The potential of deep learning on the discovery of new genes implicated in differences of sex development

Despite advances in understanding genetic causes of DSD (differences of sex development), the molecular cause remains unknown for over half of affected individuals. Next-generation sequencing (NGS) has improved diagnosis, but interpreting results can be challenging, especially when no known DSD gene mutations are found, or only variants of unknown significance appear. Identifying new genes involved in sex development from whole exome sequencing (WES) alone is difficult. To overcome this, we introduce "GONAD-ResNet," a residual convolutional neural network designed to predict novel DSD-associated genes by learning complex patterns in time-dependent single-cell gene expression data. When applied to WES data from six patients (three XX, three XY) with DSD, GONAD-ResNet prioritized genes with expression profiles similar to known DSD genes while disregarding ubiquitous or irrelevant genes. This narrowed the list of potential candidates from around 1000 to a few promising novel genes per patient. This innovative approach accelerates the discovery of new DSD-related genes, opening new research avenues and potentially improving patient outcomes.