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transferGWAS: GWAS of images using deep transfer learning.
- Source :
- Bioinformatics; Jul2022, Vol. 38 Issue 14, p3621-3628, 8p
- Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13674803
- Volume :
- 38
- Issue :
- 14
- Database :
- Complementary Index
- Journal :
- Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 158324181
- Full Text :
- https://doi.org/10.1093/bioinformatics/btac369