A review of self-supervised imaging phenotypes for genome-wide association studies.
Authors
Affiliations (5)
Affiliations (5)
- J.C. Walter Jr. Transplant Center, Department of Surgery, Houston Methodist Hospital, Houston, TX, USA.
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
- Department of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, USA.
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA. [email protected].
- Department of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, USA. [email protected].
Abstract
Biobank-scale imaging-genomic cohorts enable genome-wide association studies (GWASs) of organ structure and function. Traditional approaches rely on expert-defined image-derived phenotypes (IDPs), which capture only a fraction of the biological variation in high-content images. Powered by artificial intelligence, self-supervised learning (SSL) learns representations directly from intrinsic image structures, creating scalable, data-driven traits. Here, we review the technical frameworks and challenges of SSL-based imaging genetics and their applications in brain MRI, cardiac MRI, and retinal imaging. Early studies suggest that these data-driven approaches can complement traditional IDPs and may expand the phenotypic space available for discovery of heritable imaging traits, although broader validation across cohorts, ancestries, scanners, and imaging protocols remains needed.