transferGWAS: GWAS of images using deep transfer learning

Bioinformatics. 2022 Jul 11;38(14):3621-3628. doi: 10.1093/bioinformatics/btac369.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Genome
  • Genome-Wide Association Study* / methods
  • Machine Learning
  • Neural Networks, Computer*
  • Phenotype