| Title: | iGWAS: Image-based genome-wide association of self-supervised deep phenotyping of retina fundus images |
| Journal: | PLOS Genetics |
| Published: | 10 May 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38728357/ |
| DOI: | https://doi.org/10.1371/journal.pgen.1011273 |
| Citations: | 2 (2 in last 2 years) as of 8 Aug 2024 |
Publication 12101
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Existing imaging genetics studies have been mostly limited in scope by using imaging-derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self-supervised deep representation learning, we propose a new approach, image-based genome-wide association study (iGWAS), for identifying genetic factors associated with phenotypes discovered from medical images using contrastive learning. Using retinal fundus photos, our model extracts a 128-dimensional vector representing features of the retina as phenotypes. After training the model on 40,000 images from the EyePACS dataset, we generated phenotypes from 130,329 images of 65,629 British White participants in the UK Biobank. We conducted GWAS on these phenotypes and identified 14 loci with genome-wide significance (p<5×10-8 and intersection of hits from left and right eyes). We also did GWAS on the retina color, the average color of the center region of the retinal fundus photos. The GWAS of retina colors identified 34 loci, 7 are overlapping with GWAS of raw image phenotype. Our results establish the feasibility of this new framework of genomic study based on self-supervised phenotyping of medical images.</p>
9 Keywords
- Female
- Fundus Oculi
- Genome-Wide Association Study
- Humans
- Image Processing, Computer-Assisted
- Male
- Phenotype
- Polymorphism, Single Nucleotide
- Retina
14 Authors
- Ziqian Xie
- Tao Zhang
- Sangbae Kim
- Jiaxiong Lu
- Wanheng Zhang
- Cheng-Hui Lin
- Man-Ru Wu
- Alexander Davis
- Roomasa Channa
- Luca Giancardo
- Han Chen
- Sui Wang
- Rui Chen
- Degui Zhi
1 Application
| Application ID | Title |
|---|---|
| 24247 | Searching Identity-by-descent segments in biobank-scale cohorts |
Enabling scientific discoveries that improve human health