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Updated: Jul 13, 2025
FISH for Pre-implantation Genetic Diagnosis
Published on: February 23, 2011
Comprehensive application of multiple molecular diagnostic techniques in pre-implantation genetic testing for
Peng Huang1,2, Yueyun Lan1,2, Hong Zhou3
1PGD Laboratory, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Background:
Pre-implantation genetic testing for monogenic disorders (PGT-M) is an effective approach to reducing the incidence of birth defects by preventing the transmission of inherited diseases to offspring. However, there are still controversies regarding the detection methods and transplantation of embryos. This paper aims to evaluate the effectiveness of different detection technologies applied to PGT-M through a retrospective analysis of clinical detection data.
Methods:
The carrier status of pathogenic mutations and chromosomal copy number variants (CNVs) in 892 embryos was characterized using next-generation sequencing (NGS), single-nucleotide polymorphism (SNP) array, and PCR-based detection technologies. Clinical data from PGT-M cases were retrospectively analyzed to assess the effectiveness of these detection methods in identifying genetic abnormalities in embryos.
Results:
A total of 829 embryos were analyzed, with 63 being unsuccessful. Our study revealed that the success rate of detecting deletional mutations using Gap-PCR 84.9%, which is lower than that of SNP array (98.7%) and NGS (92.5%). However, no significant difference was observed when detecting point mutations using any of the methods. These findings suggest that, when detecting deletional mutations, SNP array and NGS are more suitable choices compared to Gap-PCR. While SNP array may have a lower resolution and success rate (80.5%) in analyzing CNVs compared to NGS (95.5%), it may still be useful for revealing certain abnormal types.
Conclusion:
In conclusion, this study found that SNP analysis is advantageous for identifying polygenic and deletional mutations, whereas NGS is more cost-efficient for detecting common monogenic diseases. Additionally, SNP-based haplotyping and PCR-based direct detection of mutations can be used together to enhance the accuracy and success rates of PGT-M. Our findings offer valuable insights for PGT technicians in choosing suitable detection methods for patients.
