The promise of carrier screening: noninvasive prenatal diagnoses without proband for spinal muscular atrophy in early gestation age

doi:10.21203/rs.3.rs-3999388/v1

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The promise of carrier screening: noninvasive prenatal diagnoses without proband for spinal muscular atrophy in early gestation age

https://doi.org/10.21203/rs.3.rs-3999388/v1

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The feasibility of traditional noninvasive prenatal diagnosis (NIPD) relying on proband-based relative haplotype dose analysis has been demonstrated. However, the prognosis of type I spinal muscular atrophy (SMA) is poor, and the proband sample is hard to collect during the second pregnancy. We investigate the feasibility of NIPD for SMA via haplotype construction without the need for a proband. Six samples were collected from both the paternal and maternal families in 36 families at risk of SMA. By enriching the SMN1/2 gene and its upstream and downstream informative SNPs, the family haplotype was constructed, and the Bayes factor was used to infer the fetal genotype by the dose changes of informational SNPs in cell-free DNA. All samples underwent MLPA testing after chorion villus sampling or amniocentesis. The MLPA results showed 100% consistency with NIPD. The earliest gestational week for successful NIPD was 7^+ 3 weeks, with a minimum fetal fraction of 1.9%. Haplotype construction based on both paternal and maternal families demonstrated significant reliability and feasibility for families without a proband. Additionally, this approach provides a safer, and earlier prenatal diagnosis option for couples identified as at-risk through SMA carrier screening.

Health sciences/Diseases

Health sciences/Health care

Health sciences/Medical research

Spinal muscular atrophy

Noninvasive prenatal diagnoses

Bayes factors

Haplotype construction

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). Normally, people have at least two copies of the SMN1 gene and one to two copies of the SMN2 gene in the 5q13 region(3). The differentiation between SMN1 and SMN2 is characterized by a paralogous sequence variant in the 6th nucleotide of exon 7, specifically a 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. Variants 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 variants within the gene(6).

Prenatal diagnosis is an essential way to diagnose infants with hereditary diseases and guide pregnancy decisions. With advancing therapeutic venues, there are opportunities for management and treatment that can lead to improved outcomes for an affected child. 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). Remarkably, the discovery of the cell-free fetal DNA (cffDNA) in maternal plasma in 1997 laid the foundation for noninvasive prenatal diagnosis (NIPD) in monogenic disorders(9). 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 the maternal genetic locus of the fetus in the context of maternal cfDNA, especially for autosomal-recessive diseases. In the clinic, the most effective and commonly used NIPD methods are relative mutation dose (RMD) analysis and relative haplotype dose (RHDO) analysis(10). The ratio of allele frequency and haplotype dosage can be calculated to infer fetal genotypes. Compared with RMD, RHDO is no longer dependent on the detection of specific variants and has a high sensitivity. Recent clinical applications based on haplotype in several monogenic disorders have been proven technically possible, such as β-thalassemia(11), congenital adrenal hyperplasia (12), and Duchenne and Becker muscular dystrophies(13).

Unlike most other monogenic disorders, SMA harbors the need and potential for a specific design of the NIPD technique. First, the highly homologous SMN2 gene to SMN1 complicates the NIPD assay, which must precisely discern the exact copy number of the SMN1 gene in the fetus. Second, the predominant variant is the homozygous absence of SMN1 (95%). Although the RMD approach for SMA has been developed, the accuracy is limited(14). Third, The proband with Type I SMA is the most severe and common type, which accounts for about 50% of patients, normally emerges within the first 6 months of life and dying within 2 years(15). Moreover, carrier screening revealed that SMA carrier frequency is around one in 50(16). For those families, the proband sample is difficult to obtain at the time of prenatal diagnosis. Nevertheless, the family structure in China tends to live in groups, parents, offspring and grandchildren are closely related, and the sample of grandparents is easy to obtain. In this study, we developed a high-accuracy assay based on RHDO but without proband to infer fetal genotype.

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. The I-1/ I-2, I-3/I-4, and II-(1–2) were confirmed as carriers of EX7_8del (Fig. 1).

