Beschreibung:
<jats:title>Abstract</jats:title><jats:sec><jats:title>Motivation</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Availability and implementation</jats:title><jats:p>Our method is implemented in Python and available at https://github.com/mkirchler/transferGWAS/.</jats:p></jats:sec><jats:sec><jats:title>Supplementary information</jats:title><jats:p>Supplementary data are available at Bioinformatics online.</jats:p></jats:sec>