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Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions

Geneton Ltd., Bratislava 841 04, Slovakia
Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, Bratislava 841 04, Slovakia
Slovak Centre of Scientific and Technical Information, Bratislava 811 04, Slovakia
Comenius University Science Park, Comenius University, Bratislava 841 04, Slovakia
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(14), 3414;
Received: 21 June 2019 / Revised: 8 July 2019 / Accepted: 9 July 2019 / Published: 11 July 2019
(This article belongs to the Special Issue Cell-Free Nucleic Acids)
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Recent advances in massively parallel shotgun sequencing opened up new options for affordable non-invasive prenatal testing (NIPT) for fetus aneuploidy from DNA material extracted from maternal plasma. Tests typically compare chromosomal distributions of a tested sample with a control set of healthy samples with unaffected fetuses. Deviations above certain threshold levels are concluded as positive findings. The main problem with this approach is that the variance of the control set is dependent on the number of sequenced fragments. The higher the amount, the more precise the estimation of actual chromosomal proportions is. Testing a sample with a highly different number of sequenced reads as used in training may thus lead to over- or under-estimation of their variance, and so lead to false predictions. We propose the calculation of a variance for each tested sample adaptively, based on the actual number of its sequenced fragments. We demonstrate how it leads to more stable predictions, mainly in real-world diagnostics with the highly divergent inter-sample coverage. View Full-Text
Keywords: non-invasive prenatal testing; statistical models; z-score non-invasive prenatal testing; statistical models; z-score

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Gazdarica, J.; Budis, J.; Duris, F.; Turna, J.; Szemes, T. Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions. Int. J. Mol. Sci. 2019, 20, 3414.

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