Noninvasive detection of fetal genetic variations through polymorphic site sequencing of maternal plasma DNA

Noninvasive prenatal testing (NIPT) for common fetal aneuploidies has been widely adopted in clinical practice for its sensitivity and accuracy. However, detection of pathogenic copy number variations (pCNVs) or monogenic disorders (MDs) is inaccurate and not cost effective. Here we developed an assay, the noninvasive prenatal testing based on goodness-of-fit and graphical analysis of polymorphic sites (GGAP-NIPT), to simultaneously detect fetal aneuploidies, pCNVs, and MDs.

Polymorphic sites were amplicon sequenced, followed by fetal fraction estimation using allelic reads counts and a robust linear regression model. The genotype of each polymorphic site or MD variant was then determined by allelic goodness-of-fit test or graphical analysis of its different alleles. Finally, aneuploidies and pCNVs were detected using collective goodness-of-fit test to select each best fit from all possible chromosomal models.

Of the simulated 1,692 chromosomes and 1,895 pCNVs, all normals and variants were correctly identified (accuracy 100%, sensitivity 100%, specificity 100%). Of the 713,320 simulated MD variants, more than 90% of the genotypes were determined correctly (accuracy: 98.3 ± 1.0%; sensitivity: 98.7 ± 1.96%; specificity: 99.7 ± 0.6%). The detection accuracies of three public MD datasets were 95.70%, 93.43%, and 96.83%. For an MD validation dataset, 75% detection accuracy was observed when a site with sample replicates was analyzed individually, and 100% accuracy was achieved when analyzed collectively.

Fetal aneuploidies, pCNVs, and MDs could be detected simultaneously and with high accuracy through amplicon sequencing of polymorphic and target sites, which showed the potential of extending NIPT to an expanded panel of genetic disorders.