Noninvasive fetal genotyping using deep neural networks

Abstract Circulating cell-free DNA (cfDNA) is a powerful diagnostics tool that is widely studied in the context of liquid biopsy in oncology and other fields. In obstetrics, maternal plasma cfDNA have already proven its utility, enabling noninvasive prenatal testing (NIPT), which has become a standard for detecting chromosomal aberrations. However, identification of point mutations responsible for monogenic diseases (NIPT-M) remains limited, even when accounting to fragment specific characteristics (i.e. fragmentomics). While genotyping of individual genomes is performed today using deep learning (DL) algorithms, cfDNA-based noninvasive fetal genotyping is performed only using traditional statistical and machine-learning methods. This study introduces the first DL-based framework for cfDNA based genotyping, heralding a significant stride toward genome-wide NIPT-M. Using unique ultra-deep whole genome sequencing (WGS) data, we were motivated to develop an efficient model, especially when compared with current DL methods for WGS. This facilitates the integration of previously overlooked levels of information, encompassing DNA nucleotides, fragments, mutation regions, samples, and familial traits. Employing this novel approach, we surpass the performance of existing methodologies, successfully detecting three deleterious mutations, and allowing for NIPT-M as early as the 7th week of gestation. Our proposed approach brings genome-wide NIPT for all mutation types closer to clinical feasibility, enabling families and healthcare providers to make well-informed decisions and alleviating the anxieties and uncertainties associated with pregnancy.