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

1
Geneton Ltd., 841 04 Bratislava, Slovakia
2
Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, 841 04 Bratislava, Slovakia
3
Slovak Centre of Scientific and Technical Information, 811 04 Bratislava, Slovakia
4
Comenius University Science Park, Comenius University, 841 04 Bratislava, Slovakia
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(14), 3414; https://doi.org/10.3390/ijms20143414
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)
PDF [1026 KB, uploaded 11 July 2019]

Abstract

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.
Keywords: non-invasive prenatal testing; statistical models; z-score non-invasive prenatal testing; statistical models; z-score
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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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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