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Development of a comprehensive noninvasive prenatal test

Overview of attention for article published in Genetics and Molecular Biology, July 2018
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Development of a comprehensive noninvasive prenatal test
Published in
Genetics and Molecular Biology, July 2018
DOI 10.1590/1678-4685-gmb-2017-0177
Pubmed ID

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


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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Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 7 26%
Other 4 15%
Student > Ph. D. Student 3 11%
Student > Master 2 7%
Lecturer > Senior Lecturer 1 4%
Other 4 15%
Unknown 6 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 37%
Medicine and Dentistry 4 15%
Agricultural and Biological Sciences 3 11%
Nursing and Health Professions 1 4%
Physics and Astronomy 1 4%
Other 1 4%
Unknown 7 26%