Using single nucleotide polymorphism array for prenatal diagnosis

single nucleotide
Introduction
Method
Result
Disccusion
Conclusion

Methods of chromosome evaluation
Criteria for invasive prenatal diagnostic tests

Single nucleotide polymorphism array �(SNP-array)
Application of SNP-array in prenatal diagnosis
To explore the performance of SNP-array-mediated evaluation with different risk factors, we split the 8386 samples into seven groups.
To perform our SNP-array testing, we used genomic DNA extracted from villus, umbilical cord, and amniotic fluid using a genomic DNA extraction kit from QIAGEN, Germany.
SNP-array Detection and Data Analysis
Result
Pregnancy out comes

These evaluations identified pCNV mutations in 8.3% (699/8386)
NIPT-positive group: pCNV rate 35.3%
abnormal ultrasound structure group: pCNV rate 12.8%
couples with chromosomal abnormalities: pCNV rate 9.5%

Chromosomal abnormalities increasing the risk of fetal pCNVs

STSS identifies high-risk abnormalities in the neural tube

1. Journal Reading 202 3 .08.21 Chimei hospital 奇美 醫院 1

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3. Outline Introduction Method Result Disccusion Conclusion 3

4. Methods of chromosome evaluation amniotic fluid umbilical cord blood chorionic villus sampling Introduction C riteria for invasive prenatal diagnostic tests non-invasive prenatal testing (NIPT)-positive results abnormal ultrasound structures chromosomal abnormalities in couples; high-risk STSS results; 5) advanced maternal age (≥ 35 years) ultrasound soft markers adverse pregnancy history.

5. Single nucleotide polymorphism array (SNP-array) high resolution, high-throughput whole genome screening detection of CNVs that cannot be detected by karyotype analysis including chromosomal microdeletion or microduplication copy number variations(CNV) Introduction Application of SNP-array in prenatal diagnosis the etiological relationship between these risk factors and CNVs

6. Study Subjects A retrospective evaluation of 8386 fetuses who underwent prenatal diagnosis at Fujian Maternity and Child Health Hospital between January 2016 and November 2021. Ages of the pregnant women: 18 ~ 48 years old
Gestational ages of the fetuses: 11 ~ 36 weeks Sample Type Sample Size (8386) Villi Samples (11–13 weeks of gestation) 62 Amniotic Fluid Samples (17–24 weeks of gestation) 6970 Cord Blood Samples (25–36 weeks of gestation) 1354 Methodology

7. Risk Factors To explore the performance of SNP-array-mediated evaluation with different risk factors, we split the 8386 samples into seven groups. The groups are: 1 NIPT positive (n=323) 2 Abnormal ultrasound structure (n=1495) 3 Chromosomal abnormalities in couples (n=232) 4 High-risk STSS (n=1100) 5 Advanced maternal age (n=1176) 6 Ultrasound soft markers (n=3423) 7 Adverse pregnancy history (n=637) Methodology

8. DNA Extraction To perform our SNP-array testing, we used genomic DNA extracted from villus, umbilical cord, and amniotic fluid using a genomic DNA extraction kit from QIAGEN, Germany. Methodology

9. SNP-array Detection and Data Analysis High-resolution CytoScan 750 K chip from Affymetrix enabled us to type SNP-arrays, generate results through Chromosome Analysis Suite software and interpreted them using reference databases. We used the clinical significance guidelines for CNV developed by the American College of Medical Genetics and Genomics to divide our results into three categories and five levels:
(1) pathogenic CNV (pCNV) : pathogenic CNV and likely pathogenic CNV
(2) benign CNV : benign CNV and likely benign CNV
(3) variants of unknown significance (VUS). The medium length of time needed to perform SNP-array was 10 days. Methodology

10. SNP-array analysis Sample Type Sample Size (8386) normal CNV 7553 (90.1%) Abnormal CNV 833 (9.9%) pCNV 699 VUS 134 Result

11. Result Relationship between different risk factors and CNV Result 35.3% 12.8% 9.5 % 2.8%

12. Result Abnormal ultrasonic structure categories and CNVs Result

13. Result Ultrasound soft markers and CNV Result 11.3% 5.8 % 4. 6 %

14. Pregnancy out comes Result

15. Pregnancy out comes Result

16. These evaluations identified pCNV mutations in 8.3% (699/8386) NIPT-positive group: pCNV rate 35.3% abnormal ultrasound structure group : pCNV rate 12.8% couples with chromosomal abnormalities: pCNV rate 9.5% C hromosomal abnormalities increasing the risk of fetal pCNVs STSS identifies high-risk abnormalities in the neural tube Discussion

17. SNP-array SNP-array is a powerful diagnostic tool for detecting CNVs and other abnormalities in fetuses. The pCNV rate varies with different risk factors, highlighting the importance of using SNP-array for prenatal diagnosis. Ultrasound Scans The pCNV rate varies with the different abnormal ultrasound structures detected, indicating that different prenatal diagnostic techniques should be selected accordingly. Ultrasound Soft Markers The rate of pCNV increases with an increase in the number of ultrasound soft markers, suggesting the need for an SNP-array detection for multiple ultrasound soft markers. Discussion

18. Limitations of the Study 1 Limited Data The study was exclusively dependent on SNP-array data. 2 Follow-up Some fetuses did not have follow-up after birth. Discussion

19. Importance of SNP-array for Prenatal Diagnosis Power of SNP-array SNP-array is a powerful tool that detects CNVs and other abnormalities in fetuses. Early Diagnosis and Prevention Early detection using SNP-array can prevent abnormalities and save the lives of the unborn. Accuracy in Prenatal Diagnosis The accuracy of the SNP-array tool in prenatal diagnosis can prevent genetic disorders and reduce the impact of pregnancy losses. Discussion

20. Conclusion Sensitivity SNP-array is sensitive to CNVs and other fetal abnormalities. Risk Factors The presence of different risk factors affects the pCNV rate. Diagnosis Techniques Different prenatal diagnosis techniques should be selected according to different abnormalities detected, indicating the potential use of SNP-array. Conclusion