Improving the calling of non-invasive prenatal testing on 13-/18-/21-trisomy by support vector machine discrimination.

With the advance of next-generation sequencing (NGS) technologies, non-invasive prenatal testing (NIPT) has been developed and employed in fetal aneuploidy screening on 13-/18-/21-trisomies through detecting cell-free fetal DNA (cffDNA) in maternal blood. Although Z-test is widely used in NIPT NGS data analysis, there is still necessity to improve its accuracy for reducing a) false negatives and false positives, and b) the ratio of unclassified data, so as to lower the potential harm to patients as well as the induced cost of retests. Combining the multiple Z-tests with indexes of clinical signs and quality control, features were collected from the known samples and scaled for model training using support vector machine (SVM). We trained SVM models from the qualified NIPT NGS data that Z-test can discriminate and tested the performance on the data that Z-test cannot discriminate. On screenings of 13-/18-/21-trisomies, the trained SVM models achieved 100% accuracies in both internal validations and unknown sample predictions. It is shown that other machine learning (ML) models can also achieve similar high accuracy, and SVM model is most robust in this study. Moreover, four false positives and four false negatives caused by Z-test were corrected by using the SVM models. To our knowledge, this is one of the earliest studies to employ SVM in NIPT NGS data analysis. It is expected to replace Z-test in clinical practice