Deep interpretability for GWAS

Deepak Sharma; Audrey Durand; Marc-André Legault; Louis-Philippe Lemieux Perreault; Audrey Lemaçon; Marie-Pierre Dubé; Joelle Pineau

arXiv · 2007.01516

Deep interpretability for GWAS

Deepak Sharma·Audrey Durand·Marc-André Legault·Louis-Philippe Lemieux Perreault·Audrey Lemaçon·Marie-Pierre Dubé·Joelle Pineau

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Abstract

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.

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BibTeX RIS

Deepak Sharma, Audrey Durand, Marc-André Legault, Louis-Philippe Lemieux Perreault, Audrey Lemaçon, Marie-Pierre Dubé, Joelle Pineau. 2020-07-03. Deep interpretability for GWAS. https://arxiv.org/abs/2007.01516

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