Predicting the prevalence of complex genetic diseases from individual genotype profiles using capsule networks

Diseases that have a complex genetic architecture tend to suffer from considerable amounts of genetic variants that, although playing a role in the disease, have not yet been revealed as such. Two major causes for this phenomenon are genetic variants that do not stack up effects, but interact in complex ways; in addition, as recently suggested, the omnigenic model postulates that variants interact in a holistic manner to establish disease phenotypes. We present DiseaseCapsule, as a capsule network based approach that explicitly addresses to capture the hierarchical structure of the underlying genome data, and has the potential to fully capture the non-linear relationships between variants and disease. DiseaseCapsule is the first such approach to operate in a whole-genome manner when predicting disease occurrence from individual genotype profiles. In experiments, we evaluated DiseaseCapsule on amyotrophic lateral sclerosis (ALS) and Parkinsons disease (PD), with a particular emphasis on ALS because known known to have a complex genetic architecture, so being affected by considerable missing heritability (40%). On ALS, Disease-Capsule achieves 86.9% accuracy on held out test data in predicting disease occurrence, thereby outperforming all other approaches by large margins. Also, DiseaseCapsule required sufficiently less training data for reaching optimal performance. Last but not leaset, the systematic exploitation of the network architecture yielded 922 genes of particular interest, and 644 "non-additive" genes that are crucial factors in DiseaseCapsule, but have no effect within linear schemes.