Related Experiment Video
Updated: Jul 3, 2025
Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform
Published on: August 17, 2022
Exploring the factors affecting classification and reporting of uncertain prenatal microarray findings, using a
Rachel Michaelson-Cohen1, Liat Sheelo Salzer2, Dana Brabbing-Goldstein2
1Faculty of Medicine, Medical Genetics Institute, Shaare Zedek Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel.
Objective:
Significant discrepancy exists between laboratories in classification and reporting of copy number variants (CNVs). Studies exploring factors affecting prenatal CNV management are rare. Our "virtual fetus" pilot study examines these factors.
Method:
Ten prenatally diagnosed CNVs of uncertain significance (VUS) > 1Mb, encompassing OMIM-morbid genes, inherited from healthy parents, were classified by 15 MD geneticists from laboratory, prenatal, and preimplantation genetic testing (PGT) units. Geneticists addressed factors affecting classification, obligation to report, and recommendation for invasive testing or PGT.
Results:
CNVs were classified likely benign (10.7%), VUS (74.7%), likely pathogenic (8.7%), or pathogenic (6.0%). Classification discrepancy was higher for losses versus gains. Classifying pathogenic/likely pathogenic was more common for losses (adjusted odds ratio [aOR] 10.9, 95% CI 1.55-76.9), and geneticists specializing in gynecology (aOR 4.9, 95% CI 1.03-23.3). 84.0% of respondents would report CNVs, depending on classification and family phenotype. Invasive testing in pregnancies was recommended for 29.3% of CNVs, depending on the classification and geneticist's specialization. PGT was recommended for 32.4%, depending on classification, experience years, and family's phenotype (38.0% for patients undergoing in vitro fertilization irrespectively, 26.7% otherwise).
Conclusion:
Factors affecting CNV classification/reporting are mainly dosage, family phenotype, geneticist specialization and experience. Understanding factors from our pilot study may facilitate developing an algorithm for clinical consensus and optimal management.
