Total number of reads affects the accuracy of fetal fraction estimates in NIPT

Miceikaitė, Ieva; Brasch‐Andersen, Charlotte; Fagerberg, Christina; Larsen, Martin Jakob

doi:10.1002/mgg3.1653

Cited by 10 publications

(10 citation statements)

References 21 publications

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“…Moreover, depending on the software used for FF calculation, FF estimator accuracy can fall at low sequencing coverages in cases of truly low FFs ( Figs 3 and S6 ). Similar results were obtained by Miceikaitė et al ., who demonstrated that FF estimator accuracy tends to decrease at low sequencing depths [ 16 ]. Severe FF estimation inaccuracies can lead to the inappropriate analysis of a very low FF sample, which should fail an FF-based quality control.…”

Section: Discussionmentioning

confidence: 99%

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Systematic evaluation of NIPT aneuploidy detection software tools with clinically validated NIPT samples

et al. 2021

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Non-invasive prenatal testing (NIPT) is a powerful screening method for fetal aneuploidy detection, relying on laboratory and computational analysis of cell-free DNA. Although several published computational NIPT analysis tools are available, no prior comprehensive, head-to-head accuracy comparison of the various tools has been published. Here, we compared the outcome accuracies obtained for clinically validated samples with five commonly used computational NIPT aneuploidy analysis tools (WisecondorX, NIPTeR, NIPTmer, RAPIDR, and GIPseq) across various sequencing depths (coverage) and fetal DNA fractions. The sample set included cases of fetal trisomy 21 (Down syndrome), trisomy 18 (Edwards syndrome), and trisomy 13 (Patau syndrome). We determined that all of the compared tools were considerably affected by lower sequencing depths, such that increasing proportions of undetected trisomy cases (false negatives) were observed as the sequencing depth decreased. We summarised our benchmarking results and highlighted the advantages and disadvantages of each computational NIPT software. To conclude, trisomy detection for lower coverage NIPT samples (e.g. 2.5M reads per sample) is technically possible but can, with some NIPT tools, produce troubling rates of inaccurate trisomy detection, especially in low-FF samples.

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Section: Discussionmentioning

confidence: 99%

“…That said, a similar downsampling approach was implemented successfully by Miceikaitė et al . in their investigation of FF estimation accuracy at low sequencing depths [ 16 ]. Our read placement variability effect analysis on Z-score inference was carried out only with the NIPTeR software, one of the most accurate tools in our comparison.…”

Section: Discussionmentioning

confidence: 99%

Systematic evaluation of NIPT aneuploidy detection software tools with clinically validated NIPT samples

et al. 2021

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“…Similar results were obtained by Miceikaitė et al . demonstrating that FF estimator accuracy can decrease at low sequencing coverages (20). Severe FF estimation inaccuracies can lead to cases where a studied sample with a very low FF (which should fail the FF-based quality control) would be analysed, and the subsequent aneuploidy/euploidy estimation would be incorrectly considered as a confident estimate.…”

Section: Discussionmentioning

confidence: 99%

“…That said, a very similar downsampling approach has also been successfully implemented by Miceikaitė et al . to investigate fetal DNA fraction estimation accuracy at lower sequencing coverages (20). Our read placement variability effect analysis on Z-score inference was carried out only with the NIPTeR software, which nevertheless, was one of the most accurate tools in our comparison.…”

Section: Discussionmentioning

confidence: 99%

Systematic evaluation of NIPT aneuploidy detection software tools with clinically validated NIPT samples

Paluoja

Teder

Ardeshirdavani³

et al. 2021

Preprint

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Motivation: Non-invasive prenatal testing (NIPT) is a powerful screening method for fetal aneuploidy detection, relying on laboratory and computational analysis of cell-free DNA. Although several published computational NIPT analysis tools are available, no comprehensive and direct accuracy comparison of these tools is published. Here, we evaluate and determine the precision of five commonly used computational NIPT aneuploidy analysis tools, considering diverse sequencing depth (coverage) and fetal DNA fraction (FF) on clinically validated NIPT samples. Methods: We evaluated computational NIPT aneuploidy analysis tools WisecondorX, NIPTeR, NIPTmer, RAPIDR, and GIPseq, on the same set of clinically validated samples, subsampled to different sequencing coverages between 1.25-20M reads per sample (RPS). These clinically validated samples consisted of 423 samples, including 19 samples with fetal chromosome 21 trisomy (T21, Down syndrome), eight trisomy 18 (T18, Edwards syndrome) and three trisomy 13 (T13, Patau syndrome) samples. For each software and sequencing coverage, we determined the number of false-negative and false-positive trisomy/euploidy calls. For a uniform trisomy detection interpretation, we defined a framework based on the percent-point function for determining the cut-off threshold for calling aneuploidy based on the sample Z-score and the reference group Z-score distribution. We also determined the effect of the naturally occurring arbitrary read placement driven uncertainty on T21 detection at very low sequencing coverage and the effect of cell-free fetal DNA fraction (FF) on the accuracy of these computational tools in the case of various sequencing coverages. Results: This is the first head-to-head comparison of NIPT aneuploidy detection tools for the low-coverage whole-genome sequencing approach. We determined that, with the currently available software tools, the minimum sequencing coverage with no false-negative trisomic cases was 5M RPS. Secondly, for these compared tools, the number of false-negative trisomic cases could be reduced if the trisomy call cut-off threshold considers the Z-score distribution of euploid reference samples. Thirdly, we observed that in the case of low FF, both aneuploidy Z-score and FF inference was considerably less accurate, especially in NIPT assays with 5M RPS or lower coverage. Conclusions: We determined that all compared computational NIPT tools were affected by lower sequencing depth, resulting in systematically increasing the proportions of false-negative trisomy results as the sequencing depth decreased. Trisomy detection for lower coverage NIPT samples (e.g. 2.5M RPS) is technically possible but can increase the proportion of false-positive and false-negative trisomic cases, especially in the case of low FF.

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“…Conversely, if the sequencing depth is too shallow, the number of read necessary to accurately estimate the ff is not reached and the sensibility and specificity of the test decreases. Higher sequencing depth (sd) can compensate for low ff, but a clear description of this relationship is missing [9] , [10] . There is no universal ff threshold applicable across sequencing platforms [4] , thus a solution allowing interlaboratory comparisons of quality parameters is missing.…”

Section: Introductionmentioning

confidence: 99%

GenomeMixer and TRUST: Novel bioinformatics tools to improve reliability of Non-Invasive Prenatal Testing (NIPT) for fetal aneuploidies

Pratella

Duboc

Milanesio

et al. 2022

Computational and Structural Biotechnology Journal

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Total number of reads affects the accuracy of fetal fraction estimates in NIPT

Cited by 10 publications

References 21 publications

Systematic evaluation of NIPT aneuploidy detection software tools with clinically validated NIPT samples

Systematic evaluation of NIPT aneuploidy detection software tools with clinically validated NIPT samples

Systematic evaluation of NIPT aneuploidy detection software tools with clinically validated NIPT samples

GenomeMixer and TRUST: Novel bioinformatics tools to improve reliability of Non-Invasive Prenatal Testing (NIPT) for fetal aneuploidies

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