Image-Derived Phenotype Extraction for Genetic Discovery via Unsupervised Deep Learning in CMR Images

Prospective studies with linked image and genetic data, such as the UK Biobank (UKB), provide an unprecedented opportunity to extract knowledge on the genetic basis of image-derived phenotypes. However, the extent of phenotypes tested within so-called genome-wide association studies (GWAS) is usually limited to handcrafted features, where the main limitation to proceed otherwise is the high dimensionality of both the imaging and genetic data. Here, we propose an approach where the phenotyping is performed in an unsupervised manner, via autoencoders that operate on image-derived 3D meshes. Therefore, the latent variables produced by the encoder condense the information related to the geometry of the biologic structure of interest. The network's training proceeds in two steps: the first is genotype-agnostic and the second enforces an association with a set of genetic markers selected via GWAS on the intermediate latent representation. This genotype-dependent optimisation procedure allows the refinement of the phenotypes produced by the autoencoder to better understand the effect of the genetic markers encountered. We tested and validated our proposed method on left-ventricular meshes derived from cardiovascular magnetic resonance images from the UKB, leading to the discovery of novel genetic associations that, to the best of our knowledge, had not been yet reported in the literature on cardiac phenotypes.