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.
@article{arxiv.2007.01516,
title = {Deep interpretability for GWAS},
author = {Deepak Sharma and Audrey Durand and Marc-André Legault and Louis-Philippe Lemieux Perreault and Audrey Lemaçon and Marie-Pierre Dubé and Joelle Pineau},
journal= {arXiv preprint arXiv:2007.01516},
year = {2020}
}
Comments
Accepted at ICML 2020 workshop on ML Interpretability for Scientific Discovery