PREFACE: In silico pipeline for accurate cell-free fetal DNA fraction prediction


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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.What’s already known about this topic?Cell-free fetal DNA fraction is an important estimate during noninvasive prenatal testing (NIPT).Most techniques to establish fetal fraction require experimental procedures, which impede routine execution.What does this study add?PREFACE is a novel software to accurately predict fetal fraction based on solely shallow-depth whole-genome sequencing data, the fundamental base of a default NIPT assay.In contrast to previous efforts, PREFACE enables user-friendly model training with a limited amount of retrospective data.

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