Title: BIG-Graph: Brain Imaging Genetics by Graph Neural Network. Abstract: Imaging genetics is one of the foremost emerging fields in neuroscience research that aims to combine neuroimaging and genetic information with phenotypes to shed light on inherent underlying mechanisms. While significant progress has been made in integrating brain imaging, like functional magnetic resonance imaging (fMRI), with genetic data, such as single nucleotide polymorphisms (SNPs), little progress has been made in studying them jointly using graph structures. To raise a new perspective and overcome challenges in analyzing data with high dimensionality and inherently complex relationships, we developed a graphical neural network model (BIG-Graph) that jointly learns to effectively represent both neuroimaging and genetic data in a nonlinear manner without any prior knowledge. Here, we demonstrate that joint learning of imaging-genetics using BIG-Graph largely outperforms existing state-of-the-art Imaging genetics models and networks trained separately on neuroimaging or genetic data in predicting a variety of phenotypes.