Geneton Ltd., Bratislava 841 04, Slovakia.
Department of Molecular Biology, Faculty of Natural Sciences, Comenius University, Bratislava 841 04, Slovakia.
Int J Mol Sci. 2019 Jul 11;20(14):3414. doi: 10.3390/ijms20143414.
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.
近年来，大规模平行 shotgun 测序技术的发展为从母体血浆中提取的 DNA 物质进行负担得起的非侵入性产前检测（NIPT）提供了新的选择，以检测胎儿非整倍体。这些测试通常将测试样本的染色体分布与一组包含正常胎儿的健康样本的控制集进行比较。超过一定阈值水平的偏差被认为是阳性发现。这种方法的主要问题是，控制集的方差取决于测序片段的数量。数量越高，对实际染色体比例的估计就越精确。因此，用训练中使用的高度不同的测序reads 数量来测试一个样本可能会导致对其方差的过高或过低估计，从而导致错误的预测。我们建议根据实际的测序片段数量，为每个测试样本自适应地计算方差。我们展示了它如何导致更稳定的预测，特别是在具有高度不同的样本间覆盖度的现实世界诊断中。
