u003cp\u003ePrenatal diagnosis is an essential way to prevent the birth of infants with hereditary diseases. Traditional prenatal diagnoses include chorionic villus sampling (CVS) and amniocentesis which are invasive and have a risk of miscarriage or stillbirth (incidence: 0.1\u0026ndash;0.3%)[7, 8]. Nevertheless, since the discovery of the cell-free fetal DNA (cffDNA) in maternal plasma in 1997[9], scientists have created several new approaches for noninvasive prenatal diagnosis (NIPD). Compared with the traditional invasive methods, NIPD is much safer and can help to diagnose congenital anomalies in the early gestational week. It is well known that the whole cell-free DNA (cfDNA) consists of cffDNA originating from the placental trophoblast and cfDNA from maternal cells. However, it is difficult to detect maternal genetic locus in the context of maternal cfDNA, especially for autosomal-recessive diseases. In clinical, the most effective and commonly used NIPD methods are relative mutation dose (RMD) analysis and relative haplotype dose (RHDO) analysis[10]. The ratio allele frequency or haplotype dosage can be calculated to infer fetal genotypes. Compared with RMD, RHDO is no longer dependent on the detection of specific mutations and has a high sensitivity. Recent clinical applications based on haplotype in several single-gene disorder (SGD) diagnoses have been proven technically possible, such as β-thalassemia[11], congenital adrenal hyperplasia (CAH)[12], and Duchenne and Becker muscular dystrophies (DMD/BMD)[13].\u003c/p\u003e \u003cp\u003eUnlike most other SGDs, SMA harbors the need and potential for a specific design of the NIPD technique. First, the predominant gene mutation is the loss of \u003cem\u003eSMN1\u003c/em\u003e (95%) instead of point mutation, which implies it is difficult for RMD analysis and there are few clinical applications[14]. Second, the pseudogene \u003cem\u003eSMN2\u003c/em\u003e results in the difficulty to estimate the copy number of \u003cem\u003eSMN1\u003c/em\u003e accurately. In this study, we developed a high-accuracy assay based on RHDO and Bayesian to infer fetal genotype.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample information and preparation\u003c/h2\u003e \u003cp\u003eThirty-six SMA families with singleton pregnancies (named P1-P36) were enrolled from December 2019 to January 2023 after genetic counseling and a receipt of informed consent. The pregnancy gestational age ranged from 7\u003csup\u003e+\u0026thinsp;3\u003c/sup\u003e weeks to 13\u003csup\u003e+\u0026thinsp;0\u003c/sup\u003e weeks (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Thirty-four families had typical trio pedigrees (Father, Mother, and Proband) and two families had normal pedigrees (Father, Mother, and completely normal offspring). For each family, 10 ml of peripheral blood from the pregnant mother and 2 ml of peripheral blood from the offspring and father were collected. The study was approved by the Ethics Committee of First Affiliated Hospital of Zhengzhou University. All methods were carried out in accordance with relevant guidelines.\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 NIPD results of 36 SMA families.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGestational weeks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFetal fraction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaternal inheritance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePaternal inheritance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNIPD results\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMLPA results\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.12%\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\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003csup\u003e+\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003csup\u003e+\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.59%\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\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003csup\u003e+\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.02%\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\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ematernal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.79%\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\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ematernal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003csup\u003e+\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.29%\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\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ematernal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003csup\u003e+\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.86%\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\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003csup\u003e+\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.29%\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\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003csup\u003e+\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.77%\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\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ematernal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.99%\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\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ematernal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003csup\u003e+\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.54%\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\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ematernal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP13-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003csup\u003e+