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. 2018;41(3):545-554.
doi: 10.1590/1678-4685-GMB-2017-0177. Epub 2018 Jul 16.

Development of a comprehensive noninvasive prenatal test

Carolina Malcher  1 , Guilherme L Yamamoto  1 , Philip Burnham  2 , Suzana A M Ezquina  1 , Naila C V Lourenço  1 , Sahilla Balkassmi  3 , David S Marco Antonio  1 , Gabriella S P Hsia  1 , Thomaz Gollop  4 , Rita C Pavanello  1 , Marco Antonio Lopes  5 , Egbert Bakker  3 , Mayana Zatz  1 , Débora Bertola  1 , Iwijn De Vlaminck  1 , Maria Rita Passos-Bueno  1
Affiliations

Development of a comprehensive noninvasive prenatal test

Carolina Malcher et al. Genet Mol Biol. 2018.

Abstract

Our aim was to develop and apply a comprehensive noninvasive prenatal test (NIPT) by using high-coverage targeted next-generation sequencing to estimate fetal fraction, determine fetal sex, and detect trisomy and monogenic disease without parental genotype information. We analyzed 45 pregnancies, 40 mock samples, and eight mother-child pairs to generate 35 simulated datasets. Fetal fraction (FF) was estimated based on analysis of the single nucleotide polymorphism (SNP) allele fraction distribution. A Z-score was calculated for trisomy of chromosome 21 (T21), and fetal sex detection. Monogenic disease detection was performed through variant analysis. Model validation was performed using the simulated datasets. The novel model to estimate FF was robust and accurate (r2= 0.994, p-value < 2.2e-16). For samples with FF > 0.04, T21 detection had 100% sensitivity (95% CI: 63.06 to 100%) and 98.53% specificity (95% CI: 92.08 to 99.96%). Fetal sex was determined with 100% accuracy. We later performed a proof of concept for monogenic disease diagnosis of 5/7 skeletal dysplasia cases. In conclusion, it is feasible to perform a comprehensive NIPT by using only data from high coverage targeted sequencing, which, in addition to detecting trisomies, also make it possible to identify pathogenic variants of the candidate genes for monogenic diseases.

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Figures

Figure 1
Figure 1. Test workflow. Red: performed only in the mother and child fastq files to generate mock samples.
Figure 2
Figure 2. Evaluation of the modeled fetal fraction (FF) and the mean coverage effect. Individual: Non-pregnant sample. Shape and color incorporate both individual classification and mean coverage value, respectively.
Figure 3
Figure 3. Fetal sex determination in 44 females and 37 males. Chromosome Y Z-score according to fetal sex.
Figure 4
Figure 4. T21 detection. Chromosome 21 Z-score as function of FF. Color and shape incorporate both disease status (blue = T21, red = Not-T21 samples) and type of sample (circle = mock, triangle= samples with pregnant women), which represents 10 T21 samples (all mock), and 73 non-T21 samples (30 mock and 43 pregnant).
See this image and copyright information in PMC

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