An Advanced Model to Precisely Estimate the Cell-Free Fetal DNA Concentration in Maternal PlasmaReportar como inadecuado

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With the speedy development of sequencing technologies, noninvasive prenatal testing NIPT has been widely applied in clinical practice for testing for fetal aneuploidy. The cell-free fetal DNA cffDNA concentration in maternal plasma is the most critical parameter for this technology because it affects the accuracy of NIPT-based sequencing for fetal trisomies 21, 18 and 13. Several approaches have been developed to calculate the cffDNA fraction of the total cell-free DNA in the maternal plasma. However, most approaches depend on specific single nucleotide polymorphism SNP allele information or are restricted to male fetuses.


In this study, we present an innovative method to accurately deduce the concentration of the cffDNA fraction using only maternal plasma DNA. SNPs were classified into four maternal-fetal genotype combinations and three boundaries were added to capture effective SNP loci in which the mother was homozygous and the fetus was heterozygous. The median value of the concentration of the fetal DNA fraction was estimated using the effective SNPs. A depth-bias correction was performed using simulated data and corresponding regression equations for adjustments when the depth of the sequencing data was below 100-fold or the cffDNA fraction is less than 10%.


Using our approach, the median of the relative bias was 0.4% in 18 maternal plasma samples with a median sequencing depth of 125-fold. There was a significant association r = 0.935 between our estimations and the estimations inferred from the Y chromosome. Furthermore, this approach could precisely estimate a cffDNA fraction as low as 3%, using only maternal plasma DNA at the targeted region with a sequencing depth of 65-fold. We also used PCR instead of parallel sequencing to calculate the cffDNA fraction. There was a significant association r = 98.2% between our estimations and those inferred from the Y chromosome.

Autor: Xiongbin Kang , Jun Xia , Yicong Wang , Huixin Xu, Haojun Jiang, Weiwei Xie, Fang Chen, Peng Zeng, Xuchao Li, Yifan Xie, Hongtai



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