Abstract
PURPOSE: This study evaluated the effectiveness and accuracy of medium-coverage whole genome sequencing (CMA-seq) for detecting triploidy in early pregnancy miscarriage tissues, comparing its performance with that of chromosomal microarray analysis (CMA) and low-coverage whole genome sequencing (CNV-seq) combined with short tandem repeat (STR) testing. METHODS: In the initial phase, the CMA-seq analytical framework was validated using four triploid miscarriage samples pre-characterized by standard methods. Three complementary algorithms were applied in parallel: X/Y chromosome dosage ratio analysis to assess read-depth-derived chromosome ratios, SNP-based variant allele frequency (VAF) modeling to differentiate triploid from diploid profiles, and CNV segmentation pattern recognition to identify fractional copy number states relative to a diploid baseline. In the subsequent phase, the validated CMA-seq pipeline was applied to 38 miscarriage tissue samples previously characterized by either CMA (n = 23) or CNV-seq combined with STR testing (n = 15). RESULTS: During validation, X/Y dosage analysis demonstrated an autosome:X:Y ratio of approximately 3:2:1 in male triploid samples and a 2:2 ratio in 69,XXX samples. The SNP-based approach produced distinct VAF profiles characterized by broader curves and higher curvature coefficients in triploid samples, while CNV segmentation Yly identified fractional copy number states (approximately 1.33, 2.67, and 3.33). In clinical evaluation, CMA-seq exhibited complete concordance with CMA results in 23 samples, yielding triploid subtype distributions of 43.5% 69,XXY, 47.8% 69,XXX, and 4.3% 69,XYY (excluding one case due to low DNA concentration), and achieved 100% agreement with CNV-seq combined with STR testing in 15 samples (46.7% 69,XXY and 53.3% 69,XXX). CONCLUSIONS: CMA-seq, integrated with a comprehensive algorithmic framework, represents a robust and sensitive approach for detecting triploidy in early pregnancy miscarriage tissues. The method provides detailed genomic profiles that may enhance clinical decision-making and genetic counseling.