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

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...

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Autores principales: Gazdarica, Juraj, Budis, Jaroslav, Duris, Frantisek, Turna, Jan, Szemes, Tomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6678500/
https://www.ncbi.nlm.nih.gov/pubmed/31336782
http://dx.doi.org/10.3390/ijms20143414
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author Gazdarica, Juraj
Budis, Jaroslav
Duris, Frantisek
Turna, Jan
Szemes, Tomas
author_facet Gazdarica, Juraj
Budis, Jaroslav
Duris, Frantisek
Turna, Jan
Szemes, Tomas
author_sort Gazdarica, Juraj
collection PubMed
description 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.
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spelling pubmed-66785002019-08-19 Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions Gazdarica, Juraj Budis, Jaroslav Duris, Frantisek Turna, Jan Szemes, Tomas Int J Mol Sci Article 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. MDPI 2019-07-11 /pmc/articles/PMC6678500/ /pubmed/31336782 http://dx.doi.org/10.3390/ijms20143414 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gazdarica, Juraj
Budis, Jaroslav
Duris, Frantisek
Turna, Jan
Szemes, Tomas
Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions
title Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions
title_full Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions
title_fullStr Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions
title_full_unstemmed Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions
title_short Adaptable Model Parameters in Non-Invasive Prenatal Testing Lead to More Stable Predictions
title_sort adaptable model parameters in non-invasive prenatal testing lead to more stable predictions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6678500/
https://www.ncbi.nlm.nih.gov/pubmed/31336782
http://dx.doi.org/10.3390/ijms20143414
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