Abstract
BackgroundNoninvasive prenatal testing (NIPT) is one of the most commonly employed clinical measures for screening of fetal aneuploidy. Fetal Fraction (ff) has been demonstrated to be one of the key factors affecting the performance of NIPT. Accurate quantification of ff plays vital role in NIPT.MethodsIn this study, we present a new approach, the accurate Quantification of Fetal Fraction with Shallow‐Coverage sequencing of maternal plasma DNA (FF‐QuantSC), for the estimation of ff in NIPT. The method employs neural network model and utilizes differential genomic patterns between fetal and maternal genomes to quantify ff.ResultsOur results show that the predicted ff by FF‐QuantSC exhibit high correlation with the Y chromosome–based method on male pregnancies, and achieves the highest accuracy compared with other ff estimation approaches. We also demonstrate that the model generates statistically similar results on both male and female pregnancies.ConclusionFF‐QuantSC achieves high accuracy in ff quantification. The method is suitable for application in both male and female pregnancies. Since the method does not require additional information upon NIPT routines, it can be easily incorporated into current NIPT settings without causing extra costs. We believe that FF‐QuantSC shall provide valuable additions to NIPT.
Highlights
In late 20th century, use of plasma DNA in molecular diagnosis has been demonstrated as a valuable potential (Sorenson, Pribish, Valone, & Memoli, 1994; D. Lo et al, 1997)
The proportion of fetal circulating cell-free DNA (ccfDNA) in maternal plasma, known as the Fetal Fraction, has been shown to be one of the fundamental factors affecting the performance of Noninvasive prenatal testing (NIPT) (Canick, Palomaki, Kloza, Lambert-Messerlian, & Haddow, 2013)
Our result shows that FF-QuantSC achieves accurate and cost-effective estimation of ff on routine NIPT settings
Summary
Yuying Yuan1 | Xianghua Chai1 | Na Liu2 | Bida Gu1 | Shengting Li3 | Ya Gao4,5 | Lijun Zhou1 | Qiang Liu1 | Fan Yang1 | Jingjuan Liu1 | Jiao Qiu1 | Jinjin Zhang1 | Yumei Hou1 | Miaolan Cen1 | Zhongming Tian6 | Weijiang Tang7 | Hongyun Zhang1 | Fang Chen3 | Ye Yin2 | Wei Wang
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have