2010
DOI: 10.1093/bioinformatics/btq544
|View full text |Cite
|
Sign up to set email alerts
|

Statistical considerations for digital approaches to non-invasive fetal genotyping

Abstract: With this in mind, we have undertaken a statistical modeling of three contemporary (digital) analytical methods in the context of prenatal diagnosis using cell free DNA for monogenic diseases that segregate in a recessive mendelian fashion. We provide an experimental framework for the future development of diagnostic methods in this context that should be considered when designing molecular assays that seek to establish proof of concept in this field.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
5
0

Year Published

2011
2011
2020
2020

Publication Types

Select...
4
1

Relationship

1
4

Authors

Journals

citations
Cited by 5 publications
(5 citation statements)
references
References 21 publications
0
5
0
Order By: Relevance
“…As mentioned above for HIV viral load monitoring, an ideal system would be able to achieve 3-fold resolution for as low as 500 molecules/mL. To be able to correctly resolve two different concentrations, the risk of both false positives (Type I error) and false negatives (Type II error) need to be taken into account. ,,,, Samples must give results at the desired confidence level (1-α, measure of Type I error) and demonstrate this confidence level again and again (Power: 1-β, measure of Type II error). When comparing two results, the null hypothesis is that the results come from samples that have statistically the same concentration.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…As mentioned above for HIV viral load monitoring, an ideal system would be able to achieve 3-fold resolution for as low as 500 molecules/mL. To be able to correctly resolve two different concentrations, the risk of both false positives (Type I error) and false negatives (Type II error) need to be taken into account. ,,,, Samples must give results at the desired confidence level (1-α, measure of Type I error) and demonstrate this confidence level again and again (Power: 1-β, measure of Type II error). When comparing two results, the null hypothesis is that the results come from samples that have statistically the same concentration.…”
Section: Resultsmentioning
confidence: 99%
“…The development of simple stand-alone devices for quantitative nucleic acid diagnostics would further enable diagnosis and treatment in point-of-care settings. Precise, absolute quantification of nucleic acid levels, especially at low levels of detection, would have particular impact in applications such as viral load analysis (e.g., HIV, hepatitis, cytomegalovirus (CMV), enterovirus), bacterial detection, and quantification in food or water sources without culturing, multiplexed diagnostics, and minimal residual disease. , Real time PCR is often considered the gold standard for nucleic acid quantification but has limited utility in the field because it requires data collection and analysis over the entire course of the reaction, careful control of conditions, and internal calibration standards and typically gives relative levels rather than absolute concentrations. , Digital PCR provides a way to obtain absolute nucleic acid levels directly using only end point analysis with high resolution and sensitivity.…”
mentioning
confidence: 99%
“…Collection of tissue samples was approved by the University of Pittsburgh Institutional Review Board (PRO07070298). CV samples were obtained between gestational weeks 11 and 13 from the Magee Women's Hospital Cytogenetic Screening Laboratory and carefully dissected by experienced technicians to remove maternal contamination as previously . The CV samples were obtained from three male and three female fetuses.…”
Section: Methodsmentioning
confidence: 99%
“…It is not trivial to tell apart the informative and non-informative SNPs (Sparks et al, 2012). The main reason for this difficulty lays in the existence of the allelic bias of the sequencing technology (Chu et al, 2010b). There are several methods for identifying the informative SNPs.…”
Section: Theorymentioning
confidence: 99%