PLoS ONEGenetic & Genomic MedicinePeer-reviewed journal article
Use of amplicon-based sequencing for testing fetal identity and monogenic traits with single circulating trophoblast (SCT) prenatal diagnosis
Xinming ZhuoiD, Qun WangiD, Liesbeth VossaertiD, Roseen Salman, Adriel KimiD, Ignatia B. Van den VeyveriD, Amy M. BremaniD, Arthur L. BeaudetiD
Abstract
A major challenge for cell-based non-invasive prenatal testing (NIPT) is to distinguish individual presumptive fetal cells from maternal cells in female pregnancies. We have sought a rapid, robust, versatile, and low-cost next-generation sequencing method to facilitate this process. Toward this goal, single isolated cells underwent whole genome amplification prior to genotyping. Multiple highly polymorphic genomic regions (including HLA-A and HLA-B) with 10-20 very informative single nucleotide polymorphisms (SNPs) within a 200 bp interval were amplified with a modified method based on other publications. To enhance the power of cell identification, approximately 40 Human Identification SNP (Applied Biosystems) test amplicons were also utilized. This method allowed reliable differentiation of fetal and maternal cells. In fully informative cases, two haplotypes were found within the maternal reads, and fetal cells showed reads with one but not the second maternal haplotype while also showing a novel paternal haplotype absent in the mother. For SNP typing, at least 2 SNPs and 10% of informative SNPs were required to differentiate a fetal cell from a maternal cell. A paternal DNA sample is not required using this method. The assay also successfully detected point mutations causing Tay Sachs disease, cystic fibrosis, and hemoglobinopathies in single lymphoblastoid cells, and monogenic disease-causing mutations in three cell-based NIPT cases. This method could be applicable for any monogenic diagnosis.
- organism: human
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: variant calling
- approach: statistical model
- Citations
- 9
- FWCI
- 1.98
- Field percentile
- top 16%
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