Yuchen Zhou, Justin Cosentino, Taedong Yun, Mahantesh I Biradar, Jacqueline Shreibati, Dongbing Lai, Tae-Hwi Schwantes-An, Robert Luben, Zachary McCaw, Jorgen Engmann, Rui Providencia, Amand Floriaan Schmidt, Patricia Munroe, Howard Yang, Andrew Carroll, Anthony P Khawaja, Cory Y McLean, Babak Behsaz, Farhad Hormozdiari
Electronic health records, biobanks, and wearable biosensors contain multiple high-dimensional clinical data (HDCD) modalities (e.g., ECG, Photoplethysmography (PPG), and MRI) for each individual. Access to multimodal HDCD provides a unique opportunity for genetic studies of complex traits because different modalities relevant to a single physiological system (e.g., circulatory system) encode complementary and overlapping information. We propose a novel multimodal deep learning method, M-REGLE, for discovering genetic associations from a joint representation of multiple complementary HDCD modalities...
March 20, 2024: medRxiv