Communications Biology· 2026Q1· Review
A review of self-supervised imaging phenotypes for genome-wide association studies
- 1citations
- Q1SCImago
- 2026year
Short summary
Self-supervised learning (SSL) creates scalable, data-driven imaging phenotypes from intrinsic image structures, expanding the discovery of heritable traits beyond traditional expert-defined measures.
AI-generated from the title and abstract; the full text is not read.
Key points
- Self-supervised learning (SSL) learns representations directly from intrinsic image structures, creating scalable, data-driven traits.
- SSL-based imaging genetics can complement traditional expert-defined image-derived phenotypes (IDPs).
- Applications of SSL in imaging genetics span brain MRI, cardiac MRI, and retinal imaging.
- Broader validation across diverse cohorts, ancestries, scanners, and protocols is needed for SSL-derived traits.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Communications Biology, 2026 · DOI ↗
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Field: Genetics (Biochemistry, Genetics and Molecular Biology)
GeneticsBiochemistry, Genetics and Molecular Biology