An extensive multi-variant deep neural network approach to enhance genomic prediction of endometriosis

Deep neural networks have shown significant advancements in modelling complex non-linear relationships in high-dimensional biomedical data. Understanding the interplay between genetic variants and disease susceptibility is still a considerable challenge that prevents certain genomic diseases to be predicted accurately for clinical interventions. In this study, we introduce the Extensive Multi-Variant Deep Neural Network (EMV-DNN), an innovative deep learning methodology designed to enhance polygenic risk prediction. Unlike conventional polygenic risk score methods, EMV-DNN incorporates single nucleotide polymorphisms (SNPs) alongside structural variants including insertions and deletions (indels), short tandem repeats (STRs), and copy number variants (CNVs) using variant-specific subnetworks to extract informative embeddings which capture a richer and holistic genomic context. Evaluated on real-world cohorts from the UK Biobank and All of Us, EMV-DNN outperforms conventional PRS methods and classic machine learning algorithms across binary and multi-class prediction tasks. Beyond predictive performance, SHapley Additive exPlanations (SHAP) analysis revealed biologically plausible variant–gene–disease associations, highlighting pathways related to endometrial cell proliferation, fibrosis, and immune regulation. Our findings underscore the value of multi-variant integration and non-linear approaches to capture the intricate genetic architecture of complex genomic diseases. Despite challenges such as dataset limitations and the complexity of diseases with multiple contributing factors, the EMV-DNN methodology presents a promising avenue for enhancing the predictive accuracy of PRS, thereby facilitating personalized healthcare interventions and advancing our understanding of genetic predispositions to disease.