Dataset Information

Clinical application of non-invasive prenatal diagnosis of phenylketonuria based on haplotypes via paired-end molecular tags and weighting algorithm.

ABSTRACT:

Background

Phenylketonuria (PKU) is a metabolic disease that can cause severe and irreversible brain damage without treatment.

Methods

Here we developed a non-invasive prenatal diagnosis (NIPD) technique based on haplotypes via paired-end molecular tags and weighting algorithm and applied it to the NIPD of PKU to evaluate its accuracy and feasibility in the early pregnancy. A custom-designed hybridization probes containing regions in phenylalanine hydroxylase (PAH) gene and its 1 Mb flanking region were used for target sequencing on genomic and maternal plasma DNA (7-13 weeks of gestation) to construct the parental haplotypes and the proband's haplotype. Fetal haplotype was then inferred combined with the parental haplotypes and the proband's haplotype. The presence of haplotypes linked to both the maternal and paternal mutant alleles indicated affected fetuses. The fetal genotypes were further validated by invasive prenatal diagnosis in a blinded fashion.

Results

This technique has been successfully applied in twenty-one cases. Six fetuses were diagnosed as patients carrying both of the mutated haplotypes inherited from their parents. Eleven fetuses were carriers of one heterozygous PAH variants, six of which were paternal and five of which were maternal. Four fetuses were absence of pathogenic alleles. All results were consistent with the prenatal diagnosis through amniotic fluid.

Conclusions

The results showed that our new technique applied to the genotyping of fetuses with high risk for PKU achieves an accurate detection at an early stage of pregnancy with low fetal fraction in cell free DNA.

SUBMITTER: Peng D

PROVIDER: S-EPMC8684071 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

Clinical application of non-invasive prenatal diagnosis of phenylketonuria based on haplotypes via paired-end molecular tags and weighting algorithm.

Peng Dai D Ganye Zhao Z Gege Sun S Yanjie Xia X Ning Liu L Xiangdong Kong K

BMC medical genomics 20211217 1

<h4>Background</h4>Phenylketonuria (PKU) is a metabolic disease that can cause severe and irreversible brain damage without treatment.<h4>Methods</h4>Here we developed a non-invasive prenatal diagnosis (NIPD) technique based on haplotypes via paired-end molecular tags and weighting algorithm and applied it to the NIPD of PKU to evaluate its accuracy and feasibility in the early pregnancy. A custom-designed hybridization probes containing regions in phenylalanine hydroxylase (PAH) gene and its 1 ...[more]

PMID: 34920737

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Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Yijun Ruan mailto:ruanyj@gis.a-star.edu.sg). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track was produced as part of the ENCODE Project. It shows the locations of protein factor mediated chromatin interactions determined by Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) data (Fullwood et al., 2010) extracted from five different human cancer cell lines (K562 (chronic myeloid leukemia), HCT116 (colorectal cancer), HeLa-S3 (cervical cancer), MCF-7 (breast cancer), and NB4 (promyelocytic)). A chromatin interaction is defined as the association of two regions of the genome that are far apart in terms of genomic distance, but are spatially proximate to each other in the 3-dimensional cellular nucleus. Additionally, ChIA-PET experiments generate transcription factor binding sites. A binding site is defined as a region of the genome that is highly enriched by specific Chromatin ImmunoPrecipitation (ChIP) against a transcription factor, which indicates that the transcription factor binds specifically to this region. The protein factors displayed in the track include estrogen receptor alpha (ERa), RNA polymerase II (RNAPII), and CCCTC binding factor (CTCF). For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Chromatin interaction analysis with paired-end tag sequencing (ChIA-PET) is a global de novo high-throughput method for characterizing the 3-dimensional structure of chromatin in the nucleus. In the ChIA-PET protocol, samples were cross-linked and fragmented, then subjected to chromatin immunoprecipitation. The DNA fragments that were brought together by the chromatin interactions were then proximity-ligated. During this proximity-ligation step, the half-linkers (created by the fragmentation) containing flanking MmeI sites (type IIS restriction enzymes) were first ligated to the DNA fragments and then ligated to each other to form full linkers. Full linkers bridge either two ends of a self-circularized fragment, or two ends of two different chromatin fragments. The material was then reverse cross-linked, purified and digested with MmeI. MmeI cuts 20 base pairs away from its recognition site. Tag-linker-tag (paired-end tag, PET) constructs were sequenced by ultra-high-throughput methods (Illumina or SOLiD paired-end sequencing). ChIA-PET reads were processed with the ChIA-PET Tool (Li et al., 2010) by the following steps: linker filtering, short reads mapping, PET classification, binding site identification, and interaction cluster identification. The high-confidence binding sites and chromatin interaction clusters were reported. Chromatin interactions identified by ChIA-PET have been validated by 3C, ChIP-3C, 4C and DNA-FISH (Fullwood et al., 2009).

Dataset Information

Clinical application of non-invasive prenatal diagnosis of phenylketonuria based on haplotypes via paired-end molecular tags and weighting algorithm.

Background

Methods

Results

Conclusions

Publications

Clinical application of non-invasive prenatal diagnosis of phenylketonuria based on haplotypes via paired-end molecular tags and weighting algorithm.

Similar Datasets

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