Combination of Fetal Fraction Estimators Based on Fragment Lengths and Fragment Counts in Non-Invasive Prenatal Testing.

Gazdarica, J.1,2,3, Hekel, R.4,5,6, Budis, J.4,6,7, Kucharik, M.4, Duris, F.8, Radvanszky, J.4,9, Turna, J.5,7,9, Szemes, T.4,5,7

1Geneton Ltd., Bratislava 84104, Slovakia
2Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, Bratislava 84104, Slovakia
3Slovak Centre of Scientific and Technical Information, Bratislava 81104, Slovakia
4Geneton Ltd., Bratislava 84104, Slovakia
5Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, Bratislava 84104, Slovakia
6Slovak Centre of Scientific and Technical Information, Bratislava 81104, Slovakia
7Comenius University Science Park, Bratislava 84104, Slovakia
8Department of Computer Science, Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava 84248, Slovakia
9Institute of Clinical and Translational Research, Biomedical Research Center, Slovak Academy of Sciences, Bratislava 84505, Slovakia

Abstract

The reliability of non-invasive prenatal testing is highly dependent on accurate estimation of fetal fraction. Several methods have been proposed up to date, utilizing different attributes of analyzed genomic material, for example length and genomic location of sequenced DNA fragments. These two sources of information are relatively unrelated, but so far, there have been no published attempts to combine them to get an improved predictor. We collected 2454 single euploid male fetus samples from women undergoing NIPT testing. Fetal fractions were calculated using several proposed predictors and the state-of-the-art SeqFF method. Predictions were compared with the reference Y-based method. We demonstrate that prediction based on length of sequenced DNA fragments may achieve nearly the same precision as the state-of-the-art methods based on their genomic locations. We also show that combination of several sample attributes leads to a predictor that has superior prediction accuracy over any single approach. Finally, appropriate weighting of samples in the training process may achieve higher accuracy for samples with low fetal fraction and so allow more reliability for subsequent testing for genomic aberrations. We propose several improvements in fetal fraction estimation with a special focus on the samples most prone to wrong conclusion.