TY  - JOUR
A1  - Kirchler, Matthias
A1  - Konigorski, Stefan
A1  - Norden, Matthias
A1  - Meltendorf, Christian
A1  - Kloft, Marius
A1  - Schurmann, Claudia
A1  - Lippert, Christoph
T1  - transferGWAS
T2  - Bioinformatics
N2  - 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.
Y1  - 2022
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/63916
SN  - 1367-4803
SN  - 1460-2059
VL  - 38
IS  - 14
SP  - 3621
EP  - 3628
PB  - Oxford Univ. Press
CY  - Oxford
ER  -