# Deep interpretability for GWAS

arXiv: https://arxiv.org/abs/2007.01516 · HTML: https://mlsanity.com/paper/2007.01516/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Interpreting artificial neural networks to detect genome-wide association signals for complex traits](https://mlsanity.com/paper/2407.18811/) (2024)

## Abstract (arXiv)

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.