The Asymmetric Threat of Maternal Cell Contamination in Prenatal Single-Gene Testing

Background/Objectives: Maternal cell contamination (MCC) is a pervasive challenge in prenatal single-gene testing, as it can cause both false-positive and false-negative results. Despite its clinical significance, quantitative tolerance thresholds for MCC in whole exome sequencing (WES) and Sanger sequencing remain limited. Methods: We established a gradient contamination model (5–95%) using genomic DNA from 20 mother–child pairs and assessed the detection fidelity for single-nucleotide variants (SNVs) and insertions/deletions (InDels) in two clinically relevant scenarios using both WES and Sanger sequencing—Scenario 1 (Fetus Heterozygous/Mother Wild-type) and Scenario 2 (Fetus Wild-type/Mother Heterozygous). Results: In Scenario 1, analysis of the 20 loci evaluated by both WES and Sanger sequencing revealed false-negative rates of 0% when MCC ≤ 30%, which then increased sharply across the 30–70% MCC range and reached 100% at 95% MCC. In Scenario 2, analysis of 12,627 WES loci showed a false-positive rate of ≤1.0% when MCC ≤ 10%, which then increased from 1.0% to 76.6% within the 10–30% MCC range and climbed to 89.8% at 50% MCC. A similar pattern was observed for the 20 loci analyzed by both WES and Sanger sequencing in this scenario. These findings potentially support a three-tier stratification: low-risk (MCC ≤ 30% in Scenario 1 and ≤10% in Scenario 2), moderate-risk (30% < MCC < 70% in Scenario 1, 10% < MCC < 30% in Scenario 2), and high-risk (MCC ≥ 70% in Scenario 1 and ≥30% in Scenario 2). Conclusions: Our findings reveal a clear directional asymmetry of MCC tolerance between the two scenarios, indicating that MCC tolerance depends not only on contamination level but also on the genotype of the contaminating DNA. We further propose a clinical reference framework based on a three-tier risk stratification for prenatal single-gene testing, which can guide result interpretation and laboratory decision-making, including decisions about reliable reporting, orthogonal validation, or re-sampling.