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

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.

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BibTeX RIS

BibTeX

@article{patel2026review,
  title = {{A review of self-supervised imaging phenotypes for genome-wide association studies}},
  author = {Khush Patel and Xingzhong Zhao and Ziqian Xie and Degui Zhi},
  journal = {Communications Biology},
  year = {2026},
  month = oct,
  doi = {10.1038/s42003-026-11199-4},
  url = {https://www.nature.com/articles/s42003-026-11199-4},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - A review of self-supervised imaging phenotypes for genome-wide association studies
AU  - Khush Patel
AU  - Xingzhong Zhao
AU  - Ziqian Xie
AU  - Degui Zhi
PY  - 2026
DA  - 2026/10/23
JO  - Communications Biology
DO  - 10.1038/s42003-026-11199-4
UR  - https://www.nature.com/articles/s42003-026-11199-4
AB  - 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.
SN  - 2399-3642
ER  -

Abstract

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