Development of a comprehensive noninvasive prenatal test

• Autor(a) principal: Malcher,Carolina
• Data de Publicação: 2018
• Outros Autores: Yamamoto,Guilherme L., Burnham,Philip, Ezquina,Suzana A.M., Lourenço,Naila C.V., Balkassmi,Sahilla, Antonio,David S. Marco, Hsia,Gabriella S.P., Gollop,Thomaz, Pavanello,Rita C., Lopes,Marco Antonio, Bakker,Egbert, Zatz,Mayana, Bertola,Débora, Vlaminck,Iwijn De, Passos-Bueno,Maria Rita
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Genetics and Molecular Biology
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572018000400545
• topic: Cell-free DNA; next-generation sequencing; trisomy; noninvasive prenatal test; fetal fraction

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.