\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP13-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003csup\u003e+\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epaternal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epaternal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003csup\u003e+\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003csup\u003e+\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epaternal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003csup\u003e+\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.71%\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\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP19-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP19-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003csup\u003e+\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.92%\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\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003csup\u003e+\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.61%\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\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003csup\u003e+\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.90%\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\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ematernal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRecombination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno call\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epaternal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.96%\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\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003csup\u003e+\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003csup\u003e+\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRecombination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eno call\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunaffected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP34-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP34-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003csup\u003e+\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epaternal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003epaternal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003csup\u003e+\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90%\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\u003eHF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ematernal carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecarrier\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=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDetection Workflow\u003c/h2\u003e \u003cp\u003eThe workflow of the NIPD for SMA families is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. First, members of at-risk of SMA families were recruited and received genetic counseling. Then, peripheral blood was collected from each family member. Then the cfDNA was extracted following the manufacturer\u0026rsquo;s instructions (Nahai Bio, Chengdu, China). gDNA was extracted from the leucocytes of three family members via the in-house protocol. Subsequently, samples were subject to end-repair, barcode ligation, PCR amplification, and target capture. The post-capture DNA libraries were subjected to another round of PCR amplification and sequenced on the Ion Proton platform. If sequencing depth met the quality control requirement, then haplotype phasing and fetal fraction calculation were conducted separately, with quality control (QC) at the end of each process. After the QC of FF and informative SNPs, the recombination event was analyzed by the circular binary segmentation (CBS) algorithm and fetal genotype was predicted using Bayes factor (BF). NIPD results were validated by invasive diagnosis and post-test genetic counseling was provided.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eProbe design and target sequencing\u003c/h2\u003e \u003cp\u003eA 168.736kb capture panel TargetSeq\u0026reg; One kit (iGeneTech, China) was designed to selectively enrich target regions based on the reference genome (GRCh37/hg19). The capture panel covered the entire \u003cem\u003eSMN1/2\u003c/em\u003e genes, including all exon and intron regions of the two genes. 758 common SNPs (EAS_MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.2, 1000 Genomes Project Phase 3) within the 2Mb genomic region both upstream and downstream of the SMN1 gene were used for the target DNA capture. Besides, the panel covered 213 highly heterozygous SNPs (MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.45) scattered on chromosomes 1\u0026ndash;22 to calculate fetal fraction. cfDNA and fragmented gDNA were captured after end repair, barcode adapter ligation, and PCR amplification. Subsequently, the post-capture libraries were subjected to PCR amplification again and sequenced on the Ion Proton platform (Thermo Fisher Scientific, Lithuania).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of fetal fraction and fetal genotype\u003c/h2\u003e \u003cp\u003e213 specific SNPs loci scattered on chromosomes 1\u0026ndash;22 were used for calculating fetal fraction. We selected SNPs homozygous in parents but with different genotypes to calculate the fetal fraction in maternal plasma (f) by the following equation: f\u0026thinsp;=\u0026thinsp;2a\u0026frasl;((a\u0026thinsp;+\u0026thinsp;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. Haplotype-phasing was conducted using genotypes of the core family according to Mendel\u0026rsquo;s law \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The maternal pathogenic and wild-type haplotypes were defined as HM1 and HM2 separately, while the paternal pathogenic and wild-type haplotypes were named HF1 and HF2. 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[13]. If BF\u0026thinsp;\u0026ge;\u0026thinsp;10, the result indicated the fetus inherited HF1/HM1. If BF\u0026thinsp;\u0026le;\u0026thinsp;0.1, it indicated the fetus inherited HF2/HM2. When BF fell between 0.1 to 10, the NIPD result is no call. After haplotype phasing, quality control for SNP numbers was performed. When the number of Type 1 or Type 2 alleles was less than 10, this indicated consanguineous marriage. Haplotype-based NIPD was not suitable for such families, and invasive diagnoses were suggested.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eQuality control (QC)\u003c/h2\u003e \u003cp\u003eFour QC criteria were implemented to ensure the reliability of the results: average sequencing depth, the fetal fraction, the number of informative SNPs, and recombination events. The average sequencing depth should be \u0026ge;\u0026thinsp;70x for cfDNA and \u0026ge;\u0026thinsp;30x for gDNA samples. If the sample failed the QC depth, it should be re-captured and sequenced. The lower limit is 10 for Type 1/2 SNPs in maternal haplotype phasing and 5 for Type 3/4 in parental haplotype phasing. Patients without sufficient informative SNPs would take invasive diagnoses instead. As for the minimum fetal fraction, if the fetal fraction is less than 1%, the pregnant mother needs to redraw blood samples after one or two weeks. When recombination events showed in target regions, it is necessary to combine bioinformatics analysis and clinical knowledge to judge whether the discrimination of pathogenic haplotypes is affected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMlPA analysis\u003c/h2\u003e \u003cp\u003eAll the recruited trio families and NIPD results were conducted MLPA analysis to validate the copy number of exons 7 and 8 in \u003cem\u003eSMN1\u003c/em\u003e and \u003cem\u003eSMN2\u003c/em\u003e genes. Besides, the NIPD results were compared with the MLPA results of the chorionic villus sampling (CVS), amniocentesis samples, or apoblema testing.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e1. Family information\u003c/h2\u003e \u003cp\u003eTotally, 36 trio families were recruited in the NIPD testing. Pregnant women's age ranges from 22 to 41 (median: 31) years old and the mean gestational age of NIPD blood drawing is 9\u003csup\u003e+\u0026thinsp;3\u003c/sup\u003e(7\u003csup\u003e+\u0026thinsp;3\u003c/sup\u003e-13\u003csup\u003e+\u0026thinsp;0\u003c/sup\u003e)weeks. 34 families consisted of a father, mother, and proband. The parents were carriers and the proband is a patient of EX7_8del. While there were two families without proband, and the offspring was confirmed in complete health without the SMA variant. These two families are speculated in the same way as trio families with proband but the results are the opposite to them.\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 36 families were sequenced by target region capture, and the average of total reads is 4265958 (803485-13211730). The average sequencing depth of each sample range from 121x to 995x (average:428x) and the ratio of more than 300x ranges from 43.3\u0026ndash;73.92% (average:59.15%). Generally, the capture ratio is around 88.00% (62.82%-98.77%). All samples met the depth quality control requirements and their average sequencing depth is more than 70x in cfDNA or 30x in gDNA samples. Hence, sufficient sequencing depth can help to screen qualified SNP sites. (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Fetal Fraction and haplotype outcomes\u003c/h2\u003e \u003cp\u003eFetal fraction is an essential factor for haplotype-based analysis in NIPD and it is affected by the maternal age, gestational week, and other maternal factors as reported before[15]. In our testing, the average fetal fraction is 6.01% (1.9%-13.52%) and all samples are above the minimum margin at last. Among the 36 families, 34 families were successfully tested for NIPD (success rate: 94.4%), and 2 families could not be determined exactly because of the recombination event near the pathogenic mutation.\u003c/p\u003e \u003cp\u003eAccording to the haplotype analysis, the maternal or paternal pathogenic haplotypes were confirmed as HF1 and HM1, respectively. The average SNP number is 91 for type 1 (18\u0026ndash;216) and 79 for type 2 (13\u0026ndash;341) which were used to speculate fetal-maternal inheritance. Similarly, type 3 (6-172) and type 4 (17\u0026ndash;154) were used to infer paternal inherence (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBF is used to judge the magnitude of informative SNP imbalance and has shown great precision in predicting fetal haplotype. In this study, the result includes 7 affected fetuses (P1, P3, P8, P9, P17, P19, P21), 6 paternal carriers, 8 maternal carriers, and 13 normal fetuses. Among the 34 successful families, three of the families obtained accurate results after redrawing blood samples due to low fetal concentration or the inefficiency of informative SNPs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Four families redrew blood samples\u003c/h2\u003e \u003cp\u003eAmong the 36 families, four families (P3, P13, P19, P34) redrew the blood sample after the first blood collection. P13 took a blood sample at week 9 and attained 13.45% fetal fraction because the fetal fraction in the first blood sample was below 1% of the QC at week 7\u003csup\u003e+\u0026thinsp;4\u003c/sup\u003e. Even though the other three families passed the QC of FF, P3 and P19 failed the targeted capture the first time, because the site coverage was not enough to judge the inheritance of the pathogenic haplotype. In these cases, redrawing blood samples after around two weeks could increase both the