Abstract
ObjectiveDuring routine noninvasive prenatal testing (NIPT), cell‐free fetal DNA fraction is ideally derived from shallow‐depth whole‐genome sequencing data, preventing the need for additional experimental assays. The fraction of aligned reads to chromosome Y enables proper quantification for male fetuses, unlike for females, where advanced predictive procedures are required. This study introduces PREdict FetAl ComponEnt (PREFACE), a novel bioinformatics pipeline to establish fetal fraction in a gender‐independent manner.MethodsPREFACE combines the strengths of principal component analysis and neural networks to model copy number profiles.ResultsFor sets of roughly 1100 male NIPT samples, a cross‐validated Pearson correlation of 0.9 between predictions and fetal fractions according to Y chromosomal read counts was noted. PREFACE enables training with both male and unlabeled female fetuses. Using our complete cohort (nfemale = 2468, nmale = 2723), the correlation metric reached 0.94.ConclusionsAllowing individual institutions to generate optimized models sidelines between‐laboratory bias, as PREFACE enables user‐friendly training with a limited amount of retrospective data. In addition, our software provides the fetal fraction based on the copy number state of chromosome X. We show that these measures can predict mixed multiple pregnancies, sex chromosomal aneuploidies, and the source of observed aberrations.
Highlights
Noninvasive prenatal testing (NIPT) has evolved into an important routine clinical practice
Performance statistics are derived from a 10‐fold cross‐ validation technique: 10% of male samples are iteratively ignored during training, followed by evaluating the correlation and mean absolute error between fetal fraction based on chromosome Y (FFY) and predictions in the left‐out test set
The classifiers are trained with male fetuses only, the inclusion of females during the unsupervised phase significantly improves performance: the correlation between predictions and FFY rises from 0.926 to 0.94, while the mean absolute error (MAE) drops 0.18 units—statistics emerging from the neural network (NN) (Figure 2A,B)
Summary
Noninvasive prenatal testing (NIPT) has evolved into an important routine clinical practice. Two other approaches have been described to predict FF, without relying on the gonosomes One of these exploits nucleosome positions, hypothesizing that shorter fetal fragments are caused by differential nucleosome packaging.[20] The spatial distribution of mapped reads should represent FF; the reported performance of the predictive model seems rather unsatisfactory.[19] SeqFF, which uses a model designed directly on bin‐wise copy number features of more than 25 000 pregnant women, reports accurate FF determination, with a Pearson correlation between predictions and FFY of 0.932.21 The inventors state that cell‐free fetal and maternal fragments are not uniformly distributed across the human reference genome: small differences in local read counts are predictive for FF. In contrast to previous efforts, PREFACE enables user‐ friendly model training with a limited amount of retrospective data
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