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 150x in cfDNA or 30x in gDNA samples. Hence, sufficient sequencing depth can help to screen qualified SNP sites.

2.2 Fetal fraction (FF) and haplotype outcomes

FF 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(17). 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 variant.

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. 5).

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, four of the families obtained accurate results after redrawing blood samples due to low fetal concentration or the low coverage 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 redrew the blood sample at week 9 and attained 13.45% fetal fraction because the fetal fraction in the first blood sample was below 1% 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 FF greatly and the BF is significant enough to identify fetal genotypes. 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 helped to secure the accuracy of haplotype judgment(18).

2.4 Three recombination families

P18, P26, and P33 were identified with recombination by the CBS algorithm (Figure S2). Luckily for P18, according to bioinformatics analysis, 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. 5), 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 chorionic villus sampling, amniocentesis, or products of conception testing. (Table S1). 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 recombinant families that opted for retention were confirmed as normal fetuses after birth.

As SMA carrier screening is commonly used in clinical practice, it gives rise to a huge demand for prenatal diagnosis in SMA carriers(19, 20). Besides, because of the poor prognosis of Type I SMA, many families have lost their probands. The traditional prenatal diagnoses include CVS, amniocentesis, fetal blood sampling, and embryo scope(21). Those methods are invasive operations and carry the risk of infection(22). Most pregnant women worry about the risk of these invasive procedures during prenatal counseling(23).

Chinese family tends to live in a large social group. It is a common phenomenon in China that three generations live together, and grandparents pay more attention to their grandchildren than parents, so it is feasible to obtain samples of six people when the proband sample is not available. The emergence of NIPD offers them another option with 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 recent research, FDA has approved Spinraza (nusinersen)(24), Zolgensma (onasemnogene abeparvovec-xioi)(25), and Evrysdir (risdiplam)(26) 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 want 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 can be performed for families who do not want invasive verification, avoiding the harm of surgical abortion to the pregnant woman(27).

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 variant 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 variant 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 variants 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, the two complete pedigree is needed to construct haplotypes. Collecting six family members’ samples might be challenging. Second, the NIPD results might be disturbed by recombination events. The CBS algorithm was used to predict the recombination event, which is used to estimate copy number variation data and identify the reasonable breakpoint(28). 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. 3), there is an absence of probes located around SMN1 and SMN2 genes (chromosome coordinates: 68,813,676 and 70,680,481)(29). 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 variants could result in NIPD failure. Luckily, it is estimated that de novo SMN1 deletions occur in approximately 2% of patients with SMA, most of which are paternal origination(30, 31). Fourth, consanguineous families are not suitable for this test. In addition, we need to rule out false positives due to parental gonadal mosaicism. Taking the above events together, we recommend that all NIPD 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(18). 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. 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 exome sequencing (ES) or genome sequencing (GS) (~ 50x sequencing depth), it increased the targeted region sequencing depth to about 300x and at the same time controlled 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(32). However, 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 but without proband is a noninvasive, high-accuracy, early pregnancy detection and cost-controllable technical method. Its considerable reliability and feasibility in early pregnancy diagnosis of SMA have been proven. At the same time, it provides hope for those high-risk couples without pregnancy and birth history identified by carrier screening. According to the existing successful research like DMD, PKU, and SMA, appropriate novel probes were designed for different diseases when clinically necessary(33). Besides, population haplotype construction that does not depend on the trio family is also in the process of continuous development(34). It is foreseen that more novel NIPD applications will emerge soon.

Sample information and preparation

Fifty-two SMA families with singleton were enrolled from December 2019 to January 2023. In thirty-six families (named P1-P36), six samples (Fig. 1) were successfully collected after genetic counseling and a receipt of informed consent. The pregnancy gestational age ranged from 7^+ 3 weeks to 13^+ 0 weeks (Table 1). For each family, 10 ml of peripheral blood from the pregnant mother and 2 ml of peripheral blood from I-(1–4) and II-(1–2) were collected (Fig. 1). The study was approved by the Ethics Committee of First Affiliated Hospital of Zhengzhou University.

