A review of self-supervised imaging phenotypes for genome-wide association studies nature.com

The review surveys self-supervised learning methods that turn brain MRI, cardiac MRI and retinal images into data-driven traits for genome-wide association studies, rather than relying only on expert-defined measurements. The authors say early studies suggest these traits can complement conventional imaging phenotypes, but broader validation across cohorts, ancestries, scanners and imaging protocols is still needed.