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Genome-wide association studies (GWAS) are used to identify relationshipsbetween genetic variations and specific traits. When applied tohigh-dimensional medical imaging data, a key step is to extractlower-dimensional, yet informative representations of the data as traits.Representation learning for imaging genetics is largely under-explored due tothe unique challenges posed by GWAS in comparison to typical visualrepresentation learning. In this study, we tackle this problem from the mutualinformation (MI) perspective by identifying key limitations of existingmethods. We introduce a trans-modal learning framework Genetic InfoMax (GIM),including a regularized MI estimator and a novel genetics-informed transformerto address the specific challenges of GWAS. We evaluate GIM on human brain 3DMRI data and establish standardized evaluation protocols to compare it toexisting approaches. Our results demonstrate the effectiveness of GIM and asignificantly improved performance on GWAS.

Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies