Deep interpretability for GWAS

TL;DR

This paper introduces a deep interpretability method using DeepLIFT to identify known and novel genetic risk factors for diabetes from GWAS data, overcoming challenges in training and interpreting deep models.

Contribution

It presents a novel application of DeepLIFT for interpretability in GWAS, enabling detection of complex genetic interactions associated with diseases.

Findings

Known diabetes risk factors identified

Potential novel genetic associations discovered

Deep models outperform linear models in capturing interactions

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.