scholarly journals Non-invasive prenatal testing by low coverage genomic sequencing: Detection limits of screened chromosomal microdeletions

2019 ◽  
Author(s):  
Marcel Kucharik ◽  
Andrej Gnip ◽  
Michaela Hyblova ◽  
Jaroslav Budis ◽  
Lucia Strieskova ◽  
...  
Keyword(s):  
In Silico ◽  
Detection Limits ◽  
Prenatal Testing ◽  
Systematic Evaluation ◽  
Whole Genome Sequencing Data ◽  
Detection Accuracy ◽  
Sequencing Data ◽  
Non Invasive ◽  
Microdeletion Syndromes ◽  
Fetal Fraction

AbstractObjectiveTo study the detection limits of chromosomal microaberrations in non-invasive prenatal testing with aim for five target microdeletion syndromes, including DiGeorge, Prader-Willi/Angelman, 1p36, Cri-Du-Chat, and Wolf-Hirschhorn syndromes.MethodWe used known cases of pathogenic deletions from ISCA database to specifically define regions critical for the target syndromes. Our approach to detect microdeletions, from whole genome sequencing data, is based on sample normalization and read counting for individual bins. We performed both an in-silico study using artificially created data sets and a laboratory test on mixed DNA samples, with known microdeletions, to assess the sensitivity of prediction for varying fetal fractions, deletion lengths, and sequencing read counts.ResultsThe in-silico study showed sensitivity of 79.3% for 10% fetal fraction with 20M read count, which further increased to 98.4% if we searched only for deletions longer than 3Mb. The test on laboratory-prepared mixed samples was in agreement with in-silico results, while we were able to correctly detect 24 out of 29 control samples.ConclusionOur results suggest that it is possible to incorporate microaberration detection into basic NIPT as part of the offered screening/diagnostics procedure, however, accuracy and reliability depends on several specific factors.What’s already known about this topic?Microdeletion detection accuracy, similarly to most common trisomies detection, was found to be dependent mostly on technical and biological parameters of the test and tested samples, such as coverage of target region, fetal fraction, size and positions of the deletions.What does this study add?Estimation of relevant regions for five chosen microdeletion syndromes. Confirmation and improvement upon previous methods. Systematic evaluation of sensitivity of microdeletion detection with read counts from 10M to 20M.

scholarly journals Common Treatment, Common Variant: Evolutionary Prediction of Functional Pharmacogenomic Variants

2021 ◽  
Vol 11 (2) ◽  
pp. 131
Author(s):  
Laura B. Scheinfeldt ◽  
Andrew Brangan ◽  
Dara M. Kusic ◽  
Sudhir Kumar ◽  
Neda Gharani
Keyword(s):  
In Silico ◽  
Drug Efficacy ◽  
Common Variant ◽  
Genomic Research ◽  
Whole Genome Sequencing Data ◽  
Mendelian Disease ◽  
Allele Frequency Distribution ◽  
Sequencing Data ◽  
Patient Race ◽  
The Impact

Pharmacogenomics holds the promise of personalized drug efficacy optimization and drug toxicity minimization. Much of the research conducted to date, however, suffers from an ascertainment bias towards European participants. Here, we leverage publicly available, whole genome sequencing data collected from global populations, evolutionary characteristics, and annotated protein features to construct a new in silico machine learning pharmacogenetic identification method called XGB-PGX. When applied to pharmacogenetic data, XGB-PGX outperformed all existing prediction methods and identified over 2000 new pharmacogenetic variants. While there are modest pharmacogenetic allele frequency distribution differences across global population samples, the most striking distinction is between the relatively rare putatively neutral pharmacogene variants and the relatively common established and newly predicted functional pharamacogenetic variants. Our findings therefore support a focus on individual patient pharmacogenetic testing rather than on clinical presumptions about patient race, ethnicity, or ancestral geographic residence. We further encourage more attention be given to the impact of common variation on drug response and propose a new ‘common treatment, common variant’ perspective for pharmacogenetic prediction that is distinct from the types of variation that underlie complex and Mendelian disease. XGB-PGX has identified many new pharmacovariants that are present across all global communities; however, communities that have been underrepresented in genomic research are likely to benefit the most from XGB-PGX’s in silico predictions.

scholarly journals Non-invasive prenatal testing (NIPT) by low coverage genomic sequencing: Detection limits of screened chromosomal microdeletions

PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0238245 ◽  
Author(s):  
Marcel Kucharik ◽  
Andrej Gnip ◽  
Michaela Hyblova ◽  
Jaroslav Budis ◽  
Lucia Strieskova ◽  
...  
Keyword(s):  
Detection Limits ◽  
Prenatal Testing ◽  
Genomic Sequencing ◽  
Non Invasive ◽  
Low Coverage

scholarly journals Combination of Fetal Fraction Estimators Based on Fragment Lengths and Fragment Counts in Non-Invasive Prenatal Testing

2019 ◽  
Vol 20 (16) ◽  
pp. 3959 ◽  
Author(s):  
Juraj Gazdarica ◽  
Rastislav Hekel ◽  
Jaroslav Budis ◽  
Marcel Kucharik ◽  
Frantisek Duris ◽  
...  
Keyword(s):  
State Of The Art ◽  
Prenatal Testing ◽  
The State ◽  
Accurate Estimation ◽  
Special Focus ◽  
Dna Fragments ◽  
Sources Of Information ◽  
Non Invasive ◽  
Genomic Locations ◽  
Fetal Fraction

The reliability of non-invasive prenatal testing is highly dependent on accurate estimation of fetal fraction. Several methods have been proposed up to date, utilizing different attributes of analyzed genomic material, for example length and genomic location of sequenced DNA fragments. These two sources of information are relatively unrelated, but so far, there have been no published attempts to combine them to get an improved predictor. We collected 2454 single euploid male fetus samples from women undergoing NIPT testing. Fetal fractions were calculated using several proposed predictors and the state-of-the-art SeqFF method. Predictions were compared with the reference Y-based method. We demonstrate that prediction based on length of sequenced DNA fragments may achieve nearly the same precision as the state-of-the-art methods based on their genomic locations. We also show that combination of several sample attributes leads to a predictor that has superior prediction accuracy over any single approach. Finally, appropriate weighting of samples in the training process may achieve higher accuracy for samples with low fetal fraction and so allow more reliability for subsequent testing for genomic aberrations. We propose several improvements in fetal fraction estimation with a special focus on the samples most prone to wrong conclusion.

scholarly journals Use of Whole Genome Sequencing Data for a First in Silico Specificity Evaluation of the RT-qPCR Assays Used for SARS-CoV-2 Detection

2020 ◽  
Vol 21 (15) ◽  
pp. 5585
Author(s):  
Mathieu Gand ◽  
Kevin Vanneste ◽  
Isabelle Thomas ◽  
Steven Van Gucht ◽  
Arnaud Capron ◽  
...  
Keyword(s):  
In Silico ◽  
Protein S ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Viral Genomes ◽  
Genome Data ◽  
Gene Coding ◽  
Control And Prevention ◽  
The Impact

The current COronaVIrus Disease 2019 (COVID-19) pandemic started in December 2019. COVID-19 cases are confirmed by the detection of SARS-CoV-2 RNA in biological samples by RT-qPCR. However, limited numbers of SARS-CoV-2 genomes were available when the first RT-qPCR methods were developed in January 2020 for initial in silico specificity evaluation and to verify whether the targeted loci are highly conserved. Now that more whole genome data have become available, we used the bioinformatics tool SCREENED and a total of 4755 publicly available SARS-CoV-2 genomes, downloaded at two different time points, to evaluate the specificity of 12 RT-qPCR tests (consisting of a total of 30 primers and probe sets) used for SARS-CoV-2 detection and the impact of the virus’ genetic evolution on four of them. The exclusivity of these methods was also assessed using the human reference genome and 2624 closely related other respiratory viral genomes. The specificity of the assays was generally good and stable over time. An exception is the first method developed by the China Center for Disease Control and prevention (CDC), which exhibits three primer mismatches present in 358 SARS-CoV-2 genomes sequenced mainly in Europe from February 2020 onwards. The best results were obtained for the assay of Chan et al. (2020) targeting the gene coding for the spiking protein (S). This demonstrates that our user-friendly strategy can be used for a first in silico specificity evaluation of future RT-qPCR tests, as well as verifying that the former methods are still capable of detecting circulating SARS-CoV-2 variants.

scholarly journals A novel high-throughput molecular counting method with single base-pair resolution enables accurate single-gene NIPT

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
David S. Tsao ◽  
Sukrit Silas ◽  
Brian P. Landry ◽  
Nelda P. Itzep ◽  
Amy B. Nguyen ◽  
...  
Keyword(s):  
Muscular Atrophy ◽  
Single Gene ◽  
Prenatal Testing ◽  
Maternal Blood ◽  
Beta Thalassemia ◽  
Analytical Sensitivity ◽  
Dna Molecules ◽  
Sequencing Data ◽  
Non Invasive ◽  
Single Base Pair

Abstract Next-generation DNA sequencing is currently limited by an inability to accurately count the number of input DNA molecules. Molecular counting is particularly needed when accurate quantification is required for diagnostic purposes, such as in single gene non-invasive prenatal testing (sgNIPT) and liquid biopsy. We developed Quantitative Counting Template (QCT) molecular counting to reconstruct the number of input DNA molecules using sequencing data. We then used QCT molecular counting to develop sgNIPTs of sickle cell disease, cystic fibrosis, spinal muscular atrophy, alpha-thalassemia, and beta-thalassemia. The analytical sensitivity and specificity of sgNIPT was >98% and >99%, respectively. Validation of sgNIPTs was further performed with maternal blood samples collected during pregnancy, and sgNIPTs were 100% concordant with newborn follow-up.

Correlation between Z score, fetal fraction, and sequencing reads in non-invasive prenatal testing

2017 ◽  
Vol 37 (9) ◽  
pp. 943-945 ◽  
Author(s):  
Marie Balslev-Harder ◽  
Stine R. Richter ◽  
Susanne Kjaergaard ◽  
Peter Johansen
Keyword(s):  
Prenatal Testing ◽  
Z Score ◽  
Non Invasive ◽  
Fetal Fraction

Two cases of placental T21 mosaicism: challenging the detection limits of non-invasive prenatal testing

2013 ◽  
Vol 33 (12) ◽  
pp. 1207-1210 ◽  
Author(s):  
Yanlin Wang ◽  
Jiansheng Zhu ◽  
Yan Chen ◽  
Shoulian Lu ◽  
Biliang Chen ◽  
...  
Keyword(s):  
Detection Limits ◽  
Prenatal Testing ◽  
Non Invasive

scholarly journals A novel high-throughput molecular counting method with single base-pair resolution enables accurate single-gene NIPT

2019 ◽  
Author(s):  
David S. Tsao ◽  
Sukrit Silas ◽  
Brian P. Landry ◽  
Nelda Itzep ◽  
Amy B. Nguyen ◽  
...  
Keyword(s):  
Muscular Atrophy ◽  
Single Gene ◽  
Prenatal Testing ◽  
Maternal Blood ◽  
Beta Thalassemia ◽  
Analytical Sensitivity ◽  
Dna Molecules ◽  
Sequencing Data ◽  
Non Invasive ◽  
Single Base Pair

ABSTRACTNext-generation DNA sequencing is currently limited by an inability to count the number of input DNA molecules. Molecular counting is particularly needed when accurate quantification is required for diagnostic purposes, such as in single-gene non-invasive prenatal testing (sgNIPT) and liquid biopsy. We developed Quantitative Counting Template (QCT) molecular counting for reconstructing the number of input DNA molecules using sequencing data. We then used QCT molecular counting to develop sgNIPT of sickle cell disease, cystic fibrosis, spinal muscular atrophy, alpha-thalassemia, and beta-thalassemia. Incorporating molecular count information into a statistical model of disease likelihood led to analytical sensitivity and specificity of >98% and >99%, respectively. Validation of sgNIPT was further performed with maternal blood samples collected during pregnancy, and sgNIPT was 100% concordant with newborn follow-up.

scholarly journals Lipid Metabolism Affects Fetal Fraction and Screen Failures in Non-invasive Prenatal Testing

2022 ◽  
Vol 8 ◽  
Author(s):  
Jun Cao ◽  
Longwei Qiao ◽  
Jieyu Jin ◽  
Sheng Zhang ◽  
Ping Chen ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Pregnant Women ◽  
Serum Lipid ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Density Lipoprotein ◽  
Prenatal Testing ◽  
Lipoprotein Cholesterol ◽  
Non Invasive ◽  
Fetal Fraction

Objective: To assess the association between lipid metabolism and fetal fraction, which is a critical factor in ensuring a highly accurate non-invasive prenatal testing (NIPT), and on the rate of screen failures or “no calls” in NIPT.Methods: A total of 4,514 pregnant women at 12–26 weeks of gestation underwent NIPT sequencing and serum lipid measurements. Univariate analysis and multivariate regression models were used to evaluate the associations of serum lipid concentrations with the fetal fraction and the rate of screen failures.Results: The fetal fraction decreased with increased low-density lipoprotein cholesterol and triglyceride (TG) levels, which were significant factors (standardized coefficient: −0.11). Conversely, high-density lipoprotein cholesterol and the interval between the two tests were positively correlated with the fetal fraction. The median fetal fraction was 10.88% (interquartile range, 8.28–13.89%) and this decreased with TG from 11.56% at ≤1.10 mmol/L to 9.51% at >2.30 mmol/L. Meanwhile, multivariate logistic regression analysis revealed that increased TG levels were independently associated with the risk of screen failures. The rate of screen failures showed an increase with TG levels from 1.20% at ≤1.70 mmol/L to 2.41% at >2.30 mmol/L.Conclusions: The fetal fraction and the rate of screen failures in NIPT are affected by TG levels. Meanwhile, in pregnant women with high TG levels, delaying the time between NIPT blood collections can significantly increase the fetal fraction.

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