We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
transferGWAS: GWAS of images using deep transfer learning.
- Authors
Kirchler, Matthias; Konigorski, Stefan; Norden, Matthias; Meltendorf, Christian; Kloft, Marius; Schurmann, Claudia; Lippert, Christoph
- Abstract
Motivation Medical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS , a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations. Results We validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases. Availability and implementation Our method is implemented in Python and available at https://github.com/mkirchler/transferGWAS/. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects
UNITED Kingdom; FUNDUS oculi; DEEP learning; ARTIFICIAL neural networks; FALSE positive error; GENOME-wide association studies; GENETIC variation; EYE color
- Publication
Bioinformatics, 2022, Vol 38, Issue 14, p3621
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btac369