Evaluating unsupervised disentangled representation learning for genomic
  discovery and disease risk prediction
Taedong Yun
High-dimensional clinical data have become invaluable resources for genetic
studies, due to their accessibility in biobank-scale datasets and the
development of high performance modeling techniques especially using deep
learning. Recent work has shown that low dimensional embeddings of these
clinical data learned by variational autoencoders (VAE) can be used for
genome-wide association studies and polygenic risk prediction. In this work, we
consider multiple unsupervised learning methods for learning disentangled
representations, namely autoencoders, VAE, beta-VAE, and FactorVAE, in the
context of genetic association studies. Using spirograms from UK Biobank as a
running example, we observed improvements in the number of genome-wide
significant loci, heritability, and performance of polygenic risk scores for
asthma and chronic obstructive pulmonary disease by using FactorVAE or
beta-VAE, compared to standard VAE or non-variational autoencoders. FactorVAEs
performed effectively across multiple values of the regularization
hyperparameter, while beta-VAEs were much more sensitive to the hyperparameter
values.