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Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy
Published on: August 25, 2019
Non-invasive prenatal diagnosis of beta-thalassemia disease using digital PCR
Chalit Tangwerapornpong1, Kuntharee Traisrisilp1, Pimlak Charoenkwan2,3
1Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
Introduction:
To evaluate the performance of digital polymerase chain reaction (dPCR) as a non-invasive prenatal test (NIPT) for assessing the risk of the fetus being affected by beta-thalassemia major and beta-thalassemia/Hb E disease.
Methods:
This cross-sectional study included 63 pregnant women from couples at risk of having a fetus with severe beta-thalassemia (homozygous beta-thalassemia or beta-thalassemia/Hb E disease), who underwent invasive prenatal diagnosis between January and September 2024. Maternal plasma cell-free DNA (cfDNA) was analyzed using dPCR to detect and quantify the paternally inherited beta-thalassemia allele (PIB) and maternally inherited beta-thalassemia (MIB) alleles. Invasive prenatal diagnosis (chorionic villus sampling, amniocentesis, or cordocentesis) served as the gold standard for fetal genotype confirmation via multiplex PCR and high-resolution melting (HRM) analysis. The mutant/normal (M/N) allele ratio was calculated, and receiver operating characteristic (ROC) analysis determined the optimal cut-off for predicting affected fetuses.
Results:
Among 54 couples with discordant mutations, PIB was identified in 25 cases. An MIB-M/N ratio cut-off of ≥0.919 yielded a sensitivity of 100% and a specificity of 81.2% for predicting fetal inheritance of MIB allele (AUC = 0.938). In nine couples with identical mutations, an M/N ratio of ≥1.043 predicted affected fetuses with both 100% sensitivity and specificity.
Conclusion:
dPCR-based NIPT accurately identifies fetuses at risk of beta-thalassemia major, potentially reducing the need for invasive procedures and associated risks. The relatively small sample size could limit the generalizability of the findings. Larger studies involving more participants from different ethnic and geographical backgrounds would provide more robust data.
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