Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology
This work addresses the challenge of enhancing gene discovery for heart morphology in genomics and medical imaging, representing an incremental advance in methodology for analyzing big databases.
The authors tackled the problem of identifying genetic associations with nuanced heart shape patterns from cardiac MRI data by proposing Unsupervised Phenotype Ensembles (UPE), which improved gene discoverability by identifying 11 loci with study-wide significance and 8 with suggestive significance.
Recent genome-wide association studies (GWAS) have been successful in identifying associations between genetic variants and simple cardiac parameters derived from cardiac magnetic resonance (CMR) images. However, the emergence of big databases including genetic data linked to CMR, facilitates investigation of more nuanced patterns of shape variability. Here, we propose a new framework for gene discovery entitled Unsupervised Phenotype Ensembles (UPE). UPE builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner, using deep learning models trained with different hyperparameters. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations across the ensemble. We apply our approach to the UK Biobank database to extract left-ventricular (LV) geometric features from image-derived three-dimensional meshes. We demonstrate that our approach greatly improves the discoverability of genes influencing LV shape, identifying 11 loci with study-wide significance and 8 with suggestive significance. We argue that our approach would enable more extensive discovery of gene associations with image-derived phenotypes for other organs or image modalities.