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.
Highlights
Prenatal testing for genomic defects of a fetus before birth is an integral component of current obstetric practice [1]
We propose several approaches that markedly improve accuracy of prediction of fetal fraction, making results of non-invasive prenatal testing more reliable
Bratislava): the first one called “non-invasive prenatal testing (NIPT) study” and the second one called “SNiPT”
Summary
Prenatal testing for genomic defects of a fetus before birth is an integral component of current obstetric practice [1]. WWee ddeemmoonnssttrraattee iinn tthhiiss ssttuuddyy tthhaatt ffrraaggmmeenntt lleennggtthh pprrooffiillee mmaayy bbee uuttiilliizzeedd aass aa ffaaiirrllyy aaccccuurraattee pprreeddiiccttoorr ooff tthhee FFFF. We conclude that low quality predictors, like regressors based on relevant anthropometric and laboratory processing attributes, may contribute to the overall accuracy Other methods utilizing their own set of distinctive attributes, both present and future, can be included, and so improve prediction compared to their stand-alone usage. Based on these findings, we conclude that additional, possibly independent, information can significantly raise the prediction accuracy of FF prediction, and should be used when possible. Appropriate weighting of samples in the training process may achieve higher accuracy for samples with low FF, and so allow a more decision regarding which samples have enough fetal fragments for subsequent testing for genomic aberrations
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