maternal and paternal SNP numbers greatly. For P34, they failed the first time because the distribution of loci was not equilibrium. The type1 sites clustered around downstream and resulted in inadequacy to judge the recombination events. However, the proportion of FF increased by redrawing blood samples and help to secure the accuracy of haplotype judgment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Three recombination families\u003c/h2\u003e \u003cp\u003eP18, P26, and P33 were identified with recombination by the CBS algorithm (\u003cb\u003eFigure S2\u003c/b\u003e). Luckily for P18, according to artificial evaluation, recombination occurred far away from downstream of the pathogenic variant and did not affect the results. However, P26 and P33 failed to get the exact accurate NIPD results because of recombination events. The maternal type1 locus recombined near the downstream of the pathogenic locus and the type2 locus recombined region crossed the SMN gene in P26. However, even if the mother's inheritance status cannot be determined, the fetus is revealed clearly with HF2 inheritance from the father. As a result, the fetus is normal or a maternal carrier without phenotype. Similarly, due to paternal recombination in P33 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, it failed to determine the father's inheritance, but it could ensure that the fetus inherited the normal haplotype from the mother. Although recombination events occurred, the two pregnant women chose to retain the fetus after fully informed consent that the fetus inherit one of the parent\u0026rsquo;s normal haplotypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3. MLPA validation and follow-up result\u003c/h2\u003e \u003cp\u003e36 families validated the NIPD results by MLPA testing. In these families, the accuracy of NIPD was verified by CVS, amniocentesis, or apoblema testing \u003cb\u003e(Table S2)\u003c/b\u003e. The consistency rate between NIPD results and MLPA diagnosis was 100%. Follow-up results showed that some families with affected NIPD results underwent abortions without invasive verifications, and the families with carrier and normal results chose to deliver fetuses. The two affected recombinant families that opted for retention were confirmed as normal fetuses after birth.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs spinal muscular atrophy (SMA) carrier screening is commonly used in clinical practice, it gives rise to a huge demand for prenatal diagnosis in SMA carriers[16, 17]. The traditional prenatal diagnoses include CVS, amniocentesis, fetal blood sampling, and embryo scope[18]. Those methods are invasive operations and carry the risk of infection[19]. Most pregnant women worry about the risk of these invasive procedures during prenatal counseling[20].\u003c/p\u003e \u003cp\u003eThe emergence of NIPD offers them another option with the advantages, such as early gestational age diagnosis and absolute safe operation. In our study, the accuracy of NIPD was verified by real clinical data. Compared with the earliest invasive diagnosis method CVS at 11 weeks, the earliest gestational age of blood collection for NIPD is week 7\u003csup\u003e+\u0026thinsp;3\u003c/sup\u003e. What\u0026rsquo;s more, the sensitivity and specificity of the NIPD were 100% with the set criterion in this study. Through MLPA verification, the NIPD results obtained by haplotype construction and Bayes factor showed a 100% accuracy rate.\u003c/p\u003e \u003cp\u003eUp to now, SMA is one of the few SGDs that can be treated. According to the literature reported previously, FDA has approved Spinraza (nusinersen)[21], Zolgensma (onasemnogene abeparvovec-xioi)[22], and Evrysdir (risdiplam)[23] for SMA treatment. Even though there are no developed programs for intrauterine treatment, excessive treatment costs need time to raise money. As reported in our previous study, we recommend the earliest noninvasive detection of gestational age could reach 7\u003csup\u003e+\u0026thinsp;0\u003c/sup\u003e weeks[13]. It earns 5 weeks compared with CVS and 9 weeks compared with amniocentesis for families who wants to retain the affected fetus. For those families who want a healthy baby, the NIPD result could help them make pregnancy decisions as early as possible. Considering the high accuracy of NIPD, early medical abortion to can be performed for families who do not want invasive verification, reducing the harm of surgical abortion to the uterus and the pregnant woman[24].\u003c/p\u003e \u003cp\u003eIn our study, the accurate, early, rapid, and safe noninvasive prenatal diagnosis of SMA is realized through targeted capture, haplotype construction, and Bayes factor calculation. Compared with the RMD, RHDO freed the dependence of the parental mutation spectrum. Besides, the MLPA test by measuring the copy number of SMN could only detect variants with deletions of exons (approximately 95%) and it is not suitable for the \u0026ldquo;2\u0026thinsp;+\u0026thinsp;0\u0026rdquo; carrier. RHDO offers a solution for all kinds of variant carriers, including the \u0026ldquo;2\u0026thinsp;+\u0026thinsp;0\u0026rdquo; families and point mutation families that could not be detected in the past. Only families with exon 7 and 8 deletions were involved in our study, and no families with point mutations were found. However, these types of families could be detected quickly and accurately in principle.\u003c/p\u003e \u003cp\u003eNevertheless, there are also some limitations associated with RHDO diagnostic methods. First, a complete pedigree is needed to construct haplotypes. In our study, two types of pedigrees were used. One is the families with a proband and the other is the families with a completely normal child (P17, P19). Actually, if the parents are carriers of different variants, the offspring used for haplotype construction can be normal, carriers, or patients. Second, the NIPD results were disturbed by recombination events extremely. The CBS algorithm was used to predict the recombination event, which is used to estimate copy number variation (CNV) data and identify the reasonable breakpoint[25]. There are two affected families (P26, P33) that showed different parents' origins of recombination. Luckily, only one of the parents had recombination, and the other haplotype could be accurately determined as a normal haplotype. In these cases, the fetuses could be confirmed as completely normal or carriers according to the NIPD results. Neither of them would have any symptoms and the parents choose to continue the pregnancy. However, if only one parental haplotype were confirmed as HF1/HM1 and another haplotype occurs recombination events, the family still needs invasive diagnosis to distinguish the carrier and patient. If both the two haplotypes occur recombination, they also need an invasive diagnosis instead of NIPD. Besides, as we can observe in the probe design of targeted capture \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, there is an absence of probes located around \u003cem\u003eSMN1\u003c/em\u003e and \u003cem\u003eSMN2\u003c/em\u003e genes (chromosome coordinates: 68 813 676 and 70 680 481)[26]. Because this segment of the gene is relatively conservative, unique probes were unable to design for this region, which means the recombination occurring in this region could not be judged. Third, de novo mutations could result in NIPD failure. It is estimated that de novo \u003cem\u003eSMN1\u003c/em\u003e deletions occur in approximately 2% of patients with SMA, most of which are paternal origination[27, 28]. In addition, we need to rule out false positives due to parental gonadal mosaicism. Taking the above events together, we recommend that all noninvasive prenatal diagnostic results should be validated at a later gestational stage.\u003c/p\u003e \u003cp\u003eQCs are essential for improving NIPD accuracy. In this study, three thresholds were set on informative SNP numbers, fetal fraction, and average sequencing depth, at the same time recombination events were assessed. Enough sequencing depth is to guarantee enough fetal fraction to calculate dose change. The larger the number of SNPs, the more accurate the haplotype construction, which is also beneficial to the judgment of recombination. In our study, the least SNPs for type 1 to type 4 were 18 (P5), 17(P34), 6(P35), and 17(P2), respectively. The distribution of SNP sites is also crucial for the judgment of recombination events. If the number of SNPs itself is limited and most concentrated at one end of the gene, then recombination cannot be accurately determined. If we can obtain enough SNPs, recombination could be fully assessed and the no-call rate would decrease. When the recombination event is far from the key area, it will not affect the judgment of the result. That is why the recombination needs a combination of manual and algorithmic assessment. For QC failure families, the current countermeasure is to redraw blood samples after two weeks. As gestational age increases, fetal fraction also increases, which can supplement the deficiency of SNP sites and sequencing depth. There are novel methods to increase fetal fraction by enriching the amount of DNA before library establishment (two-step magnetic bead screening). In this study, the minimal number of informative SNPs and fetal fractions to accurately estimate fetal haplotype was investigated and provided a useful preliminary reference for clinical application in the case of different fetal fractions. There remains room for improvement in fetal genotype determination, especially when recombination has occurred in the target region.\u003c/p\u003e \u003cp\u003eAdditionally, the evaluation of health economics is related to formulating and enforcing clinical policy. Our study showed that haplotype-based NIPD is a cost-effective, secure, and accurate method for prenatal diagnosis. Compared with whole-exome sequencing (WES) or whole-genome sequencing (WGS) (~\u0026thinsp;50x sequencing depth), it increased the targeted region sequencing depth to about 300x and at the same time controlling the cost below \u003cspan\u003e$\u003c/span\u003e500. The turnaround time of NIPD is about 7\u0026ndash;10 days, therefore the final report can come out within the first trimester of pregnancy, as the NIPD can be applied as early as 7 weeks. We have to mention that when a couple of SMA carriers want to have a healthy baby, PGT-M offers another option for them[29]. However, the success rate is not as high as it would be theoretically. What\u0026rsquo;s more, as the PGT-M procedure only detects a part of embryonic cells, prenatal diagnosis is still required at a later gestational stage. No matter the success or not, the PGT-M cost is ten times more than NIPD.\u003c/p\u003e \u003cp\u003eIn summary, NIPD based on haplotype is a noninvasive, high-accuracy, early pregnancy detection and cost-controllable technical method that has considerable reliability and feasibility in early pregnancy diagnosis and screening of SMA. According to the existing successful research like DMD, PKU, and SMA, we believe that the application of NIPD for autosomal recessive genetic diseases is relatively mature. In addition, we can design appropriate probes for different diseases and perform haplotype analysis when clinically necessary[30]. Except for the design of new probes, population haplotype construction that does not depend on the trio family is also in the process of continuous development[31]. It is foreseen that many novel NIPD applications will emerge in the near future.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement\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\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflicts of interest with the contents of this article.\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\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding support was given to XK by Key projects of medical science and technology in Henan province jointly built by the provincial departments (SBGJ202102097) and Henan province\u0026apos;s key research and development and promotion of key scientific and technological projects (222102520018) and Key scientific research projects of colleges and universities in Henan province (22A320075).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: XDK, HYL, and DW; Software: SJL; Validation: HYL; Formal Analysis: SJL, HYL, and ZHZ; Investigation: JF; Resources: XDK, HYL, JQZ, and XYF; Data curation: HYL, JQZ, and XYF; Writing\u0026mdash;original draft: HYL; Writing\u0026mdash;review and editing: HYL, SJL, and WQT; Visualization: HYL and SJL; Funding acquisition: XDK; All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMercuri E, Sumner CJ, Muntoni F, Darras BT, Finkel RS: \u003cstrong\u003eSpinal muscular atrophy\u003c/strong\u003e. \u003cem\u003eNat Rev Dis Primers \u003c/em\u003e2022, \u003cstrong\u003e8\u003c/strong\u003e(1):52.\u003c/li\u003e\n\u003cli\u003ePearn J: \u003cstrong\u003eClassification of spinal muscular atrophies\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e1980, \u003cstrong\u003e1\u003c/strong\u003e(8174):919-922.\u003c/li\u003e\n\u003cli\u003eRoy N, McLean MD, Besner-Johnston A, Lefebvre C, Salih M, Carpten JD, Burghes AH, Yaraghi Z, Ikeda JE, Korneluk RG: \u003cstrong\u003eRefined physical map of the spinal muscular atrophy 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\u003cem\u003eJ Dermatol \u003c/em\u003e2023.\u003c/li\u003e\n\u003cli\u003eChen C, Li R, Sun J, Zhu Y, Jiang L, Li J, Fu F, Wan J, Guo F, An X\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eNoninvasive prenatal testing of \u0026alpha;-thalassemia and \u0026beta;-thalassemia through population-based parental haplotyping\u003c/strong\u003e. \u003cem\u003eGenome Med \u003c/em\u003e2021, \u003cstrong\u003e13\u003c/strong\u003e(1):18.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Spinal muscular atrophy, Noninvasive prenatal diagnoses, Bayes factors, Haplotype construction","lastPublishedDoi":"10.21203/rs.3.rs-3123735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3123735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo explore the feasibility of noninvasive prenatal diagnoses (NIPD) based on haplotype construction and Bayes factor (BF) for spinal muscular atrophy (SMA) in clinical application.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e36 singleton families with pregnancy risk of SMA were recruited and all the recruited members were conducted MLPA to validate the copy number of exons 7 and 8 in \u003cem\u003eSMN1\u003c/em\u003e and \u003cem\u003eSMN2\u003c/em\u003e genes. The designed capture panel covered the entire \u003cem\u003eSMN1/2\u003c/em\u003e genes, including all exon and intron regions of the two genes. To ensure the NIPD accuracy, four quality control standards were set: sequencing depth, the number of informative SNPs, cell-free DNA fetal fraction, and the recombination event assessment. By enriching targeting genes and informative SNP sites in adjacent regions, the family haplotype was constructed and the fetal genotype was determined based on the dose change of the informative SNPs in cfDNA combined with BF algorithm. All NIPD results were verified by chorionic villus sampling (CVS), amniocentesis, or apoblema testing.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the 36 recruited SMA families, 34 (94.4%) families were successfully tested for NIPD, and 2 (5.56%) families could not be determined exactly because of the recombination event near the pathogenic mutations. In successful families, the earliest gestational week for NIPD was 7\u003csup\u003e+\u0026thinsp;3\u003c/sup\u003e weeks, and the lowest free fetal DNA fraction was 1.9%. A total of 8 affected fetuses, 6 paternal carriers, 8 maternal carriers, and 12 unaffected fetuses were detected. The consistency between NIPD results and invasive MLPA diagnosis was 100%. Four (11.1%) of the families obtained accurate results after redrawing blood samples due to low fetal fraction or insufficiency of informative SNPs. Follow-up results showed that all families with affected NIPD results underwent abortions, and the families with carrier and normal results chose to deliver the fetus.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNIPD is a noninvasive, high-accuracy, early pregnancy detection and cost-controllable technical method. The haplotype construction and BF analysis have considerable reliability and feasibility and could be used in the detection of other recessive monogenic diseases.\u003c/p\u003e","manuscriptTitle":"Haplotype-based noninvasive prenatal diagnoses of 36 fetuses with spinal muscular atrophy in the real clinical environment at early gestation age ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-19 14:28:54","doi":"10.21203/rs.3.rs-3123735/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6550ddf1-2a11-4ca7-ba7d-a618117c493d","owner":[],"postedDate":"July 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-12T07:59:09+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-19 14:28:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3123735","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3123735","identity":"rs-3123735","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}