An Efficient Computational Method to Create Positive NIPT Samples with Autosomal Trisomy

Noninvasive prenatal test (NIPT) has been widely used for screening trisomy on chromosomes 13 (T13), 18 (T18), and 21 (T21). However, the false negative rate of NIPT algorithms has not been thoroughly evaluated due to the lack of positive samples. In this study, we present an efficient computational approach to create positive samples with autosomal trisomy from negative samples. We applied the approach to establish a low coverage dataset of 1440 positive samples with T13, T18, and T21 aberrations for both mosaic and non-mosaic conditions. We examined the performance of WisecondorX and its improvement, called VINIPT, on both negative and positive datasets. Experiments showed that WisecondorX and VINIPT were able to detect all non-mosaic samples with T13, T18, and T21 aberrations (i.e., the sensitivity of 100%). When analyzing mosaic samples, both WisecondorX and VINIPT have the overall sensitivity of 99.7% on detecting T13, T18, and T21 aberrations from mosaic samples. WisecondorX has the specificity of 98.5% for the nonmosaic analysis, but a considerably lower specificity of 95% for the mosaic analysis. VINIPT has a much better specificity than WisecondorX, i.e., 99.9% for the non-mosaic analysis, and 98.2% for the mosaic analysis. The results suggest that VINIPT can play as a powerful tool for detecting autosomal trisomy from the low coverage data.