Phenotype Prediction using Graph Neural Networks This project aims to study the potential of Graph Neural Network (GNN) models for phenotype prediction from genetic data. In particular, we seek to computationally diagnose Alzheimer’s disease, one of the most complex neurodegenerative diseases. To this purpose, we explore the potential of GNNs approaches and consider different architectures, including Convolutional Graph Neural Networks (ConvGNN) and Graph Attention Networks (GAT) and show that they provide a robust tool for phenotype prediction from genetic data. Traditional phenotype prediction methods are based on logistic regression models and, in turn, are linear and do not adequately incorporate important biological information.