Deep interpretability for GWAS
This addresses the challenge of improving interpretability and detection of genetic interactions in GWAS for researchers, but it is incremental as it applies an existing interpretability method to a specific domain.
The paper tackled the problem of missing non-linear interaction effects in Genome-Wide Association Studies (GWAS) by using deep networks, and the result was that known diabetes genetic risk factors were identified along with potentially novel associations using the DeepLIFT interpretability technique.
Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases. In these studies, association testing is done on a variant-by-variant basis, possibly missing out on non-linear interaction effects between variants. Deep networks can be used to model these interactions, but they are difficult to train and interpret on large genetic datasets. We propose a method that uses the gradient based deep interpretability technique named DeepLIFT to show that known diabetes genetic risk factors can be identified using deep models along with possibly novel associations.