Comparison of Prenatal Risk Calculation (PRC) with PIA Fetal Database software in first‐trimester screening for fetal aneuploidy

Hörmansdörfer, C.; Scharf, A.; Golatta, Michael; Vaske, Bernhard; Corral, Alejandro; Hillemanns, Peter; Schmidt, P

doi:10.1002/uog.6250

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…The free beta‐human chorionic gonadotropin level tends to be higher in cases with trisomy 21, but lower in those with trisomy 18, trisomy 13 or Turner's syndrome. These patterns are evident in the paper of Hormansdorfer et al 1.…”

mentioning

confidence: 71%

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Calculating patient‐specific risk for Down syndrome: does software matter?

Alfirević

Wright

2009

Ultrasound in Obstet & Gyne

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mentioning

confidence: 71%

“…Will all available software and all laboratories give your patients the same results? The paper by Hörmansdörfer et al in this issue of the Journal suggests not1. In fact, it highlights the disconcerting issue of the extent to which the screening process depends on the software used.…”

mentioning

confidence: 99%

Calculating patient‐specific risk for Down syndrome: does software matter?

Alfirević

Wright

2009

Ultrasound in Obstet & Gyne

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“…However, the uniformity of read distribution is influenced by two main sources of bias: local GC bias and other multiplex-related bias. The GC bias has been well reported in previous papers [14], [19], [20]; moreover, there are multiplex reasons excepting GC content that could cause sequence bias, such as chromosomal structure [21]. In the case of multiplex-related bias, which is hard to observe and correct by individual/limited samples, it is necessary to recruit population-scale sequencing instead of using theoretical expectations or single comparative controls.…”

Section: Introductionmentioning

confidence: 79%

PSCC: Sensitive and Reliable Population-Scale Copy Number Variation Detection Method Based on Low Coverage Sequencing

Chen

Xie

et al. 2014

PLoS ONE

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BackgroundCopy number variations (CNVs) represent an important type of genetic variation that deeply impact phenotypic polymorphisms and human diseases. The advent of high-throughput sequencing technologies provides an opportunity to revolutionize the discovery of CNVs and to explore their relationship with diseases. However, most of the existing methods depend on sequencing depth and show instability with low sequence coverage. In this study, using low coverage whole-genome sequencing (LCS) we have developed an effective population-scale CNV calling (PSCC) method.Methodology/Principal FindingsIn our novel method, two-step correction was used to remove biases caused by local GC content and complex genomic characteristics. We chose a binary segmentation method to locate CNV segments and designed combined statistics tests to ensure the stable performance of the false positive control. The simulation data showed that our PSCC method could achieve 99.7%/100% and 98.6%/100% sensitivity and specificity for over 300 kb CNV calling in the condition of LCS (∼2×) and ultra LCS (∼0.2×), respectively. Finally, we applied this novel method to analyze 34 clinical samples with an average of 2× LCS. In the final results, all the 31 pathogenic CNVs identified by aCGH were successfully detected. In addition, the performance comparison revealed that our method had significant advantages over existing methods using ultra LCS.Conclusions/SignificanceOur study showed that PSCC can sensitively and reliably detect CNVs using low coverage or even ultra-low coverage data through population-scale sequencing.

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“…Todos los derechos reservados. doi: 10 (TN), entre las semanas 11 y 13+6 2 . Los resultados de estos marcadores, expresados como múltiplos de la mediana (MoM), se modifican con diversos factores de corrección (ej.…”

Section: Introductionunclassified

“…Se han descrito previamente comparaciones en cuanto al rendimiento de diferentes estrategias y programas comerciales de cribado 10,11 . Sin embargo, según nuestro conocimiento, este es el primer estudio en el que se aplica un simulador de cálculo con el objetivo de incrementar el rendimiento de un programa de cribado evaluando parámetros estadísticos de diferentes estudios poblacionales.…”

Section: Introductionunclassified