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
*Gray blocks indicate that the family redrew blood

Detection Workflow

The workflow of the NIPD for SMA families is illustrated in Fig. 2. First, members at risk of SMA families were recruited, and 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 the six 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 fetal fraction (FF) and informative SNPs, the recombination event was analyzed by the circular binary segmentation (CBS) algorithm, and fetal genotype was predicted using the 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 two core families by Shapeit (Fig. 4). 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. 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. 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 indicates the fetus inherits HF1/HM1. If BF ≤ 0.1, it indicates the fetus inherits HF2/HM2. When BF fall between 0.1 to 10, the NIPD result is no call.

Quality control (QC)

Four QC criteria were implemented to ensure the reliability of the results: average sequencing depth, the FF, the number of informative SNPs, and recombination events. The average sequencing depth should be ≥ 150x for cfDNA and ≥ 30x for gDNA samples. If the sample fails the QC depth, it should be re-captured and sequenced. The lower limit is 10 for Type 1/2 SNPs in the maternal haplotype and 5 for Type 3/4 in the parental haplotype. Patients without sufficient informative SNPs would take invasive diagnoses instead. As for the minimum FF, if the FF is less than 1%, the pregnant mother needs to redraw blood samples after one or two weeks. When recombination events happen 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 EX7_8 in SMN1 and SMN2 genes. Besides, the NIPD results were compared with the MLPA results of the chorionic villus sampling, amniocentesis samples, or products of conception testing.

Acknowledgments

Not applicable.

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.

Data availability statement

The relevant data is provided within the manuscript or supplementary information files. The raw datasets for this article are not publicly available due to concerns regarding participant/patient anonymity. The raw datasets used during the current study are available from the corresponding author on reasonable request via this email: [email protected]. The data can be obtained and reused within a week after offering approval from the requester's ethics committee and approved by the corresponding author.

Funding statement

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).

Conflict of Interest Disclosure

The authors have no conflicts of interest to disclose. Author Di Wu, Shaojun Li, Jun Feng, and Weiqin Tang are employed by Celula (China) Medical Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethics Approval Statement and Patient Consent Statement

The project passed the review of the ethics committee by the Ethics Committee for Scientific Research and Clinical Trials of the First Affiliated Hospital of Zhengzhou University (2019-KY-286). All patients and their family members signed informed consent. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

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):919–22.
Roy N, McLean MD, Besner-Johnston A, Lefebvre C, Salih M, Carpten JD, et al. Refined physical map of the spinal muscular atrophy gene (SMA) region at 5q13 based on YAC and cosmid contiguous arrays. Genomics. 1995;26(3):451–60.
Lefebvre S, Bürglen L, Reboullet S, Clermont O, Burlet P, Viollet L, et al. Identification and characterization of a spinal muscular atrophy-determining gene. Cell. 1995;80(1):155–65.
McAndrew PE, Parsons DW, Simard LR, Rochette C, Ray PN, Mendell JR, et al. Identification of proximal spinal muscular atrophy carriers and patients by analysis of SMNT and SMNC gene copy number. Am J Hum Genet. 1997;60(6):1411–22.
Wirth B. An update of the mutation spectrum of the survival motor neuron gene (SMN1) in autosomal recessive spinal muscular atrophy (SMA). Hum Mutat. 2000;15(3):228–37.
Salomon LJ, Sotiriadis A, Wulff CB, Odibo A, Akolekar R. Risk of miscarriage following amniocentesis or chorionic villus sampling: systematic review of literature and updated meta-analysis. Ultrasound Obstet Gynecol. 2019;54(4):442–51.
Vossaert L, Chakchouk I, Zemet R, Van den Veyver IB. Overview and recent developments in cell-based noninvasive prenatal testing. Prenat Diagn. 2021;41(10):1202–14.
Lo YM, Corbetta N, Chamberlain PF, Rai V, Sargent IL, Redman CW, et al. Presence of fetal DNA in maternal plasma and serum. Lancet (London, England). 1997;350(9076):485–7.
Li J, Liu Y, Qian Y, Zhang D. Noninvasive preimplantation genetic testing in assisted reproductive technology: current state and future perspectives. J Genet Genomics. 2020;47(12):723–6.
Lam K-WG, Jiang P, Liao GJW, Chan KCA, Leung TY, Chiu RWK, et al. Noninvasive prenatal diagnosis of monogenic diseases by targeted massively parallel sequencing of maternal plasma: application to β-thalassemia. Clin Chem. 2012;58(10):1467–75.
New MI, Tong YK, Yuen T, Jiang P, Pina C, Chan KCA, et al. Noninvasive prenatal diagnosis of congenital adrenal hyperplasia using cell-free fetal DNA in maternal plasma. J Clin Endocrinol Metab. 2014;99(6):E1022-E30.
Kong L, Li S, Zhao Z, Feng J, Chen G, Liu L, et al. Haplotype-Based Noninvasive Prenatal Diagnosis of 21 Families With Duchenne Muscular Dystrophy: Real-World Clinical Data in China. Front Genet. 2021;12:791856.
Hoskovec J, Hardisty EE, Talati AN, Carozza JA, Wynn J, Riku S, et al. Maternal carrier screening with single-gene NIPS provides accurate fetal risk assessments for recessive conditions. Genet Med. 2023;25(2):100334.
Lunn MR, Wang CH. Spinal muscular atrophy. Lancet (London, England). 2008;371(9630):2120–33.
Wilson RB, Ogino S. Carrier frequency of spinal muscular atrophy. Lancet (London, England). 2008;372(9649):1542; author reply
Deng C, Liu S. Factors Affecting the Fetal Fraction in Noninvasive Prenatal Screening: A Review. Front Pediatr. 2022;10:812781.
Kong L, Li S, Zhao Z, Feng J, Fu X, Li H, et al. Exploring factors impacting haplotype-based noninvasive prenatal diagnosis for single-gene recessive disorders. Clin Genet. 2023.
Ross LF, Clarke AJ. A Historical and Current Review of Newborn Screening for Neuromuscular Disorders From Around the World: Lessons for the United States. Pediatr Neurol. 2017;77:12–22.
Li S, Han X, Xu Y, Chang C, Gao L, Li J, et al. 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):1009–20.
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):1287–93.
Boulet SL, Kirby RS, Reefhuis J, Zhang Y, Sunderam S, Cohen B, 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, et al. Nusinersen in adults with 5q spinal muscular atrophy: a non-interventional, multicentre, observational cohort study. Lancet Neurol. 2020;19(4):317–25.
Strauss KA, Farrar MA, Muntoni F, Saito K, Mendell JR, Servais L, 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):1381–9.
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):451–61.
Winikoff B, Dzuba IG, Chong E, Goldberg AB, Lichtenberg ES, Ball C, et al. Extending outpatient medical abortion services through 70 days of gestational age. Obstet Gynecol. 2012;120(5):1070–6.
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):3763–70.
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):484–91.
Melki J, Lefebvre S, Burglen L, Burlet P, Clermont O, Millasseau P, et al. De novo and inherited deletions of the 5q13 region in spinal muscular atrophies. Science. 1994;264(5164):1474–7.
Wirth B, Schmidt T, Hahnen E, Rudnik-Schöneborn S, Krawczak M, Müller-Myhsok B, et al. 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):1102–11.
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, 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, et al. Noninvasive prenatal testing of α-thalassemia and β-thalassemia through population-based parental haplotyping. Genome Med. 2021;13(1):18.

No competing interests reported.

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The promise of carrier screening: noninvasive prenatal diagnoses without proband for spinal muscular atrophy in early gestation age

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Abstract

Figures

Introduction

Results

1. Family information

2. NIPD results

2.1 sequencing information

2.2 Fetal fraction (FF) and haplotype outcomes

2.3 Four families redrew blood samples

2.4 Three recombination families

3. MLPA validation and follow-up result

Discussion

MATERIALS AND METHODS

Sample information and preparation

Detection Workflow

Probe design and target sequencing

Measurement of fetal fraction and fetal genotype

Quality control (QC)

MlPA analysis

Declarations

References

Additional Declarations

Supplementary Files

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