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A Cell-free DNA Barcode-Enabled Single-Molecule Test for Noninvasive Prenatal Diagnosis of Monogenic Disorders: Application to β-Thalassemia.

ABSTRACT: Noninvasive prenatal testing of common aneuploidies has become routine over the past decade, but testing of monogenic disorders remains a challenge in clinical implementation. Most recent studies have inherent limitations, such as complicated procedures, a lack of versatility, and the need for prior knowledge of parental genotypes or haplotypes. To overcome these limitations, a robust and versatile next-generation sequencing-based cell-free DNA (cfDNA) allelic molecule counting system termed cfDNA barcode-enabled single-molecule test (cfBEST) is developed for the noninvasive prenatal diagnosis (NIPD) of monogenic disorders. The accuracy of cfBEST is found to be comparable to that of droplet digital polymerase chain reaction (ddPCR) in detecting low-abundance mutations in cfDNA. The analytical validity of cfBEST is evidenced by a β-thalassemia assay, in which a blind validation study of 143 at-risk pregnancies reveals a sensitivity of 99.19% and a specificity of 99.92% on allele detection. Because the validated cfBEST method can be used to detect maternal-fetal genotype combinations in cfDNA precisely and quantitatively, it holds the potential for the NIPD of human monogenic disorders.

SUBMITTER: Yang X

PROVIDER: S-EPMC6548944 | biostudies-literature |

REPOSITORIES: biostudies-literature

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A method for noninvasive prenatal diagnosis of monogenic autosomal recessive disorders.

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Project description:In the last decade, noninvasive prenatal diagnosis (NIPD) has emerged as an effective procedure for early detection of inherited diseases during pregnancy. This technique is based on using cell-free DNA (cfDNA) and fetal cfDNA (cffDNA) in maternal blood, and hence, has minimal risk for the mother and fetus compared with invasive techniques. NIPD is currently used for identifying chromosomal abnormalities (in some instances) and for single-gene disorders (SGDs) of paternal origin. However, for SGDs of maternal origin, sensitivity poses a challenge that limits the testing to one genetic disorder at a time. Here, we present a Bayesian method for the NIPD of monogenic diseases that is independent of the mode of inheritance and parental origin. Furthermore, we show that accounting for differences in the length distribution of fetal- and maternal-derived cfDNA fragments results in increased accuracy. Our model is the first to predict inherited insertions-deletions (indels). The method described can serve as a general framework for the NIPD of SGDs; this will facilitate easy integration of further improvements. One such improvement that is presented in the current study is a machine learning model that corrects errors based on patterns found in previously processed data. Overall, we show that next-generation sequencing (NGS) can be used for the NIPD of a wide range of monogenic diseases, simultaneously. We believe that our study will lead to the achievement of a comprehensive NIPD for monogenic diseases.

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Project description:Bayesian-based noninvasive prenatal diagnosis of single-gene disorders

| PRJNA484073 | ENA

Bayesian-based noninvasive prenatal diagnosis of single-gene disorders

Project description:Bayesian-based noninvasive prenatal diagnosis of single-gene disorders

| PRJNA484074 | ENA

Sensitive Monogenic Noninvasive Prenatal Diagnosis by Targeted Haplotyping

Project description:During pregnancy, cell-free DNA (cfDNA) in maternal blood encompasses a small percentage of cell-free fetal DNA (cffDNA), an easilyaccessible source for determination of fetal disease status in risk families through non-invasive procedures. In case of monogenic heritabledisease, background maternal cfDNA prohibits direct observation of the maternally inherited allele. Non-invasive prenatal diagnostics(NIPD) of monogenic diseases therefore relies on parental haplotyping and statistical assessment of inherited alleles from cffDNA,techniques currently unavailable for routine clinical practice. Here, we present monogenic NIPD (MG-NIPD), which requires a bloodsample from both parents, for targeted locus amplification (TLA)-based phasing of heterozygous variants selectively at a gene of interest.Capture probes-based targeted sequencing of cfDNA from the pregnant mother and a tailored statistical analysis enables predicting fetalgene inheritance. MG-NIPD was validated for 18 pregnancies, focusing on CFTR, CYP21A2, and HBB. In all cases we could predict theinherited alleles with >98% confidence, even at relatively early stages (8 weeks) of pregnancy. This prediction and the accuracy ofparental haplotyping was confirmed by sequencing of fetal material obtained by parallel invasive procedures. MG-NIPD is a robustmethod that requires standard instrumentation and can be implemented in any clinic to provide families carrying a severe monogenicdisease with a prenatal diagnostic test based on a simple blood draw.

| EGAS00001002622 | EGA

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Project description:BackgroundNoninvasive prenatal testing (NIPT) of recessive monogenic diseases depends heavily on knowing the correct parental haplotypes. However, the currently used family-based haplotyping method requires pedigrees, and molecular haplotyping is highly challenging due to its high cost, long turnaround time, and complexity. Here, we proposed a new two-step approach, population-based haplotyping-NIPT (PBH-NIPT), using α-thalassemia and β-thalassemia as prototypes.MethodsFirst, we deduced parental haplotypes with Beagle 4.0 with training on a large retrospective carrier screening dataset (4356 thalassemia carrier screening-positive cases). Second, we inferred fetal haplotypes using a parental haplotype-assisted hidden Markov model (HMM) and the Viterbi algorithm.ResultsWith this approach, we enrolled 59 couples at risk of having a fetus with thalassemia and successfully inferred 94.1% (111/118) of fetal alleles. We confirmed these alleles by invasive prenatal diagnosis, with 99.1% (110/111) accuracy (95% CI, 95.1-100%).ConclusionsThese results demonstrate that PBH-NIPT is a sensitive, fast, and inexpensive strategy for NIPT of thalassemia.

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