High resolution global chromosomal aberrations from spontaneous miscarriages revealed by low coverage whole genome sequencing.

OBJECTIVE: Chromosome aberrations are generally considered as one of the most substantial causative factors contributing to spontaneous miscarriages. Cytogenetic analyses like G-banded karyotype and chromosomal microarray analyses are often performed to further investigate the chromosome status of a miscarried fetus. STUDY DESIGN: Here, we describe a novel method, AnnoCNV, to detect DNA copy number variations (CNVs) using low coverage whole genome sequencing (WGS). We investigated the overall frequency of chromosomal abnormalities in 149 miscarriage specimens using AnnoCNV. RESULTS: Among 149 fetal miscarriage samples, more than two fifths of them (42.95%, 64) carried at least one chromosomal abnormality, and a subset (40) was identified as autosomal trisomy which account for 26.84% of all samples. We have also developed a robust algorithm in AnnoCNV, which is able to differentiate specifically karyotype 69,XXY from sex chromosomal aneuploidy 45,X, and to identify 45,X/46,XX mosaicism. Lastly, across the whole genome AnnoCNV identifies CNVs, which are associated with both reported symptoms and unknown clinical conditions. CONCLUSION: This cost-effective strategy reveals genome wide discovery of chromosome aberrations at higher resolution, which are consistent with parallel investigation conducted by SNP based assay.

Statistical Approach to Decreasing the Error Rate of Noninvasive Prenatal Aneuploid Detection caused by Maternal Copy Number Variation.

Analyses of cell-free fetal DNA (cff-DNA) from maternal plasma using massively parallel sequencing enable the noninvasive detection of feto-placental chromosome aneuploidy; this technique has been widely used in clinics worldwide. Noninvasive prenatal tests (NIPT) based on cff-DNA have achieved very high accuracy; however, they suffer from maternal copy-number variations (CNV) that may cause false positives and false negatives. In this study, we developed an algorithm to exclude the effect of maternal CNV and refined the Z-score that is used to determine fetal aneuploidy. The simulation results showed that the algorithm is robust against variations of fetal concentration and maternal CNV size. We also introduced a method based on the discrepancy between feto-placental concentrations to help reduce the false-positive ratio. A total of 6615 pregnant women were enrolled in a prospective study to validate the accuracy of our method. All 106 fetuses with T21, 20 with T18, and three with T13 were tested using our method, with sensitivity of 100% and specificity of 99.97%. In the results, two cases with maternal duplications in chromosome 21, which were falsely predicted as T21 by the previous NIPT method, were correctly classified as normal by our algorithm, which demonstrated the effectiveness of our approach.