Exploration of cfDNA landscape in NIPT and clinical utilities of cfDNA based gene expression inference in prenatal diagnostics

Cell-free DNA (cfDNA) is a dynamic biomarker reflecting the physiological state of the body. Its unique physical and biochemical properties, inherited from the tissue of origin, enable a wide range of clinical applications. From methylation patterns and fragmentation profiles to genetic variants, cfDNA holds immense potential for diagnosing and monitoring various diseases, including cancer. In this study, we leverage a large collection of non-invasive prenatal testing (NIPT) dataset to explore the genomic landscape of fetal cfDNA, aiming to identify novel biomarkers associated with fetal development and maternal-fetal complications. Our study identifies novel fetal-specific genomic regions, further demonstrating the potential of cfDNA as a versatile biomarker. The prediction model achieved a 100% (12 of 12) positive predictive value (PPV) for hypothyroidism. Whereas for preeclampsia the PPV is much lower (25%, 3 of 12). By establishing a foundation for early hypothyroidism prediction and preeclampsia, we contribute to the expanding applications of NIPT. This approach can be adapted to explore other complex phenotypes and inform biomarker discovery, ultimately advancing maternal-fetal medicine.