A foundation model for learning genetic associations from brain imaging phenotypes.

Motivation: Due to the intricate etiology of neurological disorders, finding interpretable associations between multiomics features can be challenging using standard approaches.

Results: We propose COMICAL, a contrastive learning approach using multiomics data to generate associations between genetic markers and brain imaging-derived phenotypes. COMICAL jointly learns omics representations utilizing transformer-based encoders with custom tokenizers. Our modality-agnostic approach uniquely identifies many-to-many associations via self-supervised learning schemes and cross-modal attention encoders. COMICAL discovered several significant associations between genetic markers and imaging-derived phenotypes for a variety of neurological disorders in the UK Biobank, as well as prediction of diseases and unseen clinical outcomes from learned representations.

Availability and implementation: The source code of COMICAL along with pretrained weights, enabling transfer learning, is available at https://github.com/IBM